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Appendix-e4. Preparation of dataset inventory

Table of Contents

Acknowledgements & Disclaimer 16

Findings 17

Table 1. Comparison of administrative datasets worldwide 17

Table 2. List of data sources with countries of origin, comorbidities evaluated and first author of publication. 23

Administrative Data Sources 30

North America 30

Canada 30

National Databases 30

Discharge Abstract Database (DAD) 30

The Hospital Morbidity Database (HMDB) 31

National Ambulatory Care Reporting System (NACRS) 32

Provincial Databases 33

Alberta 33

Alberta Health Care Insurance Plan (AHCIP) Registry 33

Inpatient Discharge Abstract Dataset (DAD) 34

Ambulatory Care Classification System Dataset 34

Practitioner Payments Dataset 35

Population Registry 35

Pharmacy Data- Alberta Blue Cross 35

Multiple Sclerosis Clinic Database, University of Calgary 36

British Columbia (BC) 36

Population Data BC 36

Medical Services Plan (MSP) Payment Information Dataset 37

Pharmacare 38

PharmaNet 38

39

Discharge Abstract Database (DAD) 39

Mental Health 39

BC Cancer Agency 40

Demographics Dataset: Consolidation File 40

British Columbia Multiple Sclerosis Database (BCMS) 40

Manitoba 41

Population Health Research Data Repository 41

Hospital Discharge Abstracts Data (DAD): 42

Medical Services Data (Physicians Claims) 42

Drug Program Information Network Data 43

Mental Health Management Information System (MHMIS) 43

Urgent Care 44

Emergency Care Database 44

Multiple Sclerosis Clinic Database and Registry, Health Sciences Centre, Winnipeg 45

Nova Scotia 45

Research Data Repository 45

Physician Billings Database 46

Hospital Discharge Abstract Database 46

Mental Health Outpatient Information System (MHOIS) 46

PharmaCare Prescriptions 47

Insured Patient Registry (MASTER) 47

Dalhousie MS Research Unit (DMSRU) Database 48

Newfoundland & Labrador 48

Newfoundland & Labrador Centre for Health Information 48

Clinical Database Management System (CDMS) (CIHI Discharge Abstract Database) 49

MCP Fee-for-Service Physician Claims Database 49

Newfoundland & Labrador Prescription Drug Program Dataset 49

Newfoundland & Labrador Chronic Disease Surveillance System 50

Cancer and Chronic Disease Research Database 50

Ontario 51

The Institute for Clinical Evaluative Sciences (ICES) 51

Ontario Health Insurance Plan (OHIP) Database 52

Discharge Abstracts Database (DAD) and Same Day Surgery Database (SDS) 52

Ontario Drug Benefits Claims 52

National Ambulatory Care Reporting System 53

Ontario Mental Health Reporting System 53

Cardiac Care Network 54

CT/MRI Abstraction Data 54

Ontario Cancer Registry 54

Ontario Diabetes Database 55

Quebec 55

EPSEBE (Environnement pour la Promotion de la Santé et du Bien-être) 55

Re´gie d’Assurance Maladie du Québec (RAMQ) (Physician Billing Database) 56

Fichier d'admissibilité au régime général d'assurance médicaments (Prescription Drug Insurance) 57

Maintenance et Exploitation des Donne´es Pour l’Etude de la Clientèle Hospitalière (Hospital Discharge Database) 57

Saskatchewan 58

Saskatchewan’s Health Services Databases 58

Prescription Drug Data 58

Hospital Services Data 59

Medical Services Data 59

Cancer Registry 60

United States 61

Medicare/Medicaid 61

Medicare 61

Medicaid 62

Chronic Condition Data Warehouse (CCW) 64

Master Beneficiary Summary File 65

Institutional and Non-institutional fee-for-service files 65

Inpatient (IP) Base Claim Files 65

Outpatient (OP) Base Claim Files 65

Part D Prescription Drug Event data 65

Medicaid Analytical Extracts (MAX) Inpatient Files 65

Medicaid Analytical Extracts (MAX) Drug Files 65

Medicaid Analytical Extracts (MAX) Personal Summary Files 65

Medicare-Medicaid Linked Enrollee Analytic Data Source 65

SEER-Medicare Linked Data 65

Veterans Administration Databases (VHA) 66

VHA Corporate Data Warehouse (CDW) 67

Inpatient 68

Outpatient 68

Mental Health Assessment 68

Patient (demographics) 68

Pharmacy 68

Staff (demographics, provider type) 68

Vital Signs 68

Decision Support System (DSS) National Data Extracts 68

DSS Discharge NDE 69

DSS Treating Specialty ND 69

Outpatient NDE 69

DSS National Pharmacy Extract: 69

Medical SAS datasets 69

Outpatient Medical SAS datasets 69

Inpatient Medical SAS datasets 70

Inpatient Encounter Medical SAS datasets 70

Kaiser Permanente 71

Kaiser Permanente Northern California (KPNC) 72

Chronic Disease Registries 73

SEER-quality KP Northern California Cancer Registry 73

KP Diabetes Registry 73

KP HIV/AIDS Registry 73

KP Asthma 73

KP Coronary Artery Disease 73

KP Congestive Heart Failure 73

KP Diabetes 73

KP Maternal Prenatal Drug/Alcohol Use 73

Inpatient Hospitalizations 73

Ambulatory Visits 74

Outside Referrals and Claims 74

Health plan Membership 74

Pharmacy 74

Patient Demographics 75

Kaiser Permanente Centre for Health Research 75

Kaiser Permanente Georgia Region (KPG) 76

Kaiser Permanente Hawaii 76

Kaiser Permanente Northwest 77

Kaiser Permanente Southern California 77

Indian Health Service (IHS) National Data Repository 78

National Hospital Discharge Survey (NHDS) 80

IMS LifeLink™ PharMetrics plus Database 81

HMO Research Network Virtual Data Warehouse (HMORN VDW) 82

Kaiser Permanente Colorado 83

Kaiser Permanente Southern California 83

Kaiser Permanente Northern California 83

Kaiser Permanente Mid-Atlantic 83

The Centre of Health Research 83

Geisinger Health System 83

Essentia Health 83

Group Health 83

Harvard Pilgrim Health Care 83

Health Partners 83

Henry Ford Health System 83

Marshfield Clinic Security Health Plan of Wisconsin 83

Fallon Community Health Plan Reliant Medical Group 83

Palo Alto Medical Foundation for Healthcare, Research and Education (PAMF) 83

Scott and White Healthcare 83

Fallon Clinic/Reliant Medical Group 84

Lovelace Clinic Foundation (LCF) Quality Research Data Warehouse 85

Demographics 85

Membership Information 85

Geographic Information 85

Inpatient 85

Outpatient 85

Pharmacy Systems 85

Geisinger Health System 86

Geisinger’s Clinical Decision Intelligence System (CDIS) 87

HealthCare Partners Medical Group database 88

United HealthCare Database 89

Medstat’s MarketScan Claims and Encounters 90

New York Department of Health Statewide Planning and Research Cooperate System (SPARCS) 91

Explorys 93

Cerner Health Facts® Database 93

New York State Multiple Sclerosis Consortium database (NYMSC database) 95

The Pacific Northwest Multiple Sclerosis Registry & Network 95

Mexico 95

National Mortality Statistics 95

Encuesta Nacional de la Dinámica Demográfica (ENADID) (National Demographics Survey) 95

South America 96

Argentina 96

Cause-of-Death Records 96

Registro Institucional de Tumores de Argentina (RITA) (Cancer Registry) 97

Brazil 98

DATASUS 98

Primary Care Information System (SIAB) 98

Sistema de Informações Hospitalares (SIH-SUS) (Hospital Systems Data) 99

Sistema de Cadastramento e Acompanhamento de Hipertensos e Diabéticos 99

Mortality Information System (MIS) 100

Europe 101

Austria 101

DIAG-Extranet 101

GAP-DRG (General Approach for Patient oriented Outpatient-based DRG) 102

Austrian National Cancer Registry 103

Cause of Death Statistics 104

Belgium 104

Résumé Clinique Minimum et Résumé Financier Minimum 105

PharmaNet Database 105

Belgium Cancer Registry 106

IMS LifeLink™ Belgian Hospital Disease Database 106

Bulgaria 107

National drug consumption database of the Bulgarian Drug Agency 107

Czech Republic 108

National Register of Hospitalized Patients 108

Czech National Cancer Registry (CNCR) 109

National Register of Cardiac Surgery (NKCHR) 110

National Register of Cardiovascular Interventions (NRKI) 111

National drug consumption database of the State Institute for Drug Control (SUKL) 112

Denmark 113

The Danish National Database of Reimbursed Prescriptions (DNDRP) 113

Danish National Registry of Patients 114

Danish Cancer Registry 115

Psychiatric Central Research Register 116

Danish Twin Registry 117

Danish Education Registers 117

Student Register (SR) 118

Academic Achievement Register (AAR) 118

Population’s Education Register (PER) 118

Adult Education and Continuing Training Register 118

Danish Heart Register 118

Danish Injury Register 119

Danish registers on personal income and transfer payments 120

Danish Multiple Sclerosis Register 120

Estonia 121

The Estonian Health Insurance Fund Administrative Database 121

Health Statistics and Health Research Database 122

Estonian Cancer Registry 122

Estonian Causes of Death Registry 123

The Estonia Health Interview Survey 123

Health Behavior among Estonian Adult Population 124

Finland 125

Finnish National Hospital Discharge Register 126

Kelasto Database (Finnish Prescription Registry) 126

Causes of Death Registry 127

Finnish Cancer Registry 128

Health 2000/2011 Interview Surveys 2000 and 2011 128

France 129

Programme de Médicalisation des Systèmes d’Information (PMSI) 129

Système National d’Information Inter-Régime de l’Assurance Maladie (SNIIR-AM) (National Claims) 130

FRANCIM (French National Cancer Register) 131

ANMS Database 132

Enquête Décennale de Santé 132

Enquête Santé et Protection Sociale (ESPS) 133

European Database for Multiple Sclerosis (EDMUS) 134

Germany 134

German Statutory Health Insurance Claims (SHIs) 134

Core Data 135

Inpatient Data 135

Outpatient Data 135

Prescription Data 135

IMS Disease Analyzer Database 135

Cancer Registry Data 136

WIdO Database 137

Robert Koch Institute (RKI) 138

Health Surveys available through RKI 138

GEDA (German Health Update survey) 138

German Health Interview and Examination Survey for Adults (DEGS) 138

Multiple Sclerosis Registry in Germany 139

Hungary 139

National Health Insurance Fund database (NHIFA) 139

National Drug Consumption Database of the Directorate General of National Institute of Pharmacy 141

Iceland 142

Icelandic National Register of Persons 142

Iceland Cancer Registry 142

Italy 143

OsMed Database 144

The Health Search Database (HSD) 144

Veneto Region Health Services 145

Hospital Discharge Records 146

Drug Dispensing Records 146

Disease-Specific Exemptions from Copayment to Health Care 146

Inhabitant Registry (IR) 146

Servizio Sanitario Regionale Emilia-Romagna 147

Hospital Discharge Records 147

Drug Dispensing Records 147

Inhabitant Registry (IR) 147

Regional Health Agency of Tuscany 148

Hospital Discharge Records 148

Drug Dispensing Records 148

Disease-Specific Exemptions from Copayment to Health Care 148

Inhabitant Registry (IR) 149

Agenzia Regionale Sanitaria Marche 149

Hospital Discharge Records 149

Drug Dispensing Records 149

Disease-Specific Exemptions from Copayment to Health Care 150

Inhabitant Registry (IR) 150

Sicily (Southern Italy) 150

Contact ricercasanitaria@formez.it or assessorato.salute@certmail.regione.sicilia.it 150

Hospital Discharge Records 151

Drug Dispensing Records 151

Disease-Specific Exemptions from Copayment to Health Care 151

Inhabitant Registry (IR) 151

Italian Multiple Sclerosis Database Network (MSDN) 152

Latvia 152

National drug consumption database of the State Agency of Medicines of Latvia 152

Statistics Latvia 152

Netherlands 153

Dutch Hospital Data (DHD) 154

Rural Hospital Care Key Register 154

National Medical Registration 154

National Ambulatory Registration 154

Diagnosis-Treatment-Combination Information System 154

Hospital Surveys 154

GIP Database 155

NIVEL Primary Care Database 156

Dutch Cancer Registry 157

PHARMO 158

General Practitioner Database (GPD) 158

Outpatient Pharmacy Database 159

In-patient Pharmacy Database 159

Norway 159

Norwegian Patient Register 159

Norwegian Cancer Registry (NCR) 160

Norwegian Prescription Database (NorPD) 161

Norwegian Cardiovascular Disease Registry 162

Cause of Death Registry 162

The Norwegian Multiple Sclerosis National Competence Centre and National Multiple Sclerosis Registry 163

Poland 163

National Health Fund Database 163

Portugal 164

INFAR Med’s Database 164

Serbia 165

Institute of Public Health 165

Diabetes Registry 165

Cancer Registry 165

Acute Coronary Syndrome 166

National Health Survey 2006 166

Institute of Neurology, Belgrade School of Medicine 166

Spain 166

National Statistics Office Datasets 166

Hospital Morbidity Survey 167

Encuesta Nacional de Salud (National Health Survey) 167

National Vital Statistics 168

FAP (Database for Pharmacoepidemiological Research in Primary Care) 168

DGFPS Database 169

MONICA-Catalonia Study 170

Slovenia 170

Cancer Registry 170

Sweden 171

National Patient Register (NPR) (Hospital Discharge Register) 171

Swedish Cancer Registry (NPR) 173

Drug Prescription Register 174

The Swedish Cause of Death Register 174

IMS LifeLink™ Longitudinal Patient Database in Sweden 175

National Swedish MS Register 176

Switzerland 176

Swiss National Cohort (SNC) 176

Vital Statistics 177

Swiss Health Survey 178

NICER Database 179

Swiss MONICA (Monitoring of trends and determinants in CVD) 180

United Kingdom 181

Clinical Practice Research Data link (CPRD) (formerly General Practice Research Database - GPRD) 181

Diagnosis 181

Procedures 181

Drug data 181

Central Mortality Data 181

Census Data 181

Disease Registries: Cardiovascular Disease, Stroke, Cancer, Diabetes, Epilepsy, Pain, IBD, Dementia, Schizophrenia 181

Health and Social Care Information Centre (HSCIC) 183

Hospital Episode Statistics (HES) 184

Payment by Results data 184

Mental Health Minimum Data Set 184

Cancer Datasets 185

Coronary Heart Disease Data Set 186

HIC (Health Informatics Centre) 186

ISD SMR00 dataset 187

ISD SMR01 dataset 187

ISD SMR04 dataset 188

ISD SMR06 dataset 188

Prescription Data 188

Accident & Emergency (A&E) 188

Ambulatory Holter Monitoring Data 188

ECHO cardiogram set 188

Epidemiology of liver disease in Tayside 189

Heart-disease Evidence-based Audit & Research Tayside Scotland (HEARTS) 189

Renal Register 189

Rheumatoid Arthritis dataset 189

Scottish Care Information - Diabetes Collaboration 189

Stroke Dataset 189

Tayside Allergy & Respiratory Disease Information System (TARDIS) 190

Thyroid Epidemiology, Audit & Research Study (TEARS) 190

GRO Death Certification 190

ISD Scotland 190

Accident and Emergency (A&E2) 191

ACaDMe 191

Data Completeness 191

Outpatients (SMR00) 191

PRISMS 192

The Scottish MS Register 192

The Health Improvement Network (THIN) 192

GPMD (General Practice Morbidity Database Project Wales) 193

Asia 194

China 194

National Central Cancer Registry (NCCR) 196

Chen W, Zheng R, Zhang S, Zhao P, Li G, Wu L, & He J. The incidences and mortalities of major cancers in China, 2009. Chinese Journal of Cancer 2013, 32 (3): 106-112. 196

India 196

Census Data 196

Annual Health Survey 197

Israel 198

Israel National Population Register 198

National hospital services data 199

Israel National Cancer Registry 199

Cause of Death Registry 200

The Maccabi Healthcare Services Clinical Database 201

MS Center Registry at the Sheba Medical Center Hospital 202

Islamic Republic of Iran 202

Iranian MS Society (IMSS) 202

National Death Registry 202

National Cancer Registration 203

Japan 204

Japan Medical Data Center (JMDC) National Claims Database 204

Osaka University Hospital 205

NBD National Claim Database 206

Medical Data Vision 207

JammNet 207

IMS NPA Data 208

Korea 209

Korea National Health insurance claims database 209

Taiwan 210

Taiwan National Health Insurance Research Database (NHIRD) 210

Turkey 211

Turkey Vital Registration 211

TÜRKİYE SAĞLIK ARAŞTIRMASI (Turkey Health Interview Survey) 212

Cancer Registry 212

Africa 213

South Africa 213

Civil Registration 213

Australia and New Zealand 214

Australia 214

Australian Institute of Heath and Welfare (AIHW) 214

National Hospital Morbidity Database (NHMD) 215

Australian Cancer Database (ACD) 215

Mental Health Data Cubes 216

Mental Health Datasets Available: 216

Admitted Patient Mental Health Care NMDS 216

Community Mental Health Care NMDS 217

Residential Mental Health Care NMDS 217

National Death Index 217

Bettering the Evaluation and Care of Health (BEACH) 218

Drug Utilization Subcommittee (DUSC) Dataset 219

Medicare Benefits Schedule (BMS) 220

Population Health Research Network 221

The Australian Multiple Sclerosis Longitudinal Study 222

Data Linkage Western Australia 223

Western Australia (Hospital Morbidity Data System) 223

Emergency Department Data Collection 224

Mental Health Information System 224

WA Cancer Registry 224

Death Registrations 224

Birth Registrations 224

New Zealand 225

National Minimum Dataset (hospital events) 225

National Non-Admitted Patient Data mart 226

New Zealand Cancer Registry (NZCR) 226

Pharmaceutical Datamart 227

PRIMHD Mental Health Dataset 228

Mortality Collection 229

Clinical Databases/Registries Global 229

MSBASE 229

Europe 231

Danish Multiple Sclerosis Register 231

National Swedish MS Register 232

The Norwegian Multiple Sclerosis National Competence Centre and National Multiple Sclerosis Registry 233

Italian Multiple Sclerosis Database Network (MSDN) 234

Multiple Sclerosis Registry in Germany 236

Institute of Neurology, Belgrade School of Medicine 237

The Scottish MS Register 238

European Register for Multiple Sclerosis (EUREMS) 239

European Database for Multiple Sclerosis (EDMUS) 240

Asia 242

Iranian MS Society 242

MS Center Registry at the Sheba Medical Center Hospital, Israel 243

North America 244

North American Research Committee on Multiple Sclerosis (NARCOMS) Registry 244

Veterans Health Administration Multiple Sclerosis National Data Repository 246

New York State Multiple Sclerosis Consortium database (NYMSC database) 247

The Pacific Northwest Multiple Sclerosis Registry & Network 248

British Columbia Multiple Sclerosis Database (BCMSD) 249

Dalhousie MS Research Unit (DMSRU) Database 251

Multiple Sclerosis Clinic Database and Registry, Health Sciences Centre, Winnipeg 252

Multiple Sclerosis Clinic Database, University of Calgary 254

Acknowledgements & Disclaimer

This document was developed by the Multiple Sclerosis Working Group on Comorbidity under the auspices of the International Advisory Committee on Clinical Trials in MS. Funding for this work was provided by the National Multiple Sclerosis Society. The opinions expressed in this publication are those of the authors/researchers, and do not necessarily reflect the official views of the National Multiple Sclerosis Society.

We aimed to create an inventory of potentially useful administrative and clinical datasets that include MS-relevant comorbid disease and health behavior data, worldwide. We expected that this inventory would support future studies of the incidence and prevalence of comorbidity in MS and the effect of comorbidity in MS. Data sources were identified using three approaches including (i) collation of data sources from the systematic review of the incidence and prevalence of comorbidity in MS,7 and published reviews regarding registries in MS;84 (ii) seeking input from members of the International Advisory Committee; and (iii) an environmental scan.

The development of this document was prepared in part through contribution of the team members who were either asked to provide names and lists of known data sources and registries in their area of expertise, helped in developing the grey literature search strategy, or helped in the formal identification of data sources and registries using our methods described later in this document. Note that many health care systems are developing datasets based on clinical data collected during the course of routine health care delivery, typically using electronic health records. We did not make an attempt to gather information on all such systems.

The information in this documented is being made available to provide general guidance on datasets available for secondary analysis for the personal use of the reader, who accepts full responsibility for its use. This document is not a definitive source of information and is based on information accessible to the authors as described in the methods. Anyone considering the use of these data sources should contact the data custodian, not the authors, to verify the accuracy of the information including whether it has changed with time. Some data sources were not captured.

For each administrative data source the following data were collected, if available: name of database, country, description, data custodian and data access procedures including potential costs, population covered, time period covered, diagnosis coding procedures, ability to link to other datasets, supporting data files required to use the dataset. If possible we identified sample references regarding the validity of the data sources and sample references showing the use of the data source to study MS. However, we did not conduct an exhaustive review to identify all such references nor evaluate their quality.

For each clinical database or registry the following data were collected, if available: primary aim of the registry, geographical coverage, how diagnosis of MS was made and whether the accuracy of MS diagnoses have been assessed, how MS is coded in the registry (e.g. ICD coding, text), and whether comorbid conditions, smoking, alcohol consumption, height and weight, and physical activity are captured. We also aimed to identify the data custodian, data access procedures and costs of data access.

We observed that in many countries the administrative datasets are population-based, common time periods for data availability can beidentified, and ICD codes are commonly used. This suggests the potential exists to conduct administrative data studies of comorbidity in multiple jurisdictions using common protocols. Some clinical datasets shared common data elements but more harmonization would be needed to create common observational study protocols. For detailed descriptions of all administrative and clinical datasets identified see below.

Findings

Each dataset is described in detail later in this document. Briefly, Table 1 summarizes characteristics of administrative datasets including whether the data are population-based, the time period for which they are available, the diagnostic coding system used, and what settings of care (e.g. hospitalizations) that they cover. Table 2 delineates the comorbidities studied using these datasets, if any.

Table 1. Comparison of administrative datasets worldwide

|Country |

|Canada |

|Canada |

|United States |

|Brazil |

|Austria |

|Israel |

|Australia |Government |National |Yes |Yes |

| |NHMD | | | |

|Alberta Health |Canada |Psychoses |Patten (2005) |Patten SB, Svenson LW, Metz LM. Psychotic disorders in MS: Population-based evidence of an association. |

|Administrative Data | | | |Neurology 2005;65:1123-1125. |

|DHDR |Denmark |AID |Eaton (2007) |Eaton WW, Rose NR, Kalaydjian A, Pedersen MG, Mortensen PB. Epidemiology of autoimmune diseases in |

| | |GI Tract |Moller (1991) |Denmark. Journal of Autoimmunity 2007;29:1-9. |

| | |Diabetes | | |

| | |Cancer | |Moller H, Kneller RW, Boice JD, Jr., Olsen JH. Cancer incidence following hospitalization for multiple |

| | | | |sclerosis in Denmark. Acta Neurol Scand 1991;84:214-220. |

|DMSR |Denmark |Cancer |Hjalgrim (2004) |Hjalgrim H, Rasmussen S, Rostgaard K, et al. Familial clustering of Hodgkin lymphoma and multiple |

| | | |Nielsen (2006) |sclerosis. J Natl Cancer Inst 2004;96:780-784. |

| | | | | |

| | | | |Nielsen NM, Rostgaard K, Rasmussen S, et al. Cancer risk among patients with multiple sclerosis: a |

| | | | |population-based register study. International Journal of Cancer 2006;118:979-984. |

|DMSRU |Canada |Anxiety |Fisk (1998) |Fisk J, Morehouse SA, Brown MG, Skedgel C, Murray TJ. Hospital-based psychiatric service utilization and |

| | |Alcohol abuse |Poder (2009) |morbidity in multiple sclerosis. Can J Neurol Sci 1998;25:230-235. |

| | |Bipolar disorder | | |

| | |Depression | |Poder K, Ghatavi K, Fisk J, et al. Social anxiety in a multiple sclerosis clinic population. Multiple |

| | | | |Sclerosis 2009;15:393-398. |

|DNRP |Denmark |Cardiac Arrhythmias |Christiansen (2010) |Christiansen CF, Christensen S, Farkas DK, Miret M, Sørensen HT, Pedersen L. Risk of arterial |

| | |Stroke |Sunesen (2010) |cardiovascular diseases in patients with multiple sclerosis: a population-based cohort study. |

| | |CHF | |Neuroepidemiology 2010;35:267-274. |

| | |IHD | | |

| | |Diabetes | |Sunesen KG, Norgaard M, Thorlacius-Ussing O, Laurberg S. Immunosuppressive disorders and risk of anal |

| | |Valvular disease | |squamous cell carcinoma: a nationwide cohort study in Denmark, 1978-2005. Int J Cancer 2010;127:675-684. |

| | |Hypertension | | |

| | |GI tract | | |

| | |Lung disease | | |

| | |MSK | | |

| | |Cancer | | |

|DNSGP |Denmark |AID |Nuyen (2006) |Nuyen J, Schellevisa FG, Satarianob WA, et al. Comorbidity was associated with neurologic and psychiatric |

| | |Stroke | |diseases: A general practice-based controlled study. J Clin Epidemiol 2006;59:1274–1284. |

| | |Diabetes | | |

| | |IHD | | |

| | |Hyperlipidemia | | |

| | |Visual disorders | | |

| | |Epilepsy | | |

| | |GI Tract | | |

| | |Lung Disease | | |

| | |Renal Disease | | |

| | |Cancer | | |

| | |Psychoses | | |

| | |Drug abuse | | |

| | |Anxiety | | |

| | |Depression | | |

|EDMUS |France |Epilepsy |Catenoix (2011) |Catenoix H, Marignier R, Ritleng C, et al. Multiple sclerosis and epileptic seizures. Mult Scler |

| | |AID |Le Scanff (2008) |2011;17:96-102. |

| | |Cancer |Lebrun (2008) | |

| | | |Lebrun (2011) |Le Scanff J, Seve P, Renoux C, Broussolle C, Confavreux C, Vukusic S. Uveitis associated with multiple |

| | | | |sclerosis. Mult Scler 2008;14:415-417. |

| | | | | |

| | | | |Lebrun C, Debouverie M, Vermersch P, et al. Cancer risk and impact of disease-modifying treatments in |

| | | | |patients with multiple sclerosis. Mult Scler 2008;14:399-405. |

| | | | | |

| | | | |Lebrun C, Vermersch P, Brassat D, et al. Cancer and multiple sclerosis in the era of disease-modifying |

| | | | |treatments. J Neurol 2011;258:1304-1311. |

|Finnish National Registries|Finland |Cancer |Sumelathi (2004) |Sumelahti M-L, Pukkala E, Hakama M. Cancer incidence in multiple sclerosis: a 35-year follow-up. |

| | | | |Neuroepidemiology 2004;23:224-227. |

|GPRD |United Kingdom |Visual disorders |Bazelier (2012) |Bazelier MT, Mueller-Schotte S, Leufkens HG, Uitdehaag BM, van Staa T, de Vries F. Risk of cataract and |

| | |Renal disease |Lawrenson (2001) |glaucoma in patients with multiple sclerosis. Mult Scler 2012;18:628-638. |

| | | | | |

| | | | |Lawrenson R, Wyndaele JJ, Vlachonikolis I, Farmer C, Glickman S. Renal failure in patients with neurogenic|

| | | | |lower urinary tract dysfunction. Neuroepidemiology 2001;20:138-143. |

|HSCIC-HES |United Kingdom |Epilepsy |Allen |Allen AN, Seminog OO, Goldacre MJ. Association between multiple sclerosis and epilepsy: large |

| | | |(2013) |population-based record-linkage studies. BMC Neurol 2013;13:189. |

|IMS Disease Analyzer |Germany |Depression |Thielscher (2013) |Thielscher C, Thielscher S, Kostev K. The risk of developing depression when suffering from neurological |

| | | | |diseases. Ger Med Sci 2013;11:Doc02. |

|KPNC |United States |Depression |Mohr (2007) |Mohr DC, Hart SL, Julian L, Tasch ES. Screening for depression among patients with multiple sclerosis: two|

| | | | |questions may be enough. Multiple Sclerosis 2007;13:215-219. |

|Manitoba Health |Canada |AID |Marrie (2012) |Marrie R, Yu B, Leung S, et al. Rising prevalence of vascular comorbidities in MS: validation of |

|Administrative Data | |Diabetes |Marrie (2012) |administrative definitions for diabetes, hypertension, hyperlipidemia. Mult Scler 2012;18:1310-1319. |

| | |Hypertension |Marrie (2012) | |

| | |Hyperlipidemia |Marrie (2013) |Marrie RA, Yu BN, Leung S, et al. The incidence and prevalence of thyroid disease do not differ in the |

| | |MSK |Marrie (2013) |multiple sclerosis and general populations: A validation study using administrative data. |

| | |Epilepsy |Marrie (2013) |Neuroepidemiology 2012;39:135-142. |

| | |IHD | | |

| | |Lung Disease | |Marrie RA, Yu BN, Leung S, et al. The incidence and prevalence of fibromyalgia are higher in multiple |

| | |Psychoses | |sclerosis than the general population: A population-based study. Multiple Sclerosis and Related Disorders |

| | |Bipolar disorder | |2012;1:162-167. |

| | |Anxiety | | |

| | |Depression | |Marrie RA, Yu BN, Leung S, et al. Prevalence and incidence of ischemic heart disease in multiple |

| | | | |sclerosis: A population-based validation study. Multiple Sclerosis and Related Disorders 2013;2:355-361. |

| | | | | |

| | | | |Marrie RA, Fisk JD, Yu BN, et al. Mental comorbidity and multiple sclerosis: validating administrative |

| | | | |data to support population-based surveillance. BMC Neurol 2013;13:16. |

| | | | | |

| | | | |Marrie RA, Yu BN, Leung S, et al. The Utility of Administrative Data for Surveillance of Comorbidity in |

| | | | |Multiple Sclerosis: A Validation Study. Neuroepidemiology 2013;40:85-92. |

|Medicare |United States |Depression |Buchanan (2003) |Buchanan RJ, Wang S, Tai-Seale M, Ju H. Analyses of nursing home residents with multiple sclerosis and |

|Medicaid | |Bipolar disorder |Demakis (2009) |depression using the Minimum Data Set. Multiple Sclerosis 2003;9:171-188. |

| | |Psychoses | | |

| | |Anxiety | |Demakis GJ, Buchanan R, Dewald L. A longitudinal study of cognition in nursing home residents with |

| | |Depression | |multiple sclerosis. Disabil Rehabil 2009;31:1734-1741. |

|Medstat’s Market Scan | |Depression |Tarrants (2011) |Tarrants M, Oleen-Burkey M, Castelli-Haley J, Lage MJ. The impact of comorbid depression on adherence to |

|Claims and Encounters | | | |therapy for multiple sclerosis. Mult Scler Int 2011;2011:271321. |

|MS Clinic Database Calgary |Canada |Depression |Patten (2000) |Patten SB, Metz LM, Reimer MA. Biopsychosocial correlates of lifetime major depression in a multiple |

| | |Psychoses |Zabad (2005) |sclerosis population. Multiple Sclerosis 2000;6:115-120. |

| | | |Patten (2005) | |

| | | |Patten (2010) |Zabad RK, Patten SB, Metz LM. The association of depression with disease course in multiple sclerosis. |

| | | | |Neurology 2005;64:359-360. |

| | | | | |

| | | | |Patten SB, Berzins S, Metz LM. Challenges in screening for depression in multiple sclerosis. Multiple |

| | | | |Sclerosis Journal 2010;16:1406-1411. |

| | | | | |

| | | | |Patten SB, Svenson LW, Metz LM. Descriptive epidemiology of affective disorders in multiple sclerosis. CNS|

| | | | |Spectr 2005;10:365-371. |

|Nova Scotia Administrative |Canada |Bipolar disorder |Fisk (1998) |Fisk J, Morehouse SA, Brown MG, Skedgel C, Murray TJ. Hospital-based psychiatric service utilization and |

|Data | |Depression | |morbidity in multiple sclerosis. Can J Neurol Sci 1998;25:230-235. |

|NARCOMS |Unites States |Depression |Buchanan (2011) |Buchanan RJ, Zuniga MA, Carrillo-Zuniga G, et al. A pilot study of Latinos with multiple sclerosis: |

| | |AID |Marrie (2008) Marrie |demographic, disease, mental health, and psychosocial characteristics. J Soc Work Disabil Rehabil |

| | |Diabetes |(2011) |2011;10:211-231. |

| | |Hypertension |Marrie (2009) | |

| | |Hyperlipidemia |Marrie (2011) |Marrie RA, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity, socioeconomic status, and |

| | |GI Tract | |multiple sclerosis. Multiple Sclerosis 2008;14:1091-1098. |

| | |Peripheral Vascular | | |

| | |Disease | |Marrie RA, Horwitz RI, Cutter G, Tyry T, Vollmer T. Association between comorbidity and clinical |

| | |Visual disorders | |characteristics of MS. Acta Neurologica Scandinavica 2011;124:135-141. |

| | |Epilepsy | | |

| | |IHD | |Marrie RA, Horwitz RI, Cutter G, Tyry T, Campagnolo D, Vollmer T. The burden of mental comorbidity in |

| | |Lung Disease | |multiple sclerosis: Frequent, underdiagnosed, and under-treated. Multiple Sclerosis 2009;15:385-392. |

| | |Renal Disease | | |

| | |MSK | |Marrie RA, Cutter G, Tyry T. Substantial adverse association of visual and vascular comorbidities on |

| | |Cancer | |visual disability in multiple sclerosis. Mult Scler 2011;17:1464-1471. |

| | |Psychoses | | |

| | |Bipolar disorder | | |

| | |Anxiety | | |

| | |Alcohol abuse | | |

|NHIRD |Taiwan |Cardiac Arrhythmias |Sheu (2013) |Sheu JJ, Lin HC. Association between multiple sclerosis and chronic periodontitis: a population-based |

| | |Stroke |Kang (2010) |pilot study. Eur J Neurol 2013;20:1053-1059. |

| | |CHF | | |

| | |AID | |Kang J-H, Chen Y-H, Lin H-C. Comorbidities amongst patients with multiple sclerosis: a population-based |

| | |IHD | |controlled study. |

| | |Peripheral Vascular | | |

| | |Disease | | |

| | |Diabetes | | |

| | |Hypertension | | |

| | |Hyperlipidemia | | |

| | |Epilepsy | | |

| | |Cancer | | |

| | |GI Tract | | |

| | |Lung Disease | | |

| | |Renal Disease | | |

| | |Psychoses | | |

| | |Depression | | |

| | |Alcohol Abuse | | |

|NHS |United Kingdom |Sleep |Goldacre (2004) |Goldacre MJ, Seagroatt V, Yeates D, Acheson ED. Skin cancer in people with multiple sclerosis: a record |

| | | | |linkage study. Journal of Epidemiology and Community Health 2004;58:142-144. |

|Norwegian Patient Register |Norway |Cancer |Midgard (1996) |Midgard R, Glattre E, Gronning M, Riise T, Edland A, Nyland H. Multiple sclerosis and cancer in Norway. A |

| | | | |retrospective cohort study. Acta Neurol Scand 1996;93:411-415. |

|Population Data British |Canada |Cancer |Kingwell (2012) |Kingwell E, Bajdik C, Phillips N, et al. Cancer risk in multiple sclerosis: findings from British |

|Columbia | | | |Columbia, Canada. Brain 2012;135:2973-2979. |

|SNIIR-AM National Claims |France |AID |Fromont (2013) |Fromont A, Binquet C, Rollot F, et al. Comorbidities at multiple sclerosis diagnosis. J Neurol 2013. |

| | |Diabetes | | |

| | |IHD | | |

| | |GI Tract | | |

| | |Cancer | | |

|SPARCS |United States |Stroke |Allen (2008) |Allen NB, Lichtman JH, Cohen HW, Fang J, Brass LM, Alderman MH. Vascular disease among hospitalized |

| | |Diabetes | |multiple sclerosis patients. Neuroepidemiology 2008;30:234-238. |

| | |IHD | | |

| | |Hypertension, | | |

| | |Hyperlipidemia | | |

|SHDR |Sweden |Cancer |Hemminki (2011) |Hemminki K, Liu X, Ji J, Sundquist K, Sundquist J. Subsequent COPD and lung cancer in patients with |

| | |Lung disease |Hemminki (2012) |autoimmune disease. European Respiratory Journal 2011;37:463-465. |

| | | |Hemminki (2012) | |

| | | |Hemminki (2012) |Hemminki K, Liu X, Ji J, Forsti A, Sundquist J, Sundquist K. Effect of autoimmune diseases on risk and |

| | | |Hemminki (2013) |survival in female cancers. Gynecol Oncol 2012;127:180-185. |

| | | |Hemminki (2013) | |

| | | |Liu (2013) |Hemminki K, Liu X, Forsti A, Ji J, Sundquist J, Sundquist K. Effect of autoimmune diseases on incidence |

| | | | |and survival in subsequent multiple myeloma. Journal of Hematology & Oncology 2012;5:59. |

| | | | | |

| | | | |Hemminki K, Liu X, Ji J, Sundquist J, Sundquist K. Autoimmune disease and subsequent digestive tract |

| | | | |cancer by histology. Annals of Oncology 2012;23:927-933. |

| | | | | |

| | | | |Hemminki K, Liu X, Forsti A, Ji J, Sundquist J, Sundquist K. Subsequent brain tumors in patients with |

| | | | |autoimmune disease. Neuro Oncol 2013;15:1142-1150. |

| | | | | |

| | | | |Hemminki K, Liu X, Försti A, Ji J, Sundquist J, Sundquist K. Subsequent leukaemia in autoimmune disease |

| | | | |patients. British Journal of Haematology 2013;161:677-687. |

| | | | | |

| | | | |Liu X, Ji J, Forsti A, Sundquist K, Sundquist J, Hemminki K. Autoimmune Disease and Subsequent Urological |

| | | | |Cancer. The Journal of Urology 2013;189:2262-2268. |

|SNPR |Sweden |Cardiac Arrhythmias |Jadidi (2013) |Jadidi E, Mohammadi M, Moradi T. High risk of cardiovascular diseases after diagnosis of multiple |

| | |Stroke |Bahmanyar (2009) |sclerosis. Multiple Sclerosis Journal 2013;19:1336-1340. |

| | |CHF | | |

| | |IHD | |Bahmanyar S, Montgomery SM, Hillert J, Ekbom A, Olsson T. Cancer risk among patients with multiple |

| | |Diabetes | |sclerosis and their parents. Neurology 2009;72:1170-1177. |

| | |Valvular disease | | |

| | |Hypertension | | |

| | |GI tract | | |

| | |Lung disease | | |

| | |Cancer | | |

|United Health Care Database|United States |Cancer |Bloomgren (2012) |Bloomgren G, Sperling B, Cushing K, Wenten M. Assessment of malignancy risk in patients with multiple |

| | | | |sclerosis treated with intramuscular interferon beta-1a: retrospective evaluation using a health insurance|

| | | | |claims database and postmarketing surveillance data. Ther Clin Risk Manag 2012;8:313-321. |

|Veterans Health |United States |Cancer |Koshiol (2011) |Koshiol J, Lam TK, Gridley G, Check D, Brown LM, Landgren O. Racial differences in chronic immune |

|Administration Databases | |Depression |Landgren (2011) |stimulatory conditions and risk of non-Hodgkin's lymphoma in veterans from the United States. J Clin Oncol|

| | | |Williams (2005) |2011;29:378-385. |

| | | | | |

| | | | |Landgren AM, Landgren O, Gridley G, Dores GM, Linet MS, Morton LM. Autoimmune disease and subsequent risk |

| | | | |of developing alimentary tract cancers among 4.5 million US male veterans. Cancer 2011;117:1163-1171. |

| | | | | |

| | | | |Williams RM, Turner AP, Hatzakis M, Jr., Bowen JD, Rodriquez AA, Haselkorn JK. Prevalence and correlates |

| | | | |of depression among veterans with multiple sclerosis. Neurology 2005;64:75-80. |

|Veterans Health |United States |Alcohol abuse |Turner (2009) |Turner AP, Hawkins EJ, Haselkorn JK, Kivlahan DR. Alcohol misuse and multiple sclerosis. Arch Phys Med |

|Administration MS National | | | |Rehabil 2009;90:842-848. |

|Data Repository | | | | |

AID: Autoimmune diseases, GI Tract: Gastrointestinal Tract, CHF: Congestive Heart Failure, IHD: Ischemic Heart Disease, MSK: Musculoskeletal disease.

