J Cross Cult Gerontol (2014) 29:353–369 DOI 10.1007/s10823-014-9251-8 ORIGINAL ARTICLE

Socioeconomic Determinants of Health Inequalities Among the Older Population in India: A Decomposition Analysis Srinivas Goli & Lucky Singh & Kshipra Jain & Ladumai Maikho Apollo Pou

Published online: 29 October 2014 # Springer Science+Business Media New York 2014

Abstract This study quantified and decomposed health inequalities among the older population in India and analyzes how health status varies for populations between 60 to 69 years and 70 years and above. Data from the 60th round of the National Sample Survey (NSS) was used for the analyses. Socioeconomic inequalities in health status were measured by using Concentration Index (CI) and further decomposed to find critical determinants and their relative contributions to total health inequality. Overall, CI estimates were negative for the older population as a whole (CI=−0.1156), as well as for two disaggregated groups, 60 to 69 years (CI=−0.0943) and 70 years and above (CI= −0.08198). This suggests that poor health status is more concentrated among the socioeconomically disadvantaged older population. Decomposition analyses revealed that poor economic status (54 %) is the dominant contributor to total health inequalities in the older population, followed by illiteracy (24 %) and rural place of residence (20 %). Other indicators, such as religion, gender and marital status were positive, while Caste was negatively associated with health inequality in the older populations. Finally, a comparative assessment of decomposition results suggest that critical contributors for health inequality vary for the older population of 60 to 69 years and 70 years and above. These findings provide important insights on health inequalities among the older population in India. Implications are advanced.

S. Goli (*) Department of Development Studies, Giri Institute of Development Studies, Sector ‘0’Aliganj Housing Scheme, Lucknow 226024 Uttar Pradesh, India e-mail: [email protected] L. Singh School of Health Systems Studies, Tata Institute of Social Sciences (TISS), Mumbai 400088, Maharashtra, India e-mail: [email protected] K. Jain : L. M. A. Pou International Institute for Population Sciences, Mumbai 400088 Maharashtra, India K. Jain e-mail: [email protected] L. M. A. Pou e-mail: [email protected]

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Keywords Decomposition . Health inequalities . Older population . Socioeconomic determinants

Introduction How do disparities in socioeconomic status, in the form of skewed distribution of wealth, education, and access to resources affect health? Globally, a growing number of studies have examined this question in an attempt to determine whether basic socioeconomic disparities have implications on the health status. Many of these studies revealed that higher socioeconomic status is correlated with the better health status (Kitagawa and Hauser 1973; Evans et al. 1994; Wilkinson 1996; Gwatkin 2000; Wilkins and Marmot 2003; Deaton 2003). Conversely, similar research has shown that lower socio-economic status is correlated with poorer health status (e.g.,Wagstaff 1991, 2002; Deaton 2003; Hosseinpoor et al. 2006; O’Donnell et al. 2008; Nedjat et al. 2012; Goli and Arokiasamy 2013; Goli et al. 2013). Studies of the older populations in both high and low income countries have pointed to the importance of income, education and racial or ethnic inequality (e.g., Schoenbaum and Waidmann 1997; Berkman and Gurland 1998; Deaton and Paxson 2001; Grundy and Sloggett 2002; Von dem Knesebeck et al. 2003; Zimmer and Amorbsirisomboon 2001; Zimmer and Kwong 2003; Zimmer and House 2003; Zimmer and Kwong 2004; Huisman et al. 2004; Matthews et al. 2006; Sun and Liu 2006; Zhang 2008). However, whether this aggregate trend holds for sub-populations, such as the older people, and in particular contexts, like developing countries, is still an open question. The effect of socioeconomic status on the older population in developing nations is a significant issue as the population of many of these nations is beginning to “grey”— a situation that is contributing to rapidly increasing health care costs incurred by states and households, as well as revealing the inadequacy of social safety nets and current public health commitments. Due to these systemic issues, health care purchasing power, health knowledge, and access to resources are critical factors for the health status of the older population in developing countries (National Research Council of the National Academies 2011). In India, the issue of socioeconomic inequalities in health has been largely discussed in the context of women and children (Jain et al. 2012; Arokiasamy et al. 2012; Goli and Arokiasamy 2013; Goli et al. 2013). Despite India having the second largest older population in the world in terms of absolute numbers, the association between socioeconomic inequality and health among the older population remains poorly understood (National Research Council of the National Academies 2011). Specifically, the extant literature in India that has attempted to establish an association between socioeconomic gradient and health status of the older population used simple bivariate percentage distributions and odds ratios in their investigation (Kumar 2003; Alam and Mukherjee 2005; Mishra 2005; Devi 2005; Rajan 2006; Mini 2009; Pou and Goli 2012). As such these studies are unable to provide critical insights into the underlying factors that create an imbalance in the health status of the older population. The aim of this study is to fill this gap. In the last three decades, research on health inequalities has made tremendous progress in terms of quantifying socioeconomic inequalities and estimation of household or individual income and wealth based Concentration Index (CI) and its decomposition has emerged as a novel tool for health inequality assessment (Wagstaff 1991, 2002; Hosseinpoor et al. 2006; O’Donnell et al. 2008; Nedjat et al. 2012; Arokiasamy et al. 2012; Jain et al. 2012; Goli and Arokiasamy 2013; Goli et al. 2013; Nawal and Goli. 2013). Using these improved tools of health inequality measurement this study investigates the issue of socioeconomic inequality in health status of the older population in India. This study is designed specifically to address two objectives: first, to quantify the socioeconomic inequality in the health of India’s older

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population by using socioeconomic status based concentration index. Second, to calculate the relative contribution of the key socioeconomic factors to the total explained health inequalities by performing the decomposition of concentration index among the older population of 60 to 69 years and 70 years and above separately. The second objective is decisive because for research and policy purposes, it is useful to distinguish between the old and the oldest old, often defined as people age 85 and over (Garfein 1995a, b; Smith et al. 1995). The oldest old have the highest population levels of chronic disease and disability that require long-term care (Smith et al. 1995). Finally, by determining the contribution of each socioeconomic factor to inequality in health status, we identify which vulnerable groups would be potential targets for an effective policy intervention to reduce health inequality among India’s older population.

