“description of data sources - global research in ... · d. 2.02 – description of data sources...

D. 2.02 – Description of Data Sources

The research leading to these results has received funding from the European Union Seventh Framework

Programme (FP7/2007-2013) under grant agreement n° 261060

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Deliverable number D2.2

“Description of Data Sources A report on the identified healthcare databases and their characteristics plus literature on their

experience with respect to paediatric studies ”

GRIP

Global Research in Paediatrics

Network of Excellence

HEALTH-F5-2010-261060

Lead Beneficiary EMC

Author(s) C.Ferrajolo, Y. Li, K. Verhamme, F. Fregonese, D. Bonifazi,

O.Osokogu, S. de Bie, I. Wong, D. Weibel, J. Bonhoeffer and M. Sturkenboom

Revision date July 1 2012

Start date 01/01/2011

Duration 5 years

Project Coordinator Dr. Carlo GIAQUINTO

Azienda Ospedaliera di Padova (AOPD)

Re fe re n c e W P W P 2 – I n t e g r a t e d i n f r a s t r u c t u r e f o r e p id e m io l o g ic a l a n d p o s t m a r k e t in g s t u d ie s

Re fe re n c e A ct iv i ty T a s k 2 . 0 2 – I d e n t i f y h e a l t h c a r e d a t a b a s e s c o m p r is in g p a e d ia t r i c d a t a

D is s e m in a t io n Le v e l

P u b l i c P U




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Table of Contents

1 List of authors and GRIP participants .......................................................................... 3 2 Abstract ........................................................................................................................ 5 3 Receivers of the document .......................................................................................... 7 4 Introduction ................................................................................................................. 8 5 Objectives of deliverable 2.2 ....................................................................................... 9 6 Healthcare databases ................................................................................................ 10

6.1 Definition ............................................................................................................ 10 7 Methods ..................................................................................................................... 11

7.1 Procedure for identification of healthcare databases ........................................ 11 7.2 Creation of the survey ........................................................................................ 15

8 Results ........................................................................................................................ 18 8.1 Databases invited to participate to the survey .................................................. 18 8.2 Response rate of survey ..................................................................................... 20 8.3 Assessment of the survey ................................................................................... 20 8.4 Nature and characteristics of the databases ...................................................... 23

8.4.1 Drug exposure ................................................................................................ 23 8.4.2 Vaccine exposure ........................................................................................... 24 8.4.3 Clinical outcome ............................................................................................. 24 8.4.4 Accessibility and costs of databases .............................................................. 24

9 Discussion and Limitations ........................................................................................ 25 10 Conclusions / Outlook and next steps ................................................................ 26 11 References .......................................................................................................... 27

12 APPENDIX ................................................................................................................. 31




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1 List of authors and GRIP participants

Name Institution

C.Ferrajolo

Y. Li,

K. Verhamme

F. Fregonese,

D. Bonifazi,

S. de Bie

I. Wong

D. Weibel

J. Bonhoeffer

M. Sturkenboom

O. Osokogu

F. Bartoloni

C. Giaquinto

EMC

BF

EMC

AOPD

CVBF-TEDDY

EMC

SoP

EMC

BF

EMC

EMC

IRIDIA (CVBF-TEDDY subcontractor)

AOPD




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In GRIP the following acronyms are used for the participating institutions

Participant organisation name Acronym Country Lead Scientist

Azienda Ospedaliera Padova – Dipartimento di Pediatria

AOPD Italy Carlo Giaquinto

National Institute of Child Health and Human Development

NICHD-NIH USA Steven Hirschfeld

European Medicines Agency EMEA UK Agnes Saint-Raymond

Erasmus Medisch Centrum Rotterdam EMC The Netherlands

Miriam Sturkenboom

University of Liverpool, MCRN ULIV-MCRN UK Rosalind Smyth

Ospedale Pediatrico Bambino Gesù OPBG Italy Paolo Rossi

Institut National de la Santé et de la Recherche Médicale

INSERM France Evelyne Jacqz Aigrain

National Center for Child Health and Development

NCCHD Japan Hidefumi Nakamura

St George's Hospital Medical School SGUL UK Mike Sharland

Consorzio per Valutazioni Biologiche e Farmacologiche

CVBF-TEDDY Italy Adriana Ceci

Universiteit Leiden UL The Netherlands

Oscar Della Pasqua

Academisch Medisch Centrum Universiteit van Amsterdam

AMC The Netherlands

Martin Offringa

Fundacion Vasca de Innovacion e Investigacion Sanitarias

BIOEF Spain Adolfo Valls-i-Soler

Instytut Pomnik Centrum Zdrowia Dziecka

PCZD Poland Marek Migdal

World Health Organization WHO Switzerland Suzanne Hill

University of Hong Kong UHK China Ian Wong

Helsingin Ja Uudenmaan Sairaanhoitopiirin Kuntayhtymä

HUS Finland Kalle Hoppu

Brighton Collaboration Foundation BF Switzerland Jan Bonhoeffer

Fondazione PENTA Italy Silvia Faggion

Dutch Genetic Alliance The Netherlands

Coe Oosterwijk

Hospital for Sick Children – Toronto SickKids Canada Shinya Ito




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2 Abstract

Introduction: The available healthcare databases on infants, children, and adolescents are not

adequately utilized to conduct post-authorization drug utilization and safety studies. The lack of

a federation of healthcare databases restricts the capacity for meaningful investigations in these

vulnerable populations. Moreover, the lack of shared methodologies to specifically retrieve

paediatric information hinders access to valuable information.

Objectives: One of the aims of the Global Research in Paediatric (GRiP) network

(http://www.grip-network.org) is to identify and describe automated population-based

healthcare databases that can provide medication and clinical information for paediatric

pharmacoepidemiological researches on a global scale.

Methods: We performed a web-based survey among all global databases that were identified

through manual revision of the pharmacoepidemiology/pharmacovigilance conference

abstracts, Bridge.to.Data software and/or by databases directly identified by members of GRiP

network. The survey included questions concerning: (i) contact information for database and

responsible person; (ii) nature of database (possible linkage of drugs prescriptions and/or clinical

data with population); (iii) demographic, clinical and drug/vaccine related data provided, (iv)

accessibility of the database for future collaboration in paediatric studies, and (v) validity of the

data.

Results: Ninty-nine databases were identified globally (in Europe, North- and South-America, in

Asian-Pacific area, and Africa) and were invited to participate to the survey. At the time this

deliverable was written, only 16 answers were received, corresponding to a response rate of

15%. In total, 75% of the respondents (N=12) accepted to collaborate with the GRiP network for

future pharmacoepidemiology studies. The collaborating databases are located in 5 different

European countries: Germany, United Kingdom, Denmark, Netherlands, and Italy, except for the

MediGuard database that is available in more than 1 country. The data sources were set up

between 1986 and 2007 providing around 16 million of total cumulative number of paediatric

population (0-18 years). Nine databases capture outpatient records and 3 both, outpatient and

inpatient data from primary care physicians and/or insurance claims. Both medication and

clinical information are described in 11 databases. Patient-level linkage between drug

prescription and clinical data is feasible for all 12 databases.

Conclusions: Those databases that replied to the survey and agreed to participate provide good

potential for paediatric pharmacoepidemiological studies. Thos databases that did not yet reply

will be contacted in the coming months which hopefully results in participation from automated

population-based healthcare databases in North- and South-America, in Asian-Pacific area, and

Africa. Creating an inventory of existing health care databases and their willingness to

http://www.grip-network.org/




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participate in future projects is important as large databases are needed for paediatric

pharmacoepidemiology research in terms of power and long term follow-up.




