Introduction
India, the second most populous country in the world, is undergoing rapid demographic and epidemiological transition (Chatterji et al., Reference Chatterji, Kowal, Mathers, Naidoo, Verdes, Smith and Suzman2008). As a result of a secular decline in fertility and rise in life expectancy, the global phenomenon of population ageing is accelerating in the country. The proportion of the elderly population (aged 60 years or over) in India increased from 6.8% in 1991 to 8.6% in 2011 (Census of India, 2011) and is projected to increase to 19.4% in 2050 (United Nations, 2015).
In India, the growing older population is facing several challenges, including almost negligible formal social and economic support, with most care being provided informally by family members (Ugargol et al., Reference Ugargol, Hutter, James and Bailey2016; Ugargol & Bailey, Reference Ugargol and Bailey2018; Bhan et al., Reference Bhan, Rao and Raj2020). Furthermore, the growing older population is experiencing a changing morbidity profile resulting in a significant health burden and disability and policy challenges (Chatterji et al., Reference Chatterji, Kowal, Mathers, Naidoo, Verdes, Smith and Suzman2008; Agarwal et al., Reference Agarwal, Lubet, Mitgang, Mohanty, Bloom, Poot and Roskruge2020). The rising prevalence of chronic conditions has significant implications for the general health and overall quality of life of a population (Arokiasamy et al., Reference Arokiasamy, Uttamacharya, Jain, Biritwum, Yawson and Wu2015). Also, the majority of older adults in India are poor underweight, poor and poorly educated (Ingle & Nath, Reference Ingle and Nath2008; Srivastava & Mohanty, Reference Srivastava and Mohanty2012; Selvamani & Singh, Reference Selvamani and Singh2018), and this is reflected in their poor physical strength, quality of life and cognitive ability (Lee et al., Reference Lee, Shih, Feeney and Langa2014; Arokiasamy & Selvamani, Reference Arokiasamy and Selvamani2018).
A high quality of life is important for healthy ageing. The life-course framework adopted by the World Health Organization (WHO) highlights that individual well-being across the different stages of life is an important factor in attaining the Sustainable Development Goals (Kuruvilla et al., Reference Kuruvilla, Sadana, Montesinos, Beard, Vasdeki and de Carvalho2018). The WHO defines quality of life (QoL) as ‘the individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals’ (WHO-QoL Group, 1993). This covers several dimensions, including physical and mental health, independence, autonomy and the environment. Several indicators have been adapted to understand the quality of life of a population, including a ‘happiness’ or’ life satisfaction’ indicator (Banjare et al., Reference Banjare, Dwivedi and Pradhan2015; Ng et al., Reference Ng, Tey and Asadullah2017). Others have used summary scores of measurement scales such as the WHO Quality of Life scale (WHO-QoL) and the Control, Autonomy, Self-realization and Pleasure (CASP) measure (Howel, Reference Howel2012; Arokiasamy et al., Reference Arokiasamy, Uttamacharya, Jain, Biritwum, Yawson and Wu2015), and others used life satisfaction (Chen, Reference Chen2001) and happiness indices (Moeini et al., Reference Moeini, Barati, Farhadian and Ara2018).
Several studies have identified the major determinants of quality of life in older adults as chronic diseases, physical capacity, frailty, physical activity, nutritional status, back pain, poor mental health, social participation, socioeconomic status, loss of spouse and living arrangements (Chen, Reference Chen2001; Öztürk et al., Reference Öztürk, Şimşek, Yümin, Sertel and Yümin2011; Layte et al., Reference Layte, Sexton and Savva2013; Arokiasamy et al., Reference Arokiasamy, Uttamacharya, Jain, Biritwum, Yawson and Wu2015; Kojima et al., Reference Kojima, Iliffe, Jivraj and Walters2016; Raggi et al., Reference Raggi, Corso, Minicuci, Quintas, Sattin and De Torres2016; Selvamani & Singh, Reference Selvamani and Singh2018). Also, a growing body of literature suggests the significant role of early-life conditions in determining health and well-being in later life, with childhood health and nutrition playing an important role in determining well-being across the life course. Better childhood socioeconomic status is significantly associated with better health (Cheval et al., Reference Cheval, Boisgontier, Orsholits, Sieber, Guessous and Gabriel2018) and cognitive ability (Aartsen et al., Reference Aartsen, Cheval, Sieber, Van der Linden, Gabriel and Courvoisier2019; Greenfield & Moorman, Reference Greenfield and Moorman2019) and slow speed of ageing (Steptoe & Zaninotto, Reference Steptoe and Zaninotto2020).
