2348 Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Public Health Rep. 2021 May;136(3):368-374. doi: 10.1177/00333549211002837. Epub 2021 Mar 17.
Understanding the pattern of population risk for coronavirus disease 2019 (COVID-19) is critically important for health systems and policy makers. The objective of this study was to describe the association between neighborhood factors and number of COVID-19 cases. We hypothesized an association between disadvantaged neighborhoods and clusters of COVID-19 cases.
We analyzed data on patients presenting to a large health care system in Boston during February 5-May 4, 2020. We used a bivariate local join-count procedure to determine colocation between census tracts with high rates of neighborhood demographic characteristics (eg, Hispanic race/ethnicity) and measures of disadvantage (eg, health insurance status) and COVID-19 cases. We used negative binomial models to assess independent associations between neighborhood factors and the incidence of COVID-19.
A total of 9898 COVID-19 patients were in the cohort. The overall crude incidence in the study area was 32 cases per 10 000 population, and the adjusted incidence per census tract ranged from 2 to 405 per 10 000 population. We found significant colocation of several neighborhood factors and the top quintile of cases: percentage of population that was Hispanic, non-Hispanic Black, without health insurance, receiving Supplemental Nutrition Assistance Program benefits, and living in poverty. Factors associated with increased incidence of COVID-19 included percentage of population that is Hispanic (incidence rate ratio [IRR] = 1.25; 95% CI, 1.23-1.28) and percentage of households living in poverty (IRR = 1.25; 95% CI, 1.19-1.32).
We found a significant association between neighborhoods with high rates of disadvantage and COVID-19. Policy makers need to consider these health inequities when responding to the pandemic and planning for subsequent health needs.
了解 2019 年冠状病毒病（COVID-19）人群风险模式对卫生系统和政策制定者至关重要。本研究的目的是描述邻里因素与 COVID-19 病例数之间的关系。我们假设贫困社区与 COVID-19 病例集群之间存在关联。
我们分析了 2020 年 2 月 5 日至 5 月 4 日期间在波士顿一家大型医疗保健系统就诊的患者数据。我们使用双变量局部连接计数程序来确定具有高邻里人口统计学特征（例如，西班牙裔种族/民族）和劣势指标（例如，健康保险状况）的普查区与 COVID-19 病例之间的位置重合。我们使用负二项模型评估邻里因素与 COVID-19 发病率之间的独立关联。
队列中共纳入 9898 例 COVID-19 患者。研究区域的总粗发病率为每 10000 人 32 例，每个普查区的调整发病率范围为每 10000 人 2 至 405 例。我们发现几个邻里因素与病例的前五分位数存在显著重合：人口中西班牙裔、非西班牙裔黑人、无健康保险、接受补充营养援助计划福利和生活在贫困中的比例。与 COVID-19 发病率增加相关的因素包括人口中西班牙裔的比例（发病率比 [IRR] = 1.25；95%置信区间，1.23-1.28）和生活在贫困中的家庭比例（IRR = 1.25；95%置信区间，1.19-1.32）。
我们发现，高劣势率社区与 COVID-19 之间存在显著关联。政策制定者在应对大流行和规划后续健康需求时需要考虑这些健康不平等现象。
