Bayesian inference in racial health inequity analyses for noncommunicable diseases: a systematic review

Health inequalities are differences in health status or in the distribution of resources and opportunities between different population groups. Bayesian models are well-suited to address the special features and uncertainties in inequality analyses, making them useful for informing policymaking. This research reviewed the use of Bayesian models in racial health equity studies focused on non-communicable diseases.

A systematic review was conducted to assess the applications and utility of Bayesian inference in racial health equity studies for non-communicable diseases (PROSPERO Registry No. CRD42024568708). A total of 2274 articles were identified through electronic databases, and 46 studies met inclusion criteria. All but three articles were from high-income countries, and all were published between 2008 and 2024. We summarized the information qualitatively, and each document included was assessed using the Bennett-Manuel checklist tool.

Studies on cancer and cardiovascular diseases were the most frequent. The most frequently used models were Poisson, spatial, and logistic regressions, with Markov-chain Monte Carlo and Integrated nested Laplace approximations being the dominant sampling strategies. The studies found that Black individuals, followed by those of Hispanic ethnicity, are the racial/ethnic groups most affected by health inequities. Data on other racial groups (e.g., Indigenous populations, people of Asian heritage) was insufficient for drawing definitive conclusions. The main factor contributing to these disparities lies within the health system, particularly in terms of access and quality, which can be understood in the context of each disease.

The integration of Bayesian modeling into health equity studies holds promise for developing methodologies that lead to insights and foster meaningful change.