Health Database analyses in Europe: Data availability

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Health Database analyses in Europe: Data availability, strengths & limitations and database-specific considerations

Milano, 10 November 2015

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Disclosure

Thomas is a Professor at the University of Wismar and partner of INGRESS-health. INGRESS-health conducts health-economic/outcomes studies for different health care companies, among them also database studies. The opinions and positions presented today are those of the presenter and do not necessarily reflect those of INGRESS-health.

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Outline of the Workshop

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Professor at Univ. of Wismar Partner of INGRESS-health

Senior project consultant at AOK PLUS

Researcher/ epidemiologist at CPRD

Manager Research Department at PHARMO

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Thomas Wilke

Andreas Fuchs

Wilhelmine Meeraus

Myrthe van Herk-Sukel

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Retrospective database studies: an important part of real world evidence studies/outcomes research studies

RWE studies/outcomes research studies

Cross sectional („single point in time“) studies

Cohort studies (also: Comparison of cohorts)

Case-control studies

Source: INGRESS, EJ Mann (2003), in Emerg Med J 2003.

Controlled (pragmatic) prospective trials

Prevalence measurement (with regard to different outcomes,

also: Treatment)

PRO measurement at a single point in time (QoL, preferences,

treatment burden etc.)

Surveys, other types of single-

point-in-time data collections

Database studies

Incidence measurement (with regard to different outcomes)

Analysis of a natural history of a condition

Prospective observational

studies, surveys

Data-base

studies

Prospective studies

Retrospective studies

Surveys, medical

chart reviews

Mainly retrospective studies – people with an outcome of interest are matched with a

control group

Database studies

Surveys, medical chart reviews

Randomized (patient or cluster randomization) controlled trials

in a real-world treatment environment

RWE study planer

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There exists an impressive number of databases

Source: ISPOR, research by Myrthe van Herk-Sukel. 5

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Three main types of retrospective databases can be differentiated

Claims data Electronic medical record

(EMR) data Registries

• Germany: AOK PLUS & others • Netherlands: Achmea • Canada: CARTaGENE • US: PharMetricsPlus

• UK: CPRD • Netherlands: PHARMO

• Netherlands: Netherlands Cancer Registry

• Sweden: Swedish Cancer Registry Examples

Source: INGRESS.

Outcomes

Socio- demographics

Clinical characteristics/

events

Surrogate out-comes (laboratory

values, disease progression)

HCRU and costs

Outcomes related to all sectors

of health care

Available (age, gender, partly education and living

circumstances)

Partly available – if covered by inpatient or outpatient diagnoses or if associated with

specific treatments

Generally not available

Generally available

Generally available – cover all areas except those that are associated with out-of-

pocket costs

Available (age, gender)

Partly available – if documented in medical records/database

Generally available – if documented in medical records/database

Generally not available; only consumption of certain units documented

No – cover regularly only a selected health care sector (outpatient or inpatient treatment; linking partly possible)

Available (age, gender)

Partly available – if documented in the registry


Usually not available in clinical registries


Database finder

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Main challenge: Select the right database for your research

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Retrospective database studies may cover different outcomes & study objectives and may differ in representativity Main challenge: data availability & data quality including potential bias

Retrospective database studies

Claims data EMR data Data from registries

Epidemiology (incidence/prevalence of

disease)

Assessment of patient/disease characteristics

Description of treatment

Description of treatment-related outcomes &

causes research

Description of HCRU/costs

X (based on population in claims

database)

X (based on population in EMR

database)

X (if registry covers all cases in a

region or country)

X (disease characteristics partly

available) X

X (if documented in the registry)

X X

(may cover only one sector of the healthcare system)


X (surrogate outcomes & disease-specific outcomes not available)

X (may cover only one sector of the

healthcare system)


X X

(may cover only one sector of the healthcare system)


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Main guidlines how to do retrospective database studies (summary of ISPOR, STROBE & ESPE guidelines)

A.