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3 Receivers of the document

User group

GRIP Beneficiaries

European Commission




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4 Introduction

The main aim of Global Research in Paediatrics – Network of Excellence (GRiP) is to implement an infrastructure matrix to stimulate and facilitate the development and safe use of medicine in children. This implementation entails the development of a comprehensive training programme and integrated use of existing research capacity, whilst reducing the fragmentation and duplication of activities.

Implementation of paediatric studies requires well trained researchers, investigators and other experts in number and capacity that currently do not exist (http://www.grip-network.org/). GRIP will address this problem by developing, as its main objective, a joint paediatric clinical pharmacology training program in collaboration with International stakeholders.

In addition, GRiP promotes sharing of best practices in research, including methodologies and research tools that can be globally used. Central to these efforts are activities that evaluate methodologies and research tools according to GRIP recommendations on the needs of researchers (including industry) and patients. Workpackage 2 aims to develop an integrated electronic infrastructure for epidemiological, pharmacovigilance and post marketing research in pediatrics. Pharmacoepidemiology has many well-established roles in paediatric drug development and monitoring of adverse events yields valuable information on safety of drugs and improves planning of trials. However, available healthcare data on infants, children, and adolescents are not adequately utilized. First, the lack of a federation of healthcare databases is a missed opportunity for meaningful investigations in these vulnerable populations (1). Second, the lack of shared methodologies to specifically retrieve paediatric information hinders access to valuable information. Third, a lack of standardized methods and study designs creates an unnecessary burden for paediatric drug development. Therefore, new approaches and standardized methodologies need to be developed and evaluated. (http://www.ema.europa.eu/docs/en_GB/document_library/Report/2011/05/WC500106554.pdf) Combining data from different databases and countries is crucial in paediatric pharmacoepidemioloy to increase the sample size and the heterogeneity of population setting and to perform long-term follow-up studies (2). The targeted electronic infrastructure should allow for virtually linking existing healthcare databases across the globe to assess the occurrence of diseases in children, plus the use and effects of drugs (including vaccines) on a large scale and in a standardized manner. Methodologies for harmonization, data exchange across national boundaries (including ethical and governance issues), data mining and comparative safety and effectiveness studies will be developed.

http://www.grip-network.org/

http://www.ema.europa.eu/docs/en_GB/document_library/Report/2011/05/WC500106554.pdf

http://www.ema.europa.eu/docs/en_GB/document_library/Report/2011/05/WC500106554.pdf




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5 Objectives of deliverable 2.2

This report describes the approach and the results of the identification and characterization of the existing databases that will be used to develop a global integrated infrastructure. The main aims were to generate:

1. An inventory of existing data sources globally 2. To describe the databases in terms of their possibilities to contribute data for

observational research in children.




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6 Healthcare databases

Computerized health care data has proven to be a valuable resource for

pharmacoepidemiological and health services research and the European Medicines Agency

(EMA) and Food and Drug Administration (FDA) now recommend and recognize the use of

electronic health records when conducting post-authorization drug utilization and safety studies

(1). To conduct proper pharmacoepidemiological studies, we need to have numerators and

denominators, and therefore outcome, exposure and demographic and clinical population data

are essential. The main focus in GRIP is on population-based healthcare databases.

6.1 Definition

Population-based healthcare databases are defined in GRIP as:

Person-level population-based databases that capture:

a) routine care information on drug prescriptions/dispensing on a person level, which can

be linked to the population file by a unique identifier; and/or

b) clinical diagnoses/events/outcomes on a person level, which can be linked to the

population file by a unique identifier.

With population-based databases we mean databases that capture the follow-up period (i.e.,

the start and end date during which data on drugs and/ or outcomes are available) on an

individual person level, independent of health status (i.e. even if healthy). Population-based

does not necessarily mean on a national level. Regional databases (e.g., if care is organized

regionally), or databases capturing GP populations (i.e., if patients need to be registered with GP

independent of being sick and GP is gatekeeper), or claims/insurance databases are regarded as

population-based databases.

Hospital medical records alone (such as neonatal intensive care unit, NICU, data) are not

considered population-based since we do not have an underlying registered catchment

population (i.e., a record of all the persons not referred to a hospital and considering the time of

referred persons prior and after hospitalization).

Immunization /drug use /disease registries alone are not considered population based

databases if they do not capture the population not being exposed /vaccinated /diseased. If

vaccination registries capture defined populations from birth to a specific date comprising all

vaccinated and unvaccinated subjects in that population, this is considered population based.




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7 Methods

7.1 Procedure for identification of healthcare databases

The procedure employed for the identification of the global population-based automated

healthcare databases is outlined in Figure 1. Three different methods were combined to

complete the total list of database contacts which were invited to participate in the on-line

survey.

a) Retrieving data from published ICPE abstracts

A systematic review of published abstracts presented at the 25th and 26th International

Conferences on Pharmacoepidemiology and Therapeutic Risk Management (ICPE) during the

years 2009-2010 was performed. At the same time, the ICPE abstract books of the Asian

meetings (ACPE) abstracts were reviewed. All doubly identified databases were excluded and

the following information was retrieved:

(i) abstract number

(ii) conference year

(iii) country

(iv) name of automated healthcare database.

Subsequently, by consulting of the corresponding websites, further data on contact details,

start-years and type of database (e.g., claims, GPs, pharmacy database, etcetera), and covering

age range were collected, whenever available. A final list namely “Abstract database contacts”

included 169 database contacts from all continents.

b) Procedure for identification of the immunization databases

The contact list for the immunization databases was compiled by the Brighton Collaboration

Foundation according to the following approach:

Step 1: The Brighton Collaboration member list was screened for potential contacts in each

country with emphasis on contacts affiliated with public health authorities

Step 2: In countries where no contacts with public health background were available,

professionals from regulatory authorities or academia or clinical care agencies were

approached for recommendation of suitable contacts in their countries.

Step3: Professionals referred to us based on Step 1 and 2 correspondence were contacted.




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Step 4: Other networks or activities such as the International Paediatric Association, INDEPTH,

the Global H1N1 vaccine safety case series were utilized to identify additional contacts.

The assistance from WHO and other international organizations is still pending.

c) Retrieving form Bridge to Data and meetings/conferences

“B.R.I.D.G.E. to data” is a non-profit organization that provides online reference to different

population-based health-care databases worldwide that can be used in epidemiologic and

health outcomes research (http://www.bridgetodata.org). Access is provided upon paying a

license fee. EMC bought an academic license and agreed with the organization that data may be

utilized for GRIP. The centralized B.R.I.D.G.E. to_data compendium contains over 170

standardized database profiles (with 75 defined data fields) representing 24 countries. It is

structured in such a way that there can be efficient side-by-side analysis of databases as well as

providing extensive database details (with the permission of the database managers). It is being

continuously updated. The types of database that “B.R.I.D.G.E. to_data” contains includes

longitudinal electronic medical records (EMR), claims databases, drug or disease specific

cohorts, registries, national surveys, national surveillance systems and spontaneous reporting

systems. For the purpose of this task however, only longitudinal electronic medical records have

been considered.

In identifying relevant databases, the strategy was to search for the presence of such databases

(longitudinal population-based) for each country following the alphabetical order. The following

steps were undertaken:

Access to the website was requested and granted.

