Full Title: Adult Mortality in India: The Health–Wealth Nexus
Debasis Barik Associate Fellow National Council of Applied Economic Research [email protected]
Sonalde Desai Professor of Sociology, University of Maryland College Park And Senior Fellow, National Council of Applied Economic Research [email protected]
Reeve Vanneman Professor of Sociology, University of Maryland College Park [email protected]
February 26, 2016
India Human Development Survey was funded by grants R01HD041455 and R01HD061048 from the US National Institutes of Health and a supplementary grant from the Ford Foundation. Data analysis was the funded by the UK government as part of its Knowledge Partnership Programme (KPP).
PAA 2016 | Session 58
Adult Mortality in India: The Health–Wealth Nexus Debasis Barik, Sonalde Desai, Reeve Vanneman
Abstract
Research on wealth and adult mortality is often stymied by the reciprocity of this relationship. While financial resources increase access to healthcare and nutrition and reduce mortality, sickness also reduces labor force participation, thereby reducing income.
Without longitudinal data, it is difficult to study the linkage between economic status and mortality. Using data from a national sample of 133,379 comprising Indian adults aged 15 years and above, this paper examines their likelihood of death between wave 1 of the India
Human Development Survey (IHDS), conducted in 2004-05 and wave 2, conducted in 2011-
12. The results show that mortality between the two waves is strongly linked to the economic status of the household at wave 1. Household wealth is positively associated with the manifestation of hypertension, diabetes and cardiac conditions, but wealth also reduces the likelihood of death conditional on having these diseases.
Keywords: Adult Mortality, health, morbidity, hypertension, wealth, SES, India, Asia
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INTRODUCTION
A strong relationship between economic deprivation and ill-health was first scientifically documented by René Villermé, who compared mortality rates and poverty across the arrondissements of Paris in the 1820s, though references to the relationship can be found even in ancient Greek and Chinese texts (Deaton 2002). It is well recognized in the literature that income is associated with declining mortality and rising longevity in cross- national comparisons (Preston 1975) as well as between the rich and the poor in the same society (Kitagawa 1973).
In spite of this observed correlation, empirical research on the causal linkages between health and economic status faces several challenges, as listed below: (a) Although poverty may lead to poor health and higher mortality, poor health may also lead to a decline in the economic status since illness may result in labor force withdrawal or higher health- related expenditure. In this case, poverty may be the result rather than the determinant of ill- health and mortality (Smith 1999); (b) Rising incomes may lead to greater consumption of processed foods and a more sedentary lifestyle, resulting in poor health (Razzell and Spence
2006); and, (c) The protective effect of income on health may depend on the nature of health systems in contexts where state-funded public health systems are effective in reducing income inequalities in health (Chen, Yang and Liu 2010).
In this paper, we examine the relationship between household economic status and adult mortality in India, taking into account the caveats mentioned above. The India Human
Development Survey conducted in 2004-05 and again in 2011-12 allows us to link household economic status and health conditions at one point in time with subsequent mortality for
133,379 adults aged 15 years and above.
Using these prospective data, we address the following three questions:
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1. Is household economic status associated with the probability of adult death in the
subsequent seven years?
2. Are individuals from higher economic strata more likely to suffer from chronic
conditions that may reduce the probability of their survival?
3. Is the relationship between chronic health conditions and mortality similar for the
rich and the poor?
Health and wealth: correlation and causation
It has long been recognized that poverty is associated with ill-health (Deaton 2002).
In England and Wales, the systematic documentation of mortality by occupational class began as early as 1851, with the publication of Decennial Supplements to the Annual Report of the Registrar General. Social class differentials in mortality became the focus of systematic study in the United States only in the latter half of the twentieth century, with the publication of Kitagawa and Hauser’s path-breaking study of demographic and socio- economic mortality differentials. This study was based on the 1960 Census matched to death certificates filed in May–August of the same year (Hummer, Rogers and Eberstein 1998;
Kitagawa 1973). Although there exists ample literature on the nexus between socio-economic status and health and mortality, research on this issue in an Asian context gained prominence only in the 1990s (Chen et al. 2010; Liang et al. 2000; Liu, Hermalin and Chuang 1998;
Zimmer 2008; Zimmer and Amornsirisomboon 2001; Zimmer, Kaneda and Spess 2007a;
Zimmer et al. 2007b).
In spite of the considerable body of evidence showing this correlation, the direction of causation has not been clearly established. Although medical practitioners and public health researchers tend to emphasize the impact of economic status on health and mortality, economists tend to argue that poor health restricts an individual’s capacity to earn income
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PAA 2016 | Session 58 and accumulate assets by limiting work or by raising medical expenses. In his pioneering article titled “Healthy Bodies and Thick Wallet”, James P. Smith (1999) concluded that the causal direction of the social health gradient is not uniform across the life-cycle. During the pre-retirement period, health affects income, whereas for older individuals, income affects health.
