Associations of Household Solid Fuel for Heating and Cooking With Hypertension in Chinese Adults
Bibliographic record
Abstract
Abstract Background: Few studies have examined the association between indoor air pollution from household solid fuel use for heating and cooking with hypertension considering the influence of outdoor particles with aerodynamic diameter < 2.5 μm. The aim of this study is to investigate the association of household solid fuel for heating and cooking with hypertension prevalence in a large diverse Chinese population.Methods: 44,007 individuals aged 35-70 years with complete information on fuels used for cooking and heating and PM2.5 air pollution levels for 279 urban and rural communities of 12 centers were recruited in this study. Generalized linear mixed models using community as the random effect were performed to estimate the association with hypertension prevalence and blood pressure after considering ambient PM2.5 and a comprehensive set of potential confounding factors at the individual and household level. Results: 47.6% and 61.2% of participants used household solid fuel for heating and cooking, respectively. Solid fuel for heating was associated with statistically insignificant increase in hypertension prevalence (adjusted OR=1.08, 95% CI: 0.98, 1.20) or elevated systolic blood pressure (0.62mmHg, 95% CI: -0.24, 1.48). No association was found between solid fuel for cooking and hypertension or blood pressure, and no additional risk was observed among participants who had the combined exposure to both solid fuels for heating and cooking compared with those using household solid heating fuel only.Conclusion: No statistical significant association between the use of solid fuel for cooking or heating with BP or prevalence of hypertension was found in this large and diverse Chinese population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".