Figures
Abstract
Background
The extant literature on gender differentials in health in developed countries suggests that women outlive men at all ages, but women report poorer health than men. It is well established that Indian women live longer than men, but few studies have been conducted to understand the gender dimension in self-rated health and self-reported disability. The present study investigates gender differentials in self-rated health (SRH) and self-reported disability (SRD) among adults in India, using a nationally representative data.
Methods
Using data on 10,736 respondents aged 18 and older in the 2007 WHO Study on Global Ageing and Adult Health in India, prevalence estimates of SRH are calculated separately for men and women by socio-economic and demographic characteristics. The association of SRH with gender is tested using a multinomial logistic regression method. SRD is assessed using 20 activities of daily living (ADL). Further, gender differences in total life expectancy (TLE), disability life expectancy (DLE) and the proportion of life spent with a disability at various adult ages are measured.
Results
The relative risk of reporting poor health by women was significantly higher than men (relative risk ratio: 1.660; 95% confidence Interval (CI): 1.430–1.927) after adjusting for socio-economic and demographic characteristics. Women reported higher prevalence of severe and extreme disability than men in 14 measures out of a total20 ADL measures. Women aged less than 60 years reported two times more than men in SRD ≥ 5 ADLs. Finally, both DLE and proportion of life spent with a disability were substantially higher for women irrespective of their ages.
Citation: Bora JK, Saikia N (2015) Gender Differentials in Self-Rated Health and Self-Reported Disability among Adults in India. PLoS ONE 10(11): e0141953. https://doi.org/10.1371/journal.pone.0141953
Editor: Spencer Moore, University of South Carolina, UNITED STATES
Received: December 12, 2014; Accepted: October 15, 2015; Published: November 4, 2015
Copyright: © 2015 Bora, Saikia. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: Data are from the (http://www.who.int/healthinfo/sage/en/) study whose authors may be contacted at (http://www.who.int/healthinfo/sage/en/).
Funding: This study was conceived when Dr. Nandita Saikia was a Max Planck India fellow at Max Planck Institute for Demographic Research (MPIDR), Germany. Dr. Saikia is grateful to Max Planck Society for providing fellowship during her stay at MPIDR, Germany; website: (http://www.demogr.mpg.de/en/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Recently, a growing body of research has looked at the gender gap in health and mortality, generally termed “the male-female health-survival paradox”[1–3]. A large body of literature confirms that women outlive men worldwide [4–6]. However, in Northern, Latin American and Caribbean countries [7–10], and in a few South Asian countries [11–12], women are more likely than men to report self-rated worse health and higher prevalence and incidence of disability and chronic morbidity.
Does this gender gap hold for India, where gender plays a crucial role from birth to death, and from nutrition to health care service utilizations? The answer is of enormous policy relevance. The literature addressing the gender gap in mortality in India is ample [13–16]. Unlike in the developed world, Indian females do not enjoy survival advantages over males in every age group. More recent studies find that the female infant mortality rate in many states of India is higher than the male infant mortality rate [17]. India still has a high volume of excess female deaths at infant and child age (1–4 years) due to discriminatory care at home, discriminatory health-care seeking and selective termination of female fetuses [18]. Despite this, Indian females enjoy a higher life expectancy at birth than males and the male-female gap in survivorship favours adult and elderly females.
Despite this important role of gender in health and mortality, few studies address the gender difference in self-rated health (SRH) [12,19–21] and in self-reported disability (SRD) in India [22–24]; these debate the gender difference in SRH and SRD side by side and arrive at different conclusions. While some studies [21,25] demonstrate that the gender differential in SRH persists even after adjusting socio-economic variables, other studies show that the gender differential in self-assessed health turns in favour of women when adjusted for the role of socio-economic factors [19]. It is found that women tend to report worse health than men, especially among the socio-economically advantaged group [25], and socio-economic status contributes significantly to this gap [19]. Studies on gender difference in SRD also reveal that, in general, women report higher disability condition than men [22–23], but the gender difference in disability may not be significant in the context of South India [24].
