Predicting Cohort-Specific Cervical Cancer Incidence From Population-Based Surveys of Human Papilloma Virus Prevalence: A Worldwide Study.

Predictions of cervical cancer burden and the impact of measures taken to control this cancer are usually data-demanding and based on complex assumptions. We propose a predictive method (called PANDORA) based on human papillomavirus (HPV) prevalence, measured 1993–2008, and cervical cancer incidence (CCI), measured 1993–2012, in the same birth cohorts from different worldwide locations, informed by data on age at detection of high-risk HPV and sexual debut. The model can predict CCI among high-risk HPV–positive women and predict CCI up to 14 years following high-risk HPV detection. We found CCI to increase during the 14 years following high-risk HPV detection in unscreened women aged <35 years but to remain mainly constant among women ≥35 years. Age at sexual debut was a significant modifier of CCI. Using our model, we accurately reproduced CCI among high-risk HPV–positive women as observed in cohort studies and in the general population of multiple countries. We also predicted the annual number of cervical cancer cases and CCI in locations with HPV prevalence data but no cancer registry. These findings could inform cervical cancer control programs in settings without cancer registries, as they can be used to predict future cervical cancer burden from population-based surveys of HPV prevalence. [ABSTRACT FROM AUTHOR]