Unequal Lives: A Sociodemographic Analysis of Covid19 Transmission and Mortality in India (original) (raw)
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Indian Journal of Community Health
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2020
Background-COVID-19 has spread worldwide and follows a growth depending upon policy and socioeconomic indicators. In India COVID-19 has been localized & measured viral disease, as per the early data till 18 th April. It is interesting and informative to look at various associations. Up till April 18 th new cases per day in India were less than 1000. Objective-What are the socioeconomic correlates with early COVID-19 data? Materials and methods-Data were extracted from national repositories and correlations were checked using SPSS16.0 for windows. Results-Places with generally better medical facilities, have a cluster of cases distributed unevenly and are traceable. Indian states with larger areas have less of the patients (r =-.458*). More populous states have less cases (r =-.551*). This may be due to extensive, early & nationwide lockdown declared by government of India. Better agriculture gains are correlated positively with cured/discharged cases (rho = +.369*). Better infrastructure in the form of highway length of states may cause clustering (r= +.379*) of fatalities due to more healthcare facilities of COVID-19. Conclusion-These trends of localized spread are reported till during intermission of phase II of local transmission and phase III of community transmission, dated up till April 18th. These trends may change later during the end phase of COVID-19 pandemic in India. Opening of lockdown in a graded & controlled manner shall be observed considering for these correlations.
Assessing Socioeconomic Vulnerabilities Related to COVID-19 Risk in India: A State-Level Analysis
Disaster Medicine and Public Health Preparedness
Objective: There is a paucity of scientific analysis that has examined spatial heterogeneities in the socioeconomic vulnerabilities related to coronavirus disease 2019 (COVID-19) risk and potential mitigation strategies at the sub-national level in India. The present study examined the demographic, socioeconomic, and health system-related vulnerabilities shaping COVID-19 risk across 36 states and union territories in India. Methods: Using secondary data from the Ministry of Health and Family Welfare (MoHFW), Government of India; Census of India, 2011; National Family Health Survey, 2015-16; and various rounds of the National Sample Survey, we examined socioeconomic vulnerabilities associated with COVID-19 risk at the sub-national level in India from March 16, 2020, to May 3, 2020. Descriptive statistics, principal component analysis, and the negative binomial regression model were used to examine the predictors of COVID-19 risk in India. Results: There persist substantial heterogene...
Birds of a feather flock together? Diversity and spread of COVID-19 cases in India
arXiv: General Economics, 2020
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2020
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District level correlates of COVID-19 pandemic in India
medRxiv (Cold Spring Harbor Laboratory), 2020
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Characterizing the Impact of Social Inequality on COVID-19 Propagation in Developing Countries
IEEE Access, 2020
The world faces a pandemic not previously experienced in modern times. The internal mechanism of SARS-Cov-2 is not well known and there are no Pharmaceutical Interventions available. To stem the spread of the virus, measures of respiratory etiquette, social distancing and hand hygiene have been recommended. Based on these measures, some countries have already managed to control the COVID-19 propagation, although in the absence of pharmaceutical interventions, this control is not definitive. However, we have seen that social heterogeneity across populations makes the effects of COVID-19 also different. Social inequality affects the population of developing countries not only from an economic point of view. The relationship between social inequality and the health condition is not new, but it becomes even more evident in times of crisis, such as the one the world has been facing with COVID-19. How does social inequality affect the COVID-19 propagation in developing countries is the object of this study. We propose a new epidemic SEIR model based on social indicators to predict outbreak and mortality of COVID-19. The estimated number of infected and fatalities are compared with different levels of Non-Pharmaceutical Interventions. We present a case study for the Deep Brazil. The results showed that social inequality has a strong effect on the propagation of COVID-19, increasing its damage and accelerating the collapse of health infrastructure. INDEX TERMS COVID-19 propagation, social inequality, non-pharmaceutical interventions, developing countries.
Health Science Reports
Background and Aims: The COVID-19 pandemic poses an extraordinary threat to global public health. We designed an ecological study to explore the association between socioeconomic factors and the COVID-19 outcomes in 184 countries, using the geographic map and multilevel regression models. Methods: We conducted a cross-sectional ecological study in 184 countries. We performed regression analysis to assess the association of various socioeconomic variables with COVID-19 outcomes in 184 countries, using ordinary least squares and multilevel modeling analysis. We performed two-level analyses with countries at Level 1 and geographical regions at Level 2 in multilevel modeling analysis, using the same set of predictor variables used in ordinary least squares. Results: There was a significant relationship between COVID-19 cases rate (Log) per 100,000 inhabitants-day at risk with human development index (HDI), percentage of the urban population, unemployment, and cardiovascular disease prevalence. The results displayed that the variances are varied between Level 1 (country level) and Level 2 (World Health Organization [WHO] regions), meaning that the geographic distribution represented a proportion of the changes in the COVID-19 outcomes. Conclusion: The study suggests that in addition to the socioeconomic status affects the COVID-19 outcomes, countries' geographical location makes a part of changes in outcomes of diseases. Therefore, health policy-makers could overcome morbidity and mortality in COVID-19 by controlling the socioeconomics factors.
Geographical Analysis of Covid 19: Its Relationship with Socio-Economic Conditions in India
2021
The present paper aims to analyse the spatial variations in spread of corona cases and corona deaths and level of socio-economic conditions in India. The causal relationship between corona cases and corona deaths and twenty selected socio-economic variables has been taken into account. The state/union territory has been taken as the smallest unit of study. The entire research work is based on secondary sources of data. The study reveals states with better socio-economic conditions recorded higher corona cases and states with poor socio-economic conditions recorded lesser corona cases. States such as Maharashtra, Kerala, Andhra Pradesh, Tamil Nadu and Karnataka with better socio-economic conditions recorded a greater number of corona deaths.
JMIR Public Health and Surveillance, 2020
Background The impact of the COVID-19 pandemic has varied widely across nations and even in different regions of the same nation. Some of this variability may be due to the interplay of pre-existing demographic, socioeconomic, and health-related factors in a given population. Objective The aim of this study was to examine the statistical associations between the statewise prevalence, mortality rate, and case fatality rate of COVID-19 in 24 regions in India (23 states and Delhi), as well as key demographic, socioeconomic, and health-related indices. Methods Data on disease prevalence, crude mortality, and case fatality were obtained from statistics provided by the Government of India for 24 regions, as of June 30, 2020. The relationship between these parameters and the demographic, socioeconomic, and health-related indices of the regions under study was examined using both bivariate and multivariate analyses. Results COVID-19 prevalence was negatively associated with male-to-female s...