European Child & Adolescent Psychiatry

, Volume 22, Issue 10, pp 605–618

An inventory of European data sources for the long-term safety evaluation of methylphenidate

  • Macey L. Murray
  • Suppachai Insuk
  • Tobias Banaschewski
  • Antje C. Neubert
  • Suzanne McCarthy
  • Jan K. Buitelaar
  • David Coghill
  • Ralf W. Dittmann
  • Kerstin Konrad
  • Pietro Panei
  • Eric Rosenthal
  • Edmund J. Sonuga-Barke
  • Ian C. K. Wong
Open Access
Original Contribution

DOI: 10.1007/s00787-013-0386-x

Cite this article as:
Murray, M.L., Insuk, S., Banaschewski, T. et al. Eur Child Adolesc Psychiatry (2013) 22: 605. doi:10.1007/s00787-013-0386-x
  • 1.3k Downloads

Abstract

To compile an inventory of European healthcare databases with potential to study long-term effects of methylphenidate (MPH) in patients with attention deficit hyperactivity disorder (ADHD). Potential databases were identified through expert opinion, the website of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance, and literature search. An online survey was conducted among database providers/coordinators to ascertain the databases’ appropriateness for inclusion into the inventory. It included questions about database characteristics, sample size, availability of information on drug exposure, clinical data and accessibility. Forty-two databases from 11 countries were identified and their coordinators invited to participate; responses were obtained for 22 (52.4 %) databases of which 15 record ADHD diagnoses. Eleven had sufficient data on ADHD diagnosis, drug exposure, and at least one type of outcome information (symptoms/clinical events, weight, height, blood pressure, heart rate) to assess MPH safety. These were Aarhus University Prescription Database, Danish National Birth Cohort (Denmark); German Health Interview and Examination Survey for Children and Adolescents; Health Search Database Thales, Italian ADHD Register, Lombardy Region ADHD Database (Italy); Avon Longitudinal Study of Parents and Children, General Practice Research Database, The Health Improvement Network, QResearch (UK) and IMS Disease Analyzer (UK, Germany, France). Of the 20 databases with no responses, information on seven from publications and/or websites was obtained; Pedianet and the Integrated Primary Care Information database were considered suitable. Many European healthcare databases can be used for multinational long-term safety studies of MPH. Methodological research is underway to investigate the feasibility of their pooling and analysis.

Keywords

Database(s) Methylphenidate Attention deficit hyperactivity disorder (ADHD) Drug safety Paediatric 

Background

As a first-line pharmacological therapy for attention deficit hyperactivity disorder (ADHD), methylphenidate (MPH) is widely prescribed in children and adolescents and to a lesser extent to adults. The efficacy of MPH in ADHD has been robustly demonstrated in randomised controlled trials with approximately 70 % of children, adolescents and adults showing a therapeutic response [1]. Other effective drugs for ADHD are the potent psychostimulant dexamfetamine and atomoxetine, a selective noradrenaline reuptake inhibitor. Although the medications for ADHD are generally well-tolerated, commonly reported adverse effects include neurological effects (such as headache, insomnia), gastroenterological effects (loss of appetite, nausea and vomiting, abdominal pain), psychiatric effects (mood, anxiety) and chronic effects such as growth restriction and increases in blood pressure [2, 3]. In order to control the acute and chronic adverse effects of medication, often patients on long-term (>1 year) drug treatment have a structured interruption of treatment (known as a drug holiday). This allows monitoring to ensure medication is still effective, and assessment of whether the balance between adverse effects and therapeutic effects favours the continuation of treatment [3].

In 2006, there were safety concerns reported about the use of amfetamines and MPH as treatments for ADHD, specifically with respect to cardiovascular safety of these products [4]. In 2007, the European Commission requested a referral to the Committee for Medicinal Products for Human Use (CHMP) under Article 31 of Directive 2001/83/EC, as amended, for MPH because of safety concerns [5]. The CHMP concluded that insufficient was known about the long-term adverse effects of MPH on growth, sexual development, the neurological system, psychiatric states and the cardiovascular system, and further assessment is needed. In January 2009, the European Medicines Agency (EMA) concluded that the benefit–risk ratio of methylphenidate in the authorised indication remains favourable, but more data are needed on the long-term effects in children, adolescents and young adults [6]. In response to the CHMP’s concerns, the ADDUCE (Attention deficit hyperactivity disorder drugs use chronic effects) research team was formed by a consortium of experts in the fields of ADHD, drug safety, neuro-psychopharmacology and cardiovascular research. The ADDUCE project, funded under the European Union’s 7th Framework Programme, will use pharmacoepidemiological research methods to investigate the long-term adverse effects of MPH on growth, the neurological system (including cognition and motivation), psychiatric states and the cardiovascular system in children, adolescents and adults. The methodologies employed will be the acquisition and analysis of existing patient databases, a 2-year prospective cohort study of MPH-treated patients and two control groups, and a cross-sectional study in late adolescents and young adults. A methodological overview of the ADDUCE project is provided by the website (http://adhd-adduce.org).

