Tool for assessing health and equity impacts of interventions modifying air quality in urban environments
Bibliographic record
Abstract
BACKGROUND: Urban outdoor air pollution (AP) is a major public health concern but the mechanisms by which interventions impact health and social inequities are rarely assessed. Health and equity impacts of policies and interventions are questioned, but managers and policy agents in various institutional contexts have very few practical tools to help them better orient interventions in sectors other than the health sector. Our objective was to create such a tool to facilitate the assessment of health impacts of urban outdoor AP interventions by non-public health experts. METHODS: An iterative process of reviewing the academic literature, brainstorming, and consultation with experts was used to identify the chain of effects of urban outdoor AP and the major modifying factors. To test its applicability, the tool was applied to two interventions, the London Low Emission Zone and the Montréal BIXI public bicycle-sharing program. RESULTS: We identify the chain of effects, six categories of modifying factors: those controlling the source of emissions, the quantity of emissions, concentrations of emitted pollutants, their spatial distribution, personal exposure, and individual vulnerability. Modifiable and non-modifiable factors are also identified. Results are presented in the text but also graphically, as we wanted it to be a practical tool, from pollution sources to emission, exposure, and finally, health effects. CONCLUSION: The tool represents a practical first step to assessing AP-related interventions for health and equity impacts. Understanding how different factors affect health and equity through air pollution can provide insight to city policymakers pursuing Health in All Policies.
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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 | 0.059 | 0.153 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.021 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".