Self-rated health (SRH) is a widely used measure based on a person's self-assessment of his/her status in response to the question “In general, how would you rate your health today?” [26–28]. Although SRH overlooks the concern of interpersonal incomparability; it is a remarkably reliable measure that is consistent with the actual health status of the respondents [26,29]. Self-rated health has been used to assess the health status of populations and predict health outcome, survival, impending morbidity and death [26,29–31]. Self-reported disability, another SRH measure, is extensively used to assess disability and is considered a reflection of true disability among the elderly population [32–33]. It is defined as functional limitations in activities of daily living involving bathing, eating, a transfer from bed to chair, use of the toilet, etc. [33]. Numerous earlier studies demonstrated empirically that data on SRH and SRD is consistent with both performed measures and medical diagnosis [32,34–35]. Earlier studies confirmed that both men and women report their disability accurately, and the higher prevalence of reported functional problems among women is perhaps a reflection of true disability for most disability measures [32]. A five-year follow-up survey of the Study on Global Ageing and Adult Health (SAGE) conducted by the World Health Organization (WHO) multi-country study on global ageing and adult health in rural India found that bad or very bad self-rated health was a strong predictor of mortality for persons aged above 50, even after controlling for socio-demographic and disability measures [26].
The purpose of the present study is to examine the gender differential in SRH and SRD among older adults in India. Our study extends the extant knowledge in several directions. First, we combine the analysis of gender difference in SRH and SRD to present a coherent picture of the gender difference in health status among adults in India. Second, we investigate gender difference in total life expectancy (TLE), disabled life expectancy (DLE) and disabled free life expectancy (DFLE) to demonstrate the health disadvantage of women despite survival advantage in adult age. Finally, unlike most of the previous studies on SRD in India, our study is based on a nationally representative survey, indicating a generalization of results at national level. The findings on gender inequality in adult health might prove useful for health planners and policy makers, especially in countries subject to rapid health transition.
Data and Methods
2.1. Ethics statement
We used cross-sectional WHO SAGE survey data, approved by the Ethics Review Committee of the WHO, Geneva and the International Institute for Population Sciences, Mumbai (IIPS) implementing the national SAGE survey. Before conducting the interviews, respondents participating in this survey were given a “Respondent information Form”, which detailed the giving details of the survey purpose, methods and data collection procedure involved in this survey. Finally, written informed consent was taken from the respondents of aged 21 and older. For participants under 21 years old of age, a parent or guardian also signed in the consent form.
2.2. Data Description
We used data primarily from the SAGE, carried out by the IIPS in 2007–08 under the WHO multi-country study on global ageing and adult health. The main objective of this study was to obtain reliable, valid and comparable data on levels of health across a range of key domains for adult populations aged 50-plus in nationally represented samples. The study aimed also to supplement and cross-validate self-reported measures of health by anchoring a vignette approach to improving the comparability of self-reported measures for selected health domains [36–37].
The first wave of the SAGE was implemented in six states selected to ensure a nationally representative sample—Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal. The same primary sampling units (PSUs) and households covered in the 2003 World Health Survey (WHS) are comprised the baseline sample for SAGE Wave 1 India in 2007–08. The SAGE Wave 1 India included a total sample of 12,198. Out of 12,198 interviews, 10,736 interviews were completed, 494 interviews were partially completed and the rest were either refused or missed. Our analysis is based on 10,736 completed interviews, of which about 58.2 percent belong to the 50+ age group.
Through face-to-face interviews, information was collected on the physical characteristics of the dwelling or household; a household roster, including the sex, age, marital status, education, and care needs of each household member; cash and non-cash transfers into and out of the household; household income and expenditure; work history and benefits; health and health behaviours; chronic conditions; health care utilization; social networks; subjective well-being and quality of life; and on the impact of caregiving. The health status of individuals was also assessed with the help of the following biomarkers: anthropometry (weight, height, body mass index, waist-to-hip ratio); physical tests (timed walk, hand grip strength, lung function, vision tests, blood pressure); cognition tests (verbal fluency, immediate and delayed verbal recall, digit span); and blood tests (from consenting respondents, to test for anaemia, diabetes, and cardiovascular disease). A detailed description of this data can be found in the India National Report of the SAGE [36].
