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

BACKGROUND: 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...

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Autores principales: Yang, Huixia, Xie, Yujin, Guan, Rui, Zhao, Yanlan, Lv, Weihua, Liu, Ying, Zhu, Feng, Liu, Huijuan, Guo, Xinxiang, Tang, Zhen, Li, Haijing, Zhong, Yu, Zhang, Bin, Yu, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794849/
https://www.ncbi.nlm.nih.gov/pubmed/36589946
http://dx.doi.org/10.3389/fpubh.2022.1052210
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author Yang, Huixia
Xie, Yujin
Guan, Rui
Zhao, Yanlan
Lv, Weihua
Liu, Ying
Zhu, Feng
Liu, Huijuan
Guo, Xinxiang
Tang, Zhen
Li, Haijing
Zhong, Yu
Zhang, Bin
Yu, Hong
author_facet Yang, Huixia
Xie, Yujin
Guan, Rui
Zhao, Yanlan
Lv, Weihua
Liu, Ying
Zhu, Feng
Liu, Huijuan
Guo, Xinxiang
Tang, Zhen
Li, Haijing
Zhong, Yu
Zhang, Bin
Yu, Hong
author_sort Yang, Huixia
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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spelling pubmed-97948492022-12-29 Factors affecting HPV infection in U.S. and Beijing females: A modeling study Yang, Huixia Xie, Yujin Guan, Rui Zhao, Yanlan Lv, Weihua Liu, Ying Zhu, Feng Liu, Huijuan Guo, Xinxiang Tang, Zhen Li, Haijing Zhong, Yu Zhang, Bin Yu, Hong Front Public Health Public Health BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. Frontiers Media S.A. 2022-12-14 /pmc/articles/PMC9794849/ /pubmed/36589946 http://dx.doi.org/10.3389/fpubh.2022.1052210 Text en Copyright © 2022 Yang, Xie, Guan, Zhao, Lv, Liu, Zhu, Liu, Guo, Tang, Li, Zhong, Zhang and Yu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Yang, Huixia
Xie, Yujin
Guan, Rui
Zhao, Yanlan
Lv, Weihua
Liu, Ying
Zhu, Feng
Liu, Huijuan
Guo, Xinxiang
Tang, Zhen
Li, Haijing
Zhong, Yu
Zhang, Bin
Yu, Hong
Factors affecting HPV infection in U.S. and Beijing females: A modeling study
title Factors affecting HPV infection in U.S. and Beijing females: A modeling study
title_full Factors affecting HPV infection in U.S. and Beijing females: A modeling study
title_fullStr Factors affecting HPV infection in U.S. and Beijing females: A modeling study
title_full_unstemmed Factors affecting HPV infection in U.S. and Beijing females: A modeling study
title_short Factors affecting HPV infection in U.S. and Beijing females: A modeling study
title_sort factors affecting hpv infection in u.s. and beijing females: a modeling study
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9794849/
https://www.ncbi.nlm.nih.gov/pubmed/36589946
http://dx.doi.org/10.3389/fpubh.2022.1052210
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