Content area
Background
The magnitude of geriatric malnutrition is under reported in developing nations. It is essential to assess the nutritional status of the elderly for achieving the better quality of life.
Objectives
To estimate and compare the prevalence and assess the risk factors that are associated with geriatric malnutrition in the urban and rural field practice areas of tertiary care hospital, Puducherry.
Setting and design
A community based cross-sectional analytical study done among elderly population in the urban and rural field areas of Government Medical College of Puducherry.
Methods
Study was conducted among elderly (≥ 60 years) for two years using multistage random sampling technique. Geriatric malnutrition was estimated using Mini Nutritional Assessment (MNA). Sociodemographic data and associated risk factors were obtained using semi-structured questionnaire.
Results
Prevalence of geriatric malnutrition was 31.3% and 19.3% in rural and urban areas. In the regression analysis, factors such as individuals with higher age group, illiterates, lower socioeconomic status, living without family, presence of two or more co-morbidity, three or more medications intake on daily basis, presence of visual impairment and reduced dietary salt intake because of their health issues were found to statistically significant (p-value < 0.05) both in urban and rural setting. Widower by their marital status was found to have statistically significant association among urban elderly whereas those who had dental problems, financially dependency was independently associated with malnutrition among the rural elderly.
Conclusions
There was a high prevalence of malnutrition among elderly residing in rural area than in urban area.
Introduction
The term malnutrition is defined as “a pathological state resulting from a relative or absolute deficiency or excess of one or more essential nutrients”. Malnutrition comprises four forms namely undernutrition, overnutrition, imbalance, specific deficiency [1]. With national health policies focusing on maternal health, child health & communicable diseases, the health status of geriatric population has not been given due consideration [2]. Since nutrition of the elderly affects immunity [3] and functional ability [4, 5] it is an important component of elderly care that warrants further attention. By 2025, the number of elderly is expected to rise to > 1.2 billion, among those, 840 million in low income countries [1]. Such a rapid rise in the elderly population will definitely pose several challenges such as scanty income to support themselves, absence of social security, loss of social status and lack of opportunities for use of their time. According to WHO, health of the elderly will be an important issue in defining the health status of a population in the coming years [6]. Elderly people are more vulnerable to malnutrition which includes physiological & functional changes that occur with age, lack of financial support & inadequate access to food [7]. In India, the magnitude of geriatric malnutrition is usually under reported and few studies have been conducted so far. As the burden is not known, the country’s nutritional intervention programs are usually directed toward children and pregnant and lactating mothers [7], and elderly were often neglected. Thus, it becomes essential to assess the nutritional status of the elderly so that it can be easily corrected and better quality of life can be achieved. The Mini Nutritional Assessment (MNA) is a useful screening tool to detect malnutrition and risk of malnutrition among elderly people [8]. The tool was first developed and published in 1994. It has been widely validated in elderly people and has been extensively used in studies worldwide [9]. Understanding the risk factors helps the physicians in assessing & treating the condition. Hence, in this present study prevalence of geriatric malnutrition & its associated risk factors will be assessed & compared between urban & rural areas of Puducherry.
Materials and methods
Study design and setting
A community based analytical cross-sectional study was conducted among elderly aged 60 years and above from September 2018 to October 2020 in the five urban and five rural service areas attached to the tertiary care hospital, Puducherry.
Sample size calculation
Sample size was calculated by using prevalence of geriatric malnutrition in urban community of Coimbatore as 19.47%,10 and prevalence of geriatric malnutrition in rural community of Karnataka as 28.4%.11 Considering the absolute precision to be 5%, Using the sample size formula of hypothesis testing for difference between two proportions, estimated sample size for each area is 357. Considering the non-response rate of 10%, minimum required sample size is 786 and so totally 800 recruited for the study.
