Ageing Int https://doi.org/10.1007/s12126-018-9342-x

Health Related Quality of Life and it’s Correlates among Older Adults in Rural District, : a Cross Sectional Study Using SF-36

Anna Pius1 & G. K. Mini1,2 & K. R. Thankappan1

# Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract Data on health-related quality of life (HRQL) from India are limited. We conducted this community based cross sectional study to find out the HRQL and its correlates among older adults in one of the most advanced Indian districts in epidemiologic and demo- graphic transition. We selected 300 older adults aged ≥60 years using a multi stage cluster sampling technique from . Information on socio- demographics and self reported morbidity was collected using a structured interview schedule. HRQL was collected using a validated Short Form (SF)-36. We used multiple linear regression analysis to find out the correlates of HRQL. Mean age of the sample was 70 years (SD 8.4). The study constituted 58% women. The mean score of SF-36 subscales ranged from 28.8 for role physical to 78.2 for role emotional. Compared to mean physical component summary (PCS) score (42.2), mean mental component summary (MCS) score (61.2) was higher. Most of the SF-36 component scores were significantly higher among men, better educated, employed, those who lived with spouse, those who did not report chronic illness and those who belonged to upper caste compared to their counterparts. However, significance of physical component summary (PCS) and mental component summary (MCS) scores was limited to better educated, employed and those who belonged to upper caste. The SF-36 scores on HRQL from this district are likely to be an upper estimate.

Keywords Health related quality of life . SF-36 . Older adults . Pathanamthitta . India

* K. R. Thankappan [email protected]; [email protected]

1 Achutha Menon Centre for Health Science Studies (AMCHSS), Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Trivandrum 695011, India 2 Global Institute of Public Health, Ananthapuri Hospitals and Research Institute, Trivandrum 695024, India Ageing Int

Introduction

Perceived health related quality of life (HRQL) has been well documented to correlate with actual health status and health outcomes (Blaum et al. 2003). Short Form 36 (SF- 36), is one of the most widely used measures to assess health related quality of life (Shapiro et al. 1996). The SF-36 score comprises of 36 items divided into eight main components such as physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Health related quality of life has been reported to steadily decline with age (Walters et al. 2001), the decline being less in the mental component summary score. In low resource settings such as India, access and utilization of health care are limited particularly for older adults (Agrawal and Keshri 2014; Channon et al. 2012) making it difficult to estimate the actual health status at the population level. In such settings SF-36 is an extremely useful tool. There are only limited data on health-related quality of life using SF-36 in rural India. One community based study using SF-36 from rural Andhra Pradesh based on a small sample of 71 older adults reported a mean physical component summary score of 49 and mental component summary score of 52 (Varma et al. 2010). state in India, the most advanced in epidemiologic and demographic transi- tions (Peters et al. 2003), had the highest proportion (13%) of older adults (60 years and above) as per the latest national census conducted in 2011(Registrar General of India 2011). Within Kerala, Pathanamthitta district had the highest proportion of elderly (18%), lowest total fertility rate (TFR) of 1.3 (Guilmoto and Rajan 2013) and highest literacy rate (96.9%) (Registrar General of India 2011) making it one of the most advanced Indian rural districts in demographic transition. Non-communicable diseases (NCDs) such as type 2 diabetes, cardiovascular diseases, cancers and chronic lung diseases are likely to be more among elderly population. Although district wise prevalence data on NCDs are not available one study reported the highest diabetes prevalence in this district of Kerala (Zachariah and Rajan 2008). Thus, this district is likely to be the most advanced in epidemiological transition also. Health related quality of life among older adults in this district has not been studied using SF-36. Data from this rural district (89% rural population) is likely to provide an estimate of the health-related quality of life of one of the most advanced rural Indian districts in epidemiologic and demographic transition. We conducted this commu- nity based study to find out the health-related quality of life and its correlates among older adults in this district.

