Ranking sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health: A Bayesian machine learning approach.
Ranking sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health: A Bayesian machine learning approach.
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DOI：
10.1016/j.ypmed.2020.106240
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发表时间：
2020-12
影响因子：
5.1
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
Hu L;Liu B;Li Y
心血管疾病是美国人死亡的主要原因。虽然已经进行了大量的研究，以确定心血管疾病的危险因素在个人层面上，很少有人知道的因素，可能会影响人口心血管健康结果在社区层面。本研究的目的是使用贝叶斯加性回归树（Bayesian Additive Regression Trees），一种最先进的机器学习方法，对预测社区心血管健康的社会人口统计学，健康行为，预防和环境因素进行排名。我们创建了一个新的社区健康数据集，结合了人口普查区域层面的三个数据集，包括疾病控制和预防中心的500个城市数据，人口普查局的2011-2015年美国社区调查5年估计，以及美国环境保护局的2015-2016年环境正义筛选数据库。结果显示，社区行为因素，如肥胖，没有闲暇时间体育活动和酗酒的人的比例，成为大多数社区心血管健康结果的前五个预测因素。这项研究的结果将使公共卫生研究人员和政策制定者能够优先考虑基于社区的干预措施，并有效地利用有限的资源来改善社区的心血管健康。
Cardiovascular disease is the leading cause of death in the United States. While abundant research has been conducted to identify risk factors for cardiovascular disease at the individual level, less is known about factors that may influence population cardiovascular health outcomes at the neighborhood level. The purpose of this study is to use Bayesian Additive Regression Trees, a state-of-the-art machine learning approach, to rank sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health. We created a new neighborhood health dataset by combining three datasets at the census tract level, including the 500 Cities Data from the Centers for Disease Control and Prevention, the 2011-2015 American Community Survey 5-Year Estimates from the Census Bureau, and the 2015-2016 Environmental Justice Screening database from the Environmental Protection Agency in the United States. Results showed that neighborhood behavioral factors such as the proportions of people who are obese, do not have leisure-time physical activity, and have binge drinking emerged as top five predictors for most of the neighborhood cardiovascular health outcomes. Findings from this study would allow public health researchers and policymakers to prioritize community-based interventions and efficiently use limited resources to improve neighborhood cardiovascular health.
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影响因子：
1.8
作者：
Bleich, Justin;Kapelner, Adam;Jensen, Shane T.
通讯作者：
Jensen, Shane T.
通过社区参与的实施研究减少心血管差异：国家心脏，肺和血液研究所研讨会报告。
影响因子：
20.1
作者：
Mensah GA;Cooper RS;Siega-Riz AM;Cooper LA;Smith JD;Brown CH;Westfall JM;Ofili EO;Price LN;Arteaga S;Green Parker MC;Nelson CR;Newsome BJ;Redmond N;Roper RA;Beech BM;Brooks JL;Furr-Holden D;Gebreab SY;Giles WH;James RS;Lewis TT;Mokdad AH;Moore KD;Ravenell JE;Richmond A;Schoenberg NE;Sims M;Singh GK;Sumner AE;Treviño RP;Watson KS;Avilés-Santa ML;Reis JP;Pratt CA;Engelgau MM;Goff DC Jr;Pérez-Stable EJ
通讯作者：
Pérez-Stable EJ
影响因子：
5
作者：
Zhang X;Holt JB;Yun S;Lu H;Greenlund KJ;Croft JB
通讯作者：
Croft JB
影响因子：
158.5
作者：
Roux, AVD;Merkin, SS;Watson, RL
通讯作者：
Watson, RL
DOI：
10.1097/phh.0000000000000817
发表时间：
2019-01-01
影响因子：
3.3
作者：
Li, Yan;Liu, Shelley H.;Liu, Bian
通讯作者：
Liu, Bian