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, 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 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) data augmentation. 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. Keywords: Bayesian statistics, health disparities, survey design, population health

翻译：理解某些社区为何及如何承受不成比例的疾病负担颇具挑战，原因在于这些社区的数据稀缺。调查为接触难以触及的人群提供了有效途径，许多调查会特意对研究不足和脆弱人群进行过度抽样。使用调查数据进行分析时，必须考虑生成数据的复杂调查设计，以避免得出有偏差的结论。贝叶斯调查统计领域旨在解释此类调查设计，同时发挥贝叶斯模型的优势，这些模型能通过信息借用灵活处理数据稀疏性，并提供连贯的推断框架以轻松获取复杂模型与数据类型下的方差。基于此，贝叶斯调查方法似乎特别适用于健康差异研究——异质性和稀疏性在此类研究中是常见考量。本文综述了贝叶斯调查方法学文献中的三大主要路径：1）多水平回归与事后分层，2）基于加权伪似然的方法，3）数据增广。我们讨论了各方法的优缺点，审视了近期应用与扩展，并思考如何利用这些方法改进人口健康公平性研究。关键词：贝叶斯统计，健康差异，调查设计，人口健康