Adapting non-parametric spline representations of outdoor air pollution health effects associations for use in public health benefits assessment
Bibliographic record
Abstract
Abstract The magnitude and shape of the association between outdoor air pollution concentrations and health need to be characterized in order to estimate public health benefits from proposed mitigation strategies. Specialized parametric functions have been proposed for this characterization. However, non-parametric spline models offer more flexibility, less bias, and predictive power, in describing these associations and are thus preferred over relatively simple parametric formulations. Unrestricted spline representations are often reported but many are not suitable for benefits analysis due to their erratic concentration-response behavior and are usually not presented in a format consistent with the requirements necessary to conduct a benefits analysis. We propose a method to adapt non-parametric spline representations of concentration-response associations that are suitable for public health benefits analysis by transforming spline predictions and its uncertainty over the study exposure range to a new spline formulation that is both monotonically increasing and restricted to concentration-response patterns suitable for use in health benefits assessment. We selected two examples of the association between long-term exposure to fine particulate matter and mortality in Canada and the USA that displayed spline fits that were neither monotonically increasing nor suitable, we suggest, for benefits analysis. We suggest our model is suitable for benefits analysis and conduct such analyses for both Canada and the USA, comparing benefits estimates to traditional models. Finally, we provide guidance on how to report spline fitting results such they can be used either in benefits analysis directly, or to fit our new model.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".