Predictors of malnutrition among elderly people above 65 Years in East Ethiopia: Neglected public health concern

Abstract

Background A nutritional problem, especially under nutrition is one of the common public health problems in elderly causing greater mortality and economic loss in developing countries. However, evidences on the risk factors for increased nutritional risk among elderly is not well stated in Ethiopia. This study aimed to assess the nutritional status and predictors of malnutrition among elderly (> = 65 years) in Eastern Ethiopia.Methods Community based survey was conducted among randomly selected 592 elderly age above 65 years of age in Harari region selected using multistage sampling Pretested full Mini Nutritional Assessment (MNA) tool was used to classify as malnourished (MNA score <17), at risk of malnutrition (MNA score of 17 to 23.5) and otherwise normal. Validated geriatric depression scale short form (15 items) was employed to screen for depression. Statistical tables, frequency, percentage and graphs. Ordinary logistic regression was employed to identify predictors of malnutrition and plum method was used to generate odds ratio. Level of statistical significance was declared at P value less than 5%. Chi-square test, crude and adjusted odds ratio with 95% confidence was reported. Results A total of 592 respondents with response rate of 93.4% were interviewed. A total of 306 (51.7%) and 93 (15.7%) were found to be at risk of malnutrition and malnourished respectively. The predicted log odds of being malnourished was higher among those from rural residents (AOR = 2.08: 1.25-3.45), not on working (AOR = 1.31: 95% CI: 0.87-1.95) and did not have health insurance (AOR = 1.58; 95% CI; 0.97-2.58). Those with chronic pain (AOR = 1.70; 95% CI: 1.15-2.51), previous hospitalization (AOR = 1.59: 95% CI: 1.27-2.38) and not able to cover their personal expense (AOR =1.61: 95% CI: 1.12-2.30) were predictors of malnutrition. The relationship between previous hospitalizations with malnutrition among elderly is moderated significantly by the presence of chronic pain (β = 0.113, p = 0.015).Conclusions Malnutrition among old age is public health concern that needs attention. Economic vulnerability, residence, depression, presence of chronic disease and hospitalization were an important risk factors for malnutrition among old age.

Prevalence of undernutrition and its associated factors among older adults using Mini Nutritional Assessment tool in Womberma district, West Gojjam Zone, Amhara Region, North West Ethiopia, 2020

“…The prevalence of undernutrition was in line with studies done in Harari region, Eastern Parts of Ethiopia (15.7%), Sodo district, Wolaita zone, Ethiopia(17.1%), Aykel town administration (17.6%), and Sri Lanka (12.6%) [13,[20][21][22]]. However, it was higher than the studies conducted in Bole sub-city, Addis Abeba, Ethiopia (6.2%),Niamey-Niger (7.8%), Turkey [23][24][25][26].…”

“…Depression could change eating behaviors and, influences people's appetite and food intake, which could lead to an increased risk of undernutrition. This finding is supported by reports from Harergie, east Ethiopia, which illustrated a significant relationship between depression and undernutrition [20].…”

Prevalence of malnutrition among old people in Africa: systematic review and meta-analysis

“…According to the quality assessment checklist, studies were either in moderate (6–8 points)39–42 44 46–48 53 61 64 65 or high-quality groups (9 or 10 points) 30–38 43 45 49–52 54 55 57–60 62 66. Study bias was mostly exacerbated as a result of convenience sampling and a low response rate among study participants.…”

“…Seven studies (18.9%) were from Ethiopia [30][31][32][33][34][35][36] and six studies (16.2%) each from Egypt [37][38][39][40][41][42] and Nigeria. [43][44][45][46][47][48] While five studies (13.5%) were from South Africa, [49][50][51][52][53] four studies (10.8%) were from the Central African Republic and the Republic of Congo, [54][55][56][57] three studies (8.1%) from Ghana [58][59][60] and two studies (6.06%) were from Tanzania.…”