One marker of childhood health and nutrition is adult height. The height advantage on various health outcomes in later life originates from childhood environmental conditions (Batty et al., Reference Batty, Shipley, Gunnell, Huxley, Kivimaki and Woodward2009; Bozzoli et al., Reference Bozzoli, Deaton and Quintana-Domeque2009; Case & Paxson, Reference Case and Paxson2010; Guven & Lee, Reference Guven and Lee2015). The height of an individual is determined by both genetics and net nutrition in early childhood (Silventoinen, Reference Silventoinen2003; Currie & Vogl, Reference Currie and Vogl2013; Perkins et al., Reference Perkins, Subramanian, Davey Smith and Özaltin2016b). Better nutritional status in childhood is associated with improved height (Victora et al., Reference Victora, Adair, Fall, Hallal, Martorell and Richter2008; Case & Paxson Reference Case and Paxson2010). Being tall in childhood is linked to several positive outcomes, such as school achievement, sporting ability and social skills (Cinnirella et al., Reference Cinnirella, Piopiunik and Winter2011).
Height has been shown to be associated with happiness (Carrieri & De Paola, Reference Carrieri and De Paola2012; Sohn, Reference Sohn2016), life satisfaction (Denny, Reference Denny2017), health and longevity (Peck & Vågerö, Reference Peck and Vågerö1989; Silventoinen et al., Reference Silventoinen, Lahelma and Rahkonen1999; Fujiwara et al., Reference Fujiwara, Kondo, Shirai, Suzuki and Kawachi2014; McGovern, Reference McGovern2014; Ihira et al., Reference Ihira, Sawada, Iwasaki, Yamaji, Goto and Noda2018), attainment of higher level of schooling, higher positions such as professionals and managers and earnings (Strauss & Thomas, Reference Strauss and Thomas1998; Case & Paxson, Reference Case and Paxson2008b, Reference Case and Paxson2010; Vogl, Reference Vogl2014; LaFave & Thomas, Reference LaFave and Thomas2017; Patel & Devaraj, Reference Patel and Devaraj2018; Murasko, Reference Murasko2019, Reference Murasko2020; Ibragimova & Salahodjaev, Reference Ibragimova and Salahodjaev2020) and improved well-being (Deaton & Arora, Reference Deaton and Arora2009). It is also reflected in better psychological health, with taller individuals having better mental health (Rees et al., Reference Rees, Sabia and Argys2009; Lee & Zhao, Reference Lee and Zhao2017) and cognitive ability (Case & Paxson, Reference Case and Paxson2008a; Guven & Lee, Reference Guven and Lee2013, Reference Guven and Lee2015; Kobayashi et al., Reference Kobayashi, Berkman, Wagner, Kahn, Tollman and Subramanian2019).
Better physical strength and cognitive ability are additional advantages of height (Case & Paxson, Reference Case and Paxson2008a; Guven & Lee, Reference Guven and Lee2013, Reference Guven and Lee2015; McGovern, Reference McGovern2014), and these improve well-being, health and survival (Gottfredson & Deary, Reference Gottfredson and Deary2004; McGuire et al., Reference McGuire, Ford and Ajani2006; Gale et al., Reference Gale, Martyn, Cooper and Sayer2007; Newman et al., Reference Newman, Kupelian, Visser, Simonsick, Goodpaster and Kritchevsky2006; Leong et al., Reference Leong, Teo, Rangarajan, Lopez-Jaramillo, Avezum and Orlandini2015; Batty et al., Reference Batty, Deary and Zaninotto2016), particularly in developing countries (Lundborg et al., Reference Lundborg, Nystedt and Rooth2009).
In India, very few studies have examined the association between height and health-related outcomes. A multi-country study based on the six middle-income countries of India, China, Ghana, Mexico, Russia and South Africa showed a significant association between height and handgrip strength and general health measures such as self-rated health and functional health (McGovern, Reference McGovern2014). However, no previous study has examined the association between height and quality of life or subjective well-being among the older population in India. Subjective well-being is an important component of healthy ageing. An understanding of the association between childhood circumstances and later-life subjective well-being is important for improving the health and well-being of the older population in India. The effect of early-life conditions on later-life outcomes is stronger in developing countries as childhood shocks are larger (Currie & Vogl, Reference Currie and Vogl2013; Perkins et al., Reference Perkins, Subramanian, Davey Smith and Özaltin2016b). However, very few studies have examined this (McEniry, Reference McEniry2013), and those that have focused on younger adults. The aim of the present study was therefore to assess the relationship between height and two measures of quality of life among older adults in India. It is hypothesized that a significant and positive association exists between height as a proxy measure of childhood health and nutrition and quality of life among older adults in India.