2.3. Measures
Self-rated health was defined by the answer to the question “In general, how would you rate your health today?” The options to answer this question were available on a five-point Likert scale: very good, good, moderate, bad and very bad. In our analysis, we converted the responses from a five-point scale to a three-point scale by combining very good and good to “good”, moderate to “moderate” and very bad and bad to “poor”. Self-reported disability in 20 activities of daily living (ADL, defined as having severe or extreme difficulties performing activities of daily living) measures—addressing mobility, self-care, pain and discomfort, cognition, interpersonal activities, sleep and energy, affect and vision were used to measure disability. For example, the first question related to mobility is “Overall in last 30 days, how much difficulty did you have with moving around?” The options to answer this question were available on a five-point Likert scale: none, mild, moderate, severe and extreme or cannot do. If the answer from the respondent “severe” or “extreme or cannot do” in moving around, it is defined as a self-reported disability in moving around ADL. The details of the questions used to define self-reported disability are given in S1 Appendix.
We analyzed gender differentials in self-rated health by a number of socio-economic and demographic variables, including age, gender, education (primary school or less, secondary school completed, tertiary or higher education); wealth quintiles of household, (Q1 lowest to Q5 highest), religion (Hindu, Muslim and Others); ethnicity (Others, Scheduled Caste (SC) and Scheduled Tribe (ST)); and marital status (currently married, never married and separated/divorced/widowed).
2.4. Statistical Methods
To estimate the prevalence of SRH and SRD by gender, we used STATA S.E. 10.0 (STATA Corp., Inc., College Station, TX). We performed chi-square test to assess whether there were significant gender differences in the sample by socio-economic and demographic factors and also in self-reported disabilities. To estimate DFLE and DLE, the Sullivan Method [38] was applied. We used the proportion of disability by age and sex on Sample Registration System (SRS)[39] life tables of India for the period 2006–2010. The SRS, a dual vital registration system in India covering six million of population in India, provides the most reliable estimates of vital rates at national and sub-national levels (state categorized by rural-urban) since the 1970s. A detailed description of SRS data can be found elsewhere [40]. We used multinomial logistic regression to assess the adjusted effect of gender on SRH. The outcome variable has three categories: self-reported good, moderate and poor health. We estimated the relative risk of (1) poor health versus good health and (2) moderate health versus good health. Age was estimated as a continuous independent variable. The gender, education and wealth quintiles of a household, religion, ethnicity and marital status were considered as categorical independent variables. A p-value less than or equal to 0.05 was considered significant. The evidence for multicollinearity was assessed by the multicollinearity diagnostic (variance inflation factor or VIF). All VIFs were less than 2, indicating that the assumption of reasonable independence among predictor variables was met. A total of 113 missing cases (1 case in age, 71 cases in wealth index and 41 cases in ethnicity) were excluded from the multinomial logistic regression analysis.
Results
3.1. Self-rated Health (SRH)
Table 1 presents gender differentials in self-rated health by socio-economic and demographic characteristics. The figures presented in the table are the percentage of men and women reporting self-rated health, categorized into good, moderate and poor health. In general, 15.1% of Indian women rated their health as “poor” compared to15.2% of Indian men. Most findings in this table are in the expected direction. For example, as age increases, the percentage of people reporting poor health increases (men: 3.8% to 38.4%; women: 4.7% to 44.6%); urbanites enjoy better health than rural people (45.4% urban men report good health against37.7% rural men; 40.7% urban women report good health against 38.0% rural women); as education increases, the percent of people reporting good health increases (Men: 28.4% to 56.9%; Women: 30.8% to 61.3%), as household wealth index increases from poorest to least poorest quintile, self-rated health improves (Men:32.6% to 47.8%; Women:34.5% to 45.0%). Finally, people belonging to deprived social groups like Muslims, STs and SCs reported poor self-rated health. Table 1 also presents the p-value of chi-square test examining gender difference in socio-economic and demographic variables. Except for a few predictors (wealth index, religion and ethnicity), p-values were always significant, indicating a clear existence of gender difference in the predictor variables.
The most noticeable finding in Table 1 is the presence of clear gender differences in self-rated health by socio-economic and demographic characteristics. Women from each age group report systematically poorer self-rated health than men. Interestingly, as age increases, the gender difference in self-rated health also increases. While a higher percentage of urban men than urban women rate their health as “good”, there is no difference in self-rated health between rural men and women. Likewise, except those in the deprived categories (say, belonging to rural, low educated, household with poor wealth quintile and scheduled tribes) all women rated their health worse than men.
