|Year : 2016 | Volume
| Issue : 2 | Page : 112-117
Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore
Anil Chankaramangalam Mathew1, Darsana Das2, Saranya Sampath2, M Vijayakumar3, N Ramakrishnan4, SL Ravishankar5
1 Professor of Biostatistics, Department of Community Medicine, PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India
2 Trainee Biostatistician, Department of Statistics and Biostatistics, St. Thomas College, Kottayam, Kerala, India
3 PGT, Department of Community Medicine, PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India
4 Medical Officer, PSG Urban Health Training Centre, Coimbatore, Tamil Nadu, India
5 Professor, Department of Community Medicine, PSG Institute of Medical Sciences and Research, Coimbatore, Tamil Nadu, India
|Date of Web Publication||23-Jun-2016|
Anil Chankaramangalam Mathew
Department of Community Medicine, PSG Institute of Medical Sciences and Research, Coimbatore - 641 004, Tamil Nadu
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Different studies in India have shown that more than 50% of elderly population of India are suffering from malnutrition and more than 90% have less than recommended intake. Objectives: The aim of this study is to estimate the prevalence and correlates of malnutrition among elderly aged 60 years and above in an urban area in Coimbatore using Mini Nutritional Assessment (MNA). Methods: A cross-sectional study was conducted on 154 households and 190 elderly were interviewed. Nutritional status was assessed using the MNA questionnaire. Results: Mean (standard deviation) age of the total population (n = 190) was 71.09 (7.93) years and 30% was male. In this population, 37 (19.47%) was malnourished (MNA <17.0) and 47 (24.73%) were at risk for malnutrition (MNA 17.0-23.5). No significant association was observed between smoking, current alcohol consumption, higher medication use, higher comorbidity, and use of walk aid with malnutrition. Among the social factors studied, lower socioeconomic status compared to higher socioeconomic status (adjusted odds ratio [OR] =5.031, P < 0.001), single/widowed/divorced compared to married (adjusted OR = 3.323, P < 0.05), and no pension compared to those having pension (adjusted OR = 3.239, P < 0.05) were significantly associated with malnutrition. Conclusion: The prevalence of malnutrition observed in the aged people is unacceptably high. The increasing total number of lifestyle, somatic, functional, and social factors was associated with lower MNA scores. The findings of the present study clearly indicate that malnutrition is a multifactorial condition associated with sociodemographic, somatic, and functional status. Hence, we recommend that the treatment of malnutrition should be multifactorial, and the treatment team should be multidisciplinary. Further research is needed to develop appropriate guidelines for nutritional screening and interventional programs among geriatric population.
Keywords: Elderly, malnutrition, Mini Nutritional Assessment
|How to cite this article:|
Mathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar S L. Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health 2016;60:112-7
|How to cite this URL:|
Mathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar S L. Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health [serial online] 2016 [cited 2020 Sep 25];60:112-7. Available from: http://www.ijph.in/text.asp?2016/60/2/112/184542
| Introduction|| |
The World Health Organization (WHO) has stated that aging populations will present new challenges to health care. The health of the elderly will be an important issue defining the health status of a population.  As the number of elderly increases, so too will their health needs. Multimorbidity associated with increasing age is common and is found to be more frequent in resource-poor countries.  It is therefore required that health policy addresses this subgroup of the population as well.  In India, the elderly (aged 60 years and above) constitutes 7.7% of the total population of 1.20 billion and this number is increasing.  With national health policy focusing on maternal health, child health, and communicable diseases, the health status of the elderly has not been given due consideration.  Since nutrition of the elderly affects immunity  and functional ability, , it is an important component of elderly care that warrants further attention. The magnitude of malnutrition among the elderly in India is under-reported. The few studies that have been done showed that more than 50% of the older population is underweight  and more than 90% has an energy intake below the recommended allowance. 
There is no gold standard for estimating malnutrition among the elderly. Body mass index (BMI; weight [kg]/height [m  ]) predicts disease risk both in those termed underweight and in those who are obese. The WHO categorizes underweight as BMI <18.5, normal 18.5-24.9, overweight 25-29.9, obese 30-30.99, and extreme obesity >40. However, BMI may be unreliable in the presence of confounding factors such as edema or ascites, and may not identify significant unintentional weight loss if used as a single assessment. ,, Furthermore, reliable measurement of height can be difficult in the elderly because of vertebral compression, loss of muscle tone, and postural changes. , Mini Nutrition Assessment (MNA) is a widely used international questionnaire to evaluate the nutritional state of seniors with high sensitivity (98.9%), specificity (94.3%), and diagnostic accuracy (97.2%). It closely correlates with biochemical (albumin, prealbumin, transferrin levels, and lymphocyte numbers) and anthropometrical markers (measuring of subcuticular fat and arms circumference) that were verified by a number of clinical studies on wide sets of geriatric patients. ,, However, in India, only a very few community studies were conducted to estimate malnutrition among elderly on MNA. In this study, we aimed to estimate the prevalence and correlates of malnutrition among elderly aged 60 years and above in an urban area in Coimbatore using MNA.
