The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

Abstract

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran’s neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including ­­air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Spatial Autocorrelation of COVID-19 in Slovakia

“…The authors state that spatially correlated data are geospatial data with spatial autocorrelation and variability that originate from each region and have adjacency to another region. Mortality rates from COVID-19 and their geographical associations with various socio-economic and ecological determinants in Tehran through the use of spatial techniques apply [ 42 ]. There is also a cross-sectional study from the Iranian environment that examined spatiotemporal patterns in northeastern Iran from 2016 to 2020.…”

Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland

“…This article uses the multiscale geographically weighted regression method (MGWR), the extended and advanced version of GWR. This spatial regression technique explores geographically varying relationships between dependent variables and explanatory variables; MGWR is being used to analyze a variety of problems in geography, urban planning, and various other disciplines [84,85]; studies proved the reliability of MGWR in modeling the COVID-19 issues [86][87][88]. Geographically weighted regression (GWR) allows the coefficients to vary spatially, but all coefficients must change at a similar rate across the study area.…”

Spatio-temporal clustering analysis of COVID-19 cases in Johor

“…This research also showed that the female mortality rate is lower than the male mortality rate. From the age aspect, the mortality rate is 68% higher among the elderly group (those aged more than 65 years old), while the young generation (those aged 25 years old and below) has the lowest mortality rate ( Mohammadi et al., 2023 ).…”