481850 research-article2013

JAGXXX10.1177/0733464813481850Journal of Applied GerontologyPandey and Ladusingh

Original Article

Socioeconomic Correlates of Gender Differential in Poor Health Status Among Older Adults in India

Journal of Applied Gerontology 2015, Vol. 34(7) 879­–905 © The Author(s) 2013 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464813481850 jag.sagepub.com

Anamika Pandey1 and Laishram Ladusingh1

Abstract Assessment of the health status of the older adults can go a long way in controlling the disease burden and monitoring the path to healthy aging in India. In the absence of a population-based clinical survey to collect data on morbidities and other health conditions through biomarkers, self-rated health by nationally representative older population is used for understanding factors contributing to the gender differential in health status. Socioeconomic status is the most important factor explaining 59% of the gender gap in self-assessed health among older adults. The vulnerability of older women in terms of educational attainment, occupational status, and economic dependency is responsible for the higher level of poor selfassessed health. The gender gap in self-assessed poor health among older Indian adults, which perpetuates over the life course resulting in severe health disadvantages at old age can be reduced considerably through social empowerment and gender sensitive public policies. Keywords aging, self-assessed health, gender differential, socioeconomic status

Manuscript received: July 19, 2012; final revision received: January 25, 2013; accepted: February 10, 2013 1International

Institute for Population Sciences, Deonar, Mumbai, India

Corresponding Author: Anamika Pandey, Research Scholar, International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai-400088, India. Email: [email protected] Downloaded from jag.sagepub.com at Karolinska Institutets Universitetsbibliotek on November 15, 2015

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In 2012, India was home to 86 million older population (60 years and above), which was projected to increase threefold by 2051 (Rajan, 2010). This rapid projected increase in the size of the older population combined with a skewed gender differential in health conditions is a growing concern for public policy and intervention. The aging of population in India will lead to increased burden of disease, particularly noncommunicable diseases (Chatterji et al., 2008). Another important aspect of population aging in India, which has considerable implications on extending health care and support systems is the feminization of population aging. This is evident from higher life expectancy at birth of 65.7 years for women as compared to 62.8 years for men (Department of Economic and Social Affairs, 2010). The turnaround in life expectancy from the 1980s in favor of women is due to the improvement in maternal health care, as evident from the National Health Profile of India (Ministry of Health & Family Welfare [MoHFW], 2010). At present, in 17 out of the 20 large states of India, the proportion of older females in the 60 plus age group is higher than that of their male counterparts (Department of Economic and Social Affairs, 2010). To enhance healthy aging, there is an urgent need to control the burden of noncommunicable and other geriatric diseases. The ability of the older adults, particularly female and socioeconomically vulnerable groups to assess the current status of their health is crucial for monitoring and controlling the disease burden of an aging population and to prevent geriatric diseases from eating into the gain in life expectancy. The capacity for objective assessment of health status is the key to reduce the disease burden of an aging population. Recent research shows that the added years of life that the older adults gained from the increase in life expectancy are spent in the shadow of disease and disability, thus jeopardizing their health in later life (Capoor, 2008). In the Indian context, there is a pronounced gender differential in the health status and utilization of healthcare services (Das Gupta, 1995; Dhak & Mutharayappa, 2009). Women are more likely to suffer from disability at older ages and also suffer from chronic health conditions, which may not be life threatening but can lead to poor self-rated health (SRH). On the other hand, older men are more likely to suffer from fatal diseases (Case & Paxson, 2005). It has been argued that the difference in SRH between women and men can either entirely (Case & Paxson, 2005) or partially (Perrachi & Rossetti, 2008) be explained by the difference in the distribution of chronic conditions. In addition to this, older women are at a higher risk of dependency, isolation, dire poverty, and neglect. They have higher chances of being left out of various social security programs due to low literacy, confinement at home, and gender relations, as experienced in daily life (Sen & Östlin, 2008). Reported poor health status among older adults is not only a public

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health concern but it also reflects the sociocultural practices in India and needs to be addressed without further delay. This calls for social awakening and targeted public health interventions. This article attempts to provide key policy inputs in terms of gender differential in health measured by reported poor self-assessed health status and socioeconomic correlates for the female male gap. This has both sociocultural and policy relevance from public health perspectives as the sociocultural practices of victimization of women in India makes them more vulnerable than men to poor health and functional disabilities. It has been argued that self-assessed health status is a subjective measure conditioned by one’s cultural and social environment (Sen, 2002). Inspite of this, it has been an extensively used indicator of health because it is associated with objective health (Ferraro, 1980; Liang, 1986; Rakowski & Cryan, 1990) and is also a predictor of mortality (Idler & Benyamini, 1997; Mossey & Shapiro, 1982). It is one of the most widely accepted global measures of health in household-based survey research (Manderbacka, Lundberg, & Martikainen, 1999; Segovia, Bartlett, & Edwards, 1989) and has also been recommended for health monitoring by the World Health Organisation (WHO; de Bruin, Picavet, & Nossikov, 1996). One of the advantages associated with self-assessed health is that it measures health in keeping with the definition of WHO, which defines health as a state of well-being and not simply the absence of disease. It describes how a person perceives his or her own health and is an indicator of well-being and quality of life in old age (Hoeymans et al., 1997). This study emphasizes the need to expand geriatric health care facilities in the public health system and also to recommend the need of gender sensitivity in all social assistance programs. In the following sections, we provide a brief review on gender and health, followed by the description of data and methods adopted for this study along with results and discussion.