3.2. Self-rated disabilities in activities of daily living (ADL)
Table 2 reports the prevalence estimates of SRD in 20 ADL measures by gender in India for 2007–2008. The most striking finding is that out of twenty ADL measures addressing mobility, self-care, pain/discomfort, cognition, interpersonal activities, sleep/ energy, affect and vision, women reported systematically higher prevalence of severe and extreme disability than men in fourteen measures. Among all these measures, the gender difference in self-rated disabilities is more pronounced in three components: pain and discomfort, interpersonal relationship and affects. The p-values of chi-square test examining gender difference in self-reported disabilities again indicate a clear existence of gender difference in performance of activities of daily living.
Table 3 and Fig 1 further present prevalence estimates (in percentage) in at least one, three and five self-reported disabilities by gender and age groups in India for 2007–2008. Table 3 and Fig 1 clearly suggest that a higher proportion of women suffer from disabilities in ADL in all adult age groups. However, the gender differential was the highest in the age group less than 50 and 50–59, with the percent of women reporting more than five disabilities being two times more than their male counterparts (Male: 3.9% against 8.1% female in age group <50; Male: 8.7% against 20.0% female in age group 50–59) etc.
Table 4 demonstrates total life expectancy (TLE), DFLE, DLE and percent of life with disability (DLE/TLE) by age and gender, calculated for at least one, three and five disabilities out of twenty disabilities as shown in Table 2. After age20, women are expected to live longer than men at each age. On average, women live two years longer than men, and the gender gap (women versus men) reduces from 4.0 years at age 20 to 0.6 years at age 80. However, women are also expected to live more years with disability, as DLE for women for any number of disabilities is higher than DLE for men at each age. Interestingly, the gender disparity in DLE is far more pronounced than the gender disparity in total life expectancy and varies greatly by age. For example, the gender difference in DLE calculated for at least one self-disability is a high as 9.8 years at age20; it drops to 5.2 years at age 50 and to 3.2 years at age 60. The DLE calculated for at least one self-rated disability shows that both Indian men and women spend a considerable proportion of their life with at least one disability; for example, at age 50, men and women are expected to live 57.3% and 70.5% of their life with at least one type of disability respectively. Once again, women spend a higher proportion of their life with poorer health status.
The magnitude of DLE and percentage of life with disability reduced to almost half and one-fourth when we calculated the same for at least three and five self-rated disability respectively. Nevertheless, we observed similar kind of gender disparity in TLE, DFLE, DLE and percent of life with a disability.
Table 5 presents the results of the multinomial logistic regression. The majority of the associations found in Table 5 are consistent with the findings of Table 2. The relative risk ratio (RRR) of having poor health versus good health increases as age increase (RRR: 1.90; CI: 1.81–2.00); decreases as wealth of the household increases; is higher among Muslims (RRR: 2.04; CI: 1.69–2.47); is lower among the never married (RRR:0.64; CI:0. 41–0.99);and is higher among the separated or divorced (RRR:1.30; CI: 1.09–1.55). All these relationships are statistically significant. Similar results are found in the RRRs of having moderate health versus good health, as shown in Table 5. The most noticeable point in Table 5 is that females have significantly higher relative risk than men of having poor health versus good health (RRR: 1.69; CI: 1.46–1.97), and of having moderate health versus good health (RRR: 1.39; CI: 1.25–1.55). Thus, even after controlling for relevant socio-economic variables, women are more likely than men to report poor or moderate health.
Discussion and Conclusion
Our first objective was to contribute to the debate over the gender difference in SRH in India. We found that, in general, Indian adult women report poorer health than Indian adult men. This finding is consistent with the findings of studies conducted in other countries [41– 43] and in India [12,19,21,25, 44–46]. This study found that the gender difference in SRH persisted even after adjusting the effect of the socio-economic condition. This is contrary to the findings of a previous study [19]. The finding of this study, however, is consistent with the previous findings [20–21] that health disadvantages among women could not be explained by the difference in demographics and socio-economic characteristics.
