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ORIGINAL ARTICLE
Year : 2016  |  Volume : 60  |  Issue : 3  |  Page : 210-215  

Quality of life and sociodemographic factors associated with poor quality of life in elderly women in Thiruvananthapuram, Kerala


1 Assistant Professor, Department of Community Medicine, Government Medical College, Manjeri, Kerala, India
2 Professor, Department of Community Medicine, Government Medical College, Thiruvananthapuram, Kerala, India
3 Assistant Professor, Department of Community Medicine, Government Medical College, Thiruvananthapuram, Kerala, India
4 Assistant Professor, Department of Community Medicine, Government Medical College, Alappuzha, Kerala, India

Date of Web Publication24-Aug-2016

Correspondence Address:
Dr. R S Rajasi
Department of Community Medicine, Government Medical College, Manjeri - 676 121, Kerala
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0019-557X.189016

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   Abstract 

Background: India is going through a phase of demographic transition leading to population aging and feminization of aging resulting in increased proportion of elderly women than men. Problems faced by the elderly women are more critical than men due to family and social conditions prevailing in India. Objective: The study made an attempt to assess the quality of life (QOL) using the World Health Organization QOL (WHOQOL-BREF) scale and sociodemographic factors affecting QOL of elderly women residing in a community setting in South Kerala. Methods: A community-based, cross-sectional study to assess the QOL of elderly women using WHOQOL-BREF questionnaire. Data were collected from 160 elderly women. Results: 2.5% (95% confidence interval [CI]: 0.07-4.84) of the study participants were having "very good" QOL. 38.8% (95% CI: 31.2-46.4) had "good," 43.1% (95% CI: 35.4-50.8) had "poor," and 15.6% (95% CI: 9.98-21.22) had "very poor" QOL, respectively. QOL was least in the psychological domain followed by physical and health-related, social, and environmental domains. Logistic regression revealed age above 70 years (adjusted odds ratio [OR] - 11.3), nonpossession of property (adjusted OR - 8.99), neglecting attitude by family (adjusted OR - 6.9), and absence of visit by friends and relatives (adjusted OR - 9.9) as risk factors, whereas residing in the urban area as a protective factor (adjusted OR - 0.1) for poor QOL. Conclusion: It is possible to improve the QOL of elderly women by providing financial security, ensuring care, and by enhancing social relationships of elderly women.

Keywords: Demographic transition, elderly women, feminization of aging, quality of life, World Health Organization quality of life-BREF


How to cite this article:
Rajasi R S, Mathew T, Nujum ZT, Anish T S, Ramachandran R, Lawrence T. Quality of life and sociodemographic factors associated with poor quality of life in elderly women in Thiruvananthapuram, Kerala. Indian J Public Health 2016;60:210-5

How to cite this URL:
Rajasi R S, Mathew T, Nujum ZT, Anish T S, Ramachandran R, Lawrence T. Quality of life and sociodemographic factors associated with poor quality of life in elderly women in Thiruvananthapuram, Kerala. Indian J Public Health [serial online] 2016 [cited 2019 Sep 18];60:210-5. Available from: http://www.ijph.in/text.asp?2016/60/3/210/189016


   Introduction Top

"Aging is a progressive, generalized impairment of functions resulting in loss of adaptive responses to stress and in increasing risk of age-related diseases." [1] Population aging, the phenomenon by which older individuals come to form a proportionately larger share of the total population, is one of the most distinctive demographic patterns of the contemporary world. [2] The United Nation defines a country as "aging" when the proportion of people over 60 years reaches 7%. India exceeded that rate of proportion by 7.8% in 2001 and had come under the purview of the United Nations definition of an aging country. [3] India's elderly population has already crossed 100 million mark during 2011 (8.2%) and is projected to cross 177 million by the year 2025. [4] In general, it is observed to have a feminization in aging since women have more life expectancy and live longer than men. As per the report of the technical group on population projections constituted by the National Commission on Population, India has a population of 50.33 million women above the age of 60. [5] As per the census 2011, though sex ratio for total Indian population is in favor of male population in ratio 940:1000, for elderly population aged 60 and above it is in favor of elderly women by 1022:1000. [6] The problems faced by the women in India are more critical compared to their male counterparts because many women are illiterate, jobless during their prime ages, and customary ownership of property in India is mostly by men. [7] Marginalization, alienation, social insecurity, restricted social interaction, limited earning possibilities, multiple medical complications, emotional isolation, limited awareness regarding their legal rights, and natural reluctance to seek justice are their other issues. [8] Among the Indian states, Kerala has the largest population of elderly and the growth rate among the aged is still increasing. [9] According to the 2011 Census, people above 60 years constitute 13% of the state's population of 33.4 million compared to the national figure of 8.2% and is projected to be 15.6% in 2012. [10] The demographic transition of Kerala outpaces the rest of the country by 25 years, according to the Planning Commission's Kerala Development Report for 2008. [11] It is estimated that by the middle of the 21 st century, more than quarter of Kerala's population will be above 60 years of age. Then, there will be more than one elderly on an average per household, and a major amount of family income will be utilized for caring them. [12] The elderly women represent the fastest growing age group in the population of Kerala, [13] and the threat of population aging is more severe here than any other state in India since Kerala continues to be the only state where females outnumber males with sex ratio of 1084:1000. [6]

