|Year : 2020 | Volume
| Issue : 3 | Page : 266-270
Nutritional status among elderly: A community-based cross-sectional study
Kriti Vaish1, Somdatta Patra2, Pragti Chhabra3
1 Senior Resident, Department of Community Health, St. Stephen's Hospital, New Delhi, India
2 Associate Professor, Department of Community Medicine, University College of Medical Sciences, Delhi, India
3 Director Professor, Department of Community Medicine, University College of Medical Sciences, Delhi, India
|Date of Submission||28-Mar-2019|
|Date of Decision||12-Dec-2019|
|Date of Acceptance||10-Jun-2020|
|Date of Web Publication||22-Sep-2020|
Department of Community Medicine, University College of Medical Sciences, Delhi - 110 095
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Malnutrition in the elderly is a significant public health problem and has serious implications on the overall health of the elderly. There are very few community-based studies on malnutrition among the elderly, especially in India. Objectives: The objective was to find out the prevalence of malnutrition and its associated risk factors among the elderly in urbanized villages of Delhi. Methods: A cross-sectional study was conducted during November 2015 to April 2017 in two urbanized villages of East Delhi among 353 elderly (>60 years) individuals. A semi-structured interview schedule was used to record the sociodemographic and relevant personal details of the elderly. To determine the prevalence of malnutrition, Mini Nutritional Assessment scale was used. Statistical analysis included simple descriptive analysis and tests of significance such as Chi-square test. Multivariable logistic regression was used to identify the predictors of malnutrition. Results: The prevalence of possible malnutrition was found to be 49.3%. On bivariate analysis, age, gender, education status, marital and residential status, per capita income, financial dependency, and family size were found to be associated with malnutrition (P < 0.05). Age >70 years, being tenant, and financial dependency were observed to be significant predictors of possible malnutrition in multiple logistic regression. Conclusion: Malnutrition needs to be identified at an early stage using appropriate tools so that proper interventions can be directed to those who need it to ensure healthy aging. Social and economic parameters are linked with the occurrence of malnutrition among the elderly and must be considered in the development of preventive strategies.
Keywords: Community, elderly, Mini Nutritional Assessment, nutrition
|How to cite this article:|
Vaish K, Patra S, Chhabra P. Nutritional status among elderly: A community-based cross-sectional study. Indian J Public Health 2020;64:266-70
|How to cite this URL:|
Vaish K, Patra S, Chhabra P. Nutritional status among elderly: A community-based cross-sectional study. Indian J Public Health [serial online] 2020 [cited 2020 Oct 30];64:266-70. Available from: https://www.ijph.in/text.asp?2020/64/3/266/295785
| Introduction|| |
Old age and nutrition has now become a global challenge. In old age, nutritional inadequacies arise as a result of age-related physiological changes, stressful life events, impaired activities of daily living, lack of financial support, inadequate access to food, food choices, frequent illness requiring higher nutrient intake or nutrient loss, and poor absorption of nutrients. All these factors often lead to malnutrition.
Malnutrition contributes to decline in health status, increased utilization of health-care services, and increased mortality. The magnitude of malnutrition among the elderly in India is not very well known., There is a scarcity of community-based studies to assess malnutrition in this particular vulnerable age group. As the burden is not known, the country's nutritional intervention programs are usually directed toward children and pregnant and lactating mothers, and elderly are often neglected. Thus, it becomes necessary to assess the nutritional status of the elderly as it can be easily corrected and a better quality of life can be achieved. A study was conducted to assess nutritional status and associated risk factors among the elderly in urbanized villages of East Delhi.
| Materials and Methods|| |
A community-based, cross-sectional study was conducted in two urbanized villages of East district of Delhi, which are the field practice areas of department of community medicine of a medical college in Delhi. The duration of the study was from November 2015 to April 2017.
Elderly residents ≥60 years, both male and female, residing in study villages for more than 6 months were included. The elderly who could not be contacted despite two consecutive visits or were not willing to give consent or not able to comprehend the questionnaire or too ill to participate were excluded from the study.
The prevalence of possible malnutrition among the elderly was reported to be 63% in a previous study from India. Sample size was calculated at 95% confidence level taking absolute error as 5% in “Epi info” software 7: A database and statistics program for public health professionals. CDC. Atlanta: CDC. 2011). The calculated sample size after rounding up to nearest ten was 360.
The study participants were selected by systematic random sampling as follows. One hundred and eighty study participants from each of the study villages were included. Both villages had a population of approximately 30,000 and 6000 families (assuming average family size as 5). Hence, the study participants were recruited from every 33rd family. The first family (n) to be included in the study was selected using a computer-generated random number. If the study participant was not found in that particular family (n), then the next family between (n) and (n + 33) family with an eligible study participant present in it was selected for the study. If there was more than one elderly present in a family, then one of them was included by random selection. A map was used to ensure that the whole village is covered. Those who were not available on the day of visit were revisited after 1 week and if could not be contacted despite two consecutive visits were excluded from the study. The age of the participants was ascertained from their birth certificates (if available) or from identity cards issued by the Election Commission of India. If both these records were not available, age was calculated using some past significant national/local event and cross-questioning the participants regarding their major life events.
