Exposure–response relationship of household air pollution on body mass index among women in rural areas of Guatemala, India, Peru and Rwanda: household air pollution intervention network trial
Bibliographic record
Abstract
Household air pollution from burning biomass materials, the main cooking fuel in low- and middle-income countries, may be linked to metabolic dysfunction. We assessed cross-sectional associations between household air pollution and body mass index (BMI), expecting to see increased BMI with higher pollution concentrations. We analyzed data from 414 women aged 40 to 79 years who resided in the households using biomass fuel and were enrolled in the multi-country Household Air Pollution Intervention Network (HAPIN) Trial. We explored associations of 24-h average personal exposure to fine particulate matter (PM2.5), black carbon (BC), and carbon monoxide (CO) with BMI through single pollutant linear and logistic models adjusted for potential confounders (i.e., age, socioeconomic indicators, education, dietary diversity, secondhand smoke exposure, alcohol and grain consumption).Sensitivity analyses explored air pollutants as quartiles, and other variables as potential confounders, such as physical activity, enrollment site, and dietary items. We examined effect modification of research site on the associations. We observed mixed evidence of associations between household air pollution and BMI in linear regression. There was no association with BMI and PM₂.₅ (1-unit increase in log-transformed PM₂.₅ estimate 0.02 kg/m2 [95% CI: -0.51, 0.54]) or CO (1-unit increase in log-transformed CO estimate 0.42 kg/m2 [95% CI -0.31, 1.14]). However, a 1-unit increase in log-transformed BC showed an association in the opposite direction as hypothesized (BC estimate -0.59 kg/m2 [95% CI -1.17, -0.003]). Using logistic regression models, we found that only CO significantly increased the odds of overweight/obesity: a 1-unit increase in log-transformed CO led to an odds ratio of 1.66 (95% CI: 1.10, 2.51). Effect modification showed inverse association between BC exposure and BMI in Peru. Evidence suggests a significant association between CO exposure and increased odds of being overweight/obese, whereas impacts of PM2.5 and BC on BMI had null or inverse effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".