DHDR: Danish Hospital Discharge Registry, DMSR: Danish Multiple Sclerosis Registry, DMSRU: Dalhousie Multiple Sclerosis Research Unit Database, DNRP: Danish National Registry of Patients, DNSGP: Dutch National Survey of General Practice, GPRD: General Practice Research Database, HSCIC-HES: Health and Social Care Information Centre- Hospital Episode Statistics, KPNC: Kaiser Permanente Northern California, NARCOMS: North American Research Committee on Multiple Sclerosis, NHIRD: Taiwan National Health Insurance Research Database, NHS: National Health Statistics, SNIIR-AM: Système National d’Information Inter-Régime de l’Assurance Maladie, SPARCS: New York Department of Health Statewide Planning and Research Cooperate System, SHDR: Swedish Hospital Discharge Register, Swedish National Patient Registries

Administrative Data Sources

North America

Canada

The universal healthcare system in Canada is administered on a provincial or territorial basis. Residents of each province are covered by health insurance plans which provide coverage for preventative care, medical treatment by physicians, as well as access to hospitals and surgery. The health administrative datasets described below are sorted by Canadian province.

National Databases

Discharge Abstract Database (DAD)

Description

The Discharge Abstract Database (DAD) captures administrative, clinical and demographic information on hospital discharges across Canada. Data is received directly from acute care facilities or from their respective health/regional authority or ministry/department of health. Facilities in all provinces and territories except Quebec are required to report. Data from Québec is submitted to CIHI directly by the ministère de la Santé et des Services sociaux du Québec. This data is appended to the DAD to create the Hospital Morbidity Database (HMDB).

Custodian

Canadian Institute for Health Information (CIHI).

Time Period Covered

From fiscal 1979-80 onward. Approval is required to access data from 1994-1995 to 2011-2012.

Population Covered

More than 3.2 million abstracts are submitted annually to the DAD, representing approximately 75% of all acute inpatient separations in Canada. Quebec’s acute inpatient separations are reported to the Hospital Morbidity Database (HMDB) and usually account for 25% of total inpatient separations in Canada.

Diagnosis Coding Procedures

Since 2004-2005, all DAD records have been reported in ICD-10-CA and CCI; prior to that, ICD-9, CCP and ICD-9-CM were used.

Data access procedures

A Data Inquiry Form can be accessed from: . After the initial submission, CIHI will provide researchers with an assessment, help finalize the request and deliver the data if project is approved.

Cost of Access

Universities, researchers and health professionals are charged $145 per hour on a cost recovery basis. Charges are applied per number of hours needed to review the data request, consult on and develop specifications, manipulate and/or analyze data, seek advice from CIHI’s support areas, perform data quality assurance and transmit data.

Mapping of fields across datasets

Health Care Numbers are assigned to individuals by provincial/territorial governments. The DAD captures a variable representing the province or territory that issued the Health Care Number, as the numbers are unique only within the province or territory. Combining the two variables with other relevant personal information data fields (such as birthdates, gender and postal code) allows individuals to be uniquely identified within the DAD. The Health Care Number also facilitates linkage to other provincial databases.

The Hospital Morbidity Database (HMDB)

Custodian

Canadian Institute for Health Information.

Time Period Covered

From fiscal 1979-80 onward. Approval is required to access data from 1994-1995 to 2011-2012.

Population Covered

The HMDB is a national data holding that captures administrative, clinical and demographic information on inpatient separations from acute care hospitals. The majority of records in the HMDB are from the Discharge Abstract Database (DAD) with the exception of data from Quebec.

Diagnosis coding procedures

As of 2004–2005, all DAD records have been reported in ICD-10-CA and CCI; prior to that, ICD-9, CCP and ICD-9-CM were used.

As of 2006–2007, Quebec began using ICD-10-CA and CCI to code diagnoses and interventions; prior to that, Quebec data was coded using ICD-9, CCP and ICD-9-CM. 

Data access procedures

A Data Inquiry Form can be accessed from: . After the initial submission, CIHI will provide researchers with an assessment, help finalize the request and deliver the data if project is approved.

Cost of Access

Universities, researchers and health professionals are charged $145 per hour. Charges are applied per number of hours needed to review the data request, consult on and develop specifications, manipulate and/or analyze data, seek advice from CIHI’s support areas, perform data quality assurance and transmit data.

Mapping of fields across datasets

Health Care Numbers are assigned to individuals by provincial/territorial governments. The DAD captures a variable representing the province or territory that issued the Health Care Number, as the numbers are unique only within the province or territory. Combining the two variables with other relevant personal information data fields (such as birthdates, gender and postal code) allows individuals to be uniquely identified within the DAD. The Health Care Number also facilitates linkage to other provincial databases.

National Ambulatory Care Reporting System (NACRS)

Description

NACRS collects demographic, administrative, clinical and service-specific data for emergency department, day surgery and other ambulatory care visits.

Custodian

Canadian Institute for Health Information (CIHI).

Time Period Covered

From fiscal 2001/02 onward.

Population covered

Contains data for all hospital-based and community-based ambulatory care: Day surgery, outpatient clinics, and emergency departments.

Diagnosis coding procedures

ICD-10-CA.

Data access procedures

A Data Inquiry Form can be accessed from: . After the initial submission, CIHI will provide researchers with an assessment, help finalize the request and deliver the data if project is approved.

Cost of Access

Universities, researchers and health professionals are charged $145 per hour on a cost recovery basis. Charges are applied per number of hours needed to review the data request, consult on and develop specifications, manipulate and/or analyze data, seek advice from CIHI’s support areas, perform data quality assurance and transmit data.

Mapping of fields across datasets

Health Care Numbers are assigned to individuals by provincial/territorial governments. NACRS captures a variable representing the province or territory that issued the Health Care Number, as the numbers are unique only within the province or territory. Combining the two variables with other relevant personal information data fields (such as birthdates, gender and postal code) allows individuals to be uniquely identified within NACRS. The Health Care Number also facilitates linkage to other provincial databases.

Provincial Databases

Alberta

Alberta Health Care Insurance Plan (AHCIP) Registry

Description

Alberta has a population of about 3.6 million people, or 10.9% of the population of Canada. Alberta Health provides universal health care to 98% of their population. All hospital, physician and prescription claims include a Personal Health Identification Number (PHIN) which uniquely identifies the person receiving the service. The Information and Analysis Branch provides access to several administrative health datasets listed below.

Custodians

Alberta Health

Diagnosis Coding Procedures

Physician claims, ICD-9 4-digit coding, up to three diagnoses.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2002, up to 16 diagnosis codes.

Prescription claims, Alberta Blue Cross: Available for persons aged ≥65 years or covered under special publicly funded programs (e.g. multiple sclerosis). Includes drug, quantity, dose, and dates for prescription dispensations.

Data access

All research data access requests are made to: health.resdata@gov.ab.ca. Requests are initially reviewed by the Information and Analysis Branch and further instructions provided to those projects that are approved.

A detailed list of information to include in the Research Request is accessible through this website:

Cost of access

$1600 /day for research related expenses. The expected cost range will be estimated after an initial consultation. Costs are for analytical time only and exclude meetings, communications and run-time.

$1000 /day for administrative functions.

Mapping of fields across datasets

Linkage of records that refer to the same individual across time and between various data sets is possible through the through the person’s unique PHIN.

Other data files needed for analyses

Alberta Vital Statistics (birth and death records) is available from 1983. Additional approval to access this data may be required.

References

Jette N, & Reid AY, Quan H, Hill MD, Wiebe S. How accurate is ICD coding for epilepsy? Epilepsia 2009, 51:1-8.

Reid AY, St.Germaine-Smith C, Liu M, Sadiq S, Quan H, Wiebe S, Faris P, Dean S, & Jette N. Development and validation of a case definition for epilepsy for use with administrative health data. Epilepsy Research 2012, 102:173-179.

Warren SA, Svenson LW, Warren KG. Contribution of incidence to increasing prevalence of multiple sclerosis in Alberta, Canada. Mult Scler 2008;14(7):872-879

Warren S, Svenson LW, Warren KG, Metz LM, Patten SB, Schopflocher DP. Incidence of multiple sclerosis among First Nations people in Alberta, Canada. Neuroepidemiology 2007; 2821-27

Datasets available through AHCIP:

Inpatient Discharge Abstract Dataset (DAD)

Custodian

Alberta Health and the Canadian Institute for Health Information (CIHI).

Time Period Covered

April 1, 1993 to present.

Population covered

Contains demographic, administrative and clinical (recipient, service, diagnosis and procedure) information for people who have been discharged from an inpatient bed. Data on Alberta residents who are admitted to a hospital in another province are included.

Diagnosis coding procedures

ICD-9-CM (prior to April 2002); ICD-10-CA/CCI (April 2002 to present).

Data access procedures

Refer to AHCIP Registry data access procedures.

Ambulatory Care Classification System Dataset

Custodian

Alberta Health.

Time Period Covered

April 1, 1997 to present.

Population covered

Facility-based ambulatory care information (same-day surgery, day procedures, emergency room visits, and community rehabilitation program services). Contains recipient, service, diagnosis, and procedure interventions.

Diagnosis coding procedures

ICD-9-CM (prior to April 2002); ICD-10-CA/CCI (April 2002 to present).

Data access procedures

Refer to AHCIP Registry data access procedures.

Practitioner Payments Dataset

Custodian

Alberta Health.

Time Period Covered

From 1983 onwards.

Population covered

Fee for Service (FFS) claims information for all medically required services covered by the AHCIP, Alberta's universal health insurance.

Diagnosis coding procedures

ICD-9-CM. Schedule of Medical Benefits used.

Data access procedures

Refer to AHCIP Registry data access procedures.

Population Registry

Custodian

Alberta Health.

Time Period Covered

From 1983 onwards.

Population covered

Basic demographic and geographic information of Albertans who are registered with AHCIP. This registry is the base for Alberta population counts.

Data access procedures

Refer to AHCIP Registry data access procedures.

Pharmacy Data- Alberta Blue Cross

Custodian

Alberta Health.

Time Period Covered

From 1994 onwards.

Population covered

Prescription drugs, prescribing information, ambulance, prosthetics/orthotics and palliative care services for those Albertans aged ≥65 years or covered under special publicly funded programs (e.g. multiple sclerosis). Includes drug, quantity, dose, and dates for fills.

Data access procedures

Refer to AHCIP Registry data access procedures.

Multiple Sclerosis Clinic Database, University of Calgary

See description under Clinical Databases/Registries.

British Columbia (BC)

Population Data BC

Description

British Columbia has a population of about four million people, or 13.1% of the population of Canada. The BC Ministry of Health provides universal health care to the residents of BC. It directly manages the Medical Services Plan, which covers most physician services; PharmaCare, which provides prescription drug insurance; and the BC Vital Statistics Agency, which registers and reports on vital events. Since 1985, all hospital, physician and prescription claims include a Personal Health Identification Number (PHIN) which uniquely identifies the person receiving the service. A list of useful datasets accessible through PopDataBC is detailed below

Custodians

The BC Ministry of Health

Population Data BC

Diagnosis Coding Procedures

Physician claims, ICD-9 4-digit coding from 1991/92, one diagnosis.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2001, up to 25 diagnosis codes.

Prescription claims, PharmaNet. Includes drug, quantity, dose, directions, day’s supply, day’s fills and physician specialty.

Data access

The various stages of the Data Access Process are outlined in this website:

Note: Only researchers who will conduct their analyses in Canada are eligible to apply for access to data. Submitted project must have approval from a recognized Research Ethics Board.

Cost of access

$8,000.00-15,000.00 per project. Complexity and external linkages can drive the charges higher. Further information regarding costs is outlined at

Mapping of fields across datasets

Approved research projects have the authorization to link records that refer to the same individual across time and between various data sets (health, education, early childhood development, workplace and the environment) through the person’s unique PHIN.

Other data files needed for analyses

Health care, demographic, occupational, early childhood and spatial data can all be linked through datasets held by PopDataBC.

References

Lacaille D, Guh D, Abrahamowicz M, Anis AH, & Esdaile JM. Use of nonbiologic disease-modifying antirheumatic drugs and risk of infection in patients with rheumatoid arthritis. Arthritis & Rheumatism 2008, 59:1074-81.

Kopec JA, Rahman MM, Sayre EC, Cibere J, Flanagan WM, Aghajanian J, Anis AH, Jordan JM, & Badley EM. Trends in physician-diagnosed osteoarthritis incidence in an administrative database in British Columbia, Canada, 1996-1997 through 2003-2004. Arthritis & Rheumatism 2008, 59 (7):929-34.

Evans C, Kingwell E, Zhu F, Oger J, Zhao Y, Tremlett H. Hospital admissions and MS: temporal trends and patient characteristics. Am J Manag Care 2012; 18(11):735-742.

Datasets Available through PopDataBC:

Medical Services Plan (MSP) Payment Information Dataset

Custodian

BC Ministry of Health.

Time Period Covered

April 1, 1985 to present.

Population covered

All medically required services covered by the Medical Services Plan (MSP), BC's universal health insurance: Physicians, supplementary benefit practitioners, laboratory, diagnostic procedures, dental and oral surgery performed in hospital. Payments made on behalf of BC residents who obtained services in Quebec, the U.S. and other countries are included. 

Diagnosis coding procedures

ICD-9 4-digit coding from 1991/92, one diagnosis.

Data access procedures

Refer to Population Data BC data access procedures.

References

Hu, W. Diagnostic Codes in MSP Claim Data, Summary Report. Victoria: Medical Services Plan; 1996.

Pharmacare

Custodian

BC Ministry of Health

Time Period Covered

April 1, 1985 to present.

Population covered

Data on prescription drugs paid for under the PharmaCare program. Note: these are a subset of the prescriptions issued in BC.

Other data files needed for analyses

Prescription drug use below the deductible level does not appear in the PharmaCare data, drugs given to hospital inpatients are not included in the PharmaCare data. PharmaNet is a province-wide network that links all BC pharmacies to a central set of data systems. PharmaNet data can be linked to PharmaCare data.

Diagnosis coding procedures

The Drug Identification Number also referred to as Canadian Drug Identity Code (CDIC).

Data access procedures

Refer to Population Data BC data access procedures.

References

Hu W. Diagnostic Codes in MSP Claim Data, Summary Report. Victoria: Medical Services Plan; 1996.

PharmaNet

Custodian

BC Ministry of Health

Time Period Covered

April 1, 1996 to present.

Population covered

It captures all prescriptions for drugs and medical supplies dispensed from community pharmacies in BC as well as prescriptions dispensed from hospital outpatient pharmacies for patient use at home. It also includes PharmaCare and patient paid prescription claim information for drugs, dispensing fees, and special services fees.

Diagnosis coding procedures

The Drug Identification Number is also referred to as Canadian Drug Identity Code (CDIC).

Data access procedures

Only one Data Access Request form will be required by researchers to access both linked and unlinked PharmaNet data.

Population Data BC will submit the PharmaNet applications on behalf of researchers to the Ministry of Health.

References



Discharge Abstract Database (DAD)

Custodian

BC Ministry of Health and the Canadian Institute for Health Information (CIHI).

Time Period Covered

April 1, 1985 to present.

Population covered

Demographic, administrative and clinical data for hospital discharges (inpatient acute, chronic, rehabilitation) and day surgeries for BC residents. Data on BC residents who are admitted to a hospital in another province are included in the database.

Diagnosis coding procedures

5-digit ICD-9 coding changed to ICD-10-CA coding in 2001, up to 25 diagnosis codes.

Data access procedures

Refer to Population Data BC data access procedures.

References

McKendry R, Reid RJ, McGrail KM, Kerluke KJ. Emergency Rooms in British Columbia: A pilot project to Validate Current Data and Describe Users. Vancouver (BC): Centre for Health Services and Policy Research; December 2002. Available at:

Mental Health

Custodian

The Ministry of Health Services, Mental Health and Addictions:

Time Period Covered

April 1, 1986 to present.

Population covered

Care Episodes and Service Events with a ‘first contact date' greater than or equal to April 1, 1986.

Diagnosis coding procedures

5-digit ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to Population Data BC data access procedures.

BC Cancer Agency

Custodian

BC Cancer Agency (BCCA):

Time Period Covered

April 1, 1985 to present.

Population covered

Information on all cancers diagnosed for BC residents. All cases include a postal code.

Diagnosis coding procedures

ICD-O-1 prior to 1992, from 1992 until 2000 ICD-O-2, ICD-O-3 since 2001.

Data access procedures

Refer to Population Data BC data access procedures.

Demographics Dataset: Consolidation File

Custodian

BC Ministry of Health

Time Period Covered

April 1, 1986 to present.

Population covered

Basic demographics such as age and sex, geo-codes indicating location of residence, and socioeconomic quintiles/deciles for all residents of BC.

Data access procedures

Refer to Population Data BC data access procedures.

References

Wilkins R. Use of postal codes and addresses in the analysis of health data. Health Reports 1993; 5(2):157-177.

British Columbia Multiple Sclerosis Database (BCMS)

See description under Clinical Databases/Registries.

Manitoba

Population Health Research Data Repository

Description

Manitoba has a population of about 1.2 million people, or 3.6% of the population of Canada. Manitoba Health provides service to 98% of the population in Manitoba. Since 1984, all hospital, physician and prescription claims submitted to Manitoba Health include a Personal Health Identification Number (PHIN) which uniquely identifies the person receiving the service. Linkage to multiple datasets included in the repository is available through the person’s unique PHIN. A list of useful datasets accessible through the repository is detailed below.

Custodians

Manitoba Health

Manitoba Centre for Health Policy (MCHP)

Diagnosis Coding Procedures

Physician claims, ICD-9 3-digit coding, one diagnosis.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2002, up to 16 diagnosis codes.

Prescription claims, DPIN. Includes drug, quantity, dose, and dates for fills.

Data access

The complete process takes a minimum of 4 months. The procedure is consistent for all datasets included in the Repository. Approval is required from 3 sources:

University of Manitoba Centre for Health Policy (MCHP).

Health Information Privacy Committee (HIPC)

University of Manitoba Human Research Ethics Board (HERB)

Cost of access

Determined based on review of research proposal by MCHP, on a cost recovery basis. Hourly rates vary from year to year.

Other data files needed for analyses

Socioeconomic status can be obtained by linking postal code to census data (through postal code conversion file).

Mapping of fields across datasets

Linkage to other datasets included in the repository is available through the person’s PHIN.

References

Marrie RA, Blanchard J, Leung S, & Elliot L. The rising prevalence and changing age distribution of multiple sclerosis in Manitoba. Neurology 2010, 74 (6): 465-471.

Marrie RA, Yu BN, Leung S, Elliot L, Caetano P, Warren S, Wolfson C, Patten SB, Svenson LW, Tremlett H, Fisk J, & Blanchard JF. Prevalence and incidence of ischemic heart disease in multiple sclerosis: A population based validation study. MSRD 2013, 2(4): 355-61.

Datasets included in the Repository:

Hospital Discharge Abstracts Data (DAD):

Custodian

Manitoba Health and the Canadian Institute for Health Information (CIHI).

Time Period Covered

1970/71 to present.

Population covered

All inpatient admissions and day surgery services for both Manitoba residents and non-Manitoba residents hospitalized in acute and chronic care facilities in Manitoba. All inpatient admissions and day surgery services for all Manitobans admitted to out-of-province facilities.

Diagnosis coding procedures

5 digits ICD-9 changed to ICD-10-CA in 2004. Up to 16 diagnostic fields. The first field is considered the most responsible (primary) diagnosis for the hospitalization.

Data access procedures

Refer to Population Health Research Data Repository data access procedures.

Medical Services Data (Physicians Claims)

Custodian

Manitoba Health.

Time Period Covered

1970/71 onward.

Population covered

Primarily reimbursement claims from 98% of all Manitoba residents receiving services from Manitoba providers. All non-Manitoba residents receiving services from Manitoba providers since 1993/94.

Diagnosis coding procedures:

5 digits ICD-9 system.

Data access procedures

Refer to Population Health Research Data Repository data access procedures.

Drug Program Information Network Data

Custodian

Manitoba Health.

Time Period Covered

1995/96 to present for DPIN PharmaCare, 1973-95 for pre-DPIN PharmaCare.

Population covered

All Manitoba residents regardless of insurance coverage or payor.

Diagnosis coding procedures

DPIN links Manitoba Health and all pharmacies in Manitoba to a central database maintained by Manitoba Health. The DPIN system generates complete drug profiles for each client including all transactions at the point of distribution.

Data Highlights

Contains prescription information (drug, dosage and date), non-adjudicated claims, ancillary programs and non-drug products.

Data access procedures

Refer to Population Health Research Data Repository data access procedures.

Mental Health Management Information System (MHMIS)

Custodian

Manitoba Health.

Time Period Covered

1990/1991 to present.

Population covered

All comprehensive case management information for Manitoba residents who received clinical, social, or rehabilitation services from the Mental Health and Spiritual Health Branch of Manitoba Health.

Diagnosis coding procedures

ICD-9-CM as well as Diagnostic and Statistical Manual of Mental Disorders coding: DSM-III for 1990 to 2001 (01/19) and DSM-IV for 2001 (01/20) to the present.

Data Highlights

Includes demographic information such as marital status, education, and living arrangements.

Data access procedures

Refer to Population Health Research Data Repository data access procedures. Additional approval required through the Winnipeg Regional Health Authority (WRHA).

Urgent Care

Custodian

Manitoba Health, Winnipeg Regional Health Authority (WRHA), Misericordia Health Centre (MHC).

Time Period Covered

2001/2002 to 2006/07

Population covered

All urgent care delivered at Misericordia Health Centre in Winnipeg, including all admission, discharge and transfer information.

Data access procedures

Refer to Population Health Research Data Repository data access procedures. Additional approval required through the Winnipeg Regional Health Authority (WRHA) and Misericordia Health Centre (MHC).

Diagnosis coding procedures:

5 digits ICD-9 system.

Other data files needed for analyses:

Data are interfaced daily with medical charts to produce a hospital-like abstract for each patient.

Emergency Care Database

Custodian

Manitoba Health, Winnipeg Regional Health Authority (WRHA).

Time Period Covered

ADT: 1999/2000 to 2009/10. E-triage: 2004/05 to present.

Population covered

Almost all Emergency Department visits in Winnipeg. 

Diagnosis coding procedures

The Canadian Emergency Department Triage & Acuity Scale (CTAS) (as of 2000/01). As of 2004 this is done electronically and the computer-generated triage codes are stored in the E Triage file. Previously a triage code was generated manually and reported in the ADT system. Effective 2007, ICD codes for ED patients are no longer submitted to Manitoba Health.

Data access procedures

Refer to Population Health Research Data Repository data access procedures. Additional approval required through the Winnipeg Regional Health Authority (WRHA).

Mapping of fields across datasets

ER data files can be linked successfully to hospital abstract data, vital statistics and long-term care files. Medical claims data can be linked to the majority of ER visits at the St. Boniface General Hospital and Health Sciences Centre.

Multiple Sclerosis Clinic Database and Registry, Health Sciences Centre, Winnipeg

See description under Clinical Databases/Registries.

Nova Scotia

Research Data Repository

Description

Nova Scotia has a population of about 950,000 people, or 2.8% of the population of Canada. Nova Scotia Medical Service Insurance (MSI) provides universal health care to 98% of their population. The Provincial government has supplied Health Data Nova Scotia (HDNS) at Dalhousie University with complete Medicare, PharmaCare and Hospital datasets for research purposes. The repository also includes postal code-based geographical mapping as well as national and provincial census data. All hospital, physician and prescription claims include a Medical Services Identification (MSI) number which uniquely identifies the person receiving the service. Linkage to multiple datasets included in the repository is available through the person’s unique MSI number. A list of useful datasets accessible through the repository is detailed below.

Custodians

Health Data Nova Scotia at Dalhousie University

Diagnosis Coding Procedures

Physician claims, ICD-9 3-digit coding, one diagnosis 1989-96, three diagnoses from 1997 onwards.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2001, up to 5 diagnoses 1989-91, 7 diagnosis 1992-95, and 16 diagnoses 1996 onwards.

Prescription claims, NS Special Therapies (MS). Available from 1989 onward. Includes drug, quantity, dose, and dates for fills.

Data access

Access to data is a 9 step process outlined in the following website:

It requires approval by HDNS and from the Dalhousie University Research Ethics Board.

Cost of access

An estimate of the cost will be provided once the project has been approved by HDNS.

Mapping of fields across datasets

Linkage of records that refer to the same individual across time and between various datasets in the repository is possible through the person’s unique MSI number.

Other data files needed for analyses

Datasets external to HDNS that require additional access procedures: Cancer Care Nova Scotia, Diabetes Care Program of Nova Scotia, Maritime Heart Centre Database, Cardiovascular Health Nova Scotia, Brain Trauma Outcomes Database.

Datasets included in the Repository:

Physician Billings Database

Custodian

HDNS

Time Period Covered

From 1989 onwards.

Population covered

Fee for Service (FFS) claims information for all medically required services covered by Nova Scotia MSI. Includes physician information (e.g. specialty), provider ID, patient demographic information (e.g. birth date, sex, postal code) and costing information.

Diagnosis coding procedures

ICD-9 3-digit coding, one diagnosis 1989-96, three diagnoses from 1997 onwards.

Data access procedures

Refer to Research Data Repository data access procedures.

Hospital Discharge Abstract Database

Custodian

HDNS and the Canadian Institute for Health Information (CIHI).

Time Period Covered

From 1989 onwards.

Population covered

Demographic, administrative, and clinical data (inpatient acute, chronic, rehabilitation, physiotherapy) as well as day surgery data for Nova Scotia residents. Data on NS residents who are admitted to a hospital in another province are included in the database.

Diagnosis coding procedures

5-digit ICD-9 coding changed to ICD-10-CA coding in 2001, up to 5 diagnoses 1989-91, 7 diagnosis 1992-95, and 16 diagnoses 1996 onwards.

Data access procedures

Refer to Research Data Repository data access procedures.

Mental Health Outpatient Information System (MHOIS)

Custodian

HDNS

Time Period Covered

From 1992 onwards.

Population covered

Each type of client encounter (registration, assessment, repeat transaction, closure) and clinically significant telephone contact from all mental health clinics across Nova Scotia.

Diagnosis coding procedures

5-digit ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to Research Data Repository data access procedures.

PharmaCare Prescriptions

Custodian

HDNS

Time Period Covered

From 1989 onwards.

Population covered

Seniors and community services.

Diagnosis coding procedures

Patient demographics, provider ID, pharmacy ID, date of prescription, drug identification number, drug cost, quantity dispensed.

Data access procedures

Refer to Research Data Repository data access procedures.

Insured Patient Registry (MASTER)

Custodian

HDNS

Time Period Covered

From 1996 onward.

Population covered

Contains longitudinal information about every individual registered as a beneficiary of provincial MSI healthcare services including: Demographic information, Insured Health Benefits program eligibility start / end dates, geography information.

Includes “Multiple Sclerosis Copayment Assistance” program information.

Data access procedures

Refer to Research Data Repository data access procedures.

Dalhousie MS Research Unit (DMSRU) Database

See description under Clinical Databases/Registries.

Newfoundland & Labrador

Newfoundland & Labrador Centre for Health Information

Newfoundland & Labrador (NL) has a population of about half a million people, or 1.5% of the population of Canada. The Centre for Health Information is responsible for the development and implementation of a confidential and secure electronic health record for the province. The Medical Care Plan (MCP) provides universal health care for residents of NL. A description of each of the datasets is provided below.

Custodians

Newfoundland & Labrador Centre for Health Information.

Diagnosis Coding Procedures

Physician claims, ICD-9 digit coding.

Hospital claims, ICD-9 coding changed to ICD-10-CA coding in 2001.

Prescription claims, the Newfoundland and Labrador Prescription Drug Program (NLPDP) provides coverage of eligible prescription drugs for residents 65 years of age and older, low-income families, and special authorization drugs (e.g. multiple sclerosis).

Data access

In order to access ICES data, researchers must submit an application to the Information Request Coordinator IM@nlchi.nl.ca

Application forms can be found on this website

Cost of access

An estimate of the cost will be provided once the project has been approved.

Mapping of fields across datasets

Data linkage is possible for all datasets available through the centre.

Other data files needed for analyses

Population, demographic, health services, disease cohort, disease registry, care provider, geography, SES and coding data are all available through the centre.

References:

Gulliver WP, MacDonald D, Gladney N, & Alaghehbandan R. Long-term prognosis and co-morbidities associated with psoriasis in the Newfoundland and Labrador founder population. Journal of Cutaneous Medicine and Surgery 2011, 15(1):37-47.

Sikdar KC, Alaghehbandan R, MacDonald D, Barrett B, Collins KD, Donnan J, & Gadag V. Adverse drug events in adult patients leading to emergency department visits in Newfoundland and Labrador, Canada. The Annals of Pharmacotherapy 2010, 44:641-649.

Clinical Database Management System (CDMS) (CIHI Discharge Abstract Database)

Custodian

Newfoundland & Labrador Centre for Health Information and the Canadian Institute for Health Information (CIHI).

Time Period Covered

From 1994 onwards.

Population covered

Demographic, clinical and administrative data for all inpatient admissions and day surgery services for residents of NL.

Diagnosis coding procedures

ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to the Centre for Health Information data access procedures.

MCP Fee-for-Service Physician Claims Database

Custodian

Newfoundland & Labrador Centre for Health Information and the Department of Health and Community Services.

Time Period Covered

From 1995 onwards.

Population covered

All services provided by fee-for-service physicians under the provincial Medical Care Plan (MCP) of NL.

Diagnosis coding procedures

ICD-9 coding.

Data access procedures

Refer to the Centre for Health Information data access procedures.

Newfoundland & Labrador Prescription Drug Program Dataset

Custodian

Newfoundland & Labrador Centre for Health Information and the Department of Health and Community Services.

Time Period Covered

1998-99 -2002-03 (identifiable), 2007-present (de-identified).

Population covered

Contains information regarding prescription drugs dispensed to beneficiaries of the NLPDP.

Data access procedures

Refer to the Centre for Health Information data access procedures.

Newfoundland & Labrador Chronic Disease Surveillance System

Custodian

Newfoundland & Labrador Centre for Health Information.

Time Period Covered

From 1995 onwards.

Population covered

A composite database that links records from the MCP Beneficiary Registration, MCP Fee-for-Service Physician Claims Databases and the Clinical Database Management System. The database tracks the prevalence, incidence, co-morbidities, and health service utilization of individuals with and without select chronic diseases in the province.

Diagnosis coding procedures

Physician claims, ICD-9 digit coding.

Hospital claims, ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to the Centre for Health Information data access procedures.

Cancer and Chronic Disease Research Database

Custodian

Newfoundland & Labrador Centre for Health Information

Time Period Covered

From 1995 onwards.

Population covered

It contains linked records from the Newfoundland and Labrador Chronic Disease Surveillance System (NCDSS) and the Oncology Patient Information System (OPIS). The database was originally created to study the relationship between cancer and diabetes.

Diagnosis coding procedures

Physician claims, ICD-9 digit coding.

Hospital claims, ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to the Centre for Health Information data access procedures.

Ontario

The Institute for Clinical Evaluative Sciences (ICES)

Ontario has a population of about thirteen million people, or 38.4% of the population of Canada. ICES is an independent, non-profit research organization that evaluates health care services and delivery to residents of Ontario. The Ontario Health Insurance Plan (OHIP) provides universal health care residents of Ontario. The provincial government has supplied ICES with various administrative datasets (health services, demographic, disease cohort, geographic) for the conduction of research. A description of each of the datasets is provided below.

Custodians

ICES

Diagnosis Coding Procedures

Physician claims, ICD-9 digit coding.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2001.

Prescription claims, available from 1990 onwards. Ontario Drug Benefit (ODB) program. Includes Drug Identification Number, drug quantity, number of days supplied, cost, plan the prescription falls under (e.g. MS therapy), dispensing date, patient and prescriber.

Data access

In order to access ICES data, researchers must either:

1. Become an ICES scientist. Information on how to become an ICES scientist is available on this site

2. Work in collaboration with an ICES scientist; Grants and projects from investigators external to ICES require an ICES co-investigator

Cost of access

An estimate of the cost will be provided once the project has been approved by ICES.

Mapping of fields across datasets

A unique identifier, the ICES Key Number (IKN), is common to all datasets and is used to link them.

Other data files needed for analyses

Population, demographic, health services, disease cohort, disease registry, care provider, geography, SES and coding data are all available through ICES.

References

Hux JE, Ivis F, Flintoft V, & Bica A. Determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 2002, 25(3): 512-516.

Mittman N, Knowles SR, Gomez M, Fish JS, Cartotto R, & Shear NH. Evaluation of the extent of under-reporting of serious adverse drug reactions: The case of toxic epidermal necrolysis. Drug Safety 2004, 27(7): 477-487.

Hopman WM, Harrison MB, Coo H, Friedberg E, Buchanan M, & VanDenKerkhof EG. Associations between chronic disease, age and physical and mental health status. Chronic Diseases in Canada 2009, 29(2):108-116.

Datasets Available through ICES

Ontario Health Insurance Plan (OHIP) Database

Custodian

ICES

Time Period Covered

From 1991 onwards.

Population covered

This dataset contains claims paid for by the OHIP, Ontario’s universal health care plan. The database contains claims (service provided, diagnosis, fee paid, date of service, physician specialty) from physicians, laboratories, and out-of-province providers.

Diagnosis coding procedures

ICD-9 coding.

Data access procedures

Refer to ICES data access procedures.

Discharge Abstracts Database (DAD) and Same Day Surgery Database (SDS)

Custodian

ICES and the Canadian Institute for Health Information (CIHI).

Time Period Covered

From 1988 onwards.

Population covered

All inpatient admissions (acute, rehabilitation, chronic, and day surgery) for institutions in Ontario. All inpatient admissions and day surgery services for residents of Ontario admitted to out-of-province facilities.

Diagnosis coding procedures

5-digit ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to ICES data access procedures.

Ontario Drug Benefits Claims

Custodian

ICES

Time Period Covered

From 1990 onwards.

Population covered

All pharmacist claims for each prescribed drug that is covered under the Ontario Drug Benefit (ODB) plan. The ODB program provides drug benefits for all adults aged 65+ and those that fall under the Exceptional Access Program (e.g. multiple sclerosis).

Diagnosis coding procedures

Includes Drug Identification Number, drug quantity, number of days supplied, cost, plan the prescription falls under (e.g. MS therapy), dispensing date, patient and prescriber.

Data access procedures

Refer to ICES data access procedures.

National Ambulatory Care Reporting System

Custodian

ICES

Time Period Covered

From 2000 onwards.

Population covered

Outpatient visits to hospital and community based ambulatory care, such as emergency departments, cancer clinics, and renal dialysis clinics.

Diagnosis coding procedures

ICD-9 coding changed to ICD-10-CA coding in 2001.

Data access procedures

Refer to ICES data access procedures.

Ontario Mental Health Reporting System

Custodian

ICES

Time Period Covered

From 2005 onwards.

Population covered

Administrative, socio-demographic information, mental health history, mental state indicators, substance use, cognition, self-care, health conditions, stressors, medications, social relations, psychiatric diagnostic information.

Diagnosis coding procedures

ICD-9 coding.

Data access procedures

Refer to ICES data access procedures.

Cardiac Care Network

Custodian

ICES

Time Period Covered

From 1991 onwards.

Population covered

Registry of adults waiting for a cardiac procedure in Ontario. Data includes: type of procedure, dates, waiting time, acuity, clinical and anatomical information, reason for removal from list (procedure, death, etc).

Data access procedures

Refer to ICES data access procedures.

CT/MRI Abstraction Data

Custodian

ICES

Time Period Covered

2006-2007

Population covered

20,000 scans. This data was created from the ICES project “Indications for and results of outpatient Computed Tomography and Magnetic Resonance Imaging in Ontario”. The study was a chart abstraction study of a consecutive series of CT and/or MRI scans. Each chart includes: Scan details, previous imaging, follow-up testing, complications, indications, findings.

Data Highlights

Data has been linked to administrative data, to obtain information on prior scans in the same region, as well as prior and subsequent testing.

Data access procedures

Refer to ICES data access procedures.

Ontario Cancer Registry

Custodian

ICES

Time Period Covered

From 1964 onwards.

Population covered

All Ontario residents who have been diagnosed with cancer or who have died of cancer. All new cases of cancer are registered, except non-melanoma skin cancer.

Data access procedures

Refer to ICES data access procedures.

Ontario Diabetes Database

Custodian

ICES

Time Period Covered

From 1991 onwards.

Population covered

All Ontario diabetic patients identified since 1991. A patient is said to be diabetic if s/he had one hospital admission with a diabetes diagnosis or an OHIP claims with a diabetes diagnosis followed within two years by either an OHIP claim or a hospital admission with a diabetes diagnosis. The gestational diabetes cases have been filtered out.

Data access procedures

Refer to ICES data access procedures.

Quebec

EPSEBE (Environnement pour la Promotion de la Santé et du Bien-être)

Quebec has a population of about eight million people, or 23.6% of the population of Canada. Quebec has a universal health insurance plan managed by the Re´gie de l’assurance Maladie du Quebec (RAMQ) and all residents are covered by this healthcare service. EPSEBE is a service supported by various provincial government agencies that gives researchers access to Quebec’s main administrative databases. All the databases available through EPSEBE are linkable through RAMQ, a 10-digit unique identifier for each resident. A list of accessible databases is provided below.

Custodians

Institute de la Statistique Québec (ISQ)

EPSEBE

Diagnosis Coding Procedures

Physician claims, ICD-9 4-digit coding, one diagnosis.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2006, up to 16 diagnoses.

Prescription claims, RAMQ drug claims database.

Data access

To access the services of EPSEBE, a researcher must meet the following conditions:

1. Be part of a recognized network of researchers.

2. Undertake to comply with ISQ’s requirements in the areas of ethics, protection of personal information and security of information.

The researcher who meets eligibility conditions can submit an application for registration to sad@stat.gouv.qc.ca

Cost of access

Varies according to request.

Mapping of fields across datasets

Approved research projects have access to link records from different databases that refer to the same individual across time through the person’s unique RAMQ number.

Other data files needed for analyses

Population, demographic, health services, disease cohort, disease registry, care provider, geography, SES and coding data are all available through EPSEBE databases.

References:

Bernatsky S, Feldman DE. Discontinuation of Methotrexate Therapy in Older Patients with Newly Diagnosed Rheumatoid Arthritis. Drugs Aging 2008, 25(10): 870-884.

