A Markov Decision Process Model for Cervical Cancer Screening Policies in Colombia

Abstract

Cervical cancer is the second most common cancer in women around the world, and the human papillomavirus (HPV) is universally known as the necessary agent for developing this disease. Through early detection of abnormal cells and HPV virus types, cervical cancer incidents can be reduced and disease progression prevented. We propose a finite-horizon Markov decision process model to determine the optimal screening policies for cervical cancer prevention. The optimal decision is given in terms of when and what type of screening test to be performed on a patient based on her current diagnosis, age, HPV contraction risk, and screening test results. The cost function considers the tradeoff between the cost of prevention and treatment procedures and the risk of taking no action while taking into account a cost assigned to loss of life quality in each state. We apply the model to data collected from a representative sample of 1141 affiliates at a health care provider located in Bogotá, Colombia. To track the disease incidence more effectively and avoid higher cancer rates and future costs, the optimal policies recommend more frequent colposcopies and Pap tests for women with riskier profiles.