Prevalence of malnutrition and associated factors among older adults from urban and rural residences of Metu district, Southwest Ethiopia
BMC Nutrition volume 8, Article number: 52 (2022)
In Ethiopia, the proportion of older adults is steadily increasing. This rapidly growing older adult population may increase the burden of malnutrition. However, malnutrition among older adults, particularly those living in rural areas, is regularly underrecognized and/or ignored. There have been no studies among older adults in the Metu district that have used the Min nutritional assessment (MNA). As a result, the purpose of this study is to determine the prevalence of malnutrition and its associated factors among older adults in Metu district, Southwest Ethiopia.
A community-based comparative cross-sectional study was conducted from May to June 2021 in Metu district. A multi-stage stratified sampling technique was employed. The nutritional status of the older adults was measured by MNA. Data from 616 older adults (308 from urban and 308 from rural residences) was collected through face-to-face interviews using a structured questionnaire. Bivariable and multivariable logistic regression analyses were done to identify factors associated with malnutrition.
According to this study, the overall prevalence of malnutrition in Metu district was 17.5% (95% CI: 14.4%–20.7%). The prevalence of malnutrition in urban and rural residences was 9.9% and 25.2%, respectively. In the overall study, insomnia (AOR: 2.0, 95%CI: 1.1–3.7), residence (AOR: 3.47, 95%CI: 1.8–6.5), and smoking (AOR: 3.7, 95%CI: 2.1–6.7) were associated with malnutrition. In urban residences, depression (AOR: 3.4, 95%CI: 1.2–9.5), dietary diversity score (DDS) (AOR: 3.5, 95%CI: 1.2–10.0), and eating problems (AOR: 2.8, 95%CI: 1.1–7.3) were associated with malnutrition. In rural residence, age (AOR:3.8; 95%CI: 1.2–11), sex (AOR:2.2,95%CI:1.0–4.8), DDS (AOR:5.4,95%CI:2.2–13.3), depression (AOR: 4.6,95%CI:2.2–9.2), and chronic disease (AOR: 3.8 95%CI: 1.8–8.2) were associated with malnutrition.
Conclusions and recommendations
Malnutrition was more prevalent among older adults living in rural areas than in urban areas. In the overall study, insomnia, residence, and smoking were significantly associated with malnutrition. DDS, depression, and eating difficulties were significantly associated with malnutrition among older adults living in urban areas, whereas sex, age, depression, chronic disease, and dietary diversity were factors influencing malnutrition in rural areas. Strengthening strategies aimed at addressing nutrition policy, as well as paying attention to the nutritional needs of the older adult population, can help to improve the health and quality of life of older adults.
Malnutrition is a condition where energy, protein, and other nutrients are insufficient, excessive, or imbalanced to sustain health, promote cell and tissue growth, and normal organ function, causing detrimental effects on body shape, function, and clinical outcomes . Globally, the prevalence of malnutrition among older adults ranges from 23 to 46% . According to the health and nutrition survey (HANES) data, 16% of community-dwelling Americans over the age of 65 consume fewer than 1000 cal per day, putting them at a high risk of malnutrition [3, 4]. In sub-Saharan Africa, the prevalence of malnutrition among older adults ranges from 6 to 48% [5, 6]. In Ethiopia, malnutrition is a common health problem, affecting 21.9% of the population .
Poor nutritional status was prevalent among older adults in both rural and urban settings . In older adults, malnutrition is caused by one or more of the following factors; inadequate food intake, food choices, increased nutrient loss, poor nutrient absorption, loss of appetite, lack of ability to chew and swallow, and increased use of prescription medications [9,10,11,12,13]. Even though diseases, inactivity, and normal aging can cause a gradual loss of muscle mass and strength, leading to malnutrition [14, 15]. This gradual loss of muscle mass and strength below the normal threshold results in functional impairment [16, 17]. Consequently, one aspect of the health-related quality of life diminishes as the individual becomes malnourished. Some of the indicators of health-related quality of life are physical health, mental health, and activity limitation .
There is a lack of community-based programs and activities that screen for malnutrition and address those risk factors among vulnerable populations. In Africa, older adults are not considered a priority for targeting nutrition interventions, so the efficacy of different types of nutrition interventions has not been described in this population. Hence, only a handful of nutritionists work with older people, and there are even fewer practitioners who perform high-quality research in their homes. Indeed, nothing else has been attempted, aside from the disaster relief and supplementary feeding programs . Correspondingly, in Ethiopia, malnutrition among older adults is not well detected, and public health research disregards the study of malnutrition among older adults .
