Cost-effectiveness of community mobilization (Camino Verde) for dengue prevention in Nicaragua and Mexico: A cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: We assessed the cost-effectiveness of Camino Verde, a community-based mobilization strategy to prevent and control dengue and other mosquito-borne diseases. A cluster-randomized controlled trial in Managua, Nicaragua, and in three coastal regions in Guerrero, Mexico (75 intervention and 75 control clusters), Camino Verde used non-governmental community health workers, called brigadistas, to support community mobilization. This donor-funded trial demonstrated reductions of 29.5% (95% confidence interval, CI: 3.8%-55.3%) on dengue infections and 24.7% (CI: 1.8%-51.2%) on self-reported cases. METHODS: We estimated program costs through a micro-costing approach and semi-structured questionnaires. We show results as incremental cost-effectiveness ratios (ICERs) for costs per disability-adjusted life-year (DALYs) averted and conducted probabilistic sensitivity analyses. FINDINGS: The Camino Verde trial spent US$16.72 in Mexico and $7.47 in Nicaragua per person annually. We found an average of 910 (CI: 487-1 353) and 500 (CI: 250-760) dengue cases averted annually per million population in Mexico and Nicaragua, respectively, compared to control communities. The ICER in Mexico was US$29 618 (CI: 13 869-66 898) per DALY averted, or 3.0 times per capita GDP. For Nicaragua, the ICER was US$29 196 (CI: 14294-72181) per DALY averted, or 16.9 times per capita GDP. INTERPRETATION: Camino Verde, as implemented in the research context, was marginally cost-effective in Mexico, and not cost-effective in Nicaragua, from a healthcare sector perspective. Nicaragua's low per capita GDP and the use of grant-funded management personnel weakened the cost-effectiveness results. Achieving efficiencies by incorporating Camino Verde activities into existing public health programs would make Camino Verde cost-effective.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".