There are a few explanations why women live longer than men but have poorer health. Several studies exhibit that this is due to biological, social and behavioral factors [47–50]. Researchers have suggested that two factors in particular contribute the excess mortality of men. First, women are more likely than men to adopt preventative health behaviors, such as routine annual visits to a physician for a check-up. Men are more likely to engage in risky behaviours, such as excessive drinking, drunken driving, illegal drug use, physical fights and violence and high tobacco consumption [48,51–55]. Early in life, boys are more susceptible to death than girls due to biological reasons [56–57]. In adolescence, youth and adulthood, men die from exposure to risks linked to their social and behavioural characteristics [48]. Studies argued that poorer health among women is due to biological as well as behavioural factors. Some studies discuss that while women suffer more than men, female ailments tend to be less lethal biologically [7,58]. Some other studies refer to the over-reporting of worse health among women [59–60]. In addition, women’s longer life expectancy also influences male-female differences in health status [3]. It is showed that larger the female excess in longevity, larger the female excess in the proportion of life in poor health [3].
Our second objective was to extend the analysis to SRD and its connotation with life expectancy at various stages of adulthood. All three measures of SRD (prevalence estimates of SRD in more than one, three and five ADL measures, corresponding DLEs and proportion of life spent with disability at various stages of life) revealed that Indian women are subject to higher levels of disability irrespective of their age. Notably, the gender gap in disability is found to be the highest in difficulty in more than five ADLs in the age groups before age 60 (women reported two times more than men). Our findings are consistent with the common wisdom that the gender difference in disability is substantial, especially in ADL measures [61– 64]. Future research should focus more on the unmet need of personal care among disabled adults whether gender plays an important role in caregiving to differential exists in proving care and treatment.
In the past several decades, life expectancy has increased in India; there are now over 192 million people aged 50 or older [39]. As a consequence of the rapid ageing of the population, the central theme of health research in India has expanded beyond longevity towards understanding the burden of disease and disability, particularly among adults. Longevity is greater among females than males, especially at adult ages, in India—as in all other countries. Does the survival advantage of females translate to their poorer health? Answering this question is of enormous importance for future health care needs and policy. A few studies address the gender differential in health and disability among older adults in India [20–21,24–25,65–68], but this study extends the knowledge by examining SRH and SRD together. By using nationally representative survey data, this study first investigated the role of gender in self-rated health among adults in India after controlling for socio-economic and demographic characteristics, and examined gender differential in SRD in performing twenty ADLs (severe and extremely difficulty) and thereafter revealed the linkage between survival and SRD measured by TLE, DFLE, DLE and percent of life spent with self-reported disability.
This study suggests that in India, as in other parts of the world, women bear the greater burden of disability in their adult age. In the absence of governmental support, the problem might be acute, especially among marginalized women living alone, widowed and poorly off socio-economically. The findings of this study strongly suggest the need for a coherent and gender sensitive health agenda for ageing populations in India. We also suggest future in-depth studies focused on the complex gender dynamics in health and disability in India at a regional level.
Limitations
The present study has a few limitations. First, we could not throw light on the regional dimensions of gender differentials in SRH and SRD because the sample size at the regional level is inadequate. Previous studies have documented the regional divide in gender differentials in health outcomes in India [44,69]. Neither could we test the relationship between female excess longevity and female excess in the proportion of life in poor health using time trend data. Second, we could not analyze the trend of gender differentials in DLE in India due to lack of such data. In the absence of it, we again could not test whether increasing SRD by women is a function of increasing survivorship or that women naturally report poorer health even in the absence of acute gender differential in survivorship.
Supporting Information
S1 Appendix. List of the questions used to define self-reported disability.
https://doi.org/10.1371/journal.pone.0141953.s001
(DOCX)
Acknowledgments
This study was conceived when Dr Nandita Saikia was a Max Planck India fellow at Max Planck Institute for Demographic Research (MPIDR), Germany. Dr Saikia is grateful to Max Planck Society for providing fellowship during her stay at MPIDR, Germany. Both authors are grateful to Dr Nadia Diamond-Smith, UCSF for the editorial assistance.
Author Contributions
Conceived and designed the experiments: JKB NS. Performed the experiments: JKB. Analyzed the data: JKB. Contributed reagents/materials/analysis tools: JKB NS. Wrote the paper: JKB NS.
