Emerg Infect Dis. 2021 Oct;27(10):2604-2618. doi: 10.3201/eid2710.210103.
We conducted a detailed analysis of coronavirus disease in a large population center in southern California, USA (Orange County, population 3.2 million), to determine heterogeneity in risks for infection, test positivity, and death. We used a combination of datasets, including a population-representative seroprevalence survey, to assess the actual burden of disease and testing intensity, test positivity, and mortality. In the first month of the local epidemic (March 2020), case incidence clustered in high-income areas. This pattern quickly shifted, and cases next clustered in much higher rates in the north-central area of the county, which has a lower socioeconomic status. Beginning in April 2020, a concentration of reported cases, test positivity, testing intensity, and seropositivity in a north-central area persisted. At the individual level, several factors (e.g., age, race or ethnicity, and ZIP codes with low educational attainment) strongly affected risk for seropositivity and death.
我们对美国加利福尼亚州南部一个人口中心（人口 320 万的橙县）的冠状病毒病进行了详细分析，以确定感染、检测阳性和死亡的风险异质性。我们使用了多种数据集，包括具有代表性的人群血清流行率调查，以评估实际疾病负担和检测强度、检测阳性率和死亡率。在当地疫情的第一个月（2020 年 3 月），病例发病率集中在高收入地区。这种模式迅速转变，接下来病例在该县中北部地区以更高的比率聚集，该地区社会经济地位较低。从 2020 年 4 月开始，在中北部地区报告病例、检测阳性率、检测强度和血清阳性率持续集中。在个人层面上，一些因素（例如年龄、种族或民族以及教育程度低的邮政编码）强烈影响血清阳性率和死亡的风险。