Electronic health care databases, comprising patient data, drug prescription data, patient outcomes and information on confounding variables, potentially provide valuable resources to examine associations between drug use and long-term adverse effects. A survey published in 2008 showed that many European healthcare databases had enormous potential for use in paediatric drug utilisation and safety studies [7]. The use of electronic health records was also recommended by the EMA when conducting post-authorisation drug utilisation and safety studies [8].

Previous studies have demonstrated that healthcare databases can be used to investigate certain adverse effects of MPH. Gau et al. used the National Health Insurance database (Taiwan) to assess the association between MPH use and psychiatric disorders in 2,109 children and adolescents with new onset ADHD between 1999 and 2003 matching 1:4 with non-ADHD controls. In this study, MPH use was associated with the occurrence of bipolar disorder (adjusted hazard ratio (HR), 4.1; 95 % CI 1.7, 9.7, p < 0.05) [9]. McCarthy et al. aimed to estimate the mortality rates associated with stimulant and non-stimulant treatment prescription using the UK General Practice Research Database (GPRD). Compared with the general population, there was a 162-fold increased risk of completed suicide in patients aged 11–14 years using psychostimulants or atomoxetine (standardized mortality ratio: 161.91 [95 % CI 19.61, 584.88]) [10]. Recently, four large US health-plan databases of insurance claims were combined to assess the use of ADHD drugs and the risk of serious cardiovascular events (sudden cardiac death, myocardial infarction, stroke) in children and young adults [11]. A total of 81 serious cardiovascular events were confirmed from over 2.5 million person-years of follow-up giving an incidence of 3.1 events per 100,000 person-years in the study cohort. Among current users of ADHD drugs, there was no increased risk of serious cardiovascular events when compared with non-users (adjusted HR, 0.75; 95 % CI 0.31, 1.85), and there was no evidence of increased risk for methylphenidate (adjusted HR, 0.96; 95 % CI 0.31, 2.97). Another study of claims data (Medicaid) from 28 US states found that the treatment of children aged 3–18 years with MPH or mixed amfetamine salts was not significantly associated with an increased short-term risk of severe cardiac events [12]. However, both US studies were unable to assess the long-term safety of stimulant treatment due to their short follow-up of 2 years [11, 12].

On the basis of these examples, we judged that analysis of existing health care databases might be useful to study the association between MPH use and long-term adverse events. Although this might not be possible for all adverse events of interest (e.g. sleep abnormalities), such databases may provide important health information relevant for the systematic study of side effects. However, many studies performed in Europe have been limited in their power and scope by the use of a single data source. Cooper et al. [11] demonstrated the value of using multiple existing databases in the US to obtain large sample sizes to study the safety of ADHD drugs; adopting a similar approach in Europe may be valuable if the issues concerning the pooling of such databases could be overcome. Our previous work has shown this to be feasible [13, 14, 15, 16]. Hence, the aim of this study is to compile an inventory of existing European databases which can be used to study the effects of long-term MPH use in patients with ADHD.

Methods

There were a number of steps taken to achieve our aim.

First, a list of all known potentially relevant European databases with individual patient information was collated. A range of different methods were used to identify eligible databases: from those listed on the website of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP), a systematic review of the published literature (including conference proceedings [17]) and finally, by nominations from members of the ADDUCE Consortium.

Second, a questionnaire comprising 33 questions to collect detailed database information was designed (by SI, TB, MLM, SM, AN and ICKW) and implemented using a web-based data collection tool (SurveyMonkey™). A request to complete the questionnaire survey was sent to the providers/coordinators of the identified databases. Information collected on each database comprised general information on the database (name, country), a description of the nature of the database (e.g. longitudinal/patient record database, disease registry, cross-sectional, observational data survey), characteristics and sample size (such as number of investigators, database starting date, and number of patients), availability of data on the exposure to medication and on symptoms and clinical events and accessibility. Examples of questions are “Is information available on ADHD diagnosis?”; “Which of the following data are recorded in the database? Weight, height, blood pressure, heart rate” and “What system is used to code diagnoses?”

Third, databases were categorised with respect to their potential suitability for investigating the long-term safety of MPH based on the availability of individual patient information. Our criteria were based on the basic data elements that are required for pharmacoepidemiologic research using healthcare databases [18]. To be useful for the investigation of the long-term safety of MPH use for ADHD, databases had to record information about the following clinical aspects of individual patients: ADHD diagnosis, MPH exposure (dose and duration), exposure to other medications, potential adverse outcomes (such as effects on height and weight, blood pressure, heart rate) and other symptoms/clinical events. Furthermore, information on the structure and standardisation of data, costs to access the database, completeness of clinical and drug information and previous applications in research or validation studies were considered. The ability to combine with data from other sources was also assessed based on whether the database used unique patient identifiers (to allow for record linkage) and coding systems for diagnoses, clinical events and medications prescribed.

Last, detailed information of all databases without survey responses were sought from publications and the websites of their data providers/coordinators, where possible information collated were based on that requested in the survey questions. The databases were then assessed for their potential suitability using similar criteria to those used above.