Methods
Participants and procedure
The study used data from the WHO Study on global AGEing and adult health (SAGE). This cross-country representative household survey of adults age 18–49 and older adults age 50 and above was conducted in six middle-income countries in 2007–10: China, Ghana, India, Mexico, the Russian Federation and South Africa. The survey interviewed 42,236 individuals aged 18 and above. The present analysis focused on 6560 older adult men (n=3304) and women (n=3256) aged 50 years and above in India.
The SAGE survey instruments are comparable to those of the Survey of Health, Ageing and Retirement in Europe (SHARE), the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA) surveys conducted in developed countries. Face-to-face interviews were conducted to obtain data on respondents’ socio-demographic characteristics, work history, health behaviours, health risk factors, self-reported and symptomatic assessment of chronic conditions, subjective health, quality of life, cognitive ability, disabilities and health care utilization. Also, objective measures of health and anthropometric measures, including height, weight, handgrip strength, lung function, hypertension, waist and hip circumferences, timed walk and a vision test were made. A detailed description and documentation of data are described elsewhere (Kowal et al., Reference Kowal, Chatterji, Naidoo, Biritwum, Fan and Lopez Ridaura2012), and data can be freely downloaded from the WHO website (http://apps.who.int/healthinfo/systems/surveydata/index.php/catalog/sage).
Outcome variables
The study had two quality-of-life outcome variables: the WHO Quality of Life (WHO-QoL) score and ‘self-rated poor quality of life’.
The WHO-QoL score was based on two SAGE survey questions in each of four broad domains (eight items in total): physical, psychological, social and environmental (Schmidt et al., Reference Schmidt, Mühlan and Power2006). A five-point scale was used to capture the response, ranging from ‘very satisfied’ to ‘very dissatisfied’. A Quality of Life Index (WHO-QoL) was generated by combining the four different domain scores. The domain composite scores and the WHO-QoL ranged from 0 to 100, with a higher score meaning a higher quality of life.
The ‘self-rated quality of life’ variable was assessed through a single question: ‘How would you rate your overall quality of life?’ The response categories were: 1) very good, 2) good, 3) moderate, 4) bad and 5) very bad. These were re-coded into the two categories ‘good’ (combining very good, good and moderate) and ‘poor’ (combining bad and very bad).
Predictor variable
The predictor variable was height. In the SAGE survey, this was measured by trained investigators in centimetres using a stadiometer. In the present study, heights were put into quintiles (lowest to highest) to examine the association between height and quality-of-life outcomes.
Covariates
Handgrip strength
‘Handgrip strength’ was measured in the SAGE survey using a Smedley Hand Dynamometer (Scandidact Aps, Denmark). With their upper arm against their body and elbow bent to 90 degrees and the palm facing inwards (like shaking hands), participants were asked to squeeze the dynamometer as hard as possible for a several seconds. Four observations were made and the best one used as the ‘handgrip strength’ score. Sex-specific handgrip quintiles (lowest, second, third, fourth and highest) were generated to examine the association between handgrip strength and quality-of-life outcomes.
Cognitive ability
A composite ‘cognitive functioning index’ was generated by combining four domains of cognition: verbal fluency, verbal recall, digit span forward and digit span backward, and used to create a ‘cognition variable’.
To assess ‘verbal recall’ the interviewer read out a list of ten commonly used words and asked the respondent to repeat them in a certain time. For ‘digit span (forward and backward)’, participants were read a series of digits and asked to repeat them immediately. In the backward test, the person had to repeat the numbers in reverse order. These tests measure concentration, attention and immediate memory.
‘Verbal fluency’ was assessed by asking participants to name as many animals as possible in a one-minute time span. This test assesses the retrieval of information from the semantic memory.
