Impact of single-dose HPV vaccination on HPV 16 and 18 prevalence in South African adolescent girls with and without HIV
Bibliographic record
Abstract
BACKGROUND: The World Health Organization has endorsed single-dose human papillomavirus (HPV) vaccination, but data on the impact on HPV prevalence in high HIV burden settings are limited. METHODS: A single-dose bivalent HPV vaccine was delivered to adolescent girls in grade 10 in a schools-based campaign in 1 district in South Africa. Impact on HPV 16 and 18 prevalence was evaluated using repeat cross-sectional surveys. A clinic-based survey in girls aged 17-18 years established HPV 16 and 18 prevalence in a prevaccine population (n = 506, including 157 living with HIV) in 2019 and was repeated in the same age group and sites in a single-dose eligible population in 2021 (n = 892, including 117 with HIV). HPV DNA was detected on self-collected vaginal swabs using the Seegene Anyplex II HPV 28. Population impact was estimated overall and by HIV status using prevalence ratios adjusted for differences in sexual behavior between surveys. RESULTS: Single-dose vaccination campaign coverage was 72% (4807 of 6673) of eligible girls attending high school (n = 66) in the district. HPV 16 and 18 prevalence was 35% lower in the postvaccine survey overall (adjusted prevalence ratio = 0.65, 95% confidence interval [CI] = 0.51 to 0.83; P < .001) and 37% lower in those living with HIV (adjusted prevalence ratio = 0.63, 95% CI = 0.41 to 0.95; P = .026). No protective effect was seen for nonvaccine oncogenic HPV types 33, 35, 39, 51, 52, 56, 58, 59, or 68 overall (adjusted prevalence ratio = 1.14, 95% CI = 1.03 to 1.26; P = .011) or in those living with HIV (adjusted prevalence ratio = 1.00, 95% CI = 0.83 to 1.21. P = 0.99). CONCLUSION: These data provide reassuring evidence of single-dose impact on population-level HPV 16 and 18 prevalence in a South African population, irrespective of HIV status.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".