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Updated: Jul 18, 2025

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Optimal age to stop prostate cancer screening and early detection
Naser Mohamadkhani1, Azin Nahvijou2, Mohammad Hadian3
1Department of Health Economics, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Background:
Prostate Cancer screening should be discontinued at older ages because competing mortality risks eventually dominate the risk of Prostate Cancer and harms exceed benefits. We explored the Prostate Cancer screening stopping age from the patient, healthcare system, and social perspectives in Iran.
Methods:
We applied Bellman Equations to formulate the net benefits biopsy and "do nothing". Using difference between the net benefits of two alternatives, we calculated the stopping age. The cancer states were without cancer, undetected cancer, detected cancer, metastatic cancer, and death. To move between states, we applied Markov property. Transition probabilities, rewards, and costs were inferred from the medical literature. The base-case scenario estimated the stopping age from the patient, healthcare system, and social perspectives. A one-way sensitivity used to find the most influential parameters on the stopping age.
Results:
Our results suggested that Prostate Cancer screening stopping ages from the patient, healthcare system, and social were 70, 68, and 68 respectively. The univariate sensitivity analysis showed that the stopping ages were sensitive to the disutility of treatment, discount factor, the disutility of metastasis, the annual probability of death from other causes, and the annual probability of developing metastasis from the hidden cancer state.
Conclusions:
Men should not be screened for Prostate Cancer beyond 70 years old, as this results in the net benefit of "do nothing" above the biopsy. Nevertheless, this finding needs to be further studied with more detailed cancer progression models (considering re-biopsy, comorbidities, and more complicated states transition) and using local utility and willingness to pay value information.
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