However, it seems likely that the nature of the relationship may be different for societies where extended households prevail, resulting in income pooling between different members of the household, and where sick members may co-reside with other family members in order to gain financial and logistical help. Thus, an examination of the relationship between wealth and health, particularly mortality, in a low to middle income country like India may offer interesting insights.
The challenge of rising prosperity
Higher economic status improves access to food, living conditions and access to medical care but also poses several challenges, particularly in a transition society like India.
Higher income individuals typically tend to engage in non-manual work, reducing physical activity, which, in turn, reduces caloric needs; but their food intake rises due to growing income. Moreover, greater incomes may lead to what individuals consider as “superior foods”, which in the case of India, include refined cereals, and the consumption of rice and wheat instead of small millets as also the higher consumption of fats, all of which may be linked to rising rates of diabetes rather than improving health (Mohan et al. 2010).
This is a distinctly different scenario from that prevalent in industrial societies where the proportion of individuals involved in manual labor is smaller and the consumption of organic and unrefined food is more expensive than mass-produced processed but less healthy foods. Consequently, obesity is associated with poverty rather than wealth. This has been
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PAA 2016 | Session 58 particularly documented in the United States where the rates of obesity and of associated chronic disease are higher among the poor rather than the rich (Levine 2011).
In contrast, India is still at a developmental stage where the poor tend to be undernourished while obesity among the rich is rising, consequently creating what has come to be known as a the ‘dual burden of malnutrition’ (Ramachandran 2016). Part of this burden may be attributable to a greater consumption of refined cereals (Mohan et al. 2010), as well as the consumption of restaurant food and declining physical activity levels among the rich urban residents (National Sample Survey Organisation 2012). Along with rising obesity, the prevalence of cardio-vascular diseases and diabetes has also been rising in India, a topic to which we return below. This brief review suggests that higher incomes may not always lead to better health outcomes, particularly in a country that is undergoing substantial transitions in lifestyles.
Factors mediating the wealth–mortality nexus
One would expect higher income-based inequalities in health under the following two conditions: (1) Where the proportion of deaths due to communicable diseases is lower; and,
(2) Where social safety nets and public health systems are more effective. In societies characterized by the prevalence of contagious diseases, both high and low-income individuals may be equally likely to experience high levels of morbidity. Research on maternal education and child health documents a far weaker relationship between maternal education (and the associated socio-economic advantages) and child health in Sub-Saharan Africa than in other settings (Desai and Alva 1998). It is also possible that in countries where public health systems are strong, even poor individuals may be able to access healthcare, thereby leading to a reduction in income inequalities in health outcomes (Mackenbach 2002; Mackenbach et al.
2015).
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India stands at the cusp of the epidemiological transition. Although communicable diseases remain dominant in the country, the prevalence of non-communicable diseases
(NCDs) is rising. Cardiovascular diseases, strokes, diabetes, and cancer are the four leading
NCDs in India (Upadhyay 2012). India has the highest number of people with diabetes in the world (Ghaffar, Reddy and Singhi 2004) and this burden has been rising over time
(Kaveeshwar and Cornwall 2014), which is why it is often referred to as the ‘diabetic capital of the world’ (IDF 2009). At least some of this increase in the occurrence of the disease could be due to the rising consumption of processed foods and refined foodgrains (Mohan et al.
2010) as unprocessed foods and healthier cereals like small millets are considered inferior foods that households abandon as they get rich. This would suggest that the relationship between wealth and mortality may be weaker in India than in richer countries.
At the same time, however, wealth may make it easier to obtain treatment for these diseases. While in theory, the Indian public health system comprises a vast array of primary health centers located throughout the country (Gangolli, Duggal and Shukla 2005), in practice nearly 3 out of 4 Indians use private health services and have to incur large out-of- pocket expenditures (Barik and Desai 2014). Thus, the extent to which an income-mortality nexus exists in India is an open question.
Nexus of Income, Lifestyle Diseases and Mortality in India
India is the second fastest-growing economy in the world. The Indian economy grew at an average rate of 7.25 per cent in the first decade of the twenty-first century (2000-10), resulting in rising per capita incomes and declining poverty in both urban and rural areas
(Dreze and Sen 2013). However, income inequality has also risen over this period (Dreze and
Sen 2013), and improvement in health and nutritional status has been slower than might have been expected on the basis of economic growth (Desai et al. 2016), and certainly slower than in other Asian nations (Gillespie, Harris and Kadiyala 2012).
