Division of Health Care Delivery Research, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA
Department of Cardiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
BMJ Open. 2022 Apr 7;12(4):e055791. doi: 10.1136/bmjopen-2021-055791.
OBJECTIVE: We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. DESIGN: We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. INTERVENTIONS: Issuance of stay-at-home orders. PRIMARY AND SECONDARY OUTCOME MEASURES: Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. RESULTS: Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. CONCLUSION: Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.
目的:我们研究了美国农村与城市县实施居家令与 COVID-19 感染和死亡人数之间的关联。
设计:我们使用混合效应零膨胀泊松模型,采用县随机截距进行分析,按人口标准化,以检验 2020 年 1 月 22 日至 6 月 10 日期间,居家令与农村和城市县每日新增 COVID-19 病例和死亡人数之间的关联。我们还使用谷歌社区流动性报告,检验了居家令与农村和城市县流动性之间的关联。
干预措施:发布居家令。
主要和次要结局指标:共同主要结局指标为 COVID-19 每日发病率(14 天滞后)和死亡率(26 天滞后)。次要结局为流动性。
结果:农村县比城市县更晚(中位数 2020 年 3 月 30 日比 2020 年 3 月 28 日)且持续时间更短(中位数 35 天比 54 天)实施居家令。在居家令实施期间和之后,农村县的室内流动性平均比城市县高 2.6%-6.9%。与基线(居家前)期相比,农村县的新 COVID-19 病例数量增加了发病率风险比(IRR)1.60(95%CI,1.57-1.64),城市县增加了 1.36(95%CI,1.30-1.42),而农村县的新 COVID-19 死亡人数增加了 IRR 14.21(95%CI,11.02-18.34),城市县为 IRR 2.93(95%CI,1.82-4.73)。在居家令实施的每一天,农村县的新发病例数量变化了 0.982(95%CI,0.981-0.982),城市县为 0.952(95%CI,0.951-0.953),与居家前相比,而农村县的新死亡人数变化了 0.977(95%CI,0.976-0.977),城市县为 0.935(95%CI,0.933-0.936)。居家令失效后的每一天,农村县的新发病例数量变化了 0.995(95%CI,0.994-0.995),城市县为 0.997(95%CI,0.995-0.999),与居家前相比,而农村县的新死亡人数变化了 0.969(95%CI,0.968-0.970),城市县为 0.928(95%CI,0.926-0.929)。
结论:居家令减少了流动性,减缓了 COVID-19 的传播速度并减轻了 COVID-19 的死亡率,但在农村县的效果不如城市县显著。这需要对农村地区居家令的设计、传达和实施方式进行批判性评估。
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