Methods Data Data from the 60th round of National Sample Survey (NSS) conducted by the National Sample Survey organization (NSSO) during 2004–2005 was used in the statistical analyses. The survey uses a stratified multi-stage design. The First Stage Units (FSU) were selected based on the 1991 census villages in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector by using Probability Proportional to Size with Replacement (PPSWR). The Ultimate Stage Units (USU) was the households in both the sectors (National Sample Survey organization 2006). The NSSO surveys provide household and individual level information on a range of socioeconomic factors as well as information on demographic and health indicators. Along with basic socioeconomic information, the 60th round of NSS focused on morbidity and health care. The NSS includes questions related to self-rated health status and disability of the older population 60 years and above. Our study uses questions related to key socioeconomic and health attributes to estimate the socioeconomic inequalities in health status of the Indian older population. For analytical purposes, this study did not replicate the classification used by previous studies in defining the age stratification among older population, that is, the "Young Old" 65–74 years, the "Old-Old" 74–84 years and the "Oldest-Old" 85+years (Garfein 1995a, b; Smith et al. 1995; Erlangsen et al. 2003; Davies et al. 2010; Cherry et al. 2011; NIA 2014). This classification was not used because it is not sensitive to issues of relative age, rather, “oldest-old” as a measure related to the overall life expectancy of individuals within a particular setting. For instance, the average additional life expectancy of 60+ populations in India is only 17.9 years—well below the mark set in the definition of "Oldest-Old" population by previous studies (Garfein 1995a, b; Smith et al. 1995; Erlangsen et al. 2003; Davies et al. 2010; Cherry et al. 2011; NIA 2014). Consideration of previous studies classification will render very few cases in the oldest-old category which will, in turn, limit any attempt to provide a robust statistical assessment. Thus, the final classification of older population in this study is comprised of only two groups: the older population of 60 to 69 years and 70 years and above. Using this classification, out of a total sample of 34, 831 older individuals, those 60 to 69 years comprise 26,855 of the total population and 7,976 belong to the 70 years and above category. Definition of Variables This section defines the variables used in the analyses. A pertinent issue in the literature on health inequality is whether all inequalities should be measured or only those that have some exclusive coherent association with indicators of socio-economic standing should be included.

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In this regard, a number of studies argue that from a policy perspective the focus should be on identifying those few critical factor that contribute the most to health inequality (Gakidou et al. 2000; Wagstaff 2002; Wagstaff et al. 2003; Subramanian et al. 2006; 2008). Therefore, we have conducted an extensive systematic literature review in Indian context (e.g. Kumar 2003; Alam and Mukherjee 2005; Mishra 2005; Devi 2005; Mini 2009; Pou and Goli 2012; National Research Council 2012; Golandaj et al. 2013; Goli et al. 2013). Subsequently, independent variables that may be able to systematically explain a major part of inequalities in health status of the older population were selected. For decomposition analysis, the variables must be in two categories (dichotomized) hence, the variables were redefined as: Health variables/Dependent variables

&

&

Poor self-rated health status (Yes = 1, Otherwise = 0): In the NSS surveys, information was collected on the current health status of the older people. The response given was purely perception-based. The respondents’ reported self-rated health was classified as poor selfrated health status (yes), while others (no) indicates good health status. Commenting on the reporting pattern of health status on the NSS data, a study by Chen and Mahal (2010) showed that except for the region, the other socioeconomic factors such as education, place of residence, gender do not have much bearing on the reporting pattern of health status. Immobility (Yes = 1, Otherwise = 0): For the older people, mobility is an important indicator of their functional health condition and indicates their degree of dependence on others in performing their daily routine. In the NSS survey, information was collected on the mobility of the older people. This item distinguishes those who cannot easily move about and are confined to the premises of their home from those who cannot move at all and are confined to bed. The latter was indicated by the response ‘yes’ classified as functional limitations or otherwise ‘no’. Socioeconomic Variables/Independent Variables To carry out the proposed inequality analyses, it is essential to construct all socioeconomic variables in two categories as Yes = 1, Otherwise = 0, where yes indicates the older population in disadvantaged socioeconomic group and ‘otherwise’ indicates the older population in advantageous position. The socioeconomic status of individuals in India is grounded not only on their position in economic and educational status, but also on their place of residence, marital status, gender, caste and religious affiliations. Based on this perspective, the following socioeconomic variables have been selected for the purpose of decomposition analyses. The variables were dichotomised as shown below:

& & &

Place of residence: Rural (1)/Urban (0) Sex: Female (1)//Male (0) Economic status (based on Monthly Per Capita Consumption Expenditure [MPCE]): Poor (1) /Non-poor (0) Household income has a strong association with the health status of the household members as well as the health care received by them (Deaton 2002). However, it is difficult to collect reliable income data in developing country like India. Hence, the NSSO surveys collects data on consumption expenditure. Data were collected through a short set of five questions rather than a detailed listing of consumption items that are used when household consumption expenditure is the main theme of the survey (National Sample Survey Organization 2006). This procedure is known to underestimate the level of MPCE

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&

&

&

&

357

in comparison with the detailed schedule but expected to provide a reasonable proxy for the relative ranking of the households according to the level of standard of living (National Sample Survey Organization 2006). Education: Illiterate (1) and Literate(0) The information on the general educational level of individuals was collected in 13 categories (National Sample Survey Organization 2006). However, for the analyses, the 13 categories were further classified into four broad categories: Illiterate, Primary, Secondary and Higher Secondary and above. For the purpose of decomposition analysis, these four categories were further categorised as illiterate and literate. Religion: Muslim(1)/others(0) The information collected on eight categories of religion was grouped into three categories (Hindu/Muslim/Others) in the bivariate analyses and two categories (Muslim/ Others) in the decomposition analyses. A number of studies have established that Muslims are in disadvantageous position not only in socioeconomic terms but also in terms of public health indicators as well (e.g., Basant 2007; Arokiasamy et al. 2012; Jain et al. 2012; Goli et al. 2013; IIPS and Macro International 1992–2006; Government of India 2006). Caste: Scheduled Castes/Scheduled Tribes(1)/others(0) The caste system is a type of social institution in India where social stratification of communities is defined by several endogamous inherited groups called Jatis. The castes in modern India can be classified into four classes: scheduled castes/scheduled tribes, Other Backward Castes (OBCs) and other castes. A number of studies provide evidence for caste based inequalities and foster that scheduled castes/scheduled tribes are socioeconomically backward compared to other castes (e.g. Srinivasan 1957; Dumont 1970; Subramanian et al. 2008; Desai 2008). Marital status: Currently married (1)/others (0) The currently married women include other than never married, widowed and divorced/ separated women.

