Temporal Trends in Cervical Human Papillomavirus Prevalence Among Females in Xiamen, China (2016-2023): Cross-Sectional Study
Bibliographic record
Abstract
Background: Human papillomavirus (HPV) is a primary causative agent of cervical cancer, accounting for more than 90% of cases worldwide. Epidemiological data on regional HPV prevalence and genotype distribution are critical for tailoring targeted cervical cancer prevention strategies, particularly in regions with limited population-based studies. Objective: This study aimed to investigate temporal trends in the prevalence of overall HPV infection and vaccine-targeted HPV genotypes among females in Xiamen between 2016 and 2023 using annual cross-sectional analyses. Methods: We analyzed retrospective deidentified data from 63,553 females who underwent HPV genotyping of cervical exfoliated cells at Zhongshan Hospital affiliated with Xiamen University from 2016 to 2023. Data on HPV genotyping, age, and detection time were collected from the hospital's electronic information system. For each year, we conducted a cross-sectional assessment of HPV infection status to calculate annual HPV prevalence. Temporal trends of HPV prevalence were analyzed across 3 pandemic periods (prepandemic: 2016-2019, pandemic: 2020-2022, and postpandemic: 2023) and by age groups. Results: The overall HPV prevalence was 25.24% (16,039/63,553), comprising high-risk human papillomavirus (HR-HPV) at 19.26% (12,242/63,553) and low-risk human papillomavirus (LR-HPV) at 10.08% (6409/63,553). Vaccine-targeted HPV prevalence rates were bivalent human papillomavirus at 3.56% (2264/63,553), quadrivalent human papillomavirus at 5.89% (3746/63,553), and nine-valent human papillomavirus at 13.64% (8666/63,553), respectively. Notably, the number of non-vaccine-targeted HPV genotypes accounted for 16.01% (10,177/63,553) of all tested females and 63.45% (10,177/16,039) of HPV-positive cases. The top 5 HR-HPV genotypes were HPV52 (3000/63,553, 4.72%), HPV58 (1895/63,553, 2.98%), HPV53 (1582/63,553, 2.49%), HPV16 (1461/63,553, 2.30%), and HPV39 (1116/63,553, 1.76%), while HPV81 (1407/63,553, 2.21%), HPV61 (1268/63,553, 2%), and HPV6 (1101/63,553, 1.73%) were the most prevalent LR-HPV genotypes. Temporal analysis revealed significant declines in the prevalence of overall HPV, HR-HPV, LR-HPV, bivalent human papillomavirus, quadrivalent human papillomavirus, nine-valent human papillomavirus, and specific genotypes (HPV52, HPV58, HPV16, HPV39, and HPV6) from 2016 to 2019 to 2023 (all P<.001). Conversely, HPV81 prevalence increased significantly in 2023 compared to 2020-2022 (2.44% vs 1.96%; P<.001). Age-stratified analysis of HPV prevalence showed a significant declining trend with increasing age (P<.001), with peak prevalence observed in the ≤20-year age group. Conclusions: Cervical HPV infection, particularly non-vaccine-targeted genotypes, remains a substantial public health burden in Xiamen, highlighting the urgency to develop broader spectrum vaccines, to enhance cervical cancer screening programs, and to implement age-specific interventions, specifically for females aged ≤20 years. Long-term surveillance of emerging HPV genotypes and vaccination coverage is recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".