The database “search” page was accessed.

The following information was entered into the relevant fields on the search page or

selected from the available options: country where the database is located and

database type (in this case longitudinal population database [same for every search

conducted]). It was also specified that only databases containing information on age of

the patient were needed.

The following fields were not utilized (no preferences were specified) in conducting the

searches: keyword; database source; “specific period of entry into a database”;

population type (example being general population, inpatient etc); active population

size; gender data; ethnicity/race data; death record; diagnosis data; birth defect data;

cancer data; procedure data; laboratory information; drug data; cost data; validation

against original source; access to original medical records.

http://www.bridgetodata.org/




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Lastly, results were returned based on the entered search criteria.

The results obtained from “B.R.I.D.G.E. to data” were finally compared with (and used in

updating) the information that was already available for each country in the list of databases

being compiled.

Matching information and creation of inventory

The list “Abstract database contacts” including 169 database contacts was matched to the

inventory retrieved by “B.R.I.D.G.E. to data”, including 74 contacts. After matching, the contact

list was updated to 214 contacts. In parallel, some members of the GRiP network established

direct contacts with the database owners met at the conferences concerning “Vaccine and drug

safety in paediatrics” of ECDC and a meeting at the Public Health Agency Canada . A parallel

inventory was set up including 28 database contacts. This latter inventory and the updated list

were matched to provide the final database contact list to be invited to participate to the survey

(Figure 1).




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Figure 1: Flowchart on the procedure for selection of the database contacts from different

sources.




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7.2 Creation of the survey

In order to conduct the survey, a questionnaire investigating the characteristics of the identified

databases was developed. Given the objectives of this project, the items included in the

questionnaire concerned the nature of the databases, the type of data collected and the

possibility for the database to contribute data to future GRIP studies.

Two previously tested questionnaires, used in surveys describing existing databases in the

European context, were used as reference. Numerous Items that had proven valid in the

previous surveys were adopted in the new questionnaire; others items were modified or

specifically developed to fulfil the needs of the GRIPsurvey.

The two questionnaires that served as guides were:

1. The questionnaire developed by the European Network of Centres for Pharmacoepidemiology

and Pharmacovigilance (ENCePP) to collect information on databases with pharmacological data

in EU (www.encepp.eu)

2. The questionnaire used by the Task-force in Europe for Drug Development for the Young

(TEDDY) for a survey on databases for paediatric medicine research (3).

These questionnaires were reviewed and each of their items compared. In this way we wanted

both to adopt question-formats that have been already tested and to avoid omitting items that

have been proven informative.

The survey from the TEDDY project, which was specifically designed to describe pediatric

databases, provided the guide for most of the pediatric specific questions.

Other original issues were developed uniquely for the GRIP questionnaire, taking into account

the pediatric focus of future studies and the nature of databases surveyed (longitudinal

healthcare databases). For example specific questions needed to address the estimation of

pediatric catchment population, or pediatric pharmacological issues (as dosing per weight). In

addition, dealing with longitudinal healthcare databases, specifications on population currently

on follow-up (defined as “active population”) was needed beside the total registered

population.

Furthermore a complete section on information available on vaccinations was specifically

developed for this survey by the GRIP partners at the Brighton Foundation.

http://www.encepp.eu/




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The final questionnaire comprised of 14 main sections for a total of 55 questions (see Appendix

1). The issues included were as follows:

Contact information for database and responsible person (name and address)

Information on nature of database (possible linkage of drugs prescriptions and/or

clinical data with population)

Years, population and geographic areas covered by database

Information on data collected: type of demographic and clinical data (including data on

referrals), type of data on drugs and vaccines

Possibility of collaboration in future studies: regulations to access the data stored,

additional information that could be collected if needed, intent on future collaborations

Previous publications on data collected (with focus on paediatric)

All questions were developed or reshaped to minimize possible misinterpretation. Complex

questions were broken down in several simple questions and whenever possible multiple-choice

answers were given. Space for open-ended comments was left in the end of the questionnaire.

Contacts of GRIP partners available to clarifications were given both in the questionnaire and in

the cover accompanying it.

A user’s guide including instructions on most questions was developed together with the

questionnaire (see Appendix 2) to be delivered with it prior to the survey.

Each survey was emailed accompanied by a cover letter explaining rational and purposes of the

GRiP project and highlighting the importance to fill the questionnaire and to collaborate to the

network (see Appendix 3).

IRIDIA made an online version of the survey and sent out the invitations with a private key to be

used (see screen shot below)




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8 Results

8.1 Databases invited to participate to the survey

A total of 238 automated population-based healthcare databases were identified through

manual revision of the ICPE/ACPE abstracts, Bridge.to.Data software and by personal contact of

the members of GRiP network (Figure 2). By continent, we collected 90 databases from

European countries, of which 37 were exclusively extracted by abstract conference revision, 22

from “B.R.I.D.G.E. to data”, 17 were matched between B.R.I.D.G.E. to data and the “Abstract

database contacts” and 14 were retrieved through networking at the meetings. Similarly, 74

databases were identified from northern America countries: 39 came exclusively from the

abstract conference revision, 17 from “B.R.I.D.G.E. to data”, 11 were matched between these

two inventories and 7 through networking at the meetings. Among Asian-Pacific countries we

identified 46 databases: 36 contacts exclusively from abstract review, 6 exclusively from

“B.R.I.D.G.E. to data”, 1 matched, and 3 through networking at the meetings. Twenty-two

databases from African countries and 6 from southern American countries were retrieved only

by abstract revision; however, we failed to found enough contact information.




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Figure 2: Global distribution of the database contacts extracted by different sources and

manually screened.

ICPE / ACPE abstracts

N=169

B.r.i.d.g.e. to data N=74

Personal contacts N=28

Overlap N=29 Overlap N=2




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After screening of the email address details and exclusion of all duplicates (n=3) and the records

with not sufficient details to be contacted (n=112), 125 databases were identified globally and

99 out of them were invited to participate to the on-line survey (see appendix 4a). A reminder

was sent up to non-responders, and repeated when no reply.

The remaining 26 databases, contacted through direct networking at meetings/conferences,

were personally invited by the leader members of the GRiP network (MS, JB, IW, HN) to fill the

questionnaires, either on-line or not (see appendix 4b). The process will be further followed by

each WP2 members who will contact the people by phone number.

8.2 Response rate of survey

To date, 99 surveys have been sent out and were received 16 users’ answers, corresponding to

the 16% of response rate. In total, 75% of the respondents (N=12) accepted to collaborate to the

GRiP network for future pharmacoepidemiology studies. Only two users did not approve or

disagreed; one of them expressing concerns about the clarity of the information provided on the

involvement in the project. Two other users only answered partially to the survey.

8.3 Assessment of the survey

Only the data sources of which the responders fully agreed to collaborate to the GRiP network

were included in the final analysis (N=12). Overall, these databases were set up between 1986

and 2007 and are located in 5 different countries, Germany, United Kingdom (UK), Denmark,

Netherlands (NL), Italy, except for the MediGuard database that is available in the following

countries United States (US), UK, France, Germany, Spain, Australia, Brazil. The databases are

listed as following:

1. German Pharmacoepidemiological Research Database (GePaRD), Germany;

2. The Health Improvement Network database (THIN), UK;

3. Clinical Practice Research Database (CPRD), UK;

4. PEDIANET, Italy;

5. (ASL) Cremona, Italy;

6. (ARS) Toscana, Italy;

7. Information system policies for health and social policies (ARS) Emilia Romagna, Italy;

8. InterAction Database (IADB), The Netherlands;

9. Integrated Primary Care Information database (IPCI), The Netherlands;




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10. Agis Health Database, The Netherlands;

11. Aarhus University Research Database, Denmark;

12. MediGuard.org (several countries: US, UK, France, Germany, Spain, and Australia).

A geographical map illustrates the global distribution of the databases involved in the GriP

network and assessd (Figure 3).

