Incidence, Trend, and Mortality of Human Exposure to Rabies in Yemen, 2011-2017: Observational Study
Bibliographic record
Abstract
BACKGROUND: Rabies remains a neglected and poorly controlled disease throughout the developing world, particularly in Africa and Asia, where most human rabies deaths occur. OBJECTIVE: This study aimed to describe the epidemiology of rabies exposures, its trend, and its geographical distribution in Yemen. METHODS: Cumulative data from a rabies surveillance system for the period 2011-2017 were obtained from the National Rabies Control Program as paper-based annual reports. Data included the number of persons bitten by a suspected rabid animal, their gender and age, and the result of the animal's laboratory test. Human cases were defined as those exposed to rabies virus bitten by a suspected rabid animal, exposed to a confirmed rabid animal and then received postexposure prophylaxis (PEP), and deaths occurred after exposure to a confirmed rabid animal after having rabies symptoms during 2011-2017. RESULTS: From 2011 to 2017, a total of 76,049 persons were bitten by a suspected rabid animal. Of these, 21,927 (28.83%) were exposed to positively confirmed rabid animals and then received PEP, and 295 (0.38%) rabies-related deaths occurred. Of all cases with rabies exposure, 50,882 (66.91%) were males. The most affected age group by animal bites (31,816/76,041, 41.84%), positive exposure (8945/21,927, 40.79%), and rabies deaths (143/295, 48.47%) was 5-14 years. Rabies vaccines and immunoglobulins quantities were least available in 2016 and 2017. The annual incidence rate of exposure to animal bites and rabies exposure was 50 and 14 per 100,000, respectively. The annual mortality rate was 2 per 1,000,000. The highest incidence rate of animal bites was in Dhamar (112 per 100,000) and Ibb (94 per 100,000), whereas the highest incidence of exposed cases was in Amanat Al Asimah (40 per 100,000) and Ibb (37 per 100,000). Mortality rate was the highest in Amanat Al Asimah (6 deaths per 1,000,000) followed by Ibb and Dhamar (4 deaths per 1,000,000 in both). CONCLUSIONS: Rabies remains a worrying health problem in Yemen with higher percentage reported among children and males. Targeting school-age populations by education, communication, and information campaigns about preventive measures is strongly recommended. An electronic system should be introduced to improve reporting. It is important to have a sufficient supply of vaccines and immunoglobulins in control units, especially in the at-risk or impacted governorates. Future studies are suggested to determine incidences and risk factors of disease progression.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".