Statistical Analyses In the first stage of statistical analyses, socioeconomic differentials in the health status of the older population were assessed by cross-tabulations. Further, the socioeconomic differentials in the health status are tested for statistical significance by Pearson’s Chi Square test. In the second stage, the effect of socioeconomic determinants of the health status of the older population was estimated by using binary logistic regression. Thirdly, concentration indices (CIs) were estimated as inequality measures. Finally, the CIs were decomposed to observe the contribution, as a percentage of different socioeconomic predictors to the total health inequality. All the statistical analyses in this paper were carried out using STATA 10.1 (STATA crop LP, College Station, Texas, USA) and Microsoft excel program. Method of Decomposing Socioeconomic Inequalities in Health Previous studies have assessed the level of socioeconomic inequalities in health using Lorenz curve, CIs and Concentration curve (Berkman and Gurland 1998). Though, the values of CIs show the degree of economic inequality, it does not highlight the pathway through which inequality occurs (Goli et al. 2013). Decomposition of inequalities is, therefore, critical to explore the prominent contributory factors that lead to socioeconomic inequalities in health status of the older population (Wagstaff et al. 2003; Hosseinpoor et al. 2006; O’Donnell et al. 2008; Sundmacher et al. 2011; Goli et al. 2013).

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After reviewing various inequality measures, the inequality decomposition measure proposed by Wagstaff et al. (2003) was considered as the most appropriate tool and used for assessing the health inequalities among the older population. The decomposition analysis enables one to estimate how proportionally the socio-economic determinants contribute to inequality (the gap between poor and rich) in a health variable. Steps in Inequality Decomposition Analysis In the first stage of analysis, the study used CIs to measure inequalities in the health status of the older population. The CI was computed as twice the (weighted) covariance of the health variable and a person’s relative rank in terms of economic status (MPCE is the rank variable for this study), divided by the variable mean according to Eq. 1. The value of CI can vary between −1 and +1. A negative value implies that the outcome of the variable is concentrated among disadvantaged people while the opposite is true for its positive values. The value of CI would be zero when there is no inequality (Wagstaff and van Doorslaer 1991). C¼

2 covw ðyi ; Ri Þ μ

ð1Þ

Where yi and Ri are, respectively, the health status of the ith individual and the fractional rank of the ith individual (for weighted data) in terms of the index of household economic status, μ is the (weighted) mean of the health variable of the sample and covw denotes the weighted covariance. In the second stage, by using Wagstaff et al. (2003) method, socioeconomic inequality among the older population is decomposed into its determinant, which enables one to estimate how the determinants are proportionally contributing to inequality (the gap between the poor and the rich) in a health variable. For any linear regression model, linking the health variable of interest, y, to a set of k health determinants, Xki: X β k Xki þ εi ð2Þ yi ¼ α þ Where ε is an error term, given the relationship between yi and Xki in equation, the concentration index for y (C) can be written as: X  βk X k  GCe GCe ¼ Cy ¼ ð3Þ Ck þ μ μ μ Equation (3) shows that C is made up of two components: deterministic or ‘explained’ component, equal to a weighted sum of the CIs of the regressors, where the weights are simply the elasticities [elasticity is a unit-free measure of (partial) association, that is, the percentage change in the dependent variable (health variable) associated with percentage change in the explanatory variables] and the residual or ‘unexplained’ component, which reflects the inequality in the health of the older population that cannot be explained by systematic variation in the Xk across social groups. The decomposition analyses are generally carried out in the following steps

& & &

Regression of the health variables against its determinants through an appropriate model for finding the coefficients of the explanatory variables (βk). Calculate the mean of the health variables and each of its determinants (μ and Xk). Calculate the CIs for the health variable and for the determinants (C and Ck) using Eq. (1)— as well as the generalized concentration index of the error term (GCε) where, yi and μ are the value of the determinants for the ith individual and the determinant mean, respectively.

Finally, the pure contribution of each determinant to the total inequality in the health variable can be quantified through the following steps:

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& &

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Calculate the absolute contribution of each factor by multiplying the health variable elasticity with respect to that determinant and its CI—(βkXk/μ) Ck. Calculate the percentage contribution of each factor by dividing its absolute contribution by CI of the health variable (βkXk/μ) Ck/C.

Results Description of Study Population The socioeconomic and demographic profile of Indian older population is described in Table 1 (weighted percentages and unweighted sample size). The results show that the average age of the sample population was 67 years. The standard deviation of the age of older population was 7 years with the maximum recorded valid age of 110 years. The percentage distribution of the older population by socioeconomic groups depicts the same pattern as the general population distribution in India. The majority of the older population are Hindu (84 %), illiterate (68 %), have poor economic status (41 %) and reside in rural areas (76 %). Overall, 59 % of the older population is currently married. However, comparison of marital status across the age groups reveal that the proportion of currently married in the 60 to 69 year age bracket was 63 %, but only 46 % in the case of those individuals 70 years and above. Stratification of the older population by educational status indicated that around 68 % of the older population were illiterate and only four percent had higher secondary education or above. The distribution of the older population by economic groups reveals that 41 % are poor and 27 % are in the highest wealth category. In general, the comparison between the older population of 60 to 69 years and 70 years and above by socioeconomic status reveals that the category of individuals 70 years and above had a higher level of socioeconomic status than those in 60 to 69 years category. Unweighted sample distribution presented in Table 1 demonstrates that the sample size in all the subcategories of the population was sufficient to carry out robust statistical analyses. Socioeconomic Differentials in the Health Status of the Older Population Socioeconomic and demographic characteristics affect the way that individuals perceive his/ her health, while the ability access required care largely depends on his/her socioeconomic status. Socioeconomic and demographic differentials of self-reported health status and functional limitations of the older population are presented in Table 2. The results show that a considerable proportion of the older population of 70 years and above were found with poor health status (38 %) and functional limitations (19 %) as compared to the older population of 60 to 69 years. Considerable and statistically significant differences in the prevalence of poor health status and functional limitations were also reflected across the socioeconomic and demographic characteristics of the older population. The poor health status and functional limitations were more pronounced among females. In both the age groups, the difference in poor health status was nearly double among the illiterate (26 %) in comparison with those with higher secondary and above schooling (14 %). Marital status also has considerable influence on the prevalence of poor health status and functional limitations in both the age groups. Those who reported being currently married had better health status and physical mobility in both age groups when compared to those living as single, divorced and widowed/widower. Similarly, the prevalence of poor health and functional limitations by economic status suggests that those older individuals living in wealthy households had a lower prevalence of poor health status and functional limitation compared to older living in households of poor economic status. The

