olaf klungel, pharmd, phd
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Explaining differences in drug-adverse event associations across and within EU databases
The PROTECT project
Olaf Klungel, PharmD, PhD
Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University
An Innovative Public-Private Partnership for New Methodologies in Pharmacovigilance and Pharmacoepidemiology
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, www.imi-protect.eu) which is a public-private partnership coordinated by the European Medicines Agency.
• The PROTECT project has received support from the Innovative Medicine Initiative Joint Undertaking (www.imi.europa.eu) under Grant Agreement n° 115004, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.
• The views expressed are those of the authors only.• PROTECT work in this presentation is work by WP2 colleagues.
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Contents
• Background PROTECT (WP2)• Selection of 6 drug-ae pairs• Analysis in 6 EU databases• Preliminary descriptive results • Population impact across EU countries• Conclusion
PROTECT Goal
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These methods will be tested in real-life situations.
To strengthen the monitoring of benefit-risk of medicines in Europe by developing
innovative methods
to enhance early detection and assessment of adverse drug reactions from different data
sources (clinical trials, spontaneous reporting and
observational studies)
to enable the integration and presentation of data
on benefits and risks
Partners
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Public PrivateRegulators:EMA (Co-ordinator)DKMA (DK)AEMPS (ES)MHRA (UK)
Academic Institutions:University of MunichFICF (Barcelona)INSERM (Paris)Mario Negri Institute (Milan)Poznan University of Medical Sciences University of GroningenUniversity of UtrechtImperial College LondonUniversity of Newcastle
EFPIA companies:GSK (Deputy Co-ordinator)Sanofi- AventisRocheNovartisPfizerAmgen GenzymeMerck SeronoBayerAstra ZenecaLundbeckNovoNordiskTakeda
SMEs:Outcome EuropePGRx
Others:WHO UMCGPRDIAPOCEIFE
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WP2 participants and their role
• WP2 has 3 Working groups (WG)
WG1Databases
WG2Confounding
WG3Drug utilization
Number of participants
n=4633 public, 13 private
n=1410 public, 4 private
n=95 public, 4 private
Public partners EMA, LMU-Muenchen, AEMPS, CEIFE, GPRD, DKMA and UU UU FIFC
Private partners Amgen, AZ, Genzyme, GSK, La-Ser, Merck, Novartis, Roche and Pfizer Amgen, Novartis, Roche and Pfizer Amgen, Novartis and Roche
WG Coordinators Raymond Schlienger 1 (Novartis)Mark de Groot2 (UU)
Nicolle Gatto (Pfizer)Rolf Groenwold (UU)
Joan Fortuny 3 (Novartis)Luisa Ibanez (FIFC)
WP2 coleaders Olaf Klungel (UU) - Robert Reynolds (Pfizer)
WP2 coleaders alternates
Tjeerd van Staa (GPRD) - Jamie Robinson (Roche)
WP2 Project Manager Ines Teixidor (UU)
1 from Oct 2010 replacing John Weil (GSK)2 from 1 Feb. 2011 replacing Frank de Vries (UU)3 from 15 March 2012 replacing Hans Petri (Roche)
WP 2: Framework for pharmacoepidemiological studies
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To:• develop• test• disseminate
of pharmacoepidemiological studies applicable to:• different safety issues• using different data sources
methodological standards for the:• design• conduct• analysis
Objectives:
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Two studies on the use of statins and the risk of fracture done in GPRD around the same period by two different groups.
Meier et al., 2000 Van Staa et al., 2011
Statins only Current use 0.55 (0.44-0.69) Current use 1.01 (0.88-1.16)
N prescriptions Time since use
• 1-4• 5-19• 20
0.51 (0.33-0.81)0.62 (0.45-0.85)0.52 (0.36-0.76)
• 0-3 months• 3-6 months• 6-12 months• > 12 months
0.71 (0.50-1.01)1.31 (0.87-1.95)1.14 (0.82-1.58)1.17 (0.99-1.40)
Recent use 0.67 (0.50-0.92)
Past use 0.87 (0.65-1.18) Past use 1.01 (0.78-1.32)
Statins (current) and type of fractures
FemurHand, wrist or armVertebralOther
0.12 (0.04-0.41)0.71 (0.52-0.96)0.14 (0.02-0.88)0.43 (0.23-0.80)
HipRadius/ulnaVertebral
0.59 (0.31-1.13)1.01 (0.80-1.27)1.15 (0.62-2.14)
Why such a difference ?
