Impact of the 2015 Yemeni War on the Under One Year Children Immunization Coverage
Bibliographic record
Abstract
Background: After two-years of the war that have crippled the capacity of the Yemeni National Health System and left only 45% of health facilities are functioning, Yemen faced increasing vaccine preventable diseases (VPD) outbreaks and may become at high risk of polio importation. Objective: To document the impact of the 2015 war on under one-year Yemeni children immunization coverage. Methods: Data on vaccination coverage for the 2012-2015 was obtained from the National Expand Program for Immunization (EPI). The vaccination coverage was calculated at national and governorate levels through dividing the number of the actually vaccinated children by the estimated under one-year children population. Results: Although there is an increase from 2012 to 2014 in the national coverage with Penta 3 (82 vs. 88%) and measles (70% vs. 75% respectively), the coverage was still below the national target (= 90%). Furthermore, the 2015 witnessed marked drop in national coverage compared to 2014 for measles (66% vs. 75%) and BCG (49% vs. 73%) but slight drop in coverage by Penta 3 (84% vs. 88%). Such drop was more marked at the governorates that witness armed confrontations e.g. Taiz governorate showed drop in Penta 3 coverage from 93% in 2014 to 73% in 2015 and Sa`dah from 50% to 38%. On the other sides, governorates that did not witness armed confirmations showed increase in coverage e.g. Rima showed increase in Penta 3 coverage from 87% to 106% for the same period. Conclusions: The analysis shows the marked negative impact of the 2015 war on immunization coverage especially in the governorates that witness armed confrontations that may put Yemen at more risk for polio importation and VPD outbreaks. Besides the ongoing struggles to stop the Yemeni war, more innovative vaccine delivery/provision and increasing demands strategies are needed especially in governorates with confrontations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".