Frailty and Quality of life among older adults: A study of six LMICs using SAGE data

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1 Extended Absract Submission for 2016 PAA Frailty and Quality of life among older adults: A study of six LMICs using SAGE data Author s Name and Affiliation Mamta Jat, M. Phil.(Public Health) Student, Tata Institute of Social Sciences Mumbai, India, address: mamta @gmail.com BACKGROUND: The increased longevity has resulted in the increase in the percentage of the global population aged 60 years or over from 8.6 per cent in 1980 to 12 per cent in 2014 and is projected to rise further to 21 per cent in 2050 (UN population devision 2013 ). Aging process is synonymous with growing frailty and failing health. With this demographic transition towards ageing, epidemiologic transition is also taking place characterized by growing share of non-communicable diseases in the overall disease burden. So many of the older adults are ageing with chronic disease and high levels of frailty which often results in lower levels of quality of life. Although frailty may be increasingly common in older adults, prevention or at least delay the onset of late life adverse health outcomes and disability is necessary to maintain the health and functional status of ageing population(seeman, Merkin, Crimmins, & Karlamangla, 2010). Despite a substantial increase in the use of the term 'frailty' over past twenty-five years, there is still no consensus on its meaning, and there are no widely accepted conceptual guidelines to operationalize frailty in clinical practice and research for identifying older adults as frail(fisher, 2005; L. P. Fried et al., 2001; Rockwood, 2005a, 2005b). For our study purpose Frailty is defined as a Syndrome associated with reduced functional reserve, impairment in multiple physiological systems, and reduced ability to regain physiological homeostasis (Bartali et al., 2006). Frailty is considered to be a well known risk factor for adverse health outcomes. Moreover, quality of life has been significantly impaired by frailty(linda P. Fried, Ferrucci, Darer, Williamson, & Anderson, 2004). Quality of life is a commonly used but seldom defined concept and there is no consensus on how to define it. Here, we will be using WHO quality of life definition. WHO defines Quality of Life as individuals 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, expectations, standards and concerns. WHO considers it as a broad ranging concept affected in a complex way by the person's physical health, psychological state, level of independence, social relationships, personal beliefs and their relationship to salient features of their environment(1994). Many studies have dealt with frailty and its relation with quality of life in elderly. Masel and colleagues, in their study found that being pre-frail or frail was strongly associated with diminished HRQoL in elderly community-dwelling Mexican Americans(2009). Bilotta and colleagues found that quality of life is negatively affected by frailty in the community dwelling outpatients in Italy (2010). Chang and colleagues, in their study found that the status of frailty is closely associated with HRQoL in elderly Taiwanese preventive health service users(2012). However, no cross country investigation has been done to examine the quality of life of frail elderly in lower and middle income countries. The main aim of this study is to assess frailty and its realtion with quality of life and correlates among frail older adults across the six countries of SAGE. Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 1

