Endocrine Disruptors and Health Effects in Africa: A Call for Action
Bibliographic record
Abstract
BACKGROUND: Africa faces a number of unique environmental challenges. Unfortunately, it lacks the infrastructure needed to support the comprehensive environmental studies that could provide the scientific basis to inform environmental policies. There are a number of known sources of endocrine-disrupting chemicals (EDCs) and other hazardous chemicals in Africa. However, a coordinated approach to identify and monitor these contaminants and to develop strategies for public health interventions has not yet been made. OBJECTIVES: This commentary summarizes the scientific evidence presented by experts at the First African Endocrine Disruptors meeting. We describe a "call to action" to utilize the available scientific knowledge to address the impact of EDCs on human and wildlife health in Africa. DISCUSSION: We identify existing knowledge gaps about exposures to EDCs in Africa and describe how well-designed research strategies are needed to address these gaps. A lack of resources for research and a lag in policy implementation slows down intervention strategies and poses a challenge to advancing future health in Africa. CONCLUSION: To address the many challenges posed by EDCs, we argue that Africans should take the lead in prioritization and evaluation of environmental hazards, including EDCs. We recommend the institution of education and training programs for chemical users, adoption of the precautionary principle, establishment of biomonitoring programs, and funding of community-based epidemiology and wildlife research programs led and funded by African institutes and private companies. https://doi.org/10.1289/EHP1774.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".