Department of Public Administration and Policy, Rockefeller College of Public Affairs and Policy, University at Albany, State University of New York (SUNY), Albany, New York.
J Public Health Manag Pract. 2020 Nov/Dec;26(6):622-631. doi: 10.1097/PHH.0000000000001236.
To evaluate predictors of stay-at-home order adoption among US states, as well as associations between order enactment and residents' mobility.
We assess associations between state characteristics and adoption timing. We also assess associations between enactment and aggregate state-level measures of residents' mobility (Google COVID-19 Community Mobility Reports).
The United States.
Adoption population: 50 US states and District of Columbia. Mobility population: state residents using devices with GPS tracking accessible by Google.
State characteristics: COVID-19 diagnoses per capita, 2016 Trump vote share, Republican governor, Medicaid expansion status, hospital beds per capita, public health funding per capita, state and local tax revenue per capita, median household income, population, percent residents 65 years or older, and percent urban residents. Mobility exposure: indicator of order enactment by March 29, 2020 (date of mobility data collection).
Order adoption timing: days since adoption of first order. Mobility: changes in mobility to 6 locations from February 6 to March 29, 2020.
In bivariate models, order adoption was associated with COVID-19 diagnoses (hazard ratio [HR] = 1.01; 95% confidence interval [CI], 1.00 to 1.01), Republican governor (HR = 0.24; 95% CI, 0.13 to 0.44), Medicaid expansion (HR = 2.50; 95% CI, 1.40 to 4.48), and hospital capacity (HR = 0.43; 95% CI, 0.26 to 0.70), consistent with findings in the multivariate models. Order enactment was positively associated with time at home (beta (B) = 1.31; 95% CI, 0.35 to 2.28) and negatively associated with time at retail and recreation (B = -7.17; 95% CI, -10.89 to -3.46) and grocery and pharmacy (B = -8.28; 95% CI, -11.97 to -4.59) locations. Trump vote share was associated with increased mobility for 4 of 6 mobility measures.
While politics influenced order adoption, public health considerations were equally influential. While orders were associated with decreased mobility, political ideology was associated with increased mobility under social distancing policies.
评估美国各州发布居家令的预测因素,以及颁布令与居民流动性之间的关联。
我们评估州特征与采用时间之间的关联。我们还评估了颁布令与居民流动性的州级综合指标(谷歌 COVID-19 社区流动性报告)之间的关联。
美国。
采用人群:美国 50 个州和哥伦比亚特区。流动性人群:使用具有谷歌可访问的 GPS 追踪功能的设备的州居民。
州特征:每千人 COVID-19 诊断数、2016 年特朗普投票份额、共和党州长、医疗补助扩展状况、每千人病床数、人均公共卫生资金、人均州和地方税收收入、家庭中位数收入、人口数、65 岁及以上居民比例、城市居民比例。流动性暴露因素:2020 年 3 月 29 日(流动性数据收集日期)前颁布令的指标。
命令采用时间:从颁布第一份命令起的天数。流动性:从 2020 年 2 月 6 日至 3 月 29 日,6 个地点的流动性变化。
在双变量模型中,采用居家令与 COVID-19 诊断(风险比[HR] = 1.01;95%置信区间[CI],1.00 至 1.01)、共和党州长(HR = 0.24;95% CI,0.13 至 0.44)、医疗补助扩展(HR = 2.50;95% CI,1.40 至 4.48)和医院容量(HR = 0.43;95% CI,0.26 至 0.70)相关,与多变量模型中的发现一致。颁布令与在家时间呈正相关(B = 1.31;95% CI,0.35 至 2.28),与零售和娱乐场所(B = -7.17;95% CI,-10.89 至 -3.46)和杂货店和药店(B = -8.28;95% CI,-11.97 至 -4.59)的时间呈负相关。特朗普的投票份额与 6 项流动性指标中的 4 项呈正相关。
虽然政治因素影响了命令的采用,但公共卫生因素同样具有影响力。虽然命令与流动性下降有关,但在社会隔离政策下,政治意识形态与流动性增加有关。