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：理解某些社区为何承担不成比例的疾病负担颇具挑战，这主要源于这些社区的数据稀缺性。调查为接触难以触及的人群提供了有效途径，因为许多调查专门对研究不足的脆弱人群进行了过度抽样。在利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，以避免得出有偏结论。贝叶斯调查统计领域旨在在考虑此类调查设计的同时，发挥贝叶斯模型的优势——该模型能够通过信息借入灵活处理稀疏性问题，并为复杂模型和数据类型提供易于获取方差的一致推断框架。基于这些原因，贝叶斯调查方法似乎天然适用于健康差异研究，而异质性和稀疏性正是此类研究中的常见考量。本综述探讨了贝叶斯调查方法文献中的三种主要方法：1）多水平回归与后分层，2）基于加权伪似然的方法，以及3）合成人群生成。我们讨论了每种方法的优缺点，审视了近期应用与扩展，并思考如何利用这些方法改进人群健康公平性研究。