Potential Predictors for HPV Vaccination Completion Rates
Bibliographic record
Abstract
Background Human papillomavirus (HPV) is the most common sexually transmitted pathogen; its ease in transmission significantly contributes to its prevalence within the population. While most HPV infections are asymptomatic, a subset of infections cause genital warts, cervical cancer and various anogenital cancers. Accordingly, the vaccine Gardasil has been designed to prevent HPV infection and its associated sequelae. While Gardasil is effective against over 75% of cervical cancers, recent studies have demonstrated its limited adoption. In 2010, only 49% of females between the ages of 13-17 had received at least one dose. Moreover, Gardasil is a three-dose vaccine, and consequently, female patients that initiate the vaccination series often do not complete it in its entirety. Methods Data obtained from researchers at the Johns Hopkins Medical Institutions (JHMI) was used to determine which socioeconomic factors influence a female’s likelihood of vaccination completion. The dataset consisted of female patients between the ages of 11-26 that had received at least one of the Gardasil vaccine doses from a JHMI clinic in Baltimore, USA, between the years 2006 and 2008. First, three logistic regression models were run with vaccination regimen completion, one shot completed and two shots completed as the dependent variables. Then, three LASSO logistic regression models were run to find relationships that were not influenced by model overfitting. The two regression methods were compared to determine if different results could be achieved. Results For the logistic regression, findings revealed that black females (P = 0.006881), females between the ages of 18-26 (P = 0.000483), and females that visited urban clinics (P = 0.004582) are at an increased risk of incomplete vaccinations. In contrast, females that were treated by obstetrician-gynecologists (P = 0.006269) had increased compliance with the Gardasil vaccination regimen compared to women that visited other healthcare professionals. For the LASSO logistic regression, the model that penalized the most for overfitting showed that black females have a higher likelihood of only receiving one shot. Conclusions Due to the retrospective nature of the data, no causation can be established. However, these correlations shed light on what female populations should be studied further and potentially targeted to improve Gardasil vaccination completion rates. Moreover, the differences in vaccination completion rates can, in turn, aggravate the existing disparities in cervical cancer risk among females.
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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 | 0.001 | 0.000 |
| 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) | 0.009 | 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".