OBJECTIVES:Quality-adjusted life years (QALYs) have been widely used in cost-effectiveness analyses (CEAs). Nonetheless, critics argue that this measure may inherently discriminate against people with disabilities. This study aims to investigate whether the use of QALYs disadvantages disabled populations through combining theoretical analysis with examination of empirical evidence. METHODS:For the theoretical analysis, we constructed 5 scenarios to compare incremental QALYs between disabled and nondisabled patients reflecting different treatment effects on health-related quality of life or survival. We then searched the Tufts CEA Registry to identify published CEAs for selected chronic diseases including rheumatoid arthritis, type 2 diabetes, and chronic kidney disease to match published evidence with these scenarios. RESULTS:Across 5 scenarios, the differences in incremental QALYs between disabled and nondisabled groups were driven by relative changes in utility and life-years, rather than indicating a systematic bias. In 9 of 11 published CEAs, the disabled subgroup achieved greater incremental QALY, lower (more favorable) incremental cost-effectiveness ratios, or dominant results. CONCLUSIONS:These findings suggest that, within the theoretical scenarios and empirical contexts examined, QALY-based CEAs do not appear to inherently disadvantage patients with disabilities.

Importance:Hispanic and non-Hispanic Black patients with ST-segment elevation myocardial infarction (STEMI) are less likely than White non-Hispanic patients to receive guideline-recommended percutaneous coronary intervention (PCI). Research suggests disparities arise before and during STEMI treatment, but it is unclear when the largest disparities in PCI emerge. Objective:To assess when in the care process the largest disparities in PCI receipt occur in patients with STEMI presenting to an emergency department. Design, Setting, and Participants:This cross-sectional study evaluated adult patients with STEMI presenting to Florida hospitals from January 1, 2011, to December 31, 2021. Data were analyzed from June 29, 2023, to May 29, 2025. Exposure:Patient race and ethnicity. Main Outcomes and Measures:The main outcomes were presentation to PCI-capable hospitals, receipt of PCI if initially presenting to PCI-capable hospitals, transfer if initially presenting to non-PCI capable hospitals, and receipt of PCI at receiving hospital if transferred. Logistic regression was used to compare outcomes for patients with STEMI by race and ethnicity, controlling for payer, age, sex, weekend presentation, time of presentation, comorbidities, and hospital characteristics. Results:Among 139 629 patients with STEMI included in the analysis, 68.81% were male. Mean (SD