Study design, period and setting
An institution based cross-sectional study was conducted from January to February 2018 in Tegede district, northwest Ethiopia. There are three high schools found in the district. In addition, the district has four health centers, and 22 health posts.
According to, 2017-2018 Amhara Region Bureau of Finance and Economics Development report, the total population in the district is 92,216, of which adolescent girls constitute 10%.
Source and study population
The source population were all late adolescent girls attending high school in Tegede district during the study period. During data collection period, about 968 late adolescents’ girls were attended high school. Late adolescent girls attending classes in the three high schools of Tegede districts, but with no serious illness and those advised by physicians to take extra meal different from their normal habit were included in this study. The sample size was determined by using a single population proportion formula, assuming a 38.7% proportion of adequate dietary diversity in Jimma town [13], 5% margin of error, 95% confidence interval and 10% non-response during the data collection period to come with 400 study participants. A simple random sampling technique was employed to select the study participants, using the school roster as a frame. The participants were proportionally allocated in the three schools according to the total number of students in each school.
Operational definitions
Dietary diversity –the number food groups consumed over a 24 hour period.
Individual dietary diversity –The number of food groups consumed by individuals in or outside the home over a 24 –hour period(32).
Adequate dietary diversity score – Those individuals who were consumed five and above food groups(15).
Minimum dietary diversity-W-was a dichotomous indicator whether or not women of reproductive age had consumed at least five out of ten defined food groups in the previous day and night(15).
Food groups - Consumption of any amount and quality of food from each food group (all starchy staple foods, Beans and peas, Nuts and seeds, Dairy Flesh foods, Eggs ,Vitamin A-rich dark green leafy vegetables ,Other vitamin A-rich vegetables and fruits, Other vegetables ,Other fruits)is sufficient to count (33).
Late Adolescents: The adolescent age group who were encompassed the latter part of the teenage years, between the ages of 15 and 19(34).
Nutritional knowledge: The participants who had responded half of and above the questions related nutrition ‘good knowledge’ if not ‘poor knowledge; who were found from age15-19.
Stunting: is defined as having a height-for-age z score (HAZ) <–2SD
Data collection procedures and variables of the study
Interviewed based questionnaire was used to collect the data. The dietary diversity was measured using a 24 hour recall dietary survey method, which was developed after reviewing literatures (FANTA-2016). The dietary diversity questionnaire used consists of 14 groups of foods, which covers almost every food taken. Some food groups in the dietary diversity questionnaire were combined into composite food groups to create the dietary diversity score.
Socio–demographic, adolescent characteristics were also collected. Wealth index also computed by principal component analysis. Primarily the tool was prepared in English and translated to Amharic, the local language, and re-translated to English to check consistency of the questionnaire. The questionnaire was pretested among 40 adolescent girls out of the study area. Six diploma nurses’ data collectors and two health officer supervisors were deployed in the data collection process. Weight and height of adolescent girls were measured using Seca beam balance with light closing and take off shoes and measurements were rounded to the nearest 0.1digits.
The data collectors were trained for one day before the actual data collection about objectives of the study and approaches to collect the data, anthropometric measurement, data recording, and ethical issues of the study. Completeness and consistency of the questionnaire were checked every day by principal investigator and supervisor.
Data processing and analysis
Data was checked, edited, coded and entered to Epi-info version 7.00 and exported to SPSS version 20.0 software for analysis. Normality was checked for all continuous variables. Interactions between different independent variables again were checked and co-linearity diagnostics was done by checking the variance inflation factor (VIF) of less than five. Means, standard deviations (SD) and percentages were used to describe the data and the results were presented with narration, frequency tables and graphs. Anthro-plus was used to enter and determine the nutritional status of girls and principal component analysis (PCA) method was used for wealth index analysis using explanatory methods of SPSS and ranked as poor, medium and rich. Binary logistic regression analysis model was fitted to identify determinants of adequate dietary diversity. Crude and adjusted odds ration with 95%CI was computed to assess the strength of association between independent and outcome variables. Determinants was considered as statistical significant at a p-value less than 0.05 in the multivariable model.