Study design and area
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.
Study population
Study population were elderly who were systematically selected and living in selected Kebeles of the Sodo Zuriya district. Those who were seriously ill and who cannot stand without aid were excluded from the study. Five participants were excluded from the study.
Sample size determination and sampling technique
The sample size was calculated using a single population proportion formula,
$$ n=\frac{z_{1-\alpha /2}^2p\left(1-p\right)}{d^2} $$
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.
Data collection tool
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.
Data quality assurance
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.
Operational definition
Seriously ill
Those who were unable to communicate due to sickness were considered as seriously sick. Moreover, this study involved height measurement that requires being on barefoot, legs straight; shoulders relaxed and maintain an erect position. Those who were unable to do so were also excluded.
Elderly: a person whose age greater than 60 years [13].
Undernutrition
Elderly who had BMI less than 18.5 kg/m2. Those who had Body Mass Index (BMI) between 18.5 Kg/m2 to 24.9 Kg/m2 were considered as normal and between 25 and 29.9 Kg/m2 were considered as overweight and obese and coded as No whereas those with BMI of less than 18.5 Kg/m2 considered as undernutrition and coded as Yes [14].
The food groups’ classification were determined using the Food and Nutrition Technical Assistance (FANTA) description as Low Dietary Diversity Score (DDS): when an elderly person consume < 3 food items per day; Moderate Dietary Diversity Score (DDS): when an elderly consume 4–5 food items per day; High Dietary Diversity Score (DDS): when an elderly consume > 5 food items per day [15].
Malnutrition
We classified malnutrition using Subjective Global Assessment form (SGA). Which is validated tool to classify malnutrition clinically. Accordingly Mild under nutrition: no decrease in food/nutrient intake; < 5% weight loss; no/minimal symptoms affecting food intake; no deficit in function; Moderately malnourished: definite decrease in food/nutrient intake; 5–10% weight loss without stabilization or gain; mild/some symptoms affecting food intake; Severely malnourished: severe deficit in food/nutrient intake; > 10% weight loss which is ongoing; significant symptoms affecting food/ nutrient intake; severe functional deficit.
Cigarette smoking: was operationalized as subjects who smoked at least one cigarette per day at the time of the study was classified as current smokers and those who smoked for at least three years in the past but had stopped by the time of the study was classified as a habitual smokers. Got was defined as a block or village that contains minimum thirty households.
Method of analysis
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.