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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 18  |  Issue : 4  |  Page : 186-190

Quality of life among malnourished elderly population in rural Puducherry, South India


1 Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
2 Department of Community Medicine, Indira Gandhi Medical College and Research Institute, Puducherry, India
3 Department of Community Medicine, Sri Lakshmi Narayana Institute of Medical Sciences (Affliated to BIHER), Puducherry, India
4 Department of Family Medicine, Private Hospital, Puducherry, India

Date of Submission17-Sep-2022
Date of Decision30-Oct-2022
Date of Acceptance11-Nov-2022
Date of Web Publication27-Dec-2022

Correspondence Address:
Dr. Premnath Dhasaram
Department of Community Medicine, Sri Lakshmi Narayana Institute of Medical Sciences, Osudu, Agaram Village, Villianur Commune, Kudupakkam Post, Puducherry - 605 502
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jiag.jiag_52_22

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  Abstract 


Context: Health of the elderly will be an important issue in defining the health status of a population in coming years. There is a paucity of information with regard to quality of life (QOL) among malnourished elderly. Aims: To assess the QOL among malnourished elderly in a rural field practice areas of tertiary care hospital, Puducherry, and to find the sociodemographic factors associated with QOL among malnourished elderly population. Settings and Design: A community-based cross-sectional study in the rural field areas of Government Medical College of Puducherry. Subjects and Methods: After obtaining ethical approval, the study was conducted among 225 malnourished elderly (>60 years) from April to June 2019 using multistage random sampling technique. Sociodemographic data were obtained using a semistructured questionnaire. Malnutrition was screened using Mini Nutritional Assessment Short Form and QOL was assessed using World Health Organization QOL-BREF questionnaire. Results: The mean and standard deviation of the study participants' age was 69.89 + 6.3 years. 57.8% of them were female, 81.3% were unemployed, and 73.3% were dependent on their old age pension for their livelihood. QOL of malnourished elderly was poor in all the domains when compared to those without malnourished and this is found to be statistically significant. In binomial logistic regression analysis, the presence of comorbidity (adjusted odds ratio [AOR]: 2.4 and 95% confidence interval: 1.3–4.4), unemployed (AOR: 4.8; 1.4–15.9), and living without family (AOR: 0.2; 0.06–0.7) revealed the statistically significant association with low QOL score among malnourished elderly. Conclusions: The mean score of QOL among malnourished elderly was below average in all four domains in which psychosocial domain was badly affected.

Keywords: Elderly, Mini Nutritional Assessment Short Form, quality of life, World Health Organization quality of life-BREF


How to cite this article:
Sivapushani A, Mathiyalagen P, Dhasaram P, Sivadamien T. Quality of life among malnourished elderly population in rural Puducherry, South India. J Indian Acad Geriatr 2022;18:186-90

How to cite this URL:
Sivapushani A, Mathiyalagen P, Dhasaram P, Sivadamien T. Quality of life among malnourished elderly population in rural Puducherry, South India. J Indian Acad Geriatr [serial online] 2022 [cited 2023 Feb 8];18:186-90. Available from: http://www.jiag.com/text.asp?2022/18/4/186/365777




  Introduction Top


Aging is an irreversible biological process which starts at conception and ends after death. According to the World Health Organization (WHO) and UNFPA (2015), the proportion of people aged 60 years and more is 12.3%.[1] As per the Indian census (2011), 8.6% of total population were estimated to be >60 years, and in Puducherry, it is 9.64%. This is likely to rise to 19% by 2050.[2] By 2025, the number of elderly is expected to rise to >1.2 billion; among those, 840 million in low-income countries.[2] According to the WHO, health of the elderly will be an important issue in defining the health status of a population in the coming years.[3] Malnutrition has been defined as “a pathological state resulting from a relative or absolute deficiency of essential nutrients or excess of one or more essential nutrients.” Malnutrition comprises four forms, which include undernutrition, overnutrition, imbalance, and specific deficiency.[4] The WHO defined quality of life (QOL) as “an individual's perception of life in the context of culture and value system in which he or she lives and in relation to his or her goals, expectations, standards and concerns.”[5] At global level, QOL is an important area of concern which reflects the health status and well-being of the elderly population. It was known that sociodemographic variables such as advanced age, education, marital status, and family structure influence the QOL among elderly population. In addition, various studies have shown that the presence of chronic morbid conditions is significantly associated with low QOL.[6] However, there is a paucity of information with regard to this in developing countries including India. This study aimed to explore QOL in four domains and its associated sociodemographic factors among malnourished elderly in rural Puducherry, South India.