The present study made an attempt to assess the quality of life (QOL) and the sociodemographic factors associated with poor QOL of elderly women residing in the geographic area under the jurisdiction of Medical College Health Unit, Pangappara, Thiruvananthapuram in South Kerala in India using the World Health Organization QOL (WHOQOL-BREF) scale.


   Materials and Methods Top


Study design and population

A community-based, cross-sectional study was carried out by the house-to-house visit to assess the QOL of elderly women using the WHOQOL-BREF scale. The WHO defines QOL as "an 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, expectations, standards, and concerns." [14] In measuring QOL therefore, the WHOQOL Group takes the view that it is important to know how satisfied or bothered people are by important aspects of their life, and this interpretation will be a highly individual matter. The study was conducted in the Medical College Health Unit, Pangappara, Thiruvananthapuram, which included both urban and rural wards and had a population of 110,286 and 26,410 households as on July 2009. Elderly women aged 60 years and above residing in Pangappara Medical College Health Unit were eligible for the study. The total population of elderly women in Pangappara was estimated to be 7180. Written informed consent was obtained from each of the study participants before collecting data. Those who were unable to talk or respond to the questionnaire, those who had preexisting mental illnesses, and those who were not willing to take part in the study were excluded. QOL was assessed using WHOQOL-BREF questionnaire. This was translated in local language Malayalam and was pretested in the field before the survey. Moreover, this tool was already validated and pilot tested in South India. [14] The total score of WHOQOL-BREF questionnaire was 400 which included items from four domains, namely physical health, psychological, social relationships, and environment. Data regarding sociodemographic variables were collected using a structured questionnaire which was finalized based on a pilot study done in a small sample of elderly women. The study period extended from July 15, 2009, to January 15, 2010.

Sample size estimation and sampling technique

The sample size was estimated using the formula 4pq/l[2] . Those scored less than the median were considered as having a poor quality of life. As per the pilot study conducted in a random sample of forty elderly women residing in Pangappara including twenty each from rural and urban areas, 27% had very poor QOL, 57.2% had moderately poor, 13.5% had moderately good, and only 2.3% had very good QOL. The prevalence of poor quality of life was expected to be around 84% accordingly. This information was used for the current study and effective sample size was estimated to be 76 with a precision of 90%. To account for the design effect of cluster sampling, twice the calculated sample was taken which came out to be 150. For the present study, data from 160 elderly women were collected. Sampling done was two-stage cluster sampling. Of the 29 wards in Pangappara area, 16 wards were identified using probability proportionate to size (PPS) sampling out of which 9 were from rural wards and 7 were from urban. Ten elderly women residing in each of these wards were selected as clusters for the study. The first house of every cluster was selected from respective wards as per convenience PPS done as follows. The total cumulative population which formed the sampling frame was calculated by taking the sum of the population residing in each of the 29 wards. As the sample size was estimated to be 160, cluster number was arbitrarily fixed as 16 for selecting ten subjects from each cluster. Sampling interval was found by dividing the total cumulative population by 16 clusters. A random number which was less than or equal to the sampling interval was selected with the help of a random number table. The first cluster was then located as the ward which contained the cumulative population same as the random number. The rest 15 clusters were identified similarly by using the formula "random number + sampling interval" for each cluster.

Ethical consideration

The study protocol was submitted to the Human Ethical Committee after attaining clearance from research committee. The study was commenced only after getting consent from the Human Ethical Committee. Informed consent was also obtained from each of the study participants and/or the head of the family by using a consent form.