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Ironically, increasing incomes have not led to improving diets. Studies of dietary diversity document declining diversity over time (Gaiha et al. 2014), anemia remains prevalent at almost all income levels (International Institute for Population Sciences and
Macro International 2007), and the proportion of individuals suffering from NCDs has grown even as India has experienced a surge of economic growth.
This issue is particularly critical for India since there is some possibility that either genetic factors or their traditional carbohydrate-based diets make Indians more susceptible to cardio-vascular diseases and diabetes. South Asian populations living abroad, particularly in
Europe and the United States, have shown very high rates of diabetes, high blood pressure and heart conditions (Gunarathne et al. 2009; Gupta et al. 2011). The rates of coronary heart disease have been reported to be unusually high in several parts of the world among people originating from the Indian subcontinent (McKeigue, Miller and Marmot 1989). A UK study showed that men and women from India had the highest standardized mortality rates due to cardiovascular diseases, and that young Indian men were at particularly high risk of contracting these diseases (Balarajan et al. 1984). The cardiovascular and cancer mortality of
South Asian migrants was also seen to increase with the duration of residence in England and
Wales, presumably as these migrants became richer (Harding 2003). Indian immigrants in the
United States show a higher prevalence of diabetes and a number of related chronic diseases such as hypertension and cardiac conditions (Bhopal 2000; Shah et al. 2015). Since Indians form the ethnic group with the highest income and education levels in the United States
(Migration Policy Institute 2014), it is not clear whether increasing wealth is a boon or a curse for Indian immigrants. An examination of the relationship between wealth and chronic conditions as well as mortality in India may help shed light on this puzzle.
While, rising incomes, on the one hand, may place individuals in sedentary lifestyles and encourage poor eating habits, on the other hand, they also make it possible for people to
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PAA 2016 | Session 58 seek better healthcare. The Indian health system is mostly privately funded with more than 60 per cent (Barik and Desai 2014) of all treatment costs borne by the family members from out- of-pocket spending. Thus, the burden of the treatment cost is disproportionately distributed across the income distribution, ranging from less than one per cent among the top income quintile to 15 per cent among the lowest quintile (Barik and Desai 2014). Although some efforts are now being made to provide hospitalization coverage to the poor (CPR 2011; IDFC
Ltd. 2014), health insurance, particularly insurance that covers outpatient services and prescription medicines, remains limited. Moreover, transportation costs can add substantially to the burden of medical costs. Although there has been some increase in secondary and tertiary care units like tehsil or district level hospitals and specialty hospitals like the All
India Institute of Medical Sciences (AIIMS) in the last decade, a majority of the rural population in India depends heavily on the usually poorly-performing primary health centers and sub-centers for most care, including emergency care. This implies that even as higher incomes increase the risk of life-style related diseases, they also allow for better treatment of those diseases, and therefore, the net impact of income on mortality remains subject to empirical examination.
The absence of research on adult mortality in India
Most of the research on mortality in India has focused on infant and child mortality
(Ghosh 2012; Kumar et al. 2013; Singh et al. 2011); and research on adult mortality is limited at best. Earlier studies of adult mortality in India concentrated more on the levels and trends
(Clark 1987; Dandekar 1972; Dyson 1984) rather than underlying processes.
Unfortunately, India has lacked comprehensive data for the analysis of individual and household level predictors of adult mortality. India has a vital registration system to record vital statistics like birth, deaths, and marriage, but it is poorly organized, and frequently incomplete, particularly in rural areas. Adult mortality statistics come mainly from the
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Sample Registration System (SRS), which is fairly complete but lacks socio-economic information about individuals.
Using retrospective data from the National Family Health Survey (NFHS), Saikia and
Ram (2010) have tried to explore the factors associated with adult death (among persons aged
15-59 years). Since the NFHS focused mainly on maternal and child health, it did not contain complete information on adult mortality, and instead relied on the retrospective reporting of adult mortality by the survey households, a method which is subject to a high level of recall lapse. Defining the universe for which retrospective data is to be obtained is difficult since households may restructure themselves after the death of a patriarch. Additionally, with retrospective recall, it is not possible to obtain data on the socio-economic status of a household before the individual’s death (Saikia and Ram 2010). Since both household structure and household income are affected by death, particularly the death of income- earning adults, it is difficult to develop an analytical model by using retrospective data. It is this niche that the present paper seeks to fill.