Therefore, it is vital to assess malnutrition among older adults living in urban and rural residences; to improve their nutritional status, advance living conditions, and develop strategies that are important to address older adult nutritional problems. So, this study aims to assess the prevalence of malnutrition and associated factors among older adults from urban and rural residences in Metu district, Southwest Ethiopia.
Methods and materials
Study design and period
A community-based comparative cross-sectional study design was conducted from May to June 2021 in Metu district, Illu Aba Bor zone, Southwest Ethiopia.
Metu district is one of the districts in the Illu Aba Bor Zone, in southwest Ethiopia. It is located 606 km from Addis Ababa, the capital city of Ethiopia. Based on the latest census, it has a population of 108,764 people, of whom 54,768 are men and 53,996 are women; of these, 18,974 are estimated to be older adults. The district has 35 kebeles, including 6 urban kebeles and 29 rural kebeles. It has an elevation of 1605 m and is located at 8°18′N 35°35′E.The area of the district is 687,230 hectares of land. The agro-climates of the residences have a winter dry season and summer rainfall. The main source of income for the indigenous community is agriculture. It is also known for its honey and coffee production. There is one public hospital (Metu Karl referral hospital) in Metu district .
Source and study population
All older adult people aged greater than or equal to 60 years old who lived in Metu district were the source population, whereas older adults randomly selected and living in selected kebeles of Metu district were the study population.
Inclusion and exclusion criteria
All people aged ≥ 60 years who had lived in the selected kebeles for the past 6 months were included in the study, whereas older adults with visible body edema, unable to respond due to critical illness, or having a mental illness were excluded from the study.
Sample size and sampling technique
Sampling size determination
The Required sample size was first determined by using a double population proportion Formula
r = the sample size n2 to n1 ratio, which is assumed to be 1.
Zα/2 = 1.96(type I error).
P1 = the proportion of urban people aged 60 and older who are malnourished.
P2 = the proportion of rural people aged 60 and older who are malnourished.
Using P1 = (17.1%  and P2 = 26.6%  and adding a 10% non-response rate and multiplying the design effect by 2, a total of 616 older adults (n1 = 308 from urban and n2 = 308 from rural) were enrolled in the study.
The second sample size was calculated by using Epi Info version 4.7. The variables were considered under the following assumptions: 95% confidence level, the ratio of unexposed to exposed (r = 1), and 80% power, among those calculated samples; the largest calculated sample size was found to be 600. As we compared the first and second calculated samples, the first method was relatively large, so we took the larger sample size of 616.
Sampling technique and procedure
A multi-stage stratified sampling technique was used. The district was stratified by residences into rural and urban kebeles. The residential stratification yields six urban kebeles (Metu town) and 29 rural kebeles (Metu district); among them, two urban and nine rural kebeles, or around 30% of the total kebeles, were randomly selected by using lottery methods. An average of 900 and 1,350 households were found in each urban and rural Kebles, respectively. Likewise, each urban and rural kebele had between 221 and 865 older people. As a result, one household was chosen for every three households in both residences using a systematic random sampling technique. Similarly, participants in the study were chosen using a systematic random sampling method that divided the total number of older adults by the total sample size (538/308) for urban kebeles and (1966/308) for rural kebeles, resulting in participants being chosen every two and six samples, respectively. If participants were not found in the selected households, the samples were taken from the next household. In each household, one participant was selected. If more than one person was found in the selected household, the lotory method was used to select one of the participants.
People over the age of sixty are considered older adults .
Based on the Mini-nutritional assessment (MNA) tool, individuals who had ≥ 17 scores were considered not malnourished and coded as "NO", while those who had a score of less than 17 were considered malnourished and coded as "YES" (which includes normal and at risk of malnutrition) .
Changes in taste, smell, and appetite generally decline with age, making it more difficult to enjoy eating and keep regular eating habits .
Geriatric depression scores
(GDS) was used to assess the psychological condition of older adults. A fifteen-item depression scale assessment was used by directly interviewing the respondent. GDS was calculated using 15 yes-or-no items, with yes being for the presence of one of the depression symptoms. Thus, depression was defined using a cut-off point greater than or equal to five .
