Prevalence patterns of overweight and obesity in the world: An age-period-cohort analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to evaluate the trends of obesity and overweight among adolescents using the age-period-cohort (APC) analysis. METHODS: Data for this study was provided by Institute for Health Metrics and Evaluation. Our data represents the cumulative prevalence trend of obesity and overweight in 195 countries between 1980 and 2015 in 5-year intervals. The age intervals were also considered to be 5 years. Besides, a subgroup analysis based on sex and socio-demographic-index subgroups were performed. To perform APC analysis, R program software was used. RESULTS: We observed an increasing trend in both obesity and overweight throughout the study period, with the trend accelerating in more recent periods. Among the fitted models, we concluded that the APC model best fit the current data. The trend for both outcomes was similar. Among the three parameters, age showed an inverted U-shaped effect on the trend of both outcomes in all subgroups. However, the effect of period and cohort differed in our subgroup analysis. CONCLUSION: Overall, this study shows that obesity and overweight are on the rise. Both phenomena were influenced by the age effect in a similar pattern. However, the period and cohort effects showed variation in our subgroup analyses based on sex and SDI subgroups, suggesting the need for country-level studies to better understand the possible impact of these two factors on the prevalence of obesity 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".