Co-infection of multiple human papillomavirus genotypes and its effects on symptomatic risks and transmission dynamics: an integrated epidemiological and modelling study in Fujian, China

Introduction Global human papillomavirus (HPV) prevalence among women rose significantly from 14% (2019) to 24% (2024), underscoring the need to understand transmission dynamics and public health impact. Although multi-genotype infections are increasingly documented, evidence remains limited on their combined effect on cervical lesion severity and transmission, especially in regional populations. Using clinical datasets from Xiamen, China, this study evaluates co-infection patterns through integrated statistical and dynamical models to quantify associations with histological severity and transmissibility. Methods Data were sourced from positive HPV nucleic acid tests at two hospitals in Xiamen. Genotyping employed multiplex PCR-based flow fluorescence hybridisation. Cumulative link models (CLMs) were used to assess associations between multi-genotype infections and cervical lesion severity. Concurrently, an ordinary differential equation transmission model was developed to estimate reproduction numbers, comparing transmission potential across infection types. Results Of 1 33 438 samples, 15 939 (11.9%) were HPV-positive, covering 27 genotypes. HPV 16, 52 and 58 were the most prevalent high-risk types. Co-infections involving these genotypes showed strong inter-hospital correlation in pairing patterns (Pearson’s r =0.851). Co-infection severity association was context-specific: the number of genotypes predicted severity in the screening population (eg, quadruple infections OR=1.47, 95% CI 1.23 to 1.71, p&lt;0.01) but not in the referral population. Conversely, co-infections exhibited consistently higher relative transmissibility indices in both settings (eg, high-low-risk co-infection median model-derived <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:mstyle> <m:mrow> <m:mstyle displaystyle="false"> <m:msub> <m:mi>R</m:mi> <m:mrow> <m:mn>0</m:mn> </m:mrow> </m:msub> </m:mstyle> </m:mrow> </m:mstyle> </m:math> : 2.57–6.82). Conclusions HPV co-infection impacts are modulated by patient population: relative transmission potential is broadly elevated, whereas histological severity effects are marked in screening cohorts but minimal in referral groups. Context-aware public health strategies—adapting co-infection screening and interventions to clinical setting—are urged for more effective and efficient disease control.

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Journal
BMJ Public Health
Published
2026-07-01
DOI
https://doi.org/10.1136/bmjph-2025-002957
Primary Topic
Cervical Cancer and HPV Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Co-infection of multiple human papillomavirus genotypes and its effects on symptomatic risks and transmission dynamics: an integrated epidemiological and modelling study in Fujian, China

Tianmu Chen, Ruiqin Tu, Huiming Ye, Weihong Lu et al.
BMJ Public Health
Cervical Cancer and HPV Research
article

Co-infection of multiple human papillomavirus genotypes and its effects on symptomatic risks and transmission dynamics: an integrated epidemiological and modelling study in Fujian, China

Tianmu Chen, Ruiqin Tu, Huiming Ye, Weihong Lu, 红师 史, Guzainuer Abudurusuli, Yue Tang, Jingwen Xu, Y Zhou
article en

Abstract

Introduction Global human papillomavirus (HPV) prevalence among women rose significantly from 14% (2019) to 24% (2024), underscoring the need to understand transmission dynamics and public health impact. Although multi-genotype infections are increasingly documented, evidence remains limited on their combined effect on cervical lesion severity and transmission, especially in regional populations. Using clinical datasets from Xiamen, China, this study evaluates co-infection patterns through integrated statistical and dynamical models to quantify associations with histological severity and transmissibility. Methods Data were sourced from positive HPV nucleic acid tests at two hospitals in Xiamen. Genotyping employed multiplex PCR-based flow fluorescence hybridisation. Cumulative link models (CLMs) were used to assess associations between multi-genotype infections and cervical lesion severity. Concurrently, an ordinary differential equation transmission model was developed to estimate reproduction numbers, comparing transmission potential across infection types. Results Of 1 33 438 samples, 15 939 (11.9%) were HPV-positive, covering 27 genotypes. HPV 16, 52 and 58 were the most prevalent high-risk types. Co-infections involving these genotypes showed strong inter-hospital correlation in pairing patterns (Pearson’s r =0.851). Co-infection severity association was context-specific: the number of genotypes predicted severity in the screening population (eg, quadruple infections OR=1.47, 95% CI 1.23 to 1.71, p&lt;0.01) but not in the referral population. Conversely, co-infections exhibited consistently higher relative transmissibility indices in both settings (eg, high-low-risk co-infection median model-derived <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:mstyle> <m:mrow> <m:mstyle displaystyle="false"> <m:msub> <m:mi>R</m:mi> <m:mrow> <m:mn>0</m:mn> </m:mrow> </m:msub> </m:mstyle> </m:mrow> </m:mstyle> </m:math> : 2.57–6.82). Conclusions HPV co-infection impacts are modulated by patient population: relative transmission potential is broadly elevated, whereas histological severity effects are marked in screening cohorts but minimal in referral groups. Context-aware public health strategies—adapting co-infection screening and interventions to clinical setting—are urged for more effective and efficient disease control.

BMJ Public HealthVol. 4(3)
Xiamen University (CN), Fudan University (CN), Fujian Women and Children Hospital (CN), Education Department of Fujian Province (CN), Zhongshan Hospital of Xiamen University (CN), Zhongshan Hospital (CN), Anhui Provincial Center for Disease Control and Prevention (CN), Children’s Hospital of Fudan University Xiamen Branch (CN)
Good health and well-being
Openalex Percentile: Top 7%
Cervical Cancer and HPV Research
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