Based on the possession of household assets (i.e., refrigerator, sofa, bicycle, television, radio, and mobile telephone), values were computed for each urban and rural area. Finally, the wealth index was ranked into three: poor, medium, and rich by using principal component analysis .
Dietary diversity score
classification were determined using the Food and Nutrition Technical Assistance (FANTA) description as Low dietary diversity score (DDS): when an older adult person consume < 3 food items per day; Moderate dietary diversity score (DDS): when an older adult consume 4–5 food items per day; High dietary diversity score (DDS): when an older adult consume > 5 food items per day .
Data collection tools
The questionnaire was prepared in English and then translated into Afan Oromo and Amharic. A variety of structured questionnaires were used to collect data, including socio-demographic information, the full mini nutritional assessment (MNA-full), the geriatrics depression scale (GDS), the dietary diversity score (DDS), and others. Malnutrition of older adults was measured by using the Min Nutritional Assessment (MNA) tool . The Mini nutritional assessment tool consists of 18 items with a maximum point value of 30, divided into four categories: general assessment (lifestyle, medication, stress, mobility, neuropsychological problems, and skin lesions); subjective assessment (perceived health and nutritional status); dietary assessment (number of meals, food and fluid intake, and mode of feeding); and anthropometric assessment (weight, height, arm, and calf circumference). Based on this tool, the nutritional status of the participants was classified as either malnourished or not-malnourished. If participants who scored < 17 points were malnourished, and if participants who scored ≥ 17 points were not malnourished (which includes normal and at risk of malnutrition) .
The weight of the study participants was measured using a beam balance to the nearest 0.1 kg without shoes and all heavy clothing, including jackets, jerseys, and belts. The weighing scale was checked against a standard weight each day for its accuracy. Calibration was performed before weighing each study participant by setting it to zero.
The height of the study participants was taken using an adult stadiometer for those who could stand. For those who are unable to stand, the arm span from the sterna notch to the tip of the finger or knee height was to be used as a proxy indicator for the height of the subjects using a specific formula for the specific sex. Participants were asked to take off their shoes and stand on the Frankfurt plane (stand erect and look straight ahead on the horizontal plane). The occipital (back of the head), shoulder blades, buttocks, and heels were touched by the measuring board, and height was recorded to the nearest 0.01 cm.
The BMI was calculated by dividing the weight in kg by the height in meters squared and was expressed in kg/m2. However, when the height measurement was not possible, calf circumference was used instead of BMI.
The Geriatric Depression Scale item 15 (GDS-15) was used to assess depression among older adults as suggested by the Royal College of Physicians, the British Geriatric Society, and the Royal College of General Practitioners. Thus, depression was defined using a cut-off point greater than or equal to five.
The short twenty-four-hour dietary recall was used to assess the dietary intake pattern of the clients, as it reduces the recall bias secondary to a memory lapse. Cronbach's Alpha was done to check the internal consistency of the tools, and each item scored an acceptable level of reliability. The data was collected daily. Data collectors were approached by introducing themselves to and interviewing the selected respondents after informed consent was obtained.
Data quality assurance
An interviewer-administered structured questionnaire and anthropometric measurements were used to collect data from interviewees. Through house to house visits, an interviewer-administered structured questionnaire was used to collect socio-demographic, health and lifestyle, geriatrics depression scale (GDS), dietary diversity score (DDS), and nutrition or dietary habits characteristics of older adults.
Eight diploma nurses who can sufficiently speak and understand both Afan Oromo and Amharic were recruited as interviewers, and three BSc nurses were recruited as supervisors. For one day, supervisors were trained on the details of the questionnaire, how to control the quality of data, and the efficiency of data collectors.
Data collectors were trained for one day in appropriate interview techniques and anthropometric measurements like height and weight. Practices were performed before the actual data collection. After that, constructive feedback was given to the data collectors by the investigators and supervisor until they became clear of the data collection procedures and instruments.
A consent form was provided for the study subject to those who voluntarily agreed to participate. Data collectors have interviewed study subjects in their homes. When study subjects were not present during data collection, the interviewer revisited the house at different time intervals, at least three times since it is a community-based study. Strict daily supervision of the data collection process was maintained throughout the data collection period. The time estimated to complete the interview was 25 to 35 min. Short and precise information regarding malnutrition prevention was provided to all participants at the end of the interview.
