The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…Such claims were reported from different countries such as the US, China, Israel, Brazil and other parts of the world. By contrast, three studies conducted in China found that a lower rate of cases was reported in cities with higher population density ( Zhang et al, 2021 ; Qiu et al, 2020 ; Q. Li et al, 2021 ). It should be noted that China had imposed stringent mobility restrictions in the early months of COVID-19 and the result of studies in China couldn't be generalized.…”

“…The association between GDP and COVID-19 case rate on the city scale is contrasting. The result of research conducted on Chinese ( X.-D. Yang et al, 2021 ; Qiu et al, 2020 , Qiu et al, 2021 ; Zhang et al, 2021 ) and Turkish ( Baser, 2021 ) cities all have shown that GDP increases the number of cases. While in the case of Brazil this relationship is insignificant ( Nakada and Urban, 2021 ), Ahmed et al (2021) reported a negative association between GDP per capita and the rate of COVID-19 cases in 70 cities throughout the world.…”

“…However, this effect is mixed for the number of cases. Health services might be a positive ( Gargiulo et al, 2020 ) or insignificant predictor of COVID-19 cases on a higher scale, but on the city scale, it is a negative predictor ( Zhang et al, 2021 ; Qiu et al, 2020 ; Lata et al, 2021 ).…”

Spatial mapping and socio-demographic determinants of COVID-19 mortality in India

“…Limited health infrastructure is associated with a greater risk of COVID-19 found in a study conducted in China. [ 23 ] In Indian states like Kerala, where health care indicators are good, COVID-19 mortality is more diminutive. In a spatial analysis conducted in Oman, purchasing power and population density are associated with COVID-19.…”

Pemodelan Spasial Kasus Kematian Akibat Covid-19 di Provinsi Jawa Timur Tahun 2020

“…Total populasi, angka kematian kasar, penyakit jangka panjang juga ditemukan sebagai faktor utama dalam kasus Covid-19 oleh Sannigrahi (2020) menggunakan regresi spasial (Sannigrahi et al, 2020). Zhang (2021) melakukan penelitian yang mengatakan bahwa model dengan bobot geografis memberikan hasil yang lebih baik (Zhang et al, 2021).…”