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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

8.1. General considerations

 

A growing number of pharmacoepidemiological studies use data from networks of databases, often from different countries. Pooling data across different databases affords insight into the generalisability of the results and may improve precision. Some of these networks are based on long-term contracts with selected partners and are very well structured (such as Sentinel, the Vaccine Safety Datalink (VSD), or the Canadian Network for Observational Drug Effect Studies (CNODES)), while others are looser collaborations based on an open community principle such as Observational Health Data Sciences and Informatics (OHDSI).

 

In Europe, collaborations for multi-database studies have been strongly encouraged by the drug safety research funded by the European Commission (EC) and public-private partnerships such as the Innovative Medicines Initiative (IMI). This funding resulted in the conduct of groundwork necessary to overcome the hurdles of data sharing across countries for specific projects (e.g. PROTECT, ADVANCE, EMIF, EHDEN, ConcePTION) and for specific post-authorisation studies. With the recent ambition of the European Commission to strive towards a so-called European Health Data Space (EHDS), major breakthroughs in this field are expected. The Joint Action Towards the European Health Data Space – TEHDAS project develops joint European principles for the secondary use of health data. Also, in collaboration with, and acting as a pathfinder to EHDS, the DARWIN EU initiative started in 2022, as a federated network designed to support scientific evaluations and regulatory decision-making.

 

The 2009 H1N1 influenza pandemic (see Safety monitoring of Influenza A/H1N1 pandemic vaccines in EudraVigilance, Vaccine 2011;29(26):4378-87) and more recently, the 2020 COVID-19 pandemic showed the importance of a formal established infrastructure that can rapidly and effectively monitor the safety of therapeutics and vaccines. In this context, EMA has established contracts with academic and private partners to support readiness of research networks to perform observational research. Three dedicated projects started in 2020: ACCESS (vACcine Covid-19 monitoring readinESS), CONSIGN (COVID-19 infectiOn aNd medicineS In preGNancy) and E-CORE (Evidence for COVID-19 Observational Research Europe). Other initiatives have emerged to address specific COVID-19 related research questions, such as the CVD-COVID-UK consortium (Linked electronic health records for research on a nationwide cohort of more than 54 million people in England: data resource, BMJ. 2021;373:n826), providing a secure access to linked health data from primary and secondary care, registered deaths, COVID-19 laboratory and vaccination data, and cardiovascular specialist audits and covering almost the entire population of England (>54 million people); similar linked data have been made available in trusted research environments for Scotland and Wales (>8 million people).

 

In this chapter, the term networking is used to reflect collaboration between researchers for sharing expertise and resources. The ENCePP Database of Research Resources may facilitate such networking by providing an inventory of research centres and data sources that collaborate on specific pharmacoepidemiology and pharmacovigilance studies in Europe. It allows the identification of research centres and data sources by country, study, type of research and other relevant fields.

 

The use of research networks in drug safety, drug utilisation and disease epidemiology is well established. A significant body of practical experience exists, while the use in effectiveness research is becoming more established (Assessing strength of evidence for regulatory decision making in licensing: What proof do we need for observational studies of effectiveness?, Pharmacoepidemiol. Drug Saf. 2020;29(10):1336-40).

From a methodological point of view, studies that adopt a multi-database design have many advantages over single database studies:

  • Increase of the size of the study populations. This especially facilitates research on rare events, drugs used in specialised setting (see Ability of primary care health databases to assess medicinal products discussed by the European Union Pharmacovigilance Risk Assessment Committee, Clin Pharmacol Ther. 2020;107(4):957-65), or when the interest is in subgroup effects.

  • Exploit the heterogeneity of treatment options across countries, which allows studying the effect of different drugs used for the same indication or specific patterns of utilisation.

  • Exploit differences in outcome/event rates across countries/regions.

  • Provide additional knowledge on the generalisability of results and on the consistency of association, for instance whether a safety issue can be identified in several countries. Possible inconsistencies might be caused by different biases or truly different effects in the databases, revealing causes of differential drug effects, and these might be investigated.

  • Involve experts from various countries addressing case definitions, terminologies, coding in databases and research practices provides opportunities to increase consistency of results of observational studies.

  • In case of primary data collection, shorten the time needed for obtaining the desired sample size and therefore accelerate investigation of drug safety issues or other outcomes.

The articles Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies (BMJ Open 2020;10(10): e037405) and Different strategies to execute multi-database studies for medicines surveillance in real world setting: a reflection on the European model (Clin Pharmacol Ther. 2020;108(2):228-35) describe key characteristics of studies using multiple data sources and different models applied for combining data or results from multiple databases. A common characteristic of all models is the fact that data partners maintain physical and operational control over electronic data in their existing environment and therefore the data extraction is always done locally. Differences, however, exist in the following areas: use of a common protocol; use of a common data model (CDM); and where and how the data analysis is conducted.

 

Use of a CDM implies that local formats are translated into a predefined, common data structure, which allows launching a similar data extraction and analysis script across several databases. Sometimes the CDM imposes a common terminology as well, as in the case of the OMOP CDM. The CDM can be systematically applied on the entire database (generalised CDM) or on the subset of data needed for a specific study (study specific CDM). The CDM transformation is assumed to faithfully represent the source data both in term of completeness and accuracy. Validation studies such as Can We Rely on Results From IQVIA Medical Research Data UK Converted to the Observational Medical Outcome Partnership Common Data Model? A Validation Study Based on Prescribing Codeine in Children (Clin Pharmacol Ther. 2020;107(4): 915-25) are recommended and any deviations that might be found should be carefully monitored and recorded.

 

In the European Union, study specific CDMs have generated results in several projects and several databases have been converted to a generalised CDM version that exists alongside the native version. The conversion was accelerated during the last year thanks also to the role that observational research had in informing the response to the COVID-19 pandemic. An example of application of generalised CDMs are the studies conducted in the OHDSI community such as Association of angiotensin converting enzyme (ACE) inhibitors and angiotensin 2 receptor blockers (ARB) on COVID-19 incidence and complications or the ConcePTION studies: From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding (Clin Pharmacol Ther. 2022;111(1):321-31).

 

 

 

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