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Socioeconomic Inequality in Life Expectancy in India

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Socioeconomic Inequality in Life Expectancy in India Research BMJ Glob Health: first published as 10.1136/bmjgh-2019-001445 on 9 May 2019. Downloaded from Socioeconomic inequality in life expectancy in India Miqdad Asaria, 1 Sumit Mazumdar, 2 Samik Chowdhury,3 Papiya Mazumdar,4 Abhiroop Mukhopadhyay,5 Indrani Gupta6 To cite: Asaria M, ABSTRACT Key questions Mazumdar S, Chowdhury S, Introduction Concern for health inequalities is an et al. Socioeconomic important driver of health policy in India; however, much of What is already known? inequality in life expectancy the empirical evidence regarding health inequalities in the in India. BMJ Global Health It is well established that health inequalities exist country is piecemeal focusing only on specific diseases or ► 2019;4:e001445. doi:10.1136/ across various social groups and in various disease on access to particular treatments. This study estimates bmjgh-2019-001445 areas in India. inequalities in health across the whole life course for the Reducing socioeconomic health inequalities consti- entire Indian population. These estimates are used to ► Handling editor Seye Abimbola tutes a key objective of health policy in the country. calculate the socioeconomic disparities in life expectancy Received 28 January 2019 at birth in the population. What are the new findings? Revised 13 April 2019 Methods Population mortality data from the Indian ► This is the first study that characterises the socio- Accepted 19 April 2019 Sample Registration System were combined with data on economic distribution of health in India as measured mortality rates by wealth quintile from the National Family by life expectancy at birth and in so doing quantifies Health Survey to calculate wealth quintile specific mortality health inequalities occurring across the lives of the rates. Results were calculated separately for males and Indian population. females as well as for urban and rural populations. Life ► The study uses data from the fourth round of the tables were constructed for each subpopulation and used National Family Health Survey, the most comprehen- to calculate distributions of life expectancy at birth by sive health survey conducted in India to date. wealth quintile. Absolute gap and relative gap indices of inequality were used to quantify the health disparity in What do the new findings imply? terms of life expectancy at birth between the richest and ► These findings act as a baseline measure of the lev- poorest fifths of households. el of inequality in lifetime health across the country Results Life expectancy at birth was 65.1 years for the that can be used to assess the relative extent that poorest fifth of households in India as compared with 72.7 the health of different subgroups within the popula- http://gh.bmj.com/ years for the richest fifth of households. This constituted tion is improving as India rolls out Universal Health an absolute gap of 7.6 years and a relative gap of 11.7 Coverage. %. 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 provided by the private sector (76%) and distribution in urban households as compared with rural on September 24, 2021 by guest. Protected copyright. © Author(s) (or their paid for out of pocket (67%)—a rich case employer(s)) 2019. Re-use households with inequalities in life expectancy widest for permitted under CC BY. men living in urban areas and narrowest for women living study in the full spectrum of market failures Published by BMJ. in urban areas. that occur when provision of healthcare is left 5–11 1LSE Health, London School of Conclusion As India progresses towards Universal Health to a largely unregulated private sector. Economics and Political Science, Coverage, the baseline social distributions of health There are a wealth of studies describing London, UK estimated in this study will allow policy makers to target 2 socioeconomic inequality in health in India in Centre for Health Economics, and monitor the health equity impacts of health policies terms of various access and process indicators University of York, York, UK introduced. 3Ambedkar University Delhi, as well as in terms of various disease specific 12–17 Delhi, India outcome measures. What is missing, 4Department of Health Sciences, however, is a quantification of socioeco- University of York, York, UK 5 nomic inequality in overall lifetime health, Economics and Planning Unit, INTRODUCTION providing a holistic measure synthesising Indian Statistical Institute Delhi 18 Centre, New Delhi, India Socioeconomic inequality in health is every- inequalities emerging across the life course. 