Results

Response to the survey

Forty-two databases from 11 European countries were identified and their providers/coordinators were invited to participate in the survey (Fig. 1). Responses were obtained from 22 (52.4 %) database coordinators/providers in seven European countries (Table 1). Seven of these 22 databases did not contain information on ADHD diagnosis; therefore, 15 databases were included in the detailed assessment. Table 2 provides detailed information of the participating databases. Only one database had an incomplete response to the survey (Generation R study), however, it was possible to obtain some of the missing details from the literature [19, 20]. There was information on over 4.5 million children and adolescents in these 15 databases (of which just over 23,000 from six databases are reported to have ADHD), this is an overestimate due to the possibility that patients and/or their clinicians can contribute information to more than one database. For example, there is an overlap between GPRD and The Health Improvement Network (THIN), where 66 % of contributing practices in THIN also contribute to GPRD between 2001 and 2008 [21].
Fig. 1

Survey and database assessment schematic flowchart

Table 1

Databases identified and invited to participate in the survey

Country

Purpose of database

Database

Website

Response

Information from websites and literature

Denmark

PM

National Psychiatric Central Register

www.psykiatriskforskning.dk/research/central-research-register/

Yes

No

Denmark

PM/Admin

Odense Pharmacoepidemiological Database

www.sdu.dk

Yes

No

Denmark

PM/Admin

Aarhus University Prescription Database

kea.au.dk/en/informaticsandstatistics/researchdatabases/theprescriptiondatabases/

Yes

No

Denmark

RC

The Danish National Birth Cohort (DNBC)

www.dnbc.dk/

Yes

No

Finland

PM/Admin

Prescription register

www.kela.fi

No

Yes

Finland

RC

The Finnish Northern Finland Birth Cohort (NFBC) 1986 study

kelo.oulu.fi/NFBC/

No

Yes

Germany

RC

The German Health Interview and Examination Survey for Children and Adolescents (KiGGS)

www.rki.de

Yes

No

Germany

PM

The German Population Based Long-Term Follow-up of ADHD

www.bips.uni-bremen.de

No

Yes

Italy

PM

Pedianet

www.pedianet.it/

No

Yes

Italy

PM

Sistema Informativo Sanitario Regionale Database-FVG region

www.regione.fvg.it

No

No

Italy

PM

Health Search Database Thales—CSD LPD (HSD)

www.healthsearch.it/

Yes

No

Italy

PM

Tuscany Regional database

www.arsanita.toscana.it

No

No

Italy

PM

Lombardy Regional ADHD database

givitiweb.marionegri.it/Centres/Public/ADHD/Default.aspx

Yes

No

Italy

PM

ARNO Observatory

osservatorioarno.cineca.org/arnoeng.htm

No

No

Italy

PM

The National ADHD Registry

www.farmaco-iss.org/

Yes

No

Netherlands

PM

Integrated Primary Care Information Database (IPCI)

www.ipci.nl

No

Yes

Netherlands

PM

PHARMO-Record-Linkage-System

www.pharmo.nl

Yes

No

Netherlands

PM

InterAction database

www.iadb.nl

Yes

No

Netherlands

RC

The Dutch KOALA Birth Cohort Study

www.koala-study.nl

Yes

No

Netherlands

RC

The Dutch TRAILS study

 

No

No

Netherlands

RC

The Dutch Generation R study

www.generationr.nl/

Yes

No

Norway

PM

The Norwegian Prescription Database

www.norpd.no/

Yes

No

Portugal

RC

Centro de Estudos e Avaliação em Saúde (Centre for Health Studies and Evaluation) (CEFAR)

 

No

No

Sweden

PM

Swedish Medical Birth Register

www.socialstyrelsen.se

Yes

No

Sweden

PM

Swedish National Health Data Registers

www.socialstyrelsen.se

Yes

No

Sweden

RC

The Swedish All Babies in Southeast Sweden

www.abis-studien.se/

No

No

Spain

PM

Base de datos para la Investigacion Farmacoepidemiologica en Atencion Primaria (BIFAP)

www.bifap.org/

No

No

UK

PM

General Practice Research Database (GPRD) (now part of Clinical Practice Research Datalink)

www.cprd.com/

Yes

No

UK

PM

The Health Improvement Network Data (THIN)

csdmruk.cegedim.com/

Yes

No

UK

PMS

Prescription Event Monitoring (PEM)

www.dsru.org/pem

Yes

No

UK

Admin

Prescription Pricing Authority (PPA)

www.nhsbsa.nhs.uk/PrescriptionServices.aspx

No

No

UK

PM

QResearch

www.qresearch.org/

Yes

No

UK

PM

Scottish Programme for Improving Clinical Effectiveness in Primary Care (SPICE) formerly known as GPASS (General Practice Administration System for Scotland)

www.abdn.ac.uk/iahs/uploads/files/PCI.pdf

No

No

UK

PM

Medicines Monitoring Unit (MEMO)

www.dundee.ac.uk/memo/

No

No

UK

RC

The Avon Longitudinal Study of Parents and Children (ALSPAC)

www.bristol.ac.uk/alspac/

Yes

No

UK

RC

The 1970 British Cohort Study

www.cls.ioe.ac.uk/bcs70

No

Yes

UK

RC

The Millennium Cohort Study

www.cls.ioe.ac.uk/mcs

No

Yes

UK

RC

The 2004 British Child and Adolescent Mental Health Survey

 