IHDS: Advantages of Panel Data
In this paper, we use prospective data from the IHDS 2004-05 and 2011-12. The
IHDS is the first Indian nationwide panel survey with a sample that is sufficiently large to study rare events like mortality. IHDS is a multi-topic panel study of 41,554 households from
33 states and union territories across India. The first wave of IHDS collected socio-economic and health data for over 215,754 individuals across 1503 villages and 971 urban neighborhoods. In 2011-12, about 83 per cent of these households were re-interviewed. In this paper, we analyze the probability that individuals aged 15 years and above, who were part of the survey in 2004-05, died before the second survey was conducted in 2011-12. This prospective panel allows us to explore the link between economic and health status at wave 1 and the probability of death by wave 2.
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The re-contact rate for IHDS was 90 per cent in rural areas and 72 per cent in urban areas. Regardless of whether the household was re-interviewed, a tracking sheet was filled out in wave 2 that contained information about the current status (including deaths) of each individual from the survey household in wave 1. Where household interviews were not possible, attempts were made to obtain information about the present whereabouts of all household members from the neighbors.
Table 1 provides a detailed description of attrition of the IHDS 2004-05 sample population. IHDS 2004-05 collected information from 215,754 individuals on various aspects like health status, education, employment, and activities of daily life, among others. Out of the entire sample of IHDS 2004-05, 8,532 individuals died and 19,841 were lost to follow-up.
Of the 187,381 persons still alive at wave 2, 150,988 were included in the IHDS 2011-12 survey. Proxy information about the remaining 36,393 individuals—mostly migrants and those suffering from diseases—was collected from the household members still residing in the original location. The loss of sample was higher among the rich and those living in the urban areas. Sample losses usually occurred due to migration or the resettlement of urban slums as governments in several large cities created affordable housing complexes between the two interviews, resulting in movements away from the original areas of residence for sample members. While attrition biases our results, given that attrition is disproportionately concentrated in richer households living in urban areas and is associated with the movement of whole families rather than single individuals, any relationship we find between economic status and mortality is likely to be an under-estimate.
[Insert Table 1 Here]
We exclude children under the age of 15 from our analysis. Hence, the analytical sample includes 133,379 adults aged 15 years or above during the first wave of the survey, of
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PAA 2016 | Session 58 which 7,996 died before the commencement of the IHDS 2011-12 survey. We have excluded the sample members for whom survival information in 2011-12 is not available.
ANALYTICAL STRUCTURE
Mortality between two waves of the survey forms our primary dependent variable while household economic status and the presence of a major health condition (diabetes, high blood pressure and cardiac condition) are the primary independent variables of interest.
Economic status at wave 1
Much of our prior discussion has used the terms ‘household economic status’,
‘income’ and ‘wealth’ interchangeably. Although measurement of the economic status of a household represents one of the crucial building blocks of social science research, it is also one of the most elusive concepts. One of the problems in studying the link between economic status, on one hand, and health and mortality, on the other, lies in what constitutes economic status. It is easier to measure economic status in modern industrial societies where most incomes are monetary than in countries where a large proportion of the individuals work in the non-wage sector either as self-employed farmers or as petty business owners.
Difficulties in collecting such information have prompted analysts to propose simple shortcuts like the creation of wealth or asset indices based on the ownership of simple durable goods like watches, televisions, and refrigerators, among other things. In a paper titled,
“Estimating Wealth Effects without Expenditure Data-or Tears: An Application to
Educational Enrollments in States of India”, Filmer and Pritchett (2001) propose the use of such an asset index to rank households in order to study the impact of household economic status on children’s school enrollment. This has been a highly influential paper (Filmer and
Pritchett 2001), garnering nearly 2500 citations in Google Scholar, and using an asset index as a primary independent variable or control variable is pretty much a standard approach for all analysis based on data from Demographic and Health Surveys.
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The IHDS is fortunate in having access to three difference sources of household economic status: (1) Household consumption expenditure; (2) Household income from a detailed questionnaire that collected both wage income and self-employment income, and (3)
Information about household asset ownership. Each, however, has a conceptually different relationship with the health and mortality of the household members. Households with sick individuals may spend more on medical care while economizing elsewhere; they may even borrow to cover medical expenses. This makes consumption expenditure the most endogenous of the three. Income is a good contemporaneous measure of economic conditions but when individuals are sick, they may have to reduce their labor force participation. Using annual income data from wave 1 and linking them to survival at wave 2 reduces the endogeneity bias but it may remain substantial. Using an index based on household ownership of assets allows us to look at the accumulation of assets over a longer period of time and is the least likely to suffer from the endogeneity bias.
