Comparative analyses of Drug- Adverse Event Associations in Various European Databases

Transcription

1 Comparative analyses of Drug- Adverse Event Associations in Various European Databases First results from PROTECT WP2-WG1 ISPE Mid-Year Meeting, Munich, 12-Apr-2013 Raymond G. Schlienger, PhD, MPH. Global Clinical Epidemiology, Novartis Pharma AG, Basel, Switzerland Mark de Groot, PhD. Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, The Netherlands

2 Disclosures RGS: full-time employee of Novartis Pharma AG, Basel Switzerland. The views expressed in this presentation are those of the author in his role of industry co-lead for IMI Protect WP2/WG1 MdG: employee of Utrecht University, The Netherlands, and partly funded by TI Pharma Mondriaan grant T

3 Acknowledgements The research leading to these results was conducted as part of the PROTECT consortium (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium, which is a public-private partnership coordinated by the European Medicines Agency PROTECT work in this presentation is work by WP2/WG1 colleagues The views expressed are those of the authors only

4 Contents Background Working Group 1 (WP2/WG1) Framework of WP2/WG1 within Protect Objectives of WP2/WG1 Methods Results Descriptive studies Current status and next steps Conclusions 4

5 WP2: Participants and their roles WG1 Databases WG2 Confounding WG3 Drug utilization Number of participants n=46 33 public, 13 private n=14 10 public, 4 private n=9 5 public, 4 private Public partners EMA, LMU-München, Witten University 4, AEMPS, CEIFE, CPRD, DKMA and UU UU FIFC, LMU and Witten University 4 Private partners Amgen, AstraZeneca, Genzyme, GlaxoSmithKline, La-Ser, Merck, Novartis, Roche and Pfizer Amgen, Novartis, Roche and Pfizer Amgen, Novartis and Roche WG Coordinators Raymond Schlienger 1 (Novartis) Mark de Groot 2 (UU) Nicolle Gatto (Pfizer) Rolf Groenwold (UU) Joan Fortuny 3 (Novartis) Luisa Ibanez (FIFC) WP2 coleaders WP2 coleaders alternates WP2 Project Manager Olaf Klungel (UU) - Robert Reynolds (Pfizer) Tjeerd van Staa (CPRD) - Jamie Robinson (Roche) Ines Teixidor (UU) 1 from October 2010 replacing John Weil (GSK), 2 from 1 February 2011 replacing Frank de Vries (UU), 3 from 15 March 2012 replacing Hans Petri (Roche), 4 New partner, accession approved by SC in January

6 WP2: Framework for pharmacoepidemiological studies To: develop test disseminate Objectives: methodological standards for the: design conduct analysis of pharmacoepidemiological studies applicable to: different safety issues using different data sources 6

7 Objective WP2 WG1 Explain differences in drug-adverse event (AE) associations due to choices in methodology and databases Reduce variation due to methodological choice of individual researchers Explain variation due to characteristics of country/database More consistency in drug-ae studies to improve B/R assessment of medicines 7

8 Methods Conduct of drug-ae studies in different EU healthcare databases, using different study designs Selection of 6 key drug-ae pairs AEs that caused regulatory decisions Public health impact (seriousness of the event, prevalence of drug exposure, etiologic fraction) Feasibility Range of relevant methodological issues Development of study protocols for all drug-ae pairs Compare results of studies Identify sources of discrepancies 8

9 Methods: Drug - AE pair selection Selection of 6 key AEs and drug classes Initial list of 55 AEs and >55 drugs Finalisation based on literature review and consensus meeting Drug AE pair Antidepressants - Hip fracture Benzodiazepines - Hip fracture Antibiotics - Acute liver injury Beta2 Agonists - Myocardial infarction Antiepileptics - Suicide Calcium Channel Blockers - Cancer 9

10 Methods: Characteristics of individual databases Database Country Cumulative population (2008) Active population (2008) Data source Coding diagnoses Coding drugs Recording of drug use BIFAP ES 3.2 Mio 1.6 Mio GP ICPC ATC Prescribing CPRD UK 11.0 Mio 3.6 Mio GP READ BNF Prescribing THIN UK 7.8 Mio 3.1 Mio GP READ BNF Prescribing Mondriaan NPCRD AHC The Danish national registries Bavarian Claims Database NL 0.7 Mio 0.26 Mio 0.34 Mio 0.17 Mio GP GP/Pharmacy DK 5.2 Mio 5.2 Mio GP + specialist doctors DE 10.5 Mio 9.5 Mio Claims health insurance ICPC ICPC ATC ATC Prescribing Prescribing + dispensing ICD-9 ATC Prescribing + dispensing ICD-10 ATC Claims 10

11 Methods: Designs Descriptive studies for drug-ae pairs in all DBs Prevalence of exposure of interest Prevalence/incidence of outcome of interest Association studies: Different study designs in selected DBs Cohort studies Nested case-control studies Case crossover studies Self-controlled case series 11