No

No

UK

RC

The Scottish SEATON Study

www.abdn.ac.uk/seatonstudy/

Yes

No

European

RC

The ADHD Observational Research in Europe

 

No

No

European

RC

The International Muti-Center ADHD Genetics (IMAGE) Project

 

No

No

UK, France, Germany

PM

IMS Disease Analyzer (IMS DA)

www.imshealth.com/

Yes

No

PM Patient management and/or disease surveillance, RC Research cohort, Admin administrative database for reimbursement and/or renumeration of prescriptions, PMS post-marketing surveillance

Table 2

Detailed information of the participating databases which record ADHD diagnoses

Name of database

Aarhus

DNBC

National Psychiatric Central Register

KiGGS

HSD

Italian ADHD Register

Lombardy Region ADHD Database

Country

Denmark

Denmark

Denmark

Germany

Italy

Italy

Italy

Type of database

Prescription Information Databasesa

Longitudinal/Patient Record Database

Longitudinal/Patient Record Databasea

Cross-sectional, Observational Data Survey

Longitudinal/Patient Record Database

ADHD specific patient cohort

ADHD specific patient cohort

Number of Investigators

N/A

N/A

N/A

5

700

84

18

Starting year

1994

1996

1969

2003

1996

2007

2007

End year

Ongoing

Ongoing

Ongoing

2006

Ongoing

Ongoing

Ongoing

Estimated no.

 Male (no. Of ADHD)

10,000 (50)

48,000 (N/A)

633,385 (N/A)

8,985 (710)

760,000 (114)

2,000

201

 Female (no. of ADHD)

10,000 (0)

46,000 (N/A)

8,656 (156)

850,000 (20)

244

28

Prescriptions

 Medication

Yes

Yes

Yes

Yes

Yes

Yes

Yes

 Medication Code

ATC

ATC

ATC

ATC

ATC

ATC

Only atomoxetine and methylphenidate

 Indication

No

No

No

Yes

Yes

Yes

Yes

 Dosage

Yes

Yes

No

Yes

Yes

Yes

Yes

 Duration

Yes

Yes

No

Yes

Yes

Yes

Yes

Outcomes/confounders

 Weight

No

Yes

No

Yes

Yes

Yes

Yes

 Height

No

Yes

No

Yes

Yes

Yes

Yes

 Blood pressure

No

No

No

Yes

Yes

Yes

Yes

 Heart rate

No

No

No

Yes

No

Yes

Yes

 Medical diagnosis

Yesa

Yes

Yes

Yes

Yes

Yes

Yes

 Symptoms/clinical events

Yesa

Yes

Yes

Yes

Yes

Yes

Yes

 Diagnoses/clinical events code

ICD-10

ICD-10

ICD-10, ICD-8

Parent reported clinician based information on ADHD

ICD-9CM

MedDRA

List of co-morbidities

 Validation of ADHD diagnosis via

N/A

Comparison with SDQ

ICD-10

Comparison with SDQ

ICD9-CM and further validation required

DSM-IV

DSM-IV

 Medical examinations

Yes

No

Yes

Yes

Yes

Yes

Yes

 Ethnicity

Yes

No

Yes

Yes

No

No

No

 Genetic information

No

Yes

Yes

No

No

No

No

Data access

 Via academic collaboration

N/A

Yes

Yes

Yes

Yes

No

Yes

 By paying a fee

N/A

Yes

No

Yes

Yes

Yes

Yes

Name of database

Generation R Study

KOALA

Swedish National Health Data Registers

ALSPAC

GPRD

THIN

QResearch

IMS DA

Country

Netherlands

Netherlands

Sweden

UK

UK

UK

UK

UK, France and Germany

Type of database

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Longitudinal/Patient Record Databasea

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Number of Investigators

5

3

N/A

1

600

495

600

>100

Starting year

2002

2000

2005

1990

1987

2003

1990

1991

End year

Ongoing

Ongoing

Ongoing

Ongoing

Ongoing

Ongoing

Ongoing

Ongoing

Estimated no.

 Male (no. Of ADHD)

3,000 (N/A)

1,400 (N/A)

32,000 (N/A)

5,000 (N/A)

2,000,000 (12,000)

1,713,823 (N/A)

200,000 (N/A)

2,500,000 (N/Ac)

 Female (no. of ADHD)

3,000 (N/A)

1,400 (N/A)

18,000 (N/A)

5,000 (N/A)

2,000,000 (8,000)

1,750,216 (N/A)

200,000 (N/A)

2,500,000 (N/Ac)

