Method(s): We formulate a partially observed Markov decision process (POMDP) model exploring the relationship between mammography screening frequency and lifetime mortality risk. This approach incorporates the unobservable disease progression, the possibility of false results, and the possibility of unsuccessful treatment upon detection. We use this model to evaluate different screening policies and to construct tradeoff curves that plot “policy effort” versus mortality risk.
Results: We conduct a numerical study for a 25 year-old patient without cancer. By our analysis, we determine which policies are dominant, draw conclusions regarding the interaction between problem parameters (disease incidence, disease aggression, comorbidities, screening efficacy, screening start age, switch age, and stop age) and answer open questions concerning the value of dynamic (two-phase), versus routine (single-phase), screening policies.
Conclusions: A patient can achieve an intermediate breast cancer mortality risk using a dynamic policy rather than a “routine” policy. Our analysis provides the following preliminary insights: (i) annual screening beginning at 40 is efficient (ii) most switch ages for efficient two-phase policies are in the 50s; (iii) the most common screening start ages are 30 and 35, these policies also require the highest policy effort; (iv) two-phase policies prescribing more frequent screening early in life and policies prescribing more frequent screening later in life are both on the frontier; and (v) screening policies that stop screening early tend to result in higher mortality risk.