Among 12 databases, 8 provided the total cumulative number of paediatric patients, accounting

for around a population of 15 million of 0-18 years old. Overall, the included databases are

primary care (general practitioner, GP or family paediatricians, FP) and/or insurance claims

databases. Concerning drug information, nine databases capture outpatient records and 3 both,

outpatient and inpatient whilst clinical data are described in 11 databases. Patient-level linkage

between drug exposure and clinical outcome is feasible for all 12 databases.

Based on the survey information and the literature of study based on their data, databases were

categorized with respect to their potential suitability for use in paediatric drug utilization and

drug safety studies. Information collected has been categorized as demographics, drug

exposure, clinical outcomes and data access.


The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n°

261060

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Figure 3: Distribution of databases included in the GriP network.




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8.4 Nature and characteristics of the databases

Six databases included in this survey were set up between 2000 and 2007 (N=6), six between

1986 and 1998. A detailed overview of the databases included in the survey is described in

Tables 1a and 1b.

Six databases from 4 countries (THIN, CPRD, PEDIANET, Emilia-Romagna, IADB.nl, IPCI) are

longitudinal, population-based databases using electronic medical record data from general

practitioners (GPs) and family pediatricians (FPs). PEDIANET comprises also claims data when

collected by the FPs. These databases were developed in countries where physicians and/or

paediatircians (in Italy) are gatekeepers for medical care and information. All of these electronic

medical record databases contain anonymous data on patient demographics, reasons for visits,

diagnoses from GPs/FPs and specialists, hospitalizations, drug prescriptions, laboratory and

other diagnostic findings for the paediatric population.

Five databases (GePaRD, ASL Cremona, ARS Toscana, Agis Health Database, Aarhus) are drug

dispensing claims databases processing all prescriptions that need to be reimbursed. However, a

patient-level linkage between drug exposure and clinical outcome and patient population file is

feasible for all of them. GePaRD provides demographic data as well as information on hospital

admissions, outpatient physician visits and outpatient prescriptions from Statutory Health

Insurances (SHI).

MediGuard is not a GP neither claim database but is a free medication monitoring service

designed specifically for patients by professionals with decades of experience in healthcare

market research, clinical drug development, and drug safety (www.mediguard.org). No more

info were found concerning the collection of data.

8.4.1 Drug exposure

All databases that participated in the survey collect information on prescription-drugs and the

units dispensed or prescribed, the formulation, and most of them also record the dosage

regimen, which is particularly important for the paediatric population. All drugs are coded

according to the Anatomical Therapeutic Chemical classification system in the majority of the

databases. Some of them use also other drug codes as z-index (IADB.nl and IPCI), DPICS (Agis),

AIC (PEDIANET), Multilex coding system (CPRD). The drug code system used in THIN database is

the British National Formulary (BNF). The indication of use is recorded only in CPRD, PEDIANET

and IPCI, using Read code, ICD-9th CM code, plus free text, and ICPC-code, respectively. In

GePaRD, prescription data contain the prescribed drugs characterized by the central

pharmaceutical number (PZN), the dates of prescription and dispensation, and information on

http://www.mediguard.org/




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the prescribing physician. They are available for all outpatient prescriptions that are reimbursed

by the SHIs. Prescription data are linked to a pharmaceutical reference database that adds

information on the defined daily dose (DDD), the ATC code, strength, packaging size, and the

generic and brand names.

8.4.2 Vaccine exposure

Immunization data are captured comprised in six databases (GePaRD, THIN, CPRD, Aarhus, ASL

Cremona, IADB.nl), they all include vaccine code and date of vaccination for routine paediatric

immunization; three databases (CPRD, ASL Cremona, IADB,nl) include also and name and four

(GeParD, THIN, Aarhus, and ASL Cremona) also data on elective childhood immunization. When

recorded by GPs, the IPCI database provides data on vaccine (e.g. influenza); childhood

vaccinations are available through linkage with a national registry from RIVM (pilot phase).

8.4.3 Clinical outcome

Past and current medical diagnoses are recorded using READ codes (a thesaurus of coded

medical terms maintained and distributed by the United Kingdom Terminology Center) in THIN,

Agis Database, MediGuard.org, according to the German modification of the International

Classification of Diseases (ICD-10 GM) in GePard, Aarhus, IADB.nl, and both code systems in

CPRD. Symptoms and medical diagnoses are either registered as free text or coded using ICPC

(International Classification of Primary Care) in IPCI and ICD-9-CM (International Classification of

Disease, 9th revision, Clinical Modification) in Pedianet. All the remained Italian databases

adopted the ICD-9th CM classification. Hospital data are reported in the majority of the

databases and include the dates of admission and discharge with their corresponding diagnoses,

and information on in-hospital diagnoses and procedures. Claims regarding outpatient physician

visits contain diagnoses, ambulatory diagnostic procedures and non-drug treatments.

8.4.4 Accessibility and costs of databases

All of the twelve databases allow access to data access for paediatric researches. The majority of

the providers require a written policy governing and a committee evaluation. Six of the

databases may be accessed free of charge, although most of them provide special conditions if

data are used for academic research or purposes.




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9 Discussion and Limitations

Combining and sharing data from different databases and countries is important to increase

sample sizes and to perform long-term studies in paediatrics. To date, combining the population

of the databases that participated in this survey results in a paediatric population of around 15

million providing a good potential for paediatric pharmacoepidemiological studies. Creating an

inventory of existing health care databases and their willingness to participate in future projects

is important as large databases are needed for paediatric pharmacoepidemiology research in

terms of power and long term follow-up.

Previous projects such as EU-ADR, SOS and others have shown that data from different

databases can be combined to conduct international observational studies (4). The majority of

health care databases are created not primarily to conduct research but are simply a collection

of electronic patient’s records accessible for the health care staff to monitor patient’s care. The

organisation of health care is country specific which in part explains the heterogeneity among

the databases in terms of disease and drug coding. The development of automatic tools such as

disease and drug mapping will further facilitate the combination of data from different health

care databases according to a common study protocol.

From the survey, we learned that the databases collect information on age, drug dosing,

mother-child linkage, immunisation status etc. Other important information such as height and

weight are however, not collected systematically. Although we appreciate that the health care

databases do not have research as primary aim, it would be an asset if databases would start to

collect crucial information that has been proven important for pediatric research.

So far, the observed response rate and collaborative opportunities are developed only in

Europe. No databases from North and South-America, Asian-Pacific Countries and Africa,

responded to the survey. The majority of non responders from Asian-Pacific countries is mainly

due to the scarce knowledge about the GRIP project and the reluctance to share data . Even,

ethical issue and governance requirement may be a concern related to data extraction from

national databases. Instead, in Europe a precedent project provided to build from national to

international efforts through common structures. Overall, the low response rate could also be

attributed to the fact that the survey specifically addressed the availability of pediatric data and

thus databases that do not collect info on the pediatric pppulation are more likely not to

respond.