Bermatsky S, Joseph L, Pineau CA, Tamblyn R, Feldman DE, & Clarke AE. A population based assessment of systemic lupus erythematosus incidence and prevalence-results and implications of using administrative data for epidemiological studies. Rheumatology 2007, 46:1814-18.

Lowe A-M, Roy P-O, Poulin MB, Michel P, Bitton A, St-Onge L, & Brassard P. Epidemiology of Crohn’s disease in Quebec, Canada. Inflamm Bowl Dis 2009, 15 (3): 429-435.

Wilchesky M, Tamblyn RM, Huang A. Validation of diagnostic codes within medical services claims. Journal of Clinical Epidemiology 2004, 57: 131-141.

Datasets available through EPSEBE

Re´gie d’Assurance Maladie du Québec (RAMQ) (Physician Billing Database)

Custodian

EPSEBE

Time Period Covered

From 1981 onwards.

Population covered

This database provides information on medical services dispensed to all residents of Quebec and prescribed medications filled in community pharmacies for 3.3 million (about 42%) residents of the province insured by the RAMQ drug-insurance plan.

Diagnosis Coding Procedure

ICD-9 4-digit coding, one diagnosis.

Data access procedures

Refer to EPSEBE data access procedures.

Fichier d'admissibilité au régime général d'assurance médicaments (Prescription Drug Insurance)

Custodian

EPSEBE

Time Period Covered

From 1983 onwards.

Population covered

RAMQ drug claims database. It covers persons aged 65 years and older and as of 2003 all persons without access to a medication insurance plan.

Data access procedures

Refer to EPSEBE data access procedures.

Maintenance et Exploitation des Donne´es Pour l’Etude de la Clientèle Hospitalière (Hospital Discharge Database)

Custodian

EPSEBE

Time Period Covered

From 1981 onwards.

Population covered

Demographic, clinical and administrative information for inpatient acute care, day surgery, as well as rehabilitation, chronic and psychiatric facilities in Quebec.

Diagnosis Coding Procedure

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2006, up to 16 diagnoses.

Data access procedures

Refer to EPSEBE data access procedures.

Saskatchewan

Saskatchewan’s Health Services Databases

Description

Saskatchewan has a population of about one million people, or 3.2% of the population of Canada. The Saskatchewan Ministry of Health provides universal health services (e.g. Prescription Drug Plan, Medical Services) to the citizens of Saskatchewan. A list of the major databases available is provided below.

Custodians

Saskatchewan Ministry of Health

Diagnosis Coding Procedures

Physician claims, 3-digit ICD-9 digit coding.

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2001.

Prescription claims, available from 1989 onwards. Includes drug, quantity, dose, dates for fills.

Data access

Researchers should contact the Epidemiology and Research Unit, Population Health Branch (epidemiology@.sk.ca) with a written proposal including:

-Name, address, research qualifications, and experience of the principal investigator and any collaborating investigators.

-Information on the working hypotheses and objectives of the proposed research project; a detailed description of the proposed methodology including: proposed measures and variables, data collection, data analysis.

-A detailed description of the output being requested (e.g., a description of the tables required or, if requesting a customized, summary dataset for statistical analyses off-site, a detailed description of the variables to be included in the dataset).

Cost of access

Varies according to request.

Mapping of fields across datasets

Available through the person’s unique PHIN. Please note that Saskatchewan is the most restrictive province regarding the release of data for linkage; all data linkage projects must be reviewed and approved by the Ministry’s Data Access Review Committee (DARC) as a separate application. In addition, research projects which require linkage with data not maintained by the Ministry of Health (e.g., cancer registry data) require approval by a recognized research ethics committee.

Datasets available through the Ministry of Health:

Prescription Drug Data

Custodian

Saskatchewan Ministry of Health.

Time Period Covered

From 1989 onwards.

Population covered

The Drug Plan provides coverage to 91% of Saskatchewan residents for drugs which are listed in the Saskatchewan Formulary (including MS drugs). Residents ineligible for coverage under the Drug Plan (9%) are primarily Registered Aboriginals who have their prescription costs paid for by another government agency. Data includes demographics, drug information (dosage, date and quantity dispensed), provider and cost information.

Diagnosis Coding Procedures

Pharmacologic-therapeutic classification of drug (based on the American Hospital Formulary Service classification system), drug identification number (DIN - assigned by Health Canada) and drug active ingredient number (AIN - also assigned by Health Canada)

Other data files needed for analyses

Postal code, SES.

Data access procedures

Refer to Saskatchewan’s Health Services Databases for data access procedures.

Hospital Services Data

Custodian

Saskatchewan Ministry of Health.

Time Period Covered

From 1970 onwards.

Population covered

Data from all hospitals in the province: all acute care in-patient separations, day surgeries, and in-patient psychiatric separations on patients treated in general hospitals. Out-of-province hospital separations for Saskatchewan residents. Data includes demographics, postal code, and diagnostic and treatment information.

Diagnosis Coding Procedures

Hospital claims, 5-digit ICD-9 coding changed to ICD-10-CA coding in 2001.

Other data files needed for analyses

Residence (postal code) information included.

Data access procedures

Refer to Saskatchewan’s Health Services Databases for data access procedures.

Medical Services Data

Custodian

Saskatchewan Ministry of Health.

Time Period Covered

From 1975 onwards.

Population covered

Data on physicians’ claims for payment on a fee-for-service basis for all Saskatchewan residents. Data also includes anesthesia, diagnoses, obstetric, surgical, chiropractic, optometric and insured dental services.

Diagnosis Coding Procedures

3-digit ICD-9 coding.

Other data files needed for analyses

Residence (postal code) information included.

Data access procedures

Refer to Saskatchewan’s Health Services Databases for data access procedures.

Cancer Registry

Custodian

Saskatchewan Cancer Agency (SCA).

Time Period Covered

From 1967 onwards.

Population covered

Data for all people in the province diagnosed with cancer. Patients who move out of the province receive continued surveillance through correspondence with the appropriate cancer clinic within Canada. Data includes demographics, postal code, diagnosis information, review information and death information.

Diagnosis Coding Procedures

All cancer cases are coded according to ICD-O-3 and causes of death are coded using ICD-10.

Data access procedures

Additional approval is needed from a recognized Ethics Research Board.

Contact: Karen Robb, Cancer Registry Coordinator, (p) 306-766-2973.

United States

Medicare/Medicaid

Medicare

Description

Medicare is a nationwide health insurance program for people aged 65 or older, people under age 65 with certain disabilities, and people of all ages with End-Stage Renal Disease. There are over 45 million beneficiaries currently enrolled in the Medicare program.

Custodians

Department of Health and Human Services, Centers for Medicare & Medicaid Services (CMS).

Population Covered

Over 98 percent of adults aged 65 and over. It also includes persons under 65 eligible for coverage due to disability or End Stage Renal Disease. Due to constraints in file sizes and processing time, researchers often request access to “5% Medicare beneficiary sample” which is a representative sample (from the 100% files) generated by CMS.

Diagnosis Coding Procedure

Physician claims, ICD-9-CM.

Hospital claims, ICD-9-CM will be replaced by ICD-10 starting October 1, 2014.

Prescription drugs, the Medicare Part D prescription drug benefit. Medicare beneficiaries have the choice to choose the Part D benefit plan from a large selection of private stand-alone prescription drug plans and Medicare Advantage plans. 

Data access

All requests for data access must be e-mailed to the Research Data Assistance Center (ResDAC) for initial review resdac@umn.edu. Once your data request has been reviewed by ResDAC and applicable changes made, your final documents will be submitted to CMS for you by ResDAC. For additional information on the submission process refer to:

Cost of access

To obtain a formal cost estimate, researchers must submit a cost estimate request form to resdac@umn.edu. Processing time for cost estimate and invoice requests is approximately 5-7 business days.

Mapping of fields across datasets

Medicare data can be linked to the following external datasets: US Census, cancer registries (e.g. Surveillance Epidemiology and End Results program [SEER]/Medicare), other providers (e.g. Veteran’s Administration, Medicaid), National death index/State vital statistics, surveys, provider information. Depending on the availability of identifying/common variables, other external data sources may be linked. Linking can take place either at the group level (based on geography, place of service, etc.) or at the person level (through SSN or Medicare ID).

Other data files needed for analyses

Medicare data includes Zip Code, Metropolitan Statistical Area (MSA), SES, and supplemental insurance information.

References:

Morley, MA, Coots LA, Forgues AL, & Gage BJ. Inpatient rehabilitation utilization for Medicare beneficiaries with multiple sclerosis. Arch Phys Med Rehabil 2012, 93: 1377-83

Pope GC, Urato CJ, Kulas ED, Kronick R, & Gilmer T. Prevalence, expenditures, utilization, and payment for persons with MS in insured populations. Neurology 2002; 58(1):37-43

Kern EFO, Maney M, Miller DR, Tseng C-L, Tiwari A, Rajan M, Aron D, & Pogach L. Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes. Health Research and Educational Trust 2006, 41 (2): 564-580.

Rector TS, Wickstrom SL, Shah M, Greenlee NT, Rheault P, Rogowski J, Freedman V, Adams J, & Escarce JJ. Specificity and sensitivity of claims based algorithms for identifying members of Medicare+Choice health plans that have chronic medical conditions. Health Services Research 2004, 39 (6), 1839-1858.

Winkelmayer WC, Schneeweiss S, Mogun H, Patrick AR, Avron J, & Solomon DH. Identification of individuals with CKD from Medicare claims data: A validation study. American Journal of Kidney Diseases 2005, 46(2): 225-232.

Nattinger AB, Laud PW, Bajorunaite R, Sparapani A, & Freeman JL. An Algorithm for the use of Medicare claims data to identify women with incident of breast cancer. Health Services Research 2004, 39(6): 1733-1750.

Medicaid

Description

Medicaid is a nationwide health insurance program for low-income Americans and people with disabilities. Although coverage varies by state, mandatory services include: inpatient hospital services, outpatient hospital services, physician services, nursing facility services for those under 21 years old, rural health clinic services, home health care, laboratory and x-ray services, pediatric and family nurse practitioner services, nurse-midwife services, federally qualified health-center services, and early and periodic screening, diagnostic, and treatment services for those under 21 years old. Note that states are not required to cover outpatient prescription drugs; enrollees have the option to choose the Medicaid Prescription Drug Program.

Custodians

CMS

Population Covered

Approximately 1 in 5 Americans, including 31.1 million children, 16.2 million nonelderly adults, 6.1 million elderly, and 9.7 million blind and disabled persons are enrolled in the program. Racial and ethnic minorities are strongly represented. Additionally, nearly all Medicaid patients over the age of 65 are also covered by Medicare and are thus known as “dual eligible”.

Due to constraints in file sizes and processing time, researchers often request access to “5% Medicare beneficiary sample” which is a representative sample (from the 100% files) generated by CMS.

Diagnosis Coding Procedure

Physician claims, ICD-9-CM.

Hospital claims, ICD-9-CM will be replaced by ICD-10 starting Oct 1, 2014.

Prescription drugs, the Medicaid Prescription Drug Program is an optional service for enrollees within Medicaid.

Data access

All requests for data access must be e-mailed to the Research Data Assistance Center (ResDAC) for initial review resdac@umn.edu. Once your data request has been reviewed by ResDAC and applicable changes made, your final documents will be submitted to CMS for you by ResDAC. For additional information on the submission process refer to this website:

Cost of access

To obtain a formal cost estimate, submit a cost estimate request form to resdac@umn.edu. Processing time for cost estimate and invoice requests is approximately 5-7 business days.

Mapping of fields across datasets

Medicaid data can be linked to the following external datasets: US Census, cancer registries (e.g. SEER/Medicaid), other providers (e.g. VA, Medicare), National death index/State vital statistics, surveys, provider information. Depending on the availability of identifying/common variables, other external data sources may be linked. Linking can take place either at the group level (based on geography, place of service, etc.) or at the person level (through SSN or Medicaid ID).

Other data files needed for analyses

Medicaid data includes Zip Code, SES, supplemental insurance information, information on race and ethnicity.

References:

Crystal S, Akincigil A, Bilder S, & Walkup JT. Studying prescription drug use and outcomes with Medicaid claims data strengths, limitations and strategies. Med Care 2007, 45 (10): S58-S65.

Pope GC, Urato CJ, Kulas ED, Kronick R, & Gilmer T. Prevalence, expenditures, utilization, and payment for persons with MS in insured populations. Neurology 2002; 58(1):37-43.

Cole JA, Rothman KJ, Cabral HJ, Zhang Y, & Farraye FA. Migraine, fibromyalgia, and depression among people with IBS: a prevalence study. BioMed Central 2006, 6(1): 26-34.

Rahul K, & Smith MJ. Utilization and costs of medical services and prescription medications for rheumatoid arthritis among recipients covered by a State Medicaid program: A retrospective, cross-sectional, descriptive, database analysis. Clinical Therapeutics 2007, 29 (11): 2456-2467.

Schulman KA, Yabroff KR, Kong J, Gold KF, Rubenstein LE, Epstein AJ, & Glick H. Health Services Research 1999, 34 (2):603-621.

Kwong WJ. Determinants of migrane emergency department utilization in the Georgia Medicaid population. Journal Compilation American Headache Society 2007, 47 (9): 1326-1333.

Mertin BC, Ganguly R, Pannicker S, Frech F, & Barghout V. Utilization patterns and net direct medical cost to Medicaid of irritable bowel syndrome. Current Medical Research and Opinion 2003, 19 (9): 771-780.

Twiggs JE, Fifield J, Apter AJ, Jackson EA, & Cushman RA. Stratifying medical and pharmaceutical administrative claims as a method to identify pediatric asthma patients in a Medicaid managed care organization. Journal of Clinical Epidemology 2002, 55(9): 938-944.

Khanna R, & Smith MJ. Utilization and costs of medical services and prescription medications for rheumatoid arthritis among recipients covered by a state Medicaid program: A retrospective, cross-sectional, descriptive, database analysis. Clinical Therapeutics 2007, 29(11): 2456-2467.

There are numerous databases, datasets and individual files available through CMS. Below are a few examples of the type of data accessible through CMS:

Chronic Condition Data Warehouse (CCW)

Custodians

CMS

Description

The CCW contains CMS administrative data and assessments (from Medicare and Medicaid) linked by a unique identifier. CCW data are available upon request for a random 5% sample or for specific chronic condition cohorts.

Time Period Covered

From 1999 onwards

Individual Files Available through CCW

Master Beneficiary Summary File

Institutional and Non-institutional fee-for-service files

Inpatient (IP) Base Claim Files

Outpatient (OP) Base Claim Files

Part D Prescription Drug Event data

Medicaid Analytical Extracts (MAX) Inpatient Files

Medicaid Analytical Extracts (MAX) Drug Files

Medicaid Analytical Extracts (MAX) Personal Summary Files

Diagnosis Coding Procedures

ICD-9-CM

References



Medicare-Medicaid Linked Enrollee Analytic Data Source

Custodians

CMS

Time Period Covered

2006-2009

Population Covered

This database has been developed to allow for the examination of all Medicare and Medicaid enrollment and claims data for those who were dually enrolled in both programs. The dataset contains: Medicaid/Medicare Beneficiary Files, Medicaid/Medicare Service Files, and Conditions Files.

Diagnosis Coding Procedures

ICD-9-CM

SEER-Medicare Linked Data

Custodians

CMS and the National Cancer Institute.

Time Period Covered

2006-2009

Population Covered

The dataset has been developed to allow for the examination of all Medicare and Medicaid enrollment and claims data for those who were dually enrolled in both programs. The dataset contains: Medicaid/Medicare Beneficiary Files, Medicaid/Medicare Service Files, and Conditions Files.

Diagnosis Coding Procedures

ICD-Oncology

Cost of access



References



Veterans Administration Databases (VHA)

Description

VHA provides comprehensive, free, or low cost healthcare to eligible war veterans nationwide. There are 3 major national databases available through VHA. The VHA Corporate Data Warehouse (CDW) is a national data repository. The Medical SAS Data Sets and Decision Support System (DSS) National Data Extracts are a set of discrete files separated by fiscal year and data type.

Custodian

US Department of Veteran Affairs, Veteran Affairs Information Resource Centre (VIReC).

Time Period Covered

Varies according to database requested (detailed information below).

Population covered

The general population of VHA enrollees consists of: War veterans (97.7%), and men (93.7%), the largest cohort (39.1%) served during the Vietnam era, and 44.6% of the population is 65 years or older1.

Diagnosis Coding Procedures

Inpatient and outpatient, ICD-9-CM.

Prescription drugs, veterans may receive their VA-prescribed medications free of charge based on their service-connected disabilities. Those veterans who do not have service-connected disabilities are charged a copayment for each 30 day supply of medications the VA provides.

Website



References

1Hines EJ. US Department of Veterans Affairs, Veterans Affairs Information Resource Center. VIReC Research User Guide: Select Variable Frequencies from the FY2003 VHA Medical SAS Datasets. VA Hospital: Hines, IL; June 2004.

Kramer BJ, Wang M, Jouldijan S, Lee ML, Finke B, & Saliba D. Veterans Health Administration and Indian Health Service. Med Care 2009, 47:670-676.

Kern EFO, Maney M, Miller DR, Tseng C-L, Tiwari A, Rajan M, Aron D, & Pogach L. Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes. Health Research and Educational Trust 2006, 41 (2): 564-580.

Peabody JW, Luck J, Jain S, Bertenthal D & Glassman P. Assessing the accuracy of administrative data in health information systems. Medical Care 2004, 42 (11): 1066-1072.

Sharafkhaneh A, Richardson P, & Hirshkowitz M. Sleep apnea in a high risk population: a study of veterans health administration beneficiaries. Sleep Medicine 2004, 5: 345-350.

Miller DR, Safford MM, & Pogach LM. Who has diabetes? Best estimates of diabetes prevalence in the department of Veterans Affairs based on computerized patient data. Diabetes Care 2004, 27(2): B10-21.

Szumski NR, & Cheng EM. Optimizing algorithms to identify Parkinson’s disease cases within an administrative database. Movement

Disorders 2009, 24(1): 51-56.

National databases available through VHA:

VHA Corporate Data Warehouse (CDW)

Description

The U.S. Department of Veterans Affairs Corporate Data Warehouse (CDW) is a national data repository comprising data from several VHA clinical and administrative systems.

Custodians

VIReC

Time Period Covered

From October 1998 onwards, data are added nightly.

Datasets available for request through CDW

Inpatient

Outpatient

Mental Health Assessment

Patient (demographics)

Pharmacy

Staff (demographics, provider type)

Vital Signs

Diagnostic Coding Procedure

ICD-9-CM

Data Access Procedures

The Data Access Request Tracker (DART) is an online application system. Researchers must use DART to request access to select VA data and as a tool for managing data request submission, review, and approval processes. Only IRB approved research projects may submit data access requests through DART. Refer to this website for detailed information on the data request process

Cost of Access

Varies according to request.

Mapping of fields across datasets

Data from several sources can be linked and requested in one data request and the requested data can often be merged into one file of person-level data. Linkage to external datasets available through the enrollee’s Social Security Number (additional access procedures are required).

Website



Decision Support System (DSS) National Data Extracts

Description

The VHA Decision Support System (DSS) is VHA’s managerial and cost accounting system; it works as an automated management information system that tracks health care workload (i.e., utilization) and assigns an approximate cost to it. The DSS National Data Extracts (NDEs) contain data originating from various DSS datasets including clinical and financial information at the patient level.

Custodians

VIReC

Time Period Covered

Individual file (1 fiscal year at a time) from 2002.

Data linked for all fiscal years available through the VHA Corporate Data Warehouse (CDW) from 2005.

Clinical Datasets available for request through DSS

DSS Discharge NDE

Data organized by inpatient stay.

DSS Treating Specialty ND

Data organized by inpatient provider specialty.

Outpatient NDE

A record represents a single interaction between a provider and a patient in an outpatient setting.

DSS National Pharmacy Extract:

Each record represents a single pharmacy product (prescription, supply).

Diagnostic Coding Procedures

ICD-9-CM

Data Access Procedures

The Data Access Request Tracker (DART) is an online application system. Researchers must use DART to request access to select VA data and as a tool for managing data request submission, review, and approval processes. Only IRB approved research projects may submit data access requests through DART. Refer to this website for detailed information on the data request process

Cost of Access

Varies according to request.

Medical SAS datasets

Description

The VHA Medical SAS Datasets contain national administrative data on patient care encounters for health care provided by VHA. The three types of Medical SAS datasets available are detailed below: Outpatient Medical SAS datasets, Inpatient Medical SAS datasets and Inpatient Encounter Medical SAS datasets.

Custodians

VIReC

Data Access Procedures

Through DART. Refer to this website for detailed information on the data request process



Outpatient Medical SAS datasets

Description

Available by visit, event, procedure or diagnosis.

Custodians

VIReC

Time Period Covered

Visit file from 1980 onward.

Events file from 1997 onward.

Procedure files from 1990-2001.

Diagnoses file from 1997-2001.

Population Covered

Visit files: Date, patient demographics, VHA facility, locations of service (clinic stops), service-connected disability percentage.

Events file: Diagnosis codes, procedure codes, location of service.

Procedure files: Procedure codes.

Diagnoses files: Diagnosis codes.

Diagnosis Coding Procedures

ICD-9-CM

Inpatient Medical SAS datasets

Description

Available by type of care (acute, extended, observation, non-VA).

Available by file (main, bed section, procedure, surgery).

Custodians

VIReC

Time Period Covered

Main file from 1976 onward.

Bed section file from 1984 onward.

Procedures file from 1986 onward.

Surgery files from 1984 onward.

Population Covered

Patient demographics, primary/secondary diagnoses, length-of-stay.

Diagnosis Coding Procedures

ICD-9-CM

Inpatient Encounter Medical SAS datasets

Description

It contains billable inpatient appointments in outpatient clinics, billable inpatient services, and inpatient professional services for all mental health care provided to patients during an inpatient stay.

Custodians

VIReC

Time Period Covered

From 2005 onward.

Population Covered

Procedure codes, diagnosis codes, location of service (clinic stop).

Diagnosis Coding Procedures

ICD-9-CM

Kaiser Permanente

Description

Kaiser Permanente (KP) is the US’ largest, not-for-profit, private health care plan. The company is comprised of 37 hospitals, 611 medical offices, and 17, 157 physicians across the country.

Custodians

The National Kaiser Foundation Research Institute.

In addition, each KP region has its own Institutional Review Board (IRB). Northern CA has two IRBs (one for biomedical research and one for health services research). Studies that involve patients and data from more than one region require IRB review and approval in each region.

Population Covered

It covers more than 9.1 million members across 8 regions: California Northern (3,403,871 members), California Southern (3,594,848 members), Colorado (540,442), Georgia (233,880), Hawaii (224,591), Ohio (86,338), Mid-Atlantic (Maryland, Virginia, D.C.) (481,755 members), and Northwest (Oregon and Washington) (484,349 members).

Diagnosis Coding Procedures

ICD-9-CM

Current Procedural Terminology (CPT)-4

Mapping of fields across datasets

Beginning in 2003, all KP members started being assigned a unique, permanent health record number (KP Health Connect) that is used when they receive their care at all KP’s facilities nationwide. Each region maintains its own KP HealthConnect system. Planning/design for inter-region data linkage is currently underway and will be available in the future. Linkage to external datasets (e.g. Medicare/Medicaid) is possible through personal identifiers (e.g. Social Security Number) although additional approval will be required.

Limitations/Other Data Files need for Analysis

A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans. Socioeconomic information can be linked through geocodes when available. Quality of life measures are not available.

Kaiser Permanente Northern California (KPNC)

Custodian

The Comprehensive Clinical Research Unit (CCRU) within the Kaiser Permanente Northern California Division of Research.

Time Period Covered

Some data available from 1971 onwards (depends on dataset being requested).

Population covered

KP Northern California provides coverage to its 3,403,871 members (as of Dec 2012), which is approximately 1/3 of the population of the Northern California service area.

Diagnosis Coding Procedure

ICD-9-CM

Data Access Procedures

To assist researchers at other institutions who want to work in partnership with KP, the CCRU employs a collaborations coordinator. The collaborations coordinator would assist in finding a KP co-investigator. All data access requests must be directed to:

Maureen Fitzpatrick, MPH

Collaborations Coordinator

Maureen.B.Fitzpatrick@

(p) (510) 627-2670

Cost of Access

Cost varies depending on a variety of factors including: salary support for extraction of data and salary for collaborating investigator/s.

Mapping of fields across datasets

Starting in 1992, all KPNC members have been probabilistically linked on an annual basis to Death Certificate data. Since 2000 all KPNC active members have been geocoded by their home addresses and linked to the 2000 U.S. Census data by their census block location. This linkage allows measures of race, ethnicity and other sociodemographic variables to be calculated. Linkage to external dataset is possible through a person’s Social Security Number.

Other data files needed for analysis

Race and ethnicity information.

References

Langer-Gould A, Albers K, Van Den Eeden S, & Nelson L. Autoimmune diseases prior to the diagnosis of multiple sclerosis: a population-based case-control study. Multiple Sclerosis 2010;16:855-861.

Herrinton LJ, Liu L, Lewis JD, Griffin PM, & Allison J. Incidence and prevalence of inflammatory bowel disease in a Northern California managed care organization, 1996–2002. American Journal of Gastroenterology 2008, 103: 1998-2006.

Lanes SF, & de Luise C. Bias due to false-positive diagnoses in an automated health insurance claims database. Drug Society 2006, 29 (11): 1069-1075.

Weng X, Liu L, Barcellos LF, Allison JE, & Herrinton LJ. Clustering of inflammatory bowel disease with immune mediated diseases among members of a Northern California-managed care organization. American Journal of Gastroenterology 2007, 102(7): 1429-1435.

Data sources available through the Kaiser Permanente Northern California Division of Research

Chronic Disease Registries

Description

There are several research disease registries available through KPNC. These registries have a high degree of validation: Via chart review, known population sensitivities and specificities for diagnoses, linkage to other databases, and ongoing updates.

Examples

SEER-quality KP Northern California Cancer Registry

KP Diabetes Registry

KP HIV/AIDS Registry

KP Asthma

KP Coronary Artery Disease

KP Congestive Heart Failure

KP Diabetes

KP Maternal Prenatal Drug/Alcohol Use

Diagnosis Coding Procedure

ICD-9-CM

Inpatient Hospitalizations

Time Period Covered

From 1976 onwards.

Population covered

Diagnostic and procedural data on all cumulative northern California Health plan members hospitalized in KP hospitals.

Diagnostic Coding Procedures

Data prior to 1979 are ICD-8 coded; subsequently data are ICD-9CM coded

Ambulatory Visits

Time Period Covered

Visit data is available from 1987; diagnostic and procedural data from 1995. 

Population covered

Date and time of each visit, diagnoses and procedures, the specific department/sub-department involved and the type of provider seen.

Diagnostic Coding Procedures

ICD-9-CM

CPT-4

Outside Referrals and Claims

Time Period Covered

From 1992 onwards

Population covered

Encounter and cost data for medical services authorized by KP providers but supplied by non-KP vendors.

Diagnostic Coding Procedures

ICD-9-CM and CPT-4 coded diagnoses and procedures; and billed and paid amounts

Health plan Membership

Time Period Covered

From 1971 onwards.

Population covered

Enrollment, disenrollment dates, zip code of residence, and primary source of insurance, for each KPNC member

Pharmacy

Time Period Covered

1994 (outpatient) and 1996 (inpatient).

Population covered

All drugs prescribed by KP physicians dispensed in KP inpatient or outpatient pharmacies.

Diagnostic Coding Procedures

Drug name, NDC code, dosage and therapeutic class, dates of prescription, dispensing and refills, identity of prescribing physician, and prescription cost.

Patient Demographics

Time Period Covered

From 1971 onwards

Population Covered

Members’ names, birthdates, sex, physical disabilities, preferred language, addresses for approximately 8 million past and current members.

Kaiser Permanente Centre for Health Research

Description

CHR is an independent, non-profit research organization founded to conduct academic research. The CHR is a single research center that conducts its research within three regions of Kaiser Permanente: Georgia (KPG), Northwest (KPNW), and Hawaii (KPH).



Time Period Covered

Data available starts from the 1990s, however KP Health Connect available from 2003/2004 onwards.

Mapping of Fields across Datasets

Beginning in 2003, all KP members started being assigned a unique, permanent health record number (KP Health Connect) that is used when they receive their care at all KP’s facilities nationwide. Each region maintains its own KP Health Connect system. Planning/design for inter-region data linkage is currently underway and will be available in the future. Linkage to external datasets is possible through identifiers (e.g. Social Security Number).

Data Access

Data cannot be extracted as an external investigator. Those investigators interested in accessing data held by CHR need to reach out to a CHR investigator with a scientific interest match, write a collaborative research proposal and obtain funding. Non-funded work can not be supported. For additional information contact:

David Mosen PhD, MPH

Senior Program Evaluation Consultant/Director, Research Response Team

david.m.mosen@

Cost of Access:

Data is only accessible to externally-funded projects.

Other data files needed for analysis

Race and ethnicity information.

Kaiser Permanente Georgia Region (KPG)

Custodians

Center for Health Research-Southeast (CHR-SE).

IRB KP Georgia.

Time Period Covered

From 1998 onwards.

Population covered

KP Georgia provides coverage to its 233,880 members (as of Dec 2012).

Datasets available

Health Information Management System.

Inpatient, outpatient, ambulatory, operating room, and emergency department.

Kaiser Anesthesia and Surgery Information System (KASIS)

Claims

Membership

Mortality

Diagnosis Coding Procedure

ICD-9-CM, CPT-4 coding diagnoses and procedures.

Pharmacy prescriptions, 98% of all drugs prescribed by KP physicians dispensed in KP inpatient or outpatient pharmacies.

Other data files needed for analysis

Race and ethnicity information.

References

Powell KE, Diseker RA, Presley RJ, Tolsma D, Harris S, Mertz KJ, Viel, K, Conn DL, & McClellan W. Administrative data as a tool for arthritis surveillance: Estimating prevalence and utilization services. J Public Health Management Practice 2003, 9 (4): 291-298.

Kaiser Permanente Hawaii

Custodians

Center for Health Research-Hawaii (CHR-HW).

IRB KP Hawaii.

Time Period Covered

From 1999 onwards.

Population covered

KP Hawaii provides coverage to about one-sixth of Hawaiian residents (224,591 members as of Dec 2012).

Datasets available

Health Information Management System

KP Health Connect inpatient, outpatient, ambulatory, operating room management, and emergency department data.

Kaiser Anesthesia and Surgery Information System (KASIS)

Claims

Membership

Mortality

Diagnosis Coding Procedure

ICD-9-CM, CPT-4 coding diagnoses and procedures.

Pharmacy prescriptions, 98% of all drugs prescribed by KP physicians dispensed in KP inpatient or outpatient pharmacies.

Other data files needed for analysis

Race and ethnicity information.

Kaiser Permanente Northwest

Custodians

Center for Health Research-Northwest (CHR-HW).

IRB KP Northwest.

Time Period Covered

From 1964 onwards.

Population covered

KP Northwest provides coverage to about 17% of the areas population (484,349 members as of Dec 2012). Data includes demographic (age, sex, residence, employer) and clinical information (primary care encounters, hospital and emergency department use, pharmacy prescriptions, laboratory tests performed and radiologic procedures).

Diagnosis Coding Procedure

ICD-9-CM, CPT-4 coding diagnoses and procedures.

Pharmacy prescriptions, 98% of all drugs prescribed by KP physicians dispensed in KP inpatient or outpatient pharmacies.

Other data files needed for analysis

Race and ethnicity information.

Kaiser Permanente Southern California

Description

KPSC provides coverage to about 20% of the areas’ population (3,594,848 members as of Dec 2012).

Custodians

IRB KPSC.

Time Period Covered

From 1978 onwards.

Datasets available

Health Information Management System.

KP Health Connect inpatient, outpatient, ambulatory, operating room management, and emergency department data.

Kaiser Anesthesia and Surgery Information System (KASIS)

Claims

Membership

Mortality

Diagnostis Coding Procedure

ICD-9-CM

Mapping of Fields Across Datasets

Beginning in 2003, all KP members started being assigned a unique, permanent health record number (KP Health Connect) that is used when they receive their care at all KP’s facilities nationwide. Each region maintains its own KP Health Connect system. Planning/design for inter-region data linkage is currently underway and will be available in the future. Linkage to external datasets is possible through identifiers (e.g. Social Security Number).

Data Access

Researchers that would like access to CHR data must initially contact a CHR collaborator in order to find a KPSC co-investigator by emailing Research.Collaboration@

Cost of Access:

Cost varies depending on a variety of factors including: salary support for extraction of data and salary for collaborating investigator/s.

References

Langer-Gould A, Brara SM, Beaber BE, & Zhang JL. Incidence of multiple sclerosis in multiple racial and ethnic groups. American Academy of Neurology 2013, 80 (19): 1734 – 1739.

Longstreth GF, Wilson A, Knight K, Wong J, Chiou C-F, Barghout Vm Frech F, & Ofman JJ. Irritable bowel syndrome, health care use, and costs: A U.S. managed care perspective. The American Journal of Gastroenterology 2003, 98(3): 600-607.

Indian Health Service (IHS) National Data Repository

Description

IHS is an agency within the Department of Health and Human Services that delivers health care to the approximately 2.4 million American Indians and Alaskan Natives in the United States. The IHS is organized into 12 geographical area offices across the United States (Aberdeen, Alaska, Albuquerque, Bemidji, Billings, Nashville, Navajo, Oklahoma, Phoenix, Tucson, excluding Portland and California service areas). The IHS National Data Warehouse gathers, and stores administrative and clinical information of Indian health users nationwide.

Custodian

U.S. Department of Health and Human Services, Indian Health Service.

Time Period Covered

From Oct 1, 2000 onwards.

Population covered

Approximately 2.4 million American Indians and Alaskan Natives living in the United States.

Data Available

Demographic information, address, social security number, admission/discharge dates, provider discipline, procedure, diagnosis, lab tests, clinical measurements, medications.

Data access procedures

Data access requests should be directed to support@

Requests need to be approved by one of the following three officers:

1. Area Statistical Officer

2. Principle Statistician, Division of Program Statistics (DPS).

3. Director, Division of Epidemiology and Disease Prevention (EPI)

For additional information on data access requests refer to this website

Diagnosis Coding Procedures

ICD-9-CM

Cost of access

Varies according to the request.

Mapping of fields across datasets

Data may be linked to external datasets through Social Security Number or other identifying variables (patient’s address).

Other data files needed for analyses

During the initial load of the data warehouse, it was asked that all geographic locations send all registration and encounter data dating back to October 1, 2000. Most sites were able to do so, but not all. Some also sent encounter data prior to that date, but this data is sparse.

References

Parko K, & Thurman DJ. Prevalence of epilepsy and seizures in the Navajo Nation 1998-2002. Epilepsia 2009, 50 (10): 2180-85.

Kramer BJ, Wang M, Jouldijan S, Lee ML, Finke B, Saliba D. Veterans Health Administration and Indian Health Service. Med Care 2009, 47:670-676.

National Hospital Discharge Survey (NHDS)

Description

The National Hospital Discharge Survey (NHDS) provides information on characteristics of inpatients discharged from non-Federal short-stay hospitals in the United States. Only hospitals with an average length of stay of fewer than 30 days for all patients, general hospitals, or children's general hospitals are included. 

Custodians

Research Data Center (RDC) of the National Center for Health Statistics (NCHS).

Website

.inchs/nhds.htm

Time Period Covered

1965-2010

Population Covered

12 million discharge records have been collected since 1970.

Data Includes

Demographic Information (age, gender, race, birth/death, geographic location, marital status)

Diagnoses

Procedures

Diagnosis Coding Procedures

ICD-9-CM, up to 7 diagnoses, up to 4 procedures.

Data access

To access data contact:

Shaleah Levant, MPH

Health Scientist, Hospital Care Team

National Centre for Helath Statistics, Centre for disease control and prevention

3311 Toledo Rd., Mailroom #3409

Hyattsville, MD 20782, USA

(301) 458-4324

Igz7@

Cost of access

The data are available to users for free and are downloadable from the internet or available by CD-ROM.

Mapping of fields across datasets

No access to medical records, no ability to validate against original source. Possibility to link the survey data to the National Death Index, Medicaid and Medicare data.

Limitations/Other Data Files need for Analysis

No drug data, no laboratory data, no cost data.

References

Colvin AC, Egorova N, Harrison AK, Moskowitz A, & Flatow EL. National trends in rotator cuff repair. J Bone Joint Surg Am 2012, 94(3): 227-33. 

IMS LifeLink™ PharMetrics plus Database

Description

The IMS LifeLink PharMetrics plus Database is the largest integrated claims database of over 100 commercial health plans in the U.S.

Custodians

IMS Health

Time Period Covered

From 2006 onwards.

Population Covered

The database contains information on demographic, medical and pharmacy claims of approximately 150 million patients across the U.S. The LifeLink population is representative of the U.S. commercially insured population in terms of age, gender, and type of health plan.

Database Includes

Inpatient/outpatient diagnoses and procedures.

Retail and mail order prescriptions.

Inpatient details: admitting diagnosis, admission source & type.

Provider/Pharmacy ID/Pharmacy Benefit Design.

Costs of services and prescriptions.

Place of service.

Plan type and time enrolled.

Diagnosis Coding Procedures

ICD-9-CM, up to 4 diagnoses.

Drug data, NDC code, product form and strength, all outpatient drug usage including retail, mail-order clinic orders, allowed amount and paid amount, days supplied and quantity dispensed, date prescription filled.

Data access

To access data held by IMS, investigators must initially contact:

Nancy Peters, CS Ops Analyst

IMSeService@

Cost of access

Access is usually costly. It depends on several factors: number of plans being linked, whether the sub-national information such as zip level is required, and whether it is a one-time or recurring deliverable.

Mapping of fields across datasets

Data is de-identified at the patient level and thus can be linked with other longitudinal IMS databases, and most external data sources.

Limitations/Other Data Files need for Analysis

A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans. Socioeconomic can be linked through geocodes. Quality of life measures not available.

References

Chen JY, Ma Q, Chen H, & Yermilov I. New Bundled World: Quality of care and readmission in diabetes patients. Journal of Diabetes Science and Technology 2012, 6(3): 563-571.

HMO Research Network Virtual Data Warehouse (HMORN VDW)

Description

The HMO Research Network VDW (soon to be renamed to the Health Care Systems Research Network) is a consortium of 15 health care delivery organizations across the U.S: Kaiser Permanente Colorado, Kaiser Permanente Southern California, Kaiser Permanente Northern California, Kaiser Permanente Mid-Atlantic, The Centre of Health Research (Kaiser Permanente Georgia, Kaiser Permanente Hawaii, Kaiser Permanente Northwest), Geisinger Health System, Essentia Health, Group Health, Harvard Pilgrim Health Care, Health Partners, Henry Ford Health System, Marshfield Clinic Security Health Plan of Wisconsin, Fallon Community Health Plan Reliant Medical Group, Palo Alto Medical Foundation for Healthcare, Research and Education, Scott and White Healthcare. The VDW is a federal database where each health plan stores their data locally in identical data structures. Each site creates a series of datasets that include the following data areas:

1. Encounters: procedures, diagnoses, providers.

2. Pharmacy: NDC, dispensing date, dispensing MD, days supply, and amount dispensed.

3. Demographics: birth date, gender, race, language.

4. Tumor: diagnoses date, tumor variables, tumor stage, treatment.

5. Enrollment: start and end date, insurance plan and type, drug coverage.

6. Lab results: procedure code & type, abnormal result indicator, dates of order, collection and results.

7. Vital signs: height, weight, diastolic & systolic blood pressure, tobacco use.

8. Census: geocode, census data, education variables, income variables, race variables.

Custodians

Institutional review board (IRB) at each institution involved in the project.

