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038 Belief-based intentions to take up cancer screening
  1. Marcus Cheetham,
  2. Christoph A Meier
  1. Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland

Abstract

Introduction Life years gained (LYG) can help individuals weigh-up the benefit and harm of cancer screening.1 Averaged across a population, LYG can be weeks or days.2 However, naïve beliefs about LYG (i.e., no knowledge of LYG data) influence intention to initiate screening.3 As proof-of-concept, we present a conceptual framework and method based on diffusion-of-innovations theory and s-shaped function research4 to characterise naïve (belief-based) intentions about initiating screening.

Methods N=30 adult participants completed a survey that included a two-alternative intention task in which they affirmed or rejected their screening intention in relation to different LYG (i.e., 0 to 5 LYG in 6-month increments). An s-shaped function curve was fitted to the response data, its slope determined by logistic function models, and parameter estimates derived to categorise participants according to intention to always screen or never screen or whether intention depends on number of LYG. For the latter, we computed the inflection point on the curve that indicates the specific tipping point in LYG between affirming and rejecting screening.

Results An s-shaped curve visualizes the diffusion trajectory (i.e., intention to take up screening over time), showing 10% of participants would never screen, 20% would always screen and an inflection point of 2.6 LYG for the remaining. Further analyses showed that moderator variables (e.g., beliefs about screening effectiveness) influence the inflection point.

Discussion Using easily acquired data, this method can help to characterise naïve belief-based, future intentions about cancer screening and the impact of moderating factors on these.5

Conclusion The initial results favour further development of this method and use of large samples to gain insights into different target populations (e.g., depending on socio-demographic factors).

References

  1. Kregting LM, Sankatsing VDV, Heijnsdijk EAM, et al. Finding the optimal mammography screening strategy: a cost-effectiveness analysis of 920 modelled strategies. Int J Cancer. 2022;151(2):287–296.

  2. Bretthauer M, Wieszczy P, Løberg M, et al. Estimated lifetime gained with cancer screening tests: a meta-analysis of randomized clinical trials. JAMA Intern Med. 2023:e233798.

  3. Pasick RJ, Barker JC, Otero-Sabogal R, et al, Intention, subjective norms, and cancer screening in the context of relational culture. Health Educ Behav. 2009;36(5):91–110.

  4. Kucharavy D, De Guio R. Application of S-Shaped Curves. Procedia Engineering. 2011;9:559–572.

  5. Sach TH, Whynes DK. Men and women: beliefs about cancer and about screening. BMC public health.2009;9:431.

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