The absence of databases from other regions is due to the diversity of healthcare systems

and our common challenge is to identify the methods and technical requirements to facilitate

bridging the different structures. In the coming weeks, we will continue contacting the non-




26

responding databases which will enrich our inventory of pediatric databases. In the end, we

hope to create an up to data inventory of all existing pediatric databases which should allow

conduction worldwide pediatric observational research.

10 Conclusions / Outlook and next steps

As next steps, those database contacts that did not yet reply will be contacted in the coming months which hopefully results in participation from automated population-based healthcare databases in North- and South-America, in Asian-Pacific area, and Africa. In order to explain in details the project and to motivate the people to respond to the survey, those databases for Asian-Pacific countries will be personally contacted. There is also the possibility collecting feedback through email and other means in parallel with the web-based survey. However, after a second reminder, the no-responders will be followed by direct calling.

In parallel, the list of 26 personal contacts, i.e., the contact persons directly informed about the project GRiP during the conferences/meetings, will be contacted by personal emails.

The anonymized data from the databases eligible to participate to the GRiP network will be combined. The analyses on the anonymized data sets that are outputted by Jerboa© (5) should be performed in a distributed fashion by using one Remote Research Environment (RRE) which will be located at EMC. The RRE allows for loading, retrieving, extracting, and transforming of the data by different institutions/partners. The report D 2.01 describes in detail the security measures taken for the Remote Research Environment (RRE) to ensure the high level of stored data protection as described in article 34 of the legislative decree 196/2003 and Directive 95/46/EC for processing of healthcare data.




27

11 References

1. Blake KV, Devries CS, Arlett P, Kurz X, Fitt H. Increasing scientific standards, independence and transparency in post-authorisation studies: the role of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance. Pharmacoepidemiol Drug Saf. 2012 Apr 23. 2. Black N, Barker M, Payne M. Cross sectional survey of multicentre clinical databases in the United Kingdom. BMJ. 2004 Jun 19;328(7454):1478. 3. Neubert A, Sturkenboom MC, Murray ML, Verhamme KM, Nicolosi A, Giaquinto C, et al. Databases for pediatric medicine research in Europe--assessment and critical appraisal. Pharmacoepidemiol Drug Saf. 2008 Dec;17(12):1155-67. 4. Trifiro G, Patadia V, Schuemie MJ, Coloma PM, Gini R, Herings R, et al. EU-ADR healthcare database network vs. spontaneous reporting system database: preliminary comparison of signal detection. Stud Health Technol Inform. 2011;166:25-30. 5. Coloma PM, Schuemie MJ, Trifiro G, Gini R, Herings R, Hippisley-Cox J, et al. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf. 2011 Jan;20(1):1-11.



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Table 1a: Demographic and clinical characteristics of the eligible databases from Germany, UK, Denmark and several countries Germany Denmark Several countries

GePaRD THINCPRD, Clinical Practice

Research Datalink

Aarhus University

Research DatabaseMediGuard.org

Start date 2004 1988 1986 1998 2007

updated yearly 3 months Continuously Continuously Continuously

Cumulative number (0-18 years) > 14 million overall > 1.8 million 12.5 million overall 18 millions overall < 5000

Demographics* Yes Yes+ height & weight Yes & weight Yes Yes

Mother-child linkage Yes Yes Yes Yes No

Drug prescriptions^ (exposure) Outpatients In- and outpatients In- and outpatients In- and outpatients In- and outpatients

Type of data source claim GP GP claim n.a.

Code ATC codeThe Multilex Coding

System - BNF

The Multilex Coding

System - ATC code ATC code WHO Drug Database

Indication No Read code -lab data Read code No No

Units (No.) Yes Yes Yes Yes No

Prescribed dosage frequency No Yes Yes No No

Prescribed duration of treatment No Yes Yes No No

Formulation Yes Yes Yes Yes No

Strength Yes Yes Yes N/A No

Route Yes Yes Yes Yes No

Clinical outcome (disease) In- and outpatients In- and outpatients In- and outpatients In- and outpatients In- and outpatients

Diagnosis for accessing to db ICD-10 Read code -lab data ICD-10 - lab data ICD-10 - lab data free text

Immunization data$ Yes (brand name n.a.) Yes (brand name n.a.)Yes (elective

immunization n.a.)Yes (brand name n.a ) No

Referral to specialist Yes Yes Yes Yes n.a.

Results on referral visits Yes Yes Yes No n.a.

Emergency room admission Yes Yes Yes Yes n.a.

Results of ER admission Yes Yes Yes Yes n.a.

Hospital admission Yes Yes Yes Yes n.a.

Hospital discharge diagnosis ICD-10 Read code Read code ICD-10 n.a.

Additional information Nophysician, patients,

genetic or samples

physician, patients,

genetic or samples

patients, genetic or

samplesphysician, patients

Data access

written policy governing n.a. Yes Yes Yes Yes

evaluation committe Yes Yes Yes Yes No

charge request Yes Yes Yes Yes Yes

UK



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Table 1b: Demographic and clinical characteristics of the eligible databases from Italy and Netherlands.

ASL Cremona PEDIANET Emilia-Romagna ARS Toscana IADB.nlAgis Health

DatabaseIPCI

Start date 2001 2000 2002 2003 16/06/1905 2001 1992

updated monthly monthly continuously yearly bi- or yearly continuously yearly

Cumulative number (0-18 years) 97400 180000 500000 about 930,000 64645 1.3 overall 300,000

Demographics Yes Yes + height & weight Yes Yes Yes Yes Yes + height & weight

Mother-child linkage Yes Yes Yes No Yes Yes Yes (probabilistic)

Drug prescriptions^ (exposure) Outpatients Outpatients Outpatients Outpatients Outpatients Outpatients Outpatients

Type of data source claim claim - GP GP claim GP claim GP

Code ATC code - AICATC code - The National Drug Code (NDC) System - MINSANATC code ATC code ATC code - z-index ATC code - DPICS ATC code - z-index

Indication No ICD-9 - free text No No No No ICPC-code

Units (No.) No Yes Yes Yes Yes Yes Yes

Prescribed dosage frequency No Yes No No Yes Yes Yes

Prescribed duration of treatment No Yes No No Yes Yes Yes

Formulation Yes Yes Yes Yes Yes Yes No

Strength No Yes Yes Yes Yes Yes Yes

Route Yes Yes Yes Yes Yes Yes Yes

Clinical outcome (disease) Inpatients Outpatients Inpatients Inpatients naIn- and

outpatientsIn- and outpatients

DiagNosis for accessing to db ICD-9 ICD-9 code - free text -

lab dataICD-9 ICD-9 code No DBC in hospital

ICPC-code - free text -

lab data

Immunization data* Yes No No No

Yes (routine and

elective

immunization n.a.)

