4.2 Article

Demographic, health, and attitudinal factors predictive of cancer screening decisions in older adults

Journal

PREVENTIVE MEDICINE REPORTS
Volume 13, Issue -, Pages 244-248

Publisher

ELSEVIER
DOI: 10.1016/j.pmedr.2019.01.007

Keywords

Cancer screening; Life expectancy; Decision-making; CART analysis

Funding

  1. National Institute on Aging of the National Institutes of Health [R03AG050912]
  2. Atlantic Philanthropies, Inc.
  3. John A. Hartford Foundation
  4. Alliance for Academic Internal Medicine-Association of Specialty Professors
  5. American Geriatrics Society
  6. Johns Hopkins KL2 Clinical Scholars program - National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) [KL2TR001077]
  7. NIH Roadmap for Medical Research
  8. Cancer Control Career Development Award from the American Cancer Society [CCCDA-16-002-01]
  9. National Institute on Aging [K76AG059984, 1K24AG056578, P30AG021334]
  10. T. Franklin Williams Scholarship Award

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Many older adults receive routine cancer screening even when it is no longer recommended. We sought to identify demographic, health-related, and attitudinal factors that are most predictive of continued breast, colorectal, and prostate cancer screening decisions in older adults under various scenarios. A sample of adults age 65 + (n = 1272) were recruited from a nationally representative panel in November 2016, of which 881 (69.3%) completed our survey. Participants were presented vignettes in which we experimentally varied a hypothetical patient's life expectancy, age, quality of life, and physician screening recommendation. The dependent variable was the choice to continue cancer screening in the vignette. Classification and regression tree (CART) analysis was used to identify characteristics most predictive of screening decisions; both the participants' characteristics and the hypothetical patient's characteristics in the vignettes were included in the analysis. CART analysis uses recursive partitioning to create a classification tree in which variables predictive of the outcome are included as hierarchical tree nodes. We used automated ten-fold cross-validation to select the tree with lowest misclassification and highest predictive accuracy. Participants' attitude towards cancer screening was most predictive of choosing screening. Among those who agreed with the statement I plan to get screened for cancer for as long as I live (n = 300, 31.9%), 73.2% chose screening and 57.2% would still choose screening if hypothetical patient had 1-year life expectancy. For this subset of older adults with enthusiasm towards screening even when presented with scenario involving limited life expectancy, efforts are needed to improve informed decision-making about screening.

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