Burden of disease attributable to PM2.5 at low exposure levels: impact of methodological choices
Bibliographic record
Abstract
Abstract Background Exposure to fine particles (PM 2.5 ) has been associated with adverse health outcomes, even at low exposure levels (< 10 µg/m 3 ). Burden of disease assessments can quantify these associations; however, their sensitivity to methodological choices limits comparability between studies. Methods This study aimed to quantify the impact of methodological choices on disease burden attributable to low levels of ambient PM 2.5 , using Norway as a case study. Key methodological choices included (i) population exposure data, (ii) concentration-response curves, and (iii) population health data. Data from national and international sources were applied, including the global burden of disease (GBD) study. Attributable mortality and disability-adjusted life years (DALY) were estimated using burden of disease methodology. Additionally, the impact of choices related to concentration-response curves was assessed for higher exposure levels, using a scenario where exposure distributions were shifted to mean exposures up to 30 µg/m 3 . Results Methodological choices related to the concentration-response curves had the largest impacts on the estimated attributable deaths, ranging from − 91% to 104% change relative to the reference estimate (1,448 deaths, 95% CI 502–1497). These choices had a smaller impact on higher exposure levels, varying from − 46% to 53%. The choice of exposure and population health data led to 40% differences in attributable death estimates. DALYs attributable to PM 2.5 were predominantly driven by years of life lost (YLL: 74%). The choice of relative risk (RR) for the concentration response curve caused around 30% variation in DALY estimates relative to the reference (11,730 DALYs; 5,980 − 16,790), with larger differences for ischemic heart disease (-44 to 79%). Conclusion Attributable burden estimates for PM 2.5 are highly sensitive to key methodological choices, particularly at low exposure levels. Consequently, transparent reporting of the methodological choices and data sources in PM 2.5 health risk assessments are required to improve comparability and facilitate interpretations of the burden estimates.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".