Kniep Nicole, Achidi Thelma, Flynn Christy, Gangur Jyoti, Khot Madhura, Kueider Lauren, Mannadiar Soumya, Pokhrel Kamana, Raghuwanshi Yash, Rajwani Aparna, Rotteck Amanda, Sharp Nialah, Shenoy Shruti, Stoney Mackenzie, Wang Haiping, Zhang Qian, Korvink Michael, Gunn Laura H
School of Data Science, University of North Carolina at Charlotte, Charlotte, North Carolina.
Department of Public Health Sciences, and School of Data Science, University of North Carolina at Charlotte, Charlotte, North Carolina.
N C Med J. 2022 Sep-Oct;83(5):366-374. doi: 10.18043/ncm.83.5.366.
There is limited research regarding associations between county-level factors and COVID-19 incidence and mortality. While the Carolinas are geographically connected, they are not homogeneous, with statewide political and intra-state socioeconomic differences leading to heterogeneous spread between and within states. Infection and mortality data from Johns Hopkins University during the 7 months since the first reported case in the Carolinas was combined with county-level socioeconomic/demographic factors. Time series imputations were performed whenever county-level reported infections were implausible. Multivariate Poisson regression models were fitted to extract incidence (infection and mortality) rate ratios by county-level factor. State-level differences in filtered trends were also calculated. Geospatial maps and Kaplan-Meier curves were constructed stratifying by median county-level factor. Differences between North and South Carolina were identified. Incidence and mortality rates were lower in North Carolina than South Carolina. Statistically significant higher incidence and mortality rates were associated with counties in both states with higher proportions of Black/African American populations and those without health insurance aged < 65 years. Counties with larger populations aged ≥ 75 years were associated with increased mortality (but decreased incidence) rates. COVID-19 data contained multiple inconsistencies, so imputation was needed, and covariate-based data was not synchronous and potentially insufficient in granularity given the epidemiology of the disease. County-level analyses imply within-county homogeneity, an assumption increasingly breached by larger counties. While statewide interventions were initially implemented, inter-county racial/ethnic and socioeconomic variability points to the need for more heterogeneous interventions, including policies, as populations within particular counties may be at higher risk.
关于县级因素与新冠病毒感染率和死亡率之间的关联，相关研究有限。卡罗来纳州在地理上相互连接，但并非同质化，全州范围的政治差异和州内社会经济差异导致了州与州之间以及州内传播的异质性。自卡罗来纳州首例报告病例以来的7个月里，约翰·霍普金斯大学的感染和死亡数据与县级社会经济/人口因素相结合。每当县级报告的感染情况不合理时，就进行时间序列插补。拟合多变量泊松回归模型，以提取按县级因素划分的发病率（感染率和死亡率）比值比。还计算了过滤趋势的州级差异。构建地理空间地图和卡普兰-迈耶曲线，并按县级因素中位数进行分层。确定了北卡罗来纳州和南卡罗来纳州之间的差异。北卡罗来纳州的发病率和死亡率低于南卡罗来纳州。在两个州中，黑人/非裔美国人口比例较高以及65岁以下无医疗保险人口比例较高的县，其发病率和死亡率在统计学上显著更高。75岁及以上人口较多的县与死亡率上升（但发病率下降）相关。新冠病毒数据存在多重不一致性，因此需要进行插补，而且考虑到该疾病的流行病学情况，基于协变量的数据不同步且粒度可能不足。县级分析意味着县内同质化，但这一假设越来越多地被大县打破。虽然最初实施了全州范围的干预措施，但县际种族/族裔和社会经济差异表明需要采取更多差异化干预措施，包括政策措施，因为特定县内的人群可能面临更高风险。