Gender and Health In western countries the phenomenon of women reporting worse health than men, but enjoying greater longevity had for long been the most widely accepted notion of the research on gender inequalities in health and mortality (Case & Paxson, 2005; Idler, 2003; Macintyre, Ford, & Hunt, 1999; Molarius & Janson, 2002; Nathanson, 1975; Verbrugge, 1985, 1989; Waldron, 1976). The sex differential in mortality among older adults has increasingly been in favor of women (Jacobsen et al., 2008) and that older women report bad health but survive and older men report good health but die (Oksuzyan, Juel, Vaupel, & Christensen, 2009). A new vibrancy was injected into the debate

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on gender inequality in health only after Macintyre, Hunt, and Sweeting (1996) and Arber and Cooper (1999) challenged the overgeneralization of the assumption that older women are more willing to acknowledge and report poor health. They have argued for the emergence of a “new paradox” among older age groups—namely, that there is a lack of gender difference in selfrated health (SAH) but that older women have a higher level of functional disability. Chan and Jatrana (2007) have shown that older men and women no longer differed in the probability of reporting poor self-assessed health status once the model was controlled for confounding variables, including the presence of chronic condition and disability, although significant gender differences continued to exist for chronic illness and disability even when the model was controlled for possible explanatory variables. Yet Lahema, Martikainen, Rahkonen, and Silventoinen (1999) did not find any gender difference in SRH in the population aged above 50 years, but the level of disability was nearly 20% higher among women than among men. It has been argued that nonolder women report significantly worse SRH than nonolder men do, but this gap either becomes narrower (Case & Deaton, 2003; Verbrugge, 1985) or disappears (Arber & Cooper, 1999; Case & Paxson, 2005; Gorman & Read, 2006; Leinonen, Heikkinen, & Jylha, 1998; Ross & Bird, 1994) and at times even reverses itself (Marks, 1996) at older ages. These empirical studies indicate that women tend to have more chronic conditions and disabilities as compared to men in older age cohorts but the differentials in self-rated health does not follow any consistent pattern. The existing sex differentials in poor self-reported health but lower mortality has been attributed to the sex differences in the distribution of chronic conditions, driven by biological, behavioral, or psychological factors (Lawlor, Shah, & Davey, 2001; Molarius & Janson, 2002; Verbrugge, 1989), or attributed to the differences in the socioeconomic status and health-reporting behavior (Crimmins & Saito, 2000; Evans & Stoddart, 1994; Idler, 2003; Marmot, Shipley, & Rose, 1984). Bird and Fremont (1991), Ross and Bird (1994), and Gorman and Read (2006) have put forward the argument that socioeconomic status (SES) plays a vital role in explaining much of the gender differentials, particularly in SRH at all ages, and that improvements in the SES of women would likely result in a remarkable upswing in how women feel about their general health. Thus it is important to understand how far the differential in characteristics of men and women account for the gender differences in health (Arber & Cooper, 1999). Few studies in India have focused on the gender differentials in health of the older adults, particularly their self-rated health status. Dhak (2009) examined the nature and extent of the gender differential in health considering selfassessed health and found that the gender gaps in health outcomes against

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older women were found to be more in the socioeconomically advantaged groups. Roy and Chaudhuri (2008) found that older women reported worse self-rated health then older men but after adjusting for economic dependence, the gender differentials disappeared or were reversed, and self-rated health status of older women was at least at par with that of their men counterparts. These studies are confined to revelation of gender differential in levels of SRH by selected individuals and other factors and determinants of SRH by sex. The present study is distinct from the aforesaid studies on the SRH of the older adults in India as it attempts to unfold the different levels of contribution of demographic, socioeconomic, disease, and health-related factors to the gender differential in poor reported health status among the older adults in India. Assessing the magnitude of the contribution of confounding factors on the gender differentials in health status will add critical policy inputs to bridge the gender gap in the health status among the older adults. Against this background, the present research attempts to fill an important research gap and provide clear insights into the gender differential in health among the older adults in India. The study intends to provide crucial inputs for feasible intervention programs to accomplish gender equality in health among the geriatric age groups. The study addresses the following research questions: Research Question 1: Is there any significant difference in self-assessed health (SAH) among older men and women in India? Research Question 2: Do the differences between older men and women with respect to demographic, social support, social-economic, and health status variables result in differences in self-assessed health? Research Question 3: Which of the socioeconomic and demographic factors contributes the most in explaining the gender differential in selfassessed health among the older adults?

Method Data Source and Sample This study used micro data on the socioeconomic conditions and health care of 17,750 males and 17,081 females aged 60 years and above collected in the 60th round of the National Sample Survey Organization (NSSO, 2006). It is a large-scale population-based survey conducted by the Ministry of Statistics and Program Implementation, Government of India. The sample design adopted for the survey was essentially a two-stage stratified design, with census villages and urban blocks as the first-stage units (FSUs) for the rural and urban areas, respectively, and households as the second-stage units (SSUs).

