Factors affecting HPV infection in U.S. and Beijing females: A modeling study

Human papillomavirus (HPV) infection is an important carcinogenic infection highly prevalent among many populations. However, independent influencing factors and predictive models for HPV infection in both U.S. and Beijing females are rarely confirmed. In this study, our first objective was to explore the overlapping HPV infection-related factors in U.S. and Beijing females. Secondly, we aimed to develop an R package for identifying the top-performing prediction models and build the predictive models for HPV infection using this R package.

This cross-sectional study used data from the 2009-2016 NHANES (a national population-based study) and the 2019 data on Beijing female union workers from various industries. Prevalence, potential influencing factors, and predictive models for HPV infection in both cohorts were explored.

There were 2,259 (NHANES cohort, age: 20-59 years) and 1,593 (Beijing female cohort, age: 20-70 years) participants included in analyses. The HPV infection rate of U.S. NHANES and Beijing females were, respectively 45.73 and 8.22%. The number of male sex partners, marital status, and history of HPV infection were the predominant factors that influenced HPV infection in both NHANES and Beijing female cohorts. However, condom application was not an independent influencing factor for HPV infection in both cohorts. R package Modelbest was established. The nomogram developed based on Modelbest package showed better performance than the nomogram which only included significant factors in multivariate regression analysis.

Collectively, despite the widespread availability of HPV vaccines, HPV infection is still prevalent. Compared with condom promotion, avoidance of multiple sexual partners seems to be more effective for preventing HPV infection. Nomograms developed based on Modelbest can provide improved personalized risk assessment for HPV infection. Our R package Modelbest has potential to be a powerful tool for future predictive model studies.