Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Prev Med. 2021 Apr;145:106435. doi: 10.1016/j.ypmed.2021.106435. Epub 2021 Jan 22.
This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy. The groups included: (1) High Change (19 states; 7-day prevalence change ≥50th percentile), (2) Low Change (19 states; 7-day prevalence change <50th percentile), (3) No SAH (11 states: did not adopt SAH order), and (4) No SAH End (2 states: did not relax SAH order). We performed regression modeling assessing the association between change in prevalence at the time of SAH order relaxation and COVID-19 prevalence days after the relaxation of SAH order for four selected groups. After adjusting for other factors, compared to the High Change group, counties in the Low Change group had 33.8 (per 100,000 population) fewer cases (standard error (SE): 19.8, p < 0.001) 7 days after the relaxation of SAH order and the difference was larger by time passing. On August 21, 2020, the No SAH End group had 383.1 fewer cases (per 100,000 population) than the High Change group (SE: 143.6, p < 0.01). A measured, evidence-based approach is required to safely relax the community mitigation strategies and practice phased-reopening of the country.
这项研究旨在评估美国在放宽居家令前一周冠状病毒病(COVID-19)流行对实施不同居家令政策的各州未来流行率的影响。我们使用截至 2020 年 8 月 21 日县一级确诊 COVID-19 病例数的数据。我们根据流行率的 7 天变化和各州对居家令政策的处理方法,将各州分为四组。这四组包括:(1)高变化(19 个州;7 天流行率变化≥第 50 百分位数),(2)低变化(19 个州;7 天流行率变化<第 50 百分位数),(3)无居家令(11 个州:未采取居家令),和(4)无居家令结束(2 个州:未放宽居家令)。我们进行回归建模,评估居家令放宽时流行率的变化与居家令放宽后 COVID-19 流行天数之间的关联,对四个选定组进行评估。在调整其他因素后,与高变化组相比,居家令放宽后 7 天,低变化组的县每 10 万人减少 33.8 例(标准误差(SE):19.8,p<0.001),且随着时间的推移差异更大。2020 年 8 月 21 日,与高变化组相比,无居家令结束组每 10 万人减少 383.1 例(SE:143.6,p<0.01)。需要采取有针对性、基于证据的方法来安全地放宽社区缓解策略,并分阶段重新开放国家。