The impact of soaring food prices on obesity in women: A longitudinal analysis in 31 developing countries
Bibliographic record
Abstract
Objectives To examine the association between food price inflation and obesity among women in low and middle‐income countries, and assess whether the association differs by socioeconomic status. Methods Longitudinal study of the international prices of food (FAO food price index), which measures the monthly change in prices of a basket of food commodities, temporally and geographically linked to 295,984 non‐pregnant adult (age>=24 y) women participants in the Demographic and Health Survey (DHS) from 31 low‐ and middle‐income countries (2000–2014). Multilevel logistic regression with two‐level random‐intercept growth models was used to estimate the joint association of food price inflation and SES differences with obesity, conditioning on known confounders. Post‐estimation computed population averaged differences in the predicted probability of being obese across the range of food price inflation values. Results We found that rising food prices were strongly associated with obesity prevalence, and particularly that the relationship showed clear differences by individual socioeconomic status (SES), regardless of the SES indicator used (–). The strongest disparities in the predicted mean obesity level were seen in education at the highest levels of food price inflation. On average, for every 1‐unit increase in food price inflation, women in the top SES categories had between 0.02 and 0.06 percentage point greater predicted obesity prevalence, compared to women in the bottom SES categories. Conclusion There is robust effect modification by socioeconomic status of the association between food price inflation and obesity in adult women in developing countries. Greater food price inflation was associated with higher levels of obesity in women in the top SES groups, who may be net food consumers most at risk of obesity in developing countries. Support or Funding Information This work was supported by the Canadian Institute for Health Research Postdoctoral Fellowship Award (MFE‐135520). No sponsors participated in the study design, data analysis or interpretation of results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".