Innovative dengue vector control interventions in Latin America: what do they cost?
Bibliographic record
Abstract
BACKGROUND: Five studies were conducted in Fortaleza (Brazil), Girardot (Colombia), Machala (Ecuador), Acapulco (Mexico), and Salto (Uruguay) to assess dengue vector control interventions tailored to the context. The studies involved the community explicitly in the implementation, and focused on the most productive breeding places for Aedes aegypti. This article reports the cost analysis of these interventions. METHODS: We conducted the costing from the perspective of the vector control program. We collected data on quantities and unit costs of the resources used to deliver the interventions. Comparable information was requested for the routine activities. Cost items were classified, analyzed descriptively, and aggregated to calculate total costs, costs per house reached, and incremental costs. RESULTS: Cost per house of the interventions were $18.89 (Fortaleza), $21.86 (Girardot), $30.61 (Machala), $39.47 (Acapulco), and $6.98 (Salto). Intervention components that focused mainly on changes to the established vector control programs seem affordable; cost savings were identified in Salto (-21%) and the clean patio component in Machala (-12%). An incremental cost of 10% was estimated in Fortaleza. On the other hand, there were also completely new components that would require sizeable financial efforts (installing insecticide-treated nets in Girardot and Acapulco costs $16.97 and $24.96 per house, respectively). CONCLUSIONS: The interventions are promising, seem affordable and may improve the cost profile of the established vector control programs. The costs of the new components could be considerable, and should be assessed in relation to the benefits in reduced dengue burden.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".