Sex differences in hepatitis A incidence rates–a multi-year pooled-analysis based on national data from nine high-income countries
Bibliographic record
Abstract
BACKGROUND: Possible sex differences in hepatitis A virus (HAV) incidence rates in different age groups are not well documented. We aimed to obtain stable pooled estimates of such differences based on data from a number of high-income countries. METHODS: We obtained data on incident cases of HAV by sex and age group over a period of 6-25 years from nine countries: Australia, Canada, Czech Republic, Finland, Germany, Israel, Netherland, New Zealand and Spain. Male to female incidence rate ratios (IRR) were computed for each year, by country and age group. For each age group, we used meta-analytic methods to combine the IRRs. Meta-regression was conducted to estimate the effects of age, country, and time period on the IRR. RESULTS: A male excess in incidence rates was consistently observed in all age groups, although in the youngest and oldest age groups, where the numbers tended to be lower, the lower bounds of the 95% confidence intervals for the IRRs were less than one. In the age groups <1, 1-4, 5-9, 10-14, 15-44, 45-64 and 65+, the pooled IRRs (with 95% CI) over countries and time periods were 1.18 (0.94,1.48), 1.22 (1.16,1.29), 1.07 (1.03,1.11), 1.09 (1.04,1.14), 1.46 (1.30,1.64), 1.32 (1.15,1.51) and 1.10 (0.99,1.23) respectively. CONCLUSIONS: The excess HAV incidence rates in young males, pooled over a number of countries, suggest that the sex differences are likely to be due at least in part to physiological and biological differences and not just behavioral factors. At older ages, differential exposure plays an important role. These findings, seen in the context of the excess incidence rates in young males for many other infectious diseases, can provide further keys to the mechanisms of the infection.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.047 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".