  Subjects and Methods Top


Study design and setting

A community-based cross-sectional study was conducted during April to June 2019 in the five rural service areas attached to the tertiary care hospital, Puducherry, namely Nirnayanpet, Sembianpalayam, Embalam, Keezhagaragaram, and Achariyapuram. Total geriatric population in those areas was about 844.

Sample size calculation

We assumed that the prevalence of poor QOL among malnourished elderly as 50%, an error of margin 7% and non-response rate of 10%, the minimum sample size was estimated to be 225.

Sampling technique

Multistage random sampling technique was used as the sampling method. Our Rural Health Training Center caters the services to 19 villages in Puducherry. Initially simple random sampling technique using lottery method without replacement was employed to select five villages from the 19. From the selected villages geriatric individuals list (sample frame) was collected from the ANM register. Total 844 elderly individuals of the selected five villages were screened using MNA-SF questionnaire to identify malnourished individuals. Around 310 were found to be malnourished. Eligible subjects were selected from each area by population proportionate to size. Applying simple random sampling, 225 malnourished elderly were recruited for the study [Figure 1]. By the same sampling technique, 225 nonmalnourished elderly were also chosen from the 844 screened population to compare the QOL.
Figure 1: Sampling technique of the study – multistage

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Study tool

Mini Nutritional Assessment Short Form (MNA-SF) questionnaire was used for screening the geriatric malnutrition, which was tested and validated.[7] This instrument contains 6 questions with the screening score of 0–14. The score of <7 was considered malnourished.[8] QOL was assessed using WHOQOL-BREF questionnaire among the malnourished elderly. WHOQOL-BREF questionnaire contains four domains, namely, physical health, psychological, social relationships, and environment with a total of 26 questions. Each of these domains was rated on a five-point Likert scale. As per the WHO guidelines, 25 raw scores for each domain were calculated by adding values of single items and it was then transformed to a score ranging from 0 to 100, where 100 is the highest and 0 is the lowest value.[9] These questionnaires were translated to Tamil and then back translated in English to assess the reliability of the instruments.

Method of data collection

Face-to-face interview was conducted in the residents of the study subjects. Data on sociodemographic profile, which include age, gender, education, living arrangement, marital status, pension, and morbidity status, were collected using the semistructured questionnaire. Morbidity status was assessed using the previous diagnosis made by the medical practitioner. Subjects not available for two consecutive visits were considered as nonrespondents.

Statistical analysis

Data collected were entered into MS-Excel sheet and analyzed using SPSS Inc. Released 2007. SPSS for Windows, Version 16.0. IBM, Chicago, USA. The findings were expressed as mean and standard deviation. The mean score difference was tested using independent t-test and analysis of variance (ANOVA), and binominal logistic regression analysis was done to determine the independent factors associated with lower QOL scores. P < 0.05 was considered statistically significant.


  Results Top


Totally 844 elderly were screened for malnutrition, of which 310 were found to be malnourished. Among 225 study participants, 57.8% (130) were female. The mean age of them was 69.89 years. 70.7% were illiterate, 81.3% were unemployed, 21.3% were living alone, and 73.3% were completely dependent on the pension for their livelihood. 58.7% were having comorbid condition.

A comparison of QOL of elderly with and without malnourishment indicates that the physical, psychological, and social domains of malnourished elderly are considerably low and this difference was statistically significant. Overall, QOL scores of malnourished elderly were below average in all four domains, of which the psychosocial domain was badly affected [Table 1].
Table 1: Quality of life domain scores of malnourished and nonmalnourished elderly

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[Table 2] shows the comparison of scores in four domains of QOL WHO-BREF scale with sociodemographic variables and comorbidity. In univariate analysis, the QOL scores of Malnourished elderly of age more than 69 years, female gender, illiterates, those who are not working, widow or unmarried, living alone, living with pension as the only source of income and those who are suffering from any of the co-morbidities currently are found to have poor QOL score in all the four domains. These differences are found to be statistically significant (p value <0.05). But in certain socio-demographic variables like age, gender of the participant and income earning capacity there were no difference in the QOL scores of the social relationship domain.
Table 2: Association of quality of life with sociodemographic profile and comorbidity (n=225)