Sampling technique
A multistage random sampling technique was used for the study. The urban and rural field practice areas of a tertiary care hospital in Puducherry consist of 30 wards and 19 villages. Simple random sampling using the lottery method without replacement was employed to select five rural and five urban areas. The selected urban areas included Muthulingapet, Kurunji Nagar, Krishna Nagar, Pakkamudianpet, and Pettichettupet, with a total elderly population of 692 residing in these wards. Similarly, the selected rural areas included Nirnayanpet, Sembianpalayam, Embalam, Keezhagaragaram, and Achariyapuram, with a total elderly population of 844 residing in these villages. A sampling frame of geriatric individuals was obtained from the ANM (Auxiliary Nurse Midwife) register. Eligible subjects were selected from each area using population proportionate to size. Subsequently, simple random sampling was applied to select individuals from each of the chosen areas.
Ethical approval and accordance
This study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethical approval for the study was obtained from the Institute Ethical Committee permission on Human studies issued by Indira Gandhi Medical College and Research Institute, Pondicherry university, (No.22/168/IEC/PP/2018), 30.10.2018. Informed consent was obtained from all individual participants included in the study.
Consent to participate
All participants provided informed consent to take part in this study. Prior to participation, the purpose of the research was explained, and participants were assured of the confidentiality and voluntary nature of their involvement. Written informed consent was obtained from all elderly participants included in the study.
Method of data collection
Face to face interview was conducted among the residents of selected urban and rural areas after obtaining Institute Ethical Committee permission. Data on socio-demographic profile which includes age, gender, education, living arrangement, marital status, pension were collected using the semi- structured questionnaire. Morbidity status was assessed using the previous diagnosis made by the medical practitioner. Subjects not available for two consecutive visits were considered as non-respondents. Part one of the questionnaire contains basic demographic details of the study participants includes identification number, name of the participant, age, gender, area, residence, religion, occupation, source of income, education, income, total number of family members, living arrangement, marital status. Part two of the questionnaire has Mini Nutritional Assessment (MNA) tool which is a validated instrument for assessing geriatric malnutrition [12, 13]. Information shall be collected from elder people of both men & women in their households. Each household will be visited thrice. Those who cannot be contacted even after three occasions will be excluded. Anthropometric measures such as weight, height, Mid Arm Circumference (MAC), Mid Calf Circumference (MCC), hip & waist circumference will be measured as per the standard measurement guidelines [13]. Before testing in the field, all the instruments will be calibrated. Weight & arm span will be measured twice and average will be considered for analysis. MAC, MCC, hip and waist circumference will be measured to the nearest 0.1 cm using non-stretchable measuring tape & average of right and left side measurements will be considered for analysis. Elderly with BMI < 19 kg/m2 will be considered as undernourished. Interviews will take around 15–20 min for each subject.
Statistical analysis
Data collected was entered in Microsoft-Excel sheet and analyzed using statistical package for social sciences version 11.0 for windows. The findings were expressed as mean and standard deviation. Chi-square test and binominal logistic regression analysis was done to determine the association between the independent factors. p-value < 0.05 was considered as statistically significant. Nutritional status was analyzed based on the review of literature from previous study, it has been dichotomized into two categories (possible malnourished vs. well-nourished).
Results
Totally 800 elderly population were recruited in the study with equal representation of 400 elderly population from rural and urban field practice areas of a Government Tertiary care Hospital of Puducherry. The mean and SD age of rural study population was 68.28 ± 6.5 years and the urban study population was 68.29 ± 6.4 years. According to MNA scores, 19.3% (77) and 31.3% (125) were malnourished in the rural and urban areas of Puducherry Table 1. Prevalence of Malnutrition is comparatively higher among rural elderly than among the urban elderly population. Variables such as higher age group, female gender, widower, illiterates, unemployed, lower Socioeconomic status, living alone, tobacco users smoke and smokeless forms, more than three medication use, Presence of Co-morbidity, Use of walking aid, presence of Visual impairment, Hearing impairment, Dental problems, unable to Household activities on their own, Salt reduction affecting their diet, presence loss of smell/taste and presence of Loss of smell/taste affecting their diet were found to be significant in the univariate analysis among urban and rural participants (Tables 2 and 3) and were subjected to the binary logistic regression analysis for finding the independent association factors. Risk factors such as Marital status, financial independency was statistically significant among rural elderly. The elderly having dental problems, those who were not financially independent, presence of loss of smell/taste sensation affecting their diet were independently associated for causing possible malnutrition only among the rural elderly population. Widower by their marital status was found to have statistically significant association for causing possible geriatric malnutrition only among urban elderly. Similarly, the financially independent factor had no significant association with the urban elderly in univariate analysis whereas it was found to be significant with rural geriatric population (Tables 2 and 3).
