Trend, Associated Factors and Concordance of Obesity by Body Mass Index, Waist Circumference and Waist-Height Ratio in Adolescents. An Analysis of a 4-Year National Survey
Bibliographic record
Abstract
Introduction: Regarding diagnosis, identifying reliable anthropometric measures to detect adolescent obesity is fundamental. However, in this age group has different definitions, either according to the body mass index (BMI), the waist circunference (WC) and the waist-height ratio (WHtR), making the measurement of this inaccurate. Objective: This study analyzed the prevalence, trends, and factors associated with obesity in Peruvian adolescents using data from the Demographic and Health Survey (ENDES) for 2019-2022. Methods: A secondary data analysis was conducted on 14,330 adolescents aged 15 to 19. The response variable was obesity, defined in three different ways. General obesity was assessed using the BMI was ≥ 2 standard deviations. 2) Abdominal obesity was defined through WC, with cutoff points ≥ 80.5 cm in men and ≥ 81 cm in women. 3) The relevant indicator for obesity was the WHtR, with a cutoff point ≥ 0.5. The associated factors to be evaluated were sex, age, natural region, marital status, education level, wealth, area of residence, alcohol consumption, and physical disability. Results: The study found that based on BMI, WC, and WHtR respectively, approximately 12.80%, 29.72%, and 24.27% of participants were considered obese. Significant associations were found between obesity and variables such as gender, natural region, marital status, wealth index, area of residence, education level, alcohol consumption, and physical disability. Conclusion: This research uncovered an alarmingly prevalence occurrence of obesity among adolescents in Peru with fluctuating patterns over time, emphasizing the need to tackle the interconnected issues contributing to this health concern. These findings can help inform and guide obesity prevention and control strategies in this population.
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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 | 0.002 |
| 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 | 0.000 | 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".