A composite cognitive ability index was derived using Principal Components Analysis (PCA) – a mathematical tool that helps create a composite index using uncorrelated components, where each component captures the largest possible variation in the original variables (Vyas & Kumaranayake, Reference Vyas and Kumaranayake2006). Selected raw scores for cognitive tasks were bundled into three domains (digit span, memory and executive functioning) to yield compound cognitive scores. This was done to condense the number of cognitive variables while refining the robustness of the underlying cognitive construct (Lezak et al., Reference Lezak, Howieson, Loring and Fischer2004). Two steps were used to make the composite cognitive ability index. As the four variables were on different scales, in Step 1 a standardized variable was created (z-score or standard score) by re-scaling each variable to have a mean of zero and a standard deviation of one. Each case’s value on the standardized variable designates its difference from the mean of the primary variable in some standard deviations (of the original variable). In Step 2, PCA was applied. The resulting index had both positive and negative values; the scores were converted into an index ranging from 0 to 100 for ease of interpretation. Higher scores indicate better cognitive ability. The ‘cognitive ability’ variable was divided into five quintiles (lowest, second, third, fourth and highest).
Body mass index
Body mass index (BMI, kg/m2) was categorized as underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9) and obese (≤30.0).
Self-reported chronic conditions
Selected chronic diseases and a edentulism variable were included as risk factors of quality of life (Emami et al., Reference Emami, de Souza, Kabawat and Feine2013; Arokiasamy et al., Reference Arokiasamy, Uttamacharya and Chatterji2016). The SAGE survey included information on the self-reported prevalence of chronic diseases, collected with the question ‘Have you ever been diagnosed with [name of the chronic disease]? Five chronic conditions were included in the analysis; high blood pressure, diabetes, angina, arthritis and asthma. The prevalence of edentulism was assessed in the survey with the question ‘Have you lost all of your natural teeth? Those who reported ‘yes’ were considered as edentulous.
Poor self-rated health
Self-rated health was assessed through a single question ‘In general, how would you rate your health today?’. The response categories were: 1. very good, 2. good, 3. moderate, 4. bad and 5. very bad. These were re-coded into two categories ‘very good’,’ good’ and ‘moderate’ as ‘good self-rated health’ and ‘bad’ and ‘very bad’ into ‘poor self-rated health’.
Socio-demographic characteristics
The socio-demographic covariates included in the analysis were age (50–59, 60–69 and 70+), place of residence (urban and rural), marital status (currently married and not currently married), years of education (0–5, 6–9 and 10+ years) and wealth quintile (poorest, poorer, middle, richer and richest).
Statistical analyses
Bivariate analysis was conducted to understand the sample distribution. Weighted mean quality of life and prevalence of self-rated poor quality of life by height quintile were estimated. Sex-stratified bivariate and multivariate linear regression models were used to assess the association between height and WHO-QoL. A logistic regression model was used to examine the relationship between height and self-rated poor quality of life. Five different models were estimated to better understand the role of socioeconomic status and handgrip strength in mediating the relationship between height and quality of life outcomes. Model 1 only included the height variable, and in a step-wise manner, Model 2 included demographic characteristics, Model 3 included socioeconomic status and Model 4 included selected chronic diseases, BMI and poor self-rated health. In Model 5, handgrip strength and cognitive ability were included along with those in Model 4. Multivariate linear regression models were used to understand the predictors of height. Multivariate linear and logistic regression analyses were used to understand the association of height with WHO-QoL and self-rated poor quality of life by age group. All statistical analyses were implemented using STATA 15.0 (Stata Corp LP).
Results
The characteristics of the study population are shown in Table 1. The mean height of the study population was 156.7 cm, the mean WHO-QoL score was 49.2 and the mean cognitive ability score and handgrip strength scores were 38.1 and 23.4, respectively. Around 11% of participants reported having a poor quality of life. A larger proportion of the sample was in the age group of 50–59. More than half of older adults had no schooling. The overall prevalence of hypertension and arthritis was higher than that of other chronic health conditions. The prevalence of poor self-rated health was 22.0%. The mean values of height, handgrip strength, cognitive ability and WHO-QoL score were lower in women than in men. The prevalence of self-rated poor quality of life was higher among women. A larger proportion of men (91.4%) were currently married than women (61.9%). A higher proportion of men (27%) than women (4%) had completed 10+ years of schooling. The prevalence of underweight was higher among men. A higher proportion of women reported poor self-rated health, edentulism, hypertension and arthritis than men.
Characteristics of the study population (weighted), India, WHO-SAGE Wave 1, 2007 a
a Values in percentages unless indicated otherwise.
