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

Publisher Correction: BMC Public Health 22, 1827 (2022)

https://doi.org/10.1186/s12889-022-14213-6

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:

• Where the data is from:

  • ○ Database or registry used

  • ○ Hospital (and if possible specific department(s) data is from)

  • ○ Geographical area(s)

• Time period of study

• Population characteristics (e.g., age range, background interventions or particular subgroup considered).

Options to minimise impact of double-counting of individuals/populations:

• Consider using a method of analysis which accounts for double-counting

• Contact authors to clarify aspects of the studies that are unclear

• Include all studies if double-counting cannot be fully determined

• Analyse studies at different time-points

• Preference of peer-reviewed studies

• Retain only one of any identified set of studies in which overlap is suspect by some rational criteria. For example, retain the:

  • ○ Largest study (i.e., study with the most participants)

  • ○ Most recent study

  • ○ Most complete data

Authors could utilise an alternative study if the selected study does not have data for a particular outcome being analysed

• Obtain individual patient data

• Always conduct sensitivity analysis to assess robustness of results.

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:

• Provide rationale for studies included in the evidence synthesis

• Discuss potential double-counting of data between studies

• Implications of double-counting and method used to account for it regarding interpretation of results.