Table 1. Comparison of nutritional status of urban and rural elderly population (N = 800)
Nutritional status | Urban n = 400(%) | Rural n = 400(%) | Z-value(p-value) |
|---|---|---|---|
Malnourished | 77(19.3) | 125(31.3) | 3.906(< 0.0001*) |
At risk of Malnutrition | 110(27.5) | 136(34) | 1.992(0.04*) |
Normal Nutritional status | 213(53.2) | 139(34.7) | 5.270(< 0.00001*) |
*Z-test used as test of significance (p-value < 0.05 considered as statistically significant
Table 2. Risk factors associated with malnutrition among the urban elderly (N = 400)
Variables | Possible malnutrition n = 287(%) | well nourished n = 213(%) | (χ2) p-value | Unadjusted PR (95% CI) |
|---|---|---|---|---|
Gender a) Female b) Male | 133(51.8) 54(37.8) | 124(48.2) 89(62.2) | (7.22) 0.007* | 1.7(1.16–2.68) |
Age a) 60–69 b)70–79 c) ≥ 80 | 70(29.6) 93(69.4) 24(80) | 166(70.3) 41(30.6) 6(20) | (68.6) 0.001* | 1.00 5.3(3.3–8.5) 9.4(3.7–24.2) |
Educational status a) Literates b) Illiterate | 64(26) 123(79.9) | 182(74) 31(20.1) | (108) 0.001* | 0.08(0.05–0.14) |
Employment status a) Unemployed b) Employed | 143(55.2) 44(31.2) | 116(44.8) 97(68.8) | (21.1) 0.001* | 2.7(1.7–4.1) |
Socio-economic status a) Upper & upper middle b) Middle c) Lower middle & lower | 7(10.1) 95(47) 85(65.9) | 62(89.9) 107(53) 44(34.1) | (29.2) 0.001* | 1.00 1.6(1.4–1.9) 2.6(2-3.3) |
Marital status a) Widow b) Married | 114(76.5) 73(29.1) | 35(23.5) 178(70.9) | (84.4) 0.000* | 7.9(4.9–12.6) |
Living Arrangement a) Alone b) With spouse/children/ family | 65(84.4) 122(37.8) | 12(15.6) 201(62.2) | (54.3) 0.001* | 0.11(0.05–0.21) |
Smokers a) Past/current b) Never | 38(60.3) 149(51.8) | 25(39.7) 188(88.3) | (5.5) 0.02* | 1.9(1.1–3.3) |
Smokeless tobacco a) Past/current b) Never | 61(79.2) 126(39.1) | 16(20.8) 197(60.9) | (40.3) 0.01* | 5.9(3.2–10.7) |
Medication use a) < 3 b) ≥ 3 | 140(41.5) 47(74.6) | 197(58.5) 16(25.4) | (23.3) 0.001* | 0.24(0.13–0.44) |
Co-morbidity a) ≥ 2 b) < 2 c) nil | 61(72.6) 74(47.4) 52(32.5) | 23(27.4) 82(52.6) 108(67.5) | (28.5) 0.001* | 5.5(3.1–9.8) 1.8(1.1–2.9) 1.00 |
Use of walking aid a) yes b) no | 26(96.3) 161(43.1) | 1(3.7) 212(56.8) | (26.4) 0.001* | 0.02(0.004–0.21) |
Visual impairment a) yes b) no | 114(61.4) 73(33.9) | 71(38.4) 142(66.1) | (30.5) 0.001* | 0.3(0.21–0.48) |
Hearing impairment a) yes b) no | 123(38.6) 64(79.0) | 196(61.4) 17(21.0) | (42.3) 0.001* | 0.16(0.093-0.29) |
Dental problems a) yes b) no | 101(62.3) 86(36.1) | 61(37.7) 152(63.9) | (26.6) 0.001* | 0.34(0.22–0.51) |
Household activities on their own a) yes b) no | 123(38.6) 64(79.0) | 196(61.4) 17(21.0) | (42.6) 0.001* | 5.9(3.35–10.7) |
Salt reduction affect your diet a) yes b) no | 73(73.0) 114(38.0) | 27(27.0) 186(62.0) | (36.9) 0.001* | 0.2(0.13–0.37) |
Loss of smell/taste a) yes b) no | 37(67.3) 150(43.5) | 18(32.7) 195(56.5) | (10.8) 0.001* | 0.37(0.2–0.6) |
Loss of smell/taste affect your diet a) yes b) no | 31(75.6) 156(43.5) | 10(24.4) 203(56.5) | (15.2) 0.001* | 0.24(0.11–0.52) |
*p-value significant in Chi-square test (< 0.05)
Table 3. Risk factors associated with malnutrition among the rural elderly (N = 400)
Variables | Possible malnutrition n = 261(%) | well nourished n = 139(%) | (χ2) p-value | Unadjusted PR (95% CI) |
|---|---|---|---|---|
Gender a) Female b) Male | 191(70.5) 70(54.3) | 80(29.5) 59(45.7) | (10.1) 0.001* | 2.01(1.30–3.10) |
Age a) 60–69 b)70–79 c) ≥ 80 | 123(53.2) 110(80.3) 28(87.5) | 108(46.8) 27(19.7) 4(12.5) | (33.2) 0.001* | 1.00 3.5(2.1–5.8) 9.4(2.0–18) |
Educational status a) Literates b) Illiterate | 60(43.2) 201(77.0) | 79(56.8) 60(23.0) | (45.8) 0.001* | 0.22(0.14–0.35) |
Employment status a) Unemployed b) Employed | 143(55.2) 44(31.2) | 74(25.6) 65(58.6) | (38.4) 0.001* | 4.1(2.5–6.5)) |