Figure 1 shows the WHO-QoL score by height quintile for the total sample by sex. The mean WHO-QoL score increased from 45 among older adults in the lowest height quintile to 53 among those in the highest height quintile. Men (47.1) and women (43.3) in the lowest height quintile had lower mean WHO-QoL scores than the men (54.6) and women (51.4) in the highest quintile.
Mean WHO-QoL score by height quintile among older adults in India, WHO-SAGE, 2007.
Figure 2 shows the prevalence of self-rated poor quality of life by height quintile and sex. The prevalence for the whole sample declined from 16.4% in the lowest height quintile to 6.1% in the highest height quintile (Figure 2) For men, it declined from 14.7% in the lowest height quintile to 5.1% in the highest height quintile. Women in the lowest height quintile (18.2%) reported a higher prevalence of poor quality of life than those in the highest height quintile (7.4%).
Prevalence of poor quality of life (%) by height quintile among older adults in India, WHO-SAGE, 2007.
Table 2 shows the association of height with socio-demographic variables by sex. Age was negatively associated with height among older adults and wealth quintile was significantly and positively associated with height.
Correlates of height (cm) among older men and women in India, WHO-SAGE Wave 1, 2007
β=coefficient value; CI=Confidence Interval.
*p<0.05; **p<0.01; ***p<0.001.
Table 3 presents the regression results of height and WHO-QoL for men The results of the multivariate regression analysis showed a positive association between height and WHO-QoL score among older men (Table 3). Across the different regression models, the association between height and WHO-QoL was positive and statistically significant among men. In the final regression model, men in the highest height quintile had a 1.87 point higher quality-of-life score than those in the lowest height quintile (p<0.001). The coefficient of height quintile declined with the inclusion of schooling, wealth quintile, handgrip strength and cognitive ability. The association between wealth quintile and WHO-QoL was strong and significant. Similarly, the association of handgrip strength and cognitive ability with WHO-QoL was significant and positive. The prevalence of chronic diseases such as high blood pressure, asthma, arthritis and angina was negatively associated with the WHO-QoL score. The association between poor self-rated health and WHO-QoL score was highly significant and negative. Measures of socioeconomic status, along with handgrip strength and cognitive ability, played important roles in mediating the relationship between height and WHO-QoL score.
Bivariate and multivariate linear regression results of the association between height and WHO-QoL among older men in India, WHO-SAGE Wave 1, 2007
β=coefficient value; CI=confidence interval.
***p<0.001; **p<0.005; *p<0.01.
Table 4 presents the regression results of height and WHO-QoL for women. The association between height and WHO-QoL was significant and positive across five different regression models, with women (final model) in the highest height quintile having a 2.65 higher WHO-QoL score than those in the lowest height quintile (p<0.001). Women who were currently not married had a significantly lower WHO-QoL score. Furthermore, wealth quintile and educational attainment showed a positive association with WHO-QoL score. High blood pressure, asthma, arthritis, edentulism and poor self-rated health were negatively associated with WHO-QoL among women. Also, handgrip strength and cognitive ability showed a positive association with WHO-QoL among women.
Bivariate and multivariate linear regression results on the association between height and WHO-QoL among older women in India, WHO-SAGE Wave 1, 2007
β=coefficient value; CI=confidence interval.
***p<0.001; **p<0.005; *p<0.01.
Table 5 shows the results of the logistic regression analysis of the association between height and self-rated poor quality of life among older men. The association between height and self-rated poor quality of life was statistically significant up to Model 4, and with the inclusion of handgrip strength and cognitive ability variables in Model 5, the association became insignificant. The association between wealth quintile and self-rated poor quality of life was negative and highly significant. The association between poor self-rated health and self-rated poor quality of life was positive and highly significant. The association between height and self-rated poor quality of life among women was significant across all regression models but the association was weak in Model 5 (Table 6). Furthermore, the factors marital status, wealth quintile, asthma, poor self-rated health and underweight were all significantly associated with self-rated poor quality of life. The age-stratified regression analysis suggested a stronger association between height and quality of life outcomes for the 50–59 and 70+ age groups (Table 7).
Bivariate and multivariate logistic regression results on the association between height and self-rated poor quality of life among older men in India, WHO-SAGE Wave 1, 2007
β=coefficient value; CI=confidence interval.
***p<0.001; **p<0.005; *p<0.01.
Bivariate and multivariate logistic regression results on the association between height and self-rated poor quality of life among older women in India, WHO-SAGE Wave 1 (2007)
β=coefficient value; CI=confidence interval.
