Hepatitis E in Bangladesh: Insights From a National Serosurvey
Bibliographic record
Abstract
BACKGROUND: Hepatitis E virus (HEV) genotypes 1 and 2 are a major cause of avoidable morbidity and mortality in South Asia. Despite the high risk of death among infected pregnant women, scarce incidence data has been a contributing factor to global policy recommendations against the introduction of licensed hepatitis E vaccines, one of the only effective prevention tools. METHODS: We tested serum from a nationally representative serosurvey in Bangladesh for anti-HEV immunoglobulin G and estimated seroprevalence. We used Bayesian geostatistical models to generate high-resolution maps of seropositivity and examined variability in seropositivity by individual-level, household-level, and community-level risk factors using spatial logistic regression. RESULTS: We tested serum samples from 2924 individuals from 70 communities representing all divisions of Bangladesh and estimated a national seroprevalence of 20% (95% confidence interval [CI], 17%-24%). Seropositivity increased with age and male sex (odds ratio, 2.2 male vs female; 95% CI, 1.8-2.8). Community-level seroprevalence ranged widely (0-78%) with higher seroprevalence in urban areas, including Dhaka, with a 3.0-fold (95% credible interval, 2.3-3.7) higher seroprevalence than the rest of the country. CONCLUSIONS: Hepatitis E infections are common throughout Bangladesh. Strengthening surveillance for hepatitis E, especially in urban areas, can provide additional evidence to appropriately target interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".