About: Abstract Objectives Socioeconomic inequalities may affect COVID-19 incidence. The goal of the research was to explore the association between deprivation of socioeconomic status (SES) and spatial patterns of COVID-19 incidence in Chennai megacity for unfolding the disease epidemiology. Study design Ecological (or contextual) study for electoral wards (sub-cities) of Chennai megacity. Methods Using data of confirmed COVID-19 cases from May 15, 2020, to May 21, 2020, for 155 electoral wards obtained from the official website of the Chennai municipal corporation, we examined the incidence of COVID-19 diseases using two count regression models namely, Poisson Regression (PR) and Negative Binomial Regression (NBR). As explanatory factors, we considered area-deprivation that represented the deprivation of socioeconomic status (SES). An index of multiple deprivations (IMD) developed to measure the area-deprivation using an advanced local statistic, Geographically Weighted Principal Component Analysis (GWPCA). Based on the availability of appropriately scaled data, five domains (i.e. poor housing condition, low asset possession, poor availability of WaSH services, lack of household amenities and services, and gender disparity) were selected as components of the IMD in this study. Results The Hot-spot analysis revealed that area-deprivation was significantly associated with higher incidences of COVID-19 in Chennai megacity. The high variations (adj. R2: 72.2%) with the lower BIC (124.34) and AIC (112.12) for the NBR compared to PR suggests that the NBR model better explains the relationship between area-deprivation and COVID-19 incidences in Chennai megacity. NBR with two-sided tests, and p<0.05 was considered statistically significant. The outcome of the PR and NBR suggests that when all other variables were constant, according to NBR, the relative risk (RR) of COVID-19 incidences was 2.19 for the wards with high housing deprivation or in other words, the wards with high housing deprivation having 119% higher probability (RR= e0.786=2.19, 95% CI=1.98 to 2.40) compared to areas with low deprivation. Similarly, in the wards with poor availability of WaSH services, chances of having COVID-19 incidence was 90% higher compared to the wards with good WaSH services (RR= e0.642=1.90, 95% CI=1.79 to 2.00). Spatial risks of COVID-19 infections were predominantly concentrated in the wards with higher levels of area-deprivation which were mostly located in the north-eastern parts of Chennai megacity. Conclusions We formulated an area-based IMD, which was substantially related to COVID-19 incidences in the Chennai megacity. This study highlights that the risks of COVID-19 infections tend to be higher in more deprived areas of SES and the north-eastern part of Chennai megacity was predominantly high-risk areas. Our results can guide measures of COVID-19 control and prevention by considering spatial risks and area-deprivation.   Goto Sponge  NotDistinct  Permalink

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  • Abstract Objectives Socioeconomic inequalities may affect COVID-19 incidence. The goal of the research was to explore the association between deprivation of socioeconomic status (SES) and spatial patterns of COVID-19 incidence in Chennai megacity for unfolding the disease epidemiology. Study design Ecological (or contextual) study for electoral wards (sub-cities) of Chennai megacity. Methods Using data of confirmed COVID-19 cases from May 15, 2020, to May 21, 2020, for 155 electoral wards obtained from the official website of the Chennai municipal corporation, we examined the incidence of COVID-19 diseases using two count regression models namely, Poisson Regression (PR) and Negative Binomial Regression (NBR). As explanatory factors, we considered area-deprivation that represented the deprivation of socioeconomic status (SES). An index of multiple deprivations (IMD) developed to measure the area-deprivation using an advanced local statistic, Geographically Weighted Principal Component Analysis (GWPCA). Based on the availability of appropriately scaled data, five domains (i.e. poor housing condition, low asset possession, poor availability of WaSH services, lack of household amenities and services, and gender disparity) were selected as components of the IMD in this study. Results The Hot-spot analysis revealed that area-deprivation was significantly associated with higher incidences of COVID-19 in Chennai megacity. The high variations (adj. R2: 72.2%) with the lower BIC (124.34) and AIC (112.12) for the NBR compared to PR suggests that the NBR model better explains the relationship between area-deprivation and COVID-19 incidences in Chennai megacity. NBR with two-sided tests, and p<0.05 was considered statistically significant. The outcome of the PR and NBR suggests that when all other variables were constant, according to NBR, the relative risk (RR) of COVID-19 incidences was 2.19 for the wards with high housing deprivation or in other words, the wards with high housing deprivation having 119% higher probability (RR= e0.786=2.19, 95% CI=1.98 to 2.40) compared to areas with low deprivation. Similarly, in the wards with poor availability of WaSH services, chances of having COVID-19 incidence was 90% higher compared to the wards with good WaSH services (RR= e0.642=1.90, 95% CI=1.79 to 2.00). Spatial risks of COVID-19 infections were predominantly concentrated in the wards with higher levels of area-deprivation which were mostly located in the north-eastern parts of Chennai megacity. Conclusions We formulated an area-based IMD, which was substantially related to COVID-19 incidences in the Chennai megacity. This study highlights that the risks of COVID-19 infections tend to be higher in more deprived areas of SES and the north-eastern part of Chennai megacity was predominantly high-risk areas. Our results can guide measures of COVID-19 control and prevention by considering spatial risks and area-deprivation.
subject
  • Zoonoses
  • Viral respiratory tract infections
  • COVID-19
  • Political economy
  • Occupational safety and health
  • Social status
  • Socioeconomics
  • 1640s establishments in Asia
  • Smart cities in India
  • Populated places established in the 1640s
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