Quantifying the Contribution to Uncertainty in Mortality Attributed to Household, Ambient, and Joint Exposure to PM<sub>2.5</sub>From Residential Solid Fuel Use
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Bibliographic record
Abstract
Abstract While there have been substantial efforts to quantify the health burden of exposure to PM 2.5 from solid fuel use (SFU), the sensitivity of mortality estimates to uncertainties in input parameters has not been quantified. Moreover, previous studies separate mortality from household and ambient air pollution. In this study, we develop a new estimate of mortality attributable to SFU due to the joint exposure from household and ambient PM 2.5 pollution and perform a variance‐based sensitivity analysis on mortality attributable to SFU. In the joint exposure calculation, we estimate 2.81 (95% confidence interval: 2.48–3.28) million premature deaths in 2015 attributed to PM 2.5 from SFU, which is 580,000 (18%) fewer deaths than would be calculated by summing separate household and ambient mortality calculations. Regarding the sources of uncertainties in these estimates, in China, India, and Latin America, we find that 53–56% of the uncertainty in mortality attributable to SFU is due to uncertainty in the percent of the population using solid fuels and 42–50% from the concentration‐response function. In sub‐Saharan Africa, baseline mortality rate (72%) and the concentration‐response function (33%) dominate the uncertainty space. Conversely, the sum of the variance contributed by ambient and household PM 2.5 exposure ranges between 15 and 38% across all regions (the percentages do not sum to 100% as some uncertainty is shared between parameters). Our findings suggest that future studies should focus on more precise quantification of solid fuel use and the concentration‐response relationship to PM 2.5 , as well as mortality rates in Africa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it