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Double-counting of populations in evidence synthesis in public health: a call for awareness and future methodological development

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posted on 2022-10-27, 10:16 authored by Humaira Hussein, Clareece Nevill, Anna Meffen, Keith Abrams, Sylwia Bujkiewicz, Alex Sutton, Laura Gray

Background

There is a growing interest in the inclusion of real-world and observational studies in evidence synthesis such as meta-analysis and network meta-analysis in public health. While this approach offers great epidemiological opportunities, use of such studies often introduce a significant issue of double-counting of participants and databases in a single analysis. Therefore, this study aims to introduce and illustrate the nuances of double-counting of individuals in evidence synthesis including real-world and observational data with a focus on public health.


Methods

The issues associated with double-counting of individuals in evidence synthesis are highlighted with a number of case studies. Further, double-counting of information in varying scenarios is discussed with potential solutions highlighted.


Results

Use of studies of real-world data and/or established cohort studies, for example studies evaluating the effectiveness of therapies using health record data, often introduce a significant issue of double-counting of individuals and databases. This refers to the inclusion of the same individuals multiple times in a single analysis. Double-counting can occur in a number of manners, such as, when multiple studies utilise the same database, when there is overlapping timeframes of analysis or common treatment arms across studies. Some common practices to address this include synthesis of data only from peer-reviewed studies, utilising the study that provides the greatest information (e.g. largest, newest, greater outcomes reported) or analysing outcomes at different time points.


Conclusions

While common practices currently used can mitigate some of the impact of double-counting of participants in evidence synthesis including real-world and observational studies, there is a clear need for methodological and guideline development to address this increasingly significant issue.

Funding

HOD1: Inferring relative treatment effects from combined randomised and observational data

Medical Research Council

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SRP Complex Reviews Research Support Unit

National Institute for Health Research

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NIHR Pre-Doctoral Research Fellowship

George Davies Charitable Trust [Registered Charity Number 1024818]

National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM)

History

Author affiliation

Department of Health Sciences, University of Leicester

Version

  • VoR (Version of Record)

Published in

BMC Public Health

Volume

22

Publisher

BioMed Central

issn

1471-2458

Copyright date

2022

Available date

2022-10-27

Language

en

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