Data analysis and processing
Data were entered into Epi-info statistical software version 7 before being exported to the Statistical Package for Social Science (SPSS) version 22 for analysis, with three separate logistic regression models (urban, rural, and both urban and rural combined) fitted. Frequencies and cross-tabulations were used to summarize descriptive statistics of the data, and tables were used for data presentation.
The significance of the association was determined using crude and adjusted odds ratios with 95% confidence intervals. The Hosmer and Lemeshow goodness of fit of overall malnutrition in urban and rural residences was 0.649, as well as 0.204 in urban and 0.287 in rural residences. All variables that were less than 0.2 during bivariable analysis were considered as candidates for multiple logistic regressions. After adjusting for their effect on the outcome variable, those variables with a p-value < 0.05 with a 95% confidence interval were regarded as factors significantly associated with malnutrition.
Socio-demographic and economic characteristics of respondents in Metu district
A total of 610 older adults participated in this study, with a 99.0% response rate. Of the total participants, 304 and 306 were from urban and rural residences, respectively. About 293 (48.0%) of the study participants were male. Of these, 160 (22.4%) were from rural residences and 133 (43.5%) of them were from rural residences. Concerning marital status, 187 (61.5%) and 195 (63.7%) of older adults were married in urban and rural residences respectively. Regarding educational status, 174 (57.2%) urban older adults had a higher formal education than rural older adults 5 (1.6%). Regarding their main source of financial support, 91 (31.9%) of older adults living in urban residences were on pensions, while 181 (59.2%) of rural older adults were from agriculture (Table 1).
Health and lifestyle characteristics of respondents in Metu district
About, 42.8% of rural older adults and 30.6% of urban older adults reported having at least one chronic disease, with 108 (35.3%) and 89 (29.3%) having a history of hospitalization in the previous year, respectively. Regarding lifestyle characteristics, 22.0% of urban and 29.7% of rural older adults had the habit of smoking cigarettes. However, only 28.6% of urban older adults reported eating problems, whereas 32% of rural older adults reported eating problems (Table 2).
Dietary Diversity Score (DDS) of older adults in Metu district
The most commonly consumed food groups by urban older adults in the last 24 h were cereals and grain food groups 222 (73.0%), followed by spices, condiments, and beverages 212 (69.7%). While for rural residences, dark green leafy vegetables (191 (62.4%), followed by spices and condiments, 185 (60.5%) of the food groups were consumed. Regarding the dietary diversity score (DDS), 18.4%, 18.8%, and 62.8% scored low, moderate, and high for urban respectively. In rural, 25.2%, 28.5%, and 46.4% scored low, moderate, and high, respectively (Table 3).
Depression status of an older adult by residence in Metu district
Among the study participants 98 (32.2%) of older adults living in an urban residence, and 103 (33.7%) of older adults living in rural residences had depression (Table 4).
Prevalence of malnutrition among older adults in Metu district
According to this study, the overall prevalence of malnutrition in Metu district was 17.5% (95% CI: 14.4%—20.7%). The prevalence of malnutrition in urban and rural residences was 9.9% (95% CI: 6.3—13.7%) and 25.2% (95% CI: 20.4—30.4%) respectively. More than half of the study participants had eaten three full meals a day 271 (89.1%) and 168 (54.9%) in both urban and rural residences respectively. And had no decrease in food intake 257 (84.5%) and 185 (60.5%) for the past 3 months in both urban and rural residences respectively.
On the other hand, the majority (98.4%) and (97.1%) of older adults in urban and rural residences did not consume selected consumption markers of protein like dairy products (milk, cheese, and yogurt), legumes, eggs, and poultry every day respectively. 58 (19.1%) of Urban older adults and 75 (24.5%) of rural older adults fed themselves without any difficulty (Table 5).
Factors associated with malnutrition among older adults in urban residences of Metu district
In the bivariable logistic regression analysis; marital status, educational status, depression, dietary diversity, smoking, chronic disease, hospitalization in the previous year, and eating problems were associated with malnutrition.
