The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Explanatory Variables
- (1)
- Built environment, land use, and urban facilities: previous research has found a relation between the density of various land uses and the COVID-19 epidemic [38,39]. The goal of this study, on the other hand, was to determine the spatial association between land use categories and COVID-19 mortality rates. As a result, the density of various uses, such as banks, restaurants, and high-rise residences, was investigated.
- (2)
- AQI: the literature has shown a strong, positive relationship between air pollutants and COVID-19 transmission and mortality in several geographic regions [40,41,42]. The major air contaminants considered as initial independent variables in this study are NO, NO2, O3, CO, and particulate matter PM2.5 and PM10 (NO and NO2 are collectively referred to as NOx). Some previous studies have used the effects of these pollutants on respiratory diseases individually or in combination with each other [23,43,44,45,46,47,48,49,50]. In the present study, both were used, and the most significant variables were included in the final model. Exposure to air pollution is an essential risk factor for many of the chronic diseases that cause people to be more likely to become seriously ill, require intensive care and mechanical ventilation, and die from COVID-19 [51]. In this study, the average of 5 years (2016 to 2021) for each pollutant was derived from the pollutant-related data of air pollution monitoring stations. Then, using the inverse distance weighted (IDW) interpolation technique, data were calculated in a GIS with pixels of 1 × 1 km in size. The 5-year average of pollution was then calculated for each neighbourhood separately using zonal statistical methods.
- (3)
- Public Transportation: public transportation contributes to the geographic spread of COVID-19 [47]. Few studies have examined the correlation between these factors and mortality rates. To analyze the association at the neighbourhood level, we considered variables such as distance from fuel stations, metro and bus rapid transit (BRT) stations, and the spatial density of main roadways.
- (4)
- Socioeconomic features: recent research has shown that socioeconomic variables impact COVID-19 transmission and mortality in various settings in developing countries, such as Iran [52]. In this study, we looked at the spatial correlations between six such variables (population density, illiteracy, unemployment, older age, having a rented home, and being an immigrant) and neighbourhood-level COVID-19 mortality.
Theme | Variable | Measure and Unit | Descriptions and Rationale | Data Source * |
---|---|---|---|---|
Built-environment, land use, and facilities | Commercial land use (V1) | Spatial density of commercial properties per km2 | Locations with high connectivity, high density, and geographic concentration of economic activity may be at a relatively higher risk of COVID-19 infection [53]. | 1 |
Industrial land use (V2) | Spatial density of industrial units per km2 | Large numbers of industrial units in a region has a significant effect on the number of active COVID-19 cases in that region [53,54]. | ||
Land use for social services (V3) | Spatial density of social services units per km2 | Social service centres may be a place of the spread of infectious diseases as they attract many people at times of epidemics [12]. | ||
Banking (V4) | Spatial density of banks per km2 | Some studies have shown that banks and automated teller machines (ATMs) are important for the spread of COVID-19 [55]. | ||
