Region, Location, and Age-Specific Comparison of Nutritional Status of In-School Adolescent Girls (10-19 years) in Nigeria
Bibliographic record
Abstract
Background Nutritional status among female adolescents in Nigeria is becoming a major concern because it determines health outcomes and productivity in their adult years. There is a growing recognition of the potential to promote a healthy start to life for the next generation by addressing health and nutritional risks in adolescents. Objective This study assessed the nutritional status of in-school adolescent girls in Nigeria and made comparisons across regions, locations, and age groups. Methods A multistage stratified random sampling procedure was used to select participants from three geopolitical zones in Nigeria for this descriptive cross-sectional study of 2261 in-schooladolescent girls aged 10 -19 years. Body mass index-for-age (BMI), waist-hip-ratio (WHR), and waist-height-ratio (WHtR) were calculated from weight, height, hip, waist, and mid-upper arm circumferences measurements. Results The mean age was 14.9 years (± 1.78 years), the mean body weight was 47.8 kg (± 9.02 kg), compared to a calculated mean ideal weight of 54.5 kg (± 9.05 kg). Using BMI, 9.8% of these adolescent girls were underweight, 7% were either overweight or obese, 47.9% were at risk judging from WHR, 10% had abdominal obesity present using WHtR, 35.7% were malnourished, and 11.8% were obese using MUAC. South East girls were eleven times more likely to have a high BMI (OR=11.341, 95%CI=6.059-21.225) and three times more likely to have a high WHtR (OR=2.870, 95%CI=1.954-4.213) than other regions. The likelihood of being overweight/obese was higher among urban than peri-urban girls; BMI (OR=1.008, 95%CI=0.728-1.395) and MUACoverweight (OR=1.280, 95%CI=0.988-1.657). Older girls, 14-16yrs; WHtR (OR = 1.426, 95%CI = 0.970-2.097) and 17 -19yrs (OR = 1.024, 95%CI = 0.617-1.699) were likely to be overweight/obese compared to 10 -13yrs (OR=3.878, 95%CI=2.385-6.305). Girls 14 -16 were three times and 17-19 were six times more likely to have higher MUACoverweight (OR = 3.878, 95%CI = 2.385-6.305) and (OR=6.371, 95%CI=3.854-10.865), respectively than those at 10-13 years. Conclusions These findings underscore the significant disparities in the nutritional status of adolescent girls across regions, locations, and age ranges in Nigeria. This highlights the urgent need for targeted, region-specific nutrition-sensitive intervention programmes among adolescent girls, potentially leading to improved public health outcomes in Nigeria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".