A systematic review and meta‐analysis of predictors of human hepatitis E virus exposure in non‐endemic countries
Bibliographic record
Abstract
The reported incidence of clinical hepatitis E cases is rising in some non-endemic countries, with concurrent concerns regarding potential hepatitis E virus (HEV) contamination of the blood supply. Therefore, the characterization of major potential sources of human HEV exposure is important to inform risk assessment and public health policy. A systematic review was conducted, including a comprehensive search in six electronic bibliographic databases, verified by hand-searching reference lists of HEV reviews, and a grey literature search, of the broad research question 'what is the evidence of the association between predictors of human HEV exposure, and HEV IgG seropositivity, in non-endemic countries?' Using forms designed a priori, captured studies were appraised at first-level screening, second-level characterization, and third-level data extraction and risk of bias assessment. Meta-analysis yielded summary estimates of association between potential predictors and odds of HEV seropositivity. Meta-analysis and meta-regression of the odds of HEV seroprevalence in specific groups characterized potential sources of HEV exposure. From 4,163 captured citations, 245 relevant studies underwent data extraction, investigating HEV seroprevalence or predictors in both healthy subjects and targeted patient groups. Across these groups, increasing age was a predictor of HEV IgG seropositivity. Both human immunodeficiency virus patients and haemodialysis patients had significantly increased odds of HEV seropositivity relative to the general population. Working with pigs, in forestry, or in hospitals, was significantly associated with increased odds of HEV seropositivity, as were consumption of meat, pork or game meat, or hunting. Chronological time was not associated with HEV seropositivity within our data sets. Further study of the distribution of potential dietary or behavioural predictors between high and lower prevalence areas within non-endemic countries could improve our understanding of the relative importance of specific HEV transmission pathways.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".