Clarifying the Trade-Offs of Risk-Stratified Screening for Prostate Cancer: A Cost-Effectiveness Study

Nathaniel Hendrix, Roman Gulati, Boshen Jiao, A. Karim Kader, Stephen T. Ryan, Ruth Etzioni

Research output: Contribution to journalArticlepeer-review

Abstract

Cancer risk prediction is necessary for precision early detection, which matches screening intensity to risk. However, practical steps for translating risk predictions to risk-stratified screening policies are not well established. We used a validated population prostate-cancer model to simulate the outcomes of strategies that increase intensity for men at high risk and reduce intensity for men at low risk. We defined risk by the Prompt Prostate Genetic Score (PGS) (Stratify Genomics, San Diego, California), a germline genetic test. We first recalibrated the model to reflect the disease incidence observed within risk strata using data from a large prevention trial where some participants were tested with Prompt PGS. We then simulated risk-stratified strategies in a population with the same risk distribution as the trial and evaluated the cost-effectiveness of risk-stratified screening versus universal (risk-agnostic) screening. Prompt PGS risk-adapted screening was more cost-effective when universal screening was conservative. Risk-stratified strategies improved outcomes at a cost of less than $100,000 per quality-adjusted life year compared with biennial screening starting at age 55 years, but risk stratification was not cost-effective compared with biennial screening starting at age 45. Heterogeneity of risk and fraction of the population within each stratum were also important determinants of cost-effectiveness.

Original languageEnglish (US)
Pages (from-to)2064-2074
Number of pages11
JournalAmerican journal of epidemiology
Volume190
Issue number10
DOIs
StatePublished - Oct 1 2021
Externally publishedYes

Keywords

  • cancer screening
  • cost-effectiveness analysis
  • genetic risk factors
  • modeling study
  • prostate cancer

ASJC Scopus subject areas

  • Epidemiology

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