Risk Ladder, Table, or Bulleted List? Identifying Formats That Effectively Communicate Personalized Risk and Risk Reduction Information for Multiple Diseases

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

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.

A qualitative study to inform adaptations to a brain health intervention for older adults with type 2 diabetes living in rural regions of Ireland

AIMS

Type 2 diabetes is a risk factor for late-life dementia, but dementia prevention strategies have yet to be comprehensively evaluated in people with diabetes. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) demonstrated cognitive benefits of a 2-year multidomain lifestyle intervention. However, given the intensive nature of FINGER, there is uncertainty about whether it can be implemented in other high-risk populations. Our aim was to explore attitudes towards dementia risk, and barriers to an intervention based on the FINGER model in older adults with type 2 diabetes living in rural areas of Ireland.

METHODS

Focus groups were conducted with 21 adults (11 men and 10 women) aged 60+ years with type 2 diabetes living in border regions of north and south Ireland. Data were analysed using thematic analysis.

RESULTS

There was limited understanding of diabetes as a risk factor for late-life dementia. The main barriers to engagement with the multidomain intervention were eating foods that were not compatible with cultural norms, time and travel constraints, and perceived lack of self-efficacy and self-motivation for adopting the desired diet, exercise and computerised cognitive training (CCT) behaviours. Facilitators for intervention acceptability included the provision of culturally tailored and personalised education, support from a trusted source, and inclusion of goal setting and self-monitoring behavioural strategies.

CONCLUSIONS

While there was high acceptability for a brain health intervention, several barriers including cultural food norms and low self-efficacy for adopting the diet, exercise and CCT components would need to be considered in the intervention design. Findings from this study will be used to inform local decisions regarding the adaptation of FINGER for people with type 2 diabetes. The feasibility of the adapted multidomain intervention will then be evaluated in a future pilot trial.

Leveraging Math Cognition to Combat Health Innumeracy

Rational numbers (i.e., fractions, percentages, decimals, and whole-number frequencies) are notoriously difficult mathematical constructs. Yet correctly interpreting rational numbers is imperative for understanding health statistics, such as gauging the likelihood of side effects from a medication. Several pernicious biases affect health decision-making involving rational numbers. In our novel developmental framework, the natural-number bias-a tendency to misapply knowledge about natural numbers to all numbers-is the mechanism underlying other biases that shape health decision-making. Natural-number bias occurs when people automatically process natural-number magnitudes and disregard ratio magnitudes. Math-cognition researchers have identified individual differences and environmental factors underlying natural-number bias and devised ways to teach people how to avoid these biases. Although effective interventions from other areas of research can help adults evaluate numerical health information, they circumvent the core issue: people's penchant to automatically process natural-number magnitudes and disregard ratio magnitudes. We describe the origins of natural-number bias and how researchers may harness the bias to improve rational-number understanding and ameliorate innumeracy in real-world contexts, including health. We recommend modifications to formal math education to help children learn the connections among natural and rational numbers. We also call on researchers to consider individual differences people bring to health decision-making contexts and how measures from math cognition might identify those who would benefit most from support when interpreting health statistics. Investigating innumeracy with an interdisciplinary lens could advance understanding of innumeracy in theoretically meaningful and practical ways.

Inclusive Recruitment Strategies to Maximize Sociodemographic Diversity among Participants: A St. Louis Case Study

