Parts greater than their sum: randomized controlled trial testing partitioned incentives to increase cancer screening

Ann N Y Acad Sci. 2019 Aug;1449(1):46-55. doi: 10.1111/nyas.14115. Epub 2019 May 20.

Abstract

Promoting healthy behavior is a challenge for public health officials, especially in the context of asking patients to participate in preventive cancer screenings. Small financial incentives are sometimes used, but there is a little scientific basis to support a compelling description of the best-practice implementation of such incentives. We present a simple behavioral strategy based on mental accounting from prospect theory that maximizes the impact of incentives with no additional cost. We show how the partition of one incentive into two smaller incentives of equivalent total amount produces substantial behavioral changes, demonstrated in the context of colorectal cancer screening. In a randomized controlled trial, eligible patients aged 50-74 (n = 1652 patients) were allocated to receive either one €10 incentive (upon completion of screening) or two €5 incentives (at the beginning and at the end of screening). We show that cancer screening rates were dramatically increased by partitioning the financial incentive (61.1%), compared with a single installment at the end (41.4%). These results support the hedonic editing hypothesis from prospect theory, and underline the importance of implementing theoretically grounded healthcare interventions. Our results suggest that, when patient incentives are feasible, healthcare procedures should be framed as multistage events with smaller incentives offered at multiple points in time.

Keywords: cancer screening; field experiment; hedonic editing; incentive; prospect theory.

Publication types

  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Colorectal Neoplasms / diagnosis*
  • Early Detection of Cancer / statistics & numerical data*
  • Female
  • Health Behavior*
  • Humans
  • Male
  • Mass Screening / statistics & numerical data*
  • Motivation
  • Public Health / methods
  • Reward*