Prescriptions

 Medication

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

 Medication Code

N/A

Not yet determinedb

ATC

ATC

Multilex

ATC, Multilex

EMIS drug database

ATC, Read

 Indication

N/A

No

No

Yes

Yes

No

No

Yes

 Dosage

N/A

Yes

Yes

No

Yes

Yes

Yes

Yes

 Duration

N/A

Yes

No

Yes

Yes

Yes

Yes

Yes

Outcomes/confounders

 Weight

Yesd

Yes

No

Yes

Yes

Yes

No

Yes

 Height

Yesd

Yes

No

Yes

Yes

Yes

No

Yes

 Blood pressure

Yesd

Yes

No

Yes

Yes

Yes

No

Yes

 Heart rate

Yesd

Yes

No

Yes

Yes

Yes

No

Yes

 Medical diagnosis

Yesd

Yes

Yes

Yes

Yes

Yes

Yes

Yes

 Symptoms/clinical events

Yesd

Yes

No

Yes

Yes

Yes

No

Yes

 Diagnoses/clinical events code

N/A

Not yet determinedb

ICD-10

ICD-10, ALSPAC generated

Read

Read

Read

ICD-10, Read

 Validation of ADHD diagnosis via

CBCL & DSM-IVe

Not yet determinedb

Uncertain

Traits and DAWBA questionnaire

GP questionnaires

GP questionnaires

None

None

 Medical examinations

Yesd

No

No

No

Yes

Yes

No

Yes

 Ethnicity

Yesd

Yes

Yes

Yes

Yes

No

Yes

Yes

 Genetic information

Yesd

No

No

Yes

No

No

No

No

Data access

 Via academic collaboration

Yesd

Yes

Yes

Yes

No

No

No

Yes

 By paying a fee

Nod

Yes

Yes

Yes

Yes

Yes

Yes

Yes

ATC Anatomical Therapeutic Chemical classification system, CBCL Child Behaviour Checklist, DAWBA Development and Well-Being Assessment, DSM-IV Diagnostic and Statistical Manual of mental disorders, 4th edition, GP general practitioner, ICD International Classification of Diseases, MedDRA Medical Dictionary for Regulatory Activities, Read Read Clinical terms, SDQ Strengths and Difficulties Questionnaire, N/A not available

aLinkable to other registries through unique identifiers of the patients

bFuture follow-up in 2012

cthe number of ADHD patients can be supplied on personal request

dfrom [19]

efrom [20]

Type of database

Most of the selected databases are longitudinal/patient record databases (n = 11). Two are ADHD specific patient cohorts (Lombardy Region ADHD Database and the Italian ADHD Register). One is a prescription information database (Aarhus University Prescription Database) and one is a cross-sectional, observational data survey (German Health Interview and Examination Survey for Children and Adolescents; KiGGS). Three databases could be linked to various other registries through unique patient identifiers (Aarhus University Prescription Database, Swedish National Health Data Registers and the National Psychiatric Central Register). The purpose of data collection of the majority of databases was for patient management and disease surveillance (n = 10); the remaining five are research cohorts: Danish National Birth Cohort (DNBC), KiGGS, Generation R Study, Dutch Child, Parent and health: Lifestyle and Genetic constitution (KOALA) Birth Cohort Study and Avon Longitudinal Study of Parents and Children (ALSPAC).

Drug exposure

Almost all databases contain information on prescribed medicines; Generation R Study does not collect details on prescriptions or drug exposure. One (Lombardy Region ADHD Database) has information on MPH and atomoxetine only. Most (n = 14) include information on medical diagnosis and the indication for prescription drugs. Four databases (National Psychiatric Central Register, Generation R study, Swedish National Health Data Registers, and ALSPAC) contain limited information on dosage and duration of treatment. The Anatomical Therapeutic Chemical classification system (ATC) is commonly used to classify medications; 10 of the 15 databases use this coding scheme [22, 23]. Multilex is a UK drug terminology system used to classify medications and is used in the GPRD and THIN databases. Only one database (KOALA) currently does not use a medication coding system.

Clinical outcomes

Full clinical data (symptoms and clinical events, weight and height, blood pressure and heart rate) are available in eight databases [KiGGS, Italian ADHD Register, Lombardy Region ADHD register, KOALA, ALSPAC, GPRD, THIN, and IMS Disease Analyzer (IMS DA)]. The International Classification of Diseases 10th Revision (ICD-10) is used as the diagnoses/clinical event coding system in six databases (Aarhus University Prescription Database, DNBC, National Psychiatric Central Register, Swedish National Health Data Registers, ALSPAC, and IMS DA) [24]. Read Clinical Terms (a UK hierarchical classification system) is used in four UK databases (GPRD, THIN, QResearch, and IMS DA). One database, Health Search Database Thales (HSD), uses ICD-9 to code diagnoses and clinical events.

Other variables

The diagnostic process for ADHD and hyperkinetic disorder includes the recognition of specific behavioural and attentional symptoms per criteria of either the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) [25] or ICD-10. However, these symptoms are also found in disorders other than ADHD [26, 27]. Therefore, information on whether the ADHD diagnosis had been validated is important. For two databases (Italian ADHD Register and Lombardy Region ADHD Database), clinicians use DSM-IV criteria to confirm diagnoses. In DNBC and KiGGS, ADHD diagnosis is supported by the scores from Strengths and Difficulties Questionnaire (SDQ) [28], which has a scale measuring ADHD symptoms. Questionnaires to general practitioners (GPs) can be used to obtain further information on diagnosis in the GPRD and THIN databases.

