Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, People's Republic of China.
BMC Infect Dis. 2021 May 7;21(1):428. doi: 10.1186/s12879-021-06128-1.
Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective.
Official surveillance data about the COVID-19 and sociodemographic information in China's 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis.
Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran's I = - 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased.
There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.
自 2019 年 12 月以来，2019 冠状病毒病（COVID-19）在人群中迅速传播，给全球带来了严重影响。然而，不同城市 COVID-19 发病率的分布存在显著的地理差异。在本研究中，我们旨在从地理角度探讨社会人口因素对中国 342 个城市 COVID-19 发病率的影响。
收集了中国 342 个城市 COVID-19 的官方监测数据和社会人口信息。比较了局部地理加权泊松回归（GWPR）模型和传统广义线性模型（GLM）泊松回归模型的最佳分析效果。
与 GLM 泊松回归模型相比，GWPR 模型的校正 Akaike 信息准则（AICc）明显更低（GLM 为 61953.0，GWPR 为 43218.9）。GWPR 模型未发现残差的空间自相关（全局 Moran's I=-0.005，p=0.468），表明 GWPR 模型捕捉到了空间自相关。国内生产总值（GDP）较高、卫生资源有限、与武汉距离较短的城市 COVID-19 发病风险较高。此外，除了一些东南城市外，随着人口密度的增加，COVID-19 的发病率呈下降趋势。
社会人口因素对 COVID-19 的发病率有潜在影响。此外，我们的研究结果和方法可以为其他国家提供指导，帮助他们了解 COVID-19 的本地传播情况，并制定有针对性的国家特定干预策略。
