Non-randomized controlled trial of the long-term efficacy of an Ecohealth intervention against Chagas disease in Yucatan, Mexico
Bibliographic record
Abstract
Non-domiciliated intrusive triatomine vectors are responsible for a low but significant transmission of Trypanosoma cruzi to humans. Their control is a challenge as insecticide spraying is of limited usefulness, and alternative strategies need to be developed for a sustainable control. We performed a non-randomized controlled trial of an Ecohealth intervention based on window insect screens and community participation to reduce house infestation by Triatoma dimidiata in two rural villages in Yucatan, Mexico. Efficacy of the intervention was measured over a three years follow-up period and entomological indicators showed that the proportion of triatomines found inside houses was significantly reduced in houses with insect screens, which effectively kept more bugs on the outside of houses. Using a previously developed model linking entomological data to the prevalence of infection in human, we predicted that the intervention would lead to a 32% reduction in yearly incidence and in the prevalence of T. cruzi infection. The cost for the coverage of all the windows of a house was of comparable magnitude to what families currently spend on various domestic insecticide, and most screens were still in good conditions after three years. In conclusion, the Ecohealth approach proposed here is effective for the long-term and sustainable control of intrusive T. dimidiata vectors in the Yucatan peninsula, Mexico. This strategy may also be easily adapted to other intrusive triatomine species as well as other regions/countries with comparable eco-epidemiological settings, and would be an excellent component of a larger integrated program for the control of a variety of other vector-borne diseases, bringing additional benefits to the communities. Our results should encourage a further scaling-up of our implementation strategy in additional villages in the region.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".