How can additional secondary data analysis of observational data enhance the generalisability of meta-analytic evidence for local public health decision making?

This paper critically explores how survey and routinely collected data could aid in assessing the generalisability of public health evidence. We propose developing approaches that could be employed in understanding the relevance of public health evidence, and investigate ways of producing meta-analytic estimates tailored to reflect local circumstances, based on analyses of secondary data. Currently, public health decision makers face challenges in interpreting global review evidence to assess its meaning in local contexts. A lack of clarity on the definition and scope of generalisability, and the absence of consensus on its measurement, has stunted methodological progress. The consequence of failing to tackle generalisability means that systematic review evidence often fails to fulfil its potential contribution in public health decision making. Three approaches to address these problems are considered and emerging challenges discussed: (1) purposeful exploration after a review has been conducted, and we present a framework of potential avenues of enquiry and a worked example; (2) recalibration of the results to weight studies differentially based on their similarity to conditions in an inference population, and we provide a worked example using UK Census data to understand potential differences in the effectiveness of community engagement interventions among sites in England and Wales; (3) purposeful exploration before starting a review to ensure that the findings are relevant to an inference population. The paper aims to demonstrate how a more nuanced treatment of context in reviews of public health interventions could be achieved through greater engagement with existing large sources of secondary data.