Cromer Sara J, Lakhani Chirag M, Wexler Deborah J, Burnett-Bowie Sherri-Ann M, Udler Miriam, Patel Chirag J
Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114.
Harvard Medical School, Boston, MA 02115.
medRxiv. 2020 Sep 30:2020.09.30.20201830. doi: 10.1101/2020.09.30.20201830.
The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities.
All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2.Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized.
This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）大流行对美国各地的种族和少数民族社区造成了不成比例的影响。我们试图梳理与这些差异相关的个体及普查区层面的社会人口和经济因素。
2020年2月1日至6月21日期间所有接受SARS-CoV-2检测的成年人，根据其地址被地理编码到一个普查区；医院员工和地址无效的个体被排除。使用线性混合模型分析个体（年龄、性别、种族/民族、首选语言、保险）和普查区层面（人口统计学、保险、收入、教育、就业、职业、家庭拥挤程度和居住情况、房屋建筑环境和交通）变量，以预测SARS-CoV-2感染、住院和死亡情况。在57865名个体中，人均检测率、个体因素（年龄较大、男性、非白人种族、非英语首选语言和非私人保险）以及普查区层面因素（人口密度增加、家庭居住人数较多和教育程度较低）与感染可能性相关。在感染者中，个体年龄、性别、种族、语言和保险，以及普查区层面教育程度较低、多家庭住房较多和极端家庭拥挤等因素与住院可能性增加相关，而人均检测率较高与住院可能性降低相关。只有个体层面变量（年龄较大、男性、医疗保险）与住院者死亡率增加相关。
这项对美国一个主要城市第一波SARS-CoV-2大流行的研究，展示了在一个大型多民族队列中SARS-CoV-2感染后的一系列结果。SARS-CoV-2感染率和住院率，但不包括住院者死亡率，与普查区层面的社会经济特征相关，包括教育程度较低以及家庭拥挤程度和居住人数较高，但与种族的邻里指标无关，且独立于个体因素。