2 Need of the Study: Adverse health outcomes of frailty have placed one of the major challenges for the medical and scientific communities as well as health care systems of various countries. Of these, the most pressing challenge is search and implementation of general and long term care effective policies for frail elderly. Public, social and health care services are not sufficient to meet situations of dramatic changes in the health needs of the population. It is of prime importance that elderly have access to resources and good quality of life to deal with frailty and failing health. As the number and proportion of elderly population increases worldwide, older adults have the potential of becoming more influential in the society. Empowerment and political participation of older generations varied significantly across countries. Older adults in some countries actively and effectively produce and promote policies and programs that improve their quality of life, but in many countries, however, older adults are ignored and they struggle to have their interests incorporated in public debate and social policy. Owing to lack of data, studies are scarce on frailty and quality of life. This is an effort to fill the gap, using SAGE data to assess levels of frailty and its socio-demographic correlates and its relation with quality of life in LMICs of India, China, Ghana, Mexico, Russia and South Africa in a comparative perspective. The Study Sample: The data for this study comes from WAVE- 1 of multi country Study on Global AGEing and Adult Health (SAGE) which is supported by the US National Institute on Aging (see SAGE consists of nationally representative samples of older adults in six low and middle income countries (LMICs): China, Ghana, India, Mexico, the Russian Federation and South Africa, accounting for 42% of the world s population aged 50-plus in 2011 (United Nation s Population prospects). At the outset of data collection, based on the world bank income categories, SAGE encompassed mix of low, lower-middle and upper-middle income countries (world Bank 2009). SAGE survey is having huge strength with large number of itmes on health, physical functions, risk-factors, chronic conditions, socioeconomic status and work, social network, healthcare utilization, subjective well-being and quality of life. The first (and to date only publicly available) full wave of SAGE was implemented between 2007 and 2010 and provides information on over 42,000 individuals aged 18-plus in total, including 34,124 individuals aged 50-plus. For our study purpose, we will consider only 50+ year s respondants. SAGE study is still going on. Frailty Index construction: There are two most commonly used approaches for frailty index construction. First one defines frailty based on a specific phenotype, consisting of five items, any three of which mark a person as recognizably frail(searle, Mitnitski, Gahbauer, Gill, & Rockwood, 2008). The second pays less attention to which items are present in a person who is frail, but rather counts the number of things that people have wrong with them, to propose a frailty index (FI) based on a count of accumulated deficits(searle et al., 2008). As more deficits are included, the precision of estimates increases, alternately, estimates become unstable when fewer than 10 deficits are included. For frailty index construction indicators are taken related to general health, medically diagonised conditions, medical symptoms, functional activities assessment, Activities of daily living, BMI grip strength and time walked at usual place. The inclusion criteria: (1) deficits are related with the health status of the individual; (2) prevalence of increases with age; (3) deficits must not saturate too early; (4) when an individual s accumulated deficts are considered as a whole, they should cover a range of systems (for example, not only impacting medically diagonised condtions) (Harttgen, Kowal, Strulik, Chatterji, & Vollmer, 2013). Based on the first wave of SHARE, a 40-item FI was created as per the standard procedure (Rockwood, Andrew, & Mitnitski, 2007). Each of the 40 deficit variables was scored such that 0 = deficit absent and 1=deficit present. The scores were added and divided by the total number of deficits evaluated (i.e. 40), to Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 2

3 produce a frailty index between 0.0 (no deficits present) and 1.0 (all deficits present). Frailty index has been categorized as non-frail(0-0.2) and frail(0.2-1). Socio-economic and Demographic variables: Socio-economic and demographic variables included Age group (50-59, 60-69, 70-79, 80+), sex (Male, Female), Educational level measured by number of years of schooling(no education, primary, secondary, higher), Wealth quintile (est, lower, middle, higher, highest), residence (Urban, rural) and Marital status (never married, married/ cohabiting, divorced/seaparated/widowed) in countries India, China, Ghana, South Africa, Russia, Mexico. Quality of life Index: WHOQoL, the World Health Organization Quality of Life instruments, are a set of international, crossculturally comparable tools used to assess quality of life and provides a measure of the evaluative component of well-being (Skevington, Lotfy, O Connell, & WHO Group, 2004). The 8-item WHOQoL instrument used in this report used two questions in each of four broad domains: physical, psychological, social, and environmental (Garratt, Schmidt, Mackintosh, & Fitzpatrick, 2002). In SAGE Quality of life was assessed by asking respondents to rate their satisfaction with different domains of their lives (namely energy, money to meet needs, satisfaction with health, respondent himself, ability to perform activities of daily living, personal relationships, conditions of living place, life as a whole, and a five point scale of overall rating of quality of life) as well as rating their overall life satisfaction. Respondents were asked to respond on a five point scale ranging from very satisfied to very dissatisfied. A composite score was created by summing the responses across the different questions and rescaling the responses from where a higher score indicated better quality of life. For the comparison purpose quality of life index has been categorized as low, middle and high. Statistical Analysis: The logistic regression model is used to assess the correlates of frailty. Multinomial logistic regression has been used to study the effect of frailty on quality of life, controlling for the effect of socio-economic and demographic correlates. STATA version 12.0 was used for this analysis. Results: Table 1 shows mean and standard deviation of frailty for socio-demographic correlates across all the LMICs. Among all the countries India is having highest mean frailty index in males() and females(0.26) and China is having lowest frailty in males() and females(). Frailty is increasing with age across all the countries. Rural people are more frail than urban people in all the countries. Decling pattern of frailty has been observed with increase in wealth and education across all the countries. Divorced/separated/widowed people are more frail than married/cohabiting in all the countries. Also never married people are having high frailty than nevr married across all the countries except in Mexico. Table 2 shows the results of logistic regression in WHO SAGE countries. The dependent variable is frailty index categorized as non-frail and frail. Females consistently have a statistically higher (p<0.01) frailty index than males across all countries. The odds of being frail are more likely with the increase in age across all the countries. Whereas, those who are in the secondary and higher level of education the chances of being frail are less as compared to the uneducated older adults. In India, China and Russia the likelihood of frailty is more among rural older adults as compared to urban older adults. For example, in China the odds are 1.45 times higher in rural older adults than the older adults who are residing in urban areas; whereas, in Ghana, South Africa and Mexico rural residence is protecting against frailty. Our result shows inverse relationship between frailty and wealth among all the countries except South Africa. In South Africa odds of being frail are 1.50 (p<0.01) times and 1.15 (p<0.01) times higher in higher and middle income group resp. as compared to those who are in the lowest category of income. The likelihood Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 3