***p<0.001; **p<0.005; *p<0.01.
Association between height and quality-of-life outcomes (WHO-QoL and self-rated poor quality of life) by age group, WHO-SAGE Wave 1, 2007
Regression results adjusted for socio-demographic characteristics, chronic diseases, BMI, poor self-rated health, handgrip strength and cognitive ability.
β=coefficient value; CI=confidence interval.
***p<0.001; **p<0.005; *p<0.01.
Discussion
Using data from WHO-SAGE, this study examined the association between height and quality-of-life outcomes among older adults (aged over 50 years) in India. Height was found to be positively associated with the WHO quality-of-life measure (WHO-QoL) and self-rated poor quality of life measures, independent of demographic, socioeconomic and health confounders. In particular, the association was consistent and stronger for women. Wealth quintile showed a strong association with quality-of-life outcomes among these older adults. Chronic diseases showed a negative association with quality of life. Furthermore, the association of handgrip strength and cognitive ability with quality of life was significant and positive, and the association between wealth quintile and height was also positive and significant.
The results of this study are consistent with those in the literature. Deaton and Arora (Reference Deaton and Arora2009) found a significant association between height and overall quality of life in the US adult population. A study based on data from Indonesia found height to be a significant predictor of happiness among younger adults (Sohn, Reference Sohn2016). Similarly, a significant relationship between height and subjective well-being measures has been demonstrated (Carrieri & De Paola, Reference Carrieri and De Paola2012; Denny, Reference Denny2017). Coste et al. (Reference Coste, Pouchot and Carel2012) found a weak association between height and health-related quality of life among adults aged 18–50. Several studies have also found a strong association between height and health outcomes. Case and Paxson (Reference Case and Paxson2008a), in their study based on Health and Retirement Study (HRS) data, documented a significant relationship between height and physical and mental health outcomes among the elderly population in the United States. In a cross-country investigation, McGovern (Reference McGovern2014) found a significant relationship between height and several health outcomes, including handgrip strength, general health, activities of daily living and lung function in six low- and middle-income countries.
The effects of poor early-life conditions have been shown to be greater in low-income settings, mainly as a result of larger childhood shocks (Currie & Vogl, Reference Currie and Vogl2013; Perkins et al., Reference Perkins, Subramanian, Davey Smith and Özaltin2016b). Early-life conditions, such as nutrition, childhood mortality and socioeconomic circumstance, play an important role in determining adult height (Bozzoli et al., Reference Bozzoli, Deaton and Quintana-Domeque2009; Mamidi et al., Reference Mamidi, Kulkarni and Singh2011; Tucker-Seeley & Subramanian, Reference Tucker-Seeley and Subramanian2011; Perkins et al., Reference Perkins, Subramanian, Davey Smith and Özaltin2016b). Studies across the globe have found a significant relationship between early-life circumstances and health, mortality and economic and subjective well-being in adulthood (Case & Paxson, Reference Case and Paxson2008b; McEniry, Reference McEniry2013), suggesting that childhood nutrition and health affect the well-being of a population throughout the life course.
This study found that handgrip strength and cognitive ability showed a positive relationship with the WHO quality-of-life score, suggesting the importance of muscle strength and cognitive ability in later life. In old age, muscle strength is a potential marker of health and overall well-being; this is supported by several studies, particularly in high-income countries (Giampaoli et al., Reference Giampaoli, Ferrucci, Cecchi, Lo Noce, Poce and Dima1999; Sayer et al., Reference Sayer, Syddall, Martin, Dennison, Roberts and Cooper2006; Gale et al., Reference Gale, Martyn, Cooper and Sayer2007; Leong et al., Reference Leong, Teo, Rangarajan, Lopez-Jaramillo, Avezum and Orlandini2015; Musalek & Kirchengast, Reference Musalek and Kirchengast2017; Celis-Morales et al., Reference Celis-Morales, Welsh, Lyall, Steell, Petermann and Anderson2018). Similarly, the association between cognitive ability and quality of life is consistent with previous findings (Singh et al., Reference Singh, Govil, Kumar and Kumar2017).
