Publisher Correction: BMC Public Health 22, 1827 (2022)
https://doi.org/10.1186/s12889-022-14213-6
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In the original publication of this article [1]: Box 1 was omitted during the publication process. Box 1 has been included in this correction article, the original article has been updated.
Box 1 Suggested approaches to include real-world data in evidence synthesis
Identify potential overlapping populations by extracting data on:
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Where the data is from:
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○ Database or registry used
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○ Hospital (and if possible specific department(s) data is from)
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○ Geographical area(s)
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Time period of study
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Population characteristics (e.g., age range, background interventions or particular subgroup considered).
Options to minimise impact of double-counting of individuals/populations:
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Consider using a method of analysis which accounts for double-counting
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Contact authors to clarify aspects of the studies that are unclear
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Include all studies if double-counting cannot be fully determined
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Analyse studies at different time-points
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Preference of peer-reviewed studies
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Retain only one of any identified set of studies in which overlap is suspect by some rational criteria. For example, retain the:
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○ Largest study (i.e., study with the most participants)
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○ Most recent study
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○ Most complete data
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Authors could utilise an alternative study if the selected study does not have data for a particular outcome being analysed
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Obtain individual patient data
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Always conduct sensitivity analysis to assess robustness of results.
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NOTE: The authors are not recommending these approaches rather highlighting possible options; further work is required to understand the implications of these methods.
Reporting on approaches taken:
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Provide rationale for studies included in the evidence synthesis
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Discuss potential double-counting of data between studies
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Implications of double-counting and method used to account for it regarding interpretation of results.
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