To assess nutritional status, the Mini Nutritional Assessment (MNA) questionnaire was used. It is a validated screening tool to provide a single, rapid assessment of nutritional status among the elderly with a sensitivity of 96%, a specificity of 98%, and a predictive value of 97%. It has been validated and translated into several languages in many countries including India.,
The full MNA includes 18 items. The response of each item has a numerical value and contributes to the final score, which has a maximum value of 30. For the current study, nutritional status was classified as normal nutrition (24–30 points) and possible malnutrition.,, Possible malnutrition included malnourished (<17 points in MNA) and those at risk of malnutrition (17–23.5 points in MNA).
Ethical clearance was obtained from the Institutional Ethics Committee Human Research (IEC-HR) of University College of Medical Sciences prior to conducting the survey. The participation was purely voluntary. The elderly selected for the study were contacted by the researcher personally at their respective houses. Written informed consent was taken from all the participants prior to conducting the study. Sufficient time was spent with the elderly to obtain the desired information. The information collected was kept confidential. If a study participant was found to have any health problem, he or she was counseled and if necessary was referred to the urban health training center or the associated hospital for further management.
The data collected were entered into MS Excel and cleaned. The cleaned data were analyzed using IBM SPSS Statistics for Windows, Version 20.0. (Armonk, NY: IBM Corp). Simple bivariate analysis was used to compute association between various sociodemographic factors and nutritional status of the elderly. To control for confounding factors, multivariable logistic regression analysis was used. Only those independent variables which had a P < 0.05 on bivariate analysis, were selected. Backward step-wise likelihood ratio was used to find the significant predictors of possible malnutrition among the elderly. The criteria for entering and removing the independent variables from the backward step-wise model were P < 0.05 and P > 0.10, respectively.
| Results|| |
A total of 360 elderly were recruited via house-to-house visit, of which 7 were excluded from the final analysis as they had obvious spinal deformity. Finally, 353 elderly were included for the final analysis.
Characteristics of the study participants
Half (51%) of the study participants were between the age group of 60 and 64 years, having a mean age of 65.2 years (standard deviation: 5.4, range: 60–85). Around two-thirds of the participants were female (66.9%) and half (54%) were illiterate. Majority (78%) lived in a joint family. Two-third (61%) of the study participants were suffering from some known existing health problem; cataract (28.9%) was the most common complaint, followed by hypertension (27.8%) and diabetes mellitus (16.4%).
Around three-fourth (77%) of the elderly were financially dependent on their family members for financial support, while others were financially independent because they were either earning or were receiving pension.
Prevalence of malnutrition and associated risk factors for malnutrition
It was observed that only around half (50.7%) of the study participants had a normal nutritional status and 4.3% of the study participants were malnourished, while a large number of participants (45%) were at risk of malnutrition [Table 1].
|Table 1: Prevalence of malnutrition among the study participants according to Mini Nutritional Assessment Scale|
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Among the different parameters, it was found that there was a statistically significant (P < 0.05) association between nutritional status and age, gender, occupation, education, marital status, residential and migration, socioeconomic status, and family size of the participant [Table 2].
|Table 2: Association of the nutritional status of the study participants and sociodemographic factors (n=353)|
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To find out the predictors of possible malnutrition among the study participants, multivariable logistic regression model was applied. Gender and occupational status were found correlated and so gender was included in the model. Hosmer–Lemeshow goodness of fit test was applied (P = 0.98); Nagelkerke R2 was 0.174, implying that 17.4% of possible malnutrition among the elderly could be explained by this model. This model could correctly classify overall 65.7% of the cases; 70.4% in whom nutrition was normal and 60.9% of cases in whom possible malnutrition was present.
The odds of having possible malnutrition among the elderly who were tenants was 3.2 times (95% confidence interval [CI]: 1.96–5.13, P = 0.00) more than those who were owner. Malnutrition among the elderly who were financially dependent on others was 2.2 (95% CI: 1.25–3.97, P = 0.00) times more than the elderly who were financially independent. Those who were ≥70 years age had 2.4 times (95% CI: 1.11–4.69, P = 0.01) more chances of being malnourished as compared to those who were in the age group of 60–64 years [Table 3].
|Table 3: Multivariable logistic regression analysis for significant predictors of possible malnutrition in the elderly (n=353)|
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| Discussion|| |
The prevalence of malnutrition among the elderly in the world ranges from 0% to 65% using MNA scale.,,, The variability of the prevalence of malnutrition in the elderly can be due to the cross-cultural differences and study setting (hospital based versus community based) in different regions. In hospital studies, there can be overestimation of the prevalence of malnutrition as hospitalized patients tend to be admitted with one or more comorbidity. In our study, the prevalence of normal nutritional status was found to be 50.7%. The prevalence of possible malnutrition was 49.3%, showing that one out of every two elderly had a chance of having malnutrition. This reflects an enormous burden especially on the health-care system as the population of the geriatric age group is on the rise with increasing life expectancy and decrease in fertility rates.