General methodology

A1. Clearly define study objectives

A2. Define settings, dates, locations, periods of recruitment, exposure, follow-up, data collection

A3. Describe other key elements of study design (outcomes, confounders, general study design)

B. Methodology: Patient selection

B1. Give eligibility criteria for selection of participants; describe, if necessary, method of finding case-controls/matching and report number of cases/case-controls

B2. Define required data – also as inclusion criteria

C. Methodology: Variables

C1. Define all outcomes, exposures, predictors, potential confounders, and effect modifiers

C2. Give diagnostic criteria, if applicable

C3. Operationalize variables in a way that abstractors can easily identify them

D. Methodology: Database selection &

data collection

D1. For each variable of interest, give details of methods of assessment (especially proxy variables)

D2. If multiple data are used, check whether reliable person-matching is possible

D3. Decide how data storage should be done

D4. Make sure that coding has been done accurately in the database

D5. Do quality assessments

E. Methodology: Bias E1. Describe any efforts to address potential sources of bias; explain how quantitative variables were handled in the analyses

E2. If applicable, describe which groupings were chosen and why

F. Methodology: Data storage and privacy &

security

F1. Comply with privacy/security policy & laws

F2. Ensure secure data storage & transfer

F3. Limit/remove identifying information

F4. Review policy & procedures

G. Methodology: Quality & validation

procedures

G1. Complete appropriate general quality checks, plan & implement study-specific quality checks

G2. Define a priori how to deal with missing/conflicting data

H. Methodology: Stat analysis

H1. Report extraction specification, output, quality testing, merging resources, responsibility for privacy and annotated programming codes for data extraction & final analysis

H2. Describe all statistical methods

I. Reporting/J. Discussion

J. Funding J1. Give the source of funding

J2. Provide the role of the funders for the present study and, if applicable, for the original study on which the publications are based

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Additional scientific & practical challenges when doing a retrospective database study

Scientific challenges Practical challenges

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Low risk of selection/observer bias

Fast, rather inexpensive access to data

High data consistency over time

Large patient samples

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Incomplete data: patient/disease characteristics or outcomes or missing codes

Incomplete data with regard to relevant health care sectors

Confounding risk if patient cohorts are compared retrospectively to each other (e.g., by matched

pairs comparisons or multivariable analyses)

Data Access

Based on scientific study protocols

Based on country-specific laws

May include data access/data linkage costs

Technical implementation

Definition of databank environment (e.g., using SQL); dealing with millions of database lines

Definition of variables & outcomes of interest

Source: INGRESS.

Database-related bias

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Wilhelmine Meeraus


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Different retrospective databases available across the World

Claims data Electronic medical record

(EMR) data Registries

. . .

Germany

UK

Netherlands

France

Spain

Italy

US/Canada

Asia/Australia

Source: INGRESS.

Database finder

. . .

YES (AOK PLUS)

NO

YES

YES

YES

YES

YES

YES

Only in specific centers/clinics

YES (CPRD)

YES (PHARMO)


YES


YES

YES

Disease-specific

Partly

Disease-specific

Disease-specific

Disease-specific

Disease-specific

YES

Disease-specific

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Take-home messages and discussion points

Source: INGRESS, CPRD, PHARMO.

• There exist three main types of retrospective databases • Claims data, EMR data, registries

• One main challenge is availability of data • Data are not always primarily collected for research purposes • Symptoms are as detailed as the used coding system allows • Only symptoms diagnosed and treated by a provider are captured -> information bias

• Another challenge is the database-related bias itself • Patients in the database may differ from national patient samples for a variety of reasons • However: It should be discussed whether this potential bias would be smaller in other

observational studies

• Medical record linkage may be a promising approach (multi-database approach) to deal with the issues of limited data availability • The linked databases offer a longitudinal perspective, allowing for observations of

healthcare utilization before, during and after (cancer) diagnosis • The strengths and limitations of the separate existing databases are maintained after

linkage

• Procedures to obtain linked datasets • Retrieve permission for obtaining data from all database separately, as technically, the data

belong to different data providers • Other conditions such as security of the data and being familiar with the contents of the

data and the healthcare system are important

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Documents

Health Database analyses in Europe: Data availability