Prospects for Health Impact Assessment in the United States: New and Improved Environmental Impact Assessment or Something Different?
Bibliographic record
Abstract
Health impact assessment (HIA) has been advanced as a means of bringing potential health impacts to the attention of policy makers, particularly in sectors where health impacts may not otherwise be considered. This article examines lessons for HIA in the United States from the related and relatively well-developed field of environmental impact assessment (EIA). We reviewed the EIA literature and conducted twenty phone interviews with EIA professionals. Successes of EIA cited by respondents included integration of environmental goals into decision making, improved planning, and greater transparency and public involvement. Reported shortcomings included the length and complexity of EIA documents, limited and adversarial public participation, and an emphasis on procedure over substance. Presently, EIAs consider few, if any, health outcomes. Respondents differed on the prospects for HIA. Most agreed that HIA could contribute to EIA in several areas, including assessment of cumulative impacts and impacts to environmental justice. Reasons given for not incorporating HIA into EIA were uncertainties about interpreting estimated health impacts, that EIA documents would become even longer and more complicated, and that HIA would gain little from the procedural and legal emphasis in EIA. We conclude that for HIA to advance, whether as part of or separate from EIA, well-formulated methodologies need to be developed and tested in real-world situations. When possible, HIA should build on the methods that have been utilized successfully in EIA. The most fruitful avenue is demonstration projects that test, refine, and demonstrate different methods and models to maximize their utility and acceptance.
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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 | 0.051 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".