Adverse health impacts of cooking with kerosene: A multi-country analysis within the Prospective Urban and Rural Epidemiology Study
Bibliographic record
Abstract
Abstract Background Kerosene, which was until recently considered a relatively clean household fuel, is still widely used in low and middle-income countries for cooking and lighting. However, there is little data on its health effects. We examined cardiorespiratory effects and mortality in households using kerosene as their primary cooking fuel within the Prospective Urban Rural Epidemiology (PURE) study. Methods We analyzed baseline and follow-up data on 31,490 individuals from 154 communities in China, India, South Africa, and Tanzania where there was at least 10% kerosene use for cooking at baseline. Baseline comorbidities and health outcomes during follow-up (median 9.4 years) were compared between households with kerosene versus clean (gas or electricity) or solid fuel (biomass and coal) use for cooking. Multi-level marginal regression models adjusted for individual, household, and community level covariates. Results Higher rates of prevalent respiratory symptoms (e.g. 34% [95% CI:15-57%] more dyspnea with usual activity, 44% [95% CI: 21-72%] more chronic cough or sputum) and lower lung function (differences in FEV 1 : -46.3 ml (95% CI: -80.5; -12.1) and FVC: -54.7 ml (95% CI: -93.6; -15.8)) were observed at baseline for kerosene compared to clean fuel users. The odds of hypertension was slightly elevated but no associations were observed for blood pressure. Prospectively, kerosene was associated with elevated risks of all-cause (HR: 1.32 (95% CI: 1.14-1.53)) and cardiovascular (HR: 1.34 (95% CI: 1.00-1.80)) mortality, as well as major fatal and incident non-fatal cardiovascular (HR: 1.34 (95% CI: 1.08-1.66)) and respiratory (HR: 1.55 (95% CI: 0.98-2.43)) diseases, compared to clean fuel use. Further, compared to solid fuel users, those using kerosene had 20 – 47% higher risks for the above outcomes. Conclusions Kerosene use for cooking was associated with higher rates of baseline respiratory morbidity and increased risk of mortality and cardiorespiratory outcomes during follow-up when compared to either clean or solid fuels. Replacing kerosene with cleaner-burning fuels for cooking is recommended. Highlights Kerosene was (until 2014) considered a relatively clean household fuel for cooking and lighting Though the WHO discouraged kerosene use, evidence in support of this position are still scarce We prospectively examined the effects of kerosene use on cardiorespiratory morbidity and mortality Kerosene use for cooking was associated with higher rates of baseline respiratory morbidity Prospectively, kerosene use was associated with increased risk of mortality and incident cardiorespiratory outcomes compared to clean fuel or solid fuel use Replacing kerosene with cleaner-burning fuels for cooking is recommended
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".