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The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis
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 rel...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962969/ https://www.ncbi.nlm.nih.gov/pubmed/36828501 http://dx.doi.org/10.3390/tropicalmed8020085 |
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author | Mohammadi, Alireza Pishgar, Elahe Fatima, Munazza Lotfata, Aynaz Fanni, Zohreh Bergquist, Robert Kiani, Behzad |
author_facet | Mohammadi, Alireza Pishgar, Elahe Fatima, Munazza Lotfata, Aynaz Fanni, Zohreh Bergquist, Robert Kiani, Behzad |
author_sort | Mohammadi, Alireza |
collection | PubMed |
description | 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 R(2) of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO(2), PM(10), and O(3)); 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. |
format | Online Article Text |
id | pubmed-9962969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99629692023-02-26 The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis Mohammadi, Alireza Pishgar, Elahe Fatima, Munazza Lotfata, Aynaz Fanni, Zohreh Bergquist, Robert Kiani, Behzad Trop Med Infect Dis Article 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 R(2) of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO(2), PM(10), and O(3)); 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. MDPI 2023-01-26 /pmc/articles/PMC9962969/ /pubmed/36828501 http://dx.doi.org/10.3390/tropicalmed8020085 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mohammadi, Alireza Pishgar, Elahe Fatima, Munazza Lotfata, Aynaz Fanni, Zohreh Bergquist, Robert Kiani, Behzad The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis |
title | The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis |
title_full | The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis |
title_fullStr | The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis |
title_full_unstemmed | The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis |
title_short | The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis |
title_sort | covid-19 mortality rate is associated with illiteracy, age, and air pollution in urban neighborhoods: a spatiotemporal cross-sectional analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962969/ https://www.ncbi.nlm.nih.gov/pubmed/36828501 http://dx.doi.org/10.3390/tropicalmed8020085 |
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