Socio-economic status a) Upper & upper middle b) Middle c) Lower middle & lower | 10(24.4) 111(62.0) 140(77.8) | 31(75.6) 68(38.0) 40(22.2) | (43.4) 0.001* | 1.00 5.06(2.3–10.9) 10.8(4.9–24) |
Marital status a) Widow b) Married | 144(86.8) 73(50.0) | 22(13.2) 117(50.0) | (57.8) 0.001* | 6.5(3.9–10.9) |
Living Arrangement a) Alone b) With spouse/children/ family | 90(89.1) 171(57.2) | 11(10.9) 128(42.8) | (33.9) 0.001* | 0.16(0.08–0.31) |
Smokeless tobacco a) Past/current b) Never | 80(85.1) 181(59.1) | 14(14.9) 125(40.9) | (23.3) 0.001* | 3.9(2.1–7.2) |
Medication use a) < 3 b) ≥ 3 | 191(59.9) 70(87.5) | 129(40.3) 10(12.5) | (21.8) 0.001* | 0.21(0.10–0.42 |
Co-morbidity a) ≥ 2 b) < 2 c) nil | 92(84.4) 100(66.7) 69(48.9) | 17(15.6) 72(51.1) 50(33.3) | (34.3) 0.001* | 5.6(3.1–10.4) 2.1(1.3–3.3) 1.00 |
Use of walking aid a) yes b) no | 45(100) 216(60.8) | 0(0) 139(39.2) | (31.6) 0.001* | 0.39(0.19–0.45) |
Visual impairment a) yes b) no | 167(77.7) 94(50.8) | 48(22.3) 91(49.2) | (30.5) 0.00* | 0.3(0.21–0.48) |
Hearing impairment a) yes b) no | 97(88.2) 164(56.5) | 13(11.8) 126(44.5) | (35.1) 0.001* | 0.17(0.093-0.32) |
Dental problems a) yes b) no | 145(78) 116(54.2) | 41(22) 98(45.8) | (24.7) 0.001* | 0.33(0.21–0.51) |
Able to wash on their own a) yes b) no | 235(63.9) 26(81.2) | 133(36.1) 6(17.8) | (3.9) 0.04* | 2.4(0.98–6.1) |
Household activities on their own a) yes b) no | 181(59.0) 80(86.0) | 126(41.0) 13(14.0) | (23.0) 0.001* | 4.2(2.28–8.03) |
Salt reduction affect your diet a) yes b) no | 104(86.7) 157(56.1) | 16(13.3) 123(43.9) | (34.6) 0.001* | 0.19(0.11–0.35) |
Loss of smell/taste a) yes b) no | 54(84.4) 207(61.1) | 10(15.6) 129(38.9) | (12.2) 0.001* | 0.29(0.14–0.60) |
Loss of smell/taste affect your diet a) yes b) no | 47(92.2) 214(61.3) | 4(7.8) 135(38.7) | (17.3) 0.001* | 0.13(0.04–0.38) |
Financially independent a) yes b) no | 13(60.6) 131(70.7) | 85(39.4) 54(29.3) | (4.3) 0.03* | 1.5(1.1–2.3) |
*p-value significant in Chi-square test (< 0.05)
The risk factors such as higher age group, illiterates by the educational status, individuals with lower socioeconomic status, living alone without the support from either of their spouse and children, presence of two or more co-morbidity, elderly using > 3 medications on daily basis, presence of two or more co-morbidity condition, presence of visual impairment and individuals taking reduced salt because of their health issues in their diet were found to statistically significant (p-value < 0.05) both in urban and rural setting (Table 4).
Table 4. Comparison of risk factors associated with geriatric malnutrition among urban and rural population (N = 800)
Variables | Urban | Rural | ||||
|---|---|---|---|---|---|---|
B | Sig. | Adjusted PR (95% C.I.) | B | Sig. | Adjusted PR (95% C.I.) | |
Age | 1.19 | 0.000* | 3.28(1.80–5.98) | 0.99 | 0.001* | 2.70(1.48–4.90) |
Education | -1.52 | 0.000* | 0.21(0.10–0.44) | -0.97 | 0.003* | 0.37(0.19–0.71) |
Socioeconomic status | 1.04 | 0.000* | 2.83(1.66–4.85) | 0.57 | 0.014* | 1.77(1.12–2.81) |
Living arrangement | -2.92 | 0.000* | 0.10(0.03–0.31) | -1.71 | 0.001* | 0.18(0.06–0.51) |
< 3 medication use | -1.38 | 0.009* | 0.25(0.08–0.71) | -1.10 | 0.032* | 0.33(0.12–0.90) |
Co-morbidity | 0.55 | 0.03* | 1.74(1.03–2.94) | 0.46 | 0.033* | 1.59(1.03–2.43) |
Visual impairment | -0.84 | 0.015* | 0.43(0.21–0.85) | -1.03 | 0.001* | 0.35(0.19–0.65) |
Salt reduction affect your diet | -0.90 | 0.040* | 0.40(0.17–0.96) | -0.95 | 0.029* | 0.38(0.16–0.90) |
Marital status | 0.35 | 0.038* | 1.54(1.02–2.33) | 0.35 | 0.077 | 1.41(0.96–2.09) |
Dental problems | 0.72 | 0.063 | 2.05(0.96–4.39) | 0.98 | 0.006* | 2.66(1.32–5.39) |
Financially independent | **NS | **NS | **NS | 0.87 | 0.009* | 2.40(1.24–4.61) |