Population Covered

Kaiser Permanente Colorado: 540, 442 (as of Dec 2012) members in the Denver-Boulder-Colorado Springs metropolitan area.

Kaiser Permanente Southern California: Coverage of about 20% of the areas’ population (3,594,848 members as of Dec 2012).

Kaiser Permanente Northern California: Coverage of 3,403,871 members (as of Dec 2012), which is approximately 1/3 of the population of the Northern California service area.

Kaiser Permanente Mid-Atlantic: 481,755 members in Maryland, Virginia and the District of Columbia.

The Centre of Health Research: Kaiser Permanente Georgia (233,880 members), Kaiser Permanente Hawaii (224,591 members), Kaiser Permanente Northwest (484,349 members).

Geisinger Health System: Geisinger Health System (GHS) serves patients in 40 counties of Central and Northeastern Pennsylvania. It includes the Geisinger Clinic, a multispecialty group practice that provides care to 450,000 primary care and 800,000 specialty care patients, and the Geisinger Health Plan, a 220,000 member network-model.

Essentia Health: Essentia Health's service area includes a rural population of 2 million people across a four-state area (Wisconsin, Minnesota, North Dakota and Idaho).

Group Health: Group Health is an integrated health care system serving more than 500,000 members in Washington State.

Harvard Pilgrim Health Care: Harvard Pilgrim Health Care is responsible for the care of 1.1 million individuals in Massachusetts, New Hampshire, and Maine.

Health Partners: Health Partners is an integrated health care system serving more than 916,000 members in the Minneapolis-Saint Paul area.

Henry Ford Health System: Henry Ford Health System is an integrated health care system serving more than 800,000 patients and health plan members in Southeast Michigan. Approximately 35% of the HFHS patient population is African American.

Marshfield Clinic Security Health Plan of Wisconsin: The plan serves 147,000 members at the Marshfield Clinic in Wisconsin.

Fallon Community Health Plan Reliant Medical Group: The Fallon Community Health Plan, servicing Massachusetts and New Hampshire, provides services to approximately 191,000 enrollees.

Palo Alto Medical Foundation for Healthcare, Research and Education (PAMF): PAMF is a non-profit health care organization serving over 700,000 patients in Northern California.

Scott and White Healthcare: The S&W Health Plan serves approximately 200,000 members in 18 counties of Central Texas, including a large rural area.

Data Access

Researchers requiring access to data must find an investigator/s at the site/s to collaborate or sponsor the project. The project needs to be approved by the IRB at each health care organization involved in the project. A directory of researchers listed by site and research interest is available from

Cost of Access

Access is usually costly, external funding sources will be required.

Diagnosis Coding Procedures

ICD-9-CM

Drug data, NDC, dispensing date, dispensing MD, days supply, and amount dispensed.

Mapping of fields across datasets

Available across the 15 HMORN sites (collaborating sites must invest time in determining local codes and linking variables of interest).

Limitations/Other Data Files need for Analysis

A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans. Socioeconomic can be linked through geocodes for most sites. Quality of life measures not available.

IRB approval is required from each site involved in the project, site-to-site variations in implementation (some variables are not available from all sites), some variables are not available continuously over time, validation of diagnosis varies by site, turn-around times differ by site).

References

Braff J. PS2-07: Institutional Review Board Review of Inter-institutional Research Conducted in the HMO Research Network. Clinical Medicine & Research 2012, 10(3): 173-174.

Fallon Clinic/Reliant Medical Group

Description

The Fallon Community Health Plan is a commercial health plan servicing Massachusetts and New Hampshire that has been operational since 1977. A multispecialty group practice, the Fallon Clinic Inc (now known as Reliant Medical Group) operates 27 medical centers throughout central Massachusetts and provides services to approximately 191,000 enrollees in cooperation with the Fallon Community Health Plan.

Custodians

Institutional review board (IRB) at FCHP, Reliant Medical Group.

Time Period Covered

From 1996 onward.

Population Covered

Database contains information on primary care physician visits, inpatient/outpatient hospital services, specialists, x-rays, laboratory tests, diagnostic procedures, rehabilitation services and prescription drugs.

Diagnosis Coding Procedures

ICD-9-CM

CPT4

Data access

For initial consultation contact Ellen Trencher with an initial research proposal and she will connect investigators with researchers from Reliant Medical Group who are interested in collaborating.

ellen.trencher@

(p) 508-595-2193

Cost of access

Cost varies depending on a variety of factors including salary support for extraction of data and salary for collaborating investigator/s.

Mapping of fields across datasets

Data is de-identified at the patient level and thus can be linked with longitudinal data held by Reliant Medical Group and most external data sources.

Other Data Files need for Analysis

Socioeconomic can be linked through geocodes. Quality of life measures not available. Another limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans.

References

Harrold LR, Li W, Yood RA, Fuller J, & Gurwitz JH. Identification of patients with arthritis and arthritis-related functional limitation

using administrative data. J Public Health Management Practice 2008, 14 (5): 487-497.

Lovelace Clinic Foundation (LCF) Quality Research Data Warehouse

Custodians

The Health Services Research Division (HSRD) within Lovelace Clinic Foundation (LCF) Research

Website



Time Period Covered

From 1990 onwards

Population Covered

Approximately 240,000 members in Albuquerque, New Mexico.

Datasets available

Demographics

Membership Information

Geographic Information

Inpatient

Outpatient

Pharmacy Systems

Diagnosis Coding Procedures

Outpatient ICD-9-CM, up to 5 diagnoses and 5 CPT codes 4th ed.

Inpatient ICD-9-CM, up to 14 diagnoses and 5 CPT codes 4th ed.

Drug data, fills at all health plan pharmacy sites and some outside pharmacies that have agreements with LHP.

Data access

Permissions for researchers to access data are granted after studies have received approval from an independent Institutional Review Board. For additional information contact:

Shelley Carter, MPH, MCRP

Director of Research and Education

LCF Research

(p) 505-938-9920

Shelley@

Cost of access

Cost varies depending on a variety of factors including: salary support for extraction of data and salary for collaborating investigator/s.

Mapping of fields across datasets

LCF data has been configured to link with other HMO Research Network data sets, of which there are approximately 17 across the US; this linkage has been established via a specific HMO Research Network data protocol. It is also possible to link data to medical charts on a case-to-case basis.

Other Data Files need for Analysis

Socioeconomic data can be linked through geocodes. Quality of life measures not available. A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans.

References

Holden EW, Grossman E, Nguyen HT, Gunter MJ, Grebosky B, Worley AV, Nelson L, Robinson S, & Thurman DJ. Developing a computer algorithm to identify epilepsy cases in managed care organizations. Disease Management 2005, 8(1): 1-14.

Geisinger Health System

Description

Geisinger Health System (GHS) serves patients in 40 counties of Central and Northeastern Pennsylvania. It includes the Geisinger Clinic, a multispecialty group practice that provides care to 450,000 primary care and 800,000 specialty care patients, and the Geisinger Health Plan, a 220,000 member network-model.

Geisinger’s Clinical Decision Intelligence System (CDIS)

Custodians

Geisinger Centre for Health Research.

Website



Time Period Covered

1996 onward

Population Covered

More than 3 million patient-years.

Data included

Primary care and specialty patients.

Inpatient, outpatient, emergency, and telephone encounters.

Socio-demographics, health insurance (surrogate for SES).

Vital signs, doctor orders, problem list.

Laboratory tests.

Medications.

Procedures.

Imaging.

Diagnosis Coding Procedure

ICD-9-CM

Data Access

Biostatistics Core

Geisinger Center for Health Research

Geisinger Health System

100 North Academy Avenue

Danville, PA 17822

(p) 570-214-8688

(f) 570-214-5170

biostatistics@geisinger.edu

Cost of Access

Access is usually costly.

Other Data Files need for Analysis

A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans.

HealthCare Partners Medical Group database

Description

HealthCare Partners is a Southern California medical group that is structured as a large network of medical practices and administration offices across the area.

Custodians

The HealthCare Partners Institute for Applied Research and Education.

Website



Population Covered

More than 420,000 patients across Southern California.

Datasets available

Inpatient.

Outpatient visits.

Office-based procedures and services.

Emergency Department.

Laboratory.

Pharmacy Services.

Diagnosis Coding Procedures

ICD-9-CM

Cost of Access

Access is usually costly.

Other Data Files need for Analysis

A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans.

References

Waters HC, Hilliard RP, Teng E, Rahman MI, Ferrer R, Pilicharam J, & Neiadnik B. An Exploratory analysis of healthcare costs and utilization of pediatric patients with chron’s disease. Dig Dis Sci 2009, 54(12): 2650-2654.

United HealthCare Database

Description

The database includes anonymous patient longitudinal data consisting of positive enrollment, inpatient and outpatient medical claims, pharmaceutical claims, and laboratory analytic results for more than 45 million unique members. Underlying information is geographically diverse across the United States.

Custodians

OPTUM Insight

Website



Time Period Covered

From May 2000 onwards.

Population Covered

Data on more than 77 million patients, 15 million active patients per year.

Datasets available

Inpatient and outpatient physician and facility claims

Pharmacy

Claims

Enrollment

Lab test results

Socioeconomic elements (e.g. income, education, race, and, ethnicity, life stage, and lifestyle variables).

Diagnosis Coding Procedures

ICD-9CM

Data access

Contact

William Crown, director of U.S. research division at Optuminsight

william.crown@

Cost of access

Cost varies according to request. Access is usually costly

Mapping of fields across datasets

Can be linked to medical charts and death certificates. Personal identifier allows tracking of individuals over time.

Other Data Files Needed for Analysis

Race is not available from claims data. Lab results data are only available for a subset of enrollees and tests. A limitation of using commercial health insurance data is that disease prevalence may be different than among people who have other forms of insurance, such as government-sponsored Medicare or Medicaid plans.

References

Bloomgren G, Sperling B, Cushing K, Wenten M. Assessment of malignancy risk in patients with multiple sclerosis treated with intramuscular interferon beta-1a: retrospective evaluation using a health insurance claims database and postmarketing surveillance data. Ther Clin Risk Manag 2012;8:313-321.

Medstat’s MarketScan Claims and Encounters

Description

The MarketScan Commercial Claims and Encounters database contains patient level enrollment and claims data for the employees, their spouses and dependents who are insured by large employers. The database contains health-care claims records from Health Maintenance Organizations and preferred provider organizations for 12 to 14 million individuals in all 50 states, representing approximately 5% of the privately insured US population.

Custodians

Truven Health Analytics (formerly Thomson Reuters)

Website



Time Period Covered

Data goes back to 1995 online; from 1988 archived.

Population Covered

Approximately 138 million unique de-identified patients that include active employees, early retirees, continuers, and their dependents insured by employer-sponsored plans. Data from all regions of the United States (the database contains information on region, state, MSA, and zip code).

Datasets available

Outpatient and inpatient drug and diagnosis data.

Procedures.

Physician and practitioner data

Cost data

Medical and Pharmacy Insurance Claims.

Diagnosis Coding Procedures

ICD-9-CM, up to 15 inpatient admissions.

Drugs, NCD, includes retail, mail order and specialty drugs.

Data access

To access data contact

Stella Chang, MPH, Sr. Director Information Assets

stella.chang@

For general information: marketscan@

Cost of access

Cost varies according to request. Access is usually costly

Mapping of fields across datasets

No ability to validate against original source, no access to medical records. Each enrollee has a unique identifier and can be identified at the three-digit ZIP Code level, thus data can been linked to Electronic Medical Records and Claims within MarketScan databases.

Limitations/Other Data Files needed for Analysis

Race is not available. Convenience samples from large employers; small and medium firms are not included. Population is generally healthier than the overall population.

References

Tarrants M, Oleen-Burkey M, Castelli-Haley J, & Lage MJ. The impact of comorbid depression on adherence to therapy for multiple sclerosis. Mult Scler Int 2011;2011:271321

Ozminkowski RJ, Marder WD, Hawkins K, Wang S, Stalling SC, Finkelstein SN, Sinskey AJ, & Wierz D. The use of disease-modifying drugs for multiple sclerosis treatment in private-sector health plans. Clinical Therapeutics 2004, 26(8): 1341-1354.

Allen LA, Tomic KE, Wilson KL, Smith DM, & Agodola I. The inpatient experience and predictors of length of stay for patients hospitalized with systolic heart failure: Comparison by commercial, Medicaid, Medicare payer type. Journal of Medical Economics 2013, 16 (1): 43-54.

New York Department of Health Statewide Planning and Research Cooperate System (SPARCS)

Description

A legislatively mandated program that collects patient level detail on patient characteristics, diagnoses and treatments, services, and charges for every hospital discharge, ambulatory surgery patient, and emergency department admission in New York State.

Custodians

The State of New York, the Bureau of Biometrics and Health Statistics (BBHS).

Website



Time Period Covered

Inpatient 1982 onwards.

Outpatient Ambulatory Services 1982 onward.

Outpatient Emergency Department 2005 onward.

Population Covered

Approximately 95% of hospital records in New York State. Master inpatient file 2.8 million records, Master outpatient file 7.3 million records, Ambulatory surgery 1.8 million records, and Emergency department 5.5 million records.

Data Available

Inpatient data contains information on all discharges from hospitals located in New York State. Data is not collected from Federal hospitals. Outpatient data consists of visits from Ambulatory Surgery and Emergency Department that do not result in a hospital admission. Emergency department visits for patients who were subsequently admitted to a hospital inpatient service are contained in the SPARCS inpatient data.

Diagnosis Coding Procedures

ICD-9-CM

Data access

Inpatient and outpatient output files are available to health care researchers. The BBHS is responsible for receiving and processing requests for SPARCS data. Detailed instructions and an application form to request SPARCS data are available on this site



SPARCS Operations

Bureau of Health Informatics

Office of Quality and Patient Safety

New York State Department of Health

Empire State Plaza

Corning Tower, Room 1970

Albany, New York 12237

Questions/Comments: sparcs.submissions@health.

Data Requests: sparcs.requests@health.

Cost of access

A fee applies to all access requests.

Mapping of fields across datasets

SPARCS can be linked to national administrative databases (i.e. Medicare, Medicaid) if researchers are granted access to identified data.

Other Data Files need for Analysis

Socioeconomic status can be obtained through the type of insurance coverage.

References

Allen NB, Lichtman JH, Cohen HW, Fang J, Brass LM, & Alderman MH. Vascular disease among hospitalized multiple sclerosis patients. Neuroepidemiology 2008;30:234-238.

Explorys

Description

The Explorys Network consists of 18 major private integrated healthcare systems (Cleveland Clinic, St. Joseph Health System, Adventist Health System, Med Star Health, Catholic Health Partners, Unity Point Health, Centura Health, Legacy Health, Meritus Health, Baylor, Scott & White, Trinity Health/Catholic Health East, Trinity Mother Frances, NOSA, Metro Health, Summa Health System, Akron General, Lancaster General Health, and Queen’s Medical Center), 300 hospitals, and over 215,000 providers collectively responsible for delivering over $53 billion in health care per year.

Custodians

Explorys

Website



Population Covered

38 million people across the United States

Data access

Not accessible to researchers.

Contact

Danielle.mack@

Cerner Health Facts® Database

Description

The Cerner Health Facts Database captures and stores de-identified, longitudinal electronic health record (EHR) patient data from approximately 480 facilities across the United States.

Custodian

Cerner Corporation

Website

solutions/Research/Real-World_Data/

Time Period Covered

From 2000 onwards

Population Covered

As of January 2012, there were 35,001,010 unique patients and 156,198,274 encounters (acute admissions, emergency and ambulatory visits) in the database.

Data Available

Patient demographics, encounters, diagnoses, prescriptions, procedures, laboratory test, locations of services/ patients (e.g., clinic, emergency department, intensive care unit, etc.) and hospital information, and billing. Data also includes cost, census region, environmental exposures, smoking history, alcohol use, substance abuse, and exposure to environmental tobacco smoke from a subset of facilities.

Diagnosis Coding Procedures

ICD-9, up to 9 principal and 8 secondary diagnoses

ICD-9, up to 6 procedures.

Drugs, NDC, prescription & OTC: drug manufacturer, dosage, days supply, generic name.

Data access

Data available through a subscription and data use agreement. For additional information contact:

Daniel Aguilar, MPH, MBA

Account executive, Cerner Life Sciences

(p) (310)598-4533

Daguilar@

Mark Levine

Director of Research Sales, Cerner Life Sciences

(p) (610)407-4250

Mark.Levine@

Cost of access

Data can be accessed directly via an annual subscription agreement or a one-time fee may apply for access to database.

Mapping of fields across datasets

No ability to validate against original source, no access to medical records. No linkage to other databases.

Other Data Files needed for Analysis

Data includes environmental exposures, smoking history, alcohol use, substance abuse, and exposure to environmental tobacco smoke

References

Salisbury AC, Reid KJ, Alexander KP, Masoudi FA, Lai SM, Chan PS, Bach RG, Wang TY, Spertus JA, & Kosiborod M. Diagnostic blood loss from phlebotomy and hospital-acquired anemia during acute myocardial infarction. Arch Intern Med 2011, 171(18):1646-53.

New York State Multiple Sclerosis Consortium database (NYMSC database)

See description under Clinical Database/Registries.

The Pacific Northwest Multiple Sclerosis Registry & Network

See description under Clinical Database/Registries.

Mexico

National Mortality Statistics

Custodian

Instituto Nacional de Estadística y Geografía (Mexican National Institute of Statistics, Geography and Informatics).

Time Period Covered

From 1955 onwards.

Website



Data Access

Aggregate data can be downloaded from the website. For additional information contact atencion.usuarios@.mx

Mapping of Fields across Datasets

Not possible.

Encuesta Nacional de la Dinámica Demográfica (ENADID) (National Demographics Survey)

Description

National survey conducted to obtain information regarding demographics, mortality and migration information of residents in Mexico.

Custodian

Instituto Nacional de Estadística y Geografía (Mexican National Institute of Statistics, Geography and Informatics).

Website



Time Period Covered

1992, 1997, 2006, and 2009

Population Covered

101.000 homes were selected for interviews. All women residing in those homes (between the ages of 15-54) were asked about regarding members of the household.

Data Access

Enquiries can be directed to: atencion.usuarios@.mx

Cost of Access

Not specified

Mapping of Fields across Datasets

Not possible.

Other Data Fields Needed for Analysis

Only limited, self-reported co-morbidities information. Self-reported mortality information.

South America

Argentina

Cause-of-Death Records

Custodian

Ministerio de Salud de la Nación (Ministry of Health), Dirección de Estadísticas e Información de Salud (National statistics and health information). .ar

Time Period Covered

Not specified

Population

When a death occurs in an Argentinean hospital or health facility, the attending physician issues a death certificate.

Diagnosis Coding Procedure

ICD-10

Data Access

Aggregate data can be downloaded from the website. Website does not specify whether they allow access to international researchers or whether they take part in collaborative projects. For additional information contact

Lic. Maria de las M. Fernandez

mfernandez@.ar

Cost of Access

Not specified

Mapping of Fields across Datasets

Not possible.

Other Data Fields Needed for Analysis

Not possible to link to other files to obtain co-morbidity, drug data.

Registro Institucional de Tumores de Argentina (RITA) (Cancer Registry)

Custodian

Ministerio de Salud de la Nación (Ministry of Health)

Time Period Covered

Not specified

Population

Demographic information, diagnosis, treatment and hospitalization data for all cancer patients treated across 13 hospitals across the country.

Diagnosis Coding Procedure

CIE-O

Data Access

Contact:

Rocio Alonzo

(p) 5239-0575

arocio.inc@

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Data Fields Needed for Analysis

Not possible to link to other files to obtain co-morbidity, drug data.

Brazil

DATASUS

Description

Brazil’s health system is based on the notion of free, universal care under the SUS (Brazilian Heath System). While 100 % of the population is able to receive services under the SUS, approximately 25% opt for private insurance coverage. The Department of Health Information of SUS (DATASUS) contains several administrative databases. DATASUS represents the primary effort of the federal government to collect data from the national health system.

Website



Population covered

Approximately 75% of the Brazilian population.

Mapping of Fields across Datasets

Not possible. Might be possible in the future as system develops.

References

Leão BF, Bernardes MM VB, Levin J, Moura L, Bandarra E, Modesto LM, Sousa AM, Cunha RE, & Filho MRM. The Brazilian National Health Informatics Strategy. MEDINFO 2001, 84: 38-42.

Data Available from DATASUS

Primary Care Information System (SIAB)

Time Period Covered

From 2001 onward

Website (in Portuguese)



Diagnosis Coding Procedures

ICD-10

Data Access

For access information contact

Julia de Figueiredo Coelho

siab@.br

(p) (21) 3985-7108

Cost of Access

Not specified

Sistema de Informações Hospitalares (SIH-SUS) (Hospital Systems Data)

Time Period Covered

From 1992 onward

Population Covered

All hospitalizations paid for by SUS in Brazil.

Diagnosis Coding Procedures

ICD-10

Data Access

Contact

Leon Avres

sisaih01@listas..br

(p) (21) 3985-7160

Cost of Access

Some data can be obtained from the DATASUS website for free. For additional information contact Leon Avres (see above)

Mapping of Fields across Datasets

Not possible.

Other Data Fields Needed for Analysis

Primary care, drug data, socioeconomic information.

References

Candiago RH, Belmonte de Abreu P. Use of Datasus to evaluate psychiatric inpatient care in Southern Brazil. Rev Sause Publica 2007, 41 (5): 821-829.

Sistema de Cadastramento e Acompanhamento de Hipertensos e Diabéticos

Description

Registration and Monitoring System for Hypertension and Diabetes.

Website (in Portuguese)



Time Period Covered

Not specified

Population Covered

Registers and monitors the situation of patients with hypertension and/ or diabetes mellitus across the country.

Data Access

Contact

Patricia Serapião Coimbra

(p) (21) 3985-7169

hiperdia@.br

Cost of Access

Not specified

Mapping of Fields across Datasets

Not possible.

Other Data Fields Needed for Analysis

Co-morbidity, drug data.

Mortality Information System (MIS)

Time Period Covered

Not specified

Website (in Portuguese)



Population

When a death occurs in a Brazilian hospital or health facility, the attending physician issues a death certificate.

Diagnosis Coding Procedure

ICD-10

Data Access

Not specified.

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

References

Franca E, de Abreu DX, Rao C, & Lopez AD. Evaluation of cause-of-death statistics for Brazil, 2002-2004. Int J Epidemiol 2008, 37 (4): 891-901.

Europe

In Europe administrative databases are more likely to be national or regional than in the United States.

European Database for Multiple Sclerosis (EDMUS)

See description under Clinical Databases/Registries.

European Register for Multiple Sclerosis (EUREMS)

See description under Clinical Databases/Registries.

Austria

DIAG-Extranet

Description

DIAG-Extranet is the most comprehensive database on hospitals in Austria. It contains data routinely collected at social-insurance funded hospitals.

Custodian

Ministry of Health

Time Period Covered

Diagnoses since 1989, treatments since 1997, intensive care since 1988.

Population Covered

All persons who have received care in a socially funded hospital in Austria. Only includes inpatient data (project underway to include outpatient care in the future).

Data available

Demographic information (sex, age, place of residence)

Diagnoses

Treatments

Diagnosis Coding Procedure

ICD-10

Data Access

Data is only available to researchers for very specific projects. Applications and requests for access can be made to

Ulrike Schermann-Ritcher

ulrike.schermann-richter@bmg.gv.at

(p) 00431711004163 or

Manfred Pregartbauer

manfred.pregartbauer@bmg.gv.at

(p) 00431711004186

Mapping of Fields across Datasets

Possibility to link through date of birth or postal code. However, linkage requires substantial justification on behalf of researchers due to data protection issues (e.g. not allowed to have postal codes and dates of birth in the same data set so that individual hospitals or patients cannot be identified). Linked to BIG (Business Intelligence in the Health Care System).

Other Data Fields Needed for Analysis

Only includes inpatient data (project underway to include outpatient care in the future).

GAP-DRG (General Approach for Patient oriented Outpatient-based DRG)

Description

GAP-DRG is a database with reimbursement data for outpatient services of sickness funds (social insurance) and Federal Ministry of Health (hospital data).

Custodian

Austrian SII and Ministry of Health.

Time Period Covered

2006-2007

Population Covered

All patients who received services in 2006/07; data includes all those covered by social insurance (approximately 98% of the population).

Data available

Demographic information (sex, age, place of residence)

Outpatient

Inpatient

Sickness leaves

Prescriptions

Diagnoses

Conditions: chronic obstructive pulmonary disease, diabetes, cancer, coronary health disease, mental health.

Diagnosis Coding Procedure

ICD-10

Drugs, ATC

Data Access

Data is only available in German. Data is only available to researchers for very specific projects. Applications and requests for access can be made to:

Nina Pfeffer

nina.pfeffer@hvb.sozvers.at

Cost of Access

Cost varies according to request.

Mapping of Fields across Datasets

Possibility to link to other national databases through a Unique Person Identifier (UPI).

Other Data Fields Needed for Analysis

Limited data on diagnosis, limited socio-economic variables, limited coverage of outpatient care (ambulatory care in hospital settings is not covered).

Austrian National Cancer Registry

Custodian

Statistic Austria.

Website



Time Period Covered

From 1983 online (archives since 1969).

Population Covered

The registry includes data on the incidence of cancer cases (per year by localization and sex) and on their vital status.

Data included

Demographic information, hospitalization, information about tumor (type, localization, histology, stage of tumor, diagnosis, treatment, anamnesis data, cancer suspected due to occupation).

Diagnosis Coding Procedure

ICD-10

Data Access Procedures

Publications available in German only. Some aggregate data available online. For access to the raw database containing patient level data a formal request is necessary. For additional information contact

nadine.zielonke@statistik.gv.at

(p) 00431711287228

Cost of data

Relatively low cost.

Mapping of Fields across Datasets

Linked to cause of death statistics (but only used for internal quality check). There are no unique individual identifiers, but data could be linked by date of birth and/or names.

Limitations/Other Fields Needed for Analysis

Cancer patients treated in outpatient care are not reported, thus cancer cases in Austria are underestimated.

Cause of Death Statistics

Custodian

Statistic Austria.

Time Period Covered

From 1970 onward

Population Covered

All deaths registered in Austria (close to 100%).

Data Access

Micro data for a 20% sample of any given year's total cause of death statistics is made available annually. Contact:

Anita Mikulasek

anita.mikulasek@statistik.gv.at

Mapping of Fields across Datasets

Linkage to cancer, births and marriages registries.

Belgium

Healthcare coverage is part of the Belgian Social Security system; citizens must join a health insurance fund mutuelle (mutualiti) or ziekenfonds (mutualiteit) and pay through payroll or bank deductions Citizens of Belgium pay and swipe a health card at the point of care. They are then reimbursed between 50% and 75% of the costs by their mutuelle/mutualiteit scheme.

Résumé Clinique Minimum et Résumé Financier Minimum

Custodian

Belgian Ministry of Public Health.

Website



Time Period Covered

From 1990 onward

Population Covered

All patients discharged from Belgium hospitals (public and private).

Data Available

Patient demographics, hospital stay (date and type of admission and discharge, referral data, admitting department, destination after discharge), clinical data (primary and secondary diagnoses), and prescriptions.

Diagnosis Coding Procedure

Diagnosis, ICD-9

Procedure, ICD-9-CM

Drugs, ATC

Mapping of Fields across Datasets

Can link patients that stayed within the same hospital over time.

PharmaNet Database

Custodian

Institut National D’assurance Maladie Invalidité (INAMI).

Website (French)



Time Period Covered

From 1996 onwards

Population Covered

90% of outpatient drug data: Outpatient, general practitioners, doctors under specialist training, specialists in internal medicine, cardiology, gastroenterology, rheumatology, pediatrics, dermatology, gynecology and other specialists, and dentists. Data is collected from pharmacies, pharmaceutical invoice offices, and by the health insurers which send data to the INAMI.

Diagnosis Coding Procedure

ATC

Data Access

Data requested should be accompanied with a full protocol (objectives, the scientific or social rationale, methodology and the way data will be disseminated), principal investigator’s name, and people with access to the data, address where data will be analyzed, source of funding and the heading under which the use of the data may be classified. A special committee will evaluate the feasibility of the project. After acceptance, a contract will be signed between INAMI and the investigator. For additional information contact:

marc.defalleur@fgov.inami.be

Cost of Access

Fee will be specified after project approval.

Mapping of Fields across Datasets

Can be linked to other INAMI datasets.

Other Files Needed for Analysis

Clinical data must be linked.

Belgium Cancer Registry

Custodian

Cancer Registry Foundation

Website



Data Access

Privacy Committee and Advisory Committee approval required. Application form can be found on this website

IMS LifeLink™ Belgian Hospital Disease Database

Custodian

IMS Health

Time Period Covered

From 2001 onwards

Population Covered

34% of Belgian acute hospital beds. Longitudinal data on patient demographics, diagnosis, treatments and procedures.

Diagnosis Coding Procedure

Primary and secondary diagnosis, ICD-9-CM.

Procedures, ICD-9-CM.

Drugs, ATC code, date and dispensed date, pack size, number of packs, strength, price per dispensed prescription.

Data Access

For information about the data application process email: heorinfo@

Cost of Data

Access is usually costly. It depends on several factors.

Mapping of Fields across Datasets

Patients can be tracked through time. No linkage to external datasets.

Bulgaria

All Bulgarian citizens are insured by the National Health Insurance Fund (NHIF). For prescription drugs the patient only pays part of the total cost of a medicine and the sickness funds pays the remaining part.

National drug consumption database of the Bulgarian Drug Agency

Custodian

Bulgarian Drug Agency.

Website

bda.bg

Time Period Covered

From 2009 onward

Population Covered

100% outpatient and inpatient.

Diagnosis Coding Procedure

ATC code, data includes trade name, international non-proprietary name (INN), pharmaceutical form and strength, legal status, number of packages sold to hospitals, pharmacy stores and other outlets.

Data Access

Contact the Department of Medicines Use Control:

Maria Popova

maria.popova@bda.bg

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Clinical data.

Czech Republic

The Czech Republic has achieved almost universal coverage of its population with mandatory health insurance. Health care services are provided on the basis of Statutory Health Insurance which is provided by 9 Health Insurance Funds. All residents have a unique personal identifier that allows linkage between national registries/databases. For prescription drugs, the patient pays part of the total cost and the Health Insurance Funds pays the rest directly to the pharmacy.

National Register of Hospitalized Patients

Custodian

Institute of Health Information and Statistics of the Czech Republic.

Website



Time Period Covered

From 1960 onward

Population Covered

The registry contains information on all patients that were hospitalized in bed departments and whose hospitalization was terminated in the monitored period.

Diagnosis Coding Procedure

ICD-10, ICD-9 prior to 1994.

Data Included

Patient demographics (includes marital status, occupation), admission, diagnosis, treatment, cause of death.

Data Access

Data is provided on the basis of written requests sent by mail or e-mail. Send request to

uzis@uzis.cz or Institute of Health Information and Statistics of the Czech Republic P.O.Box 60, Palackého nám. 4, 128 01 Praha 2.

Include:

• Name and surname of the applicant and the represented institution.

• Address, fax number and/or e-mail address.

• Project objectives and a brief annotation.

• Telephone number for the need of detailed specification.

• The purpose of use of the requested data (commercial/study/science and research/use for the needs of the state administration/media).

• Project outputs (if available) or a link to a separate project presentation.

Cost of Access

Not specified

Mapping of Fields across Datasets

Can be linked to other National registries through individuals’ unique personal identifier.

Other Files Needed for Analysis

Prescriptions.

References

Lejckova P, & Mravcik V. Mortality of hospitalized drug users in the Czech Republic. Journal of Drug Issues 2007, 37(1): 103-118.

Czech National Cancer Registry (CNCR)

Custodian

Counsel of CNCR.

Website



Time Period Covered

From 1977 onwards

Population Covered

All cancer cases in Czech Republic; the register contains more than 1.4 million entries.

Diagnosis Coding Procedure

ICD-10

Data Access

To submit a presentation for collaborative research, submit the project to onconet@iba.muni.cz.The application should include the following items:

• Author / authors or the main coordinator of the project, possibly expert guarantors.

• Project title, possibly an abbreviated title or a presentation title.

• Project objectives and a brief annotation.

• Where the project is being solved or will be solved: involved hospitals or institutions, a possible call for cooperation to other centers.

• Main monitored parameters.

• Project outputs (if available) or a link to a separate project presentation.

Cost of Access

Fees will be specified after project has been approved.

Mapping of Fields across Datasets

Can be linked to other National registries through individuals’ unique personal identifier.

Other Files Needed for Analysis

Registry includes lifestyle information such as smoking status, social status, and marital status.

References

Rericha V, Kulich M, Rericha R, Shore DL, & Sandler DP. Incidence of leukemia, lymphoma, and multiple myeloma in Czech Uranium Miners: A case cohort study. Environ Health Perspect 2006, 114 (6):818-822.

National Register of Cardiac Surgery (NKCHR)

Custodian

Institute of Health Information and Statistics of the Czech Republic.

Website



Time Period Covered

From 1997 onwards

Population Covered

The register includes demographic information, preoperative information (cardiac anamnesis, previous interventions, smoking status), operative information and post-operative information (specific medical information on the patient’s stay in intensive care unit of the health establishment, post-operative complications, on discharge from health establishment or on death of the patient). The register also contains data necessary for identification of the health establishment in which cardiac surgery was performed.

Diagnosis Coding Procedure

ICD-10

Data Included

Patient demographics (includes marital status, occupation), admission, diagnosis, treatment, cause of death.

Data Access

Data is provided on the basis of written requests sent by mail or e-mail. Send request to

uzis@uzis.cz or Institute of Health Information and Statistics of the Czech Republic P.O.Box 60, Palackého nám. 4, 128 01 Praha 2.

Include:

• Name and surname of the applicant and the represented institution.

• Address, fax number and/or e-mail address.

• Project objectives and a brief annotation.

• Telephone number for the need of detailed specification.

• The purpose of use of the requested data (commercial/study/science and research/use for the needs of the state administration/media).

• Project outputs (if available) or a link to a separate project presentation.

Cost of Access

Not specified.

Mapping of Fields across Datasets

Can be linked to other National registries through individuals’ unique personal identifier.

Other Files Needed for Analysis

Prescriptions.

National Register of Cardiovascular Interventions (NRKI)

Custodian

Institute of Health Information and Statistics of the Czech Republic.

Website



Time Period Covered

From 1997 onwards

Population Covered

Nationwide central health register of persons with socially serious ischemic heart diseases who underwent cardiovascular intervention (cathetrisation, angioplasty).

Data Included

Information regarding the health care facility, demographic information, and intervention.

Diagnosis Coding Procedure

Not applicable.

Data Access

Data is provided on the basis of written requests sent by mail or e-mail. Send request to

uzis@uzis.cz or Institute of Health Information and Statistics of the Czech Republic P.O.Box 60, Palackého nám. 4, 128 01 Praha 2.

Include:

• Name and surname of the applicant and the represented institution.

• Address, fax number and/or e-mail address.

• Project objectives and a brief annotation.

• Telephone number for the need of detailed specification.

• The purpose of use of the requested data (commercial/study/science and research/use for the needs of the state administration/media).

• Project outputs (if available) or a link to a separate project presentation.

Cost of Access

Not specified

Mapping of Fields across Datasets

Can be linked to other National registries through individuals’ unique personal identifier.

Other Files Needed for Analysis

Prescriptions.

National drug consumption database of the State Institute for Drug Control (SUKL)

Custodian

The State Institute for Drug Control (SUKL).

Website



Time Period Covered

Sales from wholesalers from 2006. Since 2011, dispensed medicines also available.

Population Covered

100 % outpatient.

Diagnosis Coding Procedure

ATC code, package size, number of packages, strength, dosage form, sales over the counter, dispensing over the counter.

Data Access

The Department of International Relations is in charge of coordination of international activities within the State Institute for Drug Control, for additional information contact mez@sukl.cz

Cost of Access

Fees apply according to request.

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Clinical information.

Denmark

All residents of Denmark are assigned a unique personal identifier, which is used to register social and health services, including prescription dispensations, in various nationwide registries. In Denmark it is called the central personal registration (CPR) number. Individual level data can be linked using the unique and national personal identification number. Data linkage between databases is allowed provided that the researcher has received permission from the appropriate authorities.

The Danish National Database of Reimbursed Prescriptions (DNDRP)

Description

The DNDRP includes data on all reimbursed prescriptions redeemed at Danish community pharmacies and hospital-based outpatient pharmacies since 2004.

Custodian

Department of Clinical Epidemiology at Aarhus University.

Time Period Covered

From 2004 onwards

Population Covered

It covers the entire Danish population including residents of long-term care institutions.

Data available

The county/region of residence of the user, civil registration number of the user, the prescriber, Anatomic Therapeutic Classification (ATC) code, item number, date of redemption, quantity of the item, strength, pack size, 24 hour dose, unit (related to strength), name on the packaging, form of doses, manufacturer, drug ID and unit (related to pack size).

Data Access Procedures

Researchers from all over Denmark can gain access to the data when the necessary approvals from the Danish Data Protection Agency are in place. Researchers who would like to use data from the Danish National Database of Reimbursed Prescriptions are encouraged to send an application along with a 3-page protocol to the board member of the region that the researcher works in. The contact information of board members can be accessed from this site

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

No information on in-hospital medications or non-reimbursable over-the-counter medicine.

References

Johannesdottir SA, Horvath-Puho E, Ehrenstein V, Schmidt M, Pederesen L, & Sorensen HT. Existing data sources for clinical epidemology: The Danish National Database of Reimbursed Prescriptions. Clin Epidemiol 2012, 4: 303-313.

Danish National Registry of Patients

Custodian

Danish Board of Health

Time Period Covered

Inpatient from 1977 onwards, Outpatient and Emergency from 1995 onwards.

Population Covered

Information of approximately 60 million instances of patient contact. All inpatient admissions to Danish hospitals since 1977 and all outpatient clinic and emergency room admissions since 1995.

Data available

CPR-number

Hospital department

Date and time for hospital arrival and departure, outpatient contact, treatment, and operation.

Waiting status

Referral diagnosis

Action diagnosis

Other diagnoses

Type of operation, examination, and treatment.

Diagnosis Coding Procedure

ICD-10, from 1994. ICD-8 prior to 1994.

SKS codes

Data Access Procedures

To obtain data from the DNRP, an application should be sent to the National Board of Health through the online system on their Website ().

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Socioeconomic status.

References

Christiansen CF, Christensen S, Farkas DK, Miret M, Sørensen HT, & Pedersen L. Risk of arterial cardiovascular diseases in patients with multiple sclerosis: a population-based cohort study. Neuroepidemiology 2010; 35: 267-274.

Sunesen KG, Norgaard M, Thorlacius-Ussing O, & Laurberg S. Immunosuppressive disorders and risk of anal squamous cell carcinoma: a nationwide cohort study in Denmark, 1978-2005. Int J Cancer 2010; 127: 675-684.

Mason K, Thygesen LC, Stenager E, Bronnum-Hansen H, & Koch-Henriksen N. Evaluating the use and limitations of the Danish National Patient Register in registered-based research using an example of multiple sclerosis. Acta Neurologica Scandinavica 2012, 125 (3): 213-217.

Lynge E, Sandegaard JL, & Rebolj M. The Danish National Patient Register. Scand J Public Health 2011, 39 (7): 30-33.

