Within-country migration and obesity dynamics: analysis of 94,783 women from the Peruvian demographic and health surveys
Bibliographic record
Abstract
BACKGROUND: Rural-to-urban migration is associated with increased obesity, yet it remains unknown whether this association exist, and to what extent, with other types of internal migration. METHODS: We conducted a secondary analysis of the Peruvian Demographic and Health Surveys (2005 to 2012) on data collected from women aged 15-49 years. Participants were classified as rural stayers, urban stayers, rural-to-urban migrants, intra-rural migrants, intra-urban migrants, and urban-to-rural migrants. Marginal effects from a logit regression model were used to assess the probabilities of being and becoming obese given both the length of time in current place of residence and women's migration status. RESULTS: Analysis of cross-sectional survey data generated between 2005 and 2012. Data from 94,783 participants was analyzed. Intra-urban migrants and rural-to-urban migrants had the highest rates of obesity (21% in 2012). A steady increase in obesity is observed across all migration statuses. Relative to rural non-migrants, participants exposed to urban environments had greater odds, two- to three-fold higher, of obesity. The intra-rural migrant group also shows higher odds relative to rural stayers (42% higher obesity odds). The length of exposure to urban settings shows a steady effect over time. CONCLUSION: Both exposure to urban environments and migration are associated with higher odds of obesity. Expanding the characterization of within-country migration dynamics provides a better insight into the relationship between duration of exposure to urban settings and obesity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".