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The reference period, January to June 2004, was split into two subrounds of 3 months each to cover all seasons. The rural and urban samples of FSUs were drawn independently in the form of two subsamples and equal numbers of FSUs of each subsample were allocated for the two subrounds. Field implementation and supervision of NSSO surveys are the responsibility of regional branches located in almost all the states in India. This ensured good response rates and quality of data, leaving no scope for selectivity bias due to poor response. The NSSO 60th round data provide detailed information relating to morbidity, utilization of health care services provided by the public and private sectors, along with expenditure on medical treatment in the 365 days and 15 days preceding the survey, collected through the “Morbidity and Health Care” schedule (Schedule No. 25). It also has a special module covering various aspects of the older population, which relates to economic independence, person financially supporting the older adults, living arrangements, number of living sons and daughters, and so forth. Apart from this, there is also information on ailments and disability suffered by the older adults on the date of survey, their physical mobility status, and information related to their perception about current state of health and relative state of health. This study used data on the self-assessed health status along with the information related to demographic, socioeconomic, and health status of the older adults.

Health Outcome The main health outcome considered in this study is self-assessed health, which has been captured from the question “What is your perception about your current state of health?” and close-ended response categories were “excellent/very good,” “good/fair,” and “poor.” For the analysis the categories “excellent/very good” and “good/fair” were considered as “good” and “poor” health status and coded as 0 and 1, respectively. In the unit-level data, about 4% of the health outcome is found to be missing and the proportional distribution of the missing and nonmissing cases by background characteristics are similar. So the missing cases are at random and shall not introduce selection bias in the results of the analysis.

Covariates of Health Outcomes Drawing from the foregoing discussion on gender and health, this study considered four domains that can induce a gender differential in SRH. These domains are a set of demographic factors, social support background, socioeconomic conditions, and other health status variables.

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The set of demographic factors include age and caste groups. We have categorized age into two categories, namely, young old (60-69 years) and old old (aged 70 years and above) with a view to study which age group is more vulnerable in terms of reporting poor self-assessed health. Caste in our analysis is categorized as non-scheduled castes (SC)/scheduled tribes (STs), which include Other Backward Classes and Others, and SCs/STs to inquire whether the adverse living conditions and poverty among the socially and economically disadvantaged groups have a negative impact on their health. We have included two important social support mechanisms that have a link with health status of the older adults, namely, living arrangement and number of non-older adult earning member in the household. The variable, “number of non-older adult earning members” has been categorized as having at least one non-older adult earning member and having no non-older adult earning member to examine if the presence of an earning member in the household other than an older adult has any positive bearing on the health status of the older adults. This variable is relevant in the Indian context where the responsibility of looking after the needs of older parents lies entirely on the shoulders of children, particularly the sons, who are supposed to be the major care providers. A variable of living arrangement is categorized as living with spouse and other members and all other living arrangements. All other living arrangements include living alone, living with spouse only, living without spouse but with children/relation/nonrelation. This study includes a range of socioeconomic indicators measured in terms of educational status, occupational status, household economic status, place of residence, and status of economic dependency of older adults to capture how far the different distribution of men and women across socioeconomic indicators is reflected in their health status. Questions related to usual activity status in the survey has been used to categorize the occupational status into two categories: 0 = involved in paid work 1 = either not working or engaged in unpaid work. The monthly per capita consumption expenditure of the household is used to create an indicator of economic status by segregating the older adults into poor and nonpoor. Economic independence of the older adults is assessed in terms of their ability to meet economic needs. All those who are taking care of their economic needs are categorized as economically independent. Others who are either partially or wholly dependent on others for the maintenance of their livelihood are categorized as economically dependent.

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The other health status conditions included are the presence of chronic diseases and disabilities and the physical mobility status, which is interlinked with the reporting of poor SRH. Chronic and disability condition has been calculated from the self-reported morbidity from which the older adults were suffering on the date of survey. The chronic diseases included in this study are gastritis/gastric or peptic ulcer, worm infection, amebiasis, hepatitis/jaundice, cardiovascular diseases, tuberculosis, disorders of joints and bones, disease of kidney/urinary system, prostatic disorders, gynecological disorders, neurological disorders, psychiatric disorders, glaucoma, cataract, goiter, diabetes mellitus, undernutrition, anemia, sexually transmitted diseases, cancer, and other tumors. A person with restriction or lack of abilities to perform an activity in the manner or within the range considered normal for a human being was treated as having disability. Illness/injury of recent origin (morbidity) resulting in temporary loss of ability to see, hear, speak, or move has been excluded from the list. This study includes four types of disability, namely, visual, locomotive, speech, and visual. The physical immobility status used in this study refers to the state of health where the ailing person is required or compelled to stay in bed or at his or her home/residence. This information is utilized to examine the presence of gender differential in the prevalence of chronic condition, disability, and physical mobility status and find out how far these contribute to the gender differential in SRH.

