Modeling HPV and Cervical Cancer in the U.S. for Analyses of Screening and Vaccination
2007
This paper discusses a model of human papillomavirus (HPV) and cervical cancer that incorporates uncertainty about the natural history of disease that was used to provide quantitative insight into U.S. policy choices for cervical cancer prevention. The authors developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF) scores were computed based on comparisons between model-predicted outcomes and calibration targets that included age-specific prevalence of HPV by type and cervical intraepithelial neoplasia (CIN), HPV type distribution within CIN and cancer, and age-specific cancer incidence.
Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets and the authors used 50 good-fitting parameter sets to assess the external consistency and face validity of the model through comparison of screening outcomes to independent data not used during calibration. Modeled screening outcomes were found to be externally consistent with results from multiple independent data sources. Based on these 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range 69-82%) and 69% (60-77%) and from vaccination alone was 75% (range 60-88%). The uncertainty was reduced when vaccination was combined with every-5-year screening to 89% (range 83-95%).
Modeling HPV and Cervical Cancer in the U.S. for Analyses of Screening and Vaccination
Source:
Goldhaber-Fiebert JD, Stout NK, Ortendahl J et al. Modeling Human Papillomavirus and Cervical Cancer in the United States for Analyses of Screening and Vaccination. Population Health Metrics 2007; 5: 11. https://doi.org/10.1186/1478-7954-5-11