Hence, we measure the household level economic resources in terms of wealth levels
(asset ownership), reflecting the accumulation of resources amassed over the lifetime. Unlike income, which signifies the flow of resources into the household, wealth helps in consumption smoothing even in the short-term absence of income. Typically, most of the old age expenses are met through wealth rather than income (Duncan et al. 2002). The IHDS asked a series of questions about the possession of various basic durable assets by a household and the quality of the housing. Similar housing and consumer goods questions are now widely used in developing country surveys as an easily administered scale measuring the household economic level. The household wealth index was constructed by using such a set of 23 dichotomous variables measuring the household possession of basic and durable assets
(Figure 1). The unweighted mean number of assets to a household was 8.52 with an unweighted standard deviation of 4.48. The wealth index was created by using a simple sum
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PAA 2016 | Session 58 of the assets; the unweighted Cronbach's reliability coefficient alpha of the wealth scale was
0.8876. The values of the wealth index used in this analysis vary from 0 to 23, where a value of ‘0’ denotes that the household possesses none of the 23 assets, and a value of ‘23’ indicates the ownership of all 23 assets by the household. Further, the asset scale has been recoded into five quintiles for facilitating the easy comprehension of the descriptive statistics in Table 2.
[Insert Figure 1 about here]
Morbidity at wave 1
We expect that wealth is positively associated with the experience of major morbidity and with the treatment of these illnesses. The IHDS asked whether any household members suffered from a range of listed conditions including high blood pressure, heart disease, diabetes, leprosy, cancer, asthma, polio, paralysis, epilepsy, cataract, mental illness, STD or
AIDS, and “others”, which included accidents. Of these, high blood pressure, diabetes, asthma and heart diseases form the bulk of the illnesses. For this paper, we focus on the existence of any of the following three conditions—high blood pressure, cardiac conditions and diabetes.1
The relationship between the prevalence of major morbidity and household wealth quintile is presented in Figure 2. This figure suggests that the prevalence of morbidity increases while that of death declines with an increase in household wealth.
[Insert Figure 2 about here]
Control variables
In addition to household wealth and morbidity at wave 1, we also control for a set of individual and household level variables like age, sex, number of completed years of
1 We have repeated this analysis with a broader range of conditions with similar results. Here, however, we focus on the three conditions most associated with rising prosperity.
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PAA 2016 | Session 58 schooling, marital status, work status, and a set of dummy variables indicating caste, ethnicity and religion. We also include controls for urban residence as well as dummy variables for the state of residence. For reasons of parsimony, the results for the state of residence are not shown in the tables. Descriptive statistics for mortality, morbidity, wealth index and other background factors are presented in Table 2.
[Insert Table 2 about here]
RESULTS
Tables 3 and 4 present a comprehensive picture of wealth effects on adult mortality in the Indian context. In Table 3, we focus on morbidity at wave 1 as the dependent variable, with household wealth and other covariates as the independent variables. The three models in Table 4 focus on mortality between the two waves as the dependent variable with the first model containing household wealth and age, sex and other background characteristics, and the second model adding the existence of morbidity in wave 1 as an additional control variable, and the third model adding an interaction term for wealth and morbidity at wave 1.
[Insert Table 3 about here]
Effect of wealth on morbidity
Table 3 presents the results from logistic regression where we examine the relationship between household wealth and the existence of major morbidity. The results show that ceteris paribus, individuals living in households with a higher number of assets face higher odds of suffering from high blood pressure, cardiac condition or diabetes. This relationship is statistically significant at the .01 level. This observation is consistent with our earlier argument that higher economic status may be associated with a greater likelihood of morbidity, particularly that resulting from a poor diet, lack of exercise or the higher consumption of tobacco and alcohol.
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It is reasonable to wonder if higher household economic status may be associated with greater access to medical care, resulting in the quicker diagnosis of illnesses like high blood pressure that may well go undetected in the case of poorer individuals. This remains a valid concern to which we return in the discussion section.
Effect of wealth on mortality
Model 1 of Table 4 examines the impact of wealth at time 1 on mortality. Model 1 shows that household economic status is negatively related to the probability of death and that this relationship is statistically significant at the 0.01 level. Consistent with the earlier research in the Asian context (Liang et al. 2002; Liang et al. 2000), these results confirm a strong inverse relationship between household economic status and adult mortality. If age, sex, education, and the place and state of residence are held constant, the risk of mortality declines as household wealth increases.
[Insert Table 4 about here]
Model 2 adds the existence of major morbidity at wave 1 to this regression. The results show that if sex, age and social background are held constant, individuals suffering from diabetes, heart condition or high blood pressure at wave 1 are far more likely to die before the survey at wave 2. The coefficient of 0.516 translates to an odds ratio of 1.67, signifying a large increase. This observation also offers some comfort about the measurement error—our measure of morbidity has indeed picked up severe conditions resulting in death.
