Trends and disparities in HPV vaccination among U.S. adolescents, 2018–2023
Bibliographic record
Abstract
Abstract Background From 2018 to 2023, Human papillomavirus (HPV) vaccination coverage in the United States was shaped by both proactive immunization efforts and the disruptions caused by the COVID-19 pandemic, leading to the first national decline in nearly a decade. This study aimed to assess vaccination trends over time and across regions to identify coverage gaps and inform health policy best practices for achieving optimal HPV vaccination rates. Methods We used provider-verified data from the National Immunization Survey–Teen for adolescents aged 13–17, focusing on vaccine initiation (≥1 dose) and up-to-date (UTD) status, as defined by Centers for Disease Control and Prevention guidelines. We used Cochran-Armitage trend tests to assess changes across the pre-pandemic, pandemic, and post-pandemic periods. We stratified our analyses by sex, race/ethnicity, and state. Results Initiation increased from 68.1% in 2018 to 76.9% in 2021, then declined to 76.8% in 2023. UTD status rose from 51.1 to 62.6% by 2022 but fell to 61.4% in 2023. Females and Hispanic/Black adolescents consistently had higher coverage than males and White adolescents. Eighteen states, mainly in the Northeast and Upper Midwest, achieved ≥80% initiation by 2023, while Southern states lagged. Conclusion Best practices for improving HPV vaccination include: (1) strengthening vaccination infrastructure in low-performing Southern states, (2) targeting male and White adolescents, (3) maintaining robust delivery systems during crises, and (4) replicating high-performing regional models. These strategies can improve vaccine equity and contribute to achieving national targets over time and space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".