Higher imported food patterns are associated with obesity and severe obesity in Tuvalu: A latent class analysis
Bibliographic record
Abstract
Tuvalu is a Pacific Island country within the small island developing states that has observed a significant and alarming increase in obesity rates over the past 40 years, affecting ∼60 %-70 % of the current population.This study aimed to investigate the association between food patterns and the proportion of obesity in a Pacific Island country.The 2022 COMmunity-based Behavior and Attitude survey in Tuvalu (COMBAT) included 985 adults with complete data on sociodemographic information and the frequency of consumption of 25 common foods. A latent class analysis determined 4 food patterns. Bayesian multilevel logistic and linear regression models estimated the association between food patterns and the proportion of obesity [body mass index (BMI) ≥30 kg/m2], severe obesity (BMI ≥40 kg/m2), and weight (kg), adjusting for potential confounders and accounting for clustering by region.The latent class analysis revealed 4 food patterns with an entropy of 0.94 and an average posterior probability of class assignment for each individual of 0.97, described as follows: 1) local: locally produced foods with moderate food diversity (proportion of individuals = 28 %); 2) diverse-local: local with greater food diversity (17 %); 3) restricted-imported: more imported with restricted diversity (29 %); and 4) imported: heavily imported with high diversity (26 %). Compared to those following the diverse-local pattern, the odds of having obesity were greater for those classified with the imported pattern [odds ratio (OR): 2.52; 95 % credible interval (CrI): 1.59, 3.99], restricted-imported pattern (OR: 1.89; 95 % CrI: 1.59, 3.99), and local pattern (OR: 1.54; 95 % CrI: 0.94, 2.50). Similar trends were observed for severe obesity while body weight was positively associated with both restricted-imported and imported food patterns.The high consumption of imported foods, together with the low consumption of plant-based foods and protein-rich foods, could be a relevant modifiable lifestyle factor explaining the high levels of obesity and severe obesity in Tuvalu, a Pacific Island country.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".