Data_Sheet_1_Bayesian network model of ethno-racial disparities in cardiometabolic-based chronic disease using NHANES 1999–2018.DOCX

BackgroundEthno-racial disparities in cardiometabolic diseases are driven by socioeconomic, behavioral, and environmental factors. Bayesian networks offer an approach to analyze the complex interaction of the multi-tiered modifiable factors and non-modifiable demographics that influence the incidence and progression of cardiometabolic disease.MethodsIn this study, we learn the structure and parameters of a Bayesian network based on 20 years of data from the US National Health and Nutrition Examination Survey to explore the pathways mediating associations between ethno-racial group and cardiometabolic outcomes. The impact of different factors on cardiometabolic outcomes by ethno-racial group is analyzed using conditional probability queries.ResultsMultiple pathways mediate the indirect association from ethno-racial group to cardiometabolic outcomes: (1) ethno-racial group to education and to behavioral factors (diet); (2) education to behavioral factors (smoking, physical activity, and—via income—to alcohol); (3) and behavioral factors to adiposity-based chronic disease (ABCD) and then other cardiometabolic drivers. Improved diet and physical activity are associated with a larger decrease in probability of ABCD stage 4 among non-Hispanic White (NHW) individuals compared to non-Hispanic Black (NHB) and Hispanic (HI) individuals.ConclusionEducation, income, and behavioral factors mediate ethno-racial disparities in cardiometabolic outcomes, but traditional behavioral factors (diet and physical activity) are less influential among NHB or HI individuals compared to NHW individuals. This suggests the greater contribution of unmeasured individual- and/or neighborhood-level structural determinants of health that impact cardiometabolic drivers among NHB and HI individuals. Further study is needed to discover the nature of these unmeasured determinants to guide cardiometabolic care in diverse populations.

背景种族和民族在心代谢疾病中的不平等现象，其成因主要在于社会经济、行为和环境等因素的相互作用。贝叶斯网络提供了一种分析多层级可调节因素与不可调节的人口统计数据之间复杂交互作用的方法，这些因素影响着心代谢疾病的发生和发展。在本研究中，我们基于美国国家健康与营养检查调查20年的数据，学习贝叶斯网络的结构和参数，以探讨种族和民族群体与心代谢结果之间的中介途径。通过对不同因素对种族和民族群体心代谢结果影响的条件概率查询分析，得出以下结果：多种途径中介了种族和民族群体与心代谢结果之间的间接关联：（1）种族和民族群体与教育及行为因素（饮食）；（2）教育因素与行为因素（吸烟、身体活动，以及通过收入影响酒精）；（3）以及行为因素与基于肥胖的慢性疾病（ABCD）和其他心代谢驱动因素。与黑人（NHB）和西班牙裔（HI）个体相比，改善饮食和身体活动与白人（NHW）个体中ABCD阶段4的概率降低更大。结论表明，教育、收入和行为因素在调节心代谢结果中的种族和民族不平等现象中起着中介作用，但在黑人或西班牙裔个体中，与传统行为因素（饮食和身体活动）相比，其影响力较小。这表明未测量的个人和/或社区层面的健康结构决定因素在黑人及西班牙裔个体中心代谢驱动因素中的影响更为显著。为进一步研究这些未测量的决定因素的性质，以指导不同人群的心代谢保健，需要进一步的研究。