References
- 1. Oksuzyan A, Petersen I, Stovring H, Bingley P, Vaupel JW, Christensen K (2009) The male-female health-survival paradox: a survey and register study of the impact of sex-specific selection and information bias. Annals of Epidemiology 19: 504–11. pmid:19457685
- 2. Austad S N (2006) Why women live longer than men: sex differences in longevity. Gender medicine 3(2): 79–92. pmid:16860268
- 3. Luy M, Minagwa Y (2014) Gender gaps-Life expectancy and proportion of life in poor health. Health Reports 25(12): 12–19. pmid:25517936
- 4. Verbrugge LM (1985) Gender and health: an update on hypotheses and evidence. Journal of health and social behavior 156–182. pmid:3905939
- 5. Verbrugge LM (1989) The twain meet: empirical explanations of sex differences in health and mortality. Journal of Health Social Behavior 30: 282–304. pmid:2778300
- 6. Barford A, Dorling D, Smith GD, Shaw M (2006) Life expectancy: women now on top everywhere. British Medical Journal 332: 808. pmid:16601021
- 7. Case A, Paxson C (2005) Sex differences in morbidity and mortality. Demography 42(2): 189–214. pmid:15986983
- 8. Al Snih S, Fisher MN, Raji MA, Markides KS, Ostir GV, Goodwin JS (2005) Diabetes mellitus and incidence of lower body disability among older Mexican Americans. Journals of Gerontology Series A: Biological Sciences and Medical Sciences 60: 1152–1156.
- 9. Oman D, Reed D, Ferrara A (1999) Do elderly women have more physical disability than men do? American Journal of Epidemiology 150: 834–842. pmid:10522654
- 10. Andrade FCD, Guevara PE, Lebrão ML, Duarte YAO, Santos JLF (2011) Gender differences in life expectancy and disability-free life expectancy among older adults in São Paulo Brazil. Women’s Health Issues 21(1): 64–70. pmid:21185991
- 11. Mohammad MA, Tareque I, Rahman KM (2013) Determinants of living arrangements, health status and abuse among elderly women: A study of rural Naogaon district, Bangladesh. Journal of International Women's Studies 11(4): 162–176.
- 12. Singh L, Arokiasamy P, Singh PK, Rai RK (2013) Determinants of Gender Differences in Self-Rated Health Among Older Population Evidence From India. SAGE Open, 3(2), 2158244013487914.
- 13. Murthi M, Guio A, Dreèze J (1995) Mortality, Fertility, and Gender Bias in India: A District-Level Analysis. Population and Development Review 21(4): 745–782.
- 14. Kishor S (1993) May God Give Sons to All: Gender and Child Mortality in India. American Sociological Review 58(2): 247–265.
- 15. Singh A, Hazra A, Ram F (2007) Women's autonomy and sex differential in child mortality in India. Genus 55–75.
- 16. Arokiasamy P (2004) Regional patterns of sex bias and excess female child mortality in India. Population E(59): 833–864.
- 17.
Annual Health Survey Fact Sheet 2010–11. Available: http://www.censusindia.gov.in/vital_statistics/ AHSBulletins/Factsheets.html
- 18. Alkema L, Chao F, You D, Pedersen J, Sawyer CC (2014) National, regional and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. The Lancet Global Health 2(9): e521–e530. pmid:25304419
- 19. Pandey A, Ladusingh L (2013) Socioeconomic correlates of gender differential in poor health status among older adults in India. Journal of Applied Gerontology, 0733464813481850.
- 20.
Dhak B, Mutharayappa R. Gender differential in disease burden: Its role to explain gender differential in mortality. Institute for Social and Economic Change; 2009.
- 21. Roy K, Chaudhuri A (2008) Influence of socioeconomic status, wealth and financial empowerment on gender differences in health and healthcare utilization in later life: evidence from India. Social Science & Medicine 66: 1951–1962.