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caste and religious affiliations of the older population were not significant predictors of differences in the prevalence of poor health status and functional limitations. Socioeconomic Inequalities in the Health Status of the Older Population In this section, we have measured the magnitude of socioeconomic inequalities in health status of the older population by using concentration indices. Figure 1 shows the values of Table 1 Percentage distribution for older population sample by background characteristics Background variables

Older population All the older population (60 year and above) Frequencya

% Sex Male Female

60 to 69 years

%

Frequencya

70 years and above

%

Frequencya

50.0

17,750

49.3

13,513

52.4

4,237

49.0

17,081

51.0

13,342

48.0

3,739

Place of residence Rural

75.7

22,265

77.0

17,544

71.1

4,721

Urban

24.3

12,566

23.0

9,311

28.9

3,255

Illiterate

67.6

22,027

68.6

17,166

63.8

4,861

Primary Secondary

17.9 10.3

6,692 4,170

16.9 10.1

4,935 3,223

21.2 10.7

1,757 947

4.3

1,926

4.3

1,520

4.3

406

Educational attainment

Higher secondary & above Marital status Currently married

59.2

20,959

62.8

17,201

46.4

3,758

Others

40.8

13,872

37.2

9,654

53.6

4,218

Hindu

84.3

27,959

84.2

21,507

84.9

6,452

Muslim Others

9.3 6.4

3,660 3,209

9.6 6.2

2,898 2,447

7.9 7.2

762 762

Schedule Tribes (ST)

6.6

3,257

7.1

2,698

4.6

559

Schedule Castes (SC)

17.4

5,274

18.7

4,344

13.1

930

Other Backward Castes (OBC)

39.8

12,948

40.0

10,061

39.0

2,887

Others

36.2

13,343

34.1

9,743

43.3

3,600

40.7 32.5

11,656 11,501

42.1 32.7

9,374 8,968

35.5 31.7

2,282 2,533

26.9

11,672

25.2

8,511

32.8

3,161

100.0

34,831

78.1

26,855

21.9

Religion

Social group

Economic groups Poor Middle Rich Total Age distribution of sample

Mean 67.58

a

Unweighted Sample Size

Standard deviation

Minimum

6.95

60

7,976 Maximum 110

25.8

Urban

Higher secondary & above

23.5

32.4

Muslim

20.4

Others

Religion

Currently married

28.3

16.5

14.6

Secondary

Marital status Others

25.8 21.9

Illiterate Primary

Educational attainment

24.5

21.2

Rural

Place of residence

21.5

Female

37.7

19.7

Physical immobility

8.6

7.9

9.3

6.9

78.64***

9.9

9.7

5.9

241.55*** 11.3

5.6

5.5

311.40*** 8.7 7.6

104.1***

59.66***

18.8

977.64*** 5.1

Poor health x2 test status

All the older population (60 year and above)

Older population

Male

70 and above Sex

60–69

Age

Covariates

11.04***

348.30***

74.87***

.009

28.0

19.4

17.6

23.3

12.8

13.7

21.6 17.9

16.9

20.6

22.0

17.5

64.57***

81.35***

225.0***

99.76***

6.3

6.1

4.2

6.7

4.2

3.2

5.7 4.1

4.9

5.2

5.8

4.4

–

60.30***

–

Physical immobility

– 63.75***

Poor health x2 test status

60 to 69 years

1472.88*** –

x2 test

–

–

39.5 33.3

9.57***

51.5

36.1

34.0

70.83*** 40.8

21.6

26.3

41.73*** 41.9 33.5

6.04

40.3

19.0

18.7

22.4

15.5

–

–

Physical immobility

26.21***

58.1***

25.4

21.0

14.2

22.8

11.0

13.6

105.95*** 20.5 17.7

45.56***

12.37***

Poor health x2 test status

70 years and above

25.68*** 35.2

x2 test

Table 2 Bivariate associations between socioeconomic status and prevalence rate (per 100) of poor health status and physical immobility among older population

7.80**

113***

45.35***

0.12

58.51***

x2 test

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19.8

23.6

Rich

Total

23.0

Middle

26.8

8.1

8.0

8.0

163.61*** 8.2

8.8

23.0

Economic groups Poor

Others

7.7

23.7

7.9

Other Backward Castes (OBC)

0.128

26.5

19.5

7.8

22.7

0.0 7.0

Physical immobility

Poor health x2 test status

All the older population (60 year and above)

Older population

Schedule Castes (SC)

Social groups Schedule Tribes (ST)

Hindu

Covariates

Table 2 (continued)