Methods

A community based cross sectional household survey was conducted among 300 older adults aged 60 years and above using a multi stage cluster sampling technique. Of the 14 districts in Kerala State, Pathanamthitta was selected since the district had the highest proportion of older adults in the state according to the latest Indian Census (Registrar General of India 2011). Based on a previous report of 37% poor perceived health status in Kerala (Mini 2009), a design effect of 1.5, 95% confidence interval and 7 % standard error, the sample size estimated as 280 was rounded off to 300. Ageing Int

In the decentralized system of Governments in Kerala there are district Panchayats (self-administration units), block Panchayats and village Panchayats in the rural areas (Varatharajan et al. 2004). At the time of this study, Pathanamthitta district consisted of nine block Panchayats, of which, three were randomly selected. From each of the selected block Panchayat,onegrama Panchayat was randomly selected. Five wards were randomly selected from each of the selected grama Panchayat.Wardisthe smallest geographical unit of the decentralized Government which has an elected representative called ward member. All random selections were done using lottery method. Each ward was considered as a cluster giving a total of 15 clusters. From each cluster 20 participants [300 total participants / 15total clusters] were selected and interviewed by one of the authors (AP). The survey was started after identifying a junction which was close to the centre of the ward. From this junction, one of the roads was randomly selected and the number of minutes taken to walk from the centre to the boundary of the ward was noted. A random number was taken between one and the number of minutes taken. The household corresponding to that number on the right side was taken as the first household for the study. All the households were continuously surveyed till we got 20 older adults in that cluster. The same procedure was followed in all the 15 selected clusters till the sample size of 300 was reached (Bennett et al. 1991). If we could not find an older adult in the selected household, the next house was chosen. Screening of the study participants was done using the criteria defined to confirm their eligibility before their participation. A total of 312 eligible older adults were approached of whom 300 consented and participated in the study providing a response rate of 96.2%. Detailed sample selection procedure is given in Fig. 1. Older adults aged 60 years or above who were residents of Pathanamthitta district for at least past six months and willing to participate in the study were included. Those who were terminally ill and mentally challenged were excluded. Data were collected from June 20 to August 20, 2014. We used SF-36 which is being widely used for measuring health related quality of life (Neale 2004). A previous study has demonstrated the validity of SF-36 in India (Sinha et al. 2013) and in Kerala (Bullinger et al. 1998). A recent study was conducted in a small rural village of Kerala using SF-36 among older adults (George et al. 2016). The validated version of SF-36 in local language () was used for data collection. Information on socio-demographic, behavioural and self-reported morbidity was collected using another pre-tested structured interview schedule. In the SF-36, current (over the past 4 weeks) self-perceived health status was assessed using 36 questions divided into eight components. The components were: physical functioning (PF) (10 items), role limitation-physical (RP) (four items), bodily pain (BP) (two items), general health perceptions (GH) (five items), energy and vitality (VT) (four items), role limitations-emotional (RE) (three items), social functioning (SF) (two items) and mental health (MH) (five items). There was also a single item on perceived changes in health status over the past year. Scoring of SF-36 was done as recommended by Ware et al. (1993). The 36 items were categorized into eight components which were then scored of 0–100. Physical component Summary (PCS) and Mental Component Summary (MCS) were calculated by averaging the values of four scores each (PF, RP, BP and GH for PCS and VT, SF, RE and MH for MCS). Socio-economic and demographic information collected included age, sex, educa- tion, marital status, living arrangement, religion, caste, monthly income and Ageing Int

Indian state of Kerala

(Total 14 districts)

Pathanamthitta District

Nine Block Panchayats

Koipuram Pulikeezh Block Panchayat Block Panchayat Block Panchayat

1/6 Grama Panchayats 1/5 Grama Panchayats 1/7 Grama Panchayats

Elanthoor Grama Panchayat Grama Panchayat Grama Panchayat

5/12 wards 5/13 wards 5/14 wards

Chalaikara Valiyavattom Kuttoor Padinjaaru Chirayirampu Elanthoor Thalayaar Thonnipuzha Elanthoor West Kuttoor Bhagavathikunnu Thengeli Nedumprayar Pulinthitta Kuttoor Vadakku