Nine databases include information on ethnicity. Three databases (DNBC, National Psychiatric Central Register, and ALSPAC) also collect genetic information.

Previous applications of databases in the field of research in paediatrics and/or ADHD

The Italian ADHD Register has been previously used in studies on medication safety in ADHD patients [29, 30, 31]. The GPRD and THIN databases have been used to study the safety and use of ADHD drug treatment [10, 32, 33]. Data from KiGGS has been used in a number of paediatric studies, including those investigating the prevalence of mental health disorders, such as ADHD and drug treatment [34, 35]. ALSPAC has been widely studied and has numerous publications in paediatric research but none in ADHD.

Data access

All databases apart from one can be accessed either by paying a fee and/or via academic collaboration. Nine of them (DNBC, National Psychiatric Central Register, KiGGS, HSD, Lombardy Region ADHD Database, KOALA, Swedish National Health Data Registers, ALSPAC, IMS DA) can be accessed via academic collaboration. The means to access the Aarhus University Prescription Database was not reported in the survey, but the literature states project-specific permission from the Danish Data Protection Agency is required and any data-linkage studies (which would be necessary to obtain outcome data) need approval from the Danish National Board of Health [36].

Assessment of non-responding databases

We obtained information on seven additional databases whose coordinators did not reply to the survey. These were Prescription Register (Finland), The Finish Northern Finland Birth Cohort (NFBC) 1986 study, The German Population Based Long-Term Follow-up of ADHD, Pedianet (Italy), Integrated Primary Care Information Database (IPCI; Netherlands), The 1970 British Cohort Study and The Millennium Cohort Study (UK). These databases were appraised using the same criteria as above with information obtained from database websites and published literature (Table 3).
Table 3

Detailed information obtained from literature and websites of non-responding databases

Name of database

Prescription register

The Finish Northern Finland Birth Cohort (NFBC) 1986 study

The German population based long-term follow-up of ADHD

Pedianet

Integrated primary care information database (IPCI)

The 1970 British cohort study

The millennium cohort study

Country

Finland

Finland

Germany

Italy

Netherlands

UK

UK

Type of database

Prescription Information Database

Longitudinal 1-year birth cohort

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Longitudinal/Patient Record Database

Longitudinal study

Longitudinal study

Number of investigators

N/A

N/A

N/A

300 paediatricians

150 GPs

N/A

N/A

Starting year

1994

1985

2010

1998

1992

1970

2000

End year

Ongoing

1986

Ongoing

Ongoing

Ongoing

Ongoing

Ongoing

Estimated no. of individuals

480,000 (0–18 years)

9,400

30,000

106,554 (0–14 years)a

>1,000,000 (161,108 of 0–18 years)b

15,500

18,800

Prescriptions

 Medication code

ATC

N/A

N/A

ATC

ATC

N/A

N/A

 Indication of prescription

Limited

N/A

N/A

No

Yes

N/A

N/A

 Dosage of prescription

Limited

N/A

N/A

Yes

Yes

N/A

N/A

 Duration of prescription

No

N/A

N/A

Yes

Yes

N/A

N/A

Outcomes/confounders

 Weight

No

N/A

N/A

Yes

Yes

Yes

Yes

 Height

No

N/A

N/A

Yes

Yes

Yes

Yes

 Blood pressure

No

Yes

N/A

N/A

Yes

N/A

N/A

 Heart rate

No

Yes

N/A

N/A

N/A

N/A

N/A

 Medical diagnosis

No

Limited

N/A

Yes

Yes

N/A

N/A

 Symptoms/clinical events

No

Limited

N/A

Yes

Yes

Limited

Limited

 Diagnoses/clinical events code

N/A

N/A

N/A

N/A

ICPC

N/A

N/A

 ADHD diagnosis tool

N/A

N/A

N/A

N/A

N/A

N/A

N/A

 Medical examinations

No

Limited

N/A

Yes

Yes

Limited

Limited

 Ethnicity

N/A

N/A

N/A

N/A

N/A

N/A

N/A

 Genetic information

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Data access

 Via academic collaboration

N/A

N/A

N/A

N/A

N/A

N/A

N/A

 By paying a fee

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A not available, GPs general practices, ICPC the international classification of primary care

ain year 2008

bin year 2004

Type of database

Three of these seven databases are longitudinal/patient record databases (the German Population Based Long-Term Follow-up of ADHD, Pedianet and IPCI). Three are longitudinal birth cohorts (NFBC, the 1970 British Cohort Study, and The Millennium Cohort Study). Only one database is a prescription information database [Prescription Register (Finland)].

Drug exposure

Only two of the databases include information on indication of prescription and/or medical diagnosis (Pedianet and IPCI). Information on dosage and duration of prescription is available in Pedianet and IPCI, and both use the WHO ATC classification system [22].