0.663

4.11

x2 test

19.7

16.0

19.4

22.3

19.1

19.7

22.8

15.6

18.9

5.1

4.9

5.7

4.9

4.9

Physical immobility

5.1

4.3

5.3

144.91*** 5.4

3.36

Poor health x2 test status

60 to 69 years

4.65

0.11

x2 test

37.7

30.4

36.5

45.7

34.0

38.6

45.9

42.0

36.5

77.34***

5.81

Poor health x2 test status

70 years and above

18.8

18.2

17.9

20.2

19.1

18.4

19.5

18.3

18.0

Physical immobility

5.14

0.77

x2 test

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60 years and above

-0.1156

70 years and above

-0.0820

60 to 69 years

-0.0943

-0.1400

-0.1200

-0.1000

-0.0800

-0.0600

-0.0400

-0.0200

0.0000

Concentration index

Fig. 1 Economics status based concentration indices for poor health status among the older population of age group 60 years and above, 60 to 69 years and 70 years and above

concentration indices for the poor health status of all the older population also separately for the older population of 60 to 69 years and 70 years and above. The results show that the values of concentration indices for all three categories of the older population were negative. This indicates that those who belonged to poor socioeconomic status were in a more disadvantageous position than their counterparts in terms of health status. The estimated CI in poor health status for the older population of 60 years and above was −0.1156. This indicates a sizeable volume of socioeconomic inequalities in the prevalence of poor health status among the older population in India. However, the comparison of CI estimates among the older population of 60 to 69 years and 70 years and above shows that the magnitude of socioeconomic inequality was greater among the older population of 60 to 69 years than 70 years and above, that was −0.0943 in comparison with −0.0821. The results of decomposition analyses of poor health status were presented in Tables 3, 4 and 5 for the older population of 60 years and above, 60 to 69 years and 70 years and above respectively. The decomposition analyses were carried out based on three components: mean, marginal effects and CIs. The mean of health variables and CIs were already explained in the previous sections. Therefore, in this section, the discussion is focused on marginal effects and the relative contributions of socioeconomic factors to overall inequality in health status of older population. The marginal effect estimates in Table 3 show that the absolute association of poor health status was highest with the age of the older population (β=0.0730), followed by poor economic status (β=0.0540), religion (β=0.0500) and illiteracy (β=0.0490). The estimates of the relative contribution of socioeconomic factors to overall health inequality in case of all the older population of 60 years and above show that the poor economic status of the older population alone contributed 54 % of total explained inequalities. However, illiteracy (24 %), rural place of residence (20 %) and higher age group (14 %) also explain considerable inequalities in poor health status of older population of 60 years and above. The residual estimate in the model in Table 3 indicates that the eight selected covariates together explained 78 % of total estimated inequalities (i.e. −0.0902 out of −0.1156). The decomposition results for two disaggregated age groups of the older population were also showed interesting findings. In case of older population of 60 to 69 years, the estimated

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marginal effects revealed that with the exception of SC/ST, all other socioeconomic factors were positively associated with the poor health status of the older population. Thus, indicating that marginalized individuals are more likely to suffer from poor health as they age. The absolute level of the association was greater between poor health status and poor economic status (β=0.0429), followed by poor health status and illiteracy (β=0.0369), poor health status and rural place of residence (β=0.0294). The results of decomposition analyses indicate that seven selected socioeconomic predictors together explained a major part of inequalities in the prevalence of poor health status in the older population of 60 to 69 years. The selected predictor variables together explain 91 % of total inequality in poor health status (CI= −0.0853 out of CI=−0.0943) and the remaining 9 % constitute the unexplained residual. The estimates of contributions from specific factors show that poor economic status made the largest contribution to total inequalities in poor health status of the older population of 60 to 69 years. Poor economic status contributed about 56 % to the total health inequality, followed by illiteracy (20 %) and residing in a rural area were contributed 20 % each respectively. Table 5 presents the results of the decomposition analysis for the older population 70 years and above. The estimates of the marginal effect for the poor health status for the older population 70 years and above by socioeconomic factors show that, except sex and marital status, all other factors were positively associated with the health variable. The absolute level of the association of poor health status, however, was greater with poor economic status (β= 0.0738), illiteracy (β=0.0668) and rural place of residence (β=0.03558). The results of decomposition analyses show that seven selected socioeconomic factors together explain a major part (84 %) of total health inequalities (CI=−0.0686 out of CI=−0.0820) among the older population of 70 years and above. The unexplained part in the model was only −0.0134 out of the total CI of −0.0820 (Fig. 1). The poor economic status makes the largest contribution (57 %) to the total inequalities in poor health status of the older population of 70 years and above followed by illiteracy (23 %) and rural place of residence (17 %). Further, the poor economic status, illiteracy and rural place of residence together explain 97 % of total health inequalities among old population of 70 years and above. A comparison of CI and its decomposition estimates for the older population 60 to 69 years and 70 years and above show that though the direction of CI estimates and individual socioeconomic factor contributions were the same, the magnitude in terms of level of CI, the marginal effect, and resultant contribution of socioeconomic factors to the poor health status considerably vary. Total explained part of health inequalities was higher in the older population of 60 to 69 years than 70 years and above. Thus, the residual part was higher among the older population of age 70 years and above than 60 to 69 years. However, at the outset, the inequality decomposition estimates suggested that the health inequalities primarily arising from inequality in the economic condition, educational attainment and place of residence as these factors significantly contribute to total health inequality in the entire older population and two disaggregated age groups of older population.

Conclusions After taking note of the previous literature on the subject of aging and health in India which has largely dealt with a cursory investigation of socioeconomic differences in health status of the older population (Kumar 2003; Alam and Mukherjee 2005; Mishra 2005; Devi 2005; Rajan 2006; Mini 2009; Goli and Pandey 2010; Pou and Goli 2012), this study fills a critical research gap. Quantification and decomposition of the socioeconomic inequalities in the health status of the older population is a much needed step. To our knowledge, this is the first study on decomposing

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Table 3 Effects and contribution of predictor variables based on decomposition analysis for poor health status of the all older population aged 60 years and above Covariates

