Commentary: 20 years online with “Your Disease Risk”

Colditz, Graham A.; Dart, Hank

doi:10.1007/s10552-020-01356-3

Cited by 6 publications

(3 citation statements)

References 25 publications

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“… 41 , 143 The consideration of numeracy and health literacy in the development of clinical decision tools could potentially help increase tool accessibility among patients from diverse backgrounds. Recent tools developed to address contextual factors 27 , 144 include a tool consisting of low-income resources in the region that health care providers could share with their eligible patients. 145 However, there is insufficient evidence on the use of contextual factors as inputs for risk prediction in clinical decision tools.…”

Section: Discussionmentioning

confidence: 99%

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A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States

Kamil,

Wojcik,

Smith

et al. 2024

MDM Policy & Practice

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Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%–96%), insured (99%; 97%–100%) women, with college education or higher (60%; 27%–100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9–39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools. Highlights There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening. Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes. Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.

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Section: Discussionmentioning

confidence: 99%

“… 24 – 26 For example, clinical decision tools for bladder cancer include contextual factors such as occupational exposures and drinking well-water to identify high-risk individuals. 27 …”

mentioning

confidence: 99%

A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States

Kamil,

Wojcik,

Smith

et al. 2024

MDM Policy & Practice

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“…We chose to study risk skepticism in the context of type 2 diabetes because diabetes is a severe and common disease, 32,33 is modifiable through health behavior change, 34 and has risk assessment tools that are understudied. Although there are several type 2 diabetes risk assessment tools available online (e.g., Waters et al 2 and Colditz 35 ), there is also a dearth of research describing people's responses to being provided personalized estimates about their risk of developing type 2 diabetes. The only exception 36 reported that providing personalized prediabetes risk estimates prompted participants' risk perceptions to become more accurate, but only among individuals who initially underestimated their risk.…”

Section: Objectives and Hypothesesmentioning

confidence: 99%

Testing Explanations for Skepticism of Personalized Risk Information

et al. 2023

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Background The promise of precision medicine could be stymied if people do not accept the legitimacy of personalized risk information. We tested 4 explanations for skepticism of personalized diabetes risk information. Method We recruited participants ( N = 356; Mage = 48.6 [ s = 9.8], 85.1% women, 59.0% non-Hispanic white) from community locations (e.g., barbershops, churches) for a risk communication intervention. Participants received personalized information about their risk of developing diabetes and heart disease, stroke, colon cancer, and/or breast cancer (women). Then they completed survey items. We combined 2 items (recalled risk, perceived risk) to create a trichotomous risk skepticism variable (acceptance, overestimation, underestimation). Additional items assessed possible explanations for risk skepticism: 1) information evaluation skills (education, graph literacy, numeracy), 2 ) motivated reasoning (negative affect toward the information, spontaneous self-affirmation, information avoidance); 3) Bayesian updating (surprise), and 4) personal relevance (racial/ethnic identity). We used multinomial logistic regression for data analysis. Results Of the participants, 18% believed that their diabetes risk was lower than the information provided, 40% believed their risk was higher, and 42% accepted the information. Information evaluation skills were not supported as a risk skepticism explanation. Motivated reasoning received some support; higher diabetes risk and more negative affect toward the information were associated with risk underestimation, but spontaneous self-affirmation and information avoidance were not moderators. For Bayesian updating, more surprise was associated with overestimation. For personal relevance, belonging to a marginalized racial/ethnic group was associated with underestimation. Conclusion There are likely multiple cognitive, affective, and motivational explanations for risk skepticism. Understanding these explanations and developing interventions that address them will increase the effectiveness of precision medicine and facilitate its widespread implementation.

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