Epidemiology of Diphtheria in Yemen, 2017-2018: Surveillance Data Analysis
Bibliographic record
Abstract
BACKGROUND: As a consequence of war and the collapse of the health system in Yemen, which prevented many people from accessing health facilities to obtain primary health care, vaccination coverage was affected, leading to a deadly diphtheria epidemic at the end of 2017. OBJECTIVE: This study aimed to describe the epidemiology of diphtheria in Yemen and determine its incidence and case fatality rate. METHODS: Data were obtained from the diphtheria surveillance program 2017-2018, using case definitions of the World Health Organization. A probable case was defined as a case involving a person having laryngitis, pharyngitis, or tonsillitis and an adherent membrane of the tonsils, pharynx, and/or nose. A confirmed case was defined as a probable case that was laboratory confirmed or linked epidemiologically to a laboratory-confirmed case. Data from the Central Statistical Organization was used to calculate the incidence per 100,000 population. A P value <.05 was considered significant. RESULTS: A total of 2243 cases were reported during the period between July 2017 and August 2018. About 49% (1090/2243, 48.6%) of the cases were males. About 44% (978/2243, 43.6%) of the cases involved children aged 5 to 15 years. Respiratory tract infection was the predominant symptom (2044/2243, 91.1%), followed by pseudomembrane (1822/2243, 81.2%). Based on the vaccination status, the percentages of partially vaccinated, vaccinated, unvaccinated, and unknown status patients were 6.6% (148/2243), 30.8% (690/2243), 48.6% (10902243), and 14.0% (315/2243), respectively. The overall incidence of diphtheria was 8 per 100,000 population. The highest incidence was among the age group <15 years (11 per 100,000 population), and the lowest incidence was among the age group ≥15 years (5 per 100,000 population). The overall case fatality rate among all age groups was 5%, and it was higher (10%) in the age group <5 years. Five governorates that were difficult to access (Raymah, Abyan, Sa'ada, Lahj, and Al Jawf) had a very high case fatality rate (22%). CONCLUSIONS: Diphtheria affected a large number of people in Yemen in 2017-2018. The majority of patients were partially or not vaccinated. Children aged ≤15 years were more affected, with higher fatality among children aged <5 years. Five governorates that were difficult to access had a case fatality rate twice that of the World Health Organization estimate (5%-10%). To control the diphtheria epidemic in Yemen, it is recommended to increase routine vaccination coverage and booster immunizations, increase public health awareness toward diphtheria, and strengthen the surveillance system for early detection and immediate response.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".