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Meier et al., 2000 Van Staa et al., 2011Source population
370 GPRD practices 683 GPRD practices
Study period Through Sept 1998 Through July 1999
Design Selected case control (3 cohorts) Conventional case-control
N Cases 3,940 81,880
N Controls 23,379 81,880
Age 50-6970-7980-89
52.2%28.9%18.9%
50-6970-84>85
47.9%38.9%13.2%
Sex Female 75.0% Female 75.6%
BMI ≥ 25 57.3% ≥ 25 52.3%
• Different patients (source population, study period, exclusion criteria)• Study design (e.g. matching criteria for age)• Definition of current statin use (last 6 months vs. last 30 days)• Possibly different outcomes (mapping)• Possibly uncontrolled/residual confounding
Work Package 2 – WG1: Databases
Conduct of adverse event - drug pair studies in different EU databases• Selection of 5 key adverse event - drug pairs• Development of study protocols for all pairs• Compare results of studies • Identify sources of discrepancies
Databases
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• British THIN databases• Spanish BIFAP project• German Bavarian claims database
• Danish National registries• Dutch Mondrian databases• British GPRD databases
Work Package 2 – WG1: Databases
Selection of key adverse events and drugs• Selection criteria:– Adverse events that caused regulatory decisions– Public health impact (seriousness of the event,
prevalence of drug exposure, etiologic fraction)– Feasibility– Range of relevant methodological issues
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Work Package 2 – WG1: Databases
Antidepressants (incl. Benzodiazepines) - Hip Fracture
Antibiotics - Acute liver injury
Beta2 Agonists - Myocardial infarction
Antiepileptics - Suicide
Calcium Channel Blockers - Cancer
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Selection of 5 key adverse events and drugs– Initial list of 55 events and >55 drugs– Finalisation based on literature review and consensus
meeting
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Population nr’s 6 EU databases
Database Country Source Cum Population nr Active population nr(2008)
GPRD UK GP 11 M 3.6 M
Mondrian NL Multisource 1.4 M (GP) 1 M (GP), 13.5 (Pharmacy), 1.2 M (Claims)
Bifap ES GP 3.2 M 1.6 M
Danish registries DK Multisource 5.2 M (All DBs) 5.2 M (All DBs)
THIN UK GP 7.8 M 3.1 M
Bavarian Claims DE Claims 10.5 M 9.5 M
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Characteristics of 6 EU DBs
Database Coding diagnoses
Coding drugs
Start year Nation wide
GPRD Read BNF 2001 7% UK
Mondrian ICPCICD
ATC 1991 90% NL (pharmacy)0.6% NL (GP)
Bifap ICPC ATC 2001 7% ESDanish registries ICD ATC 1994 (med prod)
1977 (pat register)100% DK
THIN READ BNF 2003 5.7% UKBavarian Claims ICD ATC 2001 84% (Bavaria)
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Approach
• Common protocol for each drug-ae pair– Extensive sensitivity analyses on main
methodological issues• Leader(s) for each protocol• Leader(s) for each database• Detailed data specification including definitions
of exposures, outcomes, and confounders for each database.
• Blinding of results of individual DB analyses
Preliminary results: Antibiotic use by year in 6 EU databases
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DRAFT PRELIMINARY RESULTS
Preliminary results: Antibiotic use by age in 6 EU databases
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DRAFT PRELIMINARY RESULTS
Preliminary results: Antibiotic use in 4 EU databases
• Point prevalence of Antibiotic Exposure by season from 2004-2009
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DRAFT PRELIMINARY RESULTS
Preliminary results: Antidepressant use by year in 6 EU databases
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DRAFT PRELIMINARY RESULTS
Preliminary results: BZD use per year for 6 EU databases
2001 2002 2003 2004 2005 2006 2007 2008 20090
200
400
600
800
1000
1200
1400
1600
1800
BIFAP DENMARK GPRDMONDRIAAN/LINH MONDRIAAN/ZGA THIN
Years
Prev
alen
ce p
er 1
0.00
0 pe
rson
-yea
rs
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DRAFT PRELIMINARY RESULTS
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Preliminary results: BZD use by age in 6 EU databases
0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
BIFAPDENMARKGPRDMONDRIAAN-LINHMONDRIAAN-ZGATHIN
AGE GROUP
Prev
alen
ce p
er 1
0.00
0 p-
y
Mondriaan-ZGA: results correspond to 2008
DRAFT PRELIMINARY RESULTS
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Preliminary results: Incidence of hip/femur fracture in 6 EU databases
DRAFT PRELIMINARY RESULTS
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Preliminary results: Incidence of hip/femur fracture by age in 2009 in 4 EU databases
DRAFT PRELIMINARY RESULTS
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Calculation of Population Attributable Risk
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Next steps
• Conduct of association studies 6 drug-ae pairs:
– Cohort studies (end 2012 complete)
– Nested case-control studies
– Population-based control studies
– Case-cross over studies
– Self-controlled case series
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• 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
Finally
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Members of PROTECT WP2
J. Slattery, Y. Alvarez, G. Candore, J. Durand (European Medicines Agency); J. Hasford, M. Rottenkolber (Ludwig-Maximilians-Universität-München); S. Schmiedl (Witten University); F. de Abajo Iglesias, A. Afonso, M. Gil, C. Huerta Alvarez, B. Oliva, G. Requena (Agencia Espanola de Medicamentos y Productos Sanitarios); R. Brauer, G. Downey, M. Feudjo-Tepie, M. Schoonen (Amgen NV); S. Johansson (AstraZeneca); J. Robinson, M. Schuerch, I. Tatt (Roche); L.A. Garcia, A. Ruigomez (Fundación Centro Español de Investigación Farmacoepidemiológica); J. Campbell, A. Gallagher, E. Ng, T. Van Staa (General Practice Research Database); O. Demol (Genzyme); J. Logie, J. Pimenta, K. Davis (GlaxoSmithKline Research and Development LTD); L. Bensouda-Grimaldi (L.A. Sante Epidemiologie Evaluation Recherche); U. Hesse, P. F. Rønn (Lægemiddelstyrelsen (Danish Medicines Agency) ); M. Miret (Merck KGaA ); P. Primatesta, R. Schlienger, E. Rivero, J. Fortuny (Novartis); A. Bate, N. Gatto, R. Reynolds (Pfizer); E. Ballarin, L. Ibañez, J.R. Laporte, M. Sabaté, P. Ferrer (Fundació Institut Català de Farmacologia); V. Abbing-Karahagopian, D. de Bakker, M.L. de Bruin, F. de Vries, A.C.G. Egberts, B. Leufkens, P. Souverein, L. van Dijk, E. Voogd, M. De Groot, H. Gardarsdottir, F. Rutten, R. Van den Ham, O. Klungel, S. Belitser, A. De Boer, R. Groenwold, A. Hoes, W. Pestman, K. Roes, S. Ali, J. Uddin (Universiteit Utrecht).
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