Clinical outcomes

Information on weight and height is found in Pedianet, IPCI, the 1970 British Cohort Study and the Millennium Cohort Study. Two databases provide information on medical diagnosis and symptoms/clinical events (Pedianet and IPCI). Only IPCI classifies diagnoses/clinical events using the International Classification of Primary Care (ICPC) code [37].

Other variables, previous applications in research and data access

Due to limited information on these databases, the availability of data on confounding variables and data accessibility cannot be assessed. There is one study using Pedianet to investigate the safety of paediatric drugs [38]. No studies of paediatric drug safety using the IPCI database could be identified in the literature. However, both databases have been previously used to study paediatric drug use [13, 14, 15, 16]. A study describing the incidence and prevalence of ADHD and drug treatment for ADHD using The German Population Based Long-term Follow-Up of ADHD database has been conducted [39].

Selection of data source inventory

According to our criteria for suitability, 11 of the 15 responding databases were considered to have potential value for the long-term safety evaluation of MPH. These are Aarhus University Prescription Database, DNBC, KiGGS, HSD, Italian ADHD Register, Lombardy Region ADHD Database, ALSPAC, GPRD, THIN, QResearch and IMS DA. Of the non-responding databases, Pedianet and IPCI were considered potentially suitable. Hence, 13 databases were included in our data source inventory.

Discussion

From our survey, we identified 13 sources of electronic health care records in Europe which have potential value in investigating the long-term effects of MPH treatment in patients with ADHD. These databases were selected because they record basic data elements required to conduct drug safety studies; these include validated ADHD diagnosis (or at least the ability to confirm diagnosis), other diagnoses and clinical events, exposure to MPH, and exposure to any other medications and potential adverse clinical outcomes. These data are rich resources, easily accessible, sourced from real-life practice, with the potential to provide large study populations for the long-term safety evaluation of MPH. Many are longitudinal in nature, which is essential for the long-term follow-up of patients.

However, it is generally recognised that existing sources of data (including those we have identified) may have several limitations such as selection bias, lack of control or comparison group, missing data (or limited detailed clinical information) [18], small sample sizes and issues of quality control in data collection. For example, the selection of an un-medicated group of ADHD patients from the 13 data cohorts may be challenging due to the small numbers of patients that exist (and would also affect prospective cohort studies). Some data may be unrecorded because the purpose of the database did not require it, such as details of pubertal maturation, specific psychiatric rating scales, measurements of brain function and activity (EEG, fMRI), or risk factors such as developmental history, parental history of medical and psychiatric problems and life events. Other data may not be routinely entered at specific time intervals (e.g. height, weight, blood pressure, heart rate). Missing data may restrict the ability of some data sources to be used to study specific long-term effects (such as developmental or psychiatric effects), or even threaten the reliability and validity of results, especially if confounding variables are unavailable. Misclassified diagnoses or outcomes can also affect the validity of a study. The issue of small sample sizes could be overcome if data are pooled to form larger cohorts. These issues are not pertinent to all of the selected data sources in our inventory, but they all should be carefully considered (and addressed) when selecting data sources for our drug safety research.

After consideration of these general limitations, the ADDUCE Consortium proposed that high quality data should be collected in a large prospective cohort study of patients with ADHD and their controls. This cohort study would collect specific information at set time points on efficacy measures of MPH, growth, coordination, psychiatric effects (using rating scales such as DAWBA—Developmental and Well-being Assessment modules), neurological effects (rating scales, measures of brain function and activity), as well as patient demographics, family and personal medical, psychiatric and medication histories and physical examinations (including cardiac examinations). Much of these data are not recorded in the existing data sources of our inventory. However, there are also limitations to primary data collection of a large prospective cohort; it is time- and resource-consuming, and recruitment of a sufficiently large sample size to allow the study of rare effects of MPH may be difficult. Hence studies of large existing databases (single or pooled) in our inventory would complement those of the prospective cohort.

There are opportunities to exploit the databases that we have identified in our inventory; in particular, the combination of healthcare databases can potentially generate sample sizes and statistical power for large-scale drug safety studies [7]. European colleagues have recently pooled eight existing electronic healthcare databases covering four countries to generate an early signal detection system by creating a database platform using a common data framework (EU-ADR) [40]. The different clinical terminologies of the databases (e.g. Read Clinical Terms, ICD-9) can be mapped using a biomedical terminology integration system, Unified Medical Language System®. This generated a study population of almost 20 million individuals with just under 60 million person-years of data. Five of these databases are included in our inventory (Aarhus University Prescription Database, HSD, QResearch, Pedianet and IPCI) [40]. However, the amalgamation of existing databases is complex and goes beyond issues of data structures and sources. There are ethical issues concerning the processing of anonymised healthcare data and national diversity in healthcare provision and practice [7, 40]. Also, some of the limitations described above such as the issue of data quality still remain, which affect the choice of analytical methods. Database providers may wish to improve the quality and completeness of recording by the end-user and increase the availability of more specific detailed clinical information (such as the results of diagnostic tests) [18] to enhance the research value of their databases. Despite the complexity of such a task, the EU-ADR project [40] and other studies [11] demonstrate that combining diverse databases of heterogeneous populations is feasible for drug safety research, with vast potential for further work.

