Cost-Effectiveness Analysis of Integrated Bite Case Management and Sustained Dog Vaccination for Rabies Control
Bibliographic record
Abstract
The successful prevention, control, and elimination of dog-mediated rabies is challenging due to insufficient resource availability and inadequate placement. An integrated dog bite case management (IBCM) system plus dog vaccination can help address these challenges. Based on data from the IBCM system in Haiti, we conducted a cost-effectiveness evaluation of a newly established IBCM system plus sustained vaccination and compared it with 1) a no bite-case management (NBCM) and 2) a non-risk-based (NRB) program, where bite victims presenting at a health clinic would receive post-exposure prophylaxis regardless of risk assessment. We also provide cost-effectiveness guidance for an ongoing IBCM system and for sub-optimal dog vaccination coverages, considering that not all cost-effective interventions are affordable. Cost-effectiveness outcomes included average cost per human death averted (USD/death averted) and per life-year gained (LYG). The analysis used a governmental perspective. Considering a sustained 5-year implementation with 70% dog vaccination coverage, IBCM had a lower average cost per death averted (IBCM: $7,528, NBCM: $7,797, NRB: $15,244) and cost per LYG (IBCM: $152, NBCM: $158, NRB: $308) than NBCM and NRB programs. As sensitivity analysis, we estimated cost-effectiveness for alternative scenarios with lower dog-vaccination coverages (30%, 55%) and lower implementation costs. Our results suggest that better health and cost-effectiveness outcomes are achieved with the continued implementation of an IBCM program ($118 per life-year saved) compared with a newly established IBCM program ($152 per life-year saved). Our results suggest that IBCM is more cost-effective than non-integrated programs to eliminate dog-mediated human rabies.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".