Comparative analyses of Drug-Adverse Event Associations

in Various European Databases

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

First results from PROTECT WP2-WG1 ISPE Mid-Year Meeting, Munich, 12-Apr-2013

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 T6.101

2

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

• PROTECT work in this presentation is work by WP2/WG1 colleagues

• The views expressed are those of the authors only

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

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

University4, AEMPS, CEIFE, CPRD, DKMA and UU

UU FIFC, LMU and Witten University4

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 Schlienger1 (Novartis) Mark de Groot2 (UU)

Nicolle Gatto (Pfizer) Rolf Groenwold (UU)

Joan Fortuny3 (Novartis)

Luisa Ibanez (FIFC)

WP2 coleaders Olaf Klungel (UU) - Robert Reynolds (Pfizer)

WP2 coleaders alternates

Tjeerd van Staa (CPRD) - Jamie Robinson (Roche)

WP2 Project Manager 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 2013

WP2: Participants and their roles

WP2: 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:

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

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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

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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

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Drug – AE pair Antidepressants - Hip fracture Benzodiazepines - Hip fracture Antibiotics - Acute liver injury

Beta2 Agonists - Myocardial infarction Antiepileptics - Suicide

Calcium Channel Blockers - Cancer

Methods: Characteristics of individual databases

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Data

base

Coun

try

Cum

ulat

ive

popu

latio

n (2

008)

Activ

e po

pulat

ion

(200

8)

Data

so

urce

Codi

ng

diag

nose

s

Codi

ng

drug

s

Reco

rdin

g of

dru

g us

e

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

NL 0.7 Mio

0.26 Mio

0.34 Mio 0.17 Mio

GP

GP/Pharmacy

ICPC ICPC

ATC ATC

Prescribing

Prescribing + dispensing

The Danish national registries

DK 5.2 Mio 5.2 Mio GP + specialist doctors

ICD-9 ATC Prescribing + dispensing

Bavarian Claims Database

DE 10.5 Mio 9.5 Mio Claims health insurance

ICD-10 ATC Claims

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

Methods: Overview of planned studies

Drug - AE pair

Descriptive Cohort Nested case control

Case crossover

Self-Controlled case series

AB - ALI All Databases

CPRD BIFAP

CPRD BIFAP

CPRD BIFAP

CPRD BIFAP

AED - Suicide All Databases

CPRD DKMA

n/a n/a n/a

AD - Hip All Databases

THIN Mondriaan BIFAP

THIN Mondriaan BIFAP

THIN Mondriaan

THIN Mondriaan

BZD - Hip All Databases

CPRD BIFAP Mondriaan

CPRD BIFAP Mondriaan

CPRD BIFAP

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

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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

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Methods: Standardisation to European reference population • age stratification

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Results: Benzodiazepine exposure prevalence 2001-2009

15

Crude Standardised

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

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Females Males

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

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Results: Hip fracture incidence by sex

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Adjusted Incidence of Hip Fractures in Males over 50 years old

0

5

10

15

20

25

30

35

40

2003 2004 2005 2006 2007 2008 2009

Years

Inci

d *1

0,00

0py

DKMA

BAVARIAN

AHC

BIFAP

CPRD

THIN

NPCRD

Adjusted Incidence of Hip Fractures in Females over 50 years old

0

10

20

30

40

50

60

70

80

2003 2004 2005 2006 2007 2008 2009

YearsIn

cid

*10,

000p

y

DKMA

BAVARIAN

AHC

BIFAP

CPRD

THIN

NPCRD

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

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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

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Results: Antidepressants and indications (2008)

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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

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

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

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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

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

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

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Results: Antiepileptic drug prevalence

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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

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Results - Cohort studies

• Due to blinding of results policy we cannot show any results at this point

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WG1: Progress of studies

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Drug - AE pair

Descriptive Cohort Nested case control

Case crossover

Self-controlled 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

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

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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

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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

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Questions

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