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The variables with statistically significant difference in univariate analysis were then analyzed using binary logistic regression. Factors that includes presence of co-morbidity, unemployment and those who are living alone are predictors of poor QOL among malnourished elderly. The odds of poor QOL among participants with co-morbidity and unemployment were 2.4 (1.3-4.4) and 4.8 (1.4-15.9) respectively [Table 3].
Table 3: Binary logistic regression analysis of quality of life scores

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  Discussion Top


This study was found to be the first research which assessed the QOL among malnourished elderly. However, the prevalence of geriatric malnutrition in rural Puducherry was estimated to be 24.8% using BMI as a tool[10] Ananthesh et al.'s study on community-based cross-sectional study to assess malnutrition among elderly population residing in urban and rural areas of a district in Karnataka using MNA questionnaire showed the prevalence of malnutrition to be 28.4% and 8.8% in rural and urban areas, respectively.[11] Our study intended to explore the QOL of malnourished elderly population. In the present study, 844 elderly were screened for malnutrition using MNA questionnaire, of which 310 were malnourished. Therefore, the overall prevalence of malnourished elderly in our study area was found to be 36.7%, which was almost similar to Karnataka study.

Our study highlighted the fact that the overall QOL score of malnourished elderly was found to be below average. The psychological and social domain mean scores were observed to be 44.3 and 45.4, respectively, of which psychological domain was badly affected. This finding is comparable with the study done by Thadathil SE et al in rural kerala among the elderly, where the mean score in psychological domain of QOL was 26.9 using the WHOQOL-BREF questionnaire.[12] The possible explanation could be because as age advances the loneliness, boredom, depression, grief and worrying about the future are common which alters their psychological health.[12] Mudey et al.'s study in Maharashtra showed that rural elderly population had a significant lower level of QOL than urban, especially in the social relation (55.9 ± 2.7) and environmental (57.1 ± 3.2) domain.[13] The difference observed in the four domains may be due to difference in the pattern of associated factors which influence the QOL in different settings.

In the present study, factors such as age group of 60–69 years, educated individuals, living with spouse and children, and absence of comorbidity were identified to have a better QOL score even though the elderly individuals were malnourished. Economic factors are found to have a strong relationship in four domain scores. Gureje et al. stated that economic factor is the strong predictor for QOL.[14] Furthermore, our finding that QOL was significantly affected in those who were not living with spouse was similar to those obtained from another study.[7]

McDaid O et al studied the effect of chronic health conditions on elderly QOL and revealed that chronic morbid condition adversely affects the QOL of the individual.[15] Similar finding was identified by the study done in Puducherry by Kumar et al and stressed that the presence of co-morbidities and its complications are important factors that lowers the QOL. This may be because the malnutrition affects the functional capacity leading to chronic morbid health conditions that directly affects the QOL. Also in the study done by Maseda et al in Spain shows that being female, impaired resources, low scores in physical domain are strongest determinants of risk of malnutrition which triggers chronic morbid condition and that leads to lower QOL.[16]

The limitations of the study were presence of chronic morbid conditions was recorded during oral interview in door-to door data collection visit but it was neither verified with health records nor blood investigations performed to increase the yield of undiagnosed chronic morbid conditions. Malnourished participants who are working and earning income had better QOL. So the government policies on social security schemes, nutritional support programs, geriatric health clinics, national program for healthcare of the elderly should be strengthened for creating an environment that improves the QOL domains of the malnourished elderly.


  Conclusions Top


QOL of malnourished elderly was average and it is lower compared to that of non-malnourished elderly. Of the four domains of QOL the psychological domain was the most affected. Presence of co-morbid condition, unemployed and living alone are strong predictors of QOL among malnourished elderly. Nutritional status assessment and potential determinant factors should be incorporated as part of comprehensive assessments for early identification of malnutrition and to determine appropriate intervention strategies to address this public health problem in older adults.

Ethical approval

Institute ethical committee clearance (No. 6/187/IEC-25/PP/2019) was obtained for conducting the study. Informed consent has been obtained from the study participants before the conduct of the study.

