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Background: We previously identified 3 latent classes of healthcare utilization among people with human immunodeficiency virus (PWH): adherent, nonadherent, and sick. Although membership in the "nonadherent" group was associated with subsequent disengagement from human immunodeficiency virus (HIV) care, socioeconomic predictors of class membership remain unexplored. Methods: We validated our healthcare utilization-based latent class model of PWH receiving care at Duke University (Durham, North Carolina) using patient-level data from 2015 to 2018. SDI scores were assigned to cohort members based on residential addresses. Associations of patient-level covariates with class membership were estimated using multivariable logistic regression and movement between classes was estimated using latent transition analysis. Results: A total of 1443 unique patients (median age of 50 years, 28% female sex at birth, 57% Black) were included in the analysis. PWH in the most disadvantaged (highest) SDI decile were more likely to be in the "nonadherent" class than the remainder of the cohort (odds ratio [OR], 1.58 [95% confidence interval {CI}, .95-2.63]) and were significantly more likely to be in the "sick" class (OR, 2.65 [95% CI, 2.13-3.30]). PWH in the highest SDI decile were also more likely to transition into and less likely to transition out of the "sick" class. Conclusions: PWH who resided in neighborhoods with high levels of social deprivation were more likely to have latent class membership in suboptimal healthcare utilization groupings, and membership persisted over time. Risk stratification models based on healthcare utilization may be useful tools in the early identification of persons at risk for suboptimal HIV care engagement.
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Background and Objectives. Risk-tolerance measures from patient-preference studies typically focus on individual adverse events. We recently introduced an approach that extends maximum acceptable risk (MAR) calculations to simultaneous maximum acceptable risk thresholds (SMART) for multiple treatment-related risks. We extend these methods to include the computation and display of confidence intervals and apply the approach to 3 published discrete-choice experiments to evaluate its utility to inform regulatory decisions. Methods. We generate MAR estimates and SMART curves and compare them with trial-based benefit-risk profiles of select treatments for depression, psoriasis, and thyroid cancer. Results. In the depression study, SMART curves with 70% to 95% confidence intervals portray which combinations of 2 adverse events would be considered acceptable. In the psoriasis example, the asymmetric confidence intervals for the SMART curve indicate that relying on independent MARs versus SMART curves when there are nonlinear preferences can lead to decisions that could expose patients to greater risks than they would accept. The thyroid cancer application shows an example in which the clinical incidence of each of 3 adverse events is lower than the single-event MARs for the expected treatment benefit, yet the collective risk profile surpasses acceptable levels when considered jointly. Limitations. Nonrandom sample of studies. Conclusions. When evaluating conventional MARs in which the observed incidences are near the estimated MARs or where preferences demonstrate diminishing marginal disutility of risk, conventional MAR estimates will overstate risk acceptance, which could lead to misinformed decisions, potentially placing patients at greater risk of adverse events than they would accept. Implications. The SMART method, herein extended to include confidence intervals, provides a reproducible, transparent evidence-based approach to enable decision makers to use data from discrete-choice experiments to account for multiple adverse events. Highlights: Estimates of maximum acceptable risk (MAR) for a defined treatment benefit can be useful to inform regulatory decisions; however, the conventional metric considers one adverse event at a time.This article applies a new approach known as SMART (simultaneous maximum acceptable risk thresholds) that accounts for multiple adverse events to 3 published discrete-choice experiments.Findings reveal that conventional MARs could lead decision makers to accept a treatment based on individual risks that would not be acceptable if multiple risks are considered simultaneously.
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PURPOSE: Extracurricular activities (EAs) listed on radiology residency applications can signal traits and characteristics desired in holistic reviews. The authors conducted an objective analysis to determine the influence of EAs on resident selection decisions. METHODS: A discrete-choice experiment was designed to model radiology resident selection and determine the relative weights of EAs among academic and demographic application factors. Faculty members involved in resident selection at 30 US radiology programs chose between hypothetical pairs of applicant profiles between October 2021 and February 2022. Each applicant profile included one of 22 EAs chosen for study. A conditional logistic regression model assessed the relative weights of the attributes and odds ratios (ORs) were calculated. RESULTS: Two hundred forty-four participants completed the exercise. Community-service EAs were ranked most highly by participants. LGBTQ Pride Alliance (OR, 1.56; 95% confidence interval [CI], 1.14-2.15; P = .006) and Young Republicans (OR, 0.60; 95% CI, 0.43-0.82; P = .001) significantly influenced decisions. The highest ranked EAs were significantly preferred over the lowest ranked EAs (OR, 1.916; 95% CI, 1.671-2.197; P < .001). Participants preferred EAs that reflected active over passive engagement (OR, 1.154; 95% CI, 1.022-1.304; P = .021) and progressive over conservative ideology (OR, 1.280; 95% CI, 1.133-1.447; P < .001). Participants who ranked progressive EAs more highly preferred applicants with progressive EAs (P < .05 for all). CONCLUSIONS: The influence of EAs on resident selection decisions is significant and likely to gain importance in resident selection as medical student performance metrics are further eliminated. Applicants and selection committees should consider this influence and the bias that EAs can bring to resident selection decisions.
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OBJECTIVES: Reporting of United States Medical Licensing Examination Step 1 results will transition from a numerical score to a pass or fail result. We sought an objective analysis to determine changes in the relative importance of resident application attributes when numerical Step 1 results are replaced. METHODS: A discrete choice experiment was designed to model radiology resident selection and determine the relative weights of various application factors when paired with a numerical or pass or fail Step 1 result. Faculty involved in resident selection at 14 US radiology programs chose between hypothetical pairs of applicant profiles between August and November 2020. A conditional logistic regression model assessed the relative weights of the attributes, and odds ratios (ORs) were calculated. RESULTS: There were 212 participants. When a numerical Step 1 score was provided, the most influential attributes were medical school (OR: 2.35, 95% confidence interval [CI]: 2.07-2.67), Black or Hispanic race or ethnicity (OR: 2.04, 95% CI: 1.79-2.38), and Step 1 score (OR: 1.8, 95% CI: 1.69-1.95). When Step 1 was reported as pass, the applicant's medical school grew in influence (OR: 2.78, 95% CI: 2.42-3.18), and there was a significant increase in influence of Step 2 scores (OR: 1.31, 95% CI: 1.23-1.40 versus OR 1.57, 95% CI: 1.46-1.69). There was little change in the relative influence of race or ethnicity, gender, class rank, or clerkship honors. DISCUSSION: When Step 1 reporting transitions to pass or fail, medical school prestige gains outsized influence and Step 2 scores partly fill the gap left by Step 1 examination as a single metric of decisive importance in application decisions.