OBJECTIVE To investigate the positive test of high-risk human papillomavirus (HR-HPV) among the female population undergoing physical examination in Shenyang, analyze the influencing factors and establish and validate the risk prediction model.

METHODS The data were collected from the female population who received HPV test in the physical examination center of a three-A hospital in the whole year of 2023. The prevalence rates of HR-HPV infections and subtypes were described, the influencing factors for the infections were identified. Univariate analysis and multivariate logistic regression analysis were performed for the influencing factors for positive test of HR-HPV, and the prediction model was established and validated.

RESULTS Totally 6 130 out of 7 759 female population who received HPV test were from Shenyang, the total positive rate of HR-HPV was 10.72% among the population from Shenyang, 11.11% among the population from other areas, and there was no significant difference. The population from Shenyang aged between 21 and 84 years old, with the mean age (48.58±11.64) years old. Among the local population who had the infections, 80.21% were the single HPV infection, and 19.79% were multiple infections; HPV52 was the predominant subtype of HPV causing the infections, followed by HPV58 and HPV16. The result of multivariate analysis showed age, smoking history, gynecological surgery history, allergic history, family annual income and sleep condition were the influencing factors for the positive HR-HPV. The prediction model was established based on the result of the multivariate analysis, the internal validation of the model was carried out by modeling data and receiver operating characteristic (ROC) curves, the area under the curve (AUC) of the prediction model was 0.919, and 95%CI was 0.878 to 0.960, indicating that the prediction model had a high efficiency.

CONCLUSIONS The positive rate of HR-HPV test is not relatively high among the physical examination female population in Shenyang, and the positive result is affected by a variety of factors. The population can be vaccinated for prevention and control based on the prediction model targeting to the non-variable factors such as age, meanwhile, the measures such as enhancement of health education, adjustment of health polies and intervention to health behaviors should be taken for other controllable factors.