Moller H, Kneller RW, Boice JD, Jr., & Olsen JH. Cancer incidence following hospitalization for multiple sclerosis in Denmark. Acta Neurol Scand 1991;84:214-220.

Danish Cancer Registry

Custodian

Danish Board of Health

Time Period Covered

From 1942 onwards. Reporting became compulsory for all medical doctors in Denmark in 1987.

Population Covered

Almost 100% coverage of incident cases in Denmark.

Data Available

Patient identification, place of residence, primary site of the tumor, date of diagnosis, verification of diagnosis, histological type and date and cause of death.

Diagnosis Coding Procedure

ICD-1O since 1978, prior to 1978 ICD-7

Data Access Procedures

To obtain data from the DNRP, an application should be sent to the National Board of Health through the online system on their Website

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Socioeconomic status (can be linked).

References

Storm HH, Michelsen EV, Clemmensen IH, & Pihl J. The Danish Cancer Registry- history, content, quality and use. Dan Med Bull 1997, 44(5): 535-539.

Psychiatric Central Research Register

Custodian

The Centre for Psychiatric Research at the Danish Board of Health

Time Period Covered

From 1970 onwards.

Population Covered

Every Psychiatric admission since 1970. In 1995 data on outpatient treatment and emergency room contacts was included. Thus, the Psychiatric Central Register became an integrated part of the Danish National Patient Registry.

Data Included

CPR number, dates of any admission and discharge or start and end of any outpatient treatment including emergency room visits, diagnoses, type of referral, place of treatment with identification of the specific department, municipality of residence, and mode of admission (acute or planned).

Diagnosis Coding Procedure

ICD-10 from 1994 onward, ICD-8 prior to 1994.

Data Access

Anonymous data for research purposes and planning can be released directly, whereas person-identifiable data can be retrieved for research if approved by the Data Protection Agency and the National Board of Health and in some cases the Danish Ethical Committee too. To obtain data from the PCRR, an application should be sent to the National Board of Health through the online system on their Website

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Socioeconomic status (can be linked).

References

Mors O, Petro GP, & Mortensen PB. The Danish Psychiatric Central Research Register. Scandinavian Journal of Public Health 2011, 39 (7): 54-57.

Danish Twin Registry

Custodian

University of Southern Denmark, Epidemiology, Biostatistics and Biodemography Institute of Public Health

Time Period Covered

Covers all birth cohorts of twins since 1870.

Population Covered

100%

Data Available

Vital statistical master data from church books and the Central Office of Civil Registration, information on health and life style, information on causes of death.

Data Access Procedures

Access to the twin registry data may be requested by approaching the scientific board of the Danish Twin Registry tvilling@health.sdu.dk by completing the following application

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Socioeconomic status (can be linked).

References

Skytthe A, Kyvik KO, Holm NV, Christensen K. The Danish Twin Registry. Scandinavian Journal of Public Health 2011, 39 (75): 75-78.

Danish Education Registers

Custodian

Statistics Denmark and the Danish Ministry of Education.

Time Period Covered

Varies by registry

Population Covered

97% Danish population, 85-90% education outside of Denmark.

Registries Available

Student Register (SR)

From 1974. Grade-level information on compulsory and upper secondary schooling and vocational education.

Academic Achievement Register (AAR)

From 1977. Individual specific marks for the final grades of compulsory schooling and all the formal tests in upper secondary education.

Population’s Education Register (PER)

From 1981. Information on individuals’ highest completed education.

Adult Education and Continuing Training Register

From 1997. Public adult education and training.

.Data Access Procedures

Only Danish researchers are granted authorization. Foreign researchers can, however, get access to micro data through an affiliation to a Danish authorized environment. For additional information visit



Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Socioeconomic status (can be linked).

References

Jensen VM, & Rasmussen AW. Danish Education Registries. Scandinavian Journal of Public Health 2011, 39 (7): 91-94.

Danish Heart Register

Custodian

National Institute of Public Health

Time Period Covered

Since 2000.

Population Covered

90-95%. 35,000 to 40,000 surgeries each year.

Data Available

Detailed information on select invasive procedures within cardiology and thoracic surgery.

Data Access Procedures

For customized data extracts or analyses, please contact the research assistant at Cardiovascular Diseases: Tina Birgitte Hansen (+45 3920 7777)

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Socioeconomic status (can be linked).

References

Abildstrøm SZ, & Madsen M. The Danish Heart Register. Scandinavian Journal of Public Health 2011, 39 (7): 46-49.

Danish Injury Register

Custodian

National Institute of Public Health

Time Period Covered

Since 1990-2011.

Population Covered

Approximately 12% of the Danish Population.

Data Available

All accident related primary contacts at the hospitals in Glostrup, Herlev, Frederikssund, Esbjerg, and Randers, and from 2008 also contacts due to violence and self- harm.

Data Access Procedures

For customized data extracts or analyses, please contact the Center for Injury research. Tel. +45 3920 7777, contact person: Bjarne Laursen or Hanne Møller.

Diagnosis Coding Procedure

NOMESCO classification (4th version)

Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Only includes injuries treated at emergency departments and not injuries treated at GPs.

References

Laursen B, & Møller H. The Danish Injury Register. Scandinavian Journal of Public Health 2011, 39 (7): 65-67.

Danish registers on personal income and transfer payments

Custodian

Statistics Denmark

Time Period Covered

Since 1970.

Population Covered

Approximately 12% of the Danish Population.

Data Available

Includes anyone who is economically active in Denmark.Variables describing wages, entrepreneurial income, taxes, public transfer payments, public pensions, capital income, private pension contributions and payouts, home ownership and fortunes

Data Access Procedures

Only Danish researchers are granted authorization. Foreign researchers can, however, get access to micro data through an affiliation to a Danish authorized environment. For additional information visit



Mapping of fields across datasets

Linkage to all major Danish databases through an individual’s CPR number.

Other Data files needed for Analysis

Only includes injuries treated at emergency departments and not injuries treated at GPs.

References

Laursen B, & Møller H. The Danish Injury Register. Scandinavian Journal of Public Health 2011, 39 (7): 65-67.

Danish Multiple Sclerosis Register

See description under Clinical Databases/Registries

Estonia

The health care system in Estonia is funded through mandatory health insurance. The use of acute care and primary health care is free for the insured population. All health related data, whether at aggregate or individual level is collected by the National Institute for Health Development.

The Estonian Health Insurance Fund Administrative Database

Custodian

Estonian Health Insurance Fund.

Website



Time Period Covered

From 2000 onward

Population Covered

This database contains billing data on all health care services provided to all insured population of Estonia.

Data Included

Patient identification, date of birth and place of residence.

Primary diagnoses and co-morbidities, as assigned at the end of the treatment episode.

Dates of all services provided.

List of all services provided (lab tests, diagnostic procedures, imaging, any treatment, surgical operations, bed-days, etc).

Health care institution and responsible doctor.

Amount and cost of each service provided.

Drugs

Diagnosis Coding Procedure

ICD-10

Drug, INN name and ATC code, amount purchased and paid by the EHIF and the patient (co-payment), date of prescription, date of purchase and the doctor who issued the prescription.

Data Access

Some aggregate data available online in Estonian language .

Data access for research projects need to be presented in a study proposed plan. If the proposed plan is approved, the requested data shall be extracted by the Health Insurance staff.

Cost of Access

No cost, however it can take a considerable amount of time to obtain the data requested.

Mapping of Fields across Datasets

Linkage is theoretically possible, although is not done because of ownership and data protection reasons.

Other Files Needed for Analysis

No socioeconomic or lifestyle characteristics.

Health Statistics and Health Research Database

Custodian

National Institute for Health Development.

Time Period Covered

Not available.

Website



Data Included

Characteristics of Population

Morbidity

Use of Healthcare and Reasons for Treatment

Healthcare Resources

Health Promoting Institutions

Health Behavior

Statistics on Medicines

Data Access

The tables presented in the database can be both viewed in the Internet and downloaded using different file formats (.px, .xls, .dbf, .txt). For additional information contact:

Maali Käbin, database administrator

(p)+372 659 3814

maali.kabin@tai.ee

Cost of Access

Free of charge.

Estonian Cancer Registry

Custodian

National Institute for Health Development

Website



Time Period Covered

Data available since 1968.

Population Covered

Reporting of cancer is mandatory to all physicians working in Estonia who diagnose or treat cancer. Data is also submitted by forensic experts.

Diagnosis Coding Procedure

ICD-10

Data Access

To access data contact:

Margit Magi, Head of Registry

(p) +372-659-3830

margit.magi@tai.ee

Cost of Access

Not reported

Mapping of Fields across Datasets

Linkable to other national databases and hospital databases though name, date of birth and/or personal identification code.

References

Lang K, Magi M, & Aareleid T. Study of completeness of registration at the Estonian Cancer Registry. Journal of Cancer Prevention 2003, 12(2): 153-156.

Estonian Causes of Death Registry

Custodian

National Institute for Health Development

Website



The Estonia Health Interview Survey

Description

This is nationwide (representative) survey, which has been performed in 1996 and 2006 with the aims to describe the populations’ health status and provide quantitative information on health determinants.

Custodian

National Institute for Health Development.

Website



Time Period Covered

1996 and 2006.

Population Covered

Persons aged 15-64 randomly drawn from the 1989 census. Data has been verified and is representative for the Estonia population.

Data Available

Health status and quality of life.

Prevalence of health disorders, diseases and injuries.

Occurrence and severity of disabilities.

Reproductive health and sexual behavior.

Use of health services and medicines.

Health behavior and health risks.

Education and occupation.

Attitudes and values.

Data Access

For access information contact:

Leila Oja

leila.oja@tai.ee

Mapping of Fields across Datasets

No linkage at individual level possible, it is anonymous data.

References

Aluoja A, Leinsalu M, Shlik J, Vasar V, & Luuk K. Symptoms of depression in the Estonian population : Prevalence, sociodemographic correlates and social adjustement. Journal of Affective Disorders 2004, 78(1): 27-35.

Health Behavior among Estonian Adult Population 

Description

Survey administered every 2 years since 1990 focusing on social determinants of health. Consistent questionnaire used over the years that is common to that used in Finland, Latvia and Lithuania.

Custodian

National Institute for Health Development. 

Website

pxweb.tai.ee/esf/pxweb2008/Database_en/Surveys/.../TKUinfo_en.htm

Time Period Covered

Every two years since 1990.

Population Covered

Data has been verified and is representative for the Estonia population.

Data Available

Self-perceived health.

Education and occupation.

Income and place of residence.

Prevalence of health disorders, diseases and injuries.

Occurrence and severity of disabilities.

Eating habits.

Smoking.

Alcohol consumption.

Physical activity.

Sexual behavior.

Use of health services and medicines.

Data Access

For access information contact

Leila Oja

leila.oja@tai.ee

Mapping of Fields Across Datasets

No linkage at individual level possible, it is anonymous data.

Finland

Finland provides socially funded health care to its 5.4 million residents. All residents are assigned a unique personal identifier, which is used to register social and health services, including prescription dispensations, in various nationwide registries. Individual level data can be linked using the unique and national 11-digit personal identification code (assigned since 1961). Data linkage is allowed provided that the researcher has received permission from the appropriate authorities.

Finnish National Hospital Discharge Register

Custodian

The National Institute for Health and Welfare (THL).

Website



Time Period Covered

From 1988 onward

Population covered

It contains data routinely collected from all hospitals and other institutions producing inpatient health care (close to 100%).

Data Available

Individual-level data per hospital stay (gender, age, place of residence, and number days of hospitalization), diagnoses, procedures, treatment, drug codes per hospital.

Diagnosis Coding Procedure

ICD-10, ICD-9 prior to 1995.

Data Access

Research projects must be approved by THL before the data can be used. Linkages to other registries require separate applications for permission.

Cost of Access

Relatively low cost.

Mapping of fields across datasets

Data (individual level) use is allowed provided that the researcher has received permission from the authorities (THL).

Other Files Needed for Analysis

Clinical parameters, lifestyle and quality of life variables not included.

Kelasto Database (Finnish Prescription Registry)

Custodian

Social Insurance Institution (SII)

Website





Time Period Covered

From 1985 onwards

Population Covered

Reimbursements for medical and pharmaceutical expenses, sickness and disability allowances, occupational health care, rehabilitation allowances.

Diagnosis Coding Procedure

ATC code

Data Access Procedures

Research projects must be approved by SII before the data can be used. Linkages to other registries require separate applications for permission. To obtain further information contact

Johanna Aarniom, KELA Statistics department

(p) +358 020 634 1335.

Mapping of fields across datasets

Possible to link to other data sources using the unique personal identification number.

Other Fields Needed for Analysis

OTC medicines, inpatient care, purchases compensated by an employer.

Cost of data

Relatively low cost.

Causes of Death Registry

Custodian

Statistics Finland (Tilastokeskus)

Time Period Covered

From 1986 onward

Population Covered

Cause of death, time of death, personal identification code, municipality/region at time of death.

Data Access

Research projects must be approved by Statistics Finland before the data can be used. Linkages to other registries require separate applications for permission.

Cost of Access

Relatively low cost.

Diagnosis Coding Procedure

ICD-10, ICD-9 prior to 1995.

Finnish Cancer Registry

Custodian

The National Institute for Health and Welfare (THL).

Website



Time Period Covered

From 1953 onwards

Population Covered

The prevalence, incidence and survival of all cancer patients in Finland (close to 100%). Demographic data includes residence and occupation.

Data Access

Research projects must be approved by THL before the data can be used. Linkages to other registries require separate applications for permission. For additional information contact

Katariina Peltonen, Senior Planning Officer

(p) +358 29 524 7282

Katariina.Peltonen@thl.fi

Cost of Access

Relatively low cost

Diagnosis Coding Procedure

ICD-O-3, ICD-7 prior to 2005.

Mapping of fields across datasets

Data (individual level) use is allowed provided that the researcher has received permission from the authorities (THL).

Other Data Files needed for Analysis

The resource use and costs during the treatment in the follow-up period need to be obtained by linkages to national hospital databases.

References

Hakulinen T, Isomaki H, & Knekt P. Rheumatoid arthritis and cancer studies based on linking nationwide regustries in Finland. The American Journal of Medicine 1985, 78(1): 29-32.

Health 2000/2011 Interview Surveys 2000 and 2011

Description

The survey provides detailed information on self-perceived health, chronic diseases and mental conditions, life quality, and health impairments. The survey was conducted with medical check-ups.

Custodian

The National Institute for Health and Welfare (THL).

Website



Time Period Covered

2000 and 2011

Population Covered

Representative survey (n= 10,000) of the Finnish population over a period of 18 years.

Data Access

Research projects must be approved by THL before the data can be used. Linkages to other registries require separate applications for permission. For additional information contact

Katariina Peltonen, Senior Planning Officer

(p) +358 29 524 7282

Katariina.Peltonen@thl.fi

Cost of Access

Relatively low cost

France

Citizens of France have access to universal health care services organized through the Statutory Health Insurance System (SHI). The French National Health Service generally refunds patients 70% of most health care costs (including prescription drugs), and 100% in case of costly or long-term ailments.

Programme de Médicalisation des Systèmes d’Information (PMSI)

Custodian

French Ministry of Health.

Time Period Covered

From 1996 onwards

Population Covered.

It provides data on all claims paid by the Social Security System to hospitals (public and private) and is therefore the main source of information on hospital activity and associated expenditure.

Data Available

PMSI-MCO for medical, surgical and obstetrics wards, "médecine, chirurgie, obstétrique”: from 1996 onwards.

PMSI-SSR for post-operative and rehabilitation wards, "soins de suite ou de réadaptation”: from 1999 onwards.

PMSI-PSY for psychiatric wards: from 2006 onwards (IRDES report available).

PMSI-HAD for home-based hospitalization, "hospitalisation à domicile": from 2008 onwards.

Diagnosis Coding Procedure

Diagnosis, ICD-10

Procedure, CCAM

Data Access

In French Only, some aggregate data available online. Access to the raw database requires pre-authorization with the CNIL (public agency responsible for data confidentiality). For detailed explanation (in French) refer to

Cost of Access

Fees vary according to request.

Mapping of fields across datasets

Patients can be linked across time, hospitals (public/private), PMSI emergency, PMSI psychiatry, SNIIR-AM database.

Système National d’Information Inter-Régime de l’Assurance Maladie (SNIIR-AM) (National Claims)

Custodian

National Health Insurance Fund

Website



Time Period Covered

From 1998 onward

Population Covered

Reimbursement database of the National Health Insurance Fund. Covers 100% of costs for patients with long-term illness (e.g. MS). Exhaustive data on ambulatory care consumption and private hospitals' activity.

Data Included

Patient data: age, gender, town, long term and chronic diseases, date of birth, date of death, lower income indicator, healthcare care consumption and date.

All consultations and visits to GPs and ambulatory care specialists.

All medical technical procedures.

All dispensed drugs.

All lab and diagnostics tests but not their results.

Provider level data: their activity and sales turnover, geography, prescribing behaviors.

Data Access

In French only. Access is very difficult, requires often working physically in CNAMTS ‘‘Caisse Nationale d’Assurance Maladie des Travailleurs Salarie´s’’ offices. Access to the raw database requires pre-authorization with the CNIL public agency responsible for data confidentiality and is tightly controlled.

Cost of Access

Not provided.

Mapping of Fields Across Datasets

Linkage possible at patient level with PMSI (hospital activity and expenditure data) and ESPS survey (social determinants, healthcare needs and access issues).

References

Fromont A, Binquet C, Rollot F, et al. Comorbidities at multiple sclerosis diagnosis. J Neurol 2013; 260(10):2629-37.

FRANCIM (French National Cancer Register)

Description

French network of cancer registries. Collection of cancer cases from 10-14 French Metropolitan Departments (dependent of cancer type).

Custodian

Service Biostatistics Hospices Civils de Lyon (HCL)

Website



Time Period Covered

From 1975 onward

Population Covered

15-20% of the French Population.

Diagnostic Coding Procedures

ICD-O-3

Data Access

Authorization required from HCL and from each FRANCIM registry.

Cost of Access

Not reported

Mapping of Fields Across Datasets

Linkage possible at patient level with PMSI (hospital activity and expenditure data).

References

Lebrun C, Vermersch P, Brassat D, et al. Cancer and multiple sclerosis in the era of disease-modifying treatments. J Neurol 2011;258:1304-1311.

ANMS Database

Custodian

Agence Nationale de Sécurité du médicament et des produits de santé (ANSM).

Website



Time Period Covered

From 1993 onward

Population Covered

100% outpatient and inpatient.

Data Access

Applications must be sent to communication@ansm.sante.fr.

Data only available in French.

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

No OTC medications, no ability to link to clinical records.

Enquête Décennale de Santé

Description

The Enquête Décennale de Santé is a survey conducted nationally every 10 years.

Custodian

National Institute of Statistics and Economic Studies (INSEE).

Time Period Covered

Since 1960, every ten years.

Population Covered

40,900 people in 16,800 households in the last survey (2002-2003).

Data Included

Living conditions, social protection, general state of health, disability, description of illnesses, use and contact with health care providers, surgical antecedents, interruptions of work due to illnesses and periods of bed rest, use of care facilities over the period (hospitalization, doctor, biology, paramedical interventions, etc.), incapacities, dietary habits and prevention.

Data Access

In French only. Access to the database is highly restricted and requires a request to the Comité du Secret Statistique

For additional information contact: comite-secret@cnis.fr

Cost of Access

Fees apply.

Mapping of Fields across Datasets

Linkage possible at aggregate postal level with other national databases.

Enquête Santé et Protection Sociale (ESPS)

Description

This survey is a source of information on France’s health status, healthcare coverage and access, social determinants and healthcare needs and demand.

Custodian

Conducted by the Institut de Recherche et de Documentation en Economie de la Santé (IRDES) with the support of National Health Insurance Fund (CNAMTS).

Website



Time Period Covered

From 1988, half the study population surveyed every 2 years.

Population Covered

96% representativeness of the French Population. Nationwide panel interrogation of 22,000 individuals across 8,000 households.

Data Included

Health status, socio-economic situation, healthcare utilization, insurance coverage (statutory and optional), social capital, life style.

Data Access

For additional information contact

Stéphanie Guillaume

(p) +331-53-93-43-34

esps@irdes.fr

Cost of Access

Free of Charge

Mapping of Fields across Datasets

At aggregate postal code level with other databases. At individual level with the SNIIR-AM and PMSI Databases.

European Database for Multiple Sclerosis (EDMUS)

See description under Clinical Databases/Registries.

Germany

In Germany, most of the population (85%) is insured within the social security system, which is characterized by a pay-as-you-go financing, income-dependent but not risk-dependent insurance; the remainder (10%) of the population choose private health insurance while another 4% of the population are secured by free governmental medical care, which applies to soldiers, civil servants, police men, welfare recipients and asylum seekers. The Statutory Health Insurance (SHI) directly pays costs of drugs to the pharmacists.

German Statutory Health Insurance Claims (SHIs)

Custodian

Federal Ministry of Health.

Time Period Covered

From 1991 onwards

Population Covered

70 million people (86% of German population). Pharmacy and hospitalization claims, GP data since 2004.

Data Files Available

Core Data

Subject ID, family ID, date of birth, place of residence, nationality, marital status, education, occupational code, line of business, employment status, date of entry/exit, insurance status, and contribution group.

Inpatient Data

Subject ID, hospital ID, day of admission/discharge, disability, main diagnosis, secondary diagnosis, auxiliary diagnoses, ICD-version, OPS Code (surgery).

Outpatient Data

Subject ID, Physician number, medical specialty, day of consultation, disability, diagnosis, auxiliary diagnosis, ICD-version, OPS-code (surgery).

Prescription Data

Subject ID, CPN number, prescription number, date of prescription, date of prescription delivery, SHI code, and amount prescribed.

Data Access

To access data contact: info@bmg.bund.de

Diagnosis Coding Procedure

ICD-10

CPN, generic name, brand, packaging size, strength, defined daily dose, pharmaceutical formulation, ATC code, company.

Mapping of Fields Across Datasets

Via the unique identification number (ID) assigned by SHIs the data files can be linked to each other.

Other Files Needed for Analysis

No OTC medications.

IMS Disease Analyzer Database

Description

The database contains anonymous data from a select panel of physicians' practices and patients. Data is generated directly from the computer in the physician's practice via standardized interfaces and provide daily routine information on patients' diseases and therapies.

Custodian

IMS Health

Website



Time Period Covered

From July 1, 1989 onwards

Population Covered

2,351 practices (10% of physicians in Germany) and approximately 17 million patients (representative sample).

Data available

Patient’s demographics: ID number, age, sex, insurance, risk factors (e.g. BMI), lab tests and results.

Diagnoses: Date of diagnosis, co-morbidity, treated/untreated, referrals and hospitalizations.

Drugs: Date of visit, product and quantity prescribed, treatment costs, therapy switch, dosage scheme, co-prescription.

Diagnosis Coding Procedure

ICD-10

ATC code

Data Access

Contact person:

Ms. Karin Cikes

(P): +49 (0) 69 / 66 04 - 43 81

(F): +49 (0) 69 / 66 04 - 53 49

kcikes@de.

Cost of Access

Access is usually costly. It depends on several factors.

Mapping of Fields across Datasets

Patients can be tracked over time. No ability to link to other datasets outside of IMS health.

References

Thielscher C, Thielscher S, Kostev K. The risk of developing depression when suffering from neurological diseases. Ger Med Sci 2013; 11: Doc02.

Cancer Registry Data

Description

The population-based cancer registries in each German federal state transfer data to the German Centre for Cancer Registry Data, as required by the Federal Cancer Registry Data Act. Cancer data is also available by region.

Custodian

The ZfKD

Website



Time Period Covered

From 1967 onward

Population covered

1% of cancers reported in Germany.

Data Available

Demographics (gender, date of birth, residential county code).

Tumor Diagnosis (histological findings, stage, type of primary treatment).

Case of death.

Diagnosis Coding Procedure

ICD-10

ICD-O-3

Data Access

The ZfKD allows the use of the datasets to external researchers provided a justified, especially scientific interest can be credibly demonstrated by the applicant. For a complete description of the application process visit

Cost of Access

Fees apply according to request.

WIdO Database

Custodian

Wissenschaftliches Institut der Allgemeinen Ortskrankenkassen AOK (WIdO). The Research Institute of the AOK.

Website



Time Period Covered

From 1980 onward

Population Covered

85% of reimbursed drugs through the public sickness fund system (outpatient drugs).

Diagnosis Coding Procedure

ATC code

Data Access

Data available in German. Data can be accessed through application to the data provider: valentine.coca@wido.bv.aok.de or helmut.schroeder@wid.bv.aok.de

Cost of Access

A fee applies.

Mapping of Fields across Datasets

Possible to other data files within SHI.

Other Files Needed for Analysis

Sociodemographic, inpatient and outpatient data can be linked through SHI.

Robert Koch Institute (RKI)

Description

The Robert Koch Institute (RKI) is the central federal institution responsible for disease control and prevention. RKI conducts nationwide population-based cross-sectional surveys as a part of its nationwide health monitoring.

Health Surveys available through RKI

GEDA (German Health Update survey)

Description

The GEDA is a telephone health survey that took place in 2009, 2010 and 2012 and focused on chronic diseases, health related behavior, socio-demographic characteristics, vaccinations, etc.

Custodians

RKI

Time Period Covered

2009, 2010, 2012

Population Covered

25,000 individuals in each survey; 95% representative of the German population. 200 questions about health and living conditions.

Mapping of fields across datasets

Not possible

Data access

Requests should be made to

Dr. Cornelia Lange

(p)+49 (0)30 - 18754-3748

gbe@rki.de

Cost of Access

Fees apply

German Health Interview and Examination Survey for Adults (DEGS)

Description

The study provided comprehensive and representative data on the health status of the adult population living in Germany. The study is a follow-up survey of the German National Health Interview and Examination Survey (BGS98) conducted in 1998.

Custodians

RKI

Website



Time Period Captured

1998, 2008, next survey to be conducted in 2014.

Population Covered

8,152 adults between 18-72 across 180 cities/municipalities in Germany.

Data Access

The DEGS1 data will be available as a public use file in 2014 and will include a study description, copies of the survey documents, a code plan and a manual (in German). Interested parties will be asked to fill in a request form to

Dr. Bärbel-Maria Kurth

(p) +49 (0)30 - 18754-3102

kurthB@rki.de 

Cost of Access

Fees apply

Mapping of fields across datasets

Linkage between DEGS and BGS98 possible.

Multiple Sclerosis Registry in Germany

See description under Clinical Databases/Registries.

Hungary

Hungary provides universal access to health care to all its citizens. 96% of the population is covered by the National Health Insurance Fund (NHIFA). Medicine costs are reimbursed depending on the severity of condition and the indication and type of a prescription.

National Health Insurance Fund database (NHIFA)

Description

The National Health Insurance Fund is responsible for financing health services throughout the country. The Health Insurance Fund is in charge of more than 20 sub-budgets according to different type of services (general practitioners, outpatient care, acute, chronic inpatient care, and pharmaceutical).

Custodian

Ministry of Health

Website

oep.hu

Time Period Covered

From 1994 onwards

Population Covered

96% health care services in Hungary.

Data Included

The NHIFA database contains data on age, gender, residence and all health care events (hospital units, ambulatory centers, day of admission/discharge, medical procedures) that are reported regularly by health care providers.

Diagnosis Coding Procedure

ICD-10

ATC

Data Access

Requests for data must be sent in writing to:

National Health Insurance Fund

IT Development Department

1139 Budapest Vaciut 73/A

Additionally it must be sent electronically to: ugykezeles@oep.hu

For additional information contact:

Gyula Kiràly, kiraly.gy@oep.hu

Zoltàn Szege, szege.z@oep.hu

Orsolya Szincsak, szincsak.o@oep.hu

Cost of Access

Information in Hungarian provided on the website oep.hu

Mapping of Links across Datasets

The central database contains all individuals’ Social Security Identification Number (TAJ) that identifies people attending the health care services and allows for a patient level search and follow-up within the database.

Other Files Needed for Analysis

Socioeconomic, quality of life.

References

Pentek M, Horvath C, Boncz I, Falusi Z, Toth E, Sebestyen A, Majer I, Brodszky V, & Gulacsi L. Epidemiology of osteopororsis related fractures in Hungary from the nationwide health insurance database, 1999-2003. Osteoporosis Int 2008, 19 (2): 243-249.

National Drug Consumption Database of the Directorate General of National Institute of Pharmacy

Custodian

Directorate General of National Institute of Pharmacy.

Website



Time Period Covered

Not available

Population Covered

90% of sales from wholesalers (inpatient and outpatient).

Data Included

Drugs; ATC code, quantity, package size, date dispensed, dose, strength and dosage form. For inpatient drug use, number of beds, number of admissions and average length of stay is available.

Diagnosis Coding Procedure

ATC code

Data Access

Data in Hungarian and English. Data requests must be sent to data provider ogyi@ogyi.hu

Cost of Access

Not specified

Mapping of Fields Across Datasets

Not possible

Other Files Needed for Analysis

Clinical data.

Iceland

Iceland provides its citizens with a socialized health care system. All newborns in Iceland are assigned a 10-digit personal identification number which allows tracking and linkage of individuals through national health databases.

Icelandic National Register of Persons

Custodian

Statistics Iceland

Time Period Covered

From 1953 onwards

Population Covered

100% Icelandic Population.

Data Available

Demographic Information (name, ID, sex, marital status, nationality, place of residence, religious affiliation).

National Birth Registry.

Causes-of-death (ICD-10 from 1996, ICD-9 from 1981-1995, ICD-8 prior).

Health Services data (inpatient, outpatient, pharmaceutical) (ICD-10).

Data Access

Statistics Iceland Internal Confidentiality Committee reviews and decides on applications for access to data. Initial contact can be made through this website: enquires will be answered within 1 business day.

Cost of Access

All information services which take more than half an hour are charged. Minimum price for an hour of work is ISK 11,100. Annual subscription is ISK 17,500. For a detailed list of prices refer to

References

Olafsson E, Benedikz J, &Hauser A. Risk of epilepsy in patients with multiple sclerosis: A population-based study in Iceland. Epilepsia 1999, 40 (6): 745-747.

Iceland Cancer Registry

Custodian

Icelandic Cancer Society.

Time Period Covered

From 1955 onward

Population Covered

All cancer cases in Iceland since 1955 and all breast cancers since 1910.

Diagnosis Coding Procedure

ICD-10

Data Access

In order to obtain access to clinical records, researchers must obtain access from the Icelandic Ethical Review Board and the Icelandic Data Protection Authority.

Cost of Access

Access fees may apply.

Mapping of fields across datasets

Linkage to all other national databases through the individual’s unique 10 digit identification number.

References

Sigurdardottir LG, Jonasson JG, Stefansdottir S, Jonsdottir, Olafsdottir HO, Olafsdottir EJ, & Tryggvadottir L. Data quality at the Icelandic cancer registry: Comparability, validity, timeliness and completeness. Acta Oncologica 2012, 51 (7): 880-889.

Italy

The Italian Servizio Sanitario Nazionale (SSN) provides universal coverage for all 57 million citizens. The health care system is organized into three levels: National, local and regional (approximately 10 local health units per region). Administrative data on healthcare reimbursed by SSN are routinely collected by local health units and in some regions, sent to the regional level. Transmission to the national level is mandatory, and a common data model for data transmission is mandated by law. Before data are sent to the national level, however, unique personal identifiers are removed, thus record linkage outside a single region is not possible.1

1Gini R, Francesconi P, Mazzaglia G, Cricelli I, Pasqua A, Gallina P, et al. Chronic disease prevalence from Italian administrative databases in the VALORE project: a validation through comparison of population estimates with general practice databases and national survey. Public Health 2013, 13(1): 15-26.

OsMed Database

Custodian

Agenzia Italiana del Farmaco. Osservatorio Nazionale sull'impiego dei medicinali (OsMed)

Website

’impiego-dei-medicinali-osmed

Time Period Covered

From 2000 onward

Population Covered

100% Dispensed medicines (reimbursed and non-reimbursed) by public and private pharmacies.

Data Included

Full account of the medicine dispensed, date of purchase, patient identification code (age and sex) and prescriber’s code. Number of people who have received at least one prescription, turnover, DDD.

Diagnosis Coding Procedure

ATC code

Data Access

Reports in Italian and English. Application request must be sent to data provider farmaciline@.it

Cost of Access

Not specified

Mapping of Fields across Datasets

Possible to link to other datasets at regional level.

Other Files Needed for Analysis

Diagnoses, socioeconomic information.

The Health Search Database (HSD)

Description

The database collects medical record data from a network of 500 Italian general practitioners distributed across the country who are members of the Italian College of General Practitioners.

Custodian

Italian College of General Practitioners

Website



Time Period Covered

From 1998 onward

Population Covered

800.000 patients.

Data Included

Demographic information, visits and referrals, diagnoses, drug prescriptions and clinical information.

Diagnosis Coding Procedure

ICD-9 and free text

Data Access

Approval for use of data is obtained from the Italian College of Primary Care Physicians. No ethical approval needed.

Cost of Access

Not specified

Mapping of Fields across Datasets

A unique patient code links demographic and prescription information, clinical events, diagnoses, hospital admission, and cause of death.

Other Files Needed for Analysis

Quality of life, socioeconomic.

References

Cricelli C, Mazzaglia G, Samani F, Marchi M, Sabatini A, Nardi R, Ventriglia G, & Caputi A. Prevalence estimates for chronic diseases in Italy: exploring the differences between self-report and primary care databases. Journal of Public Health 2003, 25(3):254-257.

Veneto Region Health Services

Description

The Veneto Region is situated in the North-Eastern part of Italy and it is divided into seven provinces with approximately 4.7 million people. Veneto Region Health Services holds a series of administrative health databases accessible for research projects.

Website



Data Access

Projects must be approved by the Regione del Veneto Ethical Commitee

For additional information contact

centroregionaleacquisti.sanita@regione.veneto.it,

(p) 049 877 8288 -8286

List of Databases Available:

Hospital Discharge Records

Population Covered

4.7 million residents of Veneto.

Diagnosis Coding Procedure

ICD-9, one main and five secondary diagnoses.

Mapping of Fields across Datasets

Linkable to all other files within the region

Drug Dispensing Records

Population Covered

4.7 million residents of Veneto.

Diagnosis Coding Procedure

ATC

Mapping of Fields across Datasets

Linkable to all other files within the region.

Disease-Specific Exemptions from Copayment to Health Care

Population Covered

4.7 million residents of Veneto

Diagnosis Coding Procedure

ICD-9

Mapping of Fields across Datasets

Linkable to all other files within the region

Inhabitant Registry (IR)

Population Covered

4.7 million residents of Veneto

Data Included

Demographic information (birth year, gender) and identifier of the GP.

Mapping of Fields across Datasets

Linkable to all other files within the region

Servizio Sanitario Regionale Emilia-Romagna

Description

The Emilia-Romagna region of Italy is home to approximately 4.4 million people. Servizio Sanitario Regionale Emilia-Romagna holds a number of administrative health databases that can be accessed for research projects.

Data Access

Projects must be approved by the regional Ethical Committee.

List of Databases Available:

Hospital Discharge Records

Population

4.4 million residents of Emilia-Romagna.

Diagnosis Coding Procedure

ICD-9, one main and five secondary diagnoses.

Mapping of Fields across Datasets

Linkable to all other files within the region

Drug Dispensing Records

Population

4.4 million residents of Emilia-Romagna.

Diagnosis Coding Procedure

ATC

Mapping of Fields across Datasets

Linkable to all other files within the region.

Inhabitant Registry (IR)

Population

4.4 million residents of Emilia-Romagna.

Data Included

Demographic information (birth year, gender) and identifier of the GP.

Mapping of Fields across Datasets

Linkable to all other files within the region

Regional Health Agency of Tuscany

Description

The Regional Health Agency of Tuscany holds a number of administrative health databases that can be accessed for research projects. Tuscany region is home to approximately 3.7 million people.

Website



Data Access

Contact Cristina Padovano, osservatorio.epidemiologia@ars.toscana.it, (p) +39 055 4624364 or general email at info@ars.toscana.it

Projects must be approved by the regional Ethical Committee.

List of Databases Available:

Hospital Discharge Records

Population Covered

3.7 million residents of Tuscany.

Diagnosis Coding Procedure

ICD-9, one main and five secondary diagnoses.

Mapping of Fields across Datasets

Linkable to all other files within the region.

Drug Dispensing Records

Population Covered

3.7 million residents of Tuscany.

Diagnosis Coding Procedure

ATC

Mapping of Fields across Datasets

Linkable to all other files within the region.

Disease-Specific Exemptions from Copayment to Health Care

Population Covered

3.7 million residents of Tuscany.

Diagnosis Coding Procedure

ICD-9

Mapping of Fields across Datasets

Linkable to all other files within the region.

Inhabitant Registry (IR)

Population Covered

3.7 million residents of Tuscany.

Data Available

Demographic information (birth year, gender) and identifier of the GP.

Mapping of Fields across Datasets

Linkable to all other files within the region.

Agenzia Regionale Sanitaria Marche

Description

The Agenzia Regionale Sanitaria Marche holds a number of administrative health databases that can be accessed for research projects. Marche region is home to approximately 1.6 million people.

Website



Data Access

Contact information provided on this page

Projects must be approved by the regional Ethical Committee.

List of Databases Available:

Hospital Discharge Records

Population Covered

1.6 million residents of Marche.

Diagnosis Coding Procedure

ICD-9, one main and five secondary diagnoses.

Mapping of Fields across Datasets

Linkable to all other files within the region.

Drug Dispensing Records

Population Covered

1.6 million residents of Marche.

Diagnosis Coding Procedure

ATC

Mapping of Fields across Datasets

Linkable to all other files within the region.

Disease-Specific Exemptions from Copayment to Health Care

Population Covered

1.6 million residents of Marche.

Diagnosis Coding Procedure

ICD-9

Mapping of Fields across Datasets

Linkable to all other files within the region.

Inhabitant Registry (IR)

Population Covered

1.6 million residents of Marche.

Data Included

Demographic information (birth year, gender) and identifier of the GP.

Mapping of Fields across Datasets

Linkable to all other files within the region

Sicily (Southern Italy)

Description

Regione Siciliana Assessorato della Salute holds a number of administrative health databases that can be accessed for research projects. The Regione Siciliana is home to approximately 5 million people.

Website



Data Access

Contact ricercasanitaria@formez.it or assessorato.salute@certmail.regione.sicilia.it

Projects must be approved by the regional Ethical Committee.

List of Databases Available:

Hospital Discharge Records

Diagnosis Coding Procedure

ICD-9, one main and five secondary diagnoses.

Population Covered

5 million residents of Sicily

Mapping of Fields across Datasets

Linkable to all other files within the region.

Drug Dispensing Records

Population Covered

5 million residents of Sicily.

Diagnosis Coding Procedure

ATC

Mapping of Fields across Datasets

Linkable to all other files within the region.

Disease-Specific Exemptions from Copayment to Health Care

Population Covered

5 million residents of Sicily.

Diagnosis Coding Procedure

ICD-9

Mapping of Fields across Datasets

Linkable to all other files within the region.

Inhabitant Registry (IR)

Population Covered

5 million residents of Sicily.

Data Included

Demographic information (birth year, gender) and identifier of the GP.

Mapping of Fields across Datasets

Linkable to all other files within the region.

Italian Multiple Sclerosis Database Network (MSDN)

See description under Clinical Databases/Registries.

Latvia

Latvia has a tax-funded social insurance system and universal access to healthcare for its residents. The Social Insurance Organization pays for the partial cost of prescription drugs through regional branches; certain groups of patients are fully reimbursed (e.g. low income residents).