Health service (V5) | Spatial density of health service centres (including special hospitals for COVID-19 patients, public clinics, and laboratories) per km2 | For example, hospitals for COVID-19 patients, public clinics, and laboratories naturally have an extremely strong association with the rate of COVID-19 infection [12]. | ||
Deteriorated buildings (V6) | The ratio of areas with deteriorated and old buildings to the total area of each neighbourhood in km2 * 100 (%) | Low-income and impoverished people generally reside in worn and inadequately constructed settings, where the houses are deteriorated leading to a relatively great danger of disease outbreak [12,56]. | ||
High-rise buildings (V7) | Spatial density of residential high-rise buildings per km2 | Overcrowding, dense space, and health conditions in high-rise buildings can increase the risk of COVID-19 outbreaks, which can affect people in different age groups and individuals who are suffering from underlying diseases [57]. | ||
Distance from the city business district (CBD) (V8) | Distance to the CBD in km | Previous studies show that the COVID-19 transmission decreases with the distance from the city centre [47]. | ||
Presence of restaurants (V9) | Spatial density of restaurants per km2 | Controlling transmission in restaurants is an important component of public health measures for COVID-19 [58]. | ||
Air Quality Index | Particulate matter of size ≤2.5 micron (PM2.5) (V10) | Spatial density of particles ≤2.5 micron (PM2.5) per m3 of air (µg/m3) | Studies have shown a positive correlation between the effect of delayed PM2.5 concentration and the number of confirmed COVID-19 cases, indicating an increased risk of infectious diseases [59,60,61]. | 2 |
Particulate matter of size ≤10 micron (PM10) (V11) | Spatial density of particles ≤10 micron (PM10) per m3 of air (µg/m3) | Studies have confirmed that new cases of COVID-19 are associated with elevated PM10 concentrations in urban areas [45,46]. | ||
Carbon monoxide (CO) (V12) | Concentration of carbon monoxide (CO) in parts per million (ppm) | Most studies confirm that both COVID-19 cases and deaths are positively associated with almost all pollutants [44]. | ||
Nitrogen dioxide (NO2) (V13) | Concentration of nitrogen-dioxide (NO2) in parts per billion (ppb) | Studies have shown that there is a significant association between NO2 and the risk of COVID-19 infection [23,43]. | ||
Nitrogen monoxide (NO) (V14) | Concentration of nitrogen monoxide (NO) in parts per billion (ppb) | Studies have mentioned the role of the NO pollutant in COVID-19 transmission and death [44,45,46,47]. | ||
(V15) Nitrogen oxides (NOx ppb) | Concentration of nitrogen oxides ((NO+NO2) parts per billion (ppb) | Some studies have demonstrated that NOx (NO+NO2) emissions significantly increase the incidence of COVID-19 transmission and death [48,49,50]. | ||
Ozone (O3) (V16) | Concentration of ozone (O3) in parts per billion (ppb) | Studies have demonstrated that COVID-19 outbreaks and fatalities are associated with ozone levels [44,45,46,47]. | ||
Sulfur-oxide (SO2) (V17) | Concentration of sulfur oxide (SO2) in parts per billion (ppb) | This gas is released by airplanes, trains, and other means of transportation. The importance of reducing it during quarantine situations have been highlighted [62]. | ||
Temperature (V18) | Average annual temperature (2011-2021) in degrees Celsius (°C) | High temperatures increase the risk of COVID-19 diseases and are associated with death from respiratory diseases, as well COVID-19 [47,63,64]. | ||
Public Transportation | Metro stations (V19) | Distance to the metro stations in meters | Many studies highlight the role of public transportation in the spread of infectious diseases, even in remote areas. Research shows that public transportation facilities play an important role in the geographical spread of COVID-19 [23,55,65]. | 3 |