| Materials and Methods|| |
Study design and population
For this cross-sectional study aiming to investigate the prevalence and correlates of malnutrition among elderly population aged 60 years and above, the sample size was estimated as 113. This is based on an expected correlation of a number of correlates and MNA score r = 0.30 with α = 0.05 two-sided and β = 0.10. The urban health center of PSG Institute of Medical Sciences and Research has six areas on which three areas were randomly selected. The selected areas were HUDCO Colony, AD Colony, and Pattalamman Kovil Street. The total number of households on these three areas was 762. In 565 houses, there were no elderly people and nonresponse was obtained in 43 houses. Hence, we surveyed 154 households and 190 elderly were interviewed. They were asked about demographic, medical history, medication use, and lifestyle. Some of the responses were obtained from the relatives. All elderly people aged 60 years and above residents at HUDCO Colony, AD Colony, and Pattalamman Kovil Street were included in the study. Those who were too sick, those who were not present at the time of visit, and those who could not stand unsupported due to debility were excluded from this study. Approval for the study was obtained from the Institutional Human Ethics Committee. Written informed consent will be obtained from each patient.
Nutritional status was assessed with the MNA, a validated questionnaire for older individuals.  The questionnaire comprises 18 questions clustered in four sections: Anthropometric assessment (weight, height, and weight loss); general assessment (living situation, medicine use, and mobility); dietary assessment (number of meals, food and fluid intake, and autonomy of feeding), and subjective assessment (self-perception of health and nutritional status and nutritional status). A maximum score of 30 can be obtained. A score below 17 indicates malnutrition, a score of 17-23.5 indicates a risk of malnutrition, and a score of 24 or higher indicates a satisfactory nutritional status.
Factors associated with malnutrition
Possible covariates of malnutrition were classified as lifestyle, somatic, functional, and social factors.  Lifestyle characteristics included smoking and alcohol consumption. It was assessed by checking whether the aged person was a current smoker versus never/past smoker or a current alcohol user versus never/past user. Somatic characteristics included medication use, comorbidity, and use of walking aid. The number of drugs was derived either directly from the elderly people or from the relatives. Medication use was classified as using ≤3 versus >3. Comorbidity was assessed by summing the number of underlying chronic diseases of elderly people. It was classified as having <2 versus ≥2 chronic diseases. The following chronic diseases were considered: Hypertension, diabetes, and cardiovascular disease. The use of walking aid was classified as No versus Yes.  Functional characteristics included activities of daily life (ADL) and instrumental ADL (IADL). ADL was assessed by asking if the elderly people were able to dress or wash himself independently or not. IADL was assessed by asking the elderly people were able to do the shopping and cleaning the household independently or not. Both ADL and IADL were classified as independent versus not independent. Social factors included socioeconomic status, education, marital status, pension, and living alone. Socioeconomic status was classified on the basis of modified Prasad's classification (August 2014) according to which the five classes are: Class I (>5770 per capita income), Class II (2890-5770 per capita income), Class III (1730-2890 per capita income), Class IV (870-1730 per capita income), and Class V (<870 per capita income). It was classified as Classes I, II, III versus Classes IV and V. Education was classified as schooling up to 12 th versus college and above. Marital status was classified as married versus single/widowed/divorced. Pension and living alone were assessed as Yes versus No.
Height was measured with a measuring tape (Wellknown Syndicate. Tirupur, Tamil Nadu, India) measured to the nearest centimeter (cm) and weight was assessed by a digital weighing machine to the nearest kilogram (kg). BMI was calculated as weight in kg by the square of height in meters (kg/m  ).
Characteristics in the elderly were calculated for the nutritional status categories (malnourished: MNA <17.0, at risk of malnutrition: 17.0-23.5, and normal nutritional status: >23.5). Logistic regression analysis was performed to assess the independent association of the covariates with the presence of malnutrition. Somatic, functional, social, and lifestyle characteristics were separately included as covariates in the model. Regression analysis was adjusted for age and sex. To assess the independent association of the covariates with the presence of malnutrition, all covariates for malnutrition (somatic, functional, social, and lifestyle) were included in one logistic regression model using backward elimination. Hosmer and Lemeshow test and Omnibus Chi-square statistic were performed to test the goodness of fit of the model. In addition, Nagelkerke R and Cox and Snell R as well as classification table of the models were also computed. Finally, all somatic, functional, and social correlates were summed, and median MNA score was calculated for categories of number of covariates (0-2, 3-5, 6-7) and compared using Kruskal-Wallis test. Statistical analysis was performed with SPSS software (19) IBM, New York, USA.
| Results|| |
Mean (standard deviation) age of the total population (n = 190) was 71.09 (7.93) years and 30% was male. In this population, 37 (19.47%) was malnourished (MNA <17.0) and 47 (24.73%) were at risk for malnutrition (MNA 17.0-23.5) [Figure 1]. No significant association was observed between neither smoking nor current alcohol consumption with malnutrition [Table 1].
|Figure 1: Distribution of the study participants according to malnutrition assembled by Mini Nutritional Assessment|
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|Table 1: Lifestyle, somatic, and functional characteristics associated with malnutrition|
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Among the somatic characteristics studied, no significant association was observed between higher medication use and malnutrition. No significant association was observed between higher comorbidity and malnutrition. Similarly, no significant association was observed between use of walk aid and malnutrition. Findings were presented in [Table 1].
