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Health Expect. 2009 Mar;12(1):4-17. doi: 10.1111/j.1369-7625.2008.00524.x.

Conceptual problems in laypersons' understanding of individualized cancer risk: a qualitative study.

Author information

  • 1Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA. hanp@mail.nih.gov

Abstract

OBJECTIVE:

To explore laypersons' understanding of individualized cancer risk estimates, and to identify conceptual problems that may limit this understanding.

BACKGROUND:

Risk prediction models are increasingly used to provide people with information about their individual risk of cancer and other diseases. However, laypersons may have difficulty understanding individualized risk information, because of conceptual as well as computational problems.

DESIGN:

A qualitative study was conducted using focus groups. Semi-structured interviews explored participants' understandings of the concept of risk, and their interpretations of a hypothetical individualized colorectal cancer risk estimate.

SETTING AND PARTICIPANTS:

Eight focus groups were conducted with 48 adults aged 50-74 years residing in two major US metropolitan areas. Participants had high school or greater education, some familiarity with information technology, and no personal or family history of cancer.

RESULTS:

Several important conceptual problems were identified. Most participants thought of risk not as a neutral statistical concept, but as signifying danger and emotional threat, and viewed cancer risk in terms of concrete risk factors rather than mathematical probabilities. Participants had difficulty acknowledging uncertainty implicit to the concept of risk, and judging the numerical significance of individualized risk estimates. The most challenging conceptual problems related to conflict between subjective and objective understandings of risk, and difficulties translating aggregate-level objective risk estimates to the individual level.

CONCLUSIONS:

Several conceptual problems limit laypersons' understanding of individualized cancer risk information. These problems have implications for future research on health numeracy, and for the application of risk prediction models in clinical and public health settings.

PMID:
19250148
[PubMed - indexed for MEDLINE]
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