12 Methods: Overview of planned studies Drug - AE pair AB - ALI AED - Suicide AD - Hip BZD - Hip Descriptive Cohort Nested case control All Databases All Databases All Databases All Databases CPRD BIFAP CPRD DKMA THIN Mondriaan BIFAP CPRD BIFAP Mondriaan CPRD BIFAP Case crossover CPRD BIFAP Self- Controlled case series CPRD BIFAP n/a n/a n/a THIN Mondriaan BIFAP CPRD BIFAP Mondriaan THIN Mondriaan CPRD BIFAP THIN Mondriaan CPRD BIFAP B2A - AMI All Databases CPRD Mondriaan n/a n/a n/a CCB - Cancer All Databases CPRD DKMA n/a n/a n/a 12

13 Methods: Standardization of methods Common protocol for each drug-ae pair Common standards, templates, procedures Detailed data specifications (statistical analysis plan) including definitions, codes for diseases, drugs etc. Age-/sex-standardization to European reference population Blinding of results of individual DB analyses Submission of protocols to ENCePP study registry 13

14 Methods: Standardisation to European reference population age stratification 14

15 Results: Benzodiazepine exposure prevalence Crude Standardised 15

16 Results: Benzodiazepine exposure prevalence by age & sex (2008) Females Males 16

17 Results: Benzodiazepine exposure prevalence methodological issues Marked differences of BZD exposure prevalence across countries/dbs Unlikely to be primarily explained by DB characteristics (e.g. different drug coding systems) Prescribing vs dispensing information Rather explained by different prescribing habits, e.g. driven by country guidelines/policies, marketing, reimbursement,... Age-/sex-standardization had relevant impact specifically on Mondriaan data 17

18 Results: Hip fracture incidence by sex Adjusted Incidence of Hip Fractures in Males over 50 years old Adjusted Incidence of Hip Fractures in Females over 50 years old Incid *10,000py DKMA BAVARIAN AHC BIFAP CPRD THIN NPCRD Incid *10,000py DKMA BAVARIAN AHC BIFAP CPRD THIN NPCRD Years Years 18

19 Results: Hip fracture incidence - methodological issues Hip fracture: defined as a fracture of the proximal femur in the cervix or in the trochanteric region Operational definition for this study: any femur fracture Some coding systems (International Classification of Primary Care ([ICPC-2]) don t have a specific code for hip fracture, but only a broader code for femur fracture 19

20 Results: Hip fracture incidence - methodological issues (2) Definition and coding of hip fracture/femur fracture ICPC-2: BIFAP and Mondriaan - 1 code ICD-10: Danish Registries and Bavarian Claims DB - 9 codes READ: THIN and CPRD - 64 codes 20

21 Results: Antidepressants and indications (2008) 21

22 Results: Antidepressants and indications - methodological issues Major differences across DBs regarding underlying indications for ADs Most DBs do not capture specific information on indication Time window defined ± 90 days around AD prescribing date to identify disorder which may correlate to AD prescribing 22

23 Results: ALI incidence rates by age and sex definite cases (2008) 23

24 Results: Standardised ALI incidence rates definite cases (2008) 24

25 Results: Incidence of acute liver injury - methodological issues Major challenge to define idiopathic ALI in DB which use different coding systems Codes specific of liver disease or symptoms (e.g. hepatitis, acute hepatic failure, icterus,...) Non-specific codes (e.g. liver function tests abnormal, increased transaminases) Manual review of free-text (in BIFAP and CPRD) Classification in definite, probable, non-cases based on available DB information 25

26 Results: Incidence of acute liver injury - methodological issues (2) 26

27 Results: Incidence of acute liver injury - methodological issues (3) 27

28 Results: Antiepileptic drug prevalence 28

29 Results: AED exposure prevalence - methodological issues Definition of AED - literature provides different definitions of drugs belonging to that drug class Broad range of neurological and psychiatric indications for AEDs in addition to epilepsy 29

30 Results - Cohort studies Due to blinding of results policy we cannot show any results at this point 30

31 WG1: Progress of studies Drug - AE pair Descriptive Cohort Nested case control Case crossover Selfcontrolled case series AB - ALI Completed Completed March 2013 May 2013 March 2013 AED - Suicide Completed March 2013 n/a n/a n/a AD - Hip Completed Completed Aug 2013 Dec 2013 n/a BZD - Hip Completed Completed Sept 2013 Sept 2013 Sept 2013 B2A - AMI Completed March 2013 n/a n/a n/a CCB - Cancer Completed April 2013 n/a n/a n/a 31

32 Conclusions WP2/WG1 provides unique framework for studying and explaining potential differences in drug-ae associations due to choices in methodology and DBs Descriptive studies on exposure and outcomes to better characterize the individual DBs have been finalized Association studies: Cohort studies on all outcomes across all DBs being finalized Other designs within same DB ongoing or starting soon 32

33 Conclusions (2) Challenge to dissect identified differences (both of exposure and outcome data) Due to different prescribing habits Due to true underlying differences in individual populations Life-style factors, genetics Different co-morbidities, risk factor distribution Latitude Other 33

34 Conclusions (3) Due to differences in DB characteristics/structure Information on certain life-style factors (alcohol, smoking), BMI Prescribing vs dispensing Primary care EMR db vs health claims DB vs population registries Underlying coding systems Other Due to different interpretation of protocol/data specifications Differences because of different statistical software Impact of different study designs 34

35 Questions 35