Conclusion

There are 13 European databases of birth cohorts or electronic healthcare records included in the ADDUCE data inventory, which have potential value individually or pooled for the evaluation of the long-term safety of MPH treatment in patients with ADHD. They are rich sources of data from real-life settings and easily accessible. However, there are limitations in utilising existing sources of data including small sample sizes, missing data and poor data quality. The pooling of existing data sources presents opportunities for large-scale safety studies of MPH in the future; its feasibility is currently under investigation but data quality and analysis, ethical issues and national differences in healthcare provision need to be considered.

Acknowledgments

The authors thank the database providers and coordinators for participating in the survey. The project was funded under the European Community’s 7th framework programme project number 260576: ADDUCE (Attention Deficit Hyperactivity Disorder Drugs Use Chronic Effects).

Conflict of interest

Mr. Insuk, Dr. Neubert and Dr. Panei declare they have no conflicts of interest. Dr. Murray received research funding from Shire and Pfizer. The present work is unrelated. Prof. Banaschewski served in an advisory or consultancy role for Bristol Myers-Squibb, Develco Pharma, Lilly, Medice, Novartis, Shire and Vifor Pharma. He received conference attendance support and conference support or received speaker’s fee from Lilly, Janssen McNeil, Medice, Novartis, and Shire. He has been involved in clinical trials conducted by Lilly and Shire. The present work is unrelated to the above grants and relationships. Prof. Buitelaar has been in the past 3 years a consultant to/member of advisory board of/and/or speaker for Janssen Cilag BV, Eli Lilly, Bristol-Myer Squibb, Schering Plough, UCB Pharma, Shire, Novartis and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents and royalties. Dr. McCarthy received research funding from Shire. The present work is unrelated. Prof. Dittmann is a former employee of Lilly Deutschland and now holds the Eli Lilly Endowed Chair of Paediatric Psychopharmacology at the Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany. He also holds Eli Lilly & Co. shares. He received research grants from the European Union, the US NIMH, the German Research Association (DFG), Ministry of Research/Education (BMBF), regulatory agency (BfArM), companies Ferring, Janssen-Cilag, Lilly and Shire, travel support and speaker honoraria from Lilly and Shire. Dr. Rosenthal has received conference attendance support or received speaker’s fees from Shire. The present work is unrelated. Prof. Sonuga-Barke has served in a consultancy role and on the speaker boards of Shire and UCB Pharma. He received research support from Janssen Cilag, Shire, Qbtech, Flynn Pharma and served on the Advisory Board of Shire, Flynn Pharma, UCB Pharma, Astra Zeneca. He also received conference support from Shire. The present work is unrelated to the above grants and relationships. Prof. Wong was a member of the National Institute for Health and Clinical Excellence (NICE) ADHD Guideline Group and the British Association for Psychopharmacology ADHD guideline group. He has received research grants from various pharmaceutical companies; including Shire. He has given talks at educational events sponsored by Janssen-Cilag and Eli-Lilly and acted as an advisor to Shire. The present work is unrelated to the above grants and relationships. All authors declared that they have no financial interests that may be relevant to the submitted work.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Macey L. Murray
    • 1
  • Suppachai Insuk
    • 1
  • Tobias Banaschewski
    • 2
  • Antje C. Neubert
    • 1
    • 3
  • Suzanne McCarthy
    • 4
    • 5
  • Jan K. Buitelaar
    • 6
  • David Coghill
    • 7
  • Ralf W. Dittmann
    • 2
  • Kerstin Konrad
    • 8
  • Pietro Panei
    • 9
  • Eric Rosenthal
    • 10
  • Edmund J. Sonuga-Barke
    • 11
    • 13
  • Ian C. K. Wong
    • 1
    • 12
  1. 1.Centre for Paediatric Pharmacy ResearchUniversity College London School of PharmacyLondonUK
  2. 2.Department of Child and Adolescent Psychiatry, Medical Faculty Mannheim, Central Institute of Mental HealthUniversity of HeidelbergMannheimGermany
  3. 3.Department of Paediatric and Adolescent MedicineUniversity Hospital ErlangenErlangenGermany
  4. 4.School of PharmacyUniversity College CorkCorkIreland
  5. 5.Pharmacy DepartmentCork University HospitalCorkIreland
  6. 6.Department of Cognitive NeuroscienceRadboud University Nijmegen Medical Centre, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
  7. 7.Division of Neuroscience, Medical Research InstituteUniversity of DundeeDundeeUK
  8. 8.Section of Child Neuropsychology, Department of Child and Adolescent PsychiatryUniversity Hospital AachenAachenGermany
  9. 9.Department of Therapeutic Research and Medicines EvaluationIstituto Superiore di SanitàRomeItaly
  10. 10.Department of Paediatric CardiologyEvelina Children’s Hospital, St Thomas’ HospitalLondonUK
  11. 11.Institute for Disorders of Impulse and Attention, School of PsychologyUniversity of SouthamptonSouthamptonUK
  12. 12.Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong SARChina
  13. 13.Department of Experimental Clinical and Health PsychologyGhent UniversityGhentBelgium

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