Mean

Marginal effect

CI

Contribution to CI

% contribution

Aged 70 and above

0.229

0.0730

−0.1799

−0.01215

13.5

Rural place of residence

0.639

0.0350

−0.2015

−0.01820

20.2

Female sex

0.490

0.0140

−0.0269

−0.00075

0.8

Poor economic status

0.335

0.0540

−0.6653

−0.04857

53.8 23.6

Illiterate

0.633

0.0490

−0.1700

−0.02129

Muslim religion

0.105

0.0500

−0.0762

−0.00162

1.8

SC/ST caste

0.245

−0.0010

−0.1872

0.00019

−0.2

Currently married Poor health status

0.602 0.248

−0.0420

−0.0001 −0.1156

0.00001 −0.09022

0.0 100

Residual

−0.02538

socioeconomic inequality in health status of the older population into its determinants using the concentration index. This study advances a number of intriguing findings. Through bivariate estimates of socioeconomic differentials in health status of the Indian older population, it suggests that older individuals with a lower socioeconomic status are more likely to suffer from poor health. Functional limitations, however, do not vary much across the socioeconomic groups, with an exception among the educational categories. The socioeconomic inequality measured by CI suggests considerable inequalities in poor health status by socioeconomic conditions. Poor health is concentrated among the older population with low socioeconomic status. However, a comparison of CI values among the older population of 60 to 69 years and 70 years and above suggests that the socioeconomic inequalities in health status are comparatively lower among the older population of age 70 years and above than the 60 to 69 years. This may be because of two reasons: firstly, as evident from the analyses of Table 1, a majority of the older population entering into higher age bands belongs to higher socioeconomic status—meaning this category of the older population is comparatively homogeneous in terms of socioeconomic status. The second reason could be the diminishing physical strength with increasing age, which is a common phenomenon for higher age bands, irrespective of their socioeconomic status. Further, the proportional contributions to health inequalities in older population of 60 years and above by key socioeconomic predictors suggests that some predictors contribute significantly more than others. In order of their importance, low Table 4 Effects and contribution of predictor variables based on decomposition analysis for poor health status of the older population aged 60 to 69 years Covariates

Mean

Marginal effect

CI

Contribution to CI

% contribution

Rural place of residence

0.65329

0.029437

−0.1799

−0.01694

Female sex

0.49682

0.018531

−0.0052

−0.00023

0.3

Poor economic status Illiterate

0.34909 0.63947

0.042861 0.036926

−0.6509 −0.1483

−0.04769 −0.01715

55.9 20.1

19.9

Muslim religion

0.10792

0.006257

−0.0537

−0.00185

2.2

SC/ST caste

0.26231

−0.002494

−0.1661

0.00053

−0.6

Currently married

0.64051

0.029104

Poor health status

0.20424

−0.0215

−0.00197

−0.0943

−0.08529

Residuals

−0.00897

2.3 100

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Table 5 Effects and contribution of predictor variables based on decomposition analysis for poor health status of the older population aged 70 years and above Covariates

Mean

Marginal effect

CI

Contribution to CI −0.212

−0.01164

% contribution

Rural place of residence

0.5919

0.03558

Female sex

0.46878

−0.01158

0.0102

−0.00014

17 0.2

Poor economic status

0.28611

0.07385

−0.7139

−0.03932

57.3 22.5

Illiterate

0.60984

0.06684

−0.1455

−0.01546

Muslim religion

0.09554

0.00899

−0.0671

−0.0015

2.2

SC/ST caste

0.18669

0.00443

−0.2024

−0.00044

0.6

Currently married

0.47116

−0.06343

Poor health status

0.38357

0.0015 −0.082 Residuals

−0.00011 −0.06862 −0.01336

0.2 100

economic status, illiteracy and place of residence in rural areas have emerged as key factors contributing more than 90 % of health inequalities among the older population. Hence, this study shows that the health inequalities in the older population in India are majorly the artefact of the existing inequalities in economic, educational status and place of residence. A comparative assessment of decomposed contributions of socioeconomic factors for the older population 60 to 69 years and 70 years and above suggest that the contribution from a rural place of residence, illiteracy and marital status to total health inequalities was reduced for the older population of 70 years and above as compared to the former. This may be because greater proportions of the older population moving into higher age bands are from the urban areas and higher educated categories. At higher ages, the older population are likely to be either widowed/widowers. Poor economic status is the more prominent contributor for health inequalities in case of the older population 70 years and above. Moreover, the explained part of total health inequality among the older population of 70 years and above is less than age group of 60 to 69 years. This implies there is relatively more complexity involved in terms of exploration of critical determinants causing health inequality in the older population of 70 years and above in comparison with older population of 60 to 69 years. Our findings are in tune with the studies from both high and low income countries that focused on health inequalities in older population (Zimmer and Amorbsirisomboon 2001; Bassuk et al. 2002; Von dem Knesebeck et al. 2003; Zimmer and Kwong 2003; Zimmer and House 2003; Zimmer and Kwong 2004; Huisman et al. 2004; Duda et al. 2011; Harttgen et al. 2013). The majority of global studies agree that the level of frailty in the older population is distributed along the socioeconomic gradient in both higher and lower income countries—such as individual with less education and income are more likely to be frail, which is also evident in the present study. Nevertheless, some exception exists that a low income country like Ghana shows evidence of a high prevalence of chronic and non-communicable diseases in all age groups without regard to education level or income (Duda et al. 2011). Similarly, Zimmer and Kwong (2003) note a positive association between socioeconomic status and chronic disease prevalence among the older population of urban China. While examining the social gradient theory of health and life expectancy in case of Okinawa in Japan, Cockerhama et al. (2000) found that that the social gradient theory does not apply in Japan and suggest that what is more important for health are healthy lifestyles, especially diet and social support. This could be because the state expenditure on health in Japan is one of the highest and poverty one of the lowest in world, thus, more or less health care provisions are available equally for all, irrespective of socioeconomic status. When health provisions are available equally, the

