Predictors of Seroprevalence of Hepatitis C Infection among Health Care Workers in Nigeria; A Year after Post Implementation of Nigeria’s National Hepatitis Prevention Policy
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) infection is a global public health issue. Health care workers (HCWs) are particularly at risk. Nigeria hepatitis prevention policy aims to achieve country wide elimination of hepatitis through early detection using mass screening with life-style modifications of “at risk population” which are key preventive strategies. AIM: To determine the seroprevalence of HCV infection among HCWs in a large regional referral hospital in Nigeria METHODS: A hospital-based descriptive cross-sectional study (hepatitis mass screening) was done at the University of Nigeria Teaching Hospital, Enugu, Nigeria between July and August 2016. Non-randomised sampling was used. Blood samples were assayed for antibodies to HCV. Data on knowledge, risk factors and mode of transmission were collected using a structured, pre-validated, pretested, questionnaire and analysed using SPSS version 20. RESULTS: A total of 3132 out of 5144 (60.9%) HCWs participated in the study. The seroprevalence of hepatitis C among UNTH staff was 0.90% (28/3132). The mean knowledge score of 68.95% ± 24.23 and 56.70±17.25 translates to fair knowledge level about mode of transmission and risk of transmission of hepatitis C among HCWs, respectively. There was no reported case of hepatitis B and C co-infection. Females HCWs had highest sero-prevalence for HCV 17/5144 (0.33%) (P = 0.164, AOR= 1.76, 95%CI =0.431-2.413) CONCLUSION: This study found a low seropositivity of HCV among HCWs. A pointer to the possible success of the hospital-based education awareness programme, an implementation of Nigeria’s national hepatitis prevention policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".