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Why Breast Cancer Risk by the Numbers Is Not Enough: Evaluation of a Decision Aid in Multi-Ethnic, Low-Numerate Women.

Kukafka R, Yi H, Xiao T, Thomas P, Aguirre A, Smalletz C, David R, Crew K - J. Med. Internet Res. (2015)

Bottom Line: Among 34 participants, mean age was 53.4 years, 62% (21/34) were Hispanic, and 41% (14/34) demonstrated low numeracy.However, we identified potential barriers that suggest that accurate risk perceptions will not suffice as the sole basis to support informed decision making and the acceptance of risk-appropriate prevention strategies.Findings will inform the iterative design of the RealRisks decision aid.

View Article: PubMed Central - HTML - PubMed

Affiliation: Columbia University, College of Physicians and Surgeons, Biomedical Informatics, Mailman School of Public Health, Sociomedical Sciences, New York, NY, United States. rk326@cumc.columbia.edu.

ABSTRACT

Background: Breast cancer risk assessment including genetic testing can be used to classify people into different risk groups with screening and preventive interventions tailored to the needs of each group, yet the implementation of risk-stratified breast cancer prevention in primary care settings is complex.

Objective: To address barriers to breast cancer risk assessment, risk communication, and prevention strategies in primary care settings, we developed a Web-based decision aid, RealRisks, that aims to improve preference-based decision-making for breast cancer prevention, particularly in low-numerate women.

Methods: RealRisks incorporates experience-based dynamic interfaces to communicate risk aimed at reducing inaccurate risk perceptions, with modules on breast cancer risk, genetic testing, and chemoprevention that are tailored. To begin, participants learn about risk by interacting with two games of experience-based risk interfaces, demonstrating average 5-year and lifetime breast cancer risk. We conducted four focus groups in English-speaking women (age ≥18 years), a questionnaire completed before and after interacting with the decision aid, and a semistructured group discussion. We employed a mixed-methods approach to assess accuracy of perceived breast cancer risk and acceptability of RealRisks. The qualitative analysis of the semistructured discussions assessed understanding of risk, risk models, and risk appropriate prevention strategies.

Results: Among 34 participants, mean age was 53.4 years, 62% (21/34) were Hispanic, and 41% (14/34) demonstrated low numeracy. According to the Gail breast cancer risk assessment tool (BCRAT), the mean 5-year and lifetime breast cancer risk were 1.11% (SD 0.77) and 7.46% (SD 2.87), respectively. After interacting with RealRisks, the difference in perceived and estimated breast cancer risk according to BCRAT improved for 5-year risk (P=.008). In the qualitative analysis, we identified potential barriers to adopting risk-appropriate breast cancer prevention strategies, including uncertainty about breast cancer risk and risk models, distrust toward the health care system, and perception that risk assessment to pre-screen women for eligibility for genetic testing may be viewed as rationing access to care.

Conclusions: In a multi-ethnic population, we demonstrated a significant improvement in accuracy of perceived breast cancer risk after exposure to RealRisks. However, we identified potential barriers that suggest that accurate risk perceptions will not suffice as the sole basis to support informed decision making and the acceptance of risk-appropriate prevention strategies. Findings will inform the iterative design of the RealRisks decision aid.

No MeSH data available.


Related in: MedlinePlus

Schema of the RealRisks decision aid.
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figure1: Schema of the RealRisks decision aid.

Mentions: RealRisks models patient-provider dialogue and incorporates experience-based dynamic interfaces to communicate numeric and probabilistic concepts that are central to risk communication (Figure 1). The narrative is based on a fictitious character Rose, who has a family history of breast cancer and visits her doctor for a routine check-up. We segmented the narrative into the following modules: (1) risk (what is risk, what are breast cancer risk factors), (2) genetic testing (hereditary breast cancer, inherited mutations), and (3) chemoprevention (anti-estrogens, risks/benefits). Embedded within the narrative of RealRisks are two games of experience-based risk interfaces, based upon our previous work [21]. The games demonstrate absolute 5-year and lifetime breast cancer risk for an average 50-year-old woman using a pictograph with 100 clickable women. Players are instructed to click until they “find” a woman with breast cancer. Players continue to click (eg, sample from the population of 100 women) to better learn the meaning of a given pre-set probability (ie, 12 out of 100 women or 12%). Our data suggest that this interactive experience-based format for representing risk improves accuracy of risk perception in a low-numerate population [21].