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socioeconomic status of the individual plays less role and conversely lifestyles and diet habits plays greater role in health status of the older population. Another study by Huguet et al. (2008) also found no significant association between income and health status in Canada. Similar to Japan, in Canada, the social safety net protects individuals against insecurity and impoverishment that affect the material and psychological aspects of life and, therefore, lead to improved living conditions and a healthier quality of life throughout all ages. With exception of findings from few countries where better social safety nets and health care provisions are available equally (like Japan and Canada) or in a situation where health care provisions completely lacking and larger share of the population living in poverty (like in Ghana), the outcomes of majority of the studies in the global context that assess socioeconomic inequality in health status of the older population are identical with the findings of our study. The findings of our study have not only improved our understanding of the health status of the older population, but also suggest a number of insights for health policy and planning in India. There are two patterns of health care concerns emerging from this study: firstly, social groups who are unable to survive up to the higher age bands need better socioeconomic support to acquire better health knowledge and health care to extend their survival chances. Secondly, those groups who are in higher age bands need better economic support to increase healthy life years. This study envisages that in order to provide better health status for the older population, the main challenge will be reaching to the needs of the most deprived older population, such as the poorest of the poor, the illiterate older population and the older population living in the rural areas. Therefore, health policy actions need to consider both efficiency and equity in terms of health care utilisation and health outcomes for the older population. A battle on health inequalities involves action on critical social determinants of health. However, there is a huge uncertainty in increase of public health care provisions and their standards in India (Drèze and Sen 2013). Given the hard fact that India cuts health care spending by 10 % in 2014–2015 government budgets, there is still a long way to go to achieve equity in terms of health status of the older population in India.

References Alam, M., & Mukherjee, M. (2005). Aging, activities of daily living disabilities and the need for public health initiatives: some evidences from a household survey in Delhi. Asia Pacific Population Journal, 20(2), 47–77. Arokiasamy, P., Jain, K., Goli, S., & Pradhan, J. (2012). Health inequalities among urban children in India: a comparative assessment of Empowered Action Group (EAG) and South Indian States. Journal Biosocial Science, 45, 167–185. Basant, R. (2007). Social, economic and educational conditions of Indian Muslims. Economic and Political Weekly, 42(10), 828–832. Bassuk, S. S., Berkman, L. F., & Amick, B. C. (2002). Socioeconomic status and mortality among the older: findings from four US communities. American Journal of Epidemiology, 155, 520–533. Berkman, C., & Gurland, B. J. (1998). The relationship among income, other socioeconomic indicators, and functional level in older persons. Journal of Aging and Health, 10, 81–98. Chen, B., & Mahal, A. (2010). Measuring the health of the Indian older: evidence from National Sample Survey data. Population Health Metrics, 8, 30. Cockerhama, W. C., Hattorib, H., & Yamorib, Y. (2000). The social gradient in life expectancy: the contrary case of Okinawa in Japan. Social Science and Medicine, 51(2000), 115–122. Deaton, A. S. (2002). Policy implications of the gradient of health and wealth. Health Affairs, 21, 13–30. Deaton, A. (2003). Health, inequality, and economic development. Journal of Economic Literature, 41, 113–158. Deaton, A. S., & Paxson, C. (2001). Mortality, education, income and inequality among American cohorts. In D. A. Wise (Ed.), Themes in the economics of aging (pp. 129–165). Chicago: University of Chicago Press. Desai, S. (2008). Caste in 21st century India: competing narratives. Economic and Political Weekly, 46(11), 41–49. Devi, S. B. (2005). Challenges and issues in formal care giving. Indian Journal of Gerontology, 19(4), 447–456. Drèze, J., & Sen, A. K. (2013). An uncertain glory, india and its contradictions. New Delhi: Penguin.

368

J Cross Cult Gerontol (2014) 29:353–369

Duda, R. B., Anarfi, J. K., Adanu, R. M. K., Seffah, J., Darko, R., & Hill, A. G. (2011). The health of the “older women” in Accra, Ghana: results of the women’s health study of Accra. Journal of Cross Cultural Gerontology, 26, 299–314. Dumont, L. (1970). Homo hierarchicus: The caste system and its implications. London: England Weidenfeld and Nicholson. Evans, R. G., Barer, M. L., & Marmor, T. R. (Eds.). (1994). Why are some people healthy and others not? The determinants of health of populations. New York: Aldine de Gruyter. Gakidou, E. E., Murray, C. J., & Frenk, J. (2000). Defining and measuring health inequality: an approach based on the distribution of health expectancy. Bulletin of the World Health Organization, 78(1), 42–54. Garfein, A. J. (1995a). Robust aging among the young-old, old-old, and oldest-old. Journal of Gerontology Series B: Psychological Science and Social Science, 50B(2), S77–S87. Garfein, A. J. (1995b). Robust aging among the young-old, old-old, and oldest-old. Journal Gerontology B Psychological Science Social Science, 50B(2), S77–S87. Golandaj, J. A., Goli, S., & Das, K. C. (2013). Living arrangements among older population and perceptions on old age assistance among adult population in India. International Journal of Sociology and Social Policy, 33(5/6), 367–379. Goli, S., & Arokiasamy, P. (2013). Trends in health and health inequalities among major states of India: assessing progress through convergence models. Health Economics, Policy, and Law, 26, 1–26. Goli, S. & Pandey, A. (2010). Challenges of twenty-first century-A trans disciplinary perspective. Macmillan Advanced Research Series. Goli, S., Riddhi, D., & Arokiasamy, P. (2013). Pathways of economic inequalities in maternal and child health in urban India: a decomposition analysis. PLoS One, 8(3), e58573. Government of India. (2006). Social, economic and educational status of the Muslim community of India. A report. Prime Minister’s high level committee. New Delhi Grundy, E., & Sloggett, A. (2002). Health inequalities in the older population: the role of personal capital, social resources, and socio-economic circumstances. Social Sciences and Medicine, 56, 935–947. Gwatkin, D. R. (2000). Health inequalities and the health of the poor: what do we know? What can we do? Bulletin of the World Health Organization, 78(1), 3–17. Hosseinpoor, A. R., Doorslaer, V. E., Ahmad Speybroeck, N., Hosseinpoor, R., Van Doorslaer, E., Speybroeck, N., Naghavi, M., Mohammad, K., Majdzadeh, R., Delavar, B., Jamshidi, H., & Vega, J. (2006). Decomposing socioeconomic inequality in infant mortality in Iran. International Journal of Epidemiology, 35, 1211–1219. Huguet, N., Kaplan, M. S., & Feeny, D. (2008). Socioeconomic status and health-related quality of life among older people: Results from the Joint Canada/United States Survey of Health. Social Science and Medicine, 66, 803–810. Huisman, M., Kunst, A. E., & Mackenbach, J. P. (2004). Socioeconomic inequalities in morbidity among the older: a European overview. Social Science and Medicine, 57, 861–873. IIPS and Macro International. (1992–2006). National Family Health Survey (NFHS-3), India 2005–06. International Institute for Population Sciences (IIPS), Mumbai. Jain, K., Goli, S., & Arokiasamy, P. (2012). Are self-reported morbidities deceptive in measuring socio-economic inequalities in India: a case of obstetric morbidity in EAG states. Indian Journal of Medical Research, 136, 35–42. Kitagawa, E. M., & Hauser, P. M. (1973). Differential mortality in the United States: A study of socioeconomic epidemiology. Cambridge: Harvard University Press. Kumar, V. S. (2003). Economic security for the older in India: an overview. Journal of Aging and Social Policy, 15(2–3), 45–65. Matthews, R. J., Jagger, C., & Hancock, R. M. (2006). Does socio-economic advantage lead to a longer, healthier old age? Social Science and Medicine, 62, 2489–2499. Mini, G. K. (2009). Socioeconomic and demographic diversity in the health status of older people in a transitional society, Kerala, India. Journal of Biosocial Science, 41(4), 457–467. Mishra, A. J. (2005). Is a social security for the older enough? A study of the perception of old age home residents of Orissa. Indian Journal of Gerontology, 19(4), 373–380. National Research Council. (2012). Ageing in Asia: Finding from the new and emerging data Initiatives. In J. P. Smith & M. Majumdar (Eds.), Panel on policy research and data needs to meet the challenge of ageing in Asia. Committee on Population, Division of Behavioural and Social Sciences and Education. Washington, DC: The National Academies Press. National Research Council of the National Academies. (2011). Preparing for the challenges of population aging in Asia: Strengthening the scientific basis of policy development. Washington DC: National Academies Press. National Sample Survey Organization (2006). Morbidity, Health Care and the Condition of the Aged. Report No. 507 (60/25.0/1), Department of Statistics, Government of India, New Delhi.