The addition of morbidity to this model sharpens the wealth gradient in mortality with the coefficient for ownership of household assets increasing from
-0.032 to -0.036. This is not surprising, for if wealth is associated with greater morbidity (as noted in Table 3), and morbidity, in turn, is associated with greater mortality, these two combine to reduce the protective impact of wealth on mortality. Once we determine the net of the indirect effect via morbidity, we see a sharper impact of wealth on mortality.
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Interaction between wealth and morbidity
In order to examine the role of wealth in shaping survival chances for individuals with major morbidity as compared to those without, we interact the household asset index with morbidity and add it to the regression for mortality in model 3. The interaction term is negative and significant at the 0.05 level. Figure 3 plots the predicted values for the probability of dying between the two surveys for individuals with and without major morbidity at different levels of wealth. The shaded lines show the confidence interval surrounding these predictions.
[Insert Figure 3 about here]
The event of an individual experiencing hypertension, cardiac condition or diabetes reduces survival substantially, however, this effect is mainly seen for the poor. Poor persons who suffer from a major illness such as diabetes, heart disease or high blood pressure are far more likely to die than either individuals in a similar asset group but without these diseases or those in upper income groups who have these diseases. Until the income threshold is extremely high—at an asset count of 15 or more assets, wealth plays a significant role in curbing mortality. However, only less than 7 per cent of the sample households own 16 assets or more. This suggests that the morbidity burden is mitigated by healthcare for the very rich.
Other determinants of mortality
Besides wealth, the other two socio-economic indicators, that is, education and work status, also show a significant negative relationship with adult death (Table 4). The additional years of education increase the probability of survival among adults. This is consistent with observations from both high-income countries as well as for some Asian countries (Elo and
Preston 1996; Liang et al. 2000; Liu et al. 1998). The HIV-infected population with less than a high school degree in the USA faced a 53 per cent greater risk of death than their more educated counterparts (Cunningham et al. 2005). Those who are working are at a lower risk
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PAA 2016 | Session 58 of mortality in India. This may also depend on the nature of the work they do, but investigating this would require a level of detail which is beyond the scope of the present study.
The influence of the demographic predictors on mortality is mostly supported by earlier studies. The risk of mortality is highest among the elderly, whereas the death risk is significantly higher among people above the age of 30 years, as compared to the youth below the age of 30. However, the risk of death is also lower among females, the highly educated, and married individuals as compared to their counterparts. Married individuals face a significantly lower rate of mortality than the widowed, the divorced, or the separated. This is consistent with the observations from other countries (Gove 1973; Waite 1995). The risk of mortality is significantly higher among the Adivasis (Scheduled Tribes, or STs), as compared to the high-caste Hindus. The Adivasis are indigenous tribal groups, located in mountain areas or dense forests. Poverty, low education levels, and remote locations reduce their chances of survival. Studies have also recorded higher alcohol and tobacco use among this group (Desai et al. 2010), which may also lead to higher mortality. Once we control for the state of residence and socio-economic background, we find that urban residence does not have a significant effect on morality.
DISCUSSION
Factors leading to adult mortality in developing countries have received considerably less attention than those leading to child mortality, primarily due to data limitations. While data on infant and child mortality along with socio-economic correlates thereof are routinely collected in demographic and health surveys, data about adult mortality are harder to come by. It is difficult to collect retrospective data on adult mortality in cross-sectional data as it is hard to determine the universe from which the sample should be drawn, and relatively few
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PAA 2016 | Session 58 prospective studies exist. Consequently, we are left with a fragmented literature which suggests that though the economic status of individuals may be correlated with their health and mortality, the direction and strength of this relationship and the pathways through which they operate deserve to be studied in a variety of socioeconomic contexts before robust generalizations can be made (Zimmer 2008). The present paper seeks to fill this gap.
Using prospective data from IHDS, 2004-05 and 2011-12, a large nationally representative panel survey, this paper finds that the economic status in wave 1 is negatively associated with the probability of death by wave 2 for adults aged 15 years and above. This relationship persists after controlling for age, sex, marital status, education, caste/religion and place of residence. The use of panel data allows us to address the potential problem of reverse causality. While income and wealth may affect health and mortality, sickness may also lead to labor market withdrawal and reduce income. Using panel data allows us to control for at least some of the potential endogeneity.
We also find that household wealth interacts with morbidity in the following two ways: (1) Individuals from higher economic status families appear to be more likely to contract conditions like hypertension, cardiac ailments and diabetes, possibly due to their sedentary lifestyles or obesity. The existence of these conditions at wave 1 substantially increases the risk of death by wave 2. Hence, the negative impact of household wealth on mortality is stronger once we control for risk factors due to one of the three listed conditions.