National drug consumption database of the State Agency of Medicines of Latvia

Custodian

State Agency of Medicines of Latvia

Website



Time Period Covered

Since 2003 onward

Population Covered

100% outpatient and inpatient sales of medicines from wholesalers.

Diagnosis Coding Procedure

ATC code

Data Access

Data in Latvian and English. Some data available for free online in pdf format. For specific requests contact info@.lv

Cost of Access

Fees apply for specific data requests.

Mapping of Fields across Datasets

Not possible

Other Files Needed for Analysis

Clinical information.

Statistics Latvia

Custodian

Statistika Latvijas

Website



Time Period Covered

From 1992 in electronic database. Hand-written data might be available since 1970.

Population Covered

Statistika Latvija holds mortality information for the population. The registration of cause-of-death is obtained from medical death certificates.

Diagnosis Coding Procedure

ICD-10 from 1996, ICD-9 prior.

Data Access

To request a customized dataset, contact:

Postal mail: 1 Lāčplēša Street, Riga, Latvia, LV-1301

E-mail: info@.lv

Cost of Access

Some aggregate data available for free on the website. It does not specify whether there is a cost to the customized data request.

Mapping of Fields across Datasets

Not specified

Other Files Needed for Analysis

Clinical information

References

Varnik A, Wasserman D, Palo E, & Tooding LM. Registration of external causes-of-death in the Baltic States 1970-1997. European Journal of Public Health 2011, 11 (1): 84-88.

Netherlands

The Netherlands has a privatized health care system. All residents have to purchase a basic benefit package which can be complemented by supplementary health insurance. In addition to the standard benefit package, all citizens are covered by the statutory Exceptional Medical Expenses Act scheme for a wide range of chronic and mental health illnesses. Approximately 97% of the population has health insurance.

Dutch Hospital Data (DHD)

Description

The Dutch Hospital Data foundation (DHD) was founded by the Netherlands Association of Hospitals (NVZ) and the Netherlands Federation of University Medical Centers (NFU). DHD manages collections of hospital data and is responsible for processing requests. In the Netherlands there are 85 general and 8 academic hospitals available to use for more than 16 million citizens. Every year more than 3 million people go to one of these hospitals.

Custodians

DHD

Population Covered

All public hospital admissions in the Netherlands: admission, discharge dates, primary and secondary discharge diagnosis, diagnostic, surgical and treatment procedures, consultations with specialists and length of stay.

Datasets Available

Rural Hospital Care Key Register

National Medical Registration

From 1963 onwards

National Ambulatory Registration

From 1996 onwards

Diagnosis-Treatment-Combination Information System

From 2005 onwards

Hospital Surveys

Diagnosis Coding Procedure

ICD-10

Data Access

Applicants must submit their data request to:

Dutch Hospital Data Foundation

Attn.: Data Desk

Postbus 9696

3506 GR Utrecht

For additional information email loket@hospitaldata.eu, or visit

Cost of Data

Not specified

Other files needed for Analysis

No information about private clinics or independent treatment centers.

GIP Database

Custodian

Health Care Insurance Board

Website



Time Period Covered

From 2004 onwards.

Population Covered

95% of drugs prescribed by general practitioners and specialists and dispensed by pharmacists, as well as dispensing general practitioners and other outlets being reimbursed under The Health Care Insurance Act.

Data Included

Patient demographics: gender, age, religion.

Drug data: dosage form, ATC code, DDD.

Prescriber-related information.

Dispenser-based data.

Diagnosis Coding Procedure

ATC code

Data Access

Some data available for free online. For specified access requests contact:

The Health Care Insurance Board

GIP / Afdeling Verantwoording en Signalering

P.O. Box 320

1110 AH Diemen

The Netherlands

J.F. Piepenbrink MSc, advisor

(p) +31 20 797.86.86

infogip@cvz.nl

Cost of Data

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Clinical information.

NIVEL Primary Care Database

Custodian

Netherlands Institute for Health Services Research (NIVEL)

Time Period Covered

Referrals from 1992 onward, all patients contacts (interventions, diagnoses) from 2001.

Population Covered

The NIVEL Primary Care Database holds longitudinal data on morbidity, prescribing and referrals of about 350.000 individuals. Data are collected in a representative network of about 150 general practitioners, evenly distributed over the country. Data collected from general practitioners, primary care psychologists, dietitians, exercise therapists, physiotherapists, health centers and GP out-of-service hours.

Data Included

Demographic: gender, age, type of health care insurance and place of residence.

Diagnoses

Referrals

Prescriptions

Diagnosis Coding Procedure

ICPC

Drugs, ATC

Data Access

Contact Robert Verheij, Research Coordinator

r.verheij@nivel.nl

Cost of Data

Fees apply.

Mapping of Fields across Datasets

Data cannot be traced back to individual records, health care providers/organizations. Individuals can be tracked over time within the NIVEL system.

Other Files Needed for Analysis

Data external to NIVEL cannot be linked. Socioeconomic status cannot be linked.

References

Verhaak PFM, Van Dijk CE, Nuijen J, Verheij RA, & Schellevis FG. Mental health care as delivered by Dutch general practitioners between 2004 and 2008. Scandinavian Journal of Primary Health Care 2012, 30 (3): 156-162.

Dutch Cancer Registry

Custodian

IKNL

Jurisdiction

From 1989 onwards

Population covered

95% of cancer cases in The Netherlands.

Datasets available

Demographic information

Source of data

Diagnosis/tumor data

Tumor-specific data

Treatment data

Follow-up data

Diagnosis Coding Procedure

ICD-O

Data Access

An independent National Supervisory Committee monitors compliance with the rules and evaluates the data requests. For additional information contact

Marjorie Cook

m.dekok@iknl.nl

Mapping of Fields across Datasets

Possible through an individual’s personal identification number.

References

Van Den Brandt PA, Schouten LJ, Goldbohm RA, Dorant E, & Hunen PMH. Development of a record linkage protocol for use in the Dutch Cancer Registry for epidemiological research. International Journal of Epidemiology 1990, 19 (3): 553-558.

PHARMO

Description

The PHARMO Record Linkage System links major sources of data and includes information on over 3 million residents in the Netherlands.

Time Period Covered

Varies according to database.

Custodian

The Independent Compliance Committee STIZON / PHARMO Institute.

Data Access

Each study requires permission from the Committee. For each research project, the research question, the data used for the study, the methods used to analyze the data, and the people/organizations who commissioned the study have to be described. All decisions of the Compliance Committee STIZON / PHARMO Institute are based on the applicable legislation in the Netherlands.

Cost of Access

The fee is calculated according to hours of work spent on the project.

Mapping of fields across datasets

Ability to link to full patient medical charts and all data sources. Ability to link data to other PHARMO databases.

Databases Accessible through PHARMO:

General Practitioner Database (GPD)

Description

The general practitioner (GP) database is a longitudinal observational database that contains data from computer-based patient records of GPs throughout the Netherlands

Time Period Covered

From 2002 onwards

Population Covered

1.5 million residents of The Netherlands.

Data Available

Demographic data (age, sex, patient identification, GP registration information), diagnoses, physician-linked indications for therapy, co-morbidity, drug prescriptions, laboratory values, and referrals to specialists.

Diagnosis Coding Procedure

Diagnosis, ICPC (which can be mapped to ICD-9-CM codes).

Drugs, product name, quantity dispensed, dosage regimens, strength and indication.

Mapping of fields across datasets

Ability to link data to other PHARMO databases.

Other Files Needed for Analysis

Links can be made to outpatient prescription database for information on specialist prescriptions.

Outpatient Pharmacy Database

Time Period Covered

From 1986 onwards.

Population Covered

Data on over 3 million residents with varying types of insurance; corresponds to approximately 20% of the Dutch population.

Diagnosis Coding Procedure

ATC code, dispensing date, prescriber, prescribed dosage, dispensed quantity. Costs of drugs are estimated from a third party perspective.

In-patient Pharmacy Database

Population Covered

Data on over 1.5 million patients.

Diagnosis Coding Procedure

ATC code, drug, dosage, time of admission, duration use.

Discharge diagnosis and procedures (ICD-9).

These external databases can all be linked by PHARMO:

Dutch Hospital Database

Mortality Register

Eindhoven Cancer Registry

Dutch Cancer Registry

Norway

Norway provides socially funded health care to its citizens. All citizens are assigned a unique personal identifier, which is used to register social and health services, including prescription dispensations, in various nationwide registries. Individual level data can be linked using the unique and national 11-digit personal identification code (assigned since 1964).

Norwegian Patient Register

Custodian

Norwegian Ministry of Health and Care Services

Time Period Covered

From 1997 onward

Population Covered

Contains administrative data on patients from all hospitals and private contract specialists in Norway.

Data Available

Personal Data (demographics, place of residence, etc)

Treatment

Diagnosis data

Surgical procedures (NCSP)

Medical procedures (NCMP)

Social Information

Accidents and Injuries

Diagnosis Coding Procedure

ICD-10

Data Access

Applications must be submitted electronically and can be found on the NPR's website . A decision will be made within 30 days of the application.

Cost of Access

Fees for the provision of data are calculated on the basis of an hourly rate of NOK 700 excluding VAT, subject to a minimum fee of NOK 1400, excluding VAT.

Mapping of Fields across Datasets

Linkage to all other national registries through an individual’s unique personal identification number.

Norwegian Cancer Registry (NCR)

Custodian

Cancer Registry of Norway

Website



Time Period Covered

From 1951 onward

Population Covered

100% cancer cases. The NCR contains detailed information on each case of cancer on the basis of linked data from several different sources (clinical and pathological reports, data from radiotherapy and death certificates).

Diagnosis Coding Procedure

ICD-10

Mapping of Fields Across Datasets

Linkage to all other national registries through an individual’s unique personal identification number.

Data Access

The data controller for the Cancer Registry shall, on application, disclose de-identified data from the Cancer Registry if a researcher project has been approved by the Regional Committee for Medical Research Ethics. Researchers will be notified of the decision within 30 days of initiating the application.

Cost of Access

The cost will not exceed the actual cost of preparation of the data.

References

Tingulstad S, Halvorsen T, Norstein J, Hagen B, & Skjeldestad FE. Completeness and accuracy of registration of ovarian cancer in the cancer registry of Norway. Int J Cancer 2002, 98 (6): 907-911

Larsen IK, Smastuen M, Johannesen TB, et al. Data quality at the Cancer Registry of Norway: an overview of comparability, completeness, validity and timeliness. Eur J Cancer 2009, 45(7):1218-1231

Norwegian Prescription Database (NorPD)

Custodian

Norwiegan Institute of Public Health

Website



Time Period Covered

From 2004 onward

Population Covered

Data on over 34 million dispensed prescriptions per year. The NorPD contains a complete overview about dispensing of prescribed medicines to patients, doctors and institutions from pharmacies.

Data Access

Application form can be found at . Research projects will need to have prior approval from a Human Ethics Research Board.

Cost of Access

NOK 875 per hour’s work, with a minimum price of NOK 1750, excludes VAT.

Mapping of Fileds across datasets

Linkage to all other national registries through an individual’s unique personal identification number.

Other Data Files Needed for Analysis

No OTC information.

Norwegian Cardiovascular Disease Registry

Custodian

Norwiegan Institute of Public Health

Website



Time Period Covered

From 2012 onwards

Data Access

Application form can be found at .

Research projects will need to have prior approval from a Human Ethics Research Board.

Contact

Marta Ebbing, Project Manager

(p) (+47) 53 20 40 50

Cost of Access

NOK 875 per hour’s work, with a minimum price of NOK 1750, excludes VAT.

Cause of Death Registry

Custodian

Norwegian Institute of Public Health

Website



Time Period Covered

From 1951 onward

Population covered

All deaths in Norway.

Data Access

Application form can be found at .

Research projects will need to have prior approval from a Human Ethics Research Board.

Cost of Access

NOK 875 per hour’s work, with a minimum price of NOK 1750, excludes VAT.

The Norwegian Multiple Sclerosis National Competence Centre and National Multiple Sclerosis Registry

See description under Clinical Databases/Registries.

Poland

National Health Fund Database

Custodian

Narodowy Fundusz Zdrowia (National Health Fund, NFZ).

Time Period Covered

Information available for 2 years (2004 and 2005). A report for these 2 years is available on the website.

Population Covered

100% of reimbursed medication (Information provided by pharmacies to the National Health Fund).

Data Included

Prescribing doctor, pharmacy-based data, patient identifier, and drug-base data.

Diagnosis Coding Procedure

ATC code

Data Access

Requests must be made to

Barbara Wójcik-Klikiewicz, pokój 3.14 sekretariat,

(p) 22 572 61 89

(f) 22 572 63 43

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Fields Needed for Analysis

Clinical information.

Portugal

INFAR Med’s Database

Custodian

National Authority of Medicines and Health Products, I.P.

Website



Time Period Covered

Not available

Population Covered

Data from drugs prescribed and dispensed in community pharmacies. Inpatient data also includes hospital drugs dispensed to outpatients. OTC sales also available.

Diagnosis Coding Procedure

ATC code

Data Access

Requests must be made to demps-omps@infarmed.pt

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Fields Needed for Analysis

Clinical information.

Serbia

Institute of Public Health

Description

The institute routinely collects data on health and utilization of health services, maintains databases of basic resources of the healthcare system, generates population survey data, and produces health information for the effective health reporting to authorities.

Custodian

The Institute of Public Health of Serbia “Dr. Milan Jovanovic Batut”

Website



Data Access

Approval is needed from the Review Board of the Ministry of Health of Serbia and the Institute of Public Health of Serbia.

Contact:

Tanja Kneževic, Director

Tanja_Knezevic@.rs

Cost of Access

Not specified.

Mapping on Fields across datasets

Data within the Institute of public health could be linked to each other.

Databases available through the Institute of Public Health:

Diabetes Registry

Time Period Covered

From 2006 onward

Diagnosis Coding Procedure

ICD-10

Cancer Registry

Time Period Covered

From 2006 onward

Diagnosis Coding Procedure

ICD-10

Acute Coronary Syndrome

Time Period Covered

From 2006 onward

Diagnosis Coding Procedure

ICD-10

National Health Survey 2006

Time Period Covered

2006

Website



Population Covered

Out of the 7673 households selected at random, 6156 were interviewed across the 6 geographical regions of Serbia. Within households, adults aged 20+ years were interviewed as well as children aged 7-19 years. In total 14,522 adults completed the interview (7664 women and 6858 men). Data includes prevalence of chronic disease, mental health, health literacy, use of health services, demographic, socioeconomic, and lifestyle variables.

References

Vukovic D, Bjegovic V, & Vukovic G. Prevalance of chronic diseases according to socioeconomic status measured by wealth index: Health Survey in Serbia. Croat Med J 2008, 49(6): 832-841.

Institute of Neurology, Belgrade School of Medicine

See description under Clinical Databases/Registries.

Spain

National Statistics Office Datasets

Custodian

National Statistics Office (Instituto Nacional de Estadisticas) (INE).

Website



Data Access

An official application letter for the required data needs to be addressed to the INE President and signed by the highest representative of the institution. These should be sent by fax, email or post to the General Deputy Director of International Relations at the INE. Any application for institutional cooperation that is not processed in this way will not be accepted. Documentation that should accompany the application letter:

• Technical reference terms. A detailed description of the context, characteristics, scope, objectives, main queries and questions and expected results of the activity. A proposal containing dates, place and length of the activity should also be included.

• Financial reference terms. A description of the available resources held by the institution making the application for cooperation assistance.

• Name, post and brief description of CV, professional experience and current job of the expert/s put forward to take part in this activity.

Data Accessible through the INE:

Hospital Morbidity Survey

Time Period Covered

From 1977 onwards.

Population Captured

It includes information on primary diagnosis of hospital admissions, all public and private hospitals in Spain. 98.6% of all hospital admissions in Spain each year.

Diagnosis Coding Procedure

ICD-9-CM

Mapping of Fields across datasets

Can be linked to the National Vital Statistics for mortality records. Individuals are not tracked, thus it is not possible to track a person over time (even within the same hospital).

Encuesta Nacional de Salud (National Health Survey)

Time Period Covered

2011/2012, will be conducted every 5 years.

Website (in Spanish)



Population covered

Random selection of 24.000households across 2.000 different census locations across Spain. Includes demographic information, health status, health-related behavior, use of medico-social service.

Mapping of Fields across datasets

No unique identifiers recorded. Theoretically possible to link to other national datasets through probabilistic linkage.

National Vital Statistics

Time Period Covered

From 1975 onward

Population Covered

All births, deaths, immigration records, etc

Diagnosis Coding Procedure

ICD-9-CM

Mapping of Fields across datasets

It is possible to link to other national datasets through probabilistic linkage.

FAP (Database for Pharmacoepidemiological Research in Primary Care)

Custodian

Spanish Department of Health- Spanish Agency for Medicines and Medical Devices.

Website



Time Period Covered

Data collected starting 2001.

Population covered

Data from 2,239 primary care physicians (family, pediatric doctors) on 5,061,159 patients across various regions of Spain.

Data Included

Demographic information, prescription details, clinical events, specialist referrals and laboratory test results. 

Diagnosis Coding Procedure

ICPC-2

Data Access

To request data and inquire about collaborations contact:

Miguel Gil Garcia, BIFAP Coordinator and epidemiology doctor

mgil_fcsai@

(p) 918 225 347

Cost of Access

Not specified

Mapping of fields across datasets

Linkage to external datasets not possible. Possibility to follow patients over time.

Other Files Needed for Analysis

Sociodemographic, care outside of GP.

References

Gil M, Oliva B, Timoner J, Maciá MA, Bryant V, & de Abajo FJ. Risk of meningioma among users of high doses of cyproterone acetate as compared with the general population: evidence from a population-based cohort study. Br J Clin Pharmacol. 2011; 72 (6): 965-8.

DGFPS Database

Custodian

Ministry of Health, Social Policy, and Equity.

Website



Time Period Covered

From 1985 onward.

Population Covered

100% of drugs dispensed by community pharmacies reimbursed by the National Health System. Data is collected at the regional level and centralized in the Ministry of Health.

Data Included

Region, DDD, turnover, prescriber’s code, pharmacist’s code, strength, dosage.

Diagnosis Coding Procedure

ATC code

Data Access

Data only available in Spanish. Requests must be made to farmacoepi@aemps.es

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Fields Needed for Analysis

Clinical information. Does not include medicine consumption reimbursed by private insurance.

MONICA-Catalonia Study

Description

The MONICA-Catalonia project is a longitudinal population survey conducted in Catalonia, Spain aimed at monitoring trends and determinants in CVD.

Custodian

MONICA Data Centre at the National Public Health Institute in Helsinki.

Website



Time Period Covered

1985-1997

Population Covered

35-74 year old residents (233 940 men, 246 047 women) of a geographical and administrative area (MONICA-Catalonia) near the city of Barcelona in Catalonia, in north-eastern Spain.

Mapping of fields across datasets

Can be linked to the national vital statistics and to regional hospital records.

Data Access

The principal investigator is Dr. Susana Sans, Instituto de Estudios de la Salud, susanna.sansm@

Cost of Access

Not Specified

References

Sans S, Puigdefabregas A, Paluzie G, Monterde D, & Balaguer-Vintro I. Increasing trends of acute myocardial infraction in Spain: the MONICA-Catalonia Study. Eur Heart J 2005, 26(5): 505-515.

Slovenia

Cancer Registry

Custodian

Institute of Oncology.

Website



Time Period Covered

From 1950 onward

Population Covered

2 million residents of Slovenia.

Data Access

Data is available to the Slovenian medical community and external investigators. For additional information contact the head of the Epidemiology Unit, Vesna Zadnik, MD

(p) +386 1 5879 451

register@onko-i.si  

Cost of Access

Not provided

Mapping of fields across datasets

Can be linked to the Central Population Register of Slovenia.

Sweden

Sweden provides residents with a Personal Identification Number which allows linkage across all national registries (information including migration, death records, hospitalizations, prescriptions) is accessible as long as the ethical aspects of the utilization of the data is approved by a special legislation and special authority.

National Patient Register (NPR) (Hospital Discharge Register)

Custodian

Statistics Sweden and the National Board of Health and Welfare (Socialstyrelsen).

Website



Time Period Covered

From 1968 onward

Population Covered

Inpatient care for all 21 counties in Sweden. The drop-out rate has been calculated as less than 1%. Outpatient visits include day-surgery and psychiatric care from both private and public caregivers starting in 2001.

Data Included

Patient data: age, sex, personal registration number, place of residence.

Geographical data: county, hospital, department.

Administrative data: inpatient/outpatient (date of admission, date of discharge, length of stay, etc).

Medical data: primary diagnosis, secondary diagnosis, external cause of injury, procedures.

Diagnosis Coding Procedures

ICD-10 from 1997, ICD-9 1986-97, ICD-8 1968-86. Up to 5 diagnoses.

Data Access

According to the Swedish ethical review act, issued 5 June, 2003 (SFS no 2003:460), research ethics review is mandatory in research containing sensitive personal data obtained from registers, databases or questionnaires/interviews.

Cost of Access

Not provided

Mapping of Fields across Datasets

Linkage to other national databases through an individual’s personal registration number (PNR).

Other Files Needed for Analysis

Primary care is not included.

References

Hemminki K, Liu X, Ji J, Forsti A, Sundquist J, & Sundquist K. Effect of autoimmune diseases on risk and survival in female cancers. Gynecol Oncol 2012;127:180-185.

Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim J-L, Reuterwall C, Heurgren M, &Olausson PO. External review and validation of the Swedish National Inpatient Register.BMC Public Health 2011, 11(1): 450.

Egesten A, Brandt L, Olsson T, Granath F, Inghammar M, Lofdahl C-G, & Ekbom A. Increased prevalence of multiple sclerosis among COPD patients and their first-degree relatives: a population based study. Lung 2008, 186: 173-178

Jadidi E, Mohammadi M, & Moradi T. High risk of cardiovascular diseases after diagnosis of multiple sclerosis. Multiple Sclerosis Journal 2013, 19 (10): 1336-1340.

Lindergard B. Diseases associated with multiple sclerosis and epilepsy. Acta Neurol Scand 1985, 71(4): 267-277

Bahmanyar S, Montgomery SM, Hillert J, Ekbom A, & Olsson T. Cancer risk among patients with multiple sclerosis and their parents. Neurology 2009;72:1170-1177.

Swedish Cancer Registry (NPR)

Custodian

Statistics Sweden and the National Board of Health and Welfare (Socialstyrelsen)

Website



Time Period Covered

From 1958 onward

Population Covered

100% cancer cases in Sweden. Approximately 50,000 new cases each year.

Data Available

Personal Identification number.

Demographics (sex, age, residence).

Medical information: Site of tumour, histological type , stage, basis of diagnosis, date of diagnosis, reporting hospital and department, reporting pathology/cytology department, identification number for the tissue specimen.

Follow-up data.

Diagnosis Coding Procedure

ICD-7 prior to 1987, for the years 1987-1992 tumours have been coded in ICD-9 and from 1993-2004 in ICD-O/2. From 2005 the cases has been coded in ICD-O/3.

Data Access

According to the Swedish ethical review act, issued 5 June, 2003 (SFS no 2003:460), research ethics review is mandatory in research containing sensitive personal data obtained from registers, databases or questionnaires/interviews.

Cost of Access

Not provided

Mapping of Fields Across Datasets

Linkage to other national databases through an individual’s personal registration number (PNR).

Other Files Needed for Analysis

Socioeconomic status, quality of life not included.

References

Barlow L, Westergren K, Holmberg L, &Talback M. The completeness of the Swedish Cancer Register- a sample survey for year 1998.Acta Oncologica 2009, 48 (1): 27-33.

Drug Prescription Register

Custodian

Statistics Sweden and the National Board of Health and Welfare (Socialstyrelsen).

Time Period Covered

From 2005 onward

Population Covered

The register contains data with unique patient identifiers for all dispensed prescriptions for the whole population of Sweden (9 million inhabitants).

Data Available

Drug, brand name, generic name, ATC-Code, quantity, dosage, cost, date.

Individual, Personal Identification Number, county and municipality.

Prescriber, profession, specialty, and workplace.

Diagnosis Coding Procedure

ATC code

Data Access

According to the Swedish ethical review act, issued 5 June, 2003 (SFS no 2003:460), research ethics review is mandatory in research containing sensitive personal data obtained from registers, databases or questionnaires/interviews.

Cost of Access

Not provided.

Mapping of Fields Across Datasets

Available through an individual’s PIN.

Other Files Needed for Analysis

No Over-the-Counter prescription information.

The Swedish Cause of Death Register

Custodian

Statistics Sweden and the National Board of Health and Welfare (Socialstyrelsen).

Time Period Covered

From 1952 onward

Population Covered

Underlying and contributing causes of death, date of death, gender, date of birth and county of residence for all deaths among Swedish citizens.

Diagnosis Coding Procedure

ICD-9

Data Access

According to the Swedish ethical review act, issued 5 June, 2003 (SFS no 2003:460), research ethics review is mandatory in research containing sensitive personal data obtained from registers, databases or questionnaires/interviews.

Cost of Access

Not provided.

Mapping of Fields across Datasets

Available through an individual’s PIN.

References

Egesten A, Brandt L, Olsson T, Granath F, Inghammar M, Lofdahl C-G, & Ekbom A. Increased prevalence of multiple sclerosis among COPD patients and their first-degree relatives: a population based study. Lung 2008; 186: 173-178

Jadidi E, Mohammadi M, & Moradi T. High risk of cardiovascular diseases after diagnosis of multiple sclerosis. Multiple Sclerosis Journal 2013; 19 (10): 1336-1340.

IMS LifeLink™ Longitudinal Patient Database in Sweden

Custodian

IMS Health

Population Covered

1.57 million residents

Data Included

Patient ID (anonymous, encrypted), diagnosis codes, procedure codes, clinic specialty, healthcare professional, cost and resource utilization data, hospital/care center, primary care, out-patient care, prescriptions.

Diagnosis Coding Procedure

ICD-10

Drugs, ATC code, date and dispensed date, pack size, number of packs, strength, price per dispensed prescription.

Data Access

Any research to be undertaken on the database must follow the ethical agreement between the VGR research team and the

University of Gothenburg. Any manuscript, abstract or poster to be published, which is based on the results from this research, must be reviewed by the research team responsible for the ethical agreement with Västra Götaland and Lars Wilhelmsen (principal investigator) lars.wilhelmsen@gu.se

For more information about the data application process email heorinfo@

Cost of Data

Access is usually costly. It depends on several factors: number of plans being linked, whether the sub-national information such as zip level would be required, and whether it is a one-time or recurring deliverable.

National Swedish MS Register

See description under Clinical Databases/Registries

Switzerland

Swiss National Cohort (SNC)

Description

The SNC is a longitudinal study of the entire resident population of Switzerland, based on national census information. Regularly updated mortality and migration files are linked with the census information.

Custodian

Swiss National Science Foundation (SNSF)

Website



Time Period Covered

Surveys conducted in 1990 and 2000. Regular updates have been linked from 1991-2008.

Population Covered

The SNC includes 6.8 million people at the census of 1990 and 7.3 million at the census of 2000.

Data Access

Raw data can be accessed online from . For collaborations and data linkage contact

Dr. Matthias Bopp, Institut für Sozial und Präventivmedizin

(p)+41 (0)44 634 46 14

snc_info@ispm.unibe.ch

Cost of Access

Free access to raw data. Cost applies to record linkage requests

Mapping of Fields Across Datasets

Probabilistic record linkage (record linkage with non-unique identifiers) between census, mortality and migration files.

Other Files Needed for Analysis

Able to link to mortality records, national surveys but not clinical/medical records.

References

Rohrmann, S., Braun, J., Bopp, M., & Faeh, D. Inverse association between circulating vitamin D and mortality: dependent on sex and cause of death? Nutr Metab Cardiovasc Dis. 2013, in press.

Faeh D, Braun J, Rufibach K, Puhan MA, Marques-Vidal P, & Bopp M. Population specific and up to date cardiovascular risk charts can be efficiently obtained with record linkage of routine and observational data. Plos One 2013, 8(2): e56149.

Vital Statistics

Custodian

Swiss Federal Office of Statistics

Time Period Covered

Data on births, marriages and deaths have been collected since 1871.

Population Covered

Births, stillbirths, deaths, marriages, divorces, and other variables relating to household information.

Diagnosis Coding Procedure

ICD-10, ICD-8 prior to 1995

Mapping of Fields Across Datasets

Probabilistic record linkage (record linkage with non-unique identifiers) between census, mortality and migration file

Data Access

Anonymous personal data can be supplied for research purposes as long as a data protection and confidentiality agreement has been signed. Request forms can be found in this website

Cost of Access

Detailed pricing information is available in from this website (French and German only)

Swiss Health Survey

Custodian

Swiss Federal Office of Statistics

Time Period Covered

The SHS survey has been conducted every 5 years since 1992. The data are collected over a 12-month period.

Population Covered

In 2007, 18,760 persons responded to the survey; the households contacted are selected randomly. The study population includes permanent residents of Switzerland aged 15 and older, living in private households.

Data included

Demographics, health status, health-related behavior, use of medico-social service.

Data Access

Anonymous personal data can be supplied for research purposes as long as a data protection and confidentiality agreement has been signed. Request forms can be found in this website

Cost of Access

Detailed pricing information is available in from this website (French and German only)

Mapping of Fields across Datasets

Individuals cannot be tracked over time. No ability to link to medical charts/hospital records.

Other Files needed for Analysis

No ability to link to medical charts/hospital records

References

Chiolero A, Wietlisbach V, Ruffieux C, Paccaud F, Cornuz J. Clustering of risk behaviors with cigarette consumption: A population-based survey. Preventive Medicine 2006, 42 (5): 348-353.

NICER Database

Custodian

National Institute for Cancer Epidemiology and Registration

Website



Time Period Covered

Aargau from 2013

Basel-City and Country from 1981

Berne from 2013 onwards

Fribourg from 2006

Geneva from 1970

Graubuenden from 1989

Glarus from 1992

Jura from 2005

Lucerne from 2010

Neuenburg from 1974

St. Gallen and Appenzell from 1980

Ticino from 1996

Thurgau from 2013

Wallis from 1989

Vaud from 1974

Zug from 2011

Zürich from 1980

Population covered

All cases of malignant cancer from the cantonal cancer registries since each registry were established.

Diagnosis Coding Procedure

ICD-O

Data Access

An application form can be found on this website Approval of an application depends on the availability and completeness of the information required and to what extent cantonal cancer registries are to be involved.  

Cost of Access

Varies according to request.

Mapping of Fields across Datasets

Possibility of linkage to some national registries through non-unique patient identifiers (social security number, age, sex, residence).

Swiss MONICA (Monitoring of trends and determinants in CVD)

Description

The Swiss MONICA project was a longitudinal population survey conducted in the cantons of Vaud/Fribourg and Ticino aimed at monitoring trends and determinants in CVD.

Custodian

World Health Organization.

Time Period Covered

1983-1992.

Population Covered

9,853 men and women aged 25–74 years.

Data Access

The principal investigator from the MONICA Switzerland site is F. Paccaud, University Institute of Social and Preventive Medicine, Lausanne.

Cost of Access

Not specified.

Mapping of Fields across Datasets

Ability to link to SNC through probabilistic linkage (age, sex, nationality, marital status and educational level).

References

Faeh D, Braun J, Rufibach K, Puhan MA, Marques-Vidal P, & Bopp M. Population specific and up to date cardiovascular risk charts can be efficiently obtained with record linkage of routine and observational data. Plos One 2013, 8(2): e56149.

United Kingdom

Clinical Practice Research Data link (CPRD) (formerly General Practice Research Database - GPRD)

Description

CPRD is a large database of longitudinal electronic medical records from UK General Practitioners, established in 1987. Data are currently available from a range of primary and secondary care settings, including disease registries (cancer, CVD), linked to key demographic and socioeconomic datasets. Data are subject to thorough validation1 2, audit and quality checks, and have been used in over 800 peer reviewed publications.

Custodian

NHS National Institute for Health Research (NIHR) and the Medicines and Healthcare products Regulatory Agency (MHRA).

Website



Time Period Covered

Ongoing since 1987

Population Covered

Data on over 50 million patients included in the new linkage from former GPRD. At any time, approximately 3 million residents are registered with GPs who participate in the GPRD, representing approximately 6% of the population of the UK. Individuals registered on the database are representative of the UK population in terms of age, sex and geographical distribution.

Examples of datasets linkable through CPRD

Diagnosis

Procedures

Drug data

Central Mortality Data

Census Data

Disease Registries: Cardiovascular Disease, Stroke, Cancer, Diabetes, Epilepsy, Pain, IBD, Dementia, Schizophrenia

Diagnosis Coding Procedure

Diagnosis: READ, bridged to ICD-10 codes, unlimited number of diagnosis

Procedure: OPCS-4 READ (4-digit to ICD-10 codes), Primary care - no limit; Secondary care - 12 per hospital ward the patient is in.

Drugs, Multiplex classification, prescription only: includes dosage, days supply, and manufacturer.

Data Access

Once data access has been granted, the CPRD Data Team will extract datasets for researchers based on a specific request. For an initial consultation contact:

John Parkinson, BSc, PhD 

Director the Clinical Practice Research Data link Group 

Medicines and Healthcare products Regulatory Agency 

(P) +44 (0)20 3080 6698 

(c)+44 (0)7920190541 

John.Parkinson@mhra..uk

Cost of Access

Service costs are priced based upon time of staff and use of specific IT systems. Data costs are charged at a fixed rate depending upon which data sources are required and the complexity of linkage. For a pricing arrangement contact the CPRD Knowledge Centre at kc@

Mapping of fields across datasets

Original medical records can be requested, including hospital discharge, and specialist letters for validation against original source. GPRD can be linked to any external database in an approved protocol for which it holds permission to use the data for research purposes.

Other Data Files Needed for Analysis

Over the counter drug information not available.

References

Bazelier MT, Mueller-Schotte S, Leufkens HG, Uitdehaag BM, van Staa T, de & Vries F. Risk of cataract and glaucoma in patients with multiple sclerosis. Mult Scler 2012, 18: 628-638.

1Herrett E, Thomas SL, Schoonen WM, Smeeth L, & Hall AJ. Validation and validity of diagnoses in the General Practice Research Database: a systematic review. Br J Clin Pharmacol 2010, 69: 4-14.

2Khan NF, Harrison SE, & Rose PW. Validity of diagnostic coding within the General Practice Research Database: a systematic review. Br J Gen Pract 2010, 60(572): e128-e136.

Soriano JB, Maier WC, Visick G, & Pride NB. Validation of general practitioner-diagnosed COPD in the UK General Practice Research Database. European Journal of Epidemiology 2001, 17(12): 1075-1080.

Hernan MA, Jick SS, Logroscino G, Olek MJ, Ascherio A, & Jick H. Cigarette smoking and the progression of multiple sclerosis. Brain 2005; 128 (Pt 6):1461-1465

Health and Social Care Information Centre (HSCIC)

Description

The Health and Social Care Information Centre (HSCIC) is an executive non departmental public body that collects, analyses and presents national health and care data. A detailed list of datasets linkable through HSCIC is provided below.

Custodians

Health and Social Care Information Centre (HSCIC) and Office for National Statistics (ONS) in some cases.

Website



Time Period Covered

Varies according to database requested (see detailed list of databases available below).

Data Access

In order to gain access to HSCIC data investigators will need one of the following:

1. Approval under section 251 of the NHS Act 2006. In this case researchers need to provide evidence of approval under section 251, i.e. a letter from the Health Research Authority Confidentiality Advisory Group. 

2. The appropriate statutory regulation covering your organization for the work required. In this case researchers need to provide evidence of the statutory regulation concerned. This will be reviewed by the HSCIC to ensure it is appropriate.

Note: Research projects also need approval by an NHS Research Ethics Committee.

Note: Data from the Office for National Statistics can only be released with approval from ONS. This involves an external approval process which it can be facilitated by HSCIC during the application process.

To discuss your requirements and gain advice on making an application, contact the HSCIC contact centre

enquiries@.uk

(p) 0845 300 6016

Cost of Access

Charges apply to cover the costs of processing and delivering service. There are charges for: Application and set-up, processing of data, annual service charges. A complete breakdown of charges is available from:

Mapping of fields across datasets

Ability to link all datasets held by HSCIC and ability to link data held by HSCIC to external datasets at an individual-record level.

References

Collin SM, Martin RM, Metcalfe C, Gunnell D, Albertsen PC, Neal D, Hamdy F, Stephens P, Lane JA, Moore R, & Donovan J. Prostate-cancer mortality in the USA and UK in 1975-2004: an ecological study. The Lancet Oncology 2008, 9(5): 445-452.

List of datasets linkable through HSCIC

Hospital Episode Statistics (HES)

Custodian

HSCIC

Website



Time Period Covered

Patient care from 1989 onwards, outpatient data from 2003 onwards, A&E data from 2006 onwards.

Population Covered

Approximately 1 billion records on admissions, outpatient appointments and Accident/Emergency (A&E) attendances at all NHS hospitals in England.

Diagnostic Coding Procedures

Diagnosis ICD-10

Procedure OPCS4

Payment by Results data

Custodian

HSCIC

Website



Time Period Covered

From the financial year 2009/2010 onwards.

Population Covered

Payment by Results (PbR) is a system of paying NHS healthcare providers a standard national price or tariff for each patient seen or treated, taking into account the complexity of the patient's healthcare needs.

Diagnostic Coding Procedures

Diagnosis ICD-10

Mental Health Minimum Data Set

Custodian

HSCIC

Website



Description

The Mental Health Minimum Data Set (MHMDS) contains record-level data about the care of adults and older people using secondary mental health services.

Time Period Covered

From 2000 onwards.

Population Covered

Data on over 1 million patients for services provided in hospitals, outpatient clinics and in the community.

Diagnosis Coding Procedures

ICD-10

Cancer Datasets

Custodian

HSCIC

Office for National Statistics (ONS)

Website



Description

All patients diagnosed with or receiving cancer treatment in or funded by the NHS in England are covered by the cancer dataset. Data includes demographics, first stage of patient’s pathway, imaging, diagnosis, cancer care plan, clinical trials, treatment, surgery, radiotherapy, chemotherapy and other drugs, pathology, recurrence, death details.

Datasets available from the Cancer Dataset

Core

Breast

Central Nervous System

Children, Teenagers and Young Adults

Colorectal

Gynecological

Hematology

Head and Neck

Lung

Sarcoma

Skin

Upper Gastrointestinal

Urology

Time Period Covered

From 1971 onwards.

Population Covered

Data on over 1 million patients for services provided in hospitals, outpatient clinics and in the community.

Diagnosis Coding Procedures

ICD-10

Coronary Heart Disease Data Set

Custodian

HSCIC

Website



Time Period Covered

From 1977 onward.

Population Covered

Data on all cardiac surgeries performed in NHS hospitals since 1977.

Diagnosis Coding Procedures

ICD-10

HIC (Health Informatics Centre)

Description

The Health Informatics Centre (formerly the Tayside Medicines Monitoring Unit) at the University of Dundee supports health research through the collection and management of high quality data and delivers it to academics and other users.

Custodians

University of Dundee and NHS Tayside

Website



Time Period Covered

Varies according to dataset (see individual dataset descriptions below)

Population Covered

Approximately 800,000 residents of Tayside & Fife (16% of the Scottish population).

Diagnosis Coding Procedure

ICD-9

Data Access

1. Data access inquiries should be directed to Duncan Heather or Allison Bell: info@hic.dundee.ac.uk.

2. Identify and contact necessary expertise.

3. Discuss requirements, co-ordinate team response, calculate costs.

4. Process request schedule and project manage service.

Cost of Access

Will vary depending on data linkage requirements, statistical support, clerical support, etc.

