Study setting
Wolaita Sodo town is the administrative capital for the Wolaita zonal administration in South Ethiopia located at 380 km south from Addis Ababa. The town has 3 sub-cities; 11 lower administrative units and the total population is estimated to be 182,607; 49% were female. Common staple foods in the area are cereals, roots, tubers, and vegetables.
Seven public and two private schools deliver secondary education to the population in the town and the surrounding areas.
Study design and period
We conducted a facility-based cross-sectional study involving adolescents in secondary schools from April to June in 2019.
Population and sampling
The source populations were all adolescents in secondary schools in Sodo town and selected adolescents were studied. Pregnant adolescent girls were excluded. A sample size of 670 was calculated with the following assumptions; 95% confidence level, 5% margin of error, an estimated magnitude of students’ academic performance of 72.8% taken from a similar study in Ethiopia (21), design effect of 2 and 10% non-response rate. The schools in the town were stratified into public and private by assuming socio-economic differences among the students. Among seven secondary schools (two private and five public) in the town, one private and three public schools were selected randomly to make the sampling representative. The sample size was allocated to the schools proportional to the number of students enrolled. The list of students enrolled in each school was used as a sampling frame and the study participants were selected using systematic random sampling.
Variables
Outcome
The academic performance of the students was an outcome measure. It was measured by totalling students’ average mark score of the overall subjects of the last two consecutive semesters.
Exposure variables and covariates
Socio-economic and socio-demographic: Age, Sex of adolescent, marital status of parents, Education and occupation status of parents, and Wealth status.
Wealth status was generated by using principal component analysis (PCA) and based on the result household wealth index/status was converted into quartiles and categorized as Lowest, Second, Middle, Fourth, and Highest (22).
Nutritional status indices:
Underweight- is BMI for age z-score (BAZ) of < -2 standard deviation (SD) on the WHO growth reference cut-off point (23).
Overweight- was computed with BMI for age z-score (BAZ) of >+1 SD on the WHO growth reference cut-off point (23).
Obesity- was computed with BMI for age z-score (BAZ) of >+2 SD z-score based on the WHO reference cut-off point (23).
Stunting- is the height for age z-score (HAZ) of <-2 SD on the WHO growth reference cut-off point (23).
Dietary diversity score: Dietary diversity was determined by using the dietary diversity score (DDS). Three non-consecutive days 24 hour recall of adolescents’ consumption of commonly consumed foods in the area was used to collect information for DDS (24). Foods were categorized into 10 groups based on FAO recommendations; (1) starch stable food, (2) vegetables, 3) fruits, (4) meat, (5) egg, (6) fish and other seafood, (7) legumes, nuts and seeds, (8) milk and milk products, (9) oil and fats, (10) sweets, spices, condiments and beverage (25). The response categories were "Yes" if at least one food item in the group was consumed and "No" when a food item in the group was not consumed. The results were summed and classified into <4 food items and >4 food items (26).
Behavioural factors: alcohol consumption, the purpose of spending much time on the internet, and being absent for 10% or more of school days for any reason in a calendar year.
Data collection
A structured interviewer-administered questionnaire was adopted from relevant articles and related literature (22). The questionnaire was pre-tested on 5% of the respondents who were later not included in the main study and no adjustments were made after the pre-testing. Four data collectors and two supervisors were trained for two days on different modules of the questionnaire, anthropometric measurement, and ethics.
Anthropometric measurements were standardized against an expert measurer for Technical Error of Measurement (TEM). Weight was measured using a portable digital flat Seca scale (Scale electronic scale, 770 Hamburg). Height was measured by Seca body meter (Seca 274 body meter). All measurements were taken three times, and the average was taken. Academic performance and absenteeism data were taken from school records.
Statistical analysis
Data was entered on Epi-Data Version 3.1 and analyzed by using Stata Version 15. Anthropometric data were analyzed using the WHO Anthro-plus software version 1.0.4 and compared with WHO reference (27). Frequency, percentage, mean, and standard deviation of the mean were performed for the main variables. Pearson correlation was used to check the relationship between nutritional status and academic performance. We used linear regression analysis to select exposure variables with an association to the outcome. Exposure variables with a p-value of less than 0.25 in the bivariate analysis were taken to multivariate linear regression to identify independent predictors for academic performance. A p-value < 0.05 was considered for statistical significance and parameter estimates (ß) with 95% CIs were reported.