Epidemiology of herpes simplex virus type 2 in China: Systematic review, meta-analyses, and meta-regressions
Bibliographic record
Abstract
Background: Herpes simplex virus type 2 (HSV-2) infection is prevalent and a significant public health problem. Understanding its epidemiology will help assess the current HSV-2 prevention efforts and inform future interventions in China. Methods: We followed Cochrane and PRISMA guidelines for a systematic review and included publications published in Chinese and English bibliographic systems until March 18 th , 2023. We synthesized seroprevalence, sero-incidence, and proportions of HSV-2 isolated in genital ulcer disease (GUD) and genital herpes data. We used random-effects models for meta-analyses and conducted meta-regression to assess the association between population characteristics and seroprevalence. Results: Overall, 21,849 articles were identified, and 457 publications (1,051,035 participants) were included. A total of 429 studies reported the overall seroprevalence rates (939 stratified measures), 5 reported seroincidence rates, 4 reported overall proportions of HSV-2 isolation in GUD (8 stratified proportions), and 24 reported overall proportions of HSV-2 isolation in genital herpes (59 stratified proportions). Pooled HSV-2 seroprevalence among overall populations was 14.9% (95% confidence interval (CI): 13.8-16.1%) and was 7.9% (95% CI: 6.9-8.8%) among the general population. Seroprevalence was highest among key populations (e.g., female sex workers and men who have sex with men) (32.1% (95% CI: 27.8-36.5%)). Among the general population, we found northeastern regions had a higher HSV-2 seroprevalence (12.4%, 95% CI: 7.8-17.9%). HSV-2 seroprevalence also increased with age. The pooled HSV-2 seroincidence rate was 4.3 per 100 person-years (95% CI: 1.0-7.6). Pooled HSV-2 seroprevalence among GUD and genital herpes were 45.2% (95% CI: 29.0-61.9%) and 52.8% (95% CI: 46.6-59.0%), respectively. We also found higher HSV-2 seroprevalence estimates in publications published in English bibliographic databases than those in Chinese databases (20.5% vs . 13.6%, risk ratio=1.10 (1.05-1.14)), indicating a potential existence of language bias in publication. Conclusion: Around 1 in 12 among the general population and 1 in 7 among all included populations were infected with HSV-2. The data revealed vulnerability to HSV-2 infection among higher-risk populations calling for expanding the intervention to prevent HSV-2 infection. It also revealed heterogeneities in synthesized HSV-2 prevalence results, suggesting the necessity to include Chinese bibliographic databases in conducting systematic reviews and meta-analyses of this topic.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.053 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".