On bivariate analysis, age, female gender, being illiterate, widow/living separate from spouse, being tenant, having migrated from outside state, being financially dependent, per capita income <4000 rupees/month, and having small family size were found to be risk factors for malnutrition.
Multivariable logistic regression analysis found being more than 70 years of age, being tenant, and financially dependent as significant predictors of malnutrition. It was found that participants aged ≥70 years were 2.4 times more likely to be malnourished as compared to participants in the age group of 60–64 years. Similar findings have been corroborated by some other studies.,,,,, The probable reason for this finding can be that aging is associated with physiological changes such as difficulty in chewing food, swallowing, digestive problems, and loss of appetite, which can lead to malnutrition in the elderly.
In our study, most of the elderly (54.5%) who had possible malnutrition were financially dependent on others such as their children, spouse, and other family members. They were 2.2 times more likely of having possible malnutrition than those who were financially independent. This can be due to the fact that the purchasing power determines the food intake as well as many complex social issues which are directly or indirectly associated with malnutrition.,,
In our study, tenants (the people who did not own the house on which they were living) were 3.2 times more likely to be malnourished as compared to owners. This finding could be explained by the fact that usually tenants tend to be migrants and they might not have a permanent source of income, thus leading to possible malnutrition.
The present study has its own set of limitations: (a) the results cannot be generalized to all over Delhi as it was done in a particular part of the city, (b) though validated tools were applied for data collection, the used questionnaire was focused on the events in the last 3 months, this could have led to a recall bias, thus leading to underreporting. The possibility of limitation of recall especially for this particular population also cannot be ruled out, and (c) this study was a questionnaire-based study. No laboratory investigations which could have objectively reflected the true status of nutrition were done.
| Conclusion|| |
Malnutrition needs to be identified at an early stage using appropriate tools so that proper interventions can be directed to those who need it to ensure healthy aging. This study can be used as a reference for future community-based studies on malnutrition among the elderly.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Agarwalla R, Saikia AM, Baruah R. Assessment of the nutritional status of the elderly and its correlates. J Family Community Med 2015;22:39-43.
Evans C. Malnutrition in the elderly: A multifactorial failure to thrive. Perm J 2005;9:38-41.
Kalyan G, Sarin J, Gulia R, Rani R, Malik R, Panicker RR, et al
. Diet associated problems and nutritional status of elderly of selected community of district Ambala, Haryana. Nurs Midwifery Res J 2015;11:78-86.
Vedantam A, Subramanian V, Rao NV, John KR. Malnutrition in free-living elderly in rural south India: Prevalence and risk factors. Public Health Nutr 2010;13:1328-32.
Jain P, Gupta A, Gupta K, Jain M. A pilot study on correlation of nutritional status and diet quality in the elderly. Int J Recent Sci Res 2015;6:3050-4.
Vellas B, Villars H, Abellan G, Soto ME, Rolland Y, Guigoz Y, et al
. Overview of the MNA – Its history and challenges. J Nutr Health Aging 2006;10:456-63.
Gaiki V, Wagh V. Reliability of mini-nutritional assessment scale in rural setup of a tertiary health care Hospital in Central India. J Acad Ind Res 2014;2:638-41.
Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D, Lauque S, et al
. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition 1999;15:116-22.
Lahiri S, Biswas A, Santra S, Lahiri S. Assessment of nutritional status among elderly population in a rural area of West Bengal, India. Int J Med Sci Public Health 2015;4:569-72.
Alzahrani SH, El Sayed IA, Alshamrani SM. Prevalence and factors associated with geriatric malnutrition in an outpatient clinic of a teaching hospital in Jeddah, Saudi Arabia. Ann Saudi Med 2016;36:346-51.
Verma R, Khanna P. National program of health-care for the elderly in India: A hope for healthy ageing. Int J Prev Med 2013;4:1103-7.
Goswami AK, Nongkynrih B, Kalaivani M, Gupta SK, Pandav CS. Double burden of malnutrition among elderly population of Delhi. Indian J Community Health 2016;28:324-30.
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.
Gündüz E, Eskin F, Gündüz M, Bentli R, Zengin Y, Dursun R, et al
. Malnutrition in community-dwelling elderly in Turkey: A multicenter, cross-sectional study. Med Sci Monit 2015;21:2750-6.
Andre MB, Dumavibhat N, Ngatu NR, Eitoku M, Hirota R, Suganuma N. Mini nutritional assessment and functional capacity in community-dwelling elderly in rural Luozi, Democratic Republic of Congo. Geriatr Gerontol Int 2013;13:35-42.
Wyka J, Biernat J, Mikołajczak J, Piotrowska E. Assessment of dietary intake and nutritional status (MNA) in Polish free-living elderly people from rural environments. Arch Gerontol Geriatr 2012;54:44-9.
Mathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar SL. Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health 2016;60:112-7.
] [Full text]
Ferdous T, Kabir ZN, Wahlin A, Streatfield K, Cederholm T. The multidimensional background of malnutrition among rural older individuals in Bangladesh – A challenge for the millennium development goal. Public Health Nutr 2009;12:2270-8.
[Table 1], [Table 2], [Table 3]