In the multivariable logistic regression analysis; DDS, eating problems, and depression were significantly associated with malnutrition. The odds of malnutrition were 3.5 times higher among older adults with low dietary diversity than high dietary diversity (AOR = 3.5, 95% CI = 1.2–10.0). Older adults who had an eating problem were 2.8 times more likely malnourished than their counterpart (AOR = 2.8, 95% CI: 1.1–7.3) and the odds of malnutrition was 3.4 times higher among older adults who had depression than older adults who were free of depression (AOR = 3.4, 95% CI: 1.2–9.59) (Table 6).
Factors associated with malnutrition among older adults in rural residences of Metu district
Variables like age, sex, marital status, educational status, meal, chronic disease, eating problems, previous hospitalization, dietary diversity, and depression were significantly associated with malnutrition in the bivariable logistic regression analysis.
In multivariable logistic regression analysis; sex, age, chronic disease, DDS, and depression were significantly associated with malnutrition among older adults living in a rural residence. Females were 2.2 times more likely to develop malnutrition as compared with male older adults (AOR 2.26; 95% CI: 1.06–4.8). Likewise, the odds of malnutrition were 3.81 times higher among older adults age 75 years old or above as compared with older adults age 65–74 years old (AOR: 3.81- 95% CI: 1.24–11.70). The likelihood of being malnourished was 3.8 times higher among older adults who had the chronic disease (AOR: 3.85, 95% CI: 1.80–8.23). Dietary diversity is one of the factors which affects malnutrition; the odds of malnutrition was 5.44 times higher among older adults who had low dietary diversity as compared with older adults who had high dietary diversity (AOR: 54.4; 95% CI: 2.21–13.39). And the odds of malnutrition were 4.6 times higher among older adults who had depression than older adults who were free of depression (AOR: 4.60, 95% CI: 2.28–9.26) (Table 7).
Factors associated with the overall malnutrition of older adults in Metu District
In the bivariable logistic regression analysis, factors like; age, sex, marital status, residence, educational status, source of finance, smoking, chronic disease, insomnia, eating problems, previous hospitalization, dietary diversity, depression, were significantly associated with malnutrition.
In the multivariable logistic regression analysis; sex, residence, eating problem, insomnia, cigarette smoking, and depression were statistically associated with malnutrition. The odds of malnutrition were 2.46 times higher among females than male older adults (AOR: 2.46, 95% CI: 1.3–4.4). Respective to residence; the likely hood of malnutrition was 3.4 times higher among older adults who lived in rural residences than urban residences (AOR: 3.4; 95% CI: 1.8–6.5). Eating problem was also the other problem affecting malnutrition; the odds of malnutrition was 2.47 time's higher among older adults who had an eating problem as compared with free of eating problem (AOR: 2.47, CI: 1.3–4.4). Cigarette smokers were 3.77 times more likely malnutrition as compared with non-smokers (AOR = 3.77, 95% CI: 2.1–6.7). The likelihood of malnutrition was 2.07 times higher among older adults who had insomnia as compared with older adults who were free of insomnia (AOR: 2.07; 95% CI: 1.1–3.7). Also, the odds of malnutrition were 3.75 times higher among older adults who had depression than older adults who were free of depression (AOR: 3.75, 95% CI: 2.0–6.7) (Table 8).
According to this study, the overall prevalence of malnutrition in Metu district was 17.5% (95% CI: 14.4%–20.7%). The prevalence of malnutrition in urban and rural residences was 9.9% (95% CI: 6.3—13.7%) and 25.2% (95% CI: 20.4—30.4%) respectively. The overall finding was in line with the study done in Harar, Ethiopia (15.7%)  and the study done in Egypt (17.9%) . Besides, the finding was lower than the study done in Nairobi, Machakos (29.8%), and Uganda (22.5%). The discrepancy could be attributed to a population difference or geographical variation and it might be due to age group differences . On the other hand, the current study was slightly higher than the study conducted in Senegal (16.7%)  and Kenya, Dagoretti district (11.4%) . The disparity could be attributed to differences in nutritional assessment methods as well as geographical variation. Furthermore, research findings in Senegal and Kenya used the Body Mass Index (BMI) to assess malnutrition, whereas this study used the Mini Nutritional Assessment tool to assess nutritional problems .