(2) Given that they suffer from one of these three conditions, household wealth plays a substantial role in lowering mortality. The interaction term for household wealth and morbidity at wave 1 is a significant predictor of mortality by wave 2. The affliction of diabetes, heart condition or hypertension is far more likely to lead to death in the case of poorer individuals than richer individuals.
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It is not possible to say unequivocally from our analysis that an increase in wealth leads to an increase in what have come to be termed as “lifestyle diseases”. It is possible that the availability of wealth simply makes it easier to diagnose these illnesses earlier. However, irrespective of whether wealth leads to higher rates of diabetes, heart condition and hypertension, it certainly seems to exercise a protective influence once individuals are diagnosed with these conditions. These three conditions are easily manageable by individuals who are able to obtain regular care and comply with the prescribed medication regime. Early diagnosis leads to a better health outcome. Hence, it is not surprising that the greatest rate of mortality is observed among poor individuals afflicted with one of these conditions who may be less likely to comply with the prescribed medication regime and may well have arrived at this diagnosis at a more advanced level of the disease.
Indian public health policies suffer from a curious gap when it comes to adult health.
The issue of maternal and child health has received substantial attention in the National
Health Mission; diseases that lead to hospitalization are beginning to be addressed through highly subsidized insurance programs for the poor. However, few policies address the challenge of diagnosis and treatment of NCDs. Our results suggest that tackling this challenge, particularly for the poor, may have a substantial impact on adult mortality.
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Zimmer, Z.and P. Amornsirisomboon. 2001. "Socioeconomic Status and Health among Older Adults in Thailand: An Examination using Multiple Indicators." Social
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Tables and Figures Table 1: Description of the Individual Sample Followed in India Human Development
Survey 2011-12 from 2004-05 Wave.
Still Alive Dead Lost Total Wealth Quintile
Poorest 87.8 4.9 7.3 39,472 2nd Quintile 88.7 4.1 7.1 38,792 Middle 87.6 3.9 8.5 36,475 4th Quintile 87.3 3.7 9.1 54,226 Richest 84.5 3.4 12.1 46,789 Morbidity* No 87.6 3.8 8.7 203,879 Yes 74.1 15.0 10.9 11,875 Sex
Male 86.8 4.4 8.7 109,805 Female 87.8 3.6 8.7 105,949 Age Less than 15 91.2 0.8 8.0 68,462 15-29 years 89.8 1.2 8.9 59,795 30-44 year 88.6 2.2 9.2 42,423 45-59 year 84.7 6.4 8.9 27,170 60 years or more 64.3 26.4 9.3 17,904 Educational Level# Illiterate 83.31 9.13 7.57 78,799 Up to 5th Std. 85.53 5.82 8.65 49,623 Secondary level 87.6 2.99 9.41 61,102 Metric but non-graduation 87.84 1.7 10.46 14,569 Graduate and above 83.21 1.95 14.84 10,306 Place of Residence
Rural 88.8 4.3 6.9 143,374 Urban 83.0 3.3 13.6 72,380 87.29 4.01 8.7 100.0 Total 1,87,381 8,532 19,841 2,15,754
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Notes: * Morbidity refers to any of the three conditions—Diabetes, Cardiac Condition or Hypertension.
#Some values of the education variables are missing.
Source: India Human Development Survey 2004-05 and 2011-12.
Table 2: Descriptive Statistics for Mortality, Morbidity, Wealth Index and Other
Background Factors (Weighted).
Variable Mean Std. Dev. Sample size Mortality 0.06 0.24 133,379 Household Wealth 8.34 4.26 133,379 Morbidity* 0.03 0.18 133,379 Female 0.50 0.50 133,379 Age 36.63 16.47 133379 Education# 5.35 4.89 132351 Urban 0.27 0.44 133379 Notes: * Morbidity refers to any of the three conditions—Diabetes, Cardiac Condition or Hypertension.
#Some values of the education variables are missing.
Source: India Human Development Survey 2004-05 and 2011-12.
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Table 3: Log Odds of Three Major Morbidities by Household Wealth and Other Socio- economic Characteristics in India.
