Socioeconomic inequality in life expectancy in India

Abstract

IntroductionConcern for health inequalities is an important driver of health policy in India; however, much of the empirical evidence regarding health inequalities in the country is piecemeal focusing only on specific diseases or on access to particular treatments. This study estimates inequalities in health across the whole life course for the entire Indian population. These estimates are used to calculate the socioeconomic disparities in life expectancy at birth in the population.MethodsPopulation mortality data from the Indian Sample Registration System were combined with data on mortality rates by wealth quintile from the National Family Health Survey to calculate wealth quintile specific mortality rates. Results were calculated separately for males and females as well as for urban and rural populations. Life tables were constructed for each subpopulation and used to calculate distributions of life expectancy at birth by wealth quintile. Absolute gap and relative gap indices of inequality were used to quantify the health disparity in terms of life expectancy at birth between the richest and poorest fifths of households.ResultsLife expectancy at birth was 65.1 years for the poorest fifth of households in India as compared with 72.7 years for the richest fifth of households. This constituted an absolute gap of 7.6 years and a relative gap of 11.7 %. Women had both higher life expectancy at birth and narrower wealth-related disparities in life expectancy than men. Life expectancy at birth was higher across the wealth distribution in urban households as compared with rural households with inequalities in life expectancy widest for men living in urban areas and narrowest for women living in urban areas.ConclusionAs India progresses towards Universal Health Coverage, the baseline social distributions of health estimated in this study will allow policy makers to target and monitor the health equity impacts of health policies introduced.

Caste, religion and regional differentials in life expectancy at birth in India: cross-sectional estimates from recent National Family Health Survey

“…Life expectancy has been explained by a combination of biological and behavioural factors. 47 The caste system, with its societal stratification and social restrictions, continues to have a major impact in India. 62 Caste and religion are inextricably linked to and is a proxy for SES in India.…”

“…[38][39][40] However, studies suggest that the increasing socioeconomic inequality over the years has led to poor health outcomes among population subgroups in the country. [41][42][43][44][45][46][47] The Sample Registration System (SRS) provides regular estimates of life expectancy at birth and age-specific death rate (ASDR) for major states, by sex and place of residence. However, such estimates are not available for social groups such as caste and religion and by economic groups.…”

“…Mohanty and Ram 48 stimated life expectancy at birth among the social groups using National Family Health Survey (NFHS)-2 and NFHS-3 data and found that life expectancy at birth is similar among the poor across caste groups. Recently, Asaria et al 47 have provided estimates of life expectancy at birth across wealth quintiles using NFHS-4 data and found that life expectancy at birth among the poorest wealth quintile was 65.1 years compared with 72.7 years in the richest wealth quintile.…”

Inequality in healthcare use among older people in Colombia

“…Our research question is timely and relevant, particularly as we try to study the determinants of access to healthcare. While existing evidence in Colombia suggests that within country variations in morbidity and mortality have been associated with socio-economic conditions as income, education, gender, and racial disparities, there is little evidence on how some socio-economic factors impact upon access to healthcare [ 24 – 27 ].…”

Effect of global atmospheric aerosol emission change on PM2.5-related health impacts

“…income, labor productivity, old age pension, rural-urban divide) [62,63], in air pollution-related health impact assessment. In China and India, for example, a discrepancy in life expectancy between rural and urban areas was attributed to economic, social life, and sex differences [64,65]. Using either AM/YLL in UMIC/HIC, may not necessarily alter the order of health outcome benefit, which does not alter the health impact message.…”