Loss of smell/taste affect your diet | -0.63 | 0.46 | 0.53(0.09–2.94) | -1.93 | 0.049* | 0.143(0.02–0.99) |
*p-value significant in Binary Logistic Regression (< 0.05)
**NS – Not significant in Univariate Analysis
Discussion
A study by Kalaiselvi et al. showed the prevalence to be 24.8% using BMI [14]. A study by Krishnamoorthy Y et al. revealed the prevalence of geriatric malnutrition to be 17.9% and at risk of malnutrition to be 58.8% in rural Puducherry using MNA scores [15]. In the present study the prevalence of malnutrition and at risk of malnutrition in the rural area was found to be 31.3% and 34% respectively. A study by Mathew et al. in urban area of Coimbatore revealed the prevalence to be 19.47% and at risk of malnutrition to be 24.73%.10 In the present study the prevalence of urban geriatric malnutrition and risk of malnutrition was found to be in the similar range 19.3% and 27.5% respectively. The comparative study was conducted by Ananthesh et al. in Karnataka showed that more proportion of Rural elderly were Malnourished (28.4%) and at risk of Malnutrition (40.2%) when compared to Urban elderly where 8.8% were Malnourished and 37.3% were at risk of Malnutrition [11]. Similar finding has been observed in our present study with the prevalence of malnutrition and risk of malnutrition was higher among rural elderly (31.3%, 34%) than the urban elderly (19.3%, 27.5%) population. The changes in prevalence of malnutrition among elderly can be due to the cross‑cultural differences and study setting 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 than with the study conducted in the community setting. Our study results revealed that elderly who were at risk of malnutrition found to be in higher percentage than the actual malnourished elderly. This is because the MNA questionnaire was found to be one of the best tools for identifying those at risk of malnutrition among elderly in the community. This emphasizes the fact that high prevalence of deficient protein–energy intake exists among the elderly without obvious malnutrition. Significant association between female gender and malnourishment could be attributed to the role of women in society like the effect of traditional habits of eating last after serving men, children and usually share the remaining food which may be less nutritious. In a comparative study in Karnataka, significant association (p < 0.05) was found between the nutritional status with age group and literacy level in both urban and rural areas, where more proportion of elderly aged more than 70 years and illiterates were malnourished [11]. In this study, significant association found with the educational status of the elderly population. This finding explained the fact that higher education can lead to higher income and better lifestyle which in turn results in a better nutritional status. Furthermore, educated people are usually aware about the importance of healthy dietary pattern and able to understand more about nutrition. In the current study, malnutrition was commonly observed among those with a single marital status (separated or widowed). A recent systematic review of 28 observational studies provided strong evidence for the lack of association between the death of a spouse and malnutrition, while some studies have suggested that there is a relationship between marital status and nutritional status, these results remain controversial [16, 17]. In our present study the prevalence of smoking and use of smokeless forms was observed to be 14.4% and 213% among the study participants (≥ 60 years). GATS (Global Adult Tobacco Survey) 2016–2017 revealed the prevalence of smoking and use of smokeless forms among the age group ≥ 65 years to be 9% and 