- 22. Chakrabarty D, Mandal PK, Manna N, Mallik S, Ghosh P, Chatterjee C, et al. (2010) Functional Disability and Associated Chronic Conditions among Geriatric Populations in a Rural Community of India. Ghana Medical Journal. 44(4): 150–154. pmid:21416049
- 23. Gupta PMani K, Rai SK, Nongkynrih B, Gupta SK (2014) Functional disability among elderly persons in a rural area of Haryana. Indian journal of public health 58(1): 11–16. pmid:24748351
- 24. Sengupta M, Agree EM (2002) Gender and disability among older adults in North and South India: differences associated with coresidence and marriage. Journal of cross-cultural gerontology 17(4): 313–336. pmid:14617962
- 25. Dhak B (2009) Gender difference in health and its determinants in the old-aged population in India. Journal of biosocial science 41(5): 625–643. pmid:19563694
- 26. Hirve S, Juvekar S, Sambhudas S, Lele P, Blomstedt Y, Wall S, et al. (2012) Does self-rated health predict death in adults aged 50 years and above in India? Evidence from a rural population under health and demographic surveillance. Int J Epidemiol 41(6): 1719–1727. pmid:23175517
- 27. Lundberg O, Manderbacka K (1996) Assessing reliability of a measure of self-rated health. Scandinavian Journal of Public Health 24(3): 218–224.
- 28. Fayers PM, Sprangers MA (2002) Understanding self-rated health. The Lancet 359 (9302): 187–188.
- 29. Idler EL, Benyamini Y (1997) Self-rated health and mortality: a review of twenty-seven community studies. Journal of health and social behavior: 21–37. pmid:9097506
- 30. Blazer DG (2008) How do you feel about…? Health outcomes in late life and self-perceptions of health and well-being. The Gerontologist 48(4): 415–422. pmid:18728291
- 31. Jylhä M (2009) What is self-rated health and why does it predict mortality? Towards a unified conceptual model. Social science & medicine 69(3): 307–316.
- 32. Merrill SS, Seeman TE, Kasl VS, Berkman LF (1997) Gender differences in the comparison of self-reported disability and performance measures. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 52(1): M19–M26.
- 33. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. (1994) A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. Journal of gerontology 49(2): M85–M94. pmid:8126356
- 34. Zunzunegui MV, Alvarado BE, Beland F, Vissandjee B (2009) Explaining health differences between men and women in later life: A cross-city comparison in Latin America and the Caribbean. Social Science & Medicine 68(2): 235–242.
- 35. McCallum J, Shadbolt B, Wang D (1994) Self-rated health and survival: A seven year follow-up study of Australian elderly. American Journal of Public Health 84(7): 1100–1105. pmid:8017532
- 36.
Arokiasamy P, Parasuraman S, Sekher TV, Lhungdim H. Study on global AGEing and adult health (SAGE) Wave 1, India National Report, International Institute for Population Sciences. Geneva: World Health Organization. Sep 2013. Available: http://www.who.int/healthinfo/sage/national_reports/en/.
- 37. Kowal P, Chatterji S, Naidoo N, Biritwum R, Fan W, Ridaura RL, et al. (2012) Data resource profile: the World Health Organization Study on global AGEing and adult health (SAGE). International journal of epidemiology 41(6): 1639–1649. pmid:23283715
- 38. Sullivan DF (1971) A single index of mortality and morbidity. HSMHA health reports 86(4): 347–354. pmid:5554262
- 39.
Registrar General of India (RGI) Sample registration system statistical reports 2006–2010 (2012) Office of the Registrar General, New Delhi.
- 40. Saikia N, Jasilionis D, Ram F, Shkolnikov VM (2011) Trends and geographic differentials in mortality under age 60 in India. Population Studies 65(1):73–89. pmid:21240833
- 41. Vlassoff C (2007) Gender differences in determinants and consequences of health and illness. Journal of health, population, and nutrition 25(1): 47. pmid:17615903
- 42. Andrade FCD, Guevara PE, Lebrao ML, de Oliveira Duarte YA, Santos JLF (2011) Gender differences in life expectancy and disability-free life expectancy among older adults in Sao Paulo, Brazil. Women's Health Issues 21(1): 64–70. pmid:21185991
- 43. Nummela O, Sulander T, Karisto A, Uutela A (2009) Self-rated health and social capital among aging people across the urban—rural dimension. International journal of behavioral medicine 16(2): 189–194. pmid:19225894
- 44. Basu AM (1993) Women's roles and gender gap in health and survival. Economic and Political Weekly: 2356–2362.
- 45.
Mencher J. Women’s work and poverty: Women’s contribution to household maintenance in South India. A home divided: Women and income in the Third World. Stanford University Press; 1988.