Statistical analysis

WHOQOL-BREF questionnaire was administered to get data regarding QOL and a structured questionnaire for sociodemographic variables from 160 elderly women who were residing in 16 wards. Sufficient privacy was ensured while interviewing all the elderly women to avoid respondent bias which might happen if they were interviewed in the presence of other family members. Data obtained from WHOQOL-BREF were later categorized into "very good" (scoring above 75 th percentile), "moderately good" (scoring between 75 th and 50 th percentile), "moderately poor" (scoring between 50 th and 25 th percentile), and "very poor" (scoring <25 th percentile) QOL. No missing data have been found. Univariate analysis was done using proportions for qualitative variables and using means in case of quantitative variables. Odds ratios (ORs) were calculated after classifying the participants as cases and controls based on the median. Cases included those with very poor and moderately poor QOL who scored below 50 th percentile and controls included those with very good and moderately good QOL who scored above 50 th percentile. Multivariate analysis was done using logistic regression and adjusted OR was calculated. The problem of multicollinearity was assessed using a correlation matrix analysis built using variables significantly associated with the outcome prior to the multivariate analysis. Independent variables to create the binary logistic regression model were selected accordingly. The problems with outliers were also taken care of using outlier labeling rule and Dixon's Q test. The overall reliability of WHOQOL-BREF scale for assessing QOL was good with a Cronbach's Alpha of 0.952. Alpha value of various subscale ranged from 0.91 to 0.98. This ensures the reliability of the study tool under the current setting. The statistical analysis was conducted using the statistical package SPSS for Windows version 16 ( SPSS Inc, Chicago, IL).


   Results Top


The baseline characteristics of all the 160 participants are given in [Table 1]. The mean score for QOL was found to be 177.42 (standard deviation [SD] =69.7) [Table 2]. While grading the QOL, only 2.5% (95% confidence interval [CI]: 0.07-4.84) of the study participants were found to have "very good" QOL, 38.8% (95% CI: 31.2-46.4) had "moderately good" QOL, another 43.1% (95% CI: 35.4-50.8) had "moderately poor" QOL whereas 15.6% (95% CI: 9.98-21.22) of study participants were suffering with "very poor" QOL. While doing domain-wise analysis, QOL [Table 2] score was least in the psychological domain (mean was 36.7, SD = 20) where more than one-fourth of the elderly women (28%) had "very poor" QOL and more than half (50.6%) had moderately poor QOL. Highest mean score was for physical- and health-related QOL (mean = 49.5, SD = 22) followed by environmental domain (mean = 47.38, SD = 17) and then social domain (mean = 43.7, SD = 18) where the proportion of elderly women falling under "very poor" QOL were 16.2%, 16.2%, and 14.4%, respectively. Case-control comparison revealed that the significant risk factors were age above 70 years with crude OR of 4.33 (95% CI: 2.21-8.48), absence of visit by friends and relatives with crude OR 6.1 (95% CI: 1.69-21), neglecting attitude by family members with crude OR of 4.99 (2.44-10.19), and not having any role in family decisions with crude OR of 4.2 (95% CI: 1.83-9.56). Educational qualification of 10 th and below, current unemployment, previous unemployment, monthly income below 500 rupees, monthly family income below 1500 rupees were also found as risk factors whereas living in an urban area was a protective factor [Table 3].
Table 1: Characteristics of study participants


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Table 2: Domain-wise quality of life levels of study participants


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Table 3: Factors associated with poor quality of life


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However, after doing logistic regression adjusted odds for age above 70 years increased to 11.30 (P < 0.001), nonpossession of property to 8.99 (P < 0.001), neglecting attitude by family members to 6.9 (P < 0.001), and absence of visit by friends and relatives to 9.9 (P < 0.001) whereas residing in the urban area became a significant protective factor (adjusted OR - 0.10, P < 0.001) [Table 4]. The classification table showed that the model could identify 96.9% of individuals with poor QOL correctly and could rule out 72.7%. The overall percentage is 90.7%. The model has got a Cox and Snell and Nagelkerke R2 = 0.478 and 0.613, respectively. The Omnibus tests of Model coefficients gave a χ2 = 20.70 (df = 4, P < 0.001). Hosmer and Lemeshow test for goodness of fit showed a χ2 = 13.18 (P = 0.106) suggesting a good model fit.
Table 4: Logistic regression for factors associated with poor quality of life of elderly women


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   Discussion Top


Age is an important criterion, which shows the physical and mental ability of a person. The life expectancy at birth in India during 2002-2006 was 64.2 years for females and for males it was 62.6 years. At age 60, the average remaining length of life was found to be about 16.7 years for males and 18.9 years for females whereas at age 70, it was 10.9 years for males and 12.4 years for females. [7] Menopause is considered as stepping stone to old age which usually occurs around the age of 50. As per NSSO survey (2007-2008), elderly women in India constitute 7.7% whereas elderly males constitute 7.3%. [15] When Prof. Irudayarajan studied the demographic transition in Kerala, he found that 60-plus share of the population increased from 5% in 1961 to around 8% in 20 years and is projected to be 14% by 2016. The 70-plus and 80-plus elderly population of 10 lakh and 2.9 lakh in 1991 is projected to increase up to 25 lakh and 8 lakh in 2021. [16] Economic Review 2005 shows that out of the total elderly women in Kerala state, 58% are in the age group of 60-69 years, 30.3% are in the age group of 70-79 years, and 11% are in the age group 80 and above. [17] The present study clearly indicates that the study participants can be regarded as a representative of elderly women in Kerala [Table 1]. A study by Ramamurti has found that negative effects of aging become more pronounced after the age of 70. [18] Majority of the study subjects were residing in urban areas, and this pattern is comparable with the Kerala scenario where the proportion of elderly females is more in the urban areas when compared to their rural counterparts. [9] However, the reverse is seen at the national level where three-fourth of the elderly live in the rural area. According to Barnabas, the incidence of widowhood and sufferings accelerates as age advances regardless of whether elderly women are living in the rural or urban sectors. [1]