Mapping of fields across datasets

Datasets within HIC are linkable through an individual’s unique identifier (CHI). HIC links general practice data to community pharmacies (to obtain dispensed prescriptions), hospital data, geographic data and emergency data.

Other Data Files Needed for Analysis

Ethnicity, occupation, employment, and/or socio-economic status not available as patient level data. Over the counter drug information not available.

References

Donnan PT, MacDonald TM, & Morrist AD. Adherence to prescribed oral hypoglycemic medication in a population of patients with type 2 diabetes: a retrospective cohort study. Diabetic Medicine 2002, 19 (4): 279-284.

Datasets linkable through HIC

ISD SMR00 dataset

Description

Tayside hospital outpatient.

Time Period Covered

From 1997 onwards.

ISD SMR01 dataset

Description

Tayside acute stay hospital admissions

Time Period Covered

From 1981 onward.

ISD SMR04 dataset

Description

Tayside psychiatric returns.

Time Period Covered

From 1981 onward.

ISD SMR06 dataset

Description

Tayside cancer registration.

Time Period Covered

From 1980 onward.

Prescription Data

Description

Tayside community dispensing.

Time Period Covered

From 1993 onwards.

Accident & Emergency (A&E)

Time Period Covered

From 2006 onwards

Ambulatory Holter Monitoring Data

Description

Tayside Cardiovascular patients: 24 hour heart activity monitor data

Time Period Covered

From April 2003 onwards

ECHO cardiogram set

Time Period Covered

From January 1994 onwards

Epidemiology of liver disease in Tayside

Description

Database of approximately 10,000 cases of liver disease.

Time Period Covered

From January 1991 to December 2003.

Heart-disease Evidence-based Audit & Research Tayside Scotland (HEARTS)

Description

Tayside coronary heart disease patients.

Time Period Covered

From 1998 onwards.

Renal Register

Description

Tayside dialysis and transplant patients.

Time Period Covered

From July 2002 onwards.

Rheumatoid Arthritis dataset

Description

Rheumatoid Arthritis clinical system used in support of care for patients attending Rheumatology clinics in Tayside.

Time Period Covered

From Oct 2003 onwards.

Scottish Care Information - Diabetes Collaboration

Description

Tayside & Fife diabetic patients.

Time Period Covered

From January 1996 onwards.

Stroke Dataset

Description

Stroke clinical system used in support of care of patients attending stroke clinics in Tayside.

Time Period Covered

From 1988 onwards.

Tayside Allergy & Respiratory Disease Information System (TARDIS)

Description

Tayside COPD & lung cancer patients.

Time Period Covered

From 2001 onwards.

Thyroid Epidemiology, Audit & Research Study (TEARS)

Description

Tayside thyroid patients.

Time Period Covered

From 1993 onwards.

GRO Death Certification

Description

Official death certification data for Tayside.

Time Period Covered

From 1989 onwards.

Official death certification data for Glasgow & Fife from 2006 onwards.

ISD Scotland

Population Covered

ISD holds NHS health and health related data for over 5 million people in Scotland.

Data Access

ISD's electronic Data Research and Innovation Service (eDRIS) provides a single point of contact to assist researchers in study design, approvals and data access. Any data can be requested for ethically approved research, planning and evaluation projects that will benefit the public in Scotland. ISD will provide researchers with an eDRIS Research Coordinator who will be a single point of contact to support them throughout the project. For more information email: NSS.eDRIS@ or phone 0131 275 7333.

Cost of Access

Will vary according to request.

Mapping of Fields across datasets

Datasets held by ISD Scotland can be linked to each other and individuals can be tracked over time through a personal identification number.

References

Handel AE, Jarvis L, McLaughlin R, Fries A, Ebers GC, & Ramogopalan SV. The Epidemiology of multiple sclerosis in Scotland: Inferences from hospital admissions. PLoS One 2011, 6(1): e14606

Examples of Data marts available through ISD

Accident and Emergency (A&E2)

Time Period Covered

From 2007 onward

Population Covered

All patient attendances at Emergency Departments, Minor Injuries Units and Community Hospital A&E across NHS Scotland.

ACaDMe

Time Period Covered

From 1981 onward

Population Covered

The ACaDMe Data mart contains the linked inpatient and day case (SMR01), mental health (SMR04), cancer registration (SMR06) and deaths records from General Registers of Scotland (GROS).

Data Completeness

Time Period Covered

From 1981 onwards

Population Covered

Standard reports from the following datasets: Outpatients (SMR00), Acute/general discharges (SMR01), Maternity discharges (SMR02), Mental health discharges (SMR04), Cancer registrations (Socrates), Death registrations (GRO).

Outpatients (SMR00)

Time Period Covered

From 1997 onwards

Population Covered

Outpatient appointments in NHS Scotland.

PRISMS

Time Period Covered

From 2004 onwards

Population Covered

All prescriptions dispensed in the community from April 2004 onwards.

The Scottish MS Register

See description under Clinical Databases/Registries.

The Health Improvement Network (THIN)

Description

THIN is a computerized medical research database of anonymous patient records from information entered by general practitioners and general practice staff using the Vision general practice computer system. The practices contributing data to THIN use the same computer software program as the practices contributing data to CPRD; there are several practices contributing data to both CPRD and THIN.

Custodians

In Practice Systems Ltd (INPS) and CSD Medical Research UK.

Website



Time Period Covered

Data collection started Sept 2002; however some practices have electronic medical records dating back to 1987.

Population Covered

The database contains electronic medical records of 11.1 million patients (3.7 million active patients) equivalent to 75.6 million patient years collected from 562 general practices in the UK, covering 6.2% of the UK population.

File Types within THIN

Patient: age, sex, registration date for entering and leaving the practice.

Medical: diagnoses, date of diagnosis and location.

Therapy: all prescriptions with date issued, formulation, strength, quantity and dosing instructions.

Additional Health Data: vaccinations, prescription contraceptives, smoking, height, weight, immunizations, pregnancy, birth, death, laboratory results.

Postcode Variable Indicators: postcode linked area based socio-economic, ethnicity and environmental indices.

Consultation: date, time, duration.

Staff: gender and roles of staff who entered data.

Diagnostic Coding Procedures

READ coding

Data Access

Data access will require full Multicentre Research Ethics Committee (MREC) approval. For additional information contact

Louise Pinder, Data manager

Louise.Pinder@thin-

Cost of Access

The cost of utilizing THIN will vary depending on the type and quantity of information requested.

Mapping of fields across datasets

Ability to link to full patient medical chart and questionnaires.

Limitations/Other Data Files Needed for Analysis

Ethnicity, occupation, employment, and/or socio-economic status not available as patient level data. Over the counter drug information not available.

References

Bourke A, Dattani H, & Robinson M. Feasibility study and methodology to create a quality-evaluated database of primary care data.

Inform Prim Care 2004; 12 (3):171–77.

Smith CJP, Gribbin J, Challen KB, & Hubbard RB. The impact of the 2004 NICE guideline and 2003 General Medical Services contract on COPD in primary care in the UK. Q J Med 2008, 101(2): 145-153.

James DL, Schinnar R, Bilker WB, Wang X, & Storm L. Validation studies of the health improvement network (THIN) database for pharmacoepidemiology research. Pharmacoepidemology and Drug Safety 2007, 16 (4): 393-401.

GPMD (General Practice Morbidity Database Project Wales)

Description

The GPDM is a collaborative project between primary care medical practice and public health medicine in Wales to support the assessment of health status and health needs of the population. Data is routinely collected by general practitioners into a computer system.

Website

Not available

Time Period Covered

From 1992 onward

Population Covered

10% of the Welsh population

Diagnostic Coding Procedures

READ Clinical Classification System

Data Access

Not reported

Cost of Access

Not reported

Mapping of fields across datasets

No ability to link to data external to GPMD.

Limitations/Other Data Files Needed for Analysis

Hospitalizations, prescriptions outside of general practice.

References

Tremlett HL, Evans J, Wiles CM, Luscombe DK. Asthma and multiple sclerosis: an inverse association in a case-control general practice population. Quarterly Journal of Medicine 2002;95:753-756.

Low S, Powell J. General Practice Morbidity Database Project: Working with Practices. Final Report, 1997.

Offia A. Validation of the Welsh general practice morbidity database. The International Journal of Phamacy Practice 2002; 10: R37.

Asia

China

According to the two national health services surveys, conducted by the Ministry of Health, the percentage of the Chinese population with any health insurance decreased from 30.2% in 1993 to 23.6% in 19981. Data from the 1998 survey shows that only 9.8% of rural residents have any form of health insurance. Due to this issue, nation-wide and province-wide health records are limited and cover a very small percentage of the overall population.

1Liu Y, Rao K, & Hsiao WC. Medical expenditure and rural impoverishment in China. Journal of Health, Population and Nutrition 2003, 21(3): 216-222.

China Health and Nutrition Survey (CHNS)

Custodian

The Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety at the Chinese Center for Disease Control and Prevention.

Website



Time Period Covered

The first round of the CHNS, including household, community, and health/family planning facility data, was collected in 1989. Seven additional panels were collected in 1991, 1993, 1997, 2000, 2004, 2006 and 2009. The surveyed provinces represent 56% of the Chinese population1

Population Covered

The survey covers nine provinces that vary substantially in geography, economic development, public resources, and health indicators. There are about 4,400 households in the overall survey, covering some 19,000 individuals.

Data Included

Demographics, employment, lifestyle (tobacco use, physical activity, nutrition), disease history, use of health services, marital status, physical measurements.

Mapping of Fields across Datasets

Possibility of longitudinal tracking; follow-up levels are high although families that migrate from one community to another are not followed. It is not possible to link to medical records for validation.

Data Access

Data linkage services are offered at a small fee. For a detailed description of the application process refer to:

For any additional questions, email the project manager at chns@unc.edu.

Cost of Access

Small fee applies. Some data can also be downloaded for free from their website.

References

1Attard SM, Herring AH, Mayer-Davis EJ, Popkin BM, Meigs JB, & Gordon-Larsen P. Multilevel examination of diabetes in modernizing China: what elements of urbanization are most associated with diabetes? Diabetologia 2012, 55(12): 3182-3192.

National Central Cancer Registry (NCCR)

Custodian

National Office for Cancer Prevention and Control.

Population Covered

Data from 72 registries covering 85,470,522 people (57,489,009 in urban areas and 27,981,513 in rural areas).

Time Period Covered

From 1988 onwards

Diagnosis Coding Procedure

ICD-10

ICD-0-3

Mapping of Fields across Datasets

Can be linked to the Vital Statistics Database.

Other Files Needed for Analysis

No drug data.

Data Access

Data cannot be accessed by external researchers; however they can apply for data reports and work in collaboration with the database researchers. To enquire about collaborations contact

Dr. Wanqing Chen, MD, MIPH

chenwq@cicams.

Cost of Access

External source of funding will be needed.

References

Chen W, Zheng R, Zhang S, Zhao P, Li G, Wu L, & He J. The incidences and mortalities of major cancers in China, 2009. Chinese Journal of Cancer 2013, 32 (3): 106-112.

India

Census Data

Description

A workstation for research on Census Data has been set as part of a joint collaboration between the Office of the Registrar General & Census Commissioner and the Jawaharlal Nehru University.

Custodian

Office of the Registrar General & Census Commissioner and the Jawaharlal Nehru University.

Website



Time Period Covered

Census data from 1991 to 2011.

Population Covered

The centre will provide research opportunities based on information on the country’s population size, composition, living conditions, social and cultural aspects.

Data Access

Researchers will be allowed access to de-identified micro-data. All research will be done in collaboration with the workstation. For access information contact

Dr. C. Chandramouli, I.A.S.

rgi.rgi@nic.in

(p) 011-23383761

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not specified; theoretically possible.

Other files needed for analysis

Hospital records, prescriptions would have to be linked.

Annual Health Survey

Custodian

Office of the Registrar General and Census Commissioner.

Website



Time Period Covered

2010, 2011, 2012

Population Covered

The Annual Health Survey 2011-2012 is a demographic survey covering nine high-focus states across India: Assam, Bihar, Jharkhand, Uttar Pradesh, Uttarakhand, Madhya Pradesh, Chhattisgarh, Odisha, and Rajasthan. The survey of 2010 drew a representative sample of 20,694 primary sample units, covering 4.28 million households and 20.61 million people from the 284 districts in these nine states.

Data Included

Data on mortality, disabilities, fertility, injuries, illness, access to family planning services, and maternal and child healthcare.

Data Access

Contact

Shri C. Chakravotry, Data Dissemination Unit, Consultant

cchkravorty.rgi@nic.in

(p) 011-23070629

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other files needed for analysis

Hospital records, prescriptions would have to be linked.

Israel

The National Health Insurance Law, guarantees health insurance coverage for all Israelis. Medical care is provided by four non-profit HMOs. Residents may freely choose between HMOs which are required to cover basic medical services. Residents also have the option of purchasing supplemental health insurance to cover access to private sector providers and services not included in the basic package.

Israel National Population Register

Custodian

Israel Ministry of Interior

Time Period Covered

From 1948 onwards

Population Covered

Data on all Israeli residents, including identity number, surname, first name and previous name, names of parents, date and place of birth, gender, personal status (single, married, divorced or widowed), spouse's name, names, dates of birth, and gender of children, past and present nationality, address, date of entry into Israel, date of becoming a resident.

Data Access

Data are available only to the custodial organization or in situations in which a linkage project has been approved.

Cost of Access

Fees will apply according to request.

Mapping of Fields across Datasets

Linkage to other national datasets by individual’s unique personal identification number.

National hospital services data

Custodian

Tziona Chaklai (Office of Information and Computing, Israel Ministry of Health)

Website



Time Period Covered

Not specified

Population Covered

Data includes all hospital encounters, including admissions, emergency room, outpatient clinic visits, and day hospitalizations.  Data fields include encrypted patient identity number, provider, date of admission, date of discharge, diagnoses and procedures.

Diagnosis Coding Procedure

Not specified

Data Access

Available for use within the organization, which produces periodic summary reports.

Mapping of Fields across Datasets

Not possible as hospitals report to the registry using a blinded identifier.

Israel National Cancer Registry 

Custodian

Israel Ministry of Health, National Disease Control Center

Website



Time Period Covered

Some data from 1961 onward.  Reporting mandatory from 1981 onward.

Population Covered

Approximately 93% of cases in Israel.

Data Includes

Demographic information, diagnosis, date of diagnosis, stage at diagnosis.

Diagnosis Coding Procedure

ICD-0

Data Access

Available for use within the organization, other government agencies/research organizations may obtain access after project approval. For more information contact

Micha Barchana, MD, MPH, University of Haifa

m_barchana@

Cost of Access

Fees will apply according to project.

Mapping of Fields across Datasets

Linkage to other national datasets by individual’s unique personal identification number.

References

Modan B, Ron E, Lerner-Geva L, Blumstein T, Menczer J, Rabonovici J, Oelsner G, Freedman L, Mashiach S, & Lunenfeld B. Cancer incidence in a cohort of infertile women. Am J Epidemiol 1998, 147 (11), 1038-1042.

Cause of Death Registry

Custodian

Israel National Bureau of Statistics (CBS).

Website



Time Period Covered

From 1948 onwards

Population Covered

All deaths occurring among residents of Israel, not including deaths of citizens living abroad or casualties of war.

Diagnosis Coding Procedure

ICD-10

Data Access

Available for use within the organization, other government agencies/research organizations may obtain access. Approval is granted to academic researchers and researchers from research institutions upon approval of their research proposal by the Chief Scientist of the CBS. Use of data also requires researchers to undergo a security check, become special sworn volunteers of the CBS subject to criminal sanctions for breach of confidentiality, and publish their research results in a manner prescribed by the CBS. For additional information contact info@.il

Cost of Access

Some aggregate data available online, fees apply for specific data requests.

Mapping of Fields across Datasets

Linkage to other national datasets by individual’s unique personal identification number.

References

Kristal-Boneh E, Silber H, Harari G, & Froom P. The association of resting heart rate with cardiovascular, cancer and all-cause mortality. Eight year follow-up of 3527 male Israeli employees (the CORDIS study). European Heart Journal 2000, 21 (2):116-124.

The Maccabi Healthcare Services Clinical Database

Description

Maccabi Healthcare Services (HMO) provides medical coverage to 1.85 members across the country. Maccabi has created a longitudinal database and chronic disease registries that can be accessed by researchers by request.

Custodian

Maccabi Healthcare Services

Website



Time Period Covered

From 1993 onwards

Population Covered

1.85 million residents. Stable population across time (2% turnover).

Data Included

Demographic information, comorbidities, lab results, medications, treatments, encounters (GPs, hospitalizations).

Diagnosis Coding Procedure

ICD-9

Data Access

Project has to be approved by a Helsinki committee (the Israeli equivalent of an Institutional Review Board).

Cost of Access

Fees apply according to project

Mapping of Fields across Datasets

All patients have a single national identity number that can be used to link data to other national databases and to track patients across time. Ability to validate against medical charts.

References

Halkin H, Katzir Im Kurman I, Jan J, & Malkin BB. Preventing drug interactions by online prescription screening in community pharmacies and medical practices. Clinical Pharmacology & Therapeutics 2001, 69: 260-265.

Elis A, Chodick G, Heymann AD, Kokia E, Flash S, Lishner M, & Shalev V. The achievement of target cholesterol level differs between coronary heart disease and diabetic patients. European Journal of Internal Medicine 2011, 22 (3): 262-265.

MS Center Registry at the Sheba Medical Center Hospital

See Clinical Databases/Registries for description.

Islamic Republic of Iran

Iranian MS Society (IMSS)

See Clinical Databases/Registries for description.

National Death Registry

Custodian

Civil Registration Organization (CRO)

Website



Time Period Covered

From 1999 it included 4 provinces (4.4 million people), 10 provinces (16 million people) in 2000, 18 provinces (36.8 millions) in 2001, 23 provinces (48 millions) in 2003 and 29 provinces (55.5 millions) in 2004.

Population Covered

29 out of 30 provinces included as of 2004.

Diagnosis Coding Procedure

ICD-10

Data Access

Contact the CRO

un@sabteahval.ir.

(p) (+9821)66746460

Cost of Access

Not reported.

Mapping of Fields across Datasets

No unique Identification Number included.

Other Files Needed for Analysis

Co-morbidities, prescriptions, hospitalizations.

References

Jafari N, Kabir MJ, & Motlagh ME. Death Registration System in I.R.Iran. Iranian J Public Health 2009, 38 (1): 127-129.

National Cancer Registration

Custodian

Genetic and Cancer Bureau of CDC

Time Period Covered

From 1986 onwards

Population Covered

Approximately 81% of cancer cases as of 2006.

Data Included

Patient’s demographic information (includes race and residence), name of hospital, location of which the biopsy is taken, clinical diagnosis and date of biopsy sent to histological laboratory, patient’s clinical history, primary location of tumor, date of cancer diagnosis, morphology and histology.

Diagnosis Coding Procedure

ICD-O

Data Access

Not specified.

Cost of Access

Not specified.

Mapping of Fields across Datasets

No unique Identification Number included.

Other Files Needed for Analysis

Co-morbidities, prescriptions.

References

Razavi SHE, Aaghajani H, Haghazali M, Nadali F, Ramazani R, Dabiri E, & Abedifar H. The most common cancers in Iranian women. Iranian J Publ Health 2009, 38 (1): 109-112.

Japan

Japan has universal health insurance system run through the “Social Insurance System” and the “National Health Insurance System”: The Social Insurance System covers employees and their dependents (approximately 65% of the Japanese population). The National Health Insurance System covers mainly people that are self-employed, such as farmers and fisherman (approximately 35% of the population).

Japan Medical Data Center (JMDC) National Claims Database

Custodian

Japan Medical Data Center

Website



Time Period Covered

From 2003 onward.

Population Covered

Database of health insurance claims submitted by medical institutions to health insurance societies of corporations representing one million beneficiaries (employees and their dependents). Represents 2% of the population covered by the Social Insurance System and 0.83% of the population of Japan. Contains inpatient, outpatient and pharmacy information.

Data Included

Date of service, age and gender of patient, insurance information, specialty, doctor, pharmacy, diagnosis information, treatment information, content of medical services.

Diagnosis Coding Procedure

Diseases: ICD-10

Drugs: ATC classification, brand names, general names

Data Access

For data request contact the JMDC Head Office

Sumitomo Fudosan Shibadaimon Building 12F, 2-5-5 Shibadaimon, Minato-ku, Tokyo, 105-0012 Japan

(p) (81) 3-5733-5010

Cost of Access

Not specified

Mapping of Fields across Datasets

Ability to link across data files held by JMDC. Ability to track patients longitudinally. No access to original medical records.

Other Files Needed for Analysis

Quality of life, socioeconomic information.

References

Kimura S, Sato T, Ikeda S, Noda M, & Nakayama T. Development of a database of health insurance claims: standardization of disease classifications and anonymous records linkage. J Epodemiol 2001, 20(5): 413-419.

Furukawa TA, Onishi Y, Hinotsu S, Tajika A, Takeshima N, Shinohara K. Ogawa Y, Hayasaka Y, & Kawakami K. Prescription patterns following first-line new generation antidepressants for depression in Japan: a naturalistic cohort study based on a large claims database. Journal of Affective Disorders 2013, 150(3): 916-922.

Osaka University Hospital

Description

Clinical database (EMR) based on hospital information system consists of ordering, billing, and information on medical charts.

Custodian

Osaka University

Website



Time Period Covered

From 1996 onwards.

Population Covered

87,000 annual numbers of patients. 100% of patients visited/hospitalized at Osaka University Hospital.

Diagnosis Coding Procedure

Diseases: ICD-10

Drugs: ATC classification

Data Access

Contact: matumura@hp-info.med.osaka-u.ac.jp

Cost of Access

Not specified

Mapping of Fields across Datasets

Access to original medical records.

Other Files Needed for Analysis

Quality of life, socioeconomic information.

NBD National Claim Database

Description

Will be the first nation-wide health claims database in Japan. Currently in “Pilot Phase”:

2010- First Advisory Committee

2011- The advisory committee approved 6 out of 43 applications for pilot research projects.

Custodian

Health Insurance Bureau.

Website

Not available yet

Time Period Covered

From 2009 onwards

Population Covered

Target 50 million people covered.

Diagnosis Coding Procedure

ICD-10

Drugs, ATC code

Data Access

Not available yet.

Cost of Access

Information not available yet.

Mapping of Fields across Datasets

Will not be able to link data to other datasets outside the NDB. Cannot be validated with original medical records.

Other Files Needed for Analysis

Limited demographic data, socioeconomic, quality of life.

Medical Data Vision

Description

Administrative database of inpatient and outpatient data from 124 hospitals in Japan.

Custodian

Medical Data Vision EBM Provider®

Website



Time Period Covered

From 2008 onwards

Population Covered

Approximately 3% of Japanese population, 8.2% of total number of beds for large Japanese hospitals.

Diagnosis Coding Procedure

Diseases: ICD-10

Drugs: ATC classification, brand names, general names.

Data Access

Information in Japanese on the website. Contact email: ebminfo@mdv.co.jp

Cost of Access

Information in Japanese on the website.

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Demographic variables, access to medical records.

JammNet

Description

Medical and claims database from employee’s insurance programs.

Custodian

JammNet

Website



Jurisdiction

From 2008 onwards.

Population Covered

1.2% of the total number of claims from 150 employee insurance programs across Japan. 500,000 individual patients.

Diagnosis Coding Procedure

ICD-10

Drugs, ATC code

Data Access

Data in Japanese on the website

Cost of Access

Not specified.

Mapping of Fields across Datasets

No access to medical records for validation.

Other Files Needed for Analysis

Limited inpatient prescription information. Limited demographic information.

IMS NPA Data

Description

Pharmacy claims database collecting claims from 2500 pharmacies nationwide. 160 million prescriptions annually.

Custodian

IMS

Website



Population Covered

6% of total outpatient pharmaceutical claims.

Diagnosis Coding Procedure

ATC code

Data Access

(Information in Japanese)



Cost of Access

Cost varies according to request.

Mapping of Fields across Datasets

No access to medical records for validation.

Other Files Needed for Analysis

No inpatient prescription information. Limited demographic information. No clinical diagnosis.

Korea

All residents of South Korea are eligible for coverage under the National Health Insurance Program. The NHI covers 96.3% of the total population.

Korea National Health insurance claims database

Custodian

Korea Health Insurance Review Agency (HIRA).

Time Period Covered

From 1989 onward

Population Covered

Close to 97% medical (outpatient, inpatient) and prescription drug records of the Korean population.

Diagnosis Coding Procedure

ICD-10

Data Access

Information in Korean provided in the website

Cost of Access

Information in Korean provided in the website

Mapping of Fields across Datasets

Can be linked to National Cancer Register, Mortality Statistics, and other national statistics through personal identification number.

Other Files Needed for Analysis

Quality of life, socioeconomic.

References

Choi HJ, Shin CS, Ha Y-C, Jang SM, Jang S-H, Park C M, Yoon H-K, & Lee S-S. Burden of osteoporosis in adults in Korea: a national health insurance database study. J Bone Miner Merab 2012, 30 (1): 54-58.

Taiwan

Taiwan National Health Insurance Research Database (NHIRD)

Description

Taiwan NHI is a universal, government operated health care system. The NHIRD collects all original NHI claims, which are updated and maintained regularly by National Health Research Institutes, and are open to scientific research after encryption of all personal information.

Custodian

National Health Research Institute.

Website



Time Period Covered

From 1995 onward.

Population Covered

As of 2007, 22.60 million of Taiwan’s 22.96 million residents are included.

Diagnosis Coding Procedure

ICD-9

Data Access

Only citizens of the Republic of China who fulfill the requirements of conducting research projects are eligible to request access to data. The use of NHIRD is limited to research purposes only. Applicants must follow the Computer-Processed Personal Data Protection Law found on this website

Cost of Access

Not specified.

Mapping of Fields across Datasets

Data is de-identified; files can be linked to each other within the NHIRD.

Other Files Needed for Analysis

Lifestyle variables (physical activity, smoking, alcohol consumption, diet, and family history), quality of life measures.

References

Kang J-H, Chen Y-H, Lin H-C. Comorbidities amongst patients with multiple sclerosis: a population-based controlled study. European Journal of Neurology 2010; 17(9): 1215- 1219

Chang YT, Chen TJ, Liu PC, et al. Epidemiological study of psoriasis in the national health insurance database in Taiwan. Acta Dermatologica Venereologica 2009;89(3):262-266

Sheu JJ, Lin HC. Association between multiple sclerosis and chronic periodontitis: a population-based pilot study. Eur J Neurol 2013;20:1053-1059.

Turkey

Turkey Vital Registration

Custodian

Ministry of Health

Website



Time Period Covered

From 1983 onward

Population Covered

Causes of death, mortality, and vital statistics.

Diagnosis Coding Procedure

ICD-10

Data Access

To make a data request, the quickest way is sending an e-mail to info@.tr. The information material available will immediately be sent to you by an e-mail. When an immediate answer can not be given they will put you in touch with someone who can assist you further. Any published information must quote the Turk Stat as the primary data source.

Cost of Access

Pricing of information and data is made according to the number of sheets or the size of the document in bytes, depending on the requested format of data such as electronic form or hard copy (price will vary between $10-100) depending on request. For detailed description go to

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Clinical records.

TÜRKİYE SAĞLIK ARAŞTIRMASI (Turkey Health Interview Survey)

Custodian

Turkish Statistical Institute

Website



Time Period Covered

2008, 2010, 2012.

Population Covered

This survey is based on the European Health Interview Survey questionnaire created by Eurostat. 7,886 households were chosen for interviews by stratified cluster sampling. The questionnaire was completed in 6,551 of these households.

Data Access

Researchers must fill out the Micro Data Request Form: , print out the form and get approval from their institution/organization, and apply directly to the Turkish Statistical Institute Presidency. Upon the approval of the request and signing of the Letter of Understanding, the data can be provided by CD.

Cost of Access

Pricing of data is calculated according to size of document (approximately $100 dollars).

Contact

Data Dissemination and Communication Department

Data Dissemination Group

(p) (0-312) 410 02 35,

(f) (0-312) 417 04 32

info@.tr

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Clinical records.

Cancer Registry

Custodian

Ministry of Health

Website



Time Period Covered

From 1992 onward

Population Covered

27% of cancer cases.

Diagnosis Coding Procedure

ICD-10

Data Access

Kanser Daire Baskanligi, Ilkiz Street, no: 4 Floor: 1, Sihhiye-Cankaya 06450, Ankara, turkey

Cost of Access

Not specified.

Mapping of Fields

Not specified.

Africa

South Africa

Civil Registration

Description

Statistics from civil registration are the only national source of information on mortality and causes of death in South Africa. The data is based on administrative records from death notification forms collected from the Department of Home Affairs (DHA).

Custodian

Statistics South Africa

Website



Time Period Covered

From 1997 onward

Population Covered

Estimated 93% completeness between 2001-2007.

Diagnosis Coding Procedure

ICD-10

Data Access

To request data contact info@.za

Cost of Access

Not specified.

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

Clinical records, prescriptions.

References



Australia and New Zealand

Australia

Under Medicare, all Australian residents, are entitled to free treatment in public hospitals as public patients or outpatients. For out-of-hospital services, Medicare pays a benefit of up to 85% of the schedule fee. The Pharmaceutical Benefits Scheme or PBS is a program run by the Australian Government that provides subsidized prescription drugs to residents of Australia

Australian Institute of Heath and Welfare (AIHW)

Custodian

Australian Institute of Health and Welfare (AIHW).

Website



Mapping of Fields across Datasets

All databases held by AIHW can be linked to each other. All linkage is subject to approval by ethical committee. Details on the linkage protocol can be found in this document

References

Nagle CM, Purdie, DM, Webb PM, Green AC, & Bain CJ. Searching for cancer deaths in Australia: National Death Index vs., Cancer Registries. Asian Pacific Journal of Cancer Prevention 2006, 7(1): 41.

Runciman WB, Roughhead EE, Semple SJ, & Adams RJ. Adverse drug events and medication errors in Australia. International Journal for Quality in Health Care 2003, 15 (1):49-59.

Examples of Datasets held by AIHW:

National Hospital Morbidity Database (NHMD)

Time Period Covered

From 1993 onward.

Population covered

Data for all individuals living in Australia. Separations from all public and private acute care hospitals.

Custodians

Australian Institute of Health and Welfare.

Diagnosis Coding Procedures

ICD-9-CM prior to 1998. For 1998-99 South Australia, Western Australia, Tasmania and Queensland provided data using ICD-9-CM and New South Wales, Victoria, the Northern Territory and the Australian Capital Territory used ICD-10-AM. For 1999-00 to 2007–08 all states and territories used ICD-10-AM.

Data access

The AIHW provides extracts of data from the National Hospital Morbidity Database (NHMD) on request. To request data from the database email hospitaldata@.au

Cost of access

A charge may apply. The amount charged will depend on the extract requirements and the complexity of the analysis undertaken.

Mapping of fields across datasets

All linkage is subject to approval by ethical committee.

Limitations/Other data files needed for analyses

The actual definitions used by the data providers may vary from year to year and between jurisdictions and sectors. Comparisons between the states and territories, reporting years and hospital sectors should be therefore made with caution

Australian Cancer Database (ACD)

Custodian

Australian Institute of Health and Welfare (AIHW).

Website



Time Period Covered

From 1982 onward.

Population covered

All primary, malignant cancers diagnosed in Australia since 1982.

Diagnosis Coding Procedure

ICD-0-3

Data Access

Prior to commencing the request, approvals are required from the AIHW Ethics Committee and all the state and territory cancer registries ethics committees. Securing approval can take from several months to a year and should be factored into project planning. Once Ethical approval has been granted, data can be requested through a customized data request form

Cost of Access

Requests for data are charged on a cost-recovery basis. The length of time it takes to complete a request or linkage depends on a number of factors such as the complexity of the work, competing projects, the ethics application process, additional approvals and correspondence with cancer registries where necessary.

Mental Health Data Cubes

Description

Mental health data cubes contain detailed information from several mental-health related National Minimum Datasets.

Custodian

Australian Institute of Health and Welfare (AIHW)

Website



Data Access

Data request is subject to Ethical approval first. To request data or for more information email info@.au

Mental Health Datasets Available:

Admitted Patient Mental Health Care NMDS

Jurisdiction

From 2006-07 to 2009-10

Population Covered

Data on patients admitted to hospital who received specialized psychiatric care.

Data Included

Year of collection, sex, age group, same day separation flag, mental health legal status, type of separation, hospital type and principal diagnosis.

Community Mental Health Care NMDS

Jurisdiction

From 2006-07 to 2010-11.

Population Covered

Contains data on patients’ contacts with government-operated specialized community mental health care services and hospital-based ambulatory care services, such as outpatient and day clinics.

Data Included

Year of collection, sex, age group, mental health legal status and principal diagnosis.

Residential Mental Health Care NMDS

Jurisdiction

From 2006-07 to 2010-11

Population Covered

Includes data on patients’ episodes in government-operated residential mental health care services.

Data Included

Year of collection, sex, age group, mental health legal status and principal diagnosis.

National Death Index

Custodian

Australian Institute of Health and Welfare (AIHW).

Time Period Covered

From 1980 onwards

Population Covered

All deaths occurring in Australia since 1980. Data are obtained from the Registrars of Births, Deaths and Marriages in each State and Territory.

Diagnosis Coding Procedure

Underlying cause of death (ICD9 codes until 1996, ICD10 since 1997), codes for other causes of death (ICD10 codes since 1997).

Data Access

To formally apply for access, researchers must first obtain Ethical approval. To obtain further information, or for assistance with the ethics application, including due dates for applications, refer to .au/ethics. Once you receive ethics approval for linkage request, contact the Data Linkage Unit (DLU) at Linkage@.au to obtain instructions on how to format and submit your data file for linkage. A detailed description of the application process can be found on this website

Cost of Access

The minimum cost of a small, standard, single NDI linkage is $3,500 (exclusive of GST).

Other Files Needed for Analysis

Access to postal code information must be approved by the Ethics Board.

Bettering the Evaluation and Care of Health (BEACH)

Description

BEACH is a continuous cross-sectional national study of general practice activity that began in April 1998.

Custodian

The University of Sydney.

Time Period Captured

From 1998 onwards

Population Covered

The BEACH database includes information on almost 1.1 million encounters from 10,885 participants representing more than 7,824 individual general practitioners (GPs). The sample frame for the study is all vocationally registered GPs and registrars who claimed at least 375 Medicare items of service in the most recent quarter. The Australian Government Department of Health and Ageing draws the samples from Medicare claims data.

Data Available

Encounter characteristics.

GP characteristics: Age, gender, years in general practice, number of sessions per week; country of graduation, size of practice, computer use, hours worked and on call each week, location of practice.

Patient characteristics: Age, sex, NESB status, aboriginality, Torres Strait Islander status, Health Care Card and Veterans' Affairs status, status to the practice (new/seen before).

Patient reasons for encounter (up to three).

Problems managed at the consultation (up to four).

Drugs prescribed, over the counter therapies advised, drugs supplied by the GP, status of the drug (new, continuation), dosage, regimen.

Other treatments, including therapeutic procedures and counseling, referrals to specialist; referrals to allied health professionals, admissions.

Tests and investigations: Pathology and imaging ordered at this consultation.

Supplementary analysis of nominated data: Additional questions asked of patients in subsamples of encounters: smoking status, alcohol consumption, body mass index.

Diagnosis Coding Procedure

ICPC-2

Data Access

Data can be accessed by BEACH study stakeholders. Stakeholders are given access to the BEACH data and are entitled to a number of Standard reports each year. Researchers interested in becoming stakeholders must contact

Associate Prof. Helena Britt

(p) (02) 9845 8150

It is also possible for non-stakeholders to access BEACH data, the fees that apply are detailed below.

Cost of Access

$24,200 for analysis of one year of BEACH data (include 1 day of senior analyst time).

$2,420 for each additional day of analysis required for the request.

$1,210 for each additional data year accessed.

Mapping of Fields across Datasets

Data can be linked to some datasets held by the Australian government (e.g. mortality data).

Other files needed for analysis

No data on Australian residents who do not get treated by GPs.

References

Britt H. The quality of data on general practice: a discussion of BEACH reliability and validity. Australian Family Physician 2007, 36(2): 36-40.

Drug Utilization Subcommittee (DUSC) Dataset

Description

The Pharmaceutical Benefits Scheme or PBS is a program run by the Australian Government that provides subsidized prescription drugs to residents of Australia.

Custodian

Pharmaceutical Benefits Advisory Committee.

Time Period Covered

From 1990 onward.

Population Covered

The dataset includes prescriptions dispensed under the Pharmaceutical Benefits Schedule (PBS) and the Repatriation Pharmaceutical Benefits Schedule (RPBS), which are forwarded to the government for reimbursement. Government-reimbursed prescriptions represent 70% of all prescriptions dispensed in Australia.

Data Included

Quantity dispensed, number of prescriptions, defined daily dose, pay category of recipient (e.g. general, concessional, repatriation, safety net, doctor’s bag, etc.), benefits paid (e.g. cost to Government), patients’ contributions, total cost (e.g. cost to Government plus patients’ contributions), postcode of dispensing pharmacy.

Diagnosis Coding Procedure

ATC code

Data Access

For data access request contact the Secretary of PBAC at pbac@.au or (p) 02 6289 7099.

Cost of Access

For an estimate of the cost contact the PBPA Secretariat Pricing Section at pbspricing@.au or (p) 02 6289 7083

Mapping of Fields across Datasets

No personal identifying information that can be used to link data to national data sources.

Other Files Needed for Analysis

It only includes prescribed medications that are a cost to the Australian government, not those supplied by GPs or OTC. The PBS holds no data on the problem being managed with the medication. No sociodemographic information

References

Runciman WB, Roughhead EE, Semple SJ, & Adams RJ. Adverse drug events and medication errors in Australia. International Journal for Quality in Health Care 2003, 15 (1):49-59.

Medicare Benefits Schedule (BMS)

Description

Medicare is Australia’s national health insurance scheme which is administered by the Health Insurance Commission (HIC).The Health Insurance Commission processes all claims relating to private medical services provided out of hospital and medical services for private patients in public and private hospitals.

Custodian

Department of Health and Ageing (DoHA) 

Time Period Covered

From 1974 onward

Population Covered

The Medicare claims database provides information for approximately 75% of medical services in Australia.

Data Included

Service provider number, date of service, date of processing, Medicare item number, bill type, fee charged, benefit paid, sex of patient, age of patient, postcode of patient, State and Territory of patient.

Data Access

The MBS is available online via the Department of Health and Ageing website

Cost of Access

Any permitted reproduction made must acknowledge the Commonwealth as the source of any selected passage, extract, diagram or other information or material reproduced. Any reproduction made of the information or material must include a copy of the original copyright and disclaimer notices. Requests and inquiries concerning reproduction rights should be directed to the Communications Branch, Department of Health and Ageing via: copyright@.au

Mapping of Fields across Datasets

Not possible.

Other Files Needed for Analysis

The Medicare Claims Database does not include information on services such as the nature of medical consultations provided by general practitioners and specialists. Even for specific procedures, there is no information on the underlying medical condition.

References

Menz HB, Gilheany MF, Landorf KB. Foot and ankle surgery in Australia: a descriptive analysis of the Medicare Benefits Schedule database, 1997-2006. Journal of Foot and Ankle Research 2008, 1(1): 1-10.

Population Health Research Network

Description

The Population Health Research Network (PHRN) is an Australian initiative responsible for building national data linkage infrastructure. The PHRN is developing a series of resources that will assist data users in accessing data collections held across Australia for their research. Currently the PHRN is consulting with data users from across the states and territories to gain their feedback into the design of a Centralized Metadata Database.

