Author Affiliations: Department of Social and Behavioral Sciences (Ms Rushovich and Dr Krieger) and Department of Biostatistics (Dr Nethery), Harvard T.H. Chan School of Public Health, Boston, Massachusetts; and Department of Political Science, Massachusetts Institute of Technology (Dr White), Cambridge, Massachusetts.
J Public Health Manag Pract. 2024;30(6):832-843. doi: 10.1097/PHH.0000000000001916. Epub 2024 Aug 22.
Technological innovation and access to big data have allowed partisan gerrymandering to increase dramatically in recent redistricting cycles.
To understand whether and how partisan gerrymandering, including "packing" and "cracking" (ie, respectively concentrating within or dividing specified social groups across political boundaries), distorts understanding of public health need when health statistics are calculated for congressional districts (CDs).
Cross-sectional study using 2020 CDs and nonpartisan simulated districts.
United States, 2017-2021.
United States residents.
Percent with no medical insurance (uninsured), within-district variance of percent uninsured, and between-district variance of percent uninsured.
At the state level, states where partisan redistricting plans showed greater evidence of partisan gerrymandering were more likely to contain CDs with more extreme values of uninsurance rates than districts in states with less evidence for gerrymandering (association between z-scores for gerrymandering and between-district variation in uninsurance = 0.25 (-0.04, 0.53), P = .10). Comparing variation in uninsurance rates for observed CDs vs nonpartisan simulated districts across all states with more than 1 CD, in analyses stratified by state gerrymander status (no gerrymander, Democratic gerrymander, and Republican gerrymander), we found evidence of particularly extreme distortion of rates in Republican gerrymandered states, whereby Republican-leaning districts tended to have lower uninsurance rates (the percentage of Republican-leaning districts that were significantly lower than nonpartisan simulated districts was 5.1 times that of Democratic-leaning districts) and Democrat-leaning districts had higher uninsurance rates (the percentage of Democrat-leaning districts that were significantly higher than nonpartisan simulated districts was 3.0 times that of Republican-leaning districts).
Partisan gerrymandering can affect determination of CD-level uninsurance rates and distort understanding of public health burdens.
技术创新和大数据的获取使得党派划分在最近的重新划分周期中急剧增加。
了解党派划分(包括“集中”和“分散”,即分别在政治边界内集中或划分特定社会群体)是否以及如何扭曲对国会选区(CD)中公共卫生需求的理解,当使用卫生统计数据计算时。
使用 2020 年 CD 和无党派模拟区的横断面研究。
美国,2017-2021。
美国居民。
无医疗保险(未参保)的百分比、区内无保险率的方差和区际无保险率的方差。
在州一级,党派重新划分计划显示出更多党派划分迹象的州,其 CD 中无保险率更极端的可能性更大,而在划分较少的州则更为明显(gerrymandering 的 z 分数与无保险率的区际差异之间的关联=0.25(-0.04,0.53),P=0.10)。比较所有有超过 1 个 CD 的州中观察到的 CD 与无党派模拟区之间的保险率变化,按州 gerrymander 状态(无 gerrymander、民主 gerrymander 和共和 gerrymander)分层分析,我们发现了共和党 gerrymander 州中特别极端的扭曲率的证据,共和党倾向的地区往往保险率较低(明显低于无党派模拟地区的共和党倾向地区比例是民主倾向地区的 5.1 倍),而民主党倾向地区的保险率较高(明显高于无党派模拟地区的民主党倾向地区比例是共和倾向地区的 3.0 倍)。
党派划分可能会影响 CD 级别的未参保率的确定,并扭曲对公共卫生负担的理解。