Risk-of-bias tools in air pollution epidemiology: a commentary
Bibliographic record
Abstract
SUMMARY Ambient air pollution epidemiology studies, like all other environmental epidemiology studies, are generally observational in nature, and therefore susceptible to various types of bias. As such, the assessment of risk of bias is crucial in systematic reviews of ambient air pollution epidemiology studies, particularly considering that these systematic reviews often form the basis of air quality guidelines. Although the most commonly used risk-of-bias tool in air pollution epidemiology literature is the Newcastle-Ottawa Scale (NOS), in 2020, World Health Organization (WHO) developed a tool specifically tailored to ambient air pollution epidemiology studies. Both tools assess risk of bias in terms of participant selection, exposure assessment, confounding, and outcome assessment, although in different ways. Only the NOS assesses the length of follow-up in cohort studies, while only the WHO tool assesses missing data and selective reporting. The evaluation methodology also differs between the two tools, with the WHO tool being more cumbersome to use. Owing to the subjective nature of risk-of-bias tools, conclusions regarding study quality may differ depending on the tool chosen and how it is used in a given systematic review. Despite its clear advantages over the NOS, the WHO tool is a new tool that has yet to be applied and tested extensively. We found that the WHO tool’s assessment of risk of bias associated with exposure assessment is limited. Also, it does not consider study quality with respect to the adjustment for co-pollutant exposures or the assessment of potential nonlinearity of the concentration–response function, both of which are particularly important in ambient air pollution epidemiology. A better assessment of bias in the ambient air pollution epidemiology literature requires further improvement of risk-of-bias tools, along with detailed documentation, standardization across individual studies, and quantification of bias.
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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 | 0.193 | 0.659 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.016 | 0.006 |
| Research integrity | 0.032 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".