20 participants from each ward 20 x 15=300

Fig. 1 Sample selection procedure employment status. Information on regular exercise and tobacco use was also collected. Details of self reported chronic illness such as diabetes, hypertension, heart disease, respiratory disease and cancer were also collected. Data were analysed using SPSS 17.0 (SPSS Inc., Chicago, Illinois). Both bivariate and multivariate analyses were done. Mean values were compared by Independent sample‘t’ test and one way analysis of variance. To examine the correlates of health- Ageing Int related quality of life, we used multiple linear regression analysis separately for PCS and MCS. A ‘p’ value of <0.05 was considered for statistical significance.

Results

Mean age of the sample was 70 years (SD: 8.4) ranging from 60 to 93 years, 167 (56%) were in the age group of 60–69 years and the remaining were 70 years or above. Of the participants, 174 (58%) were women, 159 (53%) were educated to high school and above, 167 (56%) were unemployed, 195 (65%) were living with spouse (men 86%, women 50%) and 168 (56%) reported at least one chronic illness. Average monthly income of the household was Indian rupees 6111 (~US $ 95) (range 1000–50,000). More than half of the older adults (55.3%) were Hindus and the remaining 44.7% were Christians. Eighty four (28%) of the sample belonged to lower caste, and the remaining were upper caste. Sixteen percent of the participants reported doing regular exercise (26% men and 10% women). Current tobacco use was reported by 14% (men 22%, women 9.2%). Nearly two third (62.7%) reported some form of disability (men 62.7%, women 62.6%). More than half of the older adults (56%) reported having at least one chronic illness; hypertension: 42.7% (men 37.3%, women 46.6%), diabetes: 34.7% (men 38.1%, women 32.2%), heart disease: 5.7% (men 5.6%, women 5.7%), respiratory disease: 2.3% (men 3.2%, women 1.7%) and cancer: 1 % (three men). Mean of the various scores are given below: physical functioning 42.7 (95% CI 38.8–46.6), role physical 28.8 (CI 23.7–33.9), role emotional 78.2 (CI 73.6–82.7), vitality 48.2 (CI 45.8–50.6), mental health 64.4 (CI 62.4–66.3), social functioning 53.9 (CI 50.0–57.8), bodily pain 50.6 (CI 47.2–54.0), general health 46.9 (CI 43.7–50.0), physical component score summary 42.2 (CI 38.9–45.5) and mental component sum- mary 61.2 (CI 58.7–63.6). All components except role emotional, vitality and mental health were significantly higher in the age group of 60–69 years compared to those aged 70 years and above. Compared to men, women scores were significantly lower in all components. The mean score of eight components of SF-36, PCS and MCS score by education and employment status are presented in Table 1, by religion and caste in Table 2 and by living arrangement and presence of chronic illness in Table 3. Christians showed significantly better scores in vitality, general health, role emo- tion and mental health compared to Hindus. Older adults who belonged to the upper caste showed higher scores in all except physical functioning, role physical and social functioning. All component scores were significantly higher among better educated and significantly lower for unemployed. All the scores were significantly higher for those who lived with spouse except that of vitality. The scores for physical functioning, role physical, social functioning, general health and physical component summary were significantly higher for those who did not report any chronic illness. Multivariate linear regression analysis results of corre- lates of PCS and MCS score are presented in Table 4. Significance of physical component summary and mental component summary scores was limited to better educated, employed and those who belonged to upper caste. Ageing Int

Table 1 Mean score of eight components of SF-36, PCS and MCS by education and employment status

