Advancing health equity in wastewater-based epidemiology: A global critical review and conceptual framework
Bibliographic record
Abstract
Population health data from wastewater-based epidemiology (WBE) are being used at unprecedented scales worldwide, yet there is limited focus on how to advance health equity in the field. Addressing this gap, we conducted a critical review of published literature in PubMed, targeting studies at the intersection of WBE and health equity. Of 145 articles assessed in full-text screening, we identified 68 studies with health equity considerations. These studies spanned various spatial scales and biochemical targets, addressing domains such as study design and methodologies, ethical and social considerations, and the feasibility and implementation of WBE monitoring. We summarize and synthesize health equity-oriented considerations across the identified domains. We further propose five key considerations to advance health equity in WBE research and practice, and integrate these considerations into a conceptual framework to illustrate how they apply to major steps in the process of conducting WBE. These considerations include global inequities in WBE access, the need to prevent potential harms and stigma via data misuse (inappropriate reporting of data and potential use of WBE for criminal surveillance), and the importance of regulation and community engagement, particularly amidst the growing privatization of WBE, especially in the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".