Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.
Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.
复制标题
南卡罗来纳州新冠肺炎感染前风险决定因素的时间地理空间分析。
DOI：
10.3390/ijerph18189673
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发表时间：
2021-09-14
影响因子：
--
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Lyu T;Hair N;Yell N;Li Z;Qiao S;Liang C;Li X
COVID-19 患者的发病率和死亡率存在差异及其地理空间模式。在感染率方面，缺乏关于差异结构、地理空间特征和感染前风险决定因素（PIDR）的研究。这项工作旨在评估南卡罗来纳州县级 PIDR 与 COVID-19 感染之间的时空关联。我们使用空间误差模型（SEM）、空间滞后模型（SLM）和条件自回归模型（CAR）作为全局模型，使用地理加权回归模型（GWR）作为局部模型。这些数据是从多个来源检索的，包括 USAFacts、美国人口普查局和人口估计计划。在全球模型中，男性和失业人口的百分比始终与 COVID-19 感染的地理分布呈正相关（p 值 < 0.05）。白人人口百分比和肥胖率在大流行的不同时期表现出不同的空间相关性。 GWR 模型比全球模型拟合得更好，表明一个区域与其邻国之间存在非平稳相关性。以时空模式为特征，COVID-19 感染率及其 PIDR 的差异与 COVID-19 患者的死亡率和发病率不同。我们的研究结果表明，在大流行的不同时期优先考虑不同人群并制定量身定制的干预措施的重要性。
Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal–geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal–geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.
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DOI：
10.1007/s42399-020-00341-w
发表时间：
2020-01-01
期刊：
SN comprehensive clinical medicine
影响因子：
--
作者：
Bwire, George M
通讯作者：
Bwire, George M
DOI：
10.1111/1467-9884.00145
发表时间：
1998-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN
影响因子：
--
作者：
Brunsdon, C;Fotheringham, S;Charlton, M
通讯作者：
Charlton, M
影响因子：
64.8
作者：
Al-Aly, Ziyad;Xie, Yan;Bowe, Benjamin
通讯作者：
Bowe, Benjamin
影响因子：
3.3
作者：
de la Vega, Ricardo;Ruiz-Barquin, Roberto;Szabo, Attila
通讯作者：
Szabo, Attila
影响因子：
11.1
作者：
Deer RR;Rock MA;Vasilevsky N;Carmody L;Rando H;Anzalone AJ;Basson MD;Bennett TD;Bergquist T;Boudreau EA;Bramante CT;Byrd JB;Callahan TJ;Chan LE;Chu H;Chute CG;Coleman BD;Davis HE;Gagnier J;Greene CS;Hillegass WB;Kavuluru R;Kimble WD;Koraishy FM;Köhler S;Liang C;Liu F;Liu H;Madhira V;Madlock-Brown CR;Matentzoglu N;Mazzotti DR;McMurry JA;McNair DS;Moffitt RA;Monteith TS;Parker AM;Perry MA;Pfaff E;Reese JT;Saltz J;Schuff RA;Solomonides AE;Solway J;Spratt H;Stein GS;Sule AA;Topaloglu U;Vavougios GD;Wang L;Haendel MA;Robinson PN
通讯作者：
Robinson PN