SARS-CoV-2 Antibody Prevalence Across Unvaccinated Health Care Workers During the COVID-19 Pandemic in Yemen: Cross-Sectional Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic presented significant challenges to health care centers across Yemen. The lack of access to COVID-19 vaccines and limited availability of personal protective equipment greatly increased the risk of SARS-CoV-2 exposure among health care workers (HCWs). Only a few studies have examined the seroprevalence and burden of SARS-CoV-2 among Yemeni HCWs. Objective: This study aimed to assess the seroprevalence of SARS-CoV-2 and associated risk factors among a cohort of unvaccinated HCWs in Ibb City, the capital of Ibb Governorate, located in the highlands of southwestern Yemen between July 2022 and January 2023. Methods: Unvaccinated HCWs employed in public and private hospitals, dispensaries, pharmacies, and laboratories in Ibb City during the past 6 months were eligible. Blood samples, occupational information, and structured interviews using a questionnaire were collected from a convenience sample of 396 unvaccinated HCWs actively providing health care services between July 2022 and January 2023. SARS-CoV-2 antibody presence was determined using a lateral flow immunoassay. Results: Of the 396 HCWs tested, 268 (67.7%) were positive for SARS-CoV-2 antibodies, with no significant difference in seropositivity between sex (P=.29). Key factors associated with seropositivity included occupation and workplace. Compared to laboratory technicians (76/124, 61%), nurses (93/124, 75%; odds ratio [OR] 1.89, 95% CI 1.10-3.26; P=.02) and physician assistants (13/14, 92.9%; OR 8.21, 95% CI 1.04-64.79; P=.046) had significantly higher odds of seropositivity. Similarly, working in hospitals was associated with significantly higher odds of seropositivity compared to working in laboratories (OR 2.77, 95% CI 1.59-4.81; P<.001). Overall, 82% (219/268) of seropositive HCWs reported COVID-19-related symptoms within the last 6 months (OR 3.82, 95% CI 2.40-6.09; P<.001), the majority being fever (191/256, 74.6%; OR 2.40, 95% CI 1.56-3.72; P<.001), headache (175/230, 76.1%; OR 2.50, 95% CI 1.62-3.84; P<.001), cough (162/205, 79%; OR 3.02, 95% CI 1.94-4.70; P<.001), or loss of taste (155/202, 76.7%; OR 2.36, 95% CI 1.53-3.65; P<.001) or smell (146/191, 76.4%; OR 2.21, 95% CI 1.43-3.41; P<.001). Conclusions: This study reveals a high prevalence of SARS-CoV-2 antibodies among HCWs in Ibb City, Yemen, underscoring the impact of limited vaccination and personal protective equipment availability in 2022 and 2023. These findings highlight the urgent need for improved protective measures and vaccination efforts in conflict-affected regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".