As per 2001 census, 20% of elderly Indian women were literate whereas in Kerala, the figure was 58%. Even in 2007-2008, only 50% men and 20% of women aged 60 years or more were literate through formal schooling. [7] Large numbers of the elderly females were found to be illiterate, and the reason might be that 60 years back, the female education was rare in our country including Kerala. 63.75% of elderly women were found to be widows in the present study. The marital status of the older women is an aspect of family structure that deeply affects their living arrangements, support systems, and QOL. It constitutes a multiple support system for them in terms of emotional, financial, and social exchanges. [10] A study done by Agewell Foundation in 2011 revealed that older women who are living with their sons/daughters and grandchildren are suffering from emotional alienation. Due to the fast changing socioeconomic scenario of the country, younger generations hardly get time to interact with their elderly family members. The nuclear family system has also crushed strong traditional bond between grandchildren and grandmothers. [4] Results brought out by Gulati on population aging and elderly women in Kerala states that 7% of the women above the age of 60 were employed. [19] Only 14.5% of elderly women were found to be economically independent whereas the figure was comparatively more for elderly male coming to be around 41.7%. [20] According to Prof. Irudayarajan, the old-age dependency ratio climbed from 10.9% in 1961 to 13.1% in 2001 for India as a whole and is projected to be 25% by 2021 [16] For females and males, the values were 13.8% and 12.5%, respectively, in 2001. A study published by Central Statistics Office Ministry of Statistics and Programme Implementation in 2011 showed that among economically dependent elderly men in India, around 7% were financially supported by their spouses, around 85% by their own children, 2% by grandchildren, and 6% by others. Among elderly women, 20% and less depended on their spouses whereas more than 70% on their children, 3% on grandchildren, and 6% or more on others. [7] Population aging is a reflection of social development and public health. However, "if aging is to be a positive experience, it must be accompanied by improvements in the QOL of those who have reached or are reaching old age." [21] Elderly women in India are vulnerable to various hazards even from their infancy. Old age is an accumulation of all these hazards and disadvantages that reduces the QOL. In the Bureau of Women's Welfare and Development's Women in India-a statistical profile, it is clearly mentioned that psychological disorders are the major clinical symptoms of elderly widows. Anupriyo's study on "elder abuse and neglect" has found that the aged mostly are worried over finance and suffer from anxiety, feelings of being unwanted, isolation, and loneliness. [22] A similar study conducted by Joshi et al. among the elderly population in Northern India showed that psychological wellbeing, physical and social support almost had identical influences on QOL. [23] Few decades back, the condition of elderly women in India was not even mentioned as the average life expectancy of females was <60 years. However, now with fast-growing elderly population, increased life expectancy and higher percentage of elderly women among the elderly population, issues of elderly women is a matter of major concern. If we ignore this matter today, this may turn into a major social development challenge tomorrow. It is high time we shift our focus toward the issues of elderly women since they have specific needs which require special attention.


   Conclusion Top


This study suggests that majority of elderly women were having poor QOL, and it was most affected for those residing in the rural areas. There are multiple factors such as financial support, family support, and education which determine the good quality of life for elderly. It is possible to improve the QOL of elderly women by providing financial security, ensuring care, and by enhancing social relationships of elderly women.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
   References Top

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Radhakrishnan MG. Old′s Own Country: Kerala′s Elderly Population Brings Forth a Crisis. India Today; 29 October, 2011.  Back to cited text no. 10
    
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Shaju P. A Youthful, Migrant Workforce Takes Root in an Ageing Kerala. Indian Express. Sec: 11; 21 February, 2013. Available from http://archive.indianexpress.com/news/a-youthful-migrant-workforce-takes-root-in-an-ageing-kerala/1077170/. [Last accessed on 2015 Jan 26].  Back to cited text no. 11
    
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Zachariah KC, Irudayarajan S. Gulf Revisited: Economic Consequences of Emigration from Kerala Emigration and Unemployment. Centre for Development Studies Thiruvananthapuram. Working Paper. No. 363; 2004.  Back to cited text no. 12
    
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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]


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