Economic burden of illness from pesticide poisonings in highland Ecuador
Bibliographic record
Abstract
Active surveillance of acute pesticide poisonings in a potato-growing region of highland Ecuador during 1991-1992 uncovered a rate of 171/100,000, due predominantly to occupational exposures to organophosphate and carbamate pesticides. Occupational exposure among agricultural workers was the most common reason for poisoning (32 male workers and 1 female worker, out of a total of 50 cases). Of these 33 cases, 28 of them reported pesticide application as the work task just prior to poisoning, with over 80% citing the use of World Health Organization Hazard Category I pesticides. The suicide rate of 17.1/100,000 and the overall mortality rate of 20.5/100,000 that we found are among the highest reported anywhere in the world. At the exchange rates prevailing at that time, median costs associated with these poisonings were estimated as follows: public and social security health care direct costs of US$ 9.85/case; private health costs of US$ 8.33/case; and lost-time indirect costs of US$ 8.33/agricultural worker. Each one of those costs was over five times the daily agricultural wage, which was then about US$ 1.50. Further costing of pesticide poisonings should be carried out in other settings to provide appropriate information for decisions about pesticide use. In addition, integrated pest management should be further evaluated as an appropriate technology to reduce the economic burden of illness from pesticide poisonings in developing countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".