4 of frailty is less among married/cohabiting than never married irrespective of the countries (except in Mexico). In Mexico odds of being frail are 1.57(p<0.05) times higher in married/cohabiting than never married. In India, Ghana and South-Africa the odds of being frail are higher in divorced/separated/widowed than not-married. Figure 1 shows that across all countries is low among frail older adults as compared to non-frail older adults. Among all countries china has high percentage (71.46) of frail people in low ; wheras Mexico has lowest percentage(36.13) of frail people in low. In China non frail older-adults are almost equally distributed in all categories of. Table 3 shows the quality of life among elderly in WHO SAGE countries. Dependent variable quality of life is categorized as low, middle and high and we specify high quality of life as base category. The risk of having low and middle quality of life relative to high quality of life is significantly higher among frail elderly as compared to non frail elderly across all countries. For ex. In South Africa, the chances of having low relative to high is highest and is 12.08(p<0.01) times higher in frail as compared to non-frail older adults; whereas risk of having middle is 3.14(p<0.05) times higher among frail as compared to non-frail older-adults. Across all the countries, the likelihood of having low relative to high is more among females as compared to males. Also in South Africa and India, chances are less of having middle among females as compared to males but it is not significant. More likelihood of having lower and middle with the increase in age has been seen in India and Ghana. Whereas in China and Ghana age is protective factor against low and middle. Across all countries, the odds are less likely of having low and middle with the rise in education level and chances are very less of having low among highest education group. In India and China, odds of having lower and middle are less among rural older adults as compared to urban older adults. For eg. In China chances of having low are 0.82(p<0.01) times lower in rural older adults as compared to urban older adults. Whereas in Russia, South Africa and Mexico odds are more of having low and middle likely among rural older adults. With the rise in wealth, risk of having low and middle is less across all the countries. In India, Ghana and Mexico, risk of having low and middle is more among marriedcohabiting than non-married. Whereas, chances are less of having low and middle among Russia and Soth Africa. In india odds of having low is 2.94(p<0.05) times higher among separated/divorced/widowed as compared to unmarried. In Russia and Mexico separated/divorced/widowhood marital status is protecting against low and middle compared to nonmarried elderly. Conclusion: This paper provides the first comparative study of frailty and quality of life among older people in LMICs. On average, women and older age groups had higher frailty levels than men and younger aged adults. The mean frailty scores demonstrated strong inverse education and income gradients, with lower levels of education and lower wealth showing higher levels of frailty. These patterns are consistent across all LMICs. These data supports a significant role of frailty With all other influences controlled, in having low quality of life as measured by WHO quality of life Index. Public health care policies should be formulated to improve quality of life among older adults across countries. Future research needs to build on this evolving concept of frailty in the public health domain, to refine its definitions and criteria, to develop standardized approaches to screening and risk assessment, in a LMICs setting to validate the findings. Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 4

5 References: Bilotta, C., Bowling, A., Casè, A., Nicolini, P., Mauri, S., Castelli, M., & Vergani, C. (2010). Dimensions and correlates of quality of life according to frailty status: a cross-sectional study on communitydwelling older adults referred to an outpatient geriatric service in Italy. Health and Quality of Life Outcomes, 8, Chang, Y.-W., Chen, W.-L., Lin, F.-G., Fang, W.-H., Yen, M.-Y., Hsieh, C.-C., & Kao, T.-W. (2012). Frailty and its impact on health-related quality of life: a cross-sectional study on elder communitydwelling preventive health service users. PloS One, 7(5), e Fisher, A. L. (2005). Just what defines frailty? Journal of the American Geriatrics Society, 53(12), Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 59(3), Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J., Cardiovascular Health Study Collaborative Research Group. (2001). Frailty in older adults: evidence for a phenotype. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56(3), M Garratt, A., Schmidt, L., Mackintosh, A., & Fitzpatrick, R. (2002). Quality of life measurement: bibliographic study of patient assessed health outcome measures. BMJ (Clinical Research Ed.), 324(7351), Harttgen, K., Kowal, P., Strulik, H., Chatterji, S., & Vollmer, S. (2013). Patterns of Frailty in Older Adults: Comparing Results from er and er Income Countries Using the Survey of Health, Ageing and Retirement in Europe (SHARE) and the Study on Global AGEing and Adult Health (SAGE). PLoS ONE, 8(10), e Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 5