Morbidity* Coefficients SE Household Wealth 0.087** 0.009 Sex (Male omitted) Female 0.208* 0.09 Age (15-29 years omitted) 30-44 years 1.735** 0.117 45-59 years 2.759** 0.118 60 years and above 3.207** 0.124 Education 0.012 0.008 Marital Status (Married/spouse absent omitted) Unmarried/No gauna -0.786** 0.144 Widowed 0.102 0.101 Divorced/Separated -0.327 0.353 Social Groups (High caste omitted) OBCs -0.143* 0.064 Dalits -0.254** 0.077 Adivasis -0.759** 0.254 Muslims -0.037 0.075 Christians/Sikhs/Jains 0.073 0.093 Place of residence (Rural omitted) Urban 0.259** 0.069 Work status (Not working omitted) Working -0.198** 0.067 Constant -5.433** 0.188
Observations 132,351 Chi-square 3121.41 DF 37 Note: ** p<0.01, * p<0.05. * Morbidity refers to any of the three conditions—Diabetes, Cardiac Condition or
Hypertension. All models include state dummy variables. The results are not shown for parsimony.
Source: India Human Development Survey 2004-05 and 2011-12.
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Table 4: Log Odds of Mortality by Household Wealth, Three Major Morbidities and
Other Socio-economic Characteristics in India (Full Model).
Model 1 Model 2 Model 3 Coefficients SE Coefficients SE Coefficients SE Wealth -0.032** 0.006 -0.036** 0.007 -0.032** 0.006 Morbidity* (None omitted) Yes 0.516** 0.1 0.927** 0.215
Morbidity*Wealth -0.041* 0.019
Sex (Male omitted)
Female -0.801** 0.05 -0.804** 0.053 - 0.808** 0.052 Age (15-29 years omitted) 30-44 years 0.573** 0.108 0.564** 0.108 0.561** 0.108 45-59 years 1.577** 0.104 1.550** 0.105 1.547** 0.105 60 years and above 2.849** 0.104 2.808** 0.105 2.808** 0.105 Education -0.053** 0.006 -0.054** 0.006 -0.053** 0.006 Marital Status (Married/spouse absent omitted) Unmarried/No gauna -0.153 0.111 -0.14 0.111 - 0.144 0.111 Widowed 0.564** 0.055 0.561** 0.057 0.564** 0.057 Divorced/Separated 0.826* 0.357 0.837* 0.355 0.839* 0.355 Social Groups (High caste omitted) OBCs -0.034 0.053 -0.027 0.053 - 0.025 0.053 Dalits 0.031 0.058 0.044 0.057 0.048 0.057 Adivasis 0.302** 0.076 0.313** 0.077 0.324** 0.077 Muslims -0.12 0.067 -0.114 0.067 -0.113 0.067 Christians/Sikhs/Jains -0.073 0.103 -0.087 0.104 -0.084 0.104 Place of residence
(Rural omitted) Urban 0.062 0.046 0.051 0.049 0.051 0.049 Work status (Not working omitted) Working -0.679** 0.046 -0.672** 0.046 - 0.672** 0.046
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Constant -3.045** 0.169 -3.060** 0.169 -3.103** 0.17
Observations 132,351 132,351 132,351 Chi-square 5360.65 5262.74 5322.40 DF 37 38 39 Note: ** p<0.01, * p<0.05. * Morbidity refers to any of the three conditions—Diabetes, Cardiac Condition or
Hypertension. All models include state dummy variables. Results are not shown for parsimony.
Source: India Human Development Survey 2004-05 and 2011-12.
Figures
Air conditioner 0.4 Computer 0.9 Generator set 1.0 Credit card 1.3 Car 1.5 Washing machine 3.0 Cell phone 6.6 Any cooling device 9.4 Refrigerator 12.7 Telephone 13.1 Any motor vehicle 16.2 Sewing machine 19.9 Mixer/grinder 21.5 Colour TV 23.5 LPG 32.2 Pressure cooker 37.6 Any TV 47.7 Electric fan 58.2 Chair/table 64.1 Any vehicle 64.2 Clock/watch 83.4 Cot 84.4 Footwear 93.2 Two set of clothes 97.1 0 10 20 30 40 50 60 70 80 90 100 Household (in per cent)
Figure 1: Possessions of Various Assets in Selected Sample Households in India, 2004-05
Source: India Human Development Survey 2004-05 and 2011-12.
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90 80 70 60 50 40 30 20 10 0 Poorest Second Middle Fourth Richest Household Wealth Quintile
Morbidity Death
Figure 2: Prevalence of One of the Three Major Diseases and Mortality by Asset
Ownership among Adults Aged 15 Years or Above.
Source: India Human Development Survey 2004-05 and 2011-12.
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Predictive Margins of morbidity with 95% CIs
.2
.15
.1
Pr(Dead)
.05 0
0 5 10 15 20+ Household possesion of assets
morbidity=0 morbidity=1
Note: * Morbidity refers to any of the three conditions --- Diabetes, Cardiac Condition or Hypertension.
Figure 3: Predicted Margins of Household Wealth and Morbidity on Adult Mortality in
India.
Source: India Human Development Survey 2004-05 and 2011-12.
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