Prevalence and factors associated with underweight, overweight and obesity among 15-49-year-old men and women in Timor-Leste
Bibliographic record
Abstract
Underweight and overweight both have a long-lasting significant effect on human health at the individual and population levels. However, in the context of Timor-Leste, a country that achieved independence around two decades ago, there is a severe scarcity of evidence regarding the underweight and obesity burden. We conducted this study to find out the prevalence of underweight, overweight and obesity and their associated factors.This study used the nationally representative data of Timor-Leste Demographic Health Survey 2016 data. We conducted descriptive analysis followed by multivariable logistic regression analysis to find out the prevalence and investigate the associated factors. Both crude and adjusted odds ratio of covariates were reported with 95% confidence interval (CI).This study analyzed the data from a weighted sample of 16,488 Timorese aged 15-49 years. The prevalence of normal weight, underweight, and overweight or obesity were found to be 55.2% (95% CI: 54.2%-56.2%), 25.5% (95% CI: 24.4%-26.7%), and 19.3% (95% CI: 18.3%-20.3%), respectively. For underweight, age, sex, type of settlement (urban/rural), township, and wealth, marital, and educational status were found to have a statistically significant relationship (p < 0.05) with Body Mass Index(BMI). After adjustment for the covariates in the logistic regression model age, sex, township, and wealth and marital status were found to be statistically significant correlates (p < .05) of underweight. For overweight and obesity, all the background characteristics included in this study (i.e, age, sex, type of settlement, township, and wealth, marital, and educational status) were found to be statistically significant correlates, after adjustment for the covariates.This study concludes that Timor-Leste has a significant underweight and overweight burden which needs to be addressed through appropriate interventions. Further studies are also warranted to delve deeper into the complex interplay of factors associated with underweight and overweight.
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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 | 0.001 | 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) | 0.002 | 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".