6Health Policy Research Unit, where evident in India with the poor living The objective of this study is to produce such Institute of Economic Growth, shorter and sicker lives than their richer a measure by describing the patterning of Delhi, India compatriots and often facing catastrophic health by wealth in India and in so doing to Correspondence to and impoverishing out-of-pocket outlays for estimate mortality probabilities and associ- 1–4 Dr Miqdad Asaria; accessing healthcare. This is unsurprising ated distributions of life expectancy at birth m. asaria@ lse. ac. uk in a country where healthcare is largely by wealth quintile in the population. These Asaria M, et al. BMJ Global Health 2019;4:e001445. doi:10.1136/bmjgh-2019-001445 1 BMJ Global Health BMJ Glob Health: first published as 10.1136/bmjgh-2019-001445 on 9 May 2019. Downloaded from estimates will provide a useful benchmark in order to Analysis gauge the level of socioeconomic inequality in overall Where detail on one or more characteristics regarding health in the country and to monitor how this changes the deaths was missing in NFHS (typically age at death), over time. we allocate these uncategorised deaths across each of As India embarks on the journey towards Universal the (age) groups in the ratio of the deaths occurring Health Coverage (UHC), public expenditure on health- with known characteristics. For example, if x% of deaths care remains modest as compared with other similarly overall are missing details about the age at death, we placed countries.19 20 This dearth of fiscal space for inflate the number of known deaths in each age group by health expenditure brings trade-offs between the various 100/(100-x). Of the 74 945 deaths overall in the dataset, dimensions of the UHC agenda: (1) widening access 466 (0.6%) were missing details on one or more charac- and hence reducing health inequalities; (2) reducing teristic. out of pocket costs and hence providing financial risk Given that the NFHS was not powered to calculate protection; and (3) increasing the range of healthcare national level mortality rates, we limited our use of the 21 services and hence improving health—into sharp focus. NFHS mortality data to adjusting the official vital statistics Methods for conducting equity informative economic data used to produce the SRS mortality rates. The adjust- evaluations of health policies have recently emerged to ments we applied serve to account for the relative differ- help inform healthcare prioritisation decisions for coun- ences observed in NFHS by wealth quintile. To do this, tries pursing UHC where reducing health inequality and we first calculated overall mortality rates for all wealth improving financial risk protection are core objectives of quintiles by age group, sex and geography from the 22–24 health policy. The estimates produced in this study NFHS using the prescribed NFHS sample weights. NFHS may serve to underpin such equity informative economic provides data about the total number of deaths over the evaluations for India. 3-year period prior to the survey. We use this information to calculate the 3-year mortality rates and simply divide by three assuming constant mortality rates across the 3 years METHODS to derive annual mortality rates. We then calibrated each Data of these rates by multiplying them by the appropriate calibration factor required to make the mortality rate for This study builds on the life tables produced by the Sample Registration System (SRS) data for 2011–2015. the particular subgroup as calculated from NFHS match The SRS in India is the largest demographic surveillance the mortality rates given for that subgroup in SRS. Finally, system in the world covering 1.7 million households and we applied these same subgroup level calibration factors 7.6 million individuals. Operated by the Registrar General to the mortality rates derived for each of the wealth quin- India working under the Ministry of Home Affairs, the tiles within each subgroup in the NFHS data. SRS continues to be the main source of information As an example, to calculate the calibration factor for http://gh.bmj.com/ on fertility and mortality both at the state and national females aged 55–60 years, living in rural households, we levels.25 The SRS life tables provide age group and sex performed the following calculation: MSRS specific mortality rates at overall, rural and urban geog- 55 60, female,rural C55 60,female,rural = NFHS− − M55 60,female,rural raphies at the national level as well as at state level for a − subset of states. The SRS data, however, do not provide where M signifies the mortality rate for the given information on socioeconomic differences in mortality subgroup from the specified dataset and C signifies the on September 24, 2021 by guest. Protected copyright. rates; for this information, we instead use the National subgroup specific calibration factor. We then assumed 26 Family Health Survey (NFHS) round 4 (2015/16). this calibration factor to be constant across the wealth The NFHS is the Indian edition of the Demographic quintiles within this subgroup and applied it to mortality and Health Surveys (DHS) programme conducted in rates further disaggregated by wealth quintile from the over 90 countries around the world.
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