Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard-to-reach populations, as many surveys specifically oversample understudied and vulnerable populations. When survey data is used for analysis, it is important to account for the complex survey design that gave rise to the data, in order to avoid biased conclusions. The field of Bayesian survey statistics aims to account for such survey design while leveraging the advantages of Bayesian models, which can flexibly handle sparsity through borrowing of information and provide a coherent inferential framework to easily obtain variances for complex models and data types. For these reasons, Bayesian survey methods seem uniquely well-poised for health disparities research, where heterogeneity and sparsity are frequent considerations. This review discusses three main approaches found in the Bayesian survey methodology literature: 1) multilevel regression and post-stratification, 2) weighted pseudolikelihood-based methods, and 3) synthetic population generation. We discuss advantages and disadvantages of each approach, examine recent applications and extensions, and consider how these approaches may be leveraged to improve research in population health equity.

翻译：理解某些社区为何及如何承担不成比例的疾病负担颇具挑战，原因在于针对这些社区的数据稀缺。调查为接触难以到达的人群提供了有效途径，因为许多调查特意对研究不足的弱势群体进行过度抽样。当使用调查数据进行分析时，必须考虑产生数据的复杂调查设计，以避免得出有偏的结论。贝叶斯调查统计学领域旨在考虑此类调查设计，同时利用贝叶斯模型的优势：通过信息借入灵活处理稀疏性，并提供一套连贯的推断框架，以轻松获取复杂模型与数据类型对应的方差。基于这些原因，贝叶斯调查方法似乎尤其适用于健康差异研究——在该领域，异质性与稀疏性是需要经常考虑的问题。本综述讨论了贝叶斯调查方法学文献中的三种主要方法：1）多水平回归与事后分层，2）基于加权伪似然的方法，以及3）合成群体生成。我们论述了每种方法的优劣，考察了近期应用与扩展，并思考如何利用这些方法增强人口健康公平性研究。