Among the functional characteristics studied, IADL dependent was significantly associated with malnutrition even after adjusting for age and sex (adjusted odds ratio [OR] =12.789, P < 0.05). Findings were presented in [Table 1].
Among the social factors studied, lower socioeconomic status compared to higher socioeconomic status (adjusted OR = 5.031, P < 0.001), single/widowed/divorced compared to married (adjusted OR = 3.323, P < 0.05), and no pension compared to those having pension (adjusted OR = 3.239, P < 0.05) were significantly associated with malnutrition [Table 2]. In the multivariate analysis also, the same variables were found to be statistically significant with malnutrition and the model was found fit well by Hosmer-Lemeshow statistic (P = 0.613) [Table 3]. The model explained between 17.9% (Cox and Snell R ) and 28.6% (Nagelkerke R ) of variance in the dependent variables studied and correctly classified 83.7% of cases.
|Table 3: Factors associated with malnutrition in the multivariate regression analysis|
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Furthermore, increasing total number of lifestyle, somatic, functional, and social factors was associated with lower MNA scores (P < 0.001) [Table 4].
|Table 4: Mini Nutritional Assessment score for categories of total number of covariates|
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| Discussion|| |
The present study among 190 geriatric populations indicates a high prevalence of malnutrition (19.47%). Poor functional status measured through IADL, poor socioeconomic status, living as single/widowed/divorced, and no pension was independently associated with malnutrition. There were only a few studies conducted in India using the MNA questionnaire to assess malnutrition. Only such study was done in Western Rajasthan and showed a prevalence of malnourishment and risk of malnourishment among rural elderly compared with urban elderly (11% and vs. 62% and 2% and 36%, respectively).  In another study at Vellore, 14% were malnourished and 49% were at risk of malnourishment. Compared with these results, our population shows slightly higher rates of malnutrition.
In our study, statistically significant association was found between poor socioeconomic status in general and malnutrition. We also observed a significant association between malnutrition and those who do not have a pension. High prevalence of malnutrition among elders in poor socioeconomic status was reported in some earlier studies. , This could be attributed to the fact that socioeconomic conditions influence dietary choices and eating patterns thereby affecting the nutritional status. We also observed that those who are living alone were having higher rates of malnutrition. This may also attribute to the practical difficulties that influence their dietary choices and eating patterns.
Many studies reported a strong association between lifestyle factors particularly smoking and alcoholism with malnutrition. However, in our study, we could not observe any significant association between lifestyle factors and malnutrition.
Our findings that the MNA score was lower in patients with multiple burdens of somatic, functional, or social characteristics provides further evidence that malnutrition could be regarded as a geriatric syndrome, next to already established geriatric syndromes such as falls, incontinence, pressure sores, and delirium. A geriatric syndrome refers to one symptom or a complex of symptoms with high prevalence in geriatric populations resulting from multiple risk factors and leading to decreased functioning.
We observed that malnutrition is associated with several adverse clinical outcomes. ,, Since malnutrition is mostly thought to be modifiable, it is important to develop and implement adequate interventions to prevent, diagnose, and treat malnutrition. Early identification of malnutrition is a first step. The MNA fulfills many criteria for both screening and diagnostic measures.  Protein and energy supplementation was considered as an effective intervention strategy not only to increase height  but also to improve functional characteristics. , However, the findings from our study clearly indicate that intervention should preferably target not only the nutritional status but also underlying problems in the somatic, social, and functional domain.
Our study has several strengths. We have used a validated questionnaire to elicit the prevalence of malnutrition. The availability of a complete and extensive dataset is another strength of the study. However, our study has some limitations. This is a cross-sectional study and hence could not establish cause-effect relationship. Second, our data collection was dependent on the response of individual person and their relatives. This could have led to both under and over reporting of comorbidity.
| Conclusion|| |
The prevalence of malnutrition observed in the aged people is unacceptably high. The increasing total number of lifestyle, somatic, functional, and social factors was associated with lower MNA scores. The findings of the present study clearly indicate that malnutrition is a multifactorial condition associated with sociodemographic, somatic, and functional status. Hence, we recommend that the treatment of malnutrition should be multifactorial, and the treatment team should be multidisciplinary. Further research is needed to develop appropriate guidelines for nutritional screening and interventional programs among geriatric population.
The authors are extremely thankful to Dr. S. Ramalingam, Principal, PSG Institute of Medical Sciences and Research for permitting us to do this study. We are grateful to Dr. Thomas V. Chacko, Professor and Head of Department of Community Medicine, PSG Institute of Medical Sciences and Research, for his constant support and encouragement for the successful completion of the study. We would like to express our deep and sincere gratitude to Dr. Y. S. Sivan, Associate Professor Social Science Research and Ms. Ashraf, Assistant Professor in Physiotherapy for their valuable guidance in the study.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]
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