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.

Low- and middle-income countries are undergoing epidemiological and demographic transitions alongside economic growth, contributing to a rise in abdominal obesity. In India, the increase in sedentary occupations and insufficient physical activity are key drivers of this growing health concern. This study investigates the relationship between occupational types and abdominal obesity markers in well-characterised adults, accounting for a wide range of confounders. Using a nationally representative sample of 99,653 women and 91,990 men, occupations were categorised into sedentary work (SW), non-sedentary work (NSW), and not working (NW). Two outcomes were assessed: abdominal obesity, measured via waist-to-hip ratio (WHR) using WHO cutoffs, and a higher-risk threshold of WHR ≥ 1. Bivariate analyses and multivariable logistic regression, adjusted for socioeconomic and demographic factors, were conducted to evaluate the risk of abdominal obesity by occupation type. Among women, abdominal obesity prevalence based on WHO criteria was 56% (95% CI: 55.60–56.46), highest in NW (57.3%; 95% CI: 56.80–57.83), followed by SW (57.1%; 95% CI: 55.39–58.78) and NSW (51.5%; 95% CI: 50.63–52.43). Among men, prevalence was 48.9% (95% CI: 48.31–49.46), highest in SW (57.8%; 95% CI: 56.51–59.14), followed by NSW (49.9%; 95% CI: 49.15–50.63) and NW (37.3%; 95% CI: 36.14–38.43). Adjusted odds of abdominal obesity were significantly higher for SW compared to NSW (women: aOR 1.08, 95% CI: 1.02–1.14; men: aOR 1.20, 95% CI: 1.16–1.25). High prevalence of abdominal obesity among both men and women implies an emerging health risk in India. The findings that contributed to associations between sedentary occupation and abdominal obesity may inform occupation-related health risks and development of interventions to limit daily sitting at work place which may reduce metabolic disease risk.

The overarching goal of this article is to sensitize the insensitive governments, institutions, and civil societies for homeless population in social sphere. Routes into homelessness are complex, multiple and interlinked. The complex triggers consist of biographical, structural and behavioural factors, when combined, increase people’s vulnerability to homelessness. Despite the high economic growth and double digit GDP growth that gives opportunity to celebrate the country’s achievement, the worsening condition of the homeless, underlaid by huge inequality, creates the huge contradiction for such celebrations. The problems and complexity associated with homelessness require several stages of prevention, intervention and system-based response for a solution. Thus, policy response requires the urgent need to move away from discussions on general welfare policies to context-specific policies; otherwise, the aspirations of sustainable development will be castles in the air.

Universal Health Coverage (UHC) has been high on national and international agendas since its adoption as one of the Sustainable Development Goals (SDGs). Within India, there is a wide variation in the total amounts per capita spent by each state government (Government Health Expenditure or GHE) on healthcare. Bihar, with a GHE of 556 per capita (per annum), has the lowest state government spending, but there are many states in which governments spend more than four times that amount on a per capita basis. However, despite this, no state offers UHC to its residents. This failure to provide UHC could be because even the highest amounts spent by the state governments are too low for them to offer UHC or because the cost differences between states are very high. It is also possible, however, that a poor design of the government-owned health system and the degree of waste embedded within it could account for this. It is important to understand which of these factors is responsible because it then provides a clue as to what the best path to UHC might be in each state. One way to do that would be to arrive at one or more broad estimates of the amounts needed to finance UHC and to compare them with actual amounts being spent by the governments in each state. Older research provides two such estimates. In this paper, using secondary data, we add to them using four additional approaches so that we can build greater confidence in the estimation of amounts needed by each state to offer UHC to its residents. We refer to these as Outside-in, Actuarial, Normative, and Inside-Out. We find that, with the exception of the approach which assumes that the current design of the government health system is optimal and only needs added investment to offer UHC (the Inside-out approach), all the other approaches give a value of between 1302 and 2703 per capita for UHC, with 2000 per capita providing a reasonable point estimate. We also find no evidence to support the view that these estimates are likely to vary between states. These results suggest that several Indian states may have an inherent ability to offer UHC with government financing alone and that a high degree of waste and inefficiency in the manner in which government funds are currently being deployed may well be behind their apparent inability to do so already. Another implication of these results is that several states may also be further away from the goal of offering UHC than an initial analysis of their GHE as a proportion of their Gross State Domestic Product (GSDP), i.e., GHE/GSDP, may suggest. Of particular concern are the states of Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh, all of which have GHE/GSDP greater than 1%, but because their absolute levels of GHE are well below 2000, in order to reach UHC, they may need to more than triple their annual health budgets. The Christian Medical College Vellore supported the second author (Sudheer Kumar Shukla) through a grant from the Infosys Foundation. Neither of these two entities had any role in the study design, data collection, data analysis, interpretation, writing of the manuscript, or the decision to submit it for publication.

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.

Equitable access to quality healthcare without financial hardship is key to achieving Universal Health Coverage (UHC), especially in low- and middle-income countries in the WHO Southeast Asia Region (SEAR). Despite health insurance programmes, high out-of-pocket expenditures remain a barrier. This study evaluates health insurance coverage in SEAR, analysing socioeconomic and demographic factors. This cross-sectional study used data from Demographic and Health Surveys (2015–2022) conducted in countries within the SEAR (data from six countries for women and five for men). Our analysis separately examined women and men aged 15–49 years using data from their respective individual Demographic and Health Survey datasets. Pooled estimates of health insurance coverage were calculated with 95% CI. Multilevel logistic regression quantified variations at the country and community-levels and identified factors influencing health insurance uptake. Health insurance coverage varied across SEAR, with Indonesia reporting highest for women (58.2%; 95% CI: 57.65–58.72) and men (56.6%; 95% CI: 55.31–57.88), while lowest in Bangladesh for women (0.3%; 95% CI: 0.22–0.39) and Myanmar for men (1.4%; 95% CI: 1.04–1.83). Indonesia also had highest social security health insurance (women: 31.0%; 95% CI: 30.49–31.49, men: 27.9%; 95% CI: 26.74–29.03). Private insurance was lowest in Myanmar (women: 0.6%; 95% CI: 0.42–0.72, men: 0.9%; 95% CI: 0.60–1.27) and highest in Indonesia (women: 28.0%; 95% CI: 27.54–28.5, men: 30.0%; 95% CI: 28.81–31.14). Health insurance coverage was higher among individuals with higher education, greater exposure to mass media, rural residence, and older age. Insurance uptake was influenced by contextual factors beyond individual characteristics. India had highest community-attributable variation in health insurance uptake [women (53.1%; 95% CI: 52.56–53.62); men (56.3%; 95% CI: 55.17–57.46)], while lowest in Indonesia among women (17.7%; 95% CI: 16.40–18.99) and Maldives among men (10.8%; 95% CI: 6.71–16.84), after adjusting for demographic and socioeconomic factors. With an ageing population, healthcare demand and costs in SEAR will rise. Context-specific health insurance policies and targeted interventions are crucial for bridging coverage gaps and achieving UHC. There is no specific funding for this study.