Council on Foreign Relations, Washington, DC, USA.
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Lancet. 2023 Apr 22;401(10385):1341-1360. doi: 10.1016/S0140-6736(23)00461-0. Epub 2023 Mar 23.
BACKGROUND: The USA struggled in responding to the COVID-19 pandemic, but not all states struggled equally. Identifying the factors associated with cross-state variation in infection and mortality rates could help to improve responses to this and future pandemics. We sought to answer five key policy-relevant questions regarding the following: 1) what roles social, economic, and racial inequities had in interstate variation in COVID-19 outcomes; 2) whether states with greater health-care and public health capacity had better outcomes; 3) how politics influenced the results; 4) whether states that imposed more policy mandates and sustained them longer had better outcomes; and 5) whether there were trade-offs between a state having fewer cumulative SARS-CoV-2 infections and total COVID-19 deaths and its economic and educational outcomes. METHODS: Data disaggregated by US state were extracted from public databases, including COVID-19 infection and mortality estimates from the Institute for Health Metrics and Evaluation's (IHME) COVID-19 database; Bureau of Economic Analysis data on state gross domestic product (GDP); Federal Reserve economic data on employment rates; National Center for Education Statistics data on student standardised test scores; and US Census Bureau data on race and ethnicity by state. We standardised infection rates for population density and death rates for age and the prevalence of major comorbidities to facilitate comparison of states' successes in mitigating the effects of COVID-19. We regressed these health outcomes on prepandemic state characteristics (such as educational attainment and health spending per capita), policies adopted by states during the pandemic (such as mask mandates and business closures), and population-level behavioural responses (such as vaccine coverage and mobility). We explored potential mechanisms connecting state-level factors to individual-level behaviours using linear regression. We quantified reductions in state GDP, employment, and student test scores during the pandemic to identify policy and behavioural responses associated with these outcomes and to assess trade-offs between these outcomes and COVID-19 outcomes. Significance was defined as p<0·05. FINDINGS: Standardised cumulative COVID-19 death rates for the period from Jan 1, 2020, to July 31, 2022 varied across the USA (national rate 372 deaths per 100 000 population [95% uncertainty interval [UI] 364-379]), with the lowest standardised rates in Hawaii (147 deaths per 100 000 [127-196]) and New Hampshire (215 per 100 000 [183-271]) and the highest in Arizona (581 per 100 000 [509-672]) and Washington, DC (526 per 100 000 [425-631]). A lower poverty rate, higher mean number of years of education, and a greater proportion of people expressing interpersonal trust were statistically associated with lower infection and death rates, and states where larger percentages of the population identify as Black (non-Hispanic) or Hispanic were associated with higher cumulative death rates. Access to quality health care (measured by the IHME's Healthcare Access and Quality Index) was associated with fewer total COVID-19 deaths and SARS-CoV-2 infections, but higher public health spending and more public health personnel per capita were not, at the state level. The political affiliation of the state governor was not associated with lower SARS-CoV-2 infection or COVID-19 death rates, but worse COVID-19 outcomes were associated with the proportion of a state's voters who voted for the 2020 Republican presidential candidate. State governments' uses of protective mandates were associated with lower infection rates, as were mask use, lower mobility, and higher vaccination rate, while vaccination rates were associated with lower death rates. State GDP and student reading test scores were not associated with state COVD-19 policy responses, infection rates, or death rates. Employment, however, had a statistically significant relationship with restaurant closures and greater infections and deaths: on average, 1574 (95% UI 884-7107) additional infections per 10 000 population were associated in states with a one percentage point increase in employment rate. Several policy mandates and protective behaviours were associated with lower fourth-grade mathematics test scores, but our study results did not find a link to state-level estimates of school closures. INTERPRETATION: COVID-19 magnified the polarisation and persistent social, economic, and racial inequities that already existed across US society, but the next pandemic threat need not do the same. US states that mitigated those structural inequalities, deployed science-based interventions such as vaccination and targeted vaccine mandates, and promoted their adoption across society were able to match the best-performing nations in minimising COVID-19 death rates. These findings could contribute to the design and targeting of clinical and policy interventions to facilitate better health outcomes in future crises. FUNDING: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.
