Predictors of undernutrition among the elderly in Sodo zuriya district Wolaita zone, Ethiopia

A community based cross sectional study design was employed in Sodo Zuriya district from February to June, 2017. Sodo Zuriya district was one of the districts in Wolaita Zone, Southern Ethiopia. It is located at 327Kms (Kilometers) far from Addis Ababa, capital city of Ethiopia and 160Kms from Hawassa, the regional capital. Based on the last census, it had a total population of 162,691, of whom 80,002 were men and 82,689 women. The number of older persons — those aged 60 years or over were 16,233. The district had 36 Kebeles (the smallest administrative unit in Ethiopia) [12]. The major type of cereal grow in the district was maize. The population mostly consumes root and tuber based products like potato, sweet potato, godere (a greenish-purple potato), and cassava. Among fruits avocado, papaya, banana and mango were commonly consumed. From vegetables kale and cabbage were frequently used.

The sample size was calculated using a single population proportion formula,

The assumptions were, estimated prevalence for undernutrition of elderly, 21.9% from study done in Gonder town [3], margin of error 5.0%, design effect of 2 and 10.0% non-response rate. Accordingly, the final sample size became 578. A multi stage simple random sampling technique were applied to select ten kebeles out of 36 kebeles.. In the selected 10 kebeles there were 207 sub-Kebeles. Then we considered a proportional allocation to the sample size to allocate participant for each sub-Kebeles, finally systematic sampling technique was used to select households included in the study. The sampling interval was calculated by dividing the total households in the selected sub-Kebeles of the Kebele by the final sample size, which gave every third household. Then after, the first household was selected from each sub-Kebeles, by spinning a pen, where the tip of the pen was pointed taken to be the first household. Then the sampling interval was added on to the first household to identify the consecutive households.

The questionnaires contains socio-demographic factors, life style factors, 24 h food diary and nutritional factors, physiological factors, psychological factors and health related factors. During the preparation of the questionnaire, related published articles and Mini Nutritional Assessment Questionnaire were reviewed and contextualized. It was prepared in English and translated into Amharic and back translated into English to maintain consistency.

Before actual data collection pre-test was done among 10% of sample size in relatively similar district but out of actual study area to check accuracy, to estimate time and any inconsistency and necessary corrections were made. Experts with a nutrition and dietetic background assessed content validity. Trained ten diploma holders in clinical nursing collected data and two bachelor holders supervised the collection. The data collection tool had two parts; interview using questionnaire and target population assessment using measurement scales. Regarding the target population assessment, weight and height were measured using digital weighing scale and Stadio-meter respectively. During the measurement participants were barefoot, legs straight, shoulders relaxed and look straight ahead at the horizontal plane. In addition, they were asked to inhale deeply, hold the breath and maintain an erect position just before taking the measurement. Reading of height measurement was taken twice to the nearest 0.1 cm. A digital weighing scale was used to measure weight; it was measured twice in light clothes without shoes and stand still in the middle of the scale’s platform; and the average weight was taken. The district grows most of the stable crops through out the year. Hence, the difference might not exist when it comes to when data was collected.

Data were entered into Epi-Data version 3.1 and exported to SPSS version 20 for analysis. All continuous data were checked for normality using histogram and other normal plots. Univariate analysis was used to determine the frequencies. Bivariate analysis was applied to determine associations between outcome and exposure variables. Independent variables having P-value less than 0.05 on bivariate analysis were candidates for multivariable analysis for further confounding effect control. Hosmer and Lemeshew goodness of fit test was done to assess the fitness of the model during multivariate analysis. With p-value of 0.25, the model was ensured being fit well for the multivariate analysis and accepted. Both crude and adjusted odds ratio with 95% confidence interval were reported to measure the strength of association between exposure and outcome variable. The results on multiple logistic regression were considered statistically significant at P-value< 0.05.

Of the total 578, 554 elderly participated in the study giving response rate of 96.0%. The median age of participants was 65 years with (SD+ 8.1). Among the study participants majority of them were aged 60–65 years, 52.2% (n = 281). Majority of the participants, 76.5% (n = 424) cannot read and write. Most of the participants, 83.0% (n = 460) had monthly income less than $20 (Table 1).

Regarding cigarette smoking and alcohol intake, 79.4% (n = 31) smoke on daily basis and 27.5% (n = 27) took alcohol on daily basis. Regarding health status of the participants, 49.3% (n = 273) were sick in the last three months. From the total participants, 33.6% (n = 183) of them were suffering from decline in food intake in the last three months and most mentioned loss of appetite as a reason. Of the total participants, 81.8% (n = 453) of participants do not feel lonely (Table 2).

The most commonly consumed food groups in the last 24 h were dark green vegetables (81.9%), followed by dairy products (60.6%) and legumes and nuts (54.5%). Regarding the dietary diversity score (DDS), 57.0, 28.0 and 15.0% scored low, moderate and high respectively (Table 3).

The overall prevalence of undernutrition among elderly in Sodo Zuriya districts was 17.1% while majority 80.6% (n = 446) have normal BMI and only 2.3% (n = 13) of them were overweight. Among the undernourished subjects 11.0% (n = 61) of them fall under mild category, 5.0% (n = 27) at moderate and 1.1% (n = 7) of them fall under severe category. The prevalence of undernutrition was slightly high among females 20.5% compared to male 14.2%. The prevalence is also relatively high among study participants whose age are greater than 65 years 24.6% than those whose age are between 60 and 65 years 14.8%.

On bivariate logistic regression, factors such as age group, being unable to read and write status, sex, not being married, illness in the last three month, weight lost, taking medication, decline in food intake, smoking and monthly income <$20 were positively associated with undernutrition. Whereas, on the multivariable logistic regression, being unable to read and write, not being married, decline in food intake, monthly income <$20 and smoking were positively associated with undernutrition (Table 4).

Elderly who cannot read and write were two times more likely to be undernourished than those who can read and write (AOR = 2.09, 95%CI: 1.02–4.2). Study participants whose marital status either widowed or divorced were two times more likely to be undernourished than those married (AOR = 2.02,95%CI: 1.2–3.4). This study also showed that elderly who have history of decline in food intake had 2.1 times odds of being undernourished than their counter parts (AOR = 2.1, 95%CI: 1.02–4.2). Those who smoke on daily basis were five times (AOR = 4.9, 95%CI: 1.5–15.4) more likely to be undernourished than those who do not. Elderly whose monthly income less than 500 ETB (Ethiopian Birr) ($20) was 7.5 times (AOR = 7.5, 95%CI: 2.1–26.5) high likely to be undernourished than their counterparts (Table 4).