Bus rapid transit (BRT) stations (V20) | Distance to the BRT stations in meters | Sense places, such as BRT stations, are at risk for many physical contacts and disease transmission [66,67]. | ||
Density of roads (V21) | Spatial density of main roads per km2 | Where the urban road network creates the most intersections, individual and collective contacts increase. In the long run, this increases the spread of the disease in the surrounding areas [68]. | ||
Fuel stations (V22) | Distance to the fuel (petrol and gas) stations in meters | As with other location based public facilities, fuel stops may increase the transmission and spread of the COVID-19 virus in nearby areas [55]. | ||
Socio-economic characteristics | Population density (V23) | Total population/neighborhood area (km2) = persons/km2. | There is a significant relationship between population density, overcrowding, and the spread of COVID-19 virus [64,69]. | 4 |
Illiteracy rate in % (V24) | Ratio of illiteracy in the total population ≥ 6 years = illiterate population/population (6+) * 100, (%). | Health literacy enables people to understand the reasons behind medical recommendations and to become aware of the possible outcomes of their actions. Instead, higher levels of adult’s illiteracy rates can be seen as a social risk factor for rising COVID-19 related deaths [70]. | ||
Unemployment rate in % (V25) | Ratio of unemployment in the total population = unemployed population/active population (15–65 years) * 100 (%). | Areas with a higher unemployment rate positively associated with COVID-19 high mortality rates [71]. | ||
Age rate in % (V26) | Ratio of elderly in the total population = elderly (65+ years) population/total population * 100 (%). | Older age groups experienced higher COVID-19 mortality rates. Subsequently, areas with a large proportion of elderly people face a high risk of infection [72,73]. | ||
Rate of rented homes in % (V27) | Total number of rented houses/total number of all types of housing units * 100 (%) | Mostly recent studies have examined the correlation between COVID-19 outbreaks and poor housing condition [74,75]. | ||
Rate of Immigrants in % (V28) | Ratio of immigrants in the total population = immigrant population/total population * 100 (%). | Areas with higher rates of immigration appear to have been more affected by COVID-19 [76,77]. |
2.4. Methods, Tools, and Procedure
2.4.1. Geographical Smoothing of Mortality Rates
2.4.2. Spatio-Temporal Analysis
2.4.3. Linear and Geographically Multivariate Data Analysis
3. Results
3.1. COVID-19 Mortality Rates
3.2. Spatio-Temporal Distribution Patterns and Clusters
3.2.1. Retrospective Purely Temporal Analysis
3.2.2. Global and Local Purely Spatial Analysis
3.2.3. Retrospective Space-Time Analysis
3.3. Modeling Spatial Associations
3.3.1. OLS Model Results
3.3.2. GWR Model Results
3.3.3. MGWR Model Results
3.3.4. Model Performance (Validation)
3.4. Mapping and Spatial Analysis of the MGWR Model
3.4.1. Local t-Values
3.4.2. Local Parameter Estimates
4. Discussion
4.1. Policy Implications
4.2. Limitations and Futures Research Strategy
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Cluster | Neighbourhood | Coordinates/Radius | Population | Cases (No.) | Expected Cases (No.) | Annual Cases/100,000 (No.) | Observed/Expected Cases (No.) | Relative Risk | Log Likelihood Ratio | Monte Carlo Rank | p-Value |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | Includes seventy-one neighbourhoods | (35.671100° N, 51.403600° E)/4.63 km | 1,551,335 | 1956 | 1210.76 | 2192.9 | 1.62 | 1.85 | 242.78 | 1/1000 | <0.001 |
