Estimating the burden of rabies in Ethiopia by tracing dog bite victims
Bibliographic record
Abstract
In developing countries where financial resources are limited and numerous interests compete, there is a need for quantitative data on the public health burden and costs of diseases to support intervention prioritization. This study aimed at estimating the health burden and post-exposure treatment (PET) costs of canine rabies in Ethiopia by an investigation of exposed human cases. Data on registered animal bite victims during the period of one year were collected from health centers in three districts, i.e. Bishoftu, Lemuna-bilbilo and Yabelo, to account for variation in urban highland and lowland areas. This data collection was followed by an extensive case search for unregistered victims in the same districts as the registered cases. Victims were visited and questioned on their use of PET, incurred treatment costs and the behavioral manifestations of the animal that had bitten them. Based on the collected data PET costs were evaluated by financial accounting and the health burden was estimated in Disability-Adjusted Life Years (DALYs). In total 655 animal bite cases were traced of which 96.5% was caused by dog bites. 73.6% of the biting dogs were suspected to be potentially rabid dog. Annual suspected rabid dog exposures were estimated per evaluated urban, rural highland and rural lowland district at, respectively, 135, 101 and 86 bites, which led, respectively, to about 1, 4 and 3 deaths per 100,000 population. In the same district order average costs per completed PET equaled to 23, 31 and 40 USD, which was significantly higher in rural districts. Extrapolation of the district results to the national level indicated an annual estimate of approximately 3,000 human deaths resulting in about 194,000 DALYs per year and 97,000 exposed persons requiring on average 2 million USD treatment costs per year countrywide. These estimations of the burden of rabies to the Ethiopian society provide decision makers insights into the potential benefits of implementing effective interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".