Using pollutant release and transfer register data in human health research: a scoping review
Bibliographic record
Abstract
Pollutant release and transfer registers (PRTRs) collect and provide information on chemicals released to the environment or otherwise managed as waste. They support the public’s right-to-know and provide useful information in gauging performance of facilities, sectors, and governments. The extent to which these data have been used in research, particularly in relation to human health, has not been documented. In this scoping review our objective was to learn from scholarly literature the extent and nature of the use of PRTR data in human health research. We performed literature searches (1994–2011) using various search engines and (or) key words. Articles selected for review were chosen following predefined criteria, to extract and analyze data. One hundred and eighty four papers were identified. Forty investigated possible relations with health outcomes: 33 of them identified positive associations. The rest explored other uses of PRTR data. Papers identified challenges, some imputable to the PRTR. We conclude that PRTR data are useful for research, including health-related studies, and have significant potential for prioritizing research needs that can influence policy, management, and ultimately human health. In spite of their inherent limitations, PRTRs represent a perfectible, unique useful source, whose application to human health research appears to be underutilized. Developing strategies to overcome these limitations could improve data quality and increase its utility in future environmental health research and policy applications.
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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 | 0.037 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.033 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".