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Understanding how and why certain communities bear a disproportionate burdenof disease is challenging due to the scarcity of data on these communities.Surveys provide a useful avenue for accessing hard-to-reach populations, asmany surveys specifically oversample understudied and vulnerable populations.When survey data is used for analysis, it is important to account for thecomplex survey design that gave rise to the data, in order to avoid biasedconclusions. The field of Bayesian survey statistics aims to account for suchsurvey design while leveraging the advantages of Bayesian models, which canflexibly handle sparsity through borrowing of information and provide acoherent inferential framework to easily obtain variances for complex modelsand data types. For these reasons, Bayesian survey methods seem uniquelywell-poised for health disparities research, where heterogeneity and sparsityare frequent considerations. This review discusses three main approaches foundin the Bayesian survey methodology literature: 1) multilevel regression andpost-stratification, 2) weighted pseudolikelihood-based methods, and 3)synthetic population generation. We discuss advantages and disadvantages ofeach approach, examine recent applications and extensions, and consider howthese approaches may be leveraged to improve research in population healthequity.

Bayesian estimation methods for survey data with potential applications to health disparities research