No Yes (linkage)

Referral to specialist No Yes Yes Yes No Yes Yes

Results on referral visits No Yes Yes No No Yes Yes

Emergency room admission Yes Yes Yes No No Yes Yes

Results of ER admission No Yes Yes No No Yes Yes

Hospital admission Yes Yes Yes Yes No Yes Yes

Hospital discharge diagNosis ICD-9 free text ICD-9 ICD-9 No DBC ICPC-code - free text

Additional information physicianphysician, genetic or

samplesphysician

patients, genetic or

samplespatients physician physician, patients

Data access

written policy governing Yes Yes Yes Yes Yes Yes Yes

evaluation committe Yes Yes Yes Yes Yes Yes Yes

charge request No No Yes No n.a. Yes No

Italy Netherlands



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Legend table 1a and 1b: *demographics include age and gender ^in all databases the drugs are indicated by name $immunization data include vaccine code and brand name, date, routine paediatric and elective childhood immunization




31

12 APPENDIX

Appendix 1: Survey on electronic healthcare databases

GRIP Survey on electronic health care databases

01 - Main Info

S001: Name of the database

S002: Database URL

S003: Contact persons

Administrative Contact person

Title

Name

Address

City, Postcode, Country, Phone number (incl. country code), Alternative phone number, Fax num.

Email address for administrative contact

Scientific Contact person

Title

Name

Address

City, Postcode, Country, Phone number (incl. country code), Alternative phone number, Fax num.

Email address for scientific contact

S004: Brief Description

02 – Nature of the database

S005: Does the database capture drug prescriptions? Yes No

If yes: does it capture drug prescriptions for Outpatients? Yes No

- through Insurance claims Yes No

- through Medical records Yes No

Does the database capture drug prescriptions for Inpatients? Yes No




32

- through Insurance claims Yes No

- through Medical records Yes No

S006: Does the database capture clinical data? Yes No

If yes: Outpatient clinical data Yes No

Inpatient clinical data Yes No

S007: Linkage with population data

Is patient-based linkage of clinical data to follow-up time (population file) possible? Yes No

If Yes:

- Probabilistic linkage Yes No

- Deterministic linkage (with unique identifier) Yes No

S008: Is patient-based linkage between drug prescriptions and clinical data possible? Yes No

If Yes

- Probabilistic linkage Yes No

- Deterministic linkage (with unique identifier) Yes No

03 - General Characteristics

S009: Start date of data collection

S010: Is the database updated:

Continuously Yes No

At intervals Yes No

If Yes, please specify the interval..............................................

S011: Total Cumulative number of registered subjects, including adults

S012: Total Cumulative number of registered children (0-18 years of age)

S013: Number of active (registered) children (0-18 years of age) in 2010

04 - Geographical Coverage

S014: Are the patients in the database representative for national population? (according to age and gender distribution) Yes No

S015: Names of covered regions or provinces

05 - Collected Data – Demographics

S016: Exact date of birth available as




33

⃝ Date, Month, Year

⃝ Month, Year

⃝ Year

⃝ None

S017: Gender Yes No

S018: Height Yes No

S019: Weight Yes No

S020: Mother-child linkage Yes No

06 - Collected Data - Clinical Data

S021: Reason for accessing care Yes No

S022: Diagnosis Yes No

If Yes, how is diagnosis collected?:

⃝ as text

⃝ as code:

⃝ ICD-10

⃝ Read code

⃝ ICPC code

⃝ ICD-9 code

⃝ Others (please specify).......................................................

S023: Measurements (laboratory/diagnostics) Yes No

07 - Collected Data – Drugs

S024: Name of drugs prescribed Yes No

S025: Identification code for each drug Yes No

If Yes, which codes are used?:

⃝ The Anatomical Therapeutic Chemical (ATC) Classification System

⃝ The Drug Products Information Coding System (DPICS)

⃝ The Multilex Coding System

⃝ The National Drug Code (NDC) System

⃝ Others (please specify)..............................................................

S026: Indication for prescription Yes No




34

If Yes, how is indication collected?:

⃝ as text

⃝ as code:

⃝ ICD-10

⃝ Read code

⃝ ICPC code

⃝ ICD-9 code

S027: Total number of prescribed units (tablets/ml, suppositories etc) for each drug Yes No

S028: Prescribed dosage frequency for each drug Yes No

S029: Prescribed duration of treatment for each drug Yes No

S030: Drug Formulation Yes No

S031: Drug Strength (of each unit) Yes No

S032: Route of administration Yes No

08 - Collected Data – Vaccines

S033: immunizations Yes No

If Yes:

S034: Routine paediatric immunization Yes No

if Yes: select from the list (all possible):

BCG Cholera Diphteria Haemophilus influenzae Hepatitis A Hepatitis B HPV Influenza Japanese encephalitis Measles Meningococci Mumps Pertussis Pneumococci Poliomyelitis Rabies Rotavirus Rubella Tetanus Tick born encephalitis Typhoid Varicella




35

Yellow fever

S035: Additional (elective) childhood immunisation Yes No

if Yes: select from the list: (as above)

S036: Date of vaccination Yes No

S037: Brand name of vaccination Yes No

09 - Collected Data – Referrals

S038: Referral to specialist Yes No

S039: Results of referral visits Yes No

S040: Emergency room admission Yes No

S041: Results of emergency room admission Yes No

S042: Hospital admission Yes No

S043: Hospital discharge diagnosis Yes No

If Yes:

S044: How is the diagnosis collected:

⃝ as text

⃝ as code:

⃝ ICD-10

⃝ Read code

⃝ ICPC code

⃝ ICD-9 code

⃝ Others (please specify).......................................................

10 – Would it in principle be possible to obtain the following additional information on the patient?

S045: Clinical information from treating physician? Yes No

S046: Data from questionnaires completed by the patient? Yes No

S047: Genetic information or samples? Yes No

11 – Data access

S048: Is there a written policy governing data access? Yes No

S049: Do you have a committee (governance/ethics) to evaluate requests for data access? Yes No

S050: Is a charge made for data access Yes No




36

S051: Are you allowed to provide data / do industry sponsored studies Yes No

S052: Would you allow for auditing of the data/studies by external parties?

⃝ yes to regulators

⃝ yes to companies for whom studies are done

⃝ No

12 - Please list the 5 most relevant publications using your data for the last five calendar years (please focus on paediatrics). If there is a publication explicitly reporting on assessment of data quality, please include first

13- Comments (please add comments or questions on this survey, or additional information on your database)

14-Survey completed by: …………………………

on: DD/MM/YY




37

Appendix 2: Guide to fill the survey

Guide for survey completion

Please find below some information to complete the survey. This survey includes 14 sections

and takes approximately 5-10 minutes to fill. If you have additional questions, please contact

Osemeke Osokogu ([email protected]) from the Erasmus medical Center, Rotterdam.

Thank you for your participation.

Section 01: Contact information

Section 01 collects general information on the database and contact information for future

correspondence with the database managers.

-S001: Current database name (full name and acronym, if applicable)

-S002: Database URL: current web address of the database

-S003: Administrative contact: person in charge of administration of database; Scientific

contact: scientific advisor responsible for the database

If for your database administrative and scientific contacts are the same person, please

fill only Administrative contact and write SAME in Scientific contact.

-S004: Brief Description: Please give a brief description (1-3 lines) of the main purpose of

the database and type of data collected. For example “Database created to keep the

records of all prescriptions given by GPs in the public health system. Collects data on

drugs prescriptions and demographic data on adults and children”

Section 02: Nature of database

Section 2 collects information on the nature of data collected in the database and on the

structure of the database.

- S005: Select Yes if you collect data on ‘complete’ drug prescriptions/dispensing for the

patients registered in the database. Please specify if you collect data on Outpatients (GP

or ambulatory specialist visits) and/or Inpatients (prescriptions during

hospitalizations/stay in nursing homes) - check both if applicable. Please specify as well

mailto:o.osokogu@




38

if data are collected through Insurance claims (payers national/private) or Medical

records (from prescribers) –check both if applicable.