The current study compares the prevalence of malnutrition among older adults in Metu district between urban and rural residences. As a result, malnutrition among older adults in urban areas was 9.9%, while malnutrition among older adults in rural areas was 25.2%. A study conducted in Senegal found that 10.5% of older adults living in urban areas were malnourished, while 23.5 percent of older adults living in rural areas were malnourished. . It was supported by the studies done in Benin, where malnutrition was found to be 17.0% and 22.2% in urban and rural older adults, respectively . Furthermore, it was supported by a study conducted in Harar, Ethiopia, which found that older adults in rural residences had a higher proportion of malnutrition 17.1% than older adults in urban residences.4.9% .
In terms of associated factors, rural residents were found to be at a higher risk of malnutrition than urban residents. According to the findings of this study, the likelihood of malnutrition was 3.4 times higher among older adults living in rural areas than in urban areas. This finding was consistent with the findings of a study conducted in France. . This might be attributed to the difference in socioeconomic status and dietary habits, these puts rural residences at risk of malnutrition.
Furthermore, the likelihood of malnutrition was 2.07 times higher in older adults who had insomnia compared to older adults who did not have insomnia. This findings agree with studies done in Lebanon  and Turkey . This could be due to a lack of sleep, which increases energy consumption and contributes to malnutrition, and/or insomnia, which increases the risk of falls, cognitive impairment, and depression. These conditions, when combined, raise the risk of malnutrition .
Besides that, smoking is another factor that has been linked to malnutrition. Thus, cigarette smokers were 3.77 times more likely to suffer from malnutrition than nonsmokers. This finding was supported by a study conducted in Taiwan  and Cuba . The possible justification could be that smoking is one of the risk factors for malnutrition. .
In rural areas, the likelihood of older adults being malnourished increases with age. As a result, the odds of malnutrition were 3.81 times higher in adults 75 years or older compared to older adults 65–74 years old. This finding was consistent with the data reported in Senegal , Addis Ababa, Ethiopia , and Gonder, Ethiopia . This could be due to a change in taste and smell, as well as a general decline in appetite with age, making it more difficult to enjoy eating and maintain regular eating habits, likely to result in malnutrition .
In rural residences, females were 2.2 times more likely to develop malnutrition as compared with male older adults. This result was supported by the study done in rural India  and Gonder, Ethiopia . This could be because women are more responsible for the care of household members and are more financially reliant on family members than men.
Correspondingly, depression was found to be a factor for both people living in urban and rural areas. In urban areas, the odds of malnutrition were 3.4 times higher among depressed older adults than among non-depressed older adults, and in rural areas, the odds of malnutrition were 4.6 times higher among depressed older adults than among non-depressed older adults. This finding was consistent with the results of the Addis Abeba study , India , and Italy . The possible justification could be that depression is one of the risk factors for malnutrition, which decreases food intake .
This research revealed that dietary diversity scores were strongly associated to malnutrition in both urban and rural areas. In urban areas, the odds of malnutrition were 3.5 times higher among older adults with low dietary diversity compared to those with high dietary diversity, and in rural areas, the odds of malnutrition were 5.44 times higher among older adults with low dietary diversity compared to those with high dietary diversity. This was consistent with research conducted in Gonder, Ethiopia , and Kenya . A possible reason could be that a change in dietary diversity could cause a change in nutritional status .
In urban kebeles, eating difficulties were significantly associated with malnutrition. Older adults who had an eating problem were 2.8 times more likely to have malnutrition than their counterparts. This study was consistent with the study done in Debre Markos, Ethiopia . The possible explanation is that malnutrition may occur as a result of increasing age and a decrease in the senses of taste, smell, and appetite .
Moreover, the presence of chronic illness was a significant factor influencing the nutritional status of older adults living in rural areas. Thus, the risk of malnutrition was 3.8 times higher in older adults with chronic diseases. This finding was consistent with the findings of a study conducted in Eastern Ethiopia  and rural Lebanese . This could be because chronic disease can account for nearly half of all malnutrition in older adults .
Conclusions and recommendations
Malnutrition was more prevalent among older adults who lived in rural areas than in urban areas. In the overall study, insomnia, residence, and smoking were significantly associated with malnutrition. DDS, depression, and eating difficulties were significantly associated with malnutrition among older adults living in urban areas, whereas sex, age, depression, chronic disease, and dietary diversity were factors influencing malnutrition in rural areas. Strengthening strategies aimed at addressing nutrition policies, as well as paying attention to the nutritional needs of the older adult population, can help to improve the health and quality of life of older adults. Besides this, public health policies and interventions should address these nutritional risk factors separately in urban and rural areas.
