Medical Decision Making最新文献

Background: To assess the impact of risk perceptions on prevention efforts or behavior change, best practices involve conditional risk measures, which ask people to estimate their risk contingent on a course of action (e.g., "if not vaccinated").

Purpose: To determine whether the use of conditional wording-and its drawing of attention to one specific contingency-has an important downside that could lead researchers to overestimate the true relationship between perceptions of risk and intended prevention behavior.

Methods: In an online experiment, US participants from Amazon's MTurk (N = 750) were presented with information about an unfamiliar fungal disease and then randomly assigned among 3 conditions. In all conditions, participants were asked to estimate their risk for the disease (i.e., subjective likelihood) and to decide whether they would get vaccinated. In 2 conditional-wording conditions (1 of which involved a delayed decision), participants were asked about their risk if they did not get vaccinated. For an unconditional/benchmark condition, this conditional was not explicitly stated but was still formally applicable because participants had not yet been informed that a vaccine was even available for this disease.

Results: When people gave risk estimates to a conditionally worded risk question after making a decision, the observed relationship between perceived risk and prevention decisions was inflated (relative to in the unconditional/benchmark condition).

Conclusions: The use of conditionals in risk questions can lead to overestimates of the impact of perceived risk on prevention decisions but not necessarily to a degree that should call for their omission.

Highlights: Conditional wording, which is commonly recommended for eliciting risk perceptions, has a potential downside.It can produce overestimates of the true relationship between perceived risk and prevention behavior, as established in the current work.Though concerning, the biasing effect of conditional wording was small-relative to the measurement benefits that conditioning usually provides-and should not deter researchers from conditioning risk perceptions.More research is needed to determine when the biasing impact of conditional wording is strongest.

Background: The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey.

Design: We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up.

Results: Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds.

Limitations: We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape.

Conclusions: Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making.

Implications: Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs.

Highlights: The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

Purpose: To examine the effects of training general practitioners and nurses in shared decision-making (SDM) support as perceived by cancer patients and survivors.

Design: An innovative, experimental design was adopted that included analogue patients (APs), that is, people who have or have had cancer and who imagine themselves in the position of the actor-patient presented in a video. Each AP assessed a video-recorded simulated consultation of a health care professional (HCP) conducted before or after an SDM support training program. The primary outcome was the APs' perceived SDM support with 13 self-developed items reflecting the perceived patient benefit of SDM support as well as the perceived HCP support behavior. Secondary outcomes included an overall rating of SDM support, AP-reported extent of SDM (CollaboRATE), satisfaction with the communication (Patient Satisfaction Questionnaire), conversation appreciation and helpfulness, as well as decision-making satisfaction and confidence (visual analog scale, 0-100). In addition, patient and HCP characteristics associated with AP-perceived SDM support were examined.

Results: APs (n = 131) did not significantly differentiate trained from untrained HCPs in their perceptions of SDM support nor in secondary outcomes. Agreement between APs' perceptions was poor. The higher the perceived comparability of the consultation with APs' previous personal experiences, the higher their rating of SDM support.

Limitations: We used a nonvalidated primary outcome and an innovative study design that should be tested in future work.

Conclusions: Despite the limitations of the study design, the training seemed to not affect cancer patients' and survivors' perceived SDM support.

Implications: The clinical relevance of the training on SDM support needs to be established. The variation in APs' assessments suggests patients differ in their perception of SDM support, stressing the importance of patient-tailored SDM support.

Highlights: Cancer patients and survivors did not significantly differentiate trained from untrained HCPs when evaluating SDM support, and agreement between their perceptions was poor.The clinical relevance of training GPs and nurses in SDM support needs to be established.Patient-tailored SDM support may be recommended, given the variation in APs' assessments and their possible diverging perceptions of SDM support.This innovative study design (having patients watch and assess videos of simulated consultations made in the context of training evaluation) needs to be further developed.

Background: Health economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health-related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses.

Methods: The value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample (N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned.

Results: The obtained coefficients indicated that "physical health,""mood," and "relationships" were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension "future." The predicted values for the MHQoL ranged from -0.741 for the worst state to 1 for the best state.

Conclusions: This study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions.

Highlights: A discrete choice experiment was used to derive a Dutch value set for the MHQoL.This allows the use of the MHQoL in Dutch cost-utility analyses.The dimensions physical health, mood, and relationships were the most important.The utility values range from -0.741 for the worst state to 1 for the best state.

Background: While shared decision making (SDM) is advocated for ethical reasons and beneficial outcomes, SDM might also negatively affect patients with incurable cancer. The current study explored whether SDM, and an oncologist training in SDM, are associated with adverse outcomes (i.e., patient anxiety, tension, helplessness/hopelessness, decisional uncertainty, and reduced fighting spirit).