- S006: Select Yes if data in your database comprise clinical data (diagnoses, reasons for

visits, laboratory assessments, hospitalizations etc.). Please then specify if data capture

clinical data for Outpatients (GP and/or ambulatory specialist visits) or Inpatients

(hospitalizations) - check both if applicable.

- S007, Linkage with population: Please select Yes if it is possible to link data on patients

in your database with data in a population file. Please specify if this linkage is

Deterministic: this means that can you link directly based on a unique identifier that is

the same in all files; or Probabilistic if you link by matching a series of variables between

files (e.g. date of birth, physician, sex, initials) since a unique identifier is not available.

- S008: Select Yes if it is possible in your database to link the information on drug

prescriptions and on clinical data for the same patient. Specify whether this linkage is

done in a deterministic or probabilistic manner (see above)

Section 03: general characteristics of the database

- S009: please write which has been the date of the first data entered in the database

(DD/MM/YYYY or MM/YYYY or YYYY).

- S010: check Continuously if database is updated automatically at each new data entry;

check At intervals if update is done periodically (e.g. every 3 months, or annually) and

then specify the approximate average interval between updates (for example 1 month,

3 months, 1 year, etc).

- S011: total number of persons (of any age) for whom you have data in the database

over all years.

- S012: total number of subjects <19 years old for whom you have data in the database

over all years.

- S013: total number of subjects <19 years old for whom you have data in the database

and who are active (registered and not departed) in the database (and in the age range

0-18) at January 1, 2010

Section 04: Geographical coverage of your database.




39

- S014: Please select Yes if population in your database has the same age AND gender

distribution than population of your Country.

-S015: List the provinces or regions for which there are data entered for at least one

subject in the database. Please specify if you are listing Provinces or Regions.

Section 05: Demographic data collected

- S016: if date of birth is recorded in your database, please specify in which format (e.g.

some databases only have month/year for privacy issues whereas others may exact

dates)

- S020: check Yes if children can be linked to their biological mother in the database.

Section 06: Clinical data collected

- S021: check Yes if in the database the reason is recorded of why the person is

accessing care (for either inpatients or outpatients).

- S022: check Yes if in the database data are recorded on diagnoses, please specify in

which format checking all the options given that are applicable. If the format you use is

not in the list, please specify it.

- S023: Check Yes if in the database data are recorded on clinical or laboratory

measurements (e.g. blood pressure, temperature, blood test results, urine culture, etc.).

Section 07: Data collected on drugs

In this section we aim to collect information on the detail of information that is available in the

database on drug prescriptions. Please answer to questions S024-S032 considering the

availability of original records on drugs in the database, not what could be found as result of

analysis /algorithms.




40

-S024: check Yes if the database captures the names of the drugs prescribed,

either as commercial name (e.g. Batroban) or generic name (e.g. mupirocine).

-S025: Please specify if the database records a unique product code (either

based on commercial product or active compound) for each drug and which one

(choose all what is applicable).

-S026: check Yes if the database captures the reason for prescribing the drug, as

stated by the physician, and specify in which format the reason is coded.

-S027: check Yes if database captures the total amount of units prescribed by

the physician. For example a bottle of syrup: the total amount of ml in the

bottle, for tablets the total number of tablets, for parenteral drugs: the number

of vials.

- S028: check Yes if database captures information on the frequency of dosing

e.g. twice a day, once a day, three times/week, etc.

- S029: check Yes if database captures information on the prescribed duration

of treatment (number of days).

- S030: check Yes if database captures information on the drug formulation for

each drug prescribed (e.g. syrup, tablets, capsules, suppositories)

-S031: check Yes if database captures information on the drug strength or

concentration for drug prescribed (e.g. paracetamol: 1000mg tablets or 500mg

tablets or injectable 5mg/ml).

- S032 Check Yes if database captures information on the route of

administration or the drug (intra venous, per os, etc).

Section 08: Vaccines

-S033: check Yes if database captures data on immunizations

-S034: check Yes if database captures data on national programs of routine

pediatric immunizations (e.g. DTP); and if yes, specify which from the list (check

all applicable).




41

-S035: check Yes if database captures data on specific additional (non-routine

but special) immunizations for the paediatric population; and if yes, specify

which from the list (check all applicable).

Section 09: Data on referrals

-S038-S043: If in your database captures data on referrals to specialists,

emergency room or for Hospital admission: check Yes for any that applies. For

each referral collected, specify if the results of the referral are collected.

-S044: If your database captures the discharge diagnosis from hospitals, please

specify in which format.

Section 10: Possibilities to obtain further data

In this section we aim to explore possibilities for future studies which would involve additional

data on the patients whose other data are already in your database.

Please check Yes for any of S045, S046, S047, if you think it would in principle be feasible to

arrange specific studies to collect this additional information.

Section 11 Regulations and charges for data access.

In this section we aim to collected data on ethical /governance procedures that may be in place

to govern which type of projects are being done.

In addition we would like to ask you whether you could do studies paid by industry and whether

data/analyses may be audited (from governance principles)

Section 12: Publications.

If there are more than 5 publications with data from your database in the last 5 years, please

choose the 5 most relevant, giving preference to paediatric studies. If there are no publications

yet with data from your database, please write None.

Thank you for completing the survey.




42

Appendix 3: Cover letter

Dear colleague,

we would like to ask you to help us by completing a short survey on automated healthcare databases that you may hold or be aware of in your country. We specifically aim for databases that could be useful for the conduct of post marketing studies on the use and effects (positive and adverse) of drugs (including vaccines) in children.

This request comes from The Global Research in Paediatrics (GRIP) Network of Excellence. GRIP is a project funded by the European Commission (FP7). It aims to implement a global infrastructure to stimulate and facilitate the development and safe use of medicines in children. The GRIP Network of Excellence is a consortium comprising 21 participant organizations across the world, including WHO, NIH, European Medicines Agency etc. It is coordinated by Prof. Dr. C Giaquinto from Padova Italy.

Why do we ask your help now?

We are all aware of the fact that the evidence about the effects of drugs in children is sparse since few prelicensure studies are conducted in children. This has led to regulatory changes, which may improve the situation for new drugs in the long term. In GRIP we are convinced however, that we have the ability to collect evidence on the use and effects of drugs in children much faster and on a wider scale if we would use existing healthcare data. Millions of children are treated on a daily basis and we could use their data to obtain information on usage patterns as well as safety and eventual effectiveness. To allow for this, GRIP aims to build a platform for global studies of drug effects in paediatrics. With this survey we are at the first step of assessing which systems and healthcare databases are available in each of the countries. We are specifically looking for databases containing

Electronic person-level drug prescription/dispensing information (e.g. pharmacy claims data, primary care prescription databases)

Electronic person-level immunization information (e.g. vaccine registry, primary care/ immunization clinical databases) for routine childhood vaccines

Electronic person-level disease diagnoses (e.g. primary care medical record databases, claims databases, hospital databases)

You can access the survey by registering at the following link:

http://survey.grip-network.org/index.php?sid=51458&newtest=Y&lang=en

You are kindly invited to complete a separate registration for each different database you can provide information about (in case you have access to more than one database).

http://survey.grip-network.org/index.php?sid=51458&newtest=Y&lang=en




43

What may be offered in the future?

