Effective coverage measures assess the proportion of a population that receive a health intervention with sufficient quality to achieve health benefit. Linking population-based surveys and health facility data is a promising means of generating effective coverage estimates, however, little guidance exists on methodological considerations for these analyses. We conducted a systematic review to assess existing knowledge related to 1) the suitability of data used in linking analyses, 2) the implications of the design of existing data sources commonly used in linking analyses, and 3) the impact of choice of method for combining datasets to obtain linked coverage estimates. The primary search was completed in Medline, with additional reviews of select sources. Of 3192 papers reviewed, 62 publications addressed issues related to linking household and provider datasets. Limited data suggest household surveys can be used to identify sources of care, but their validity in estimating a denominator of intervention need was variable. Methods for collecting provider data and constructing quality indices were variable and presented limitations. There was little empirical data supporting an association between structural, process, and outcome quality. Few studies addressed the influence of the design of common data sources on linking analyses, including imprecise household GIS data, provider sampling frame and sampling design, and estimate stability. There was a lack of concrete evidence around the impact of these factors on linked effective coverage estimates. The most consistent evidence suggested under certain conditions, combining data sets based on geographical proximity (ecological linking) produced similar estimates to linking based on the specific provider utilized (exact-match linking). Linking household and healthcare provider can leverage existing data sources to generate more informative estimates of intervention coverage and care. However, there is need for additional research to develop evidence-based, standardized best practices for these analyses.
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