6 Masel, M. C., Graham, J. E., Reistetter, T. A., Markides, K. S., & Ottenbacher, K. J. (2009). Frailty and health related quality of life in older Mexican Americans. Health and Quality of Life Outcomes, 7, Rockwood, K. (2005a). Frailty and Its Definition: A Worthy Challenge. Journal of the American Geriatrics Society, 53(6), Rockwood, K. (2005b). What would make a definition of frailty successful? Age and Ageing, 34(5), Rockwood, K., Andrew, M., & Mitnitski, A. (2007). A comparison of two approaches to measuring frailty in elderly people. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 62(7), Searle, S. D., Mitnitski, A., Gahbauer, E. A., Gill, T. M., & Rockwood, K. (2008). A standard procedure for creating a frailty index. BMC Geriatrics, 8, Seeman, T. E., Merkin, S. S., Crimmins, E. M., & Karlamangla, A. S. (2010). Disability trends among older Americans: National Health And Nutrition Examination Surveys, and American Journal of Public Health, 100(1), Skevington, S. M., Lotfy, M., O Connell, K. A., & WHO Group. (2004). The World Health Organization s WHO-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHO group. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 13(2), United Nations Department of Economic and Social Affairs, Population Devision. World Popuation Prospects: The 2012 Revision. New York: United Nations; WHO Group. Development of the WHO: rationale and current status. Int J Ment Health 1994; 23(3): World Bank. Country classification Avaialable from: 31~pagePK: ~piPK: ~theSitePK: ,00.html (cited 24 september 2015). Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 6

7 Table 1: and Standard deviation of the frailty Index in SAGE countries by sex, age, education and wealth, marital status. Countries Sex Male Female Age-group Residence Urban Rural Education level Non-educated India (N=5134) China (N=10533) Ghana (N=3564) Russia (N=2313) South-Africa (N=2909) Mexico (N=2755) Primary Secondary er Wealth Index est er Middle er est Marrita status Never-married 0.18 Tata Institute of Social Sciences Mumbai, India, -id: Page 7

8 (Married/ Cohabiting) Divorced/ Separated /Widowed) Tata Institute of Social Sciences Mumbai, India, -id: Page 8

9 Table 2: logistic regression estimates showing the effects of socio-economic background characteristics on frailty of respondents in age 50 and above from WHOSAGE Countries Countries India China Ghana Russian Federation South Africa Mexico Frailty Odds Ratio Odds Ratio Odds Ratio Odds Ratio Odds Ratio Odds Ratio Sex Male Female 1.88*** 1.57*** 1.29*** 1.54*** *** Age-group *** 2.71*** 1.95*** 2.49*** 1.39*** 1.32*** *** 6.67*** 4.47*** 6.17*** 2.33*** 2.43*** *** 17.77*** 11.27*** 9.66*** 3.00*** 6.38*** Education level No-education Primary.861* ** 0.80* Secondary.692** 0.82** *** 0.70*** er.498*** 0.787** 0.538*** 0.42** 0.42*** 0.59*** Residence Urban Rural *** * 0.78*** 0.78** Wealth Index est er Middle ** *** 0.85 er.759* 0.682*** *** 0.87 est.653*** 0.462*** ** * Marital-Status Never-married Married-Cohabiting * 1.57** Separated/Divorced/ Widowed * Note: represents the reference category; *** = p <.01; ** =.01 < p <.05; *=.05 < p <.10 Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 9

10 Figure 1: Association between frailty and quality of life in six countries of SAGE study 100% 90% 80% 70% Quality of Life 60% 50% 40% 30% 20% Middle 10% 0% Non-frail frail Non-frail frail Non-frail frail Non-frail frail Non-frail frail Non-frail frail India China Ghana Russia South Africa Mexico Tata Institute of Social Sciences Mumbai, India, -id: Page 10

