Background COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. Objective The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. Data and methods We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. Results The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. Conclusion The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

Purpose:The objective of this study is to investigates the relationship between domestic violence and use of contraception among married women in rural India.Data:Third round of National Family Health Survey (NFHS-III).Methodology:Cross tabulation as bivariate analysis and Binary Logistic Regression as multivariate analysis has been employed to fulfill the objective.Findings:The result shows that there are several hidden factors. between physical violence and contraception use. Alternate explanatory variables are significantly affected the use of contraception. With physical violence which reflects that there is a relationship between physical violence and socioeconomic status such as education, awareness, empowerment of women and subsequently the use of contraception.Originality/value:The paper throws light on the hidden factors which are obstacle in use of contraception with physical violence. Results of this study have potentially important implications for programs aimed at preventing violence and promoting family planning programs.

Background The number of patients with coronavirus infection (COVID-19) has amplified in India. Understanding the district level correlates of the COVID-19 infection ratio (IR) is essential for formulating policies and intervention. Objectives The present study examines the association between socioeconomic and demographic characteristics of India's population and the COVID-19 infection ratio at the district level. Data and Methods Using crowdsourced data on the COVID-19 prevalence rate, we analyzed state and district level variation in India from March 14 to July 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out a regression analysis to highlight the district level demographic, socioeconomic, infrastructure, and health-related correlates of the COVID-19 infection ratio. Results The results showed that the IR is 42.38 per one hundred thousand population in India. The highest IR was observed in Andhra Pradesh (145.0), followed by Maharashtra (123.6), and was the lowest in Chhattisgarh (10.1). About 80 percent of infected cases and 90 percent of deaths were observed in nine Indian states (Tamil Nadu, Andhra Pradesh, Telangana, Karnataka, Maharashtra, Delhi, Uttar Pradesh, West Bengal, and Gujarat). Moreover, we observed COVID-19 cold-spots in central, northern, western, and north-eastern regions of India. Out of 736 districts, six metropolitan cities (Mumbai, Chennai, Thane, Pune, Bengaluru, and Hyderabad) emerged as the major hotspots in India, containing around 30 percent of confirmed total COVID-19 cases in the country. Simultaneously, parts of the Konkan coast in Maharashtra, part of Delhi, the southern part of Tamil Nadu, the northern part of Jammu & Kashmir were identified as hotspots of COVID-19 infection. Moran's- I value of 0.333showed a positive spatial clustering level in the COVID-19 IR case over neighboring districts. Our regression analysis found that district-level population density (β: 0.05, CI:004-0.06), the percent of urban population (β:3.08, CI: 1.05-5.11), percent of Scheduled Caste Population (β: 3.92, CI: 0.12-7.72),and district-level testing ratio (β: 0.03, CI: 0.01-0.04) are positively associated with the prevalence of COVID-19. Conclusion COVID-19 cases were heavily concentrated in 9 states of India. Several demographic, socioeconomic, and health-related variables are correlated with the COVID-19 prevalence rate. However, after adjusting the role of socioeconomic and health-related factors, the COVID-19 infection rate was found to be more rampant in districts with a higher population density, a higher percentage of the urban population, and a higher percentage of deprived castes and with a higher level of testing ratio. The identified hotspots and correlates in this study give crucial information for policy discourse. Keywords COVID-19, socioeconomic, co-morbidity, geographical, hot-cold spot, districts, India.

Right to Education (RTE) Act is intended to provide free and compulsory elementary education to all children aged 6–14 years. This article examines key constituents of elementary education in view of the RTE Act such as current attendance rate, types of institutions, medium of instruction, neighbourhood schools, Monthly per capita expenditure on elementary education (MPCEE)and incentives during pre- and post-RTE period using National Sample Survey Organisation’s 64th (2007–2008) and 71st (2014) round of unit level data. The result shows that far from the universalisation, exclusion is getting entrenched across gender, sector, and socio-religious and economic groups. Female children, children from deprived socio-religious groups, rural areas and from the bottom MPCE quintile have not only fared lower in most of the studied parameters during the pre-RTE period, but the gap from their counterpart has widened immensely during the post-RTE period. Free education has declined and monthly per capita expenditure on elementary education has increased sharply. Children are moving out of the government to private schools. The findings raise serious questions on the intention of the government to fulfil its mandate under RTE.

Affordable housing signifies the importance of one’s capacity to pay for housing, and it is essentially a market-based concept. The fundamental difficulty with India’s urban poor is that some households simply cannot afford adequate housing at any stage. The interface between their poverty and the real estate reality in India makes affordable housing a distant dream. The article, using the National Sample Survey Office 68th Round Consumer Expenditure Data and India’s Consumer Economy (ICE 360) Data, tests Engel’s law to determine the disposable income of the poor for housing. The poor spend a greater share of their income on basic needs and do not have disposable income to pay for an affordable house. Data collated from a private property website for 22 cities show that the government’s approach of affordable housing through its celebrated flagship programme, the Pradhan Mantri Awas Yojana (PMAY) is far from the market reality.