背景:美国在应对 COVID-19 大流行方面举步维艰,但并非所有州都同样艰难。确定各州间感染率和死亡率差异的相关因素可能有助于改善对此类和未来大流行的应对措施。我们试图回答五个与以下方面相关的关键政策问题:1)社会、经济和种族不平等在 COVID-19 结果的州际差异中扮演了什么角色;2)医疗保健和公共卫生能力较强的州是否有更好的结果;3)政治如何影响结果;4)实施更多政策命令并长期维持的州是否有更好的结果;5)一个州的累计 SARS-CoV-2 感染数和总 COVID-19 死亡人数与其经济和教育结果之间是否存在权衡。
方法:从包括 IHME 的 COVID-19 数据库中的 COVID-19 感染和死亡率估计、美国人口普查局按州划分的种族和族裔数据在内的公共数据库中提取按美国州划分的数据。我们对感染率进行了标准化,以反映人口密度,对死亡率进行了标准化,以反映年龄和主要合并症的流行程度,以便比较各州减轻 COVID-19 影响的成功程度。我们将这些健康结果与大流行前的州特征(如教育程度和人均医疗保健支出)、各州在大流行期间采取的政策(如口罩令和商业关闭)以及人口水平的行为反应(如疫苗接种率和流动性)进行回归分析。我们使用线性回归探索连接州级因素与个体行为的潜在机制。我们量化了大流行期间州 GDP、就业和学生标准化考试成绩的下降,以确定与这些结果相关的政策和行为反应,并评估这些结果与 COVID-19 结果之间的权衡。显著性定义为 p<0·05。
结果:2020 年 1 月 1 日至 2022 年 7 月 31 日期间,美国各州的标准化累计 COVID-19 死亡率存在差异(全国死亡率为每 100000 人 372 人[95%不确定区间[UI]364-379]),死亡率最低的州是夏威夷(每 100000 人 147 人[127-196])和新罕布什尔州(每 100000 人 215 人[183-271]),死亡率最高的州是亚利桑那州(每 100000 人 581 人[509-672])和华盛顿特区(每 100000 人 526 人[425-631])。较低的贫困率、较高的平均受教育年限和较高的人际信任比例与较低的感染率和死亡率呈统计学相关,而人口中黑人(非西班牙裔)或西班牙裔比例较高的州与较高的累计死亡率相关。医疗保健获取(由 IHME 的医疗保健获取和质量指数衡量)与总 COVID-19 死亡人数和 SARS-CoV-2 感染人数较少有关,但州级医疗保健支出和每万人拥有的公共卫生人员数量较高则没有关系。州州长的政治派别与 SARS-CoV-2 感染或 COVID-19 死亡率的降低无关,但一个州的选民中投票给 2020 年共和党总统候选人的比例较高与较差的 COVID-19 结果相关。州政府使用保护命令与较低的感染率有关,与口罩使用、较低的流动性和较高的疫苗接种率有关,而疫苗接种率与较低的死亡率有关。州 GDP 和学生阅读测试成绩与州 COVID-19 政策反应、感染率或死亡率无关。然而,就业与餐馆关闭以及更多的感染和死亡有统计学上的显著关系:在就业增长率提高一个百分点的州,每 10000 人将增加 1574(95% UI 884-7107)例感染。几项政策命令和保护性行为与四年级数学考试成绩较低有关,但我们的研究结果并未发现与州级学校关闭估计值有关。
结论:COVID-19 放大了美国社会中已经存在的极化和持续的社会、经济和种族不平等,但下一次大流行威胁不必如此。减轻这些结构性不平等、部署基于科学的干预措施(如疫苗接种和有针对性的疫苗命令)并促进其在整个社会中推广的美国各州能够将死亡率降至最低,与表现最好的国家相匹配。这些发现可能有助于设计和定位临床和政策干预措施,以促进未来危机中的更好健康结果。
资金来源:比尔及梅琳达·盖茨基金会、J 斯坦顿、T 吉列斯皮、J 和 E 诺德斯特姆、彭博慈善基金会。
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