16.3% in Puducherry. The percentage was found to be higher in the study area from which our study participants were recruited for the study [18]. In the present study somatic characteristics such as presence of co-morbidity and use of walking aid found to have higher odds among possible malnutrition individuals in both urban and rural settings. Use of less than three medications, absence of visual, hearing impairment and dental problems had protective Prevalence ratio among urban and rural elderly. Among the somatic characteristics, more than three oral medication usage was only found to be statistically significant in previous studies done in urban Maharashtra [19]. This could be due to presence of two or more co-morbid condition and usage of medications for those conditions tend to impair the functional characteristics of the elderly individuals indirectly thereby leading to malnutrition. Oral medications among elderly people may lead to malnutrition by impairing food absorption or enhancing excretion or by causing nausea, vomiting, diarrhea, constipation. Previous studies have also observed the same association between the use of oral medications and malnutrition [16]. In this study there was significant association between malnourishment and financial dependency among rural elderly which was consistent with other studies [7, 20, 21]. This could be because intake of food or choices all depend on the purchasing power. Therefore, if an elderly individual is financially independent, he or she can be decisive about food intake. In our study, on multivariate regression analysis, lower MNA scores were significantly associated with higher age group, lower middle and lower socio-economic status, living alone, use of more than 3 medications, presence of co-morbidity, visual impairment, salt reduction affecting the diet were the independent common factors for both urban and rural elderly. Higher MNA scores was observed to be independently associated with married urban elderly individuals and the factors such as financially independent, absence of dental problems, loss of smell/taste sensation not affecting the diet were independently significant with rural elderly. Similar significant association with lower age group, higher socio-economic status, higher literacy level, good food intake and financial independence was in the previous studies [11] , [21]. In the BIMARU states, the challenges faced by the elderly are more acute due to higher poverty levels, lower literacy rates, and inadequate healthcare facilities. For instance, in Uttar Pradesh, the health behaviour of older individuals is generally poorer in rural areas compared to urban counterparts, influenced by factors such as economic hardship and social deprivation [22]. Recent studies by Zaidi et al. (2023, 2024) provide valuable insights into the healthcare challenges faced by elderly populations in rural India, highlighting factors such as limited mobility, inadequate transportation, financial constraints, and lack of awareness about available services. These findings are consistent with the barriers identified in our study and further underscore the systemic nature of healthcare access issues in underserved regions. Incorporating this evidence not only strengthens the generalizability of our conclusions but also reinforces the need for targeted, context-sensitive policy interventions aimed at improving healthcare accessibility for vulnerable elderly populations in rural settings [23, 24]. Our results conclude that a greater number of elderly are at risk of malnourishment. By using MNA tool, it can be easy to identify those who are malnourished and also who are at risk of malnutrition who can benefit from early intervention.
