Female Reproductive Disorders, Diseases, and Costs of Exposure to Endocrine Disrupting Chemicals in the European Union
Bibliographic record
Abstract
CONTEXT: A growing body of evidence suggests that endocrine-disrupting chemicals (EDCs) contribute to female reproductive disorders. OBJECTIVE: To calculate the associated combined health care and economic costs attributable to specific EDC exposures within the European Union (EU). DESIGN: An expert panel evaluated evidence for probability of causation using the Intergovernmental Panel on Climate Change weight-of-evidence characterization. Exposure-response relationships and reference levels were evaluated, and biomarker data were organized from carefully identified studies from the peer-reviewed literature to represent European exposure and approximate burden of disease as it occurred in 2010. Cost-of-illness estimation used multiple peer-reviewed sources. SETTING, PATIENTS AND PARTICIPANTS AND INTERVENTION: Cost estimation was carried out from a societal perspective, ie, including direct costs (eg, treatment costs) and indirect costs such as productivity loss. RESULTS: The most robust EDC-related data for female reproductive disorders exist for 1) diphenyldichloroethene-attributable fibroids and 2) phthalate-attributable endometriosis in Europe. In both cases, the strength of epidemiological evidence was rated as low and the toxicological evidence as moderate, with an assigned probability of causation of 20%–39%. Across the EU, attributable cases were estimated to be 56 700 and 145 000 women, respectively, with total combined economic and health care costs potentially reaching €163 million and €1.25 billion. CONCLUSIONS: EDCs (diphenyldichloroethene and phthalates) may contribute substantially to the most common reproductive disorders in women, endometriosis and fibroids, costing nearly €1.5 billion annually. These estimates represent only EDCs for which there were sufficient epidemiologic studies and those with the highest probability of causation. These public health costs should be considered as the EU contemplates regulatory action on EDCs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".