Background. Sociodemographically diverse study samples are critical for research related to health decision making. However, not all researchers have the training, capacity, and funding to engage research methods that recruit the most diverse populations. Objective and Methods. We used participant-generated data, staff salary data, and participant observation to examine the effectiveness and cost of strategies that we used for screening, enrolling, and retaining a sociodemographically diverse sample for a risk communication and behavior change randomized controlled trial. Results. It took approximately 646 hours to contact 1,626 individuals and enroll 554 participants (505 of whom completed the baseline survey; 45.2% were members of a underrepresented racial/ethnic group, 19.4% had no college education, 49.5% were age 30-49 y). Retention at 90-d follow-up was 93%. The total cost was USD$19,898.50. The average cost was $35.92 per participant enrolled. In-person recruitment was most successful in identifying the largest proportion of screened and eligible participants who were members of underrepresented racial/ethnic populations (32.8% and 27.8%, respectively) and with no college experience (39.7% and 33.5%, respectively); it also had the highest total cost ($8,079.17). Existing research pools identified the largest proportion of younger participants (ages 30-49 y; 39.3% and 43.4% for screened and eligible, respectively). Existing listservs yielded the smallest proportion of individuals with no college experience and the fewest members of underrepresented racial/ethnic populations but had the lowest total cost ($290.33). Newspaper ads identified the fewest younger individuals and also had the highest cost per participant enrolled ($166.21). Word of mouth had the lowest cost per participant enrolled ($10.47). Conclusion. Results help medical decision-making researchers formulate recruitment plans that increase sociodemographic diversity in study samples. We also ask funders to accommodate increased costs required to maximize sociodemographic diversity in medical decision-making research.

Highlights

We provide concrete strategies for recruiting, enrolling, and retaining a sociodemographically diverse study sample.We offer cost estimates for all stages of study recruitment and found that in-person recruitment was the most effective, but also the most expensive, way to identify Black participants and participants with no college experience.It is critical for investigators to have access to institutional infrastructure and resources to support conducting research that is inclusive of diverse sociodemographic groups.An intentionally diverse recruitment staff supports a diverse study sample.

Health Literacy and eHealth: Challenges and Strategies

Given the impact of health literacy (HL) on patients' outcomes, limited health literacy is a major barrier to improve cancer care globally. HL refers to the degree in which an individual is able to acquire, process, and comprehend information in a way to be actively involved in their health decisions. Previous research found that almost half of the population in developed countries have difficulties in understanding health-related information. With the gradual shift toward the shared decision making process and digital transformation in oncology, the need for addressing low HL issues is crucial. Decision making in oncology is often accompanied by considerable consequences on patients' lives, which requires patients to understand complex information and be able to compare treatment methods by considering their own values. How health information is perceived by patients is influenced by various factors including patients' characteristics and the way information is presented to patients. Currently, identifying patients with low HL and simple data visualizations are the best practice to help patients and clinicians in dealing with limited health literacy. Furthermore, using eHealth, as well as involving HL mediators, supports patients to make sense of complex information.

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

BACKGROUND

Decision making for the "best" treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC).

METHODS

In German claims data for the calendar years 2014-2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation.

RESULTS

A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: -0.78 % [-1.40; -0.03]).

CONCLUSIONS

If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients' complexity deviates from "typical" study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making.HighlightsIt was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly.ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding.When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option.Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.

Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts

BACKGROUND

Decision aid developers have to convey complex task-specific numeric information in a way that minimizes bias and promotes understanding of the options available within a particular decision. Whereas our companion paper summarizes fundamental issues, this article focuses on more complex, task-specific aspects of presenting numeric information in patient decision aids.

METHODS

As part of the International Patient Decision Aids Standards third evidence update, we gathered an expert panel of 9 international experts who revised and expanded the topics covered in the 2013 review working in groups of 2 to 3 to update the evidence, based on their expertise and targeted searches of the literature. The full panel then reviewed and provided additional revisions, reaching consensus on the final version.

RESULTS

Five of the 10 topics addressed more complex task-specific issues. We found strong evidence for using independent event rates and/or incremental absolute risk differences for the effect size of test and screening outcomes. Simple visual formats can help to reduce common judgment biases and enhance comprehension but can be misleading if not well designed. Graph literacy can moderate the effectiveness of visual formats and hence should be considered in tool design. There is less evidence supporting the inclusion of personalized and interactive risk estimates.

DISCUSSION

More complex numeric information. such as the size of the benefits and harms for decision options, can be better understood by using incremental absolute risk differences alongside well-designed visual formats that consider the graph literacy of the intended audience. More research is needed into when and how to use personalized and/or interactive risk estimates because their complexity and accessibility may affect their feasibility in clinical practice.