The association between socioeconomic status and quality of life has been found to be highly significant in India (Alcañiz & Solé-Auró, Reference Alcañiz and Solé-Auró2018). In particular, wealth quintile strongly predicts the quality of life of older Indians. These factors, together with handgrip strength, play an important role in mediating the relationship between height and quality-of-life outcome (Deaton & Arora, Reference Deaton and Arora2009; Carrieri & De Paola, Reference Carrieri and De Paola2012; Sohn, Reference Sohn2016). Socioeconomic status has been shown to play an important role in determining health and quality of life through nutrition and access to health and social care (Marmot et al., Reference Marmot, Stansfeld, Patel, North, Head and White1991; Nédó & Paulik, Reference Nédó and Paulik2012; Selvamani & Singh, Reference Selvamani and Singh2018). Furthermore, marital status is closely linked to quality-of-life outcomes among older women in India. In India, many (nearly half) of women over the age of 50 are widowed, largely as a result of the large age gap at marriage, associated changes in living arrangements and economic factors that can contribute to a decrease in their quality of life (Perkins et al., Reference Perkins, Lee, James, Oh, Krishna and Heo2016a). The association of poor self-rated health with WHO-QoL and poor quality of life in this study was highly significant. Self-rated health is an important measure of general health and a strong predictor of future health and mortality (Idler & Benyamini, Reference Idler and Benyamini1997).
This study found considerable differences in height by socio-demographic characteristics. Age was shown to be negatively associated with the height of older adults, replicating a secular trend in height and living standards found among succeeding cohorts across developed (Silventoinen et al., Reference Silventoinen, Lahelma and Rahkonen1999; Onland-Moret et al., Reference Onland-Moret, Peeters, Van Gils, Clavel-Chapelon, Key and Tjønneland2005) and developing countries (Mamidi et al., Reference Mamidi, Kulkarni and Singh2011). Age-associated physiological changes, such as declining muscle mass and bone density, have been shown to be significant contributors to height decline in old age (Fernihough & McGovern, Reference Fernihough and McGovern2015). Years of education and wealth quintile have also been shown to be positively associated with height (Silventoinen et al., Reference Silventoinen, Lahelma and Rahkonen1999; Mamidi et al., Reference Mamidi, Kulkarni and Singh2011), with a suggestion of a bidirectional relationship. Tallness is associated with better socioeconomic achievement through better cognitive abilities and job performance (Vogl, Reference Vogl2014; Kim & Han, Reference Kim and Han2017). On the other hand, life-course circumstances such as better socioeconomic status predict height through better nutrition and health (Case & Paxson, Reference Case and Paxson2008b; Tucker-Seeley & Subramanian, Reference Tucker-Seeley and Subramanian2011).
The strengths of this study are twofold. First, most previous studies examining the associations between height, health and quality-of-life outcomes used self-reported height, whereas the present study used objectively measured height. Second, while most previous studies used a single metric to capture well-being, such as a happiness or life satisfaction scale, this study used the WHO’s widely accepted composite quality-of-life index. The study’s limitations included the data being cross-sectional so no causal relationship could be determined, and that the measures used to construct the WHO-QoL score were based on self-reported data.
In conclusion, this study found adult height to be significantly associated with quality of life among older adults in India, and highlights the long-term effects of early-life conditions on later-life subjective well-being. Shorter height, as a proxy for childhood nutrition/health, showed a significant negative association with self-rated poor quality of life in later life, suggesting that childhood health and nutrition play an important role in determining health and well-being in later life. An understanding of the well-being of the growing older population must therefore consider the role of early-life conditions. Improvement in early-life conditions, such as childhood nutrition, is important for well-being across the life course. The study also observed significant socioeconomic differentials in quality of life and height in older adults India, suggesting a role for socioeconomic status in overall well-being in later life in developing countries. The role of handgrip strength and cognitive ability in predicting quality of life highlight the importance of physical and mental capacity in later life.
Acknowledgments
The authors are grateful to WHO-SAGE for providing access to data. The SAGE survey was supported by the World Health Organization, Division of Behavioral and Social Research at the National Institute on Aging and US Agency for International Development (USAID) in New Delhi.
Funding
This research received no specific grant from any funding agency, commercial entity or not-for-profit organization.
Conflicts of Interest
The authors report no conflicts of interest.
Ethical Approval
This study used the secondary data collected by the WHO in collaboration with country research organizations. The SAGE study was approved by the Ethics Review Committee, WHO, Geneva, Switzerland and the Institutional Review Board, International Institute of Population Sciences, Mumbai, India. Informed written consent was obtained from each participant, who were reassured that data would remain confidential and used for research purposes only.