Availability of data and materials
For patient confidentiality reasons, the raw data would not be provided. But the summary data is available in the main document.
Body Mass Index
Dietary Diversity Score
European Society for Clinica, Nutrition and Metabolism
Food and Nutrition Technical Assistance
Geriatric Depression Score
Institutional Review Board
Mini Nutritional Assessment
Mid Upper Arm Circumference
Proportional allocation to size
Statistical Package for Social Sciences
World Health Organization
Chen CCH, Schilling LS, Lyder CH. A concept analysis of malnutrition in the elderly. J Adv Nurs. 2001;36(1):131–42.
Kaiser MJ, et al. Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. J Am Geriatr Soc. 2010;58(9):1734–8.
Wallace J, Malnutrition and enteral/parenteral alimentation. Principles of geriatric medicine and gerontology. 4th ed. New York: McGraw-Hill; 1999. p. 1455–69.
Endoy M. Anorexia among older adults. Am J Nurs Pract. 2005;9(5):31–8.
World Health Organization. 2019. Health situation and trend assessment: Elderly population. http://www.searo.who.int/entity/health_situation_trends/data/chi/elderly-population/en/. Accessed 14 Feb 2021.
Mahfouz, E, Mohammed E, Abd El-Rhman T. Assessment of nutritional status of elderly populations in rural Minia, Egypt. J Aging Res Clin Pract. 2013;2(3):300–302.
Tessfamichael D, Gete AA, Wassie MM. High prevalence of undernutrition among elderly people in Northwest Ethiopia: a cross sectional study. Read Write. 2014;244:32.2.
Serrano Moreno W, et al. A Comparison of Nutritional Habits, Physical Function and Psychological Constructs between Urban and Rural Costa Rican Older Adults. 2019.
Smith ML, et al. A brief intervention for malnutrition among older adults: stepping up your nutrition. Int J Environ Res Public Health. 2020;17(10):3590.
Adhana Z, Tessema G, Getie G. Prevalence of under nutrition and associated factors among people of old age in debre markos town northwest ethiopia. 2015.
Raynaud-Simon A, Revel-Delhom C, Hébuterne X. Clinical practice guidelines from the french health high authority: nutritional support strategy in protein-energy malnutrition in the elderly. Clin Nutr. 2011;30(3):312–9.
Hamren K, Chungkham HS, Hyde M. Religion, spirituality, social support and quality of life: measurement and predictors CASP-12 (v2) amongst older Ethiopians living in Addis Ababa. Aging Ment Health. 2015;19(7):610–21.
Demling R, DeSanti L. 2001. Involuntary weight loss and proteinenergy malnutrition: Diagnosis and treatment. New York: Medscape Nursing Clinical Management Modules.
Evans WJ, Lexell J. Human aging, muscle mass, and fiber type composition. J Gerontol A Biol Sci Med Sci. 1995;50(Special_Issue):11–6.
Hurley BF. Age, gender, and muscular strength. J Gerontol A Biol Sci Med Sci. 1995;50:41–4.
Samson MM, et al. Relationships between physical performance measures, age, height and body weight in healthy adults. Age Ageing. 2000;29(3):235–42.
Bassey EJ, et al. Leg extensor power and functional performance in very old men and women. Clin Sci (London, England: 1979). 1992;82(3):321–7.
Ford ES, et al. Self-reported body mass index and health-related quality of life: findings from the behavioral risk factor surveillance system. Obes Res. 2001;9(1):21–31.
Charlton KE, Rose D. Nutrition among older adults in Africa: the situation at the beginning of the millenium. J Nutr. 2001;131(9):2424S-2428S.
Abate T, et al. Malnutrition and its determinants among older adults people in Addis Ababa. Ethiopia BMC geriatrics. 2020;20(1):1–9.
Wakshuma F. Urban potable water supply in Metu town, Southwestern Ethiopia. 2017.
Wondiye K, et al. Predictors of undernutrition among the elderly in Sodo zuriya district Wolaita zone. Ethiopia BMC nutrition. 2019;5(1):1–7.
AbouZahr C, Boerma T. Health information systems: the foundations of public health. Bull World Health Organ. 2005;83:578–83.
Vellas BJ, Garry PJ, Guigoz Y. 1999. Mini Nutritional Assessment (MNA): research and practice in the elderly. Vol. 1. Basel: Karger Medical and Scientific Publishers.
