Latent Tuberculosis Infection among Healthcare Workers in Abidjan: Prevalence and Risk Factors for a Tertiary Hospital Cohort,
Bibliographic record
Abstract
Healthcare workers in high tuberculosis burden countries face an elevated occupational risk of infection. Data on latent tuberculosis infection among healthcare workers in Côte d'Ivoire, particularly in major urban centres like Abidjan, remain scarce. This gap hinders the development of effective, targeted occupational health policies. This policy brief presents evidence on the prevalence and key risk factors for latent tuberculosis infection among healthcare workers in a tertiary hospital in Abidjan. Its objective is to inform hospital infection prevention and control policy and national occupational health guidelines for the healthcare sector. A cross-sectional study was conducted at a large tertiary hospital in Abidjan. A cohort of healthcare workers from various departments was tested for latent tuberculosis infection using tuberculin skin tests. Data on demographic and occupational risk factors were collected via structured questionnaires. Analysis determined prevalence and identified associated risk factors. The prevalence of latent tuberculosis infection among the healthcare worker cohort was 49%. A strong occupational gradient was observed: nurses and clinical support staff had significantly higher odds of testing positive compared to administrative staff. Longer duration of employment in healthcare was also a major risk factor. Latent tuberculosis infection is highly prevalent among healthcare workers in this Abidjan tertiary hospital, representing a significant occupational health issue. The risk is not uniform and is strongly linked to specific clinical roles and cumulative exposure time. Implement routine latent tuberculosis infection screening and preventive therapy programmes for high-risk healthcare worker groups. Strengthen infection prevention and control measures, particularly in high-exposure clinical areas. Develop and enforce national policies for tuberculosis occupational health protection in the healthcare sector. latent tuberculosis, healthcare workers, occupational health, infection prevention and control, Côte d'Ivoire, policy This brief provides crucial local evidence to guide occupational health policy and practice for protecting healthcare workers from tuberculosis in Côte d'Ivoire.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".