key: cord-1020615-k6y7f9d2
authors: Das, Arijit; Ghosh, Sasanka; Das, Kalikinkar; Basu, Tirthankar; Das, Manob; Dutta, Ipsita
title: Modelling the Effect of Area-deprivation on COVID-19 Incidences: A Study of Chennai Megacity, India
date: 2020-06-12
journal: Public Health
DOI: 10.1016/j.puhe.2020.06.011
sha: ee4c3569203a966855b41389909dc96e74da50fe
doc_id: 1020615
cord_uid: k6y7f9d2

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.

Coronavirus disease (COVID-19) is an epidemic illness that was discovered in Wuhan of China at the end of 2019. Shortly after, it rapidly spreads worldwide to emerge as a 'Public Health Emergency of International Concern'. Subsequently, WHO declares COVID-19 as a global pandemic. As of May 22, 2020, COVID-19 has affected about 4.99 million people and claimed over 3,27,738 deaths globally 1 and these figures are increasing every day. In India, the first COVID-19 case was reported on January 30, 2020, in Kerala, and then gradually spreads across in all states and union territories except Daman and Diu, Nagaland, and Lakshadweep 2 . The COVID-19 has mostly affected the urban areas particularly the megacities of India which became the epicenters of COVID-19 spreads. The geographic distribution of COVID-19 incidences showed that the disease was not uniformly affecting all parts of the Indian megacities but lead to spatial clustering of cases 3 .

The existence of an inverse relationship between the socioeconomic status (SES) of populations and higher incidences of lower respiratory tract infection among populations in society was well recognized 4 . Despite the preliminary evidence of social inequality in COVID-19 incidences and the possibility of SES deprivation as a major contributor to COVID-19 infection, research on the relationship between SES deprivation and COVID-19 incidences is inadequate. It has, therefore, become imperative that the role of SES deprivation on COVID-19 incidences be evaluated. To fill up the existing research gap this study attempted to provide scientific evidence about the influence of SES deprivation on spatial clustering of COVID-19 hotspots in Chennai megacity.

In the absence of individual data, we have employed ecological (or contextual) measures of SES deprivation to describe the inequalities in COVID-19 incidences. In this article, we have modified and improved the IMD developed by Isa Baud 5 for Chennai megacity by incorporating specific indicators of SES deprivation that affect COVID-19, such as nonavailability to a drinking water source within premises or not having a toilet inside houses. The households (HH) without having the availability of a drinking water source within premises were compelled to collect drinking water from community stand posts and tube well. Similarly, the HH not having a toilet inside houses also was compelled to use a community toilet. These situations will certainly reduce compliance with social distancing. Limited availability of community latrines, stand posts, and tube well certainly increases the chance of community transmission of the virus. Examining the spatial inequalities in COVID-19 incidences in Chennai megacity will provide important insights into the spatial pattern of the disease. The development of an IMD representing SES disadvantage would explore the linkages between living environment deprivation and COVID-19 incidences.

Ultimately, the outcome of this study will provide useful insights to policymakers for targeted interventions to combat the COVID-19 pandemic.

The study area is Chennai megacity (13.04°N-80.17°E) is the fourth-largest metropolis in India (after Mumbai, New Delhi, and Kolkata) with a population of 10.2 million. It is the most important urban centre in the south-east coastal region of India, which has a typical subtropical, hot-humid, monsoon climate classified as Aw (tropical wet and dry) in the Köppen climate classification. With mild and moderate winters and very hot summers, the average air temperature ranges from 21-35°C (70-95°F) and relative humidity varies from 45-95%.

The first COVID-19 in Chennai was detected on March 9, 2020, and later community transmission has taken place rapidly. The number of confirmed COVID-19 cases in 155 wards of Chennai megacity reported in this study was collected from the official website of Greater Chennai online database releases from May 15, 2020, to May 21, 2020. Ward wise confirmed cases during this same period were also obtained from The News Minute coronavirus data repository 6 . The two datasets were compared to ensure consistency of COVID-19 incidences before executing the statistical analysis.

Since SES is a complex and multidimensional phenomenon, in the absence of individual data, which were not available at this pandemic situation, we developed well recognized ecological (or contextual) measures of SES in the form of an index that represents area-based deprivation for 155 electoral wards (sub-cities) of Chennai megacity. Table.S1 summarizes the domains of selected widely used IMDs developed earlier and most IMDs includeincome, employment, socioeconomic status, education, housing quality, and ownership of goods or items 7, 8 . The dimensions and indicators selected to devise an IMD for 155 electoral wards of Chennai megacity are slightly different from IMDs developed earlier because information on income is not available in the Census of India (see Table. S1). The IMD is devised by employing GWPCA. GWPCA is now recognized as a very effective tool for the detection of the local non-stationary effects of variance in a data structure 8 . The local principal components and local variance derived from GWPCA are suitable in devising IMD 8 .