Conclusion
In conclusion, this study highlights a high prevalence of malnutrition among elderly individuals in rural areas compared to urban areas. Nutritional status was significantly associated with factors such as age, literacy, socioeconomic status, and living arrangements in both rural and urban settings. Additionally, marital status showed a significant association only among urban participants. Changes in lifestyle, physical health, functional ability, and social circumstances were linked to lower Mini Nutritional Assessment (MNA) scores. Elderly individuals in lower socioeconomic groups require targeted attention to address their specific nutritional needs. Implementing appropriate nutritional support can help alleviate undernutrition among community-dwelling elderly individuals and potentially reduce healthcare costs. Identifying malnourished and at-risk elderly individuals is crucial to promoting healthy dietary practices and improving their overall well-being.
Recommendation
The prevalence of malnutrition among the elderly, particularly in rural areas, remains alarmingly high. Addressing this issue requires a multifaceted approach, with a multidisciplinary team involved in the management and treatment of malnutrition. Further research is essential to establish standardized guidelines for nutritional screening and intervention programs tailored to the geriatric population. The Mini Nutritional Assessment (MNA) tool offers an effective means of identifying elderly individuals who are malnourished or at risk, enabling early intervention. Timely detection and targeted nutritional strategies are critical to preventing the adverse effects of untreated malnutrition, which can lead to increased morbidity and hinder healthy aging. Given that undernutrition is most prevalent among economically vulnerable elderly individuals, initiatives such as employment guarantee schemes and subsidized food distribution systems can play a vital role in ensuring food security, especially in rural regions. While general trends in elderly health can be observed, their experiences vary significantly based on geographic, economic, and social factors. Recognizing and addressing these disparities is crucial for enhancing the applicability and impact of policies aimed at improving elderly well-being across India. Primary care physicians should be trained to identify and manage undernutrition among the elderly. Implementing opportunistic screening and periodic household surveys can aid in early detection and intervention. A proactive, region-specific approach is necessary to mitigate malnutrition and promote healthy aging among India’s elderly population.
Limitation of the study
Majority of the answers were self-reported and may lead to over reporting or under reporting of conditions.
Acknowledgements
The authors sincerely thank the elderly population who contributed to this study. We would like to thank Nestle institute of Nutrition for granting permission to use the MNA-SF questionnaire. We sincerely thank our Institute and our department for guiding us for the conduct of this study.
Author contributions
S.A conceived the idea and concepts, planned this study, literature search, collected data, analyzed data, prepared the initial draft and finalized. D.K and P.M reviewed the draft, finalized and approved the final version of manuscript. S.T helped in data collection and analysis. All authors reviewed the manuscript before submission.
Funding
Not applicable.
Declarations.
Data availability
https://drive.google.com/drive/folders/1muQDANXsWU8P-A3G_V0e33pcBYJppU1J? usp=sharing.
Declarations
Ethics approval and consent to participate
Institute ethical committee clearance (No.22/168/IEC/PP/2018) was obtained for conducting the study. Informed consent has been obtained from the study participants before the conduct of the study.
Competing interests
The authors declare no competing interests.
Publisher’s note
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