11 Table 2: Multinomial logistic Regression(Relative Risk Ratio) for the effect of Frailty on Quality of life () India China Ghana Russian South Africa Mexico Medium Medium Medium Medium Medium Medium Relative Risk Ratio Relative Risk Ratio Relative Risk Ratio Relative Risk Ratio Relative Risk Ratio Relative Risk Ratio Frailty Non-Frail Frail 8.74*** 2.85*** 8.32*** 1.82*** 10.67*** 2.80*** ** 3.14*** 6.81*** 2.08*** Sex Male Female ** *** 1.33** 1.36** 1.47*** Age-group ** *** ** ** 0.77** *** *** *** 1.59*** *** 0.67*** 0.67** 0.65** *** *** * ** ** 0.74 Education level No-education Primary * ** * Secondary 0.61*** 0.79* 0.72*** 0.76*** ** 0.20** ** 0.72** 0.41*** 0.63** er 0.43*** 0.55*** 0.50*** 0.64*** 0.70** 0.59*** 0.18** *** 0.53*** 0.28*** 0.39*** Residence Urban Rural 0.78** 0.73*** 0.73*** 0.82*** *** 1.32** 1.50*** 1.46*** 1.63*** 1.72*** Wealth Index est er 0.57*** *** 0.66*** 0.41*** 0.58*** 0.66* *** 0.72* Middle 0.36*** 0.64*** *** 0.45*** 0.31*** 0.51*** 0.51*** 0.79 *** 0.52*** er 0.27*** 0.65*** *** 0.57*** 0.18*** 0.36*** 0.43*** 0.71 *** 0.44*** 0.68* 1.13 est *** 0.39*** *** 0.39*** *** 0.27*** 0.24*** *** *** 0.56** 0.93 Marital-Status Never-married Married-Cohabiting 2.29* * 0.38** ** Separated/Divorced/ Widowed 2.94** Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 11

12 Appendix: List of the 40 variables included in the frailty index and cut-off points by domain Topic/Variable General Health: Self related health Medically Diagonised Conditions Arthritis Asthma Cataracts Chronic lung disease Depression Diabetes Hypertension Angina Stroke Oral teeth Medical symptoms In last 30 days how much Bodily ach or pain did you have? Of a problem did you have with sleeping? Difficulty did you have in seeing( person or object) across the road? Difficulty did you have in seeing an object at arm s length? Functional activities assessment In last 30 days how much difficulty did you have with.. sitting for long periods walking 100 meters standing up from sitting down standing for long periods climbing one flight of stairs without resting stooping, kneeling or crouching picking up things with fingers extending arms above shoulders concentrating for 10 minutes Response categories and cut-points Very good=0, Good=0.25, Moderate=0.50, Bad=0.75, Very Bad=1 1=yes, 0=no Very good=0, Good=0.25, Moderate=0.50, Bad=0.75, Very Bad=1 Very good=0, Good=0.25, Moderate=0.50, Bad=0.75, Very Bad=1 walking long distance(1 km) carrying things getting out of your home Concentrating or remembering things Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 12

13 Cont Activities of daily living In last 30 days how much difficulty did you have with... taking care of household responsibilities Joining community activities bathing/washing dressing day-to-day work moving around inside home eating getting up from lying down getting to and using the toilet getting up from lying down getting to and using toilet getting where you want to go BMI: (Weight/Height in meters)^2 Grip Strength: Grip(in Kg), (Left+Right hand)/2 Very good=0, Good=0.25, Moderate=0.50, Bad=0.75, Very Bad=1 BMI>=18.5-<25=0 (Normal) BMI>=25-<30=0.5(Overwieght) BMI<=18.5=1(Underweight) BMI>=1(Obese) (Male and 0< BMI<=24 and grip<=29) or (Male and 24<BMI<=26 and grip<=30) or (Male and 26<BMI<=28 and grip<=30) or (Male and 28<BMI<=40 and grip<=32) or (Female and 0<BMI<=23 and grip<=17) or Time walked at usual pace (Female and 23<BMI<=29 and grip<=17.3) or (Female and 26<BMI<=29 and grip<=21) =1 (weak grip) (<=0.4 m/sec)=0 (Slow) (.0.4 m/sec)=1(normal) Time (sec) over 20 feet 4 meters (6 meters) Tata Institute of Social Sciences Mumbai, India, -id: mamta @gmail.com Page 13