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 R 0 : 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.
Tang et al. (2026) studied this question.