- 46. Moser KA, Agrawal S, Smith GD, Ebrahim S (2014) Socio-demographic inequalities in the prevalence, diagnosis and management of hypertension in India: analysis of nationally representative survey data. PloS one 9(1): e86043. pmid:24465859
- 47. Nathanson CA (1984) Sex differences in mortality. Annual review of sociology: 191–213. pmid:12339750
- 48. Rogers RG, Everett BG, Onge JMS, Krueger PM (2010) Social, behavioral, and biological factors, and sex differences in mortality. Demography 47(3): 555–578. pmid:20879677
- 49. Friedman HS, Tucker JS, Schwartz JE, Tomlinson-Keasey C, Martin LR, Wingard DL, et al. (1995) Psychosocial and behavioral predictors of longevity: The aging and death of the “termites”. American Psychologist 50(2): 69–78. pmid:7879989
- 50. Waldron I (1976) Why do women live longer than men? Social Science & Medicine 10(7): 349–362.
- 51. Kalben BB (2000) Why men die younger: causes of mortality differences by sex. North American Actuarial Journal 4(4): 83–111.
- 52. Pampel FC (2005) Patterns of tobacco use in the early epidemic stages: Malawi and Zambia, 2000–2002. American journal of public health 95(6): 1009. pmid:15914826
- 53. Rieker PP, Bird CE (2005) Rethinking gender differences in health: why we need to integrate social and biological perspectives. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences 60(2): S40–S47.
- 54. Waldron I, McCloskey C, Earle I (2005) Trends in gender differences in accidents mortality. Demographic Research 13(17): 415–454.
- 55. Rosenbloom T, Beigel A, Eldror E (2011) Attitudes, behavioral intentions, and risk perceptions of fatigued pedestrians. Social Behavior and Personality: an international journal 39(9): 1263–1270.
- 56. Drevenstedt GL, Crimmins EM, Vasunilashorn S, Finch CE (2008) The rise and fall of excess male infant mortality. Proceedings of the National Academy of Sciences of the United States of America; 105(13): 5016–5021. pmid:18362357
- 57.
Waldron I (1998) Sex differences in infant and early childhood mortality: Major causes of death ad possible biological causes. In: Nations, United (ed.). Too young to die: Genes or gender. New York, NY: United Nations: 64–83.
- 58. Crimmins EM, Kim JK, Sole-Auro A (2010) Gender differences in health: results from SHARE, ELSA and HRS. The European Journal of Public Health, ckq022.
- 59. Verbrugge LM, Wingard DL, Features Submission HC (1987) Sex differentials in health and mortality. Women & Health 12(2): 103–145.
- 60. Kroenke K, Spitzer RL (1998) Gender differences in the reporting of physical and somatoform symptoms. Psychosomatic Medicine 60(2): 150–155. pmid:9560862
- 61. Sara A, Cooper H (1999) Gender differences in health in later life: the new paradox? Social science & medicine 48(1): 61–76.
- 62. Murtagh KN, Hubert HB (2004) Gender differences in physical disability among an elderly cohort. American journal of public health 94(8): 1406–1411. pmid:15284051
- 63. Wray LA, Blaum CS (2001) Explaining the Role of Sex on Disability A Population-Based Study. The Gerontologist 41(4): 499–510. pmid:11490048
- 64. Kandrack MA, Karen R, Grant AS (1991) Gender differences in health related behaviour: some unanswered questions. Social science & medicine 32(5): 579–590.
- 65. Agrawal G, Arokiasamy P (2010) Morbidity prevalence and health care utilization among older adults in India. Journal of Applied Gerontology 29: 155–179.
- 66. Benson TM, James KS, Sulaja S (2014) Does living longer means living healthier? Exploring Disability Free Life Expectancy in India. Indian Journal of Gerontology 28 (3): 321–347.
- 67. Chen B, Mahal A (2010) Measuring the health of the Indian elderly: evidence from National Sample Survey data. Population Health Metrics 8: 30–42. pmid:21080940
- 68. Srinivasan K, Vaz M, Thomas T (2010) Prevalence of health related disability among community dwelling urban elderly from middle socioeconomic strata in Bangaluru, India. Indian Journal of Medical Research 31(4): 515–521.
- 69. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray C (2006) Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. The Lancet 367(9524): 1747–1757.