Search

This corrects the article DOI: 10.1038/gim.2016.61.

PurposeWhole-genome sequencing (WGS) can be used as a powerful diagnostic tool as well as for screening, but it may lead to anxiety, unnecessary testing, and overtreatment. Current guidelines suggest reporting clinically actionable secondary findings when diagnostic testing is performed. We examined preferences for receiving WGS results.MethodsA US nationally representative survey (n = 410 adults) was used to rank preferences for who decides (an expert panel, your doctor, you) which WGS results are reported. We estimated the value of information about variants with varying levels of clinical usefulness by using willingness to pay contingent valuation questions.ResultsThe results were as follows: 43% preferred to decide themselves what information is included in the WGS report. 38% (95% confidence interval (CI): 33-43%) would not pay for actionable variants, and 3% (95% CI: 1-5%) would pay more than $1,000. 55% (95% CI: 50-60%) would not pay for variants for which medical treatment is currently unclear, and 7% (95% CI: 5-9%) would pay more than $400.ConclusionMost people prefer to decide what WGS results are reported. Despite valuing actionable information more, some respondents perceive that genetic information could negatively impact them. Preference heterogeneity for WGS information should be considered in the development of policies, particularly to integrate patient preferences with personalized medicine and shared decision making.Genet Med 18 12, 1295-1302.

Introduction: Medication non-adherence remains a significant challenge in healthcare, impacting treatment outcomes and the overall effectiveness of medical interventions. This article introduces a novel approach to understanding and predicting medication non-adherence by integrating patient beliefs, efficacy expectations, and perceived costs. Existing theoretical models often fall short in quantifying the impact of barrier removal on medication adherence and struggle to address cases where patients consciously choose not to follow prescribed medication regimens. In response to these limitations, this study presents an empirical framework that seeks to provide a quantifiable model for both individual and population-level prediction of non-adherence under different scenarios.Methods: We present an empirical framework that includes a health production function, specifically applied to antihypertensive medications nonadherence. Data collection involved a pilot study that utilized a double-bound contingent-belief (DBCB) questionnaire. Through this questionnaire, participants could express how efficacy and side effects were affected by controlled levels of non-adherence, allowing for the estimation of sensitivity in health outcomes and costs.Results: Parameters derived from the DBCB questionnaire revealed that on average, patients with hypertension anticipated that treatment efficacy was less sensitive to non-adherence than side effects. Our derived health production function suggests that patients may strategically manage adherence to minimize side effects, without compromising efficacy. Patients' inclination to manage medication intake is closely linked to the relative importance they assign to treatment efficacy and side effects. Model outcomes indicate that patients opt for full adherence when efficacy outweighs side effects. Our findings also indicated an association between income and patient expectations regarding the health of antihypertensive medications.Conclusion: Our framework represents a pioneering effort to quantitatively link non-adherence to patient preferences. Preliminary results from our pilot study of patients with hypertension suggest that the framework offers a viable alternative for evaluating the potential impact of interventions on treatment adherence. [ABSTRACT FROM AUTHOR]

Background: Medical decisions require weighing expected benefits of treatment against multiple adverse outcomes under uncertainty (i.e., risks) that must be accepted as a bundle. However, conventional maximum acceptable risk (MAR) estimates derived from discrete-choice experiment benefit-risk studies evaluate the acceptance of individual risks, assuming other risks are fixed, potentially leading decision makers to misinterpret levels of risk acceptance. Design: Using simulations and a published discrete-choice experiment, we demonstrate a method for identifying multidimensional risk-tolerance measures given a treatment level of benefit. Results: Simultaneous Maximum Acceptable Risk Thresholds (SMART) represents combinations of risks that would be jointly accepted in exchange for specific treatment benefits. The framework shows how the expectation of utility associated with treatments that involve multiple risks are related even when preferences for potential adverse events are independent. We find that the form of the marginal effects of adverse-event probabilities on the expected utility of treatment determines the magnitude of differences between SMART and conventional single-outcome MAR estimates. Limitations: Preferences for potential adverse events not considered in a study or preferences for adverse-event attributes held constant in risk-tolerance calculations may affect estimated risk tolerance. Further research is needed to understand the right balance between realistically reflecting clinical treatments with many potential adverse events and the cognitive burden of evaluating risk-risk tradeoffs in research and in practice. Conclusions and Implications: SMART analysis should be considered in preference studies evaluating the joint acceptance of multiple potential adverse events. Highlights: Conventional approaches to calculate maximum-acceptable risk (MAR) using discrete-choice experiment data account for 1 adverse-event risk at a time, requiring that decision makers infer the acceptability of treatments when patients are exposed to multiple risks simultaneously. The Simultaneous Maximum Acceptable Risk Threshold (SMART) maps combinations of adverse-event risks that would be jointly acceptable given a specific treatment benefit and provides a transparent and precise portrayal of acceptance of multiple risks. Risk levels that would be accepted using individual MAR estimates might not be acceptable when simultaneous risks are considered, especially when marginal expected disutility of risk is decreasing nonlinearly with risk probabilities. Preference researchers should calculate SMARTs in any discrete-choice study in which 2 or more adverse-event risks are presented, particularly if risk preferences are nonlinear. [ABSTRACT FROM AUTHOR]