Design: A secondary analysis of a randomized clinical trial investigating the effects of SDM interventions in the context of advanced cancer. The relations between observed SDM (OPTION12), specific SDM elements (4SDM), oncologist SDM training, and adverse outcomes were analyzed. We modeled adverse outcomes as a multivariate phenomenon, followed by univariate regressions if significant.

Results: In total, 194 patients consulted by 31 oncologists were included. In a multivariate analysis, observed SDM and adverse outcomes were significantly related. More specifically, more observed SDM in the consultation was related to patients reporting more tension (P = 0.002) and more decisional uncertainty (P = 0.004) at 1 wk after the consultation. The SDM element "informing about the options" was especially found to be related to adverse outcomes, specifically to more helplessness/hopelessness (P = 0.002) and more tension (P = 0.016) at 1 wk after the consultation. Whether the patient consulted an oncologist who had received SDM training or not was not significantly related to adverse outcomes. No relations with long-term adverse outcomes were found.

Conclusions: It is important for oncologists to realize that for some patients, SDM may temporarily be associated with negative emotions. Further research is needed to untangle which, when, and how adverse outcomes might occur and whether and how burden may be minimized for patients.

Highlights: Observed shared decision making was related to more tension and uncertainty postconsultation in advanced cancer patientsHowever, training oncologists in SDM did not affect adverse outcomes.Further research is needed to untangle which, when, and how adverse outcomes might occur and how burden may be minimized.

Background: In orthopedics, the use of patient decision aids (ptDAs) is limited. With a mixed-method process evaluation, we investigated patient factors associated with accepting versus declining the use of the ptDA, patients' reasons for declining the ptDA, and clinicians' perceived barriers and facilitators for its use.

Methods: Patients with an indication for joint replacement surgery (N = 153) completed questionnaires measuring demographics, physical functioning, quality of life (EQ-5D-3L), and a visual analog scale (VAS) pain score at 1 time point. Subsequently, their clinician offered them the relevant ptDA. Using a retrospective design, we compared patients who used the ptDA (59%) with patients who declined (41%) on all these measures as well as the chosen treatment. If the use of the ptDA was declined, patients' reasons were recorded by their clinician and analysed (n = 46). To evaluate the experiences of clinicians (n = 5), semistructured interviews were conducted and thematically analyzed. Clinicians who did not use the ptDA substantially (<10 times) were also interviewed (n = 3).

Results: Compared with patients who used the ptDA, patients who declined use had higher VAS pain scores (7.2 v. 6.2, P < .001), reported significantly worse quality of life (on 4 of 6 EQ-5D-3L subscales), and were less likely to receive nonsurgical treatment (4% v. 28%, P < .001). Of the patients who declined to use the ptDA, 46% said they had enough information and felt ready to make a decision without the ptDA. The interviews revealed that clinicians considered the ptDAs most useful for newly diagnosed patients who had not received previous treatment.

Conclusion: These results suggest that the uptake of a ptDA may be improved if it is introduced in the early disease stages of hip and knee osteoarthritis.

Highlights: Patients who declined the use of a patient decision aid (ptDA) for hip and knee osteoarthritis reported more pain and worse quality of life.Most patients who declined to use a ptDA felt sufficiently well informed to make a treatment decision.Patients who declined the ptDA were more likely to have received prior treatment in primary care.Clinicians found the ptDA to be a helpful addition to the consultation, particularly for newly diagnosed patients.

Background: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions. At a given benefit-cost ratio (the number of false-positive predictions one would trade for a true positive prediction) or risk threshold (the probability of developing disease at indifference between treatment and no treatment), the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction. For example, a test tradeoff of 3,000 invasive tests per true-positive prediction of cancer may suggest that risk prediction is not worthwhile. A test tradeoff curve plots test tradeoff versus benefit-cost ratio or risk threshold. The test tradeoff curve evaluates risk prediction at the optimal risk score cutpoint for treatment, which is the cutpoint of the risk score (the estimated risk of developing disease) that maximizes the expected utility of risk prediction when the receiver-operating characteristic (ROC) curve is concave.

Methods: Previous methods for estimating the test tradeoff required grouping risk scores. Using individual risk scores, the new method estimates a concave ROC curve by constructing a concave envelope of ROC points, taking a slope-based moving average, minimizing a sum of squared errors, and connecting successive ROC points with line segments.

Results: The estimated concave ROC curve yields an estimated test tradeoff curve. Analyses of 2 synthetic data sets illustrate the method.

Conclusion: Estimating the test tradeoff curve based on individual risk scores is straightforward to implement and more appealing than previous estimation methods that required grouping risk scores.

Highlights: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions.At a given benefit-cost ratio or risk threshold, the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction.Unlike previous estimation methods that grouped risk scores, the method uses individual risk scores to estimate a concave ROC curve, which yields an estimated test tradeoff curve.