Bayesian Kernel Machine Regression for Social Epidemiologic Research.

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Epidemiology Pub Date : 2024-11-01 Epub Date: 2024-08-01 DOI:10.1097/EDE.0000000000001777
Jemar R Bather, Taylor J Robinson, Melody S Goodman
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引用次数: 0

Abstract

Background: Little attention has been devoted to framing multiple continuous social variables as a "mixture" for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects.

Methods: Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable.

Results: We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31).

Conclusion: With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.

用于社会流行病学研究的贝叶斯核机器回归。
背景:在社会流行病学分析中,很少有人关注将多个连续社会变量作为 "混合物 "进行分析。我们建议使用贝叶斯核机器回归分析框架,该框架可产生单变量、双变量和总体暴露混合效应:利用 2023 年种族主义与公共健康调查的数据,我们进行了贝叶斯核机器回归分析,以研究作为暴露混合物的若干个人、社会和结构因素及其与至少有一次被警方逮捕的个人的心理困扰之间的关系。这些因素包括种族和经济两极分化、邻里贫困、歧视感知、警察感知、主观社会地位和药物使用。我们针对每个暴露混合变量建立了一系列未调整和调整模型,对上述分析进行了补充:我们发现,过去一年中自我报告的歧视经历越多(后纳入概率 = 1.00),药物使用越多(后纳入概率 = 1.00),心理压力就越大。这些关联与未调整和调整线性回归分析的结果一致:过去一年感知到的歧视(未调整 b = 2.58,95% CI:1.86,3.30;调整 b = 2.20,95% CI:1.45,2.94)和药物使用(未调整 b = 2.92,95% CI:2.21,3.62;调整 b = 2.59,95% CI:1.87,3.31):随着大数据的兴起以及长期队列和普查研究变量的扩大,相邻学科方法的新颖应用在确定社会流行病学中的暴露混合物关联和满足社会弱势群体的健康需求方面向前迈进了一步。
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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
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