Custodian

PHRN (Note: other custodians could be involved, e.g. Australian Institute of Health and Welfare)

Website



Time Period Covered

The initiative is under development.

Data Access

There are 4 steps in obtaining data from PHRN:

1. Obtain approval from Human Research Ethics Committees and all data custodians.

2. Sign legally binding contracts and confidentiality agreements with data custodians.

3. Successful completion of compulsory online researcher training covering privacy and security from PHRN.

4. Receive data from custodians in encrypted format.

Cost of Access

Fees will vary according to request of linkage.

Mapping of Fields across Datasets

PHRN is a nation-wide initiative in which datasets from all over Australia could be linked to each other.

The Australian Multiple Sclerosis Longitudinal Study

Custodian

MS Research Australia

Website



Time Period Covered

An original cohort of 2,000 volunteers with MS was recruited in 2002.

Population Covered

Representative sample of Australians living with MS1. Currently, the database has information on over 3000 Australians living with MS. Since 2002 surveys have been conducted on topics such as economic impact, employment, quality of life, needs, air conditioning use, medication use and online information-seeking.

Data Access

For information about opportunities for collaboration, contact

Dr Ingrid van der Mei

ingrid.vandermei@utas.edu.au

Cost of Access

Not specified

Mapping of Fields across Datasets

No ability to link to medical charts.

References

1 Taylor BV, Palmer A, Simpson S Jr, Lucas, R, NZMSPS study group, Simmons RD, Mason D, Pearson J, Clarke G, Sabel C, Willoughby E, Richardson A, & Abernethy D. Assessing possible selection bias in a national voluntary MS longitudinal study in Australia. Multiple Sclerosis Journal 2013 e-pub ahead of print DOI: 10.1177/1352458513481511

Data Linkage Western Australia

Description

Data Linkage Western Australia was established by the collaboration of the Department of Health Western Australia, the University of Western Australia, the Telethon Institute for Child Health Research and Curtin University with the purpose of connecting all available health information for the population of Western Australia.

Custodian

Data Linkage WA.



Time Period Covered

Varies according to the dataset requested.

Data Access

Data access to Western Australia’s linked data is granted to researchers who meet the requirements of a Human Research Ethics Committee, the DLB Access Policy and the Data Custodians Involved. The complete application process is detailed on this site .

Cost of Access

Charges vary according to the size, complexity, number of datasets, number of years and number of individuals in the data extract. To obtain a quote prior to submitting an application contact

Diana Rosman

Diana.rosman@health..au

Mapping of Fields across Datasets

Linkage possible across all datasets available through Data Linkage WA.

Datasets available through Data Linkage WA

Western Australia (Hospital Morbidity Data System)

Time Period Covered

From 1970 onwards

Population covered

All inpatient episodes for defined admitted patients to public, private and freestanding day-hospitals in Western Australia.

Data Included

Inpatient: Demographics (age, sex, postal code, indigenous status, marital status, employment, insurance).

Diagnosis: Primary, secondary and unlimited number of additional diagnoses.

Procedure: Principal procedure and all other significant procedures.

Diagnosis Coding Procedure

Diagnosis, ICD-10, ICD-9 prior to July 1998.

Procedure, ICD-10, ICD-9 prior to July 1998.

Emergency Department Data Collection

Time Period Covered

From 2002 onwards

Population Covered

Data on emergency department activity in WA’s public hospitals as well as activity in private hospitals under contract with the WA government.

Mental Health Information System

Time Period Covered

From 1966 onwards

Population Covered

Inpatient (public and private) and outpatient (public only).

WA Cancer Registry

Time Period Covered

From 1982 onwards

Population Covered

All cases of cancer and some neotypes.

Death Registrations

Time Period Covered

From 1969 onwards

Birth Registrations

Time Period Covered

From 1974 onwards

New Zealand

New Zealand provides universal health care to its residents; 95% of New Zealand citizens have their own unique NHI number to assist with coordination and provision of health and disability support services across the country.

National Minimum Dataset (hospital events)

Custodian

Ministry of Health

Website



Jurisdiction

From 1993 (public hospitals) and 1997 (private hospitals).

Population Covered

The NMDS is a national collection of public and private hospital discharge information, including clinical information, for inpatients and day patients.

Diagnosis Coding Procedures

ICD-10-AM

Data Access

Data can be accessed by request to the Information Analysts. For additional information contact one of the Information Analysts

(p) (04) 496 2000

data-enquiries@t.nz.

Researchers requiring access to identifiable data will need approval by a Research Ethics Board.

Cost of Access

Charges will apply depending on request.

Mapping of Fields across Datasets

Can be linked to other datasets/data collections held by the Ministry of Health through the unique NHI number.

Other Files Needed for Analysis

Behavior/lifestyle variables, quality of life.

References

Langley J, Stephenson S, Thorpe C, & Davie G. Accuracy of injury coding under the ICD-9 for New Zealand public hospital discharges. Injury Prevention 2006, 12: 58-61

National Non-Admitted Patient Data mart

Custodian

Ministry of Health

Website



Jurisdiction

From July 1, 2006.

Population Covered

The NMDS is a national collection of data on non admitted patient (outpatient and emergency department) activity. Each record contains the date, facility and type of service provided.

Diagnosis Coding Procedures

Does not include information on diagnosis.

Data Access

Data can be accessed by request to the Information Analysts. For additional information contact one of the Information Analysts

(p) (04) 496 2000

data-enquiries@t.nz.

Researchers requiring access to identifiable data will need approval by a Research Ethics Board.

Cost of Access

Charges will apply depending on request.

Mapping of Fields across Datasets

Can be linked to other datasets/data collections held by the Ministry of Health through the unique NHI number.

Other Files Needed for Analysis

Behavior/lifestyle variables, quality of life.

New Zealand Cancer Registry (NZCR)

Custodian

Ministry of Health

Website



Jurisdiction

From 1948 onwards

Population Covered

NZCR collects data for almost all malignant tumors (invasive and in-situ) first diagnosed in New Zealand. The data collected includes information on the site, stage and pathology of the cancer, as well as demographic information (ethnicity, age, sex, and residence).

Diagnosis Coding Procedures

ICD-0

Data Access

Data can be accessed through the Information Analysts or directly through Business Objects if access is approved. For more information contact one of the Information Analysts

(p)(04) 496 2000

data-enquiries@t.nz.

Cost of Access

Charges may apply depending on request.

Mapping of Fields across Datasets

Can be linked to other datasets/data collections held by the Ministry of Health.

Other Files Needed for Analysis

NZCR does not record basal cell and squamous cell cancers of the skin. Benign neoplasms, including those situated in the brain, are also not registered.

References

Brewer N, Borman B, Sarfati D, Jeffreys M, Fleming ST, Cheng S, & Pearce N. Does comorbidity explain the ethnic inequalities in cervical cancer survival in New Zealand? A retrospective cohort study. BMC Cancer 2011, 11: 132-139.

Pharmaceutical Datamart

Custodian

Ministry of Health and Pharmac.

Website



Jurisdiction

From 1992 onwards

Population Covered

The Pharmaceutical Collection contains claim and payment information from pharmacists for subsidized dispensing drugs.

Diagnosis Coding Procedure

Drug name, code, cost, brand, source of supply, pharmacode, price, claim, etc.

Data Access

Data can be accessed if approved by Pharmac. For more information contact one of the Information Analysts

(p) (04) 496 2000

data-enquiries@t.nz.

Cost of Access

Charges may apply depending on request.

Mapping of Fields across Datasets

Can be linked to other datasets/data collections held by the Ministry of Health.

Other Files Needed for Analysis

Medications but not conditions.

PRIMHD Mental Health Dataset

Custodian

Ministry of Health

Website



Jurisdiction

From July 1, 2008.

Population Covered

The PRIMHD includes information on mental health and addiction services provided by secondary organizations that are funded by the New Zealand Government and also by non-governmental agencies (NGO’s).

Data Available

Patient’s demographics, mental health and addiction services provided, outcomes of this service use.

Diagnosis Coding Procedure

ICD-10

Mapping of Fields across Datasets

Cannot be linked to medical charts for validation.

Other Files Needed for Analysis

No information for patients treated by GP or patients with a mental illness who are not seen by services. Limited information on diagnosis and outcomes.

Mortality Collection

Custodian

Ministry of Health

Website



Jurisdiction

From 1988 onwards

Population Covered

All deaths and still birth registrations in New Zealand.

Diagnosis Coding Procedures

ICD-10

Data Access

Data can be accessed through the Information Analysts or directly through Business Objects if access is approved. For more information contact one of the Information Analysts

(p) (04) 496 2000

data-enquiries@t.nz.

Cost of Access

Charges may apply depending on request.

Mapping of Fields across Datasets

Can be linked to other datasets/data collections held by the Ministry of Health.

Clinical Databases/Registries Global

MSBASE

Primary Aim

Unique international database dedicated to sharing, tracking, and evaluating outcomes date in Multiple Sclerosis (MS).

Country and Geographical Coverage

Global

How was diagnosis made for the condition?

Varies according to data source. Physician-diagnosed MS.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Varies according to data source.

Has the accuracy of the condition in registry been validated?

Not explicitly reported but physician-diagnosed.

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Not reported.

Is smoking captured? If yes, provide details.

Not reported.

Is alcohol consumption captured? If yes, provide details.

Not reported.

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

Not reported.

Data access

Not reported.

Cost of accessing data

Not reported.

Contact Information

The MSBase Foundation Ltd

Department of Neurology

Royal Melbourne Hospital

Grattan St.

Parkville VICTORIA 3050

Australia

(f) +61 3 9342 8070

(p) +61 3 9342 8070

Name and web address of website if available



Europe

Danish Multiple Sclerosis Register

Primary Aim

To monitor the incidence, prevalence, and survival of MS and provide a basis for studies of epidemiology in MS.

Country and Geographical Coverage

Since 1956, all Danish cases of MS have been registered including all cases (or suspicion of cases) diagnosed by a neurologist or a department of neurology1.

How was diagnosis of MS made?

Clinical Evaluation (neurologist)/Allison or Poser.

How is the condition coded in registry (e.g. ICD? Text?)

ICD

Has the accuracy of MS in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

No

Is smoking captured? If yes, provide details.

Not reported.

Is alcohol consumption captured? If yes, provide details.

Not reported.

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

Not reported.

Data access

Contact Melinda Magyari, Head of Danish MS Registry  Melinda)magyari@dadlnet.dk

Registry data may be linked to administrative databases.

Cost of accessing data

Not reported.

Contact information

Professor Per Soelberg Sorensen

Multiple Sclerosis Research Unit and Neuroimmunology Laboratory: Copenhagen University Hospital

Rigshospitalet, sect. 6311

Blegdamsvej 9

2100 Copenhagen, Denmark

(p) +45 3545-6311

(f) +45 3545-6316

pss@rh.dk

Name and address of website if available



Additional comments

1 Flachenecker P, & Stuke K. National MS registries. Journal of Neurology 2008, 255 (6): 102-108.

Nielsen N, Frisch M, Rostgaard K, et al. Autoimmune diseases in patients with multiple sclerosis and their first-degree relatives: a nationwide cohort study in Denmark. Multiple Sclerosis 2008;14:823-829

Brønnum-Hansen H, Koch-Henriksen N, & Stenager, E. The Danish multiple sclerosis registry. Scandinavian Journal of Public Health 2011, 39 (7): 62-4.

National Swedish MS Register

Primary Aim

To contribute towards high-quality, equitably distributed MS care in Sweden. To assure that prevailing treatment indications for MS are followed. To assess the long-term effects of modern drugs that modify the progression of MS. To produce and broadcast new knowledge of MS by research and information.

Country and Geographical Coverage

All counties in Sweden are involved and there are about 50 regional departments/units in the registry. In total there is information from 13,000 patients with MS which is more than 70% of the estimated Swedish MS patient population.

How was diagnosis of MS made?

Clinical evaluation.

How is the condition coded in registry (e.g. ICD? Text?)

Not reported.

Has the accuracy of MS in registry been validated?

Physician-diagnosed

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Not reported.

Is smoking captured? If yes, provide details.

Not reported.

Is alcohol consumption captured? If yes, provide details.

Not reported.

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

Not reported.

Data access

Information in Swedish.

Cost of accessing data

Not specified.

Contact information

Jan Hillert

Jan.hillert@ki.se

Name and address of website if available

.

Additional comments

Flachenecker P. National MS Registries. Neurol Sci 2011, 31 (3): S289–S293

The Norwegian Multiple Sclerosis National Competence Centre and National Multiple Sclerosis Registry

Primary Aim

The main focus has been on MS epidemiology, clinical characterization, treatment, immunology and genetics.

Country and Geographical Coverage

Norway, 50-60% MS patients in Norway.

How was diagnosis of MS made?

Based on the McDonald criteria and previously Poser criteria.

How is the condition coded in registry (e.g. ICD? Text?)

Not Reported.

Has the accuracy of MS in registry been validated?

Not Reported. Physician-diagnosed

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Yes, no specified which ones.

Is smoking captured? If yes, provide details.

Yes.

Is alcohol consumption captured? If yes, provide details.

Yes.

Are height and weight captured? Measured or self-reported?

Yes, measured.

Is physical activity captured? If yes, provide details.

Not Reported.

Data access

Researchers are responsible for acquiring all necessary licences. This could be a licence from the Norwegian Data Inspectorate, exemption from confidentiality from the Norwegian Directorate of Health or approval from the Regional Committee for Medical Research Ethics (REK). An application form can be found on this site:

Cost of accessing data

Not specified.

Contact information

Kjell-Morten Myhr

kjell-morten.myhr@helse-bergen.no

Name and address of website if available

MainArea_5811=5895:0:15,4626:1:0:0:::0: 0&MainLeft_5895=5825:75022::1:5860:1:::0:0

Additional comments

Myhr KM, Grytten N, Aarseth JH, & Nyland H. The Norwegian Multiple Sclerosis National Competence Centre and National Multiple Sclerosis registry: a resource for clinical practice and research. Acta Neurologica Scandinavica 2006, 113 (s183): 37–40.

Italian Multiple Sclerosis Database Network (MSDN)

Primary Aim

To establish a large, comprehensive and easily accessible  source of  information  on  patients'  clinical, MRI, laboratory and specific treatment data. To provide a basis for epidemiological studies and observational studies aimed to evaluate effectiveness and safety of available disease modifying drugs (DMD) in real world

Country and Geographical Coverage

Italy. To date 45 Italian MS centers contribute to MS data collection. Information on 27.000 patients (which is more than 50% of the estimated Italian MS patient population and 80% of patients estimated to be taking disease-modifying therapies) is currently included in the database.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Text String.

How was diagnosis made for the condition?

Neurologists/McDonald and Poser criteria

Has the accuracy of the condition in registry been validated?

The completeness  and accuracy of  data  collected  are checked, at  each participating center, by a trained monitoring  neurologist  and , after their upload in the central Server (at the Fondazione Mario Negri Sud, Chieti, Italy) by a staff  composed by neurologists, statisticians and technicians. 

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

No. Potentially available by linkage to administrative data in some Italian Regions ( i.e. Apulia and Sicily)

Is smoking captured? If yes, provide details.

No

Is alcohol consumption captured? If yes, provide details.

No

Are height and weight captured? Measured or self-reported?

No

Is physical activity captured? If yes, provide details.

No

Data access

Data access needs previous written authorization from each owner (original physician/department).

Research projects must be clearly stated and approved by an Independent Scientific Advisory Board which is responsible for the registry and data integrity and oversees all scientific objectives of the project: study design, implementation, data analysis and publication policy.

For additional information contact

Maria Trojano

mtrojano@neurol.uniba.it

Cost of accessing data

Negotiable

Contact information

Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari

Piazza Giulio Cesare 11, I-70124 Bari, Italy

maria.trojano@ uniba.it

Name and address of website if available

Additional Comments

In some Italian regions (i.e. Apulia) data can be linked to administrative health care claims data held by National Health.

All patients’ data are requested to be updated biannually or at least annually in each center. With the growing number of participating MS centers and the recent involvement of the Italian MS Foundation (FISM) in the management of the database network, the goal is to establish an Italian population based MS Registry.

Trojano M, Paolicelli D, Lepore V, Fuiani A, Di Monte E, Pellegrini F, Russo P, Livrea P, Comi G, & MSDN Study group. Italian Multiple Sclerosis Database Network. Neurological Sciences 2006, 27 (5): S358-361.

Trojano M, Russo P, Fuiani A, Paolicelli D, Di Monte E, Granieri E, Rosati G, Savettieri G, Comi G, Livrea P, & MSDN Study Group. The Italian Multiple Sclerosis Database Network (MSDN): The risk of worsening according to IFNβ exposure in multiple sclerosis. Multiple Sclerosis 2006, 12 (5): 578-585.

Multiple Sclerosis Registry in Germany

Primary Aim

Collecting epidemiological data and information on health care provision for MS patients in Germany.

Country and Geographical Coverage

Standardized data sets of 18,029 patients across 86 MS centers in Germany (2008)1

How was diagnosis made for the condition?

McDonald criteria.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

No

Is smoking captured? If yes, provide details.

No

Is alcohol consumption captured? If yes, provide details.

No

Are height and weight captured? Measured or self-reported?

No

Is physical activity captured? If yes, provide details.

No

Data access

Information in German on the following website

Cost of accessing data

Information in German on the following website

Contact information

MS Forschungs- und Projektenwicklungs-gGmbH

Küsterstraße 8

30519 Hannover

msregister@dmsg.de

Name and address of website if available





Additional Comments

1Stuke K. Flachenecker P, Zettl UK, Ellias WG, Freidel M, Haas J, Pitshnau-Michel D, Schimrigk S, & Rieckman P. Symptomatology of MS: results from the German MS registry. Journal of Neurology 2009, 256 (11): 1932-1935.

Flachenecker P, & Stuke K. National MS registries. Journal of Neurology 2008, 255 (6): 102-108.

Institute of Neurology, Belgrade School of Medicine

Primary Aim

Not reported.

Country and Geographical Coverage

Serbia

How was diagnosis made for the condition?

Neurologist/McDonald criteria

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Website



Contact

Prof Jelena Drulovic

jelena60@eunet.yu

References

Tepavcevic DK, Kostic J, Basuroski ID, Stojsavljevic N, Pekmezovic T, & Drulovic J. The impact of sexual dysfunction on the quality of life measured by MSQoL-54 in patients with multiple sclerosis. Multiple Sclerosis 2008, 14(8): 1131-1136.

Dragoslav VS, Stojsavlijevic N, Drulovic J, Dujmovic I, Mesaros S, Ercegovac M, Peric V, Dragutinovic G, & Levic Z. Seizures in multiple sclerosis. Epilepsia 2001, 42(1): 72-79.

The Scottish MS Register

Primary Aim

To establish the incidence of MS in Scotland and enable service evaluation and improvement of the care of newly diagnosed individuals.

Country and Geographical Coverage

Scotland, Europe.

How was diagnosis made for the condition?

Neurologist; Poser 1983 and McDonald 2005.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

ICD

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

No

Is smoking captured? If yes, provide details.

No

Is alcohol consumption captured? If yes, provide details.

No

Are height and weight captured? Measured or self-reported?

No

Is physical activity captured? If yes, provide details.

No

Data access

Any data can be requested for ethically approved research, planning and evaluation projects that will benefit the public in Scotland. See contact information additional information. Registry data may be linked to National Health System (NHS) datasets such as hospitalizations.

Cost of accessing data

Not specified.

Contact information

For general inquiries nss.isdscottishmsregister@

Amanda Gilmour, Information Analyst, Healthcare Audits, National Services Scotland

amanda.gilmour@

(p) 0141 282 2135

Name and address of website if available



European Register for Multiple Sclerosis (EUREMS)

Primary Aim

To exchange and disseminate information relating to MS considering all issues relevant to people affected by MS and to encourage research of all kinds that is appropriate to MS. It is a project of the European Multiple Sclerosis Platform (EMSP).

Country and Geographical Coverage

EMSP includes 34 European Countries, 39 member organizations

How was diagnosis made for the condition?

Not reported.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Has the accuracy of the condition in registry been validated?

Not reported.

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Not reported.

Is smoking captured? If yes, provide details.

Not reported.

Is alcohol consumption captured? If yes, provide details.

Not reported.

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

Not reported.

Data access

Not reported.

Cost of accessing data

Not reported.

Name and web address of website if available



Contact Information

Not reported.

Additional Comments

EUREMS conducted a study of registries in Europe.

European Database for Multiple Sclerosis (EDMUS)

Primary Aim

To facilitate the fight against MS and the related diseases through the use of a common, standardized language elaborated through the continuous concentration of national and international experts with the aim of improving care and treatment of patients with of MS, and promoting research into this disease.

Country and Geographical Coverage

Europe

How was diagnosis made for the condition?

Neurologists/ Poser before 2000, McDonald after 2001.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

May be captured depending on forms used. Autoimmune disease, hypertension, migraine, cancer.

Is smoking captured? If yes, provide details.

May be captured depending on forms used. No specific details.

Is alcohol consumption captured? If yes, provide details.

May be captured depending on forms used. No specific details.

Are height and weight captured? Measured or self-reported?

Yes (Detailed Form)

Is physical activity captured? If yes, provide details.

No

Data access

A written request to the EDMUS Coordinating Center is needed to obtain data; the study project for which data are requested should be clearly stated. For each project, the applicant needs previous written authorization from each owner (original physician/department).

Cost of accessing data

Not specified.

Name and web address of website if available



Contact Information

EDMUS Coordinating Center

Service de Neurologie A, Hôpital Neurologique 59 boulevard Pinel, 69677 LYON Bron Cedex, France

(p) +33 4 72 68 13 10

(f) +33 4 72 35 75 25

support@

Additional Comments

Lebrun C, Vermersch P, Brassat D, et al. Cancer and multiple sclerosis in the era of disease-modifying treatments. J Neurol 2011; 258:1304-1311.

Confavreux C. Establishment of multiple sclerosis registers. Annals of Neurology 1994, 36 (1): S136-139.

Asia

Iranian MS Society

Primary Aim

Not reported.

Country and Geographical Coverage

Iran, Asia

How was diagnosis of MS made?

Poser (up to 2001) or McDonald criteria approved by neurologists.

How is the condition coded in registry (e.g. ICD? Text?)

ICD

Has the accuracy of MS in registry been validated?

Not Reported.

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Some conditions might be self-reported or reported by family members.

Is smoking captured? If yes, provide details.

No

Is alcohol consumption captured? If yes, provide details.

No

Are height and weight captured? Measured or self-reported?

No

Is physical activity captured? If yes, provide details.

No

Data access

Not specified.

Cost of accessing data

Not specified.

Contact information

No 35 Mohammad Shams Alley

Vesal-Shirazi Avenue

Islamic Republic of Iran

(p) (98)219-669-5397

(f) (98)219-669-53907

Name and address of website if available (in Farsi)



Additional comments

Elhami S-R, Mohammad K, Sahraian MA, & Eftekhar H. A 20-year incidence trend (1989-2008) and point prevalence (March 20, 2009) of multiple sclerosis in Tehran, Iran: A population-based study. Neuroepidemiology 2011, 36 (3): 141-147.

Etemadifar M, & Maghzi A-H. Sharp increase in the incidence and prevalence of multiple sclerosis in Isfahan, Iran. Multiple Sclerosis Journal 2011, 17 (8): 1022-1027.

MS Center Registry at the Sheba Medical Center Hospital, Israel

Primary Aim

Not reported.

Country and Geographical Coverage

Israel.

How was diagnosis made for the condition?

Neurologist/Poser.

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Yes. Have not been validated.

Is smoking captured? If yes, provide details.

Not reported.

Is alcohol consumption captured? If yes, provide details.

Not reported.

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

Not reported.

Contact Information

Dr. Anat Achiron

Director of the Multiple Sclerosis Center

Anat.Achiron@sheba..il

(p) 972-3-530-3932 

Additional Comments

Harel Y, Barak Y, Achiron A. Dysregulation of affect in multiple sclerosis: new phenomenological approach. Psychiatry Clin Neurosci 2007;61:94-98.

Achiron A, Barak Y, Gail M, et al. Cancer incidence in multiple sclerosis and effects of immunomodulatory treatments. Breast Cancer Research and Treatment 2005;89:265-270.

North America

North American Research Committee on Multiple Sclerosis (NARCOMS) Registry

Description

NARCOMS is the largest voluntary patient-driven MS registry in the world . Participants may enroll by completing a questionnaire online, or by mailing in a questionnaire. Following enrolment, participants are asked to complete surveys semi-annually, containing clinical and socio-demographic information. Diagnoses of MS as well as various clinical characteristics have been previously validated1.

Country and Geographical Coverage

Global (most from US)

Time Period Covered

1996 first enrollment.

2001 first online survey introduced.

Population Covered

The registry contains information on over 36,000 persons with MS.

How was diagnosis made for the condition?

Individuals with MS voluntarily enroll through direct mailings, MS support groups and the CMSC/NARCOMS registry page. Self-reported data.

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Yes2, diabetes, hypertension, hyperlipidemia, heart disease, migraine, irritable bowel syndrome, chronic lung disease, cancer, obstructive sleep apnea, autoimmune thyroid disease, depression and anxiety. Questionnaire for comorbidity validated against medical records review3.

Is smoking captured? If yes, provide details.

Yes, smoker/non-smoker.

Is alcohol consumption captured? If yes, provide details.

Yes

Are height and weight captured? Measured or self-reported?

Yes, self-report.

Is physical activity captured? If yes, provide details.

Yes, from 2011 onwards.

Data access

Researchers requesting access must submit a Researcher Information Form .

NARCOMS can provide access to researchers either as Data Collection or Data Analysis. The project will be reviewed by the NARCOMS team; the review process generally takes about 3-4 weeks. For additional information refer to

Cost of access

Cost varies according to request.

Contact Information

Tuula Tyry, PhD, MAED, NARCOMS Program Manager, MSRegistry@

Robert Fox, MD, NARCOMS Medical Director, foxr@

Additional Comments

1Marrie RA, Cutter G, Tyry T, Campagnolo D, & Vollmer T. Validation of the NARCOMS registry: Diagnosis. Multiple Sclerosis 2007, 13, 770–775.

2Marrie RA, Horwitz R, Cutter G, Tyry T, Campagnolo D, & Vollmer T. Comorbidity, socioeconomic status and multiple sclerosis. Multiple Sclerosis 2008, 14(8): 1091-1098.

3 Horton M, Rudick RA, Hara-Cleaver C, Marrie RA. Validation of a Self-Report Comorbidity Questionnaire for Multiple Sclerosis. Neuroepidemiology 2010;35:83-90.

Marrie RA, Cutter G, Tyry T, Vollmer T, & Campagnolo D. Does multiple sclerosis-associated disability differ between races? Neurology 2006, 66, 1235–1240.

Marrie RA, & Goldman MD. Validity of Performance Scales for disability assessment in multiple sclerosis. Multiple Sclerosis 2007, 13, 1176–1182.

Marrie RA, Cutter G, & Tyry T. Substantial adverse association of visual and vascular comorbidities on visual disability in multiple sclerosis. Multiple Sclerosis 2011, 17(12):1464-1471.

Veterans Health Administration Multiple Sclerosis National Data Repository

Primary Aim

To improve services for Veterans with MS and provide a resource for providers through a specialized and collaborative integration of clinical care, education, research, and informatics.

Country and Geographical Coverage

USA, North America. VHA medical data of every patient diagnosed with MS since 1998; over 44,000 unique and de-identified cases.

How was diagnosis of MS made?

Validated search algorithm 1

How is the condition coded in registry (e.g. ICD? Text?)

ICD

Has the accuracy of MS in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Can be obtained by chart review.

Is smoking captured? If yes, provide details.

No.

Is alcohol consumption captured? If yes, provide details.

No.

Are height and weight captured? Measured or self-reported?

Can be obtained by chart review.

Is physical activity captured? If yes, provide details.

No.

Data access

MS researchers can access data by contacting Steven.Leipertz@

Cost of accessing data

Not specified.

Contact information

Steven.Leipertz@

Name and address of website if available



Additional comments

1Culpepper WJ, Ehrmantraut M, Wallin MT, Flannery K, & Bradham DD. Veterans Health Administration multiple sclerosis surveillance registry: The problem of case-finding from administrative databases. J Rehabil Res Dev 2006 ; 43(1): 17-24

Turner AP, Hawkins EJ, Haselkorn JK, Kivlahan DR. Alcohol misuse and multiple sclerosis. Arch Phys Med Rehabil 2009;90:842-848.

New York State Multiple Sclerosis Consortium database (NYMSC database)

Primary Aim

Designed to assess demographic and clinical characteristics of MS patients and to determine characteristics of the population that could form the basis of research projects.

Country and Geographical Coverage

15 sites within the state of New York; the database has over 9,000 registered patients (2006).

How was diagnosis made for the condition?

Physician/nurse assessment/McDonald criteria.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Has the accuracy of the condition in registry been validated?

Not reported. Neurologist diagnosed.

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Yes, allergies, asthma, cancer, chronic respiratory disorders, Crohn’s disease, IBS, lupus, lymphoma, migraines, myasthenia gravis, psoriasis, rheumatoid disorders, thyroid disease, diabetes.

Is smoking captured? If yes, provide details.

Yes.

Kavak K, Teter B, & Weinstock-Guttman B. Smoking in multiple sclerosis patients is negatively associated with patient perceived psychosocial factors. Neurology 2014, 82 (10): P6. 177.

Is alcohol consumption captured? If yes, provide details.

Yes.

Are height and weight captured? Measured or self-reported?

Yes, measured.

Is physical activity captured? If yes, provide details.

No

Data access

External Entities requesting collaboration are required to submit a proposal of said collaboration to the NYSMSC Executive Director. Proposals will be submitted to the Scientific Review Committee.

Cost of accessing data

An External Entity requesting collaboration with the NYSMSC may be subject to a fee.

Contact information

Bianca Weinstock-Guttman, Executive Director, BWeinstock-Guttman@

Barbara Teter MPH, PhD, Director of Research & Development, beteter@

Name and address of website if available



Additional comments

Jacobs, LK, Wende, et al. The New York State Multiple Sclerosis Consortium: Establishment of a patient registry and centralized database. Annual Meeting of Multiple Sclerosis, Atlanta, GA. September 1996.

The Pacific Northwest Multiple Sclerosis Registry & Network

Primary Aim

To accurately estimate the prevalence and geographic distribution of persons diagnosed with MS in the Northwest. The registry serves as a database for ongoing epidemiological and health services research in MS for the people of this region.

Country and Geographical Coverage

Oregon, USA

How was diagnosis made for the condition?

Clinical Evaluation.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Not reported.

Is smoking captured? If yes, provide details.

Not reported.

Is alcohol consumption captured? If yes, provide details.

Not reported.

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

Not reported.

Data access

To obtain access information contact

msregistry@

(p) (503) 216-1022.

Cost of accessing data

Fees will vary according to request.

Contact information

msregistry@

Name and address of website if available



Additional comments

Flachenecker P, & Stuke K. National MS registries. Journal of Neurology 2008, 255 (6): 102-108.

British Columbia Multiple Sclerosis Database (BCMSD)

Primary Aim

A resource for research, training, and education.

Country and Geographical Coverage

Canada, North America. Data collected from 4 MS clinics in BC (Kelowna, Prince George, Vancouver, and Victoria), covers approximately 80% of MS population in the province.

How was diagnosis made for the condition?

Neurologists/Poser

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Not reported.

Has the accuracy of the condition in registry been validated?

Neurologist-diagnosed

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

No. Potentially available by linkage to administrative data.

Is smoking captured? If yes, provide details.

No

Is alcohol consumption captured? If yes, provide details.

No

Are height and weight captured? Measured or self-reported?

No

Is physical activity captured? If yes, provide details.

No

Data access

Approval will be needed from the University of British Columbia's Research Ethics Board. Data cannot leave British Columbia. To discuss collaborations contact

Dr Helen Tremlett

tremlett@interchange.ubc.ca

Cost of accessing data

Not specified.

Contact information

S192

Division of Neurology

The University of British Columbia

UBC Hospital

2211 Westbrook Mall

Canada V6T 1Z4

(604) 822-7929

Name and address of website if available

Not available.

Additional comments

Tremlett H, Zhao Y, & Devonshire V. Natural History of secondary-progressive multiple sclerosis. Multiple Sclerosis 2008, 14 (3): 314-324.

Kingwell E, Bajdik C, Phillips N, et al. Cancer risk in multiple sclerosis: findings from British Columbia, Canada. Brain 2012;135:2973-2979.

Dalhousie MS Research Unit (DMSRU) Database

Primary Aim

Tracking all clinical contacts and activities of patients attending the DMSRU.

Country and Geographical Coverage

Maritime Provinces of Canada (primarily Nova Scotia)

How was diagnosis made for the condition?

By DMSRU-affiliated neurologists

How is condition coded in registry (e.g. ICD? Text string? etc.)

Numeric codes designating diagnosis and disease classification.

Has the accuracy of the condition in registry been validated?

Diagnosis of MS is established by neurologists affiliated with the DMSRU according to established diagnostic criteria. Information is updated at each clinic visit.

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Yes, self-reported: hypertension, hypercholesterolemia, heart disease, diabetes, thyroid disease, cancer, asthma, renal disease, liver disease, inflammatory bowel disease, trauma, migraine, epilepsy, osteoarthritis, rheumatoid arthritis, psoriasis, systematic lupus erythematosus, Sjogren’s syndrome, depression, anxiety, Parkinson disease, other illnesses.

Is smoking captured? If yes, provide details.

Self-reported as of ?2007 (ever vs. never, # cigarettes/ day) at first visit to DMSRU

Is alcohol consumption captured? If yes, provide details.

Self-reported as of ?2007 (yes, no, average weekly amount) at first visit to DMSRU

Are height and weight captured? Measured or self-reported?

Not reported.

Is physical activity captured? If yes, provide details.

No

Data access

Access requires approval from the Dalhousie University Research Ethics Board and collaboration with the DMSRU.

Cost of accessing data

Fees vary according to request.

Contact information

Information regarding the database is available from the DMSRU Database Manager, Karen Stadnyk Karen.Stadnyk@cdha.nshealth.ca.

Information regarding the DMSRU is available from the DMSRU Director, Dr. Virender Bhan

Virender.Bhan@cdha.nshealth.ca

Name and address of website if available



Additional comments

Fisk JD, Morehouse SA, Brown MG, Skedgel C, Murray TJ. Hospital-based psychiatric service utilization and morbidity in multiple sclerosis. Can J Neurol Sci 1998;25:230-235.

Sketris I, Hicks V, Brown MJ, Fisk JD, Lummis H, Bhan V, & Murray TJ. Multiple sclerosis disease-modifying drug utilization patterns following introduction of a provincially funded program in Nova Scotia, Canada, 1998-2004. Journal of Applied Therapeutic Research 2011, 8(2):65-78.

Veugelers PJ, Fisk JD, Brown MG, Stadnyk K, Sketris IS, Murray TJ, & Bhan V. Disease progression among multiple sclerosis patients before and during a disease modifying drug program: a longitudinal population based evaluation. Multiple Sclerosis 2009, 15(11):1286-1294.

Poder K, Ghatavi K, Fisk JD, Campbell T, Kisely S, Sarty I, Stadnyk K, & Bhan V. Social anxiety in a multiple sclerosis clinic population. Multiple Sclerosis 2009, 15 (3):393-398.

Multiple Sclerosis Clinic Database and Registry, Health Sciences Centre, Winnipeg

Primary Aim

To establish a clinical database for patients followed at the MS Clinic to facilitate patient care. To establish a registry of patients who are willing to be contacted for research purposes.

Country and Geographical Coverage

Manitoba, Canada

How was diagnosis made for the condition?

Neurologist/McDonald.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

ICD (includes age/year onset symptoms, age/year diagnosis, and clinical course).

Has the accuracy of the condition in registry been validated?

Yes, neurologist diagnosed

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

Yes: reported on patient intake questionnaire and recorded by neurologist

Is smoking captured? If yes, provide details.

Yes (recorded as not at all, some days, every day, quit).

Is alcohol consumption captured? If yes, provide details.

Yes (recorded as never, times per week or times per month).

Are height and weight captured? Measured or self-reported?

Yes, measured at each visit.

Is physical activity captured? If yes, provide details.

Yes (recorded as vigorous/moderate and times per week) as of March 2014.

Data access

Requests to access data will be submitted in writing to the Principal Investigator (PI). Such requests will include a written description of the intended research inquiry, data needed to address the research question, and data analyses to be used. All such requests will be reviewed by the PI to:

i) ascertain whether the data analyses are in keeping with the intended research

ii) preclude possible duplication by other investigators of projects with similar intents

iii) determine if they meet the objectives of the database

Under some circumstances, persons who are not co-investigators in the database may potentially be provided access to a dataset from the database provided that the conditions listed above are met. If approved, the researcher would be provided with a de-identified dataset, limited to the variables necessary to complete the proposed research project. The researcher will sign a statement that the protected health information contained in this database will not be re-used or disclosed to any other person or entity without the PIs approval.

Cost of accessing data

Negotiable.

Contact information

Dr. Ruth Ann Marrie, MD, PhD, FRCPC

rmarrie@exchange.hsc.mb.ca

Name and address of website if available

Not available

Additional comments

Data can be linked to administrative health care claims data held by Manitoba Health.

Data captures Disability progression (recorded at each visit using the following validated instruments: the Expanded Disability Status Scale, Multiple Sclerosis Functional Composite, Patient Determined Disease Steps, Performance Scales), functional status (Activities of Daily Living and Instrumental Activities of Daily Living), employment status, MS-specific drug therapies and quality of life.

Multiple Sclerosis Clinic Database, University of Calgary

Primary Aim

Not reported.

Country and Geographical Coverage

Southern Alberta, Canada.

How was diagnosis made for the condition?

Neurologist/Poser.

How is condition coded in registry (e.g. ICD? Text string? Etc.)

Text string, entered by neurologist. Includes date of onset, date of diagnosis, onset localization, and clinical course

Has the accuracy of the condition in registry been validated?

Yes

Are comorbid conditions captured? If yes, list which ones and indicate if validated?

No, although Alberta Health captures prescription, hospitalization and physician billing data that can be linked to the clinical database.

Is smoking captured? If yes, provide details.

Yes (text strings), details entered by neurologist.

Is alcohol consumption captured? If yes, provide details.

Yes (text strings), details entered by neurologist.

Are height and weight captured? Measured or self-reported?

Yes (text strings), measured.

Is physical activity captured? If yes, provide details.

Yes (text strings), details entered by neurologist.

Data access

For inquiries about access contact Winona Wall or Dr. Luanne Metz (see details below)

Cost of accessing data

In the past there has been no charge for access to data that falls within the scope of Alberta Health Data Management Team.

Contact information

Winona Wall, MS Health Outcomes Manager

winona.wall@albertahealthservices.ca

206 South Tower; Foothills Medical Centre, 1403 29 St NW, Calgary, AB  T2N 2T9

(p) (403) 944-2684

(f) (403) 270-8742

Dr. Luanne Metz, MD, FRCPC

lmetz@ucalgary.ca

Director Multiple Sclerosis Program, University of Calgary

(p)(403) 944-4241

Name and address of website if available

The clinical system used at the Calgary MS clinic for capturing all visits (face to face and non-face-to-face) is Sunrise Clinical Manager (SCM):

Additional comments

Patten SB, Lavorato DH, Metz LM. Clinical correlates of CES-D depressive symptom ratings in an MS population. General Hospital Psychiatry 2005;27:439-445.

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