Mathematically, the local eigen decomposition of GWPCA transformation can be written in its algebraic expression as: ) is a diagonal matrix obtained from optimal bandwidths (here adaptive) based on the 'Bi-square' kernel weighting scheme. The detailed description of GWPCA is given in the appendix section. To reduce noise and locate important factors of IMD, the first 3 PCs with eigenvalues greater than 1 (i.e., λi ≥ 1) were retained. The hotspot analysis tool of ArcGIS 10.2 software (Getis-Ord Gi*) was used to explore the spatial clustering of COVID-19 incidences and high IMD values (mathematical expression given in the supplementary material). The distribution of COVID-19 cases in Chennai megacity was negative binomial because its variance was higher than the means (see Table. S2). Therefore, we employed Poisson regression (PR) and negative binomial regression (NBR) to analyze the impact of individual domains of IMD on COVID-19 incidences in Chennai megacity (see supplementary material for details).

The descriptive analysis of COVID-19 incidences is reported in Table. S2 shows that variability (σ =54.52) of COVID-19 incidences is higher than mean (µ=49.49) and it is following negative binomial distribution. The first three components with eigenvalues greater than 1 (i.e., λi ≥ 1) accounted for 80.7% of the total variance in the data and the first component alone explained more than 47 percent variance in the data. The product of the proportion of local variance explained by three components and the component score was summed to devise the initial IMD. The IMD score of 0 stands for the least deprived ward (Ward No. 125) and 100 for most deprived wards (Ward No. 40). Figure. 1(e) showed the spatial distribution of COVID-19 and IMD hotspots. Hotspot areas for both COVID-19 and IMD were mainly located in the northern and central parts. This area is crowded with a higher concentration of slums, the majority of the HH with poor housing conditions and lack of HH services are located in this zone of the megacity. Figure 1( . Spatial risks of COVID-19 infections were predominantly concentrated in the wards with high IMD which were mostly located in the north-eastern parts of Chennai megacity. 

We formulated IMD using multiple socioeconomic indicators at the electoral ward level in Chennai megacity and examined the relationship between the IMD and COVID-19 incidences. The hot-spot analysis indicates that formulated IMD was significantly related to higher incidences of COVID-19 in areas with higher IMD.

The critical matters of the study were the selection of domains and the method of index formulation. The selection of domains was based on the previous IMDs, data availability in the Indian context, and factors that directly or indirectly affect the transmission of COVID-19. The indicators of IMD were drawn from the Census of India, the most reliable data sources on SES in India. To formulate a composite index of SES deprivation, we used GWPCA which is preferred over widely used normal PCA at recent. GWPCA as an emerging and promising tool 7 has a certain advantage over PCA such as it provides covariance structure, component scores, loadings, and explained variance for each electoral wards 8 . Therefore, we were able to devise IMD by utilizing the local component weights of IMD indicators which were not possible in normal PCA 8 .

The regression results of this study support the findings of the earlier study that addressed the possible impact of SES on COVID 19 incidences 9 . To the best of our knowledge, this study was the first attempt on COVID-19 which quantitatively establish the influence of areadeprivation on COVID-19 incidences. The results of Poisson Regression and Negative Binomial Regression suggested that area-deprivation has both positive and inverse associations with the incidences of COVID-19 infections in Chennai megacity. This further strengthens the findings of Ahmed et al. (2020) 10 that the socioeconomic disadvantages and inequalities have a profound role in the spread of COVID-19. The findings of this study suggest approaches to combat the COVID-19 pandemic must incorporate SES dynamics to develop a mitigation strategy.

In conclusion, this study formulated an IMD for area-deprivation measures of SES disadvantage, and the index showed a substantial relation to COVID-19 incidences, especially for poor availability of WaSH services and poor housing conditions. This study highlights that the risks of COVID-19 infections tend to be higher in SES deprived areas and the north-eastern part of Chennai megacity was predominantly high-risk areas. Although a more contextual discussion on IMD formulation is needed, the proposed index based on a common set of SES indicators may be readily applicable for research on the relationship between COVID-19 and SES deprivation. Our results can guide measures of COVID-19 control and prevention by considering spatial risks and area-deprivation.

COVID-19) Situation Report-103

Uneven distribution of Covid-19 among Megacities

Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Crowding: Risk factor or protective factor for lower respiratory disease in young children? BMC Public Health

Matching deprivation mapping to urban governance in three Indian mega-cities

The News Minute, Coronavirus data repository

Higher mortality in areas of lower socioeconomic position measured by a single index of deprivation in Japan

Urban Deprivation in a Global South City-a Neighborhood Scale Study of Kolkata, India

Poverty, Inequality & COVID-19: The Forgotten Vulnerable. Public Health, Available online 14

2020:Why inequality could spread COVID-19

The incidence of COVID-19 diseases was modelled using two count regression models namely, Poisson Regression and Negative binomial regression

An index of multiple deprivation (IMD) was devised using Geographically Weighted Principal Component Analysis

Area deprivation has both positive and inverse associations with the COVID-19 incidences in Chennai megacity

Our results can guide COVID-19 control and prevention by considering spatial risk detection through area-deprivation