Components Employment status

Unemployed Currently Employed Retired P value*

Physical Functioning 33.0(28.5–37.6) 57.3(48.7–65.8) 52.7(44.1–61.3) <0.001 Role physical 16.7(11.1–22.4) 43.4(30.8–56.0) 44.4(32.9–55.9) 0.014 Role Emotional 72.4(65.7–79.1) 82.5(73.3–91.6) 87.9(80.4–95.4) <0.001 Vitality 41.7(38.8–44.5) 51.8(47.0–56.5) 60.4(55.3–65.5) <0.001 Mental Health 59.7(57.2–62.2) 66.8(62.8–70.9) 73.3(69.3–76.9) <0.001 Social Functioning 44.6(39.7–49.5) 67.8(60.4–75.2) 63.5(54.8–72.2) <0.001 Bodily Pain 42.5(38.3–46.7) 56.1(49.3–62.9) 64.7(57.1–72.4) <0.001 General Health 38.1(34.0–42.2) 53.2(46.6–59.7) 61.8(55.8–67.7) <0.001 Physical Component 32.6(28.8–36.4) 52.5(45.5–59.4) 55.9(48.7–63.1) <0.001 Summary (PCS) Mental Component 54.6(51.5–57.8) 67.2(62.3–72.1) 71.2(66.3–76.1) <0.001 Summary (MCS) Education Up to High School High School and above P value** Mean (95% CI) Mean (95% CI) Physical Functioning 33.4 (28.2–38.6) 50.9 (45.5–56.3) <0.001 Role physical 16.1(10.1–22.1) 40.0 (32.5–47.6) <0.001 Role Emotional 71.3 (64.0–78.7) 84.2 (78.7–89.8) 0.006 Vitality 41.6 (38.4–44.8) 54.1(50.8–57.3) <0.001 Mental Health 59.5 (57.0–62.0) 68.7 (66.0–71.4) <0.001 Social Functioning 45.1 (39.4–50.7) 61.7 (56.5–66.8) <0.001 Bodily Pain 41.3 (36.8–45.7) 58.9 (54.1–63.6) <0.001 General Health 36.1 (31.8–40.4) 56.4 (52.3–60.5) <0.001 Physical Component 31.7 (27.7–35.7) 51.6 (46.9–56.2) <0.001 Summary(PCS) Mental Component 54.4 (50.8–57.9) 67.2 (64.0–70.3) <0.001 Summary (MCS)

*Significance between employment status based on one way analysis of variance test **Significance between different educational level and earning status based on student’s t test PCS Physical Component Summary, MCS Mental Component Summary

Discussion

In this community based cross sectional study among a representative sample of 300 older adults aged 60 years and above we report the health-related quality of life of rural Pathanamthitta district which is one of the most advanced Indian districts, probably the most advanced, in epidemiologic and demographic transi- tion. The study by George et al. was limited to a small rural village population in Kerala whereas our study was based on a representative sample of a whole district in Kerala. To our knowledge this is the first comprehensive study on health-related quality of life using SF-36 among older adults in Kerala, the most advanced Ageing Int

Table 2 Mean score of eight components of SF-36, PCS and MCS by religion and caste

Scales Religion P value*

Hindus Christians Mean (95% CI) Mean (95% CI)