Analyses In the first step of analysis, simple percentage distribution of components of four domains by sex is used to provide a contrast in the characteristics of older male and female adults. Second, prevalence of poor health status among male and female older adults by each factor in the four domains is presented. This provides a foundation for comprehensive understanding of the current status of older adults in India. In the third part of the analysis, the univariate odds ratio is calculated to find out whether the variables considered have a significant impact on the self-rated poor health status. This is important because for a variable to be a possible confounder of gender differential in health, it must significantly affect the health status. Next, multivariate logistic regression models are employed to identify the significant predictor of the likelihood of the older adults reporting poor self-assessed health after controlling for covariates in the four domains. Six separate multivariate logistic models have been proposed to measure the association between vectors of demographic variables, vector of social support variables, vector of socioeconomic variables, and a

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vector of health variables with the probability of reporting poor self-assessed health. To examine the interplay of various covariates in molding the magnitude and direction of the gender differences in self-assessed health status of older adults, the modeling strategy is designed to integrate the vectors in the aforesaid four domains in a stepwise manner so that the final model contains all the domains. By comparing the magnitude of the odds ratio, we can identify the vector that has significant bearing on gender differential in selfassessed health status. This approach allows us to examine whether nullifying differences in current socioeconomic conditions is sufficient to eliminate the gender gaps in health, and to test if these differences are more important in explaining the gender gaps in health than the gender differences in demographic, family support, and health status variables. Multivariate logistic regression analysis has been used to model the dichotomous outcome coded as 1 = reporting poor health and 0 = not reporting poor health. The independent variables are coded as categorical variables and all results are presented in terms of odds ratios. In the fourth part of the analysis, our aim is to compute the differences in the reporting of poor self-assessed health among older adults by sex and, then, decompose these differentials into their separate underlying factors. We have employed Fairlie’s (1999, 2005) decomposition technique for the decomposition analysis, as it is particularly suited to calculating gaps for binary outcomes, that is, poor self-assessed health in the present study. The procedure computes the difference in outcome between two groups and quantifies the contribution of group differences in the independent variable to outcome differential. Following Fairlie (1999), the decomposition for nonlinear equation of the  type Y = F X β , can be written as

( )

  M M  N F X iF β F N F Xi β  = ∑ −∑ NF NM  i =1 i =1  F

Y − Y F

M

(

)

M

(

)  +   

NM



 i =1 

 F X iM β F

(

NM

)−

 F X iM β M   ∑ NM  i =1 

NM

(

)

Where, NF and NM is the sample size for female and male older adults, F is the cumulative distribution function of logistic distribution and Y F and Y M is the average probability of the binary outcome of interest for older female and male. In the above equation, the first term in brackets represents the part of the gender gap that is due to group differences in distributions of characteristics of the independent variables X’s also known as the “explained part,” whereas, the second term represents the portion of gender differences due to differences in the coefficients or “returns” to the exogenous covariates. The second term also captures the portion of the gender gap due to group differences in unmeasurable or unobserved endowments. Similar to most previous studies

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applying the decomposition technique, we do not focus on this “unexplained” portion of the gap because of the difficulty in interpreting results (Cain, 1986; Jones, 1983).

Results Distribution of male and female older adults by the selected demographic, social support, socioeconomic, and health-related variables are shown in Table 1. It is apparent from the table that the majority of the older adults are in younger old-age cohort (less than 70 years), non-SC/STs, illiterate, economically dependent, engaged in unpaid work/not working, having at least one non-older adult earning member, physically mobile and not suffering from any chronic disease, disability, or physical immobility. A major difference between older men and women is noticed in pattern of living arrangement, educational status, working status, and state of economic dependency. Majority of the older males are living with spouse and other members but among older females only 29% are currently residing with their spouse and other members. Nearly 81% of the female older adults are illiterate and around half of the male older adults are illiterate. There is a striking gender difference in occupational status of older adults in the study population. An equal proportion of older men are engaged in paid work and unpaid work/not working while among older women, nearly 90% are engaged in unpaid work/not working. Majority of the female older adults (85%) are economically dependent as against 48% of male older adults. Not much differential is observed in the age distribution, economic status, and place of residence of male and female older adults in India. The proportion of older women in age bracket 70 years and above is slightly less (34%) than that of older men (36%), which is in contrast to the trend in developed countries where women make up a significant majority of older population and their share in population increases with age. The variables of health status, which include the presence of chronic and disability condition and physical immobility status, is slightly higher among older female than among older male adults. Older adults reporting poor self-assessed health and male-female differential in reported poor self-assessed health by selected variables are presented in Table 2. At the country level, nearly 24% of older adults reported poor self-assessed health status. Sex-wise segregation show that higher percentage of female older adults (26%) report poor self-assessed health as compared to male older adults (22%). The distribution of self-assessed health status by different background characteristics indicated that the highest percentages of older adults who are illiterate, economically dependent, involved in unpaid

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%

66.1 33.9 76.3 23.7

29.1 70.9 88.5 11.6

9.5 90.5 14.7 85.3

13,269 4,476

11,042 6,360 16,164 1,586

8,412 9,338 9,367 8,383

%

11,380 6,370

N#

N#

2,433 14,648

1,406 15,675

15,236 1,845

5,085 11,617

13,022 4,055

11,166 5,915

Female % N#

 Education   Literate 49.6 9,657   Illiterate 50.4 8,085   Economic status   Nonpoor 67.1 13,015   Poor 32.9 4,734   Place of residence   Urban 23.7 6,222   Rural 76.3 11,528 Health status variables   Suffering from at least one chronic disease   No 77.6 13,570   Yes 22.4 4,180   Suffering from at least one disability   No 93.9 16,678   Yes 6.1 1,072   Physical mobility status   Mobile 93.1 15,933   Immobile 6.9 1,435   Total 50.0 17,750

Background variables

Male

90.7 9.3 50.0

14,888 1,789 17,081  

15,892 1,189

92.8 7.2

6,344 10,737

24.8 75.2

12,776 4,305

12,406 4,674

66.1 33.9

76.2 23.8

3,857 13,216

N#

18.7 81.3

%

Female

Note. N# samples are unweighted. aage 70 and above. bOther Backward Class and Others. cAlone/with spouse only/without spouse but with children/nonrelation/others.

