Children’s Health in Latin America: The Influence of Environmental Exposures
Bibliographic record
Abstract
BACKGROUND: Chronic diseases are increasing among children in Latin America. OBJECTIVE AND METHODS: To examine environmental risk factors for chronic disease in Latin American children and to develop a strategic initiative for control of these exposures, the World Health Organization (WHO) including the Pan American Health Organization (PAHO), the Collegium Ramazzini, and Latin American scientists reviewed regional and relevant global data. RESULTS: Industrial development and urbanization are proceeding rapidly in Latin America, and environmental pollution has become widespread. Environmental threats to children's health include traditional hazards such as indoor air pollution and drinking-water contamination; the newer hazards of urban air pollution; toxic chemicals such as lead, asbestos, mercury, arsenic, and pesticides; hazardous and electronic waste; and climate change. The mix of traditional and modern hazards varies greatly across and within countries reflecting industrialization, urbanization, and socioeconomic forces. CONCLUSIONS: To control environmental threats to children's health in Latin America, WHO, including PAHO, will focus on the most highly prevalent and serious hazards-indoor and outdoor air pollution, water pollution, and toxic chemicals. Strategies for controlling these hazards include developing tracking data on regional trends in children's environmental health (CEH), building a network of Collaborating Centres, promoting biomedical research in CEH, building regional capacity, supporting development of evidence-based prevention policies, studying the economic costs of chronic diseases in children, and developing platforms for dialogue with relevant stakeholders.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".