Physical Functioning 42.6(37.2–47.9) 42.8(37.1–48.5) 0.949 Role physical 24.5(17.9–31.1) 34.1(26.1–42.1) 0.064 Role Emotional 73.0(66.5–79.6) 84.5(78.5–90.6) 0.013 Vitality 41.1(38.0–44.2) 57.1(53.9–60.3) <0.001 Mental Health 62.0(59.6–64.3) 67.3(64.1–70.5) 0.006 Social Functioning 54.1(49.1–59.0) 53.6(47.3–59.9) 0.900 Bodily Pain 47.8(43.4–52.3) 54.0(48.8–59.3) 0.706 General Health 39.8(35.4–44.2) 55.6(51.3–59.9) <0.001 Physical Component Summary (PCS) 38.7(34.3–43.1) 46.6(41.7–51.5) 0.018 Mental Component Summary (MCS) 57.5(54.2–60.8) 65.6(62.1–63.6) 0.001 Caste Lower Upper Physical Functioning 38.2(30.8–45.7) 44.4(39.9–49.0) 0.158 Role physical 22.3(13.4–31.2) 31.3(25.2–37.5) 0.116 Role Emotional 63.4(53.3–73.6) 83.9(79.1–88.7) <0.001 Vitality 37.5(33.6–41.4) 52.4(49.6–55.1) <0.001 Mental Health 57.7(54.2–61.1) 67.0(64.7–69.2) <0.001 Social Functioning 49.1(41.9–56.2) 55.7(51.1–60.4) 0.130 Bodily Pain 40.2(34.1–46.4) 54.6(50.6–58.6) <0.001 General Health 33.9(27.9–39.8) 51.9(48.3–55.5) <0.001 Physical Component Summary (PCS) 33.7(27.6–39.7) 45.6(41.7–49.4) 0.001 Mental Component Summary (MCS) 51.9(47.2–56.6) 64.7(62.0–67.5) <0.001

* Significance between religion & caste based on student’sttest

Indian state in demographic and epidemiologic transition (Peters et al. 2003). The SF-36 scores on health-related quality of life from this district are likely to provide an upper estimate of these scores for rural India. The lowest mean score of 28.8 in our study was for role physical. A recent study from China reported a mean score of 19.2 for role physical which was also the lowest among the eight mean scores of SF 36 in that study (Liu et al. 2013). In our study, role emotional had the highest mean score of 78.2 where as in the above Chinese study the highest mean score of 82.6 was for physical functioning. The mean score of 86.1 for role emotional was the highest in a study from Brazil (Lima et al. 2009). In this Brazilian study, all the mean scores were higher than our study, probably because of the overall better health and development status in Brazil compared to India. Mean score for men in our study was 49.2 for physical functioning and 58.0 for social functioning which was lower than the population norm for men in the US: 52.0 for physical functioning and 66.2 for social functioning. Older adults in the US was 65 years and above whereas in our study it was 60 years and above making the difference even more. Ageing Int

Table 3 Mean score of eight components of SF-36, PCS and MCS by living arrangement and self reported chronic illness

Scales Living arrangement P value*

Without spouse With spouse Mean (95% CI) Mean (95% CI)

Physical Functioning 31.6(25.8–37.3) 48.7(43.8–53.6) <0.001 Role physical 20.4(12.7–28.2) 33.3(26.7–39.8) 0.013 Role Emotional 65.0(56.1–73.9) 85.2(80.4–90.1) <0.001 Vitality 45.1(40.9–49.3) 49.9(47.0–52.8) 0.057 Mental Health 60.6(57.1–64.0) 66.4(64.1–68.7) 0.004 Social Functioning 44.7(38.2–51.2) 58.8(54.0–63.6) 0.001 Bodily Pain 46.0(40.5–51.4) 53.1(48.8–57.4) 0.049 General Health 40.2(34.7–45.6) 50.4(46.6–54.3) 0.002 Physical Component Summary (PCS) 34.5(29.4–39.7) 46.4(42.2–50.5) <0.001 Mental Component Summary (MCS) 53.8(49.5–58.2) 65.1(62.3–67.9) <0.001 Self reported Chronic illness Yes No Physical Functioning 36.0(31.4–40.6) 51.2(44.8–57.5) <0.001 Role physical 24.1(17.6–30.5) 34.8(26.7–42.9) 0.041 Role Emotional 78.9(72.9–84.9) 77.2(70.1–84.3) 0.717 Vitality 47.2(44.1–50.2) 49.6(45.8–53.4) 0.321 Mental Health 63.2(60.7–65.8) 65.8(62.8–68.8) 0.192 Social Functioning 48.8(43.7–54.0) 60.3(54.5–66.1) 0.004 Bodily Pain 48.1(43.6–52.6) 53.8(48.6–58.9) 0.106 General Health 43.0(39.0–46.9) 51.8(46.7–56.9) 0.007 Physical Component Summary (PCS) 37.8(33.7–41.8) 47.9(42.6–53.2) 0.003 Mental Component Summary (MCS) 59.5(56.4–62.6) 63.2(59.2–67.2) 0.149