Why Breast Cancer Risk by the Numbers Is Not Enough: Evaluation of a Decision Aid in Multi-Ethnic, Low-Numerate Women.

Kukafka R, Yi H, Xiao T, Thomas P, Aguirre A, Smalletz C, David R, Crew K - J. Med. Internet Res. (2015)

Schema of the RealRisks decision aid.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526996&req=5

figure1: Schema of the RealRisks decision aid.
Mentions: RealRisks models patient-provider dialogue and incorporates experience-based dynamic interfaces to communicate numeric and probabilistic concepts that are central to risk communication (Figure 1). The narrative is based on a fictitious character Rose, who has a family history of breast cancer and visits her doctor for a routine check-up. We segmented the narrative into the following modules: (1) risk (what is risk, what are breast cancer risk factors), (2) genetic testing (hereditary breast cancer, inherited mutations), and (3) chemoprevention (anti-estrogens, risks/benefits). Embedded within the narrative of RealRisks are two games of experience-based risk interfaces, based upon our previous work [21]. The games demonstrate absolute 5-year and lifetime breast cancer risk for an average 50-year-old woman using a pictograph with 100 clickable women. Players are instructed to click until they “find” a woman with breast cancer. Players continue to click (eg, sample from the population of 100 women) to better learn the meaning of a given pre-set probability (ie, 12 out of 100 women or 12%). Our data suggest that this interactive experience-based format for representing risk improves accuracy of risk perception in a low-numerate population [21].

Bottom Line: Among 34 participants, mean age was 53.4 years, 62% (21/34) were Hispanic, and 41% (14/34) demonstrated low numeracy.However, we identified potential barriers that suggest that accurate risk perceptions will not suffice as the sole basis to support informed decision making and the acceptance of risk-appropriate prevention strategies.Findings will inform the iterative design of the RealRisks decision aid.

View Article: PubMed Central - HTML - PubMed

Affiliation: Columbia University, College of Physicians and Surgeons, Biomedical Informatics, Mailman School of Public Health, Sociomedical Sciences, New York, NY, United States. rk326@cumc.columbia.edu.

ABSTRACT

Background: Breast cancer risk assessment including genetic testing can be used to classify people into different risk groups with screening and preventive interventions tailored to the needs of each group, yet the implementation of risk-stratified breast cancer prevention in primary care settings is complex.

Objective: To address barriers to breast cancer risk assessment, risk communication, and prevention strategies in primary care settings, we developed a Web-based decision aid, RealRisks, that aims to improve preference-based decision-making for breast cancer prevention, particularly in low-numerate women.

Methods: RealRisks incorporates experience-based dynamic interfaces to communicate risk aimed at reducing inaccurate risk perceptions, with modules on breast cancer risk, genetic testing, and chemoprevention that are tailored. To begin, participants learn about risk by interacting with two games of experience-based risk interfaces, demonstrating average 5-year and lifetime breast cancer risk. We conducted four focus groups in English-speaking women (age ≥18 years), a questionnaire completed before and after interacting with the decision aid, and a semistructured group discussion. We employed a mixed-methods approach to assess accuracy of perceived breast cancer risk and acceptability of RealRisks. The qualitative analysis of the semistructured discussions assessed understanding of risk, risk models, and risk appropriate prevention strategies.

Results: Among 34 participants, mean age was 53.4 years, 62% (21/34) were Hispanic, and 41% (14/34) demonstrated low numeracy. According to the Gail breast cancer risk assessment tool (BCRAT), the mean 5-year and lifetime breast cancer risk were 1.11% (SD 0.77) and 7.46% (SD 2.87), respectively. After interacting with RealRisks, the difference in perceived and estimated breast cancer risk according to BCRAT improved for 5-year risk (P=.008). In the qualitative analysis, we identified potential barriers to adopting risk-appropriate breast cancer prevention strategies, including uncertainty about breast cancer risk and risk models, distrust toward the health care system, and perception that risk assessment to pre-screen women for eligibility for genetic testing may be viewed as rationing access to care.

Conclusions: In a multi-ethnic population, we demonstrated a significant improvement in accuracy of perceived breast cancer risk after exposure to RealRisks. However, we identified potential barriers that suggest that accurate risk perceptions will not suffice as the sole basis to support informed decision making and the acceptance of risk-appropriate prevention strategies. Findings will inform the iterative design of the RealRisks decision aid.

No MeSH data available.


Related in: MedlinePlus