2 | Includes one neighbourhood | (35.723200° N, 51.357100° E)/1.3 km | 27,371 | 66 | 21.36 | 4193.9 | 3.09 | 3.11 | 29.95 | 1/1000 | <0.001 |
3 | Includes fourteen neighbourhoods | (35.776700° N, 51.402900° E)/3.17 km | 374,205 | 421 | 292.05 | 1956.7 | 1.44 | 1.47 | 26.25 | 1/1000 | <0.001 |
4 | Includes one neighbourhood | (35.695400° N, 51.486500° E)/0.8 km | 20,374 | 47 | 15.90 | 4012.2 | 2.96 | 2.97 | 19.9 | 1/1000 | <0.001 |
5 | Includes one neighbourhood | (35.726200° N, 51.520600° E)/1.87 km | 56,873 | 87 | 44.39 | 2660.6 | 1.96 | 1.97 | 16.06 | 1/1000 | <0.001 |
6 | Includes one neighbourhood | (35.772600° N, 51.468100° E)/2.6 km | 22,574 | 41 | 17.62 | 3158.9 | 2.33 | 2.33 | 11.28 | 3/1000 | <0.001 |
7 | Includes two neighbourhoods | (35.702800° N, 51.228200° E)/2.05 km | 18,598 | 34 | 14.52 | 3179.6 | 2.34 | 2.35 | 9.48 | 25/1000 | <0.001 |
Diagnostic Name | Value | Value | |
---|---|---|---|
Residual sum of squares | 114.911 | AICc | 660.788 |
Effective number of parameters (trace(S)) | 25.428 | BIC | 758.252 |
Degree of freedom (n–trace(S)) | 324.572 | R2 | 0.672 |
Sigma estimate | 0.595 | Adj. R2 | 0.646 |
Log-likelihood | −301.718 | Adj. alpha (95%) | 0.012 |
Degree of dependency (DoD) | 0.753 | Adj. critical t value (95%) | 2.531 |
AIC | 656.293 | - |
GWR Model | ||||||
Variable | Bandwidth | Mean | STD | Minimum | Median | Maximum |
Intercept | 172 | −0.108 | 0.176 | −0.399 | −0.125 | 0.217 |
PM10 | 172 | 0.118 | 0.046 | 0.001 | 0.124 | 0.222 |
NO2 | 172 | 0.332 | 0.164 | 0.055 | 0.340 | 0.616 |
O3 | 172 | 0.319 | 0.123 | −0.136 | 0.318 | 0.559 |
Illiteracy rate (%) | 172 | 0.139 | 0.129 | −0.074 | 0.126 | 0.377 |
Ageing rate (%) | 172 | 0.369 | 0.084 | 0.195 | 0.368 | 0.654 |
MGWR Model | ||||||
Variable | Bandwidth | Mean | STD | Minimum | Median | Maximum |
Intercept | 59 | −0.049 | 0.280 | −0.545 | −0.070 | 0.620 |
PM10 | 348 | 0.115 | 0.008 | 0.103 | 0.113 | 0.129 |
NO2 | 335 | 0.375 | 0.030 | 0.311 | 0.384 | 0.408 |
O3 | 253 | 0.327 | 0.053 | 0.236 | 0.317 | 0.422 |
Illiteracy rate (%) | 196 | 0.125 | 0.065 | 0.013 | 0.130 | 0.241 |
Ageing rate (%) | 348 | 0.374 | 0.004 | 0.365 | 0.374 | 0.383 |
Diagnostic Name | Value | Value | |
---|---|---|---|
Residual sum of squares (RSS) | 108.666 | AICc | 641.051 |
Effective number of parameters (trace (S)) | 25.352 | BIC | 738.247 |
Degree of freedom (n–trace (S)) | 324.648 | R2 | 0.690 |
Sigma estimate | 0.579 | Adj. R2 | 0.665 |
Log-likelihood | −291.940 | ||
Degree of dependency (DoD) | 0.754 | ||
AIC | 636.583 | - |
Model | RRS | Log-Likelihood | AIC | AICc | R2 | Adj. R2 |
---|---|---|---|---|---|---|
OLS | 151.110 | −349.642 | 1027.85 | 3455.81 | 0.568 | 0.561 |
GWR | 114.911 | −301.718 | 656.293 | 660.788 | 0.672 | 0.64 |
MGWR | 108.666 | −291.940 | 636.583 | 641.051 | 0.690 | 0.665 |
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Mohammadi, A.; Pishgar, E.; Fatima, M.; Lotfata, A.; Fanni, Z.; Bergquist, R.; Kiani, B. The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis. Trop. Med. Infect. Dis. 2023, 8, 85. https://doi.org/10.3390/tropicalmed8020085
Mohammadi A, Pishgar E, Fatima M, Lotfata A, Fanni Z, Bergquist R, Kiani B. The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis. Tropical Medicine and Infectious Disease. 2023; 8(2):85. https://doi.org/10.3390/tropicalmed8020085
Chicago/Turabian StyleMohammadi, Alireza, Elahe Pishgar, Munazza Fatima, Aynaz Lotfata, Zohreh Fanni, Robert Bergquist, and Behzad Kiani. 2023. "The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis" Tropical Medicine and Infectious Disease 8, no. 2: 85. https://doi.org/10.3390/tropicalmed8020085