Life Satisfaction (LS) is an indicator of subjective well-being (SWB) among the elderly, and is directly associated with health and mortality. Present study deals with the factors associated with the LS among the rural elderly in Odisha, India. A cross-sectional survey using multi-stage random sampling procedure was conducted among elderly (60+ years) in Bargarh district of Odisha. The survey was conducted among 310 respondents. Hierarchical regression analysis was used to assess the adjusted effect of various socio-economic, demographic, health conditions (physical and mental), social support and effects of multi-morbidity on LS. Cognitive health was the most influential factor in determining LS among both men (β = 0.327) and women (β = 0.329). Individual’s social support also plays an influential role in LS among rural elderly. Elderly who are living alone and have any sort of disability and had low score of activities of daily living (ADL) have also reported significantly lower perceived LS for both the genders. It is necessary to analyze and identify the major factors which can improve upon the level of LS among the elderly population. Better understanding of these factors can help in removing the superfluous anxiety of old age in the mindset of people which is pervading in the society.

Equity and justice in healthcare payment form an integral part of health policy and planning. In the majority of low and middle-income countries (LMICs), healthcare inequalities are further aggravated by Out of Pocket Expenditure (OOPE). This paper examines the pattern of health equity and regional disparities in healthcare spending among Indian states by applying Andersen’s behavioural model of healthcare utilization. The present study uses data from the 66th quinquennial round of Consumer Expenditure Survey, of the National Sample Survey Organization (NSSO), conducted in 2009–10 by Ministry of Statistics and Programme Implementation (MoSPI), Government of India (GoI). To measure equity and regional disparities in healthcare expenditure, states have been categorized under three heads on the basis of monthly OOPE i.e., Category A (OOPE > =INR 100); Category B (OOPE between INR 50 to 99) and Category C (OOPE < INR 50). Multiple Generalised Linear Regression Model (GLRM) has been employed to explore the effect of various socio-economic covariates on the level of OOPE. The gap in the ratio of average healthcare spending between the poorest and richest households was maximum in Category A states (richest/poorest = 14.60), followed by Category B (richest/poorest 11.70) and Category C (richest/poorest 11.40). Results also indicate geographical concentration of lower level healthcare spending among Indian states (e.g., Odisha, Chhattisgarh and all the north-eastern states). Results from the multivariate analysis suggest that people residing in urban areas, having higher economic status, belonging to non-Muslim communities, non-Scheduled Tribes (STs), and non-poor households spend more on healthcare than their counterparts. In spite of various efforts by the government to reduce the burden of healthcare spending, widespread inequalities in healthcare expenditure are prevalent. Households with high healthcare needs (SCs/STs, and the poor) are in a more disadvantaged position in terms of spending on health care. It has also been observed that spending on healthcare was comparatively lower among backward or isolated states. No doubt, the overall social security measures should be enhanced, but at the same time, looking at the regional differences, more priority should be assigned to the disadvantaged states to reduce the burden of OOPE. It is proposed that there is need to increase government spending, especially for the disadvantaged states and population, to minimise the burden of OOPE.

This study examines the incidence, prevalence, deaths, and disability-adjusted life years (DALYs) related to non-communicable diseases (NCDs) in South Asia, exploring the environmental, metabolic, and behavioural risk factors, and exploring changes in deaths and DALYs driven by population growth, aging, and mortality rates. Using data from the Global Burden of Disease (GBD) study 2021, we estimated age-standardized incidence, prevalence, deaths, and DALYs for four major NCDs: cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases from 2010 to 2021. Gender and age-specific estimations were conducted across all NCDs, with 95% uncertainty intervals and a decomposition analysis was employed to estimate change in death and DALYs attributable to NCDs. The burden of NCDs in South Asia increased by 3.00% in incidence from 2010 to 2021, while overall prevalence decreased by 1.00%, yet the age-standardized prevalence rate remains above the global rate (91,570 per 100,000 population). Incidences of cardiovascular and respiratory diseases declined by 3.00% and 13.00%, respectively, whereas diabetes and cancer rose by 21.00% and 13.00% in South Asia. Nepal faced the highest environmental impact (23.4% of DALYs), Bangladesh the greatest metabolic impact (25.62%), and India the highest from behavioural factors (23.95%). Population growth and aging were primary drivers of changes in deaths and DALYs across the region. This finding emphasizes the need for targeted public health interventions addressing environmental, metabolic, and behavioral risks for NCDs in South Asia, alongside strategies to support healthy aging and effective disease management across diverse demographic groups.

Accidental Injury is a traumatic event which not only influences physical, psychological, and social wellbeing of the households but also exerts extensive financial burden on them. Despite the devastating economic burden of injuries, in India, there is limited data available on injury epidemiology. This paper aims to, first, examine the socio-economic differentials in Out of Pocket Expenditure (OOPE) on accidental injury; second, to look into the level of Catastrophic Health Expenditure (CHE) at different threshold levels; and last, to explore the adjusted effect of various socio-economic covariates on the level of CHE. Data was extracted from the key indicators of social consumption in India: Health, National Sample Survey Organisation (NSSO), conducted by the Government of India during January–June-2014. Logistic regression analysis was employed to analyse the various covariates of OOPE and CHE associated to accidental injury. Binary Logistic analysis has demonstrated a significant association between socioeconomic status of the households and the level of OOPE and CHE on accidental injury care. People who used private health services incurred 16 times higher odds of CHE than those who availed public facilities. The result shows that if the person is covered via any type of insurance, the odd of CHE was lower by about 28% than the uninsured. Longer duration of stay and death due to accidental injury was positively associated with higher level of OOPE. Economic status, nature of healthcare facility availed and regional affiliation significantly influence the level of OOPE and CHE. Despite numerous efforts by the Central and State governments to reduce the financial burden of healthcare, large number of households are still paying a significant amount from their own pockets. There are huge differentials in cost for the treatment among public and private healthcare providers for accidental injury. It is expected that the findings would provide insights into the prevailing magnitude of accidental injuries in India, the profile of the population affected, and the level of OOPE among households.