Demographic variables  Age   Young old 64.4   Othersa 35.6   Social group   Non SCs/STsb 75.6   SCs/STs 24.4 Social support variables   Living arrangement    With spouse and other members 62.0    All other arrangementsc 38.0   Atleast one earning non-older adult in HH   Yes 91.5   No 8.5 Socioeconomic variables  Occupation   Paid work 50.1    Unpaid work/not working 50.0   Economic dependency   Independent 52.3   Dependent 47.7

Background variables

Male

Table 1.  Percent Distribution of Population Aged 60 Years and Above by Selected Variables According to Sex in India, 2004-2005.

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Male

1.27 1.16 1.21 1.16

1.03 1.13 1.22 0.97

1.23 0.86 1.53 0.83

25.6 26.4

20.2 28.0 25.6 25.0

14.3 27.0 17.9 27.1

F/M#

19.7 37.7

Female

13.1 29.1

12.1 30.0

23.3 27.5

19.8 26.9

23.4 24.6

17.6 35.1

Male

Female

 Education   Literate 19.1 21.1 23.9 26.9   Illiterate   Economic status   Nonpoor 19.7 24.1   Poor 25.3 29.2   Place of residence   Urban 19.0 23.2   Rural 22.3 26.6 Health status variables   Suffering from at least one chronic disease   No 16.7 20.8   Yes 37.8 41.4   Suffering from at least one disability   No 19.8 23.7   Yes 46.9 52.3   Physical mobility status   Mobile 17.6 20.8   Immobile 76.0 74.4   Total 21.5 25.8

Total Background variables

1.18 0.98 1.20

19.1 75.1 23.7  

21.7 49.9

1.19 1.11

21.2 24.5

1.22 1.19

18.7 39.6

21.9 27.2

1.22 1.15

1.24 1.10

19.6 25.7

Total

1.10 1.12

F/M#

Self-perceived poor health

Note. # Female/Male. aage 70 and above. bOther Backward Class and Others. cAlone/with spouse only/without spouse but with children/nonrelation/others.

Demographic variables  Age 15.4   Young old   Othersa 32.6   Social group   Non SCs/STsb 21.1   SCs/STs 22.8 Social support variables   Living arrangement    With spouse and other members 19.6    All other arrangementsc 24.8   Atleast one earning non-older adult in HH   Yes 20.9   No 22.3 Socioeconomic variables  Occupation   Paid work 11.6    Unpaid work/not working 31.4   Economic dependency   Independent 11.7   Dependent 32.5

Background Variables

Self-perceived poor health

Table 2.  Gender Differential in Self-Perceived Poor Health Status Among Population Aged 60 Years and Above by Selected Variables in India, 2004-2005.

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work/not working, belonging to the poor economic group, and residing in rural areas reported poor self-assessed health. We also found that perceiving overall health status as poor is more common among older adults suffering from chronic condition (40%) and disability (50%). Among older adults who are physically immobile, as high as 75% reported being in poor health in our study population. The phenomenon of the declining perception about one’s own health of older adults is quite evident in our study population—only 18% of older adults below 70 years of age reported poor self-assessed health as against 46% of older adults aged 70 years and above. A high proportion of older adults (27%) not living with their spouse and other members have reported poor self-assessed health as compared to their counterparts. The figures of self-assessed health status by different socioeconomic, demographic, social support and health variables distributed by sex supports, the general view that older women are more likely than older men to report poor self-assessed health status. The only exception is seen in case of unpaid work/not working, economically dependent, and physically immobile older adults, where older women reporting poor self-assessed health are slightly less than that of older men.

Multivariate analysis Univariate odds ratio of reporting poor self-assessed health status among the older adults in India is presented in Table 3. The univariate odds ratio clearly indicates that all the variables that we have considered in our analysis excluding social group has a significant association (p < .01) with reporting poor self-perceived health status among the older adults. This provides enough ground for us to model them in multivariate analysis to examine how these variables modify the gender differentials in self-assessed health status. Although variable on the caste of the older adults is not significant, we will include it in our analysis because caste is unique to Indian society and plays a very important role. Studies have consistently shown that groups higher in the social ladder have better health outcomes than groups lower down suggesting the existence of a “social gradient” to health outcomes in India (Borooah, 2010; Guha, 2007; Nayar, 2007). The results from the different version of the proposed models in methods section are shown in Table 4. Model 1 showed the odds ratio of reporting poor self-assessed health for older women taking men as the reference category. Female older adults are 30% more likely to report poor self-assessed health as compared to older men and the difference was found to be highly significant (p < .01).

