BACKGROUND With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. METHODS AND FINDINGS County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran's I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%-29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37-49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34-44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. CONCLUSIONS Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

中文翻译：

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调查美国 COVID-19 死亡率与人口健康和社会经济指标之间的关联：一项模型研究。

背景随着多种 2019 冠状病毒病 (COVID-19) 疫苗的出现以及预计在不久的将来供应短缺，有必要以尽量减少严重后果（尤其是死亡）的方式分配疫苗。迄今为止，美国的疫苗接种策略主要关注年龄和职业等个人特征。在这里，我们评估人口健康和社会经济指标作为疫苗地理分配的附加标准的效用。方法和结果 从公共数据源中提取了县级与 COVID-19 死亡率相关的 14 项指标的估计值。使用单变量模型计算各个指标的效果估计。使用 Moran's I 统计数据确定空间自相关的存在。使用考虑响应和预测变量空间自相关的空间同时自回归 (SAR) 模型来评估 (i) 可通过确定的健康/社会经济指标 (R2) 解释的县级 COVID-19 死亡率的方差比例；(ii) 每个预测变量的效果估计。调整病例发生率后，选定的指标分别解释了 24%-29% 的死亡率变异性。慢性肾病的患病率和居住在疗养院的人口比例的 R2 最高。据估计，随着慢性肾病患病率增加 1%，每千名居民死亡率将增加 43 人（95% CI：37-49；p < 0.001），每千人死亡人数将增加 39 人（95% CI：34-44；p < 0.001）。 p < 0.001），居住在疗养院的人口增加了 1%。使用多种健康/社会经济指标的 SAR 模型解释了美国各县 43% 的 COVID-19 死亡率变异性（根据病例发生率进行调整）。发现R2对SAR模型形式的选择不敏感。研究的局限性包括使用未进行年龄标准化的死亡率、未捕获县间人口流动的空间邻接矩阵以及对预测变量之间相互作用的解释不足。结论 美国的 COVID-19 死亡率存在显着的空间自相关性，人口健康/社会经济指标导致县级死亡率存在相当大的变异性。在美国和全球疫苗推广的背景下，国家和地方对疾病负担的估计可以为疫苗的最佳地理分配提供信息。