Prescription registers in Denmark

1. Prescription registers in Denmark Morten Andersen Senior Researcher, PhD Clinical Pharmacologist Nordic Congress of General Practice Copenhagen, May 2009

2. Prescription registers in Denmark used for pharmacoepidemiologic research • Odense University Pharmacoepidemiologic Database, OPED, Funen (1990) • Northern Jutland, PDNJ (1991) • Aarhus (1996) • Viborg (1998) • Research registers in Statistics Denmark:National register of drug statistics of the Danish Medicines Agency (1995)

3. Sources of pharmacydispensing data • Regional health insurance registers • Data from pharmacies to regional health insurance • Drugs in the general reimbursement scheme: All dispensings, regardless of copayment • Drugs with individual reimbursement: Only reimbursed dispensings • National register of drug statistics • Data from pharmacies to the Danish Medicines Agency • All prescriptions dispensed at community pharmacies • Drugs on prescription regardless of reimbursement

4. Geographical bias

5. Odense PharmacoEpidemiologic Database (OPED) • All computer-registered purchases of reimbursed prescription drugs in pharmacies of Funen County (population 470,000) since October 1990 • Complete for the whole county since November 1992 • West Zealand 2000 • Region of Southern Denmark 2007 (1.2 million) • Data on the individual level (CPR-number) • Anonymised version available • Research registry maintained by the university

6. Data recorded in OPED Prescription data Population data CPR-number of patient CPR-number Date of purchase Date of birth Package number Sex Package Municipality of residence Volume Dates of migration Strength Date of death Dispensing form ATC-code and DDD Number of packages Pharmacy Prescribing practice Price and reimbursement

7. National register of drug statistics • Data collected by the Danish Medicines Agency • Available under the research registers in Statistics Denmark • Anonymous data, person identifier not accessible • Record linkage to other registers in Statistics Denmark • Health registers • Demographic data (residence, migration, death, family) • Socioeconomic data (education, occupation, employment status, income)

8. National register of drug statistics • Authorised research institutions offered remote access • Externally acquired data with CPR-number can be linked to the research registers (one-way procedure) • Programs for data processing and analysis can be e-mailed and placed on server • On-line access (secure connection) • Results e-mailed back to user (screened for misuse: single records or person identifiable data)

9. Incomplete coverage of dispensing registers • Non-reimbursed drugs (regional registers) • Benzodiazepines • Oral contraceptives • Certain antibiotics • ASA (only when prescribed to aquire reimbursement) • Paracetamol (only when prescribed to aquire reimbursement) • OTC use • In-hospital use • Drugs dispensed through hospital pharmacies/outpatient clinics • HIV treatment • Anti-tuberculosis drugs • Biologicals

10. Record linkage of register data POPULATION REGISTER ID, date, residence, birth, death, migration PRESCRIPTIONREGISTER ID Date Drug Dose HOSPITAL REGISTER ID Date Diagnoses Procedures

11. Letigen (ephedrine/caffeine)marketing suspended 2002 in DK

12. Confounding by indication Letigen Myocardial infarction Obesity

13. Case-crossover design Each person serves as his/her own control, adjusting for time-independent confounders Exposure statusCase / Control No / Yes Yes / No Yes / No Case time: MI Control time (1 year before)Exposure: Letigen Effect period

14. Ephedrine/caffeine study results • Among 2,316 case subjects, 282 (12.2%) were current users of ephedrine/caffeine • Case-crossover OR 0.84 (95% CI: 0.71, 1.00) • After adjustment for trends in ephedrine/caffeine use OR 0.95 (95% CI: 0.79, 1.16). • Subgroup analyses: no strata with significantly elevated risk • Case-control substudy: no increased risk among naïve users or users with large cumulative doses

15. Important information on medication and patient factors missing • Confounding factors in register-based epidemiological studies • Indication for drug (diagnosis) • Recommended dosage • Patient’s medical history, co-morbidities • Lifestyle factors (BMI, physical activity, alcohol, smoking, diet)

16. Information in patient records POPULATION REGISTER PRESCRIPTIONREGISTER HOSPITAL REGISTER HOSPITAL RECORDS ID Date Clinical examination Lab data Diagnostic procedures Drug use Discharge summary GENERAL PRACTICE ID Date Diagnoses Procedures Prescriptions with indications Other clinical and lab data Lifestyle factors SOCIO-ECONOMIC DATA SPECIALISED CLINICALREGISTERS

17. Other current research examples • Quality indicators for asthma treatment (patient questionnaires and spirometry) • Treatment of hypertension in general practice (GP clinical information, patient questionnaires) • Generic substitution, patient concerns and compliance (patient questionnaires and interviews )

18. Conclusions • Prescription databases are important sources of information on medication use, including the quality of prescribing, and adverse effects • General practice is responsible for the majority of prescribing, treatment initiations and follow-up in the population • Important patient characteristics and information on drug use are captured in the GP patient record systems • Pharmacoepidemiological studies should more often have general practice as the starting point