Wasfy Jason H, Stewart Charles, Bhambhani Vijeta
Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Department of Political Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS One. 2017 Oct 2;12(10):e0185051. doi: 10.1371/journal.pone.0185051. eCollection 2017.
In the U.S. presidential election of 2016, substantial shift in voting patterns occurred relative to previous elections. Although this shift has been associated with both education and race, the extent to which this shift was related to public health status is unclear.
To determine the extent to which county community health was associated with changes in voting between the presidential elections of 2016 and 2012.
Ecological study with principal component analysis (PCA) using principal axis method to extract the components, then generalized linear regression.
General community.
All counties in the United States.
Physically unhealthy days, mentally unhealthy days, percent food insecure, teen birth rate, primary care physician visit rate, age-adjusted mortality rate, violent crime rate, average health care costs, percent diabetic, and percent overweight or obese.
The percentage of Donald Trump votes in 2016 minus percentage of Mitt Romney votes in 2012 ("net voting shift").
Complete public health data was available for 3,009 counties which were included in the analysis. The mean net voting shift was 5.4% (+/- 5.8%). Of these 3,009 counties, 2,641 (87.8%) had positive net voting shift (shifted towards Trump) and 368 counties (12.2%) had negative net voting shift (shifted away from Trump). The first principal component ("unhealthy score") accounted for 68% of the total variance in the data. The unhealthy score included all health variables except primary care physician rate, violent crime rate, and health care costs. The mean unhealthy score for counties was 0.39 (SD 0.16). Higher normalized unhealthy score was associated with positive net voting shift (22.1% shift per unit unhealthy, p < 0.0001). This association was stronger in states that switched Electoral College votes from 2012 to 2016 than in other states (5.9% per unit unhealthy, p <0.0001).
Substantial association exists between a shift toward voting for Donald Trump in 2016 relative to Mitt Romney in 2012 and measures of poor public health. Although these results do not demonstrate causality, these results suggest a possible role for health status in political choices.
在2016年美国总统选举中，投票模式相对于以往选举发生了重大转变。尽管这种转变与教育和种族都有关联，但这种转变与公众健康状况的关联程度尚不清楚。
确定2016年与2012年总统选举期间，县社区健康状况与投票变化之间的关联程度。
采用主成分分析（PCA）的生态研究，使用主轴法提取成分，然后进行广义线性回归。
一般社区。
美国所有县。
身体不健康天数、精神不健康天数、粮食不安全百分比、青少年出生率、初级保健医生就诊率、年龄调整死亡率、暴力犯罪率、平均医疗保健费用、糖尿病百分比以及超重或肥胖百分比。
2016年唐纳德·特朗普的得票率减去2012年米特·罗姆尼的得票率（“净投票转变”）。
分析纳入了3009个县的完整公共卫生数据。平均净投票转变为5.4%（±5.8%）。在这3009个县中，2641个（87.8%）有正的净投票转变（转向特朗普），368个县（12.2%）有负的净投票转变（背离特朗普）。第一个主成分（“不健康得分”）占数据总方差的68%。不健康得分包括除初级保健医生率、暴力犯罪率和医疗保健费用之外的所有健康变量。各县的平均不健康得分为0.39（标准差0.16）。较高的标准化不健康得分与正的净投票转变相关（每单位不健康得分转变22.1%，p<0.0001）。在2012年至2016年选举中改变总统选举团投票的州，这种关联比其他州更强（每单位不健康得分转变5.9%，p<0.0001）。
相对于2012年投票给米特·罗姆尼，2016年投票给唐纳德·特朗普的转变与不良公共卫生指标之间存在显著关联。尽管这些结果并未证明因果关系，但这些结果表明健康状况在政治选择中可能发挥作用。