Acknowledgment

The authors sincerely thank the elderly population who contributed to this study. We would like to thank the World Health Organization and Nestle Institute of Nutrition for granting permission to use the WHOBREF-QOL and MNA-SF questionnaire. We sincerely thank our institute and our department for guiding us for the conduct of this study

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest



 
  References Top

1.
WHO Ageing and Health Factsheet; 2017. Available from: https://www.who.int/features/factfiles/ageing/en. [Last accessed on 2019 May 12].  Back to cited text no. 1
    
2.
Population Composition-Census of India; 2011 Available from: www.censusindia.gov.in>9chap2-2011. [Last accessed on 2019 May 12].  Back to cited text no. 2
    
3.
World Health Organization. The World Health Report 2008: Primary Health Care (Now More Than Ever. Geneva: World Health Organization; 2008. Available from: http://www.who.int/whr/2008/en. [Last accessed on 2019 May 12].  Back to cited text no. 3
    
4.
Park K. Park's Textbook of Preventive and Social Medicine. 24th ed. Jabalpur: Bhanot Publishers; 2011. p. 631.  Back to cited text no. 4
    
5.
World Health Organization. WHOQOL-BREF: Introduction, Administration, Scoring and Generic Version of the Assessment. Programme on Mental Health. Geneva: World Health Organization; 1996. Available from: http://www.who.int/mental_health/media/en/76.pdf. [Last accessed on 2018 Dec 30].  Back to cited text no. 5
    
6.
Kumar SG, Majumdar A, Pavithra G. Quality of Life (QOL) and its associated factors using WHOQOL-BREF among elderly in Urban Puducherry, India. J Clin Diagn Res 2014;8:54-7.  Back to cited text no. 6
    
7.
Barua A, Mangesh R, Harsha Kumar HN, Mathew S. A cross-sectional study on quality of life in geriatric population. Indian J Community Med 2007;32:146-7.  Back to cited text no. 7
  [Full text]  
8.
Nestle Nutrition Institute-MNA Elderly-MNA User Guide pdf. Available from: https://www.mna-elderly.com>user_guide. [Last accessed on 2018 Dec 18].  Back to cited text no. 8
    
9.
Qadri SS, Ahluwalia SK, Ganai AM, Bali SP, Wani FA, Bashir H. An epidemiological study on quality of life among rural elderly population of Northern India. Int J Med Sci Public Health 2013;2:514-22.  Back to cited text no. 9
    
10.
Kalaiselvi S, Arjumand Y, Jayalakshmy R, Gomathi R, Pruthu T, Palanivel C. Prevalence of under-nutrition, associated factors and perceived nutritional status among elderly in a rural area of Puducherry, South India. Arch Gerontol Geriatr 2016;65:156-60.  Back to cited text no. 10
    
11.
Ananthesh BG, Bathija VG, Bant DD. A community based cross-sectional study to assess malnutrition among elderly population residing in Urban & Rural areas of a district in Karnataka India. Int J Community Med Public Health 2017;4:51-8.  Back to cited text no. 11
    
12.
Thadathil SE, Jose R, Varghese S. Assessment of domain wise quality of life among elderly population using WHO-BREF scale and its determinants in a rural setting of Kerala. Int J Curr Med Appl Sci 2015;7:43-6.  Back to cited text no. 12
    
13.
Mudey A, Ambekar S, Ramchandra C, Agarekar S, Vasant V. Assessment of quality of life among Rural and Urban elderly population of Wardha District, Maharashtra, India. Ethno Med 2011;5:89-93.  Back to cited text no. 13
    
14.
Gureje O, Kola L, Afolabi E, Olley BO. Determinants of quality of life of elderly Nigerians: Results from the Ibadan study of ageing. Afr J Med Med Sci 2008;37:239-47.  Back to cited text no. 14
    
15.
McDaid O, Hanly MJ, Richardson K, Kee F, Kenny RA, Savva GM. The effect of multiple chronic conditions on self-rated health, disability and quality of life among the older populations of Northern Ireland and the Republic of Ireland: A comparison of two nationally representative cross-sectional surveys. BMJ Open 2013;3:e002571.  Back to cited text no. 15
    
16.
Maseda A, Diego-Diez C, Lorenzo-López L, López-López R, Regueiro-Folgueira L, Millán-Calenti JC. Quality of life, functional impairment and social factors as determinants of nutritional status in older adults: The VERISAÚDE study. Clin Nutr 2018;37:993-9.  Back to cited text no. 16
    


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