Ineffectiveness of the Current Strategy to Prevent Hepatitis A in Travelers
Bibliographic record
Abstract
BACKGROUND: Each year, a large number of Canadians travel to regions of the world where hepatitis A remains endemic. Many of these travelers are not immune and the current preventive strategy relies wholly on self-referral to a travel clinic. All of the costs associated with such a visit are assumed by the traveler. We estimated the effectiveness of this strategy. METHODS: This case-control study included 108 travel-related hepatitis A cases with onset of disease between 1997 and 1999 and 620 controls who traveled during the same period. RESULTS: Hepatitis A was strongly associated with high-risk travel (Odds Ratio = 7.2, 95% Confidence Interval 1.76-29.4), but only 7% of cases were found in this category. The risk of hepatitis A was 5 times lower in travelers who visited a travel clinic than in those who did not (80% efficacy). However, only 14% of the controls visited a travel clinic. As a result, the effectiveness of the current strategy is estimated to be 11% (80% of 14%). CONCLUSIONS: Hepatitis A in travelers can be prevented effectively by attendance at a travel clinic. Unfortunately, most travelers do not visit such clinics prior to departure. Even if all high-risk travelers were to visit a travel clinic and receive vaccination, this would have negligible impact on the number of travel-related hepatitis A cases (approximately 7% reduction). The current strategy for the prevention of hepatitis A in travelers is ineffective and should be reexamined.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".