Labor Model Health Management Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Respiratory Rehabilitation Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Front Public Health. 2022 Dec 14;10:1052210. doi: 10.3389/fpubh.2022.1052210. eCollection 2022.
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.
人乳头瘤病毒（HPV）感染是一种在许多人群中普遍存在的重要致癌感染。然而，美国和北京女性的 HPV 感染的独立影响因素和预测模型很少得到证实。在这项研究中，我们的第一个目标是探索美国和北京女性 HPV 感染相关因素的重叠。其次，我们旨在开发一个 R 包，用于识别表现最佳的预测模型，并使用该 R 包构建 HPV 感染预测模型。
本横断面研究使用了 2009-2016 年 NHANES（一项全国性人口研究）和 2019 年北京各行业女性工会工人的数据。探讨了两个队列中 HPV 感染的流行率、潜在影响因素和预测模型。
共纳入 2259 名（NHANES 队列，年龄：20-59 岁）和 1593 名（北京女性队列，年龄：20-70 岁）参与者进行分析。美国 NHANES 和北京女性 HPV 感染率分别为 45.73%和 8.22%。性伴侣数量、婚姻状况和 HPV 感染史是影响 NHANES 和北京女性 HPV 感染的主要因素，但 condom 的应用不是两个队列 HPV 感染的独立影响因素。建立了 R 包 Modelbest。基于 Modelbest 包开发的列线图在性能上优于仅包含多元回归分析中显著因素的列线图。
尽管 HPV 疫苗广泛应用，但 HPV 感染仍然很普遍。与推广 condom 相比，避免多个性伴侣似乎更能有效预防 HPV 感染。基于 Modelbest 开发的列线图可以提供更准确的 HPV 感染个性化风险评估。我们的 R 包 Modelbest 有可能成为未来预测模型研究的有力工具。