J Cross Cult Gerontol (2014) 29:353–369

369

Nawal, D., & Goli, S. (2013). Inequality in utilization of maternal health care services in Nepal. Ethnicity and Inequalities in Health and Social Care, 6(1), 3–15. Nedjat, S., Hosseinpoor, A. R., et al. (2012). Decomposing socioeconomic inequality in self-rated health in Tehran. Journal of Epidemiology Community Health, 66(6), 495–500. O’Donnell, O., Van Doorslaer, E., Wagstaff, A., & Lindelow, M. (2008). Analyzing health equity using household survey data. A guide to techniques and their implementation. Washington D. C: World Bank Institute. Pou, L. M. A., & Goli, S. (2012). Burden of multiple disabilities among the older population in India: an assessment of socioeconomic differentials. International Journal of Sociology Social Policy, 33(1/2), 63–76. Rajan, S. I. (2006). Population aging and health in India. Mumbai: The Centre for Enquiry into Health and Allied Themes (CEHAT). Schoenbaum, M., & Waidmann, T. (1997). Race, socioeconomic status, and health: accounting for race differences in health. The Journals of Gerontology Series B 1997, 52B(Special Issue), 61–73. Smith, J., Borchelt, M., Maier, H., & Jopp, D. (1995). Health and well–being in the young old and oldest old. Journal of Social Issues, 58(4), 715–732. Srinivasan, M. N. (1957). Caste in modern India. Journal of South Asian Studies, 16(4), 529–548. Subramanian, S. V., et al. (2006). The mortality divide in India: the differential contributions of gender, caste, and standard of living across the life course. American Journal of Public Health, 96(5), 818–825. Subramanian, S. V., Leland, K. A., Malavika, A. S., & Sivaramakrishnan, K. (2008). Health inequalities in India: the axes of stratification. Brown Journal of World Affairs, 14(2), 127–138. Sun, R., & Liu, Y. (2006). Mortality of the oldest old in China: the role of social and solitary customary activities. Journal of Aging and Health, 18, 37–55. Sundmacher, L., Scheller-Kreinsen, D., & Busse, R. (2011). The wider determinants of inequalities in health: a decomposition analysis. International Journal for Equity in Health, 10(30), 5–13. Von dem Knesebeck, O., Uschen, G. L., Cockerham, W. C., & Siegrist, J. (2003). Socioeconomic status and health among the aged in the United States and Germany: a comparative cross-sectional study. Social Science and Medicine, 57(2003), 1643–1652. Wagstaff, A. (1991). QALYs and the equity-efficiency trade-off. Journal of Health Economics, 10(1), 21–41. Wagstaff, A. (2002). Poverty and health sector inequalities. Bulletin of the World Health Organization, 80(2), 97– 105. Wagstaff, A., & van Doorslaer, E. (1991). On the measurement of inequalities in health. Social Sciences and Medicines, 33, 545–557. Wagstaff, A., Van Doorslaer, E., & Wantanbe, N. (2003). On decomposing the causes of Health sector inequalities, with an application to malnutrition inequalities in Vietnam. Journal of Econometrics, 112(1), 219–227. Wilkins, R., Marmot, M. (ed). (2003). The social determinants of health: The solid facts, 2nd edition. World Health Organization Europe. Retrieved 2013-03-27. Wilkinson, R. G. (1996). Unhealthy societies: The afflictions of inequality. New York: Routledge. Zhang, W. (2008). Religious participation and mortality risk among the oldest old in China. The Journals of Gerontology: Social Sciences, 63B, S293–S297. Zimmer, Z., & Amorbsirisomboon, P. (2001). Socioeconomic status and health among older adults in Thailand: an examination using multiple indicators. Social Science and Medicine, 52(8), 1297–1311. Zimmer, Z., & House, J. S. (2003). Education, income, and functional limitation transitions among American adults: contrasting onset and progression. International Journal of Epidemiology, 32, 1089–1097. Zimmer, Z., & Kwong, J. (2003). Family size and support of older adults in rural and urban China: current effects and future implications. Demography, 40(1), 23–44. Zimmer, Z., & Kwong, J. (2004). Socioeconomic status and health among older adults in rural and urban China. Journal of Aging and Health, 16, 44–70.