Prevalence and factors associated with stunting and thinness among adolescent students in Northern Ethiopia: a comparison to World Health Organization standards
Bibliographic record
Abstract
BACKGROUND: Adolescence is last chance for curbing the consequences of malnutrition and breaking the intergenerational cycle of malnutrition and poor health. This study aimed to assess the prevalence and the factors associated with stunting and thinness among in-school adolescents in northern Ethiopia using the 2006 World Health Organization (WHO) standards. METHODS: In-school adolescents (n = 348, 10-19 years old) were randomly selected to participate in this cross-sectional study. Anthropometric measurements were carried out to determine the proportion of adolescents who were stunted (height-for-age < -2 Standard Deviation (SD)) and thin (body-mass-index-for-age < -2 SD). T-test was employed to evaluate mean weight and height differences between groups. Pearson chi-square, chi-square trend and Fisher's exact tests were used to explore the crude association of categorical outcome variables and associated factors. Crude and adjusted associations between the outcome variables (stunting and thinness) and independent variables (socio-demographic, eating behavior and sanitation) were also determined using logistic regression. Stata version 11.1 was used to analyze the data. RESULTS: The height of the adolescents was 147.6 ± 11.2 cm (mean ± SD) and weight was37.2 ± 9.5 kg. The mean Z-scores of height-for-age and body-mass-index (BMI)-for-age of adolescents were -1.49 and -1.29, respectively. The prevalence of stunting and thinness among adolescents was 28.5 % (boys = 37.7 %; girls = 21.2 %; P = 0.001) and 26.1 % (boys = 32.4; girls = 21.6 %; p = 0.017), respectively. Adolescents in 13-15 year old age group (Adjusted Odds ratio (AOR) = 2.23; 95 % CI: 1.22, 4.08), boys (AOR = 2.53; 95 % CI: 1.52, 4.21) and rural residents (AOR = 2.15; 95 % CI: 1.20, 3.86) had significantly higher odds of being stunted compared to their counterparts. Furthermore, boys had higher (AOR = 1.97; 95 % CI: 1.19, 3.25) odds of being thin compared to girls. Compared to those 10 to 12 years of age, adolescents in 16 to 19 years of age were 53 % (AOR = 0.47; 95 % CI: 0.23, 0.95) less likely to be thin. CONCLUSIONS: Undernutrition is widely prevalent among adolescents in northern Ethiopia. Sex, age and area of residence significantly associated with adolescent undernutrition. The study underlines the need for nutrition interventions targeting rural and boy adolescents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".