Hepatitis C Virus in the US Military Retiree Population: To Screen, or Not to Screen?
Bibliographic record
Abstract
BACKGROUND: In 2012, the Centers for Disease Control (CDC) recommended hepatitis C virus (HCV) screening for those born between 1945 and 1965. Prior recommendations endorsed screening based on risk factors (RFs). Because United States (US) military retirees have had at least 20 years of access to free comprehensive health care, mandatory physical fitness tests, periodic health assessments and mandatory drug screening, we hypothesized that the prevalence of HCV amongst military retirees is lower than the national average. Thus the new CDC screening guidelines may not be applicable or cost effective in this particular population. METHODS: A quality improvement (QI) initiative implemented the new birth-cohort CDC screening guidelines for the internal medicine (IM) clinic of our hospital (QI group). An age-matched group from the same IM clinic, screened based on RFs for HCV infection, served as the comparator (RF group). The prevalence of the anti-HCV antibody and chronic infection was determined and compared with each other and with the national average. RESULTS: The prevalence of the HCV antibody was 2.1% and 2.3% in the QI and RF groups, respectively (odds ratio (OR): 1.08, 95% CI: 0.37 - 3.21, P = 1.000). The prevalence of chronic infection was 0.4% and 1.8% in the QI and RF groups, respectively (OR: 4.39, 95% CI: 0.80 - 24.13, P = 0.083). When our data were compared with the national average, there were no statistical differences in the prevalence of the HCV antibody; however, there was statistically more viral clearance, and subsequently less chronic infection, in the QI group versus the national average. CONCLUSIONS: The military retiree population did not have a lower prevalence of the HCV antibody than the American populace whether screened based on age or traditional RFs. Thus, the CDC guidelines are applicable in this population. One interesting finding of this study is the higher rate of viral clearance in military retirees when compared with the national average. It is therefore possible that military retirees may be more likely to have natural viral eradication than the civilian population.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".