*Significance between different living arrangement and self reported chronic illness based on student’s t test

Another scale for measuring quality of life is the World Health Organization Quality of Life (WHOQOL-BREF), which is measuring global quality of life where as SF-36 measures health related quality of life (Huang et al. 2006). The various facets incor- porated within physical health domain of WHOQOL-BREF are, activities of daily living; dependence on medicinal substances and medical aids; energy and fatigue; mobility, pain and discomfort; sleep and rest and work capacity, whereas the various components included in the physical component summary score of SF-36 are physical functioning, role physical, role emotional and vitality. Psychological domain of WHOQOL-BREF includes, bodily image, and appearance; negative feelings; positive feelings; self-esteem; spirituality/ religion/personal beliefs; and thinking, learning, memory and concentration and mental component summary score of SF-36 includes mental health, social functioning, bodily pain and general health. The mean physical component summary (PCS) score of 42.2 in our study was lower than the PCS of 55.0 reported from Tehran (Tajvar et al. 2008) and from the Indian state Ageing Int

Table 4 Correlates of health-related quality of life: Results of multivariate linear regression analysis of physical component summary and mental component summary scores

Variable Reference Category Regression coefficient (SE)

PCS MCS

Age 70+ years 60–69 years 12.35 (3.05)** 4.18 (2.41) Education Up to high school High school and above 13.18 (3.27)** 7.76 (2.58)* Employment Unemployed Currently employed 15.81 (4.34)** 9.81 (3.42)* Retired 17.14(4.35)** 12.07(3.43)** Living arrangement Living without spouse living with spouse 0.97 (3.40) 5.69 (2.68)* Self reported chronic illness At least one chronic illness 10.09 (2.93)** 3.81 (2.31) No chronic illness Caste Lower Upper 7.44(3.72)* 8.74(2.89)*

Other variable considered in the two models but not found significant were religion, monthly income and sex. SE: Standard Error, *p <=0.05,**p < =0.001 of Andhra Pradesh (49.0) (Varma et al. 2010) among older adults. Our PCS score was similar to the normative PCS value of 41.0 for older adults in United States (Mishra and Schofield 1998). Possible explanations for lower PCS score in our study compared to the other rural study from India could be the small sample size of 71 older adults in the other Indian study and the other explanation could be that Kerala had the highest morbidity rates among the Indian states (Kumar 1993; Kannan et al. 1991) and morbidity is associated with lower self-rated quality of life as seen in our study and a few previous studies (McDaid et al. 2013). The poorer health related quality of life among women in our study was similar to that reported by Ghosh in India (Ghosh 2015). It could be due to poorer access to health care, lower autonomy and education among Indian women. In Kerala and in Pathanamthitta district, people live longer probably with lots of morbidity. In Kerala, the infant mortality rate has been much lower than India as a whole from early 1960s. This means that a lot of low birth weight babies survived in Kerala compared to other Indian states where the survival rates were lower. These babies born in Kerala are now elderly who are more likely to suffer from several chronic illnesses such as diabetes as postulated by Barker hypothesis (Barker 1995). Rural Kerala (Thankappan et al. 2010) and Pathanamthitta district (Vijayakumar et al. 2009) were reported to have very high prevalence of diabetes almost double than that of rural India. The mean mental component summary (MCS) score of 61.2 in our study was similar to the mean score of 63.8 reported from Tehran (Tajvar et al. 2008) and higher than the mean MCS score of 52.0 reported from the other Indian study (Varma et al. 2010). MCS score in our study of 61.2 was higher than the normative MCS score of 51.0 in the United States (Mishra and Schofield 1998). Ageing Int