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Odds ratio

bOther

70 and above. **p < .05. ***p < .01.

aage

Backward Class and Others.

2.352

0.984

1.385

1.127

1.312

0.096

0.046

0.057

0.069

0.056

1.531

1.397

1.607

1.165

2.728

1.362

SE

  Work status   Paid work    Unpaid work/not working 2.91*** 0.143   Economic dependency   Independent   Dependent 2.73*** 0.121   Economic status   Nonpoor   Poor 1.34*** 0.053  Sector   Urban   Rural 1.21*** 0.049 Health status variables   Suffering from atleast one chronic disease   No   Yes 2.85*** 0.115   Suffering from atleast one disability    No   Yes 3.58*** 0.231   Physical mobility status   Mobile   Immobile 12.79*** 0.827

Background variables

Odds ratio

spouse only/without spouse but with children/nonrelation/others.

1.178

0.047

SE

cAlone/with

Sex  Male  Female 1.27*** Demographic variables  Age   Young old   Old olda 2.53***   Social group   Non SC/STsb   SC/STs 1.07 Social support variables   Living arrangement    Living with spouse and other members    All other arrangementsc 1.49***   At least one earning non-older adult in HH       No 1.25*** Socioeconomic variables   Educational status    Literate   Illiterate 1.42***  

Background variables

95 % Confidence interval

4.061

3.085

1.307

11.227 14.479

3.154

2.635

1.113

1.444

2.975

2.498

1.237

3.205

2.645

95 % Confidence #nterval

Table 3.  Univariate Odds Ratios for Logistic Regression of Reporting Poor Self-Perceived Health Status Among Older Adults in India, 2004-2005.

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Table 4.  Odds Ratios for Logistic Regression of Reporting Poor Self-Perceived Health Status Among Older Adults in India, 2004-2005. Background variables

Model 1

Model 2

Sex  Male  Female 1.27*** 1.30*** Demographic variables  Age   Young old   Othersa 2.58***   Social group   Non SC/STsb   SC/STs 1.17*** Social support variables   Living arrangement   With spouse and other members    All other arrangementsc   At least one earning non-older adult in HH   Yes   No Socioeconomic variables   Educational status   Literate   Illiterate   Work status   Paid work    Unpaid work/not working   Economic dependency     Independent   Dependent   Economic status   Nonpoor   Poor  Sector   Urban   Rural Health status variables   Suffering from at least one chronic disease   No   Yes   Suffering from at least one disability   No   Yes   Physical mobility status   Mobile   Immobile   –2 log likelihood 36216 34937

Model 3

Model 4

Model 5

Model 6

1.26***

0.72***

1.19***

0.79***

1.61***   1.12**

1.42***

  1.18***

1.10***

  1.09

35921

1.20**

  1.22***

2.41***

  1.57***

1.86***

  1.57***

1.21***

  1.30***

1.17**

  1.26***

34567

2.59***

  2.65***

2.55***

  2.11***

9.63*** 31377

  7.25*** 30115

aage 70 and above. bOther Backward Class and Others. cAlone/with spouse only/without spouse but with children/nonrelation/others. **p < .05. ***p < .01.

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Adjustment of demographic variable in Model 2 did not change either the pattern or the magnitude of the gender differential in poor self-assessed health noticed in Model 1, the difference being highly significant (p < .01). This showed that demographic variables measured in terms of age and social group did not have any impact on the gender differential in the self-assessed health of the older adults. In Model 3, with the inclusion of the vector of social support mechanism to the base model, the likelihood of female older adults reporting poor selfassessed health reduces slightly from 1.27 to 1.13, the difference still being statistically significant. This indicates a part of the gender differential in selfreported poor health status can be explained by the gender differential in the pattern of living arrangement and the presence of earning non-older adult members in the household. In Model 4, we add a vector of socioeconomic factors to the base model. The gender differential in self-assessed health not only disappears but also turns in favor of females. Now older women are 24% less likely to report poor self-assessed health status (p < .01) than older men. From this model, we learn that a major reason for the higher level of poor self-assessed health among older women relates to their more disadvantaged position in terms of educational status, occupational status, economic dependency, economic status, and place of residence. In Model 5, we have included a set of other health conditions, which include the presence of chronic diseases, disability, and physical immobility. Even after controlling for the other heath conditions, we did not find much change in odds of female older adults reporting poor self-assessed health as compared to male older adults and found that older women are 19% more likely (p < .01) to report poor self-assessed health. The analysis also found that as compared to young old (aged less than 70 years) those adults who are older than 70 years are 1.6 times more likely (p < .01) to report poor self-assessed health when other covariates are controlled in the analysis. Older adults suffering from chronic and disability conditions have 2.7 and 2.1 times higher odds of reporting poor self-assessed health status, respectively, as compared to the reference group. Physical immobility has a negative impact on the self-assessed health status. The physically immobile older adults are 7.3 times more likely to report poor self-assessed health and this is found to be statistically significant at 1% level of significance. The illiterate and those not working/working in unpaid jobs are 22% and 57% more likely to be in poor health status, respectively. A similar conclusion holds good for economically dependent and poorer older adults. The rural older adults are 26% more likely (p < .01) to report poor self-assessed health as compared to their urban counterparts. The older adults who are not