The healthcare databases in the inventory could become part of a global federation (platform) of databases for paediatric pharmacoepidemiological studies. Based on the data available, local researchers could be invited to participate in harmonization and proof of concept studies as third parties to the GRIP network of excellence. We appreciate your support and would be happy to answer any questions you may have. Please contact: Osemeke Osokogu

Jan Bonhoeffer /Yulin Li

Brighton Collaboration Foundation

Basel, Switzerland

Miriam Sturkenboom / Osemeke Osokogu

Erasmus University Medical Center

Rotterdam, The Netherlands

mailto:[email protected]?subject=GRIP%20Survey%20-%20information%20request

mailto:[email protected]







44

Appendix 4a: Database invited to fill the survey (n=99)

Continent Country Name of Database

Asia-Pacific Australia (2) Vaccine Assessment using Linked Data (VALiD)

General Practice Research Network (GPRN)

China (3) China Health and Nutrition Survey (CHNS)

Shanghai Food and Drug Administration (FDA) Hospital Medical Record Database

immunization registry

Japan (2) IMS LifeLink? Longitudinal Rx (LRx) Database: Japan

National Claims Database

Laos (1) immunization information system

New Zealand (1)

New Zealand's Mortality Database (MORT) (New Zealand)

Sri Lanka (1) Birth and Immunization Register

Europe Belgium (2) IMS Lifelink: Belgium Hospital Disease Database

CSD Longitudinal Patient Database: Belgium

Denmark (5)

Nation-wide Danish immunization registry

Danish National Patient Registry (NPR)

Aarhus University Prescription Database

Danish Fertility Database

Odense University Pharmacoepidemiologic Database (OPED - Denmark)

Estonia (1) Estonian Health Insurance Fund (EHIF) Prescription Database

Finland (1) THL sentinel hospital databases

France (9) Pharmacoepidemiology

Evaluation chez la Femme Enceinte des MEdicaments et de leurs RISque (EFEMERIS)

THALES

CSD Longitudinal Patient Database: France

Enqu

IMS LifeLink? Electronic Medical Records (EMR) Database - France [aka LifeLink EMR-EU - France]

European Database for Multiple Sclerosis (EDMUS)

French Communicable Diseases Computer Network (FCDN - The Sentinel Network)

Claims databases from the French Rhone-Alpes Region

Germany (3)

CSD Longitudinal Patient Database: Germany

IMS LifeLink? Electronic Medical Records (EMR) Database - Germany [aka LifeLink EMR-EU - Germany]

The German Pharmacoepidemiological Research Database (GePaRD - BIPS database)




45

Iceland (2) The Pharmaceuticals Database (PDB)

National Patient Registry (NPR)

Ireland (1) Irish Health Services Executive (HSE) Primary Care Reimbursement Services (PCRS) pharmacy database

Italy (12) Lombardy database

Regional Agency of Healthcare services of Abruzzi.

Regione Emilia Romagna

OSSIFF

Regione Toscana

Marche database

Friuli Venezia Giulia

CSD Longitudinal Patient Database: Italy

Gruppo Italiano di Farmacovigilanza nell Anziano - Italian Group of Pharmacoepidemiology in the Elderly (GIFA)

PEDIANET (Italy)

Panor@mica GP database

Farmaceutica database

Netherlands (8)

Dutch Foundation for Pharmaceutical Statistics (SFK)

Praeventis Immunization registry

PHARMO Record Linkage System

InterAction Database (IADB)

AGIS Health Database (AHD - Netherlands)

Integrated Primary Care Information Database (IPCI)

IMS LifeLink? Longitudinal Rx (LRx) Database: Netherlands

LINH Database (The Netherlands Information Network of General Practice) (Netherlands)

Norway (2) Norwegian hospital databases

Norwegian Prescription Database (NORPD)

Scotland (1) Tayside Medicines Monitoring Unit (MEMO) (UK)

Spain (2) CSD Longitudinal Patient Database: Spain

BIFAP(Database for Pharmacoepidemiological Research in Primary Care/Base de datos para la Investigaci

Sweden (4) Swedish Prescribed Drug Register

Swedish National Patient Register (Sweden)

Dental Health Register of Sweden

Swedish Medical Birth Register

UK (6) IMS Oncology Analyzer (United Kingdom)

The Primary Care Clinical Informatics Unit (PCCIU) Database (UK)

IMS LifeLink? Electronic Medical Records (EMR) Database- UK [aka LifeLink EMR-EU - UK]

QRESEARCH




46

General Practice Research Database (GPRD) (UK)

The Health Improvement Network (THIN)

North-America Canada (7) Quebec Pregnancy Registry

Manitoba Population Health Research Data Repository:Bone Mineral Density (BMD) Database

Canadian Cancer Registry (CCR)

IMS LifeLink? Longitudinal Rx (LRx) Database: Canada

Saskatchewan Health, Multiple Linkable Population Databases

MedEcho

Longitudinal database

US (23) MEDSTAT

Health care cost and utilization project nationwide inpatient sample

the Veterans Affairs (VA) database

National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (USA)

i3 Invision Data Mart (formerly LabRx) (USA)

Slone Epidemiology Unit Case Control Surveillance Study (USA)

Analyticare Long Term Care (LTC) Database (USA)

IMS LifeLink? Longitudinal Rx (LRx) Database: USA

IMS LifeLink Health Plan Claims Database (formerly PharMetrics Patient-Centric Database) (USA)

Medi-Cal Paid Claims File (USA

Systematic Assessment of Geriatric drug use via Epidemiology (SAGE) Database (USA)

MediGuard (formerly iGuard) (USA)

Vaccine Safety Data Link (VSD) (USA)

Premier Perspective

Rochester Epidemiology Project (REP) (Mayo Clinic) (USA)

Geisinger Health Care System (USA)

IntrinsiQ Database (USA)

Regenstrief Medical Record System (RMRS) (USA)

HealthCore Integrated Research Database (HIRD) (USA)

United States Renal Data System (USRDS) (USA)

MarketScan Commercial Claims and Encounters (USA)

Pharmaceutical Assistance Contract for the Elderly (PACE) (USA)

The Cardiovascular Health Study (CHS) (USA)




47

Appendix 4b: Personal contacts invited after networking in the meetings (n=26+2 with the

same contact persons).

Continent Country Name of automated health care database Conference/Meeting

North-America US MarketScan Commercial Claims and Encounters (USA)

Mid-Year Meeting ISPE

MarketScan Medicaid Database (USA) Mid-Year Meeting ISPE

MarketScan Medicare Supplemental and COB Database (USA)

Mid-Year Meeting ISPE

OPTUM Insight Mid-Year Meeting ISPE

Canada Quebec Health Insurance Agency (vermoedelijk ook RAMQ)

PHAC

Immunization registry (linkable) PHAC

PHAC

Manitoba Immunization registry PHAC

Ottawa Hospital Research Institute PHAC

Ontario Public Healtg PHAC

Europe Belgium Insurance database ECDC

Austria

ECDC

Bulgaria

ECDC

Cyprus

ECDC

Czech republic

ECDC

Estonia

ECDC

Hungary

ECDC

Iceland

ECDC

Ireland

ECDC

Lithuania

ECDC

Poland

ECDC

Portugal

ECDC

Slovakia

ECDC

Slovenia

ECDC

Sweden

ECDC

Asia-Pacific SoutkKorea Immunization data PHAC

China, Republic of (a.k.a. Taiwan)

PHAC

Thailand Immunization data PHAC

“description of data sources - global research in ... · d. 2.02 – description of data sources...

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