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社会、个人和行为风险因素及 PM 对社区卫生中心患者队列中心血管代谢差异的影响。

The Effects of Social, Personal, and Behavioral Risk Factors and PM on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients.

机构信息

Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA.

School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA.

出版信息

Int J Environ Res Public Health. 2020 May 19;17(10):3561. doi: 10.3390/ijerph17103561.

Abstract

(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM. (3) Results: The study resulted in four key findings: (1) PM concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m increase in PM; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.

摘要

(1) 背景:心血管代谢疾病(CMD)包括心血管疾病、中风和糖尿病,有许多共同的个体和环境危险因素。然而,迄今为止,很少有研究考虑这些多种危险因素如何共同影响黑人和白人之间的 CMD 差异。(2) 方法:我们将每日细颗粒物(PM)测量值与南方社区队列研究(SCCS)参与者的调查回复联系起来。广义线性混合模型(GLMM)用于估计 CMD 风险与社会人口统计学特征、行为和个人危险因素以及 PM 暴露水平之间的关系。(3) 结果:研究结果有四个关键发现:(1)PM 浓度水平与报告的 CMD 显著相关,PM 每增加 1 µg/m3,风险增加 2.6%;(2)当考虑临床、生活方式和环境危险因素时,种族并不能预测 CMD 风险;(3)在各州之间,参与者的 CMD 风险存在显著差异;(4)多个个人、临床、社会人口统计学和环境危险因素在预测 CMD 发生方面发挥作用。(4) 结论:低社会地位人群的 CMD 风险差异反映了自然、建筑和社会环境中不同个人和环境因素对 CMD 暴露和累积风险的复杂相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/15f2/7277630/9d7cbf2ddeaa/ijerph-17-03561-g001.jpg

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