Mean PCS score and MCS score in our study decreased with age as reported from Australia (Mishra and Schofield 1998), although the magnitude of decrease in MCS score was lower than the PCS score. Similar to the findings reported earlier (Walters et al. 2001;Wyssetal.1999), women had significantly lower scores compared to men in our study. Although women in Kerala live about six years more than men (77.8 Vs 71.8 years) (Office of the Registrar General and Census Commissioner India 2013) as in several developed countries, the women in Kerala seem to be living with poor quality of life for a longer number of years compared to men. Higher educated, currently employed and retired older adults in our study were having better PCS score and MCS score which were in the expected lines. Similar findings were reported in other studies also (Heydari et al. 2012). Older adults belonging to lower caste were reported to have poorer self-rated health and higher levels of disability compared to those of upper caste in a previous study from India (International Institute for Population Studies 2013). Those living with spouse reported significantly better scores for all components similar to that reported earlier (Michael et al. 2001). Similar to what is reported earlier (Aghamolaei et al. 2010) our study found that suffering from chronic illness decrease the physical component score, where as such association was not significant in the MCS score. One of the limitations of our study was that we did not collect details on the accessibility or knowledge of various social security schemes for older adults. The study has also the limitation of generalizability to other parts of India since the findings from the study area are likely to provide an upper estimate of the various scores.

Conclusions

Physical component summary and mental component summary scores were signifi- cantly higher among better educated, employed and those who belonged to upper caste in this population. It seems that the higher life expectancy in this Indian district similar to the entire state of Kerala is due to increased access to health care helping people to survive longer. However, these additional years of life gained due to better access and utilization of health care in the district are with lots of morbidity particularly chronic disease morbidity resulting in lower self-reported physical wellbeing. The SF-36 scores on HRQL from this district are likely to be an upper estimate of these scores for rural India. Improving education and access to income through some form of pension, employment or social security for older adults are likely to improve the PCS and MCS scores in rural India.

Compliance with Ethical Standards

Conflict of Interest There are no conflicts of interest.

Informed Consent Written informed consent was obtained from all the participants before the study.

Ethical Treatment of Experimental Subjects (Animal and Human) Ethical clearance for the study was obtained from Institute Ethics Committee of a national institute. Ageing Int

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References

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Ms. Anna Pius obtained her nursing degree from the Christian Medical College (CMC) Vellore, Tamil Nadu, India. After her nursing training, she worked as a clinical instructor in the community department in the college of nursing, CMC Vellore for a few months and then as a staff nurse in CMC Vellore for about two years. She completed her Master of Public Health from Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum in the year 2014. Her research interests are in Palliative Care, community psychiatry and public health.

Dr. G.K. Mini is adjunct faculty in Global Institute of Public Health, Ananthapuri Hospitals and Research Institute, Trivandrum, Kerala. She worked in Sree Chitra Tirunal Institute for Medical Sciences and Technol- ogy, Trivandrum, for a period of more than ten years. She is a trained Demographer. Dr. Mini has a long record of research publications in public health. She also served United Nations Development Program in strength- ening state plans for human development and contributed the development of human development reports of Kerala and now working in World Food Program India as UN Consultant.

K R Thankappan is Emeritus Professor at the Achutha Menon Centre for Health Science Studies of Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India that hosts India’s first Master of Public Health (MPH) program. He has been trained as a public health physician and took his MD in social and preventive medicine from Trivandrum medical college in 1986 and an MPH in international health from the Harvard School of Public Health, USA in 1997. He is a fellow of the National Academy of Medical Sciences, India. His research interests are chronic non-communicable diseases and their risk factors.