Kluzek M. Nutrition status and malnutrition. Illinois Department on Aging. 2021. https://www2.illinois.gov/aging/programs/nutrition/Documents/Nutrition-Status-and-Malnutrition-2021.pdf. Accessed 26 Feb 2021.
Yesavage JA, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982;17(1):37–49.
Abdu AO, Yimamu ID, Kahsay AA. Predictors of malnutrition among older adults aged above 65 years in eastern Ethiopia: neglected public health concern. BMC Geriatr. 2020;20(1):1–11.
Intemationai H. Summary of Research Findings on the Nutritional Status and Risk Factors for Vulnerability of Older People in Africa. 2004.
Clausen T, Gobotswang K, Holmboe-Ottesen G. 4 Association between nutrition and socio-economic status. In: Nhongo TM, editor. Summary of research findings on the nutritional status and risk factors for vulnerability of older people in Africa. Nairobi: HelpAge International Africa Regional Centre; 2004. p. 34-47.
Guigoz Y, Vellas B, Garry PJ. Assessing the nutritional status of the elderly: the mini nutritional assessment as part of the geriatric evaluation. Nutr Rev. 1996;54(1):S59.
Torres MJ, et al. Nutritional status in community-dwelling elderly in France in urban and rural areas. PLoS One. 2014;9(8):e105137.
Boulos C, Salameh P, Barberger-Gateau P. The AMEL study, a cross sectional population-based survey on aging and malnutrition in 1200 elderly Lebanese living in rural settings: protocol and sample characteristics. BMC Public Health. 2013;13(1):1–13.
Isik AT, et al. Relationship Between Nutritional Status and Insomnia Severity in Older Adults. 2019.
Everson CA, Wehr TA. Nutritional and metabolic adaptations to prolonged sleep deprivation in the rat. Am J Physiol. 1993;264(2):R376–87.
Poda GG, et al. Impact of socio-demographic factors, lifestyle and health status on nutritional status among the elderly in Taiwan. Nurs Res Pract. 2019;13(3):222–9.
Coqueiro RDS, Barbosa AR, Borgatto AF. Nutritional status, health conditions and socio-demographic factors in the elderly of Havana, Cuba: data from SABE survey. J Nutr Health Aging. 2010;14(10):803–8.
Collins PF, Stratton R, Elia M. The influence of smoking status on malnutrition risk and 1-year mortality in outpatients with chronic obstructive pulmonary disease. J Hum Nutr Diet. 2011;24(4):382–3.
Lahiri S, et al. Assessment of nutritional status among elderly population in a rural area of West Bengal, India. Int J Med Sci Public Health. 2015;4(4):569–72.
Semwal J, et al. Nutritional status and associated comorbidities among the elderly in Doiwala block, Dehradun. Indian J Community Health. 2014;26(Supp 2):197–203.
Donini LM, et al. Malnutrition in elderly: social and economic determinants. J Nutr Health Aging. 2013;17(1):9–15.
Maila G, Audain K, Marinda PA. Association between dietary diversity, health and nutritional status of older persons in rural Zambia. South Afr J Clin Nutr. 2021;34(1):34–9.
Cano N, Melchior J-C. Malnutrition in chronic diseases. Rev Prat. 2003;53(3):268–73.
We would like to thank the study participants, data collectors, and Metu district leaders for their unlimited day-to-day support throughout the data collection process.
The authors received no specific funding for this work.
Ethical approval and consent to particepate
All methods were performed in accordance with the relevant guidelines and regulations. Ethical clearance was obtained from the Institutional Review Board (IRB) of the University of Gondar, College of Medicine and Health Science, Institute of Public Health. The reference number for the ethical clearance for this paper was IPH/1484/2013. Supportive letters were obtained from the local focal person of Metu district. Each study participant was informed about the purpose, method, expected benefit, and risk of the study. They were also informed about their full right to not participate in or withdraw from the study at any time. Written informed consent was obtained from each study participant.
Consent for publication
The author declares that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Ferede, Y.M., Derso, T. & Sisay, M. Prevalence of malnutrition and associated factors among older adults from urban and rural residences of Metu district, Southwest Ethiopia. BMC Nutr 8, 52 (2022). https://doi.org/10.1186/s40795-022-00532-9