Potential Predictors for HPV Vaccination Completion Rates
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».