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Pandey and Ladusingh Table 5.  Results of Fairlie Decomposition Analysis. Raw difference#

Total difference explained

Difference unexplained

0.056 132

–0.013 –32

0.042 In percent Background variables

% contribution in explained difference

p>z

–2.5 –0.2

0.000 0.484

–0.002 0.000

–0.001 0.000

14.1 0.6

0.014 0.314

0.002 0.000

0.014 0.001

18.1 28.3 12.6

0.003 0.009 0.124

0.003 0.004 –0.002

0.017 0.028 0.016

0.7 –0.3

0.121 0.402

0.000 –0.001

0.001 0.000

5.8 3.3 19.1

0.000 0.000 0.000

0.002 0.001 0.010

0.004 0.002 0.012

Demographic variables  Age   Social group Social support variables   Living arrangement   Nonelderly earning member in HH Socioeconomic variables   Education status   Work status  Economic dependency   Economic status  Sector Health status variables   Chronic conditions   Disability conditions   Physical mobility status

95% confidence interval

Note. #weighted figure.

residing with their spouse and other members are 18% more likely to report poor health.

Decomposition Analysis Table 5 shows the Fairlie decomposition results of the gender gap in reporting poor self-assessed health among older adults in India. The female and male gap in reported poor self-assessed health is 0.042, that is, 4.2% higher for female. The set of covariates under the four major domains we have considered are able to explain 132% of the overall gender gap (i.e., the explained part or due to differences in the distribution of characteristics). From the

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results of Fairlie decomposition, we found that work status, educational status, and state of economic dependence explain the majority (59%) of the gender gap in self-assessed health. Out of the three variables, work status accounts for a larger share of differences in poor self-perceived health status among older females and males. The difference in the distribution of older men and women in productive employment constitutes 28% of the explained gender gap. Being illiterate and economically dependent contributed 18% and 13% respectively to the explained difference in reported self-perceived health status of older female and male. The contribution of chronic diseases and disability is marginal in explaining the gender gap in reporting poor selfassessed health. Among the other health conditions physical immobility has the most significant bearing in explaining gender contributing 19% of the explained gap. The contribution of living arrangement is around 14% in explaining the gender gaps in reporting poor self-assessed health. Age, caste, and sector have a marginal negative contribution in explaining the gender differences in poor self-assessed health among the older adults. Above all it is the socioeconomic background that contributes significantly in explaining the gender gap in reported poor health status as evidenced from the multivariate analysis.

Discussion Health inequality by gender is important information but is not adequate to draw appropriate health intervention strategies. This should be complemented by a comprehensive knowledge of the sources of factors that contribute to the inequalities. Under this premise, the present article makes an attempt to understand the pattern and determinants of gender differences in health measured in terms of self-assessed health and carries out a decomposition analysis to facilitate in the identification of factors that contribute significantly to gender inequalities in self-assessed health among the older adults in India. This study unfolds that the socioeconomic factor is the most important factor affecting the self-rated health status of the older adults. Majority of older adults reporting poor self-perceived health status are those belonging to socioeconomically disadvantaged group, and this is in line with the findings of Kawachi, Kennedy, and Glass (1999), Yngwe, Diderichsen, Whitehead, Holland, and Burström (2001), Lantz et al., (2001), and Knesebeck, Luschen, Cockerham, and Siegrist (2003). Persons with a lower socioeconomic status invariably report poor health because they are exposed to more hardships and stress and have limited access to resources for preventive care and treatment as mentioned in Ross and Bird (1994) and Walters, McDonough, and Strohschein (2002). Results reflect the advantageous situation of the urban

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older adults in terms of the availability and accessibility of health services. Among the social support mechanisms considered in the study, living arrangement emerges as a significant predictor of poor self-assessed health among the older adults. Among demographic variables, age and caste have a strong bearing on the self-assessed health status of the older adults. We found that poor selfassessed health is a common problem among the aged population. With increase in age, the perception of the older adults regarding their health deteriorates, and this is compatible with the findings of previous studies (Goldstein, Siegel, & Boyer, 1984; Moum, 1992; Haseen, Adhikari, & Soonthorndhada, 2010; Kanagae et al., 2006). Our findings also confirm the fact that the older adults belonging to scheduled castes (SC)/scheduled tribes (STs), which are considered to be socioeconomically disadvantaged groups, are more prone to poor health status. Presence of chronic conditions or long-term disability and physical immobility made the older adults assess their overall health status as poor in our study population, and this is consistent with the findings of previous literatures (Cott, Gingac, & Badley, 1999; Damian, Ruigomez, Pastor, & MartinMoreno, 1999) Results from the multivariate analysis provide enough evidence to conclude that significant gender differences exist among the older adults in reporting poor self-rated health and that nullification of gender differences on the socioeconomic front can effectively contribute to the reduction of gender differences in the poor health of the older adults in India. It is a widely accepted view that education works through complex ways and has a large and persistent effect on health, which is quite evident in our study population. One assertion for this could be that those with higher education have a greater likelihood of being knowledgeable about health conditions and preventive care, and have better financial resources at their disposal to enable them to have more access to health care services, thus increasing the likelihood of their being in good health (Wróblewska, 2002). Occupation was found to be an important determinant of gender differences in ill health, which mirrors the findings of Nathanson (1975), Waldron (1983) and Arber (1997). Owing to lack of education and involvement in paid work, older women do not have financial resources at their disposal resulting in greater economic dependency making them disadvantaged on the socioeconomic front. Adjusting for a wide array of socioeconomic variables not only narrows down and reduces the gender gap in the poor health status of the older adults but can also reverse it in favor of females at older ages. Our findings corroborate with the widely accepted notion that the lower the socioeconomic status, the more pessimistic is the self-rated health status (Arber & Cooper,

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1999; Bird & Fremont, 1991; Cooper, 2002; Dhak, 2009; Liu & Zhang, 2004; Ross & Bird, 1994; Verbrugge, 1988). A part of the older female excess in poor self-assessed health is also because older women are, on an average, more likely to be living without spouse and other members, which has a negative impact on their health. The presence of spouse and other household members provides a sense of security and opportunities for companionship and intimacy, which is important for the physical and mental well-being of older people (Choi & Wodarski, 1996; Fajemilehin, 2009; Giang & Dfau, 2009; McNicholas, 2002; Seeman & Crimmins, 2001). It can be argued that controls for some of the compositional differences between the genders can go a long way in ameliorating the gender differences in self-rated health (Hraba, Lorenz, Lee, & Pechacova, 1996). Decomposition analysis provides further evidence on the importance of socioeconomic variables in explaining the gender differences in self-assessed health among the older adults. We found that much of the gender differential in reporting poor self-perceived health status among the older adults can be explained by the higher level of engagement of female older adults in unpaid work/not working followed by their higher level of illiteracy and economic dependency when compared to the older men. Apart from this, living arrangements also contributed much to the gender gaps in SRH. Older women were found to be more disadvantaged in terms of their current living arrangement, which contributed to the existing gender gaps. The contribution of disability and chronic condition in explaining the difference in older men’s and women’s health measured in terms of self-assessed health is found to be meager, which is in contrast to the findings by recent studies. But we do find that immobility status of the older adults account for nearly 19% of the explained gap in gender differential.

Study Limitations The limitations of this study relate to the fact that both the dependent and the independent variables are self-reported and likely to have reporting bias and recall lapse. The data used is cross-sectional and, therefore, we cannot establish any cause and effect relationship between self-assessed health and different socioeconomic, demographic, social support, and health-related variables. There is no information collected on psychological and behavioral aspects in NSSO (60th round) micro data on morbidity and health care. Hence, we have used a limited set of independent variables in our analysis, ignoring the psychological and behavioral factors affecting the perception of health among the older adults.

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It would have been more interesting to study the contribution of socioeconomic factors in explaining the gender gap in poor self-assessed health among older men and women with similar health conditions, but this is not included in this study. Besides, we could not identify the precise channel through which better socioeconomic status translates into more positive perception about one’s health by using this data. Despite these limitations, this study is important in that it gives a comprehensive understanding and quantification of the principal factors that explain the gender differences in health status.

Conclusion We conclude that among the multiple factors responsible for the gender differential in self-assessed health among the older adults in India, socioeconomic conditions make the most important significant contribution. This indicates that the improvements on these fronts will augment the health status of older women. Thus, to combat the gender differences in health in old age, we need to formulate policies and programs that can tackle the social and economic inequality among older men and women. Acknowledgment The comments and suggestions of two anonymous reviewers have led to revision and considerable improvement to the initial draft of the article. Their contribution is duly acknowledged with thanks.

Authors’ Note Data used in this study do not involve human subject as it is from a cross-sectional survey where sampled households were asked to provide information on self-assessment of health conditions of members of households without any clinical or laboratory examination of individuals. Survey was conducted by National Sample Survey Organization, Ministry of Statistics and Programme Implementation, Government of India and data are made available for public use on charge. Details are available in the website http://mospi.nic.in/Mospi_New/site/inner.aspx?status=2&menu_id=5. In view of the foregoing background of data collection, IRB is not applicable for this study.

Declaration of Conflicting Interests The author(s) declare no potential conflict of interest with respect of the authorship and/or publication of this article. There is no ethical issue as the study is based on data available in public domain.

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Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research is supported by Junior Research Fellowship grant from the University Grant Commission, Government of India, to the first author.

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Author Biographies Anamika Pandey is currently a doctoral student at the International Institute for Population Sciences (IIPS), Mumbai, India. She has completed masters in economics from the Banaras Hindu University, Varanasi, and masters in population sciences from the International Institute for Population Sciences. Her research interests are aging, gender differentials in health, health expenditure burden, child malnutrition and demographic dividend. She has presented papers in various national and international conferences.

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Pandey and Ladusingh

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Laishram Ladusingh is a full-time professor of demography & statistics at the International Institute for Population Sciences, Mumbai, India. He has written numerous articles on stochastic modeling, longevity and work participation, health care cost and health inequality, which have appeared in national and international scholarly journals. He is one of the international advisory members of a few scholarly journals. His current research interest centers on economic implications of aging, postretirement life and pension policies in developing countries.

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Socioeconomic Correlates of Gender Differential in Poor Health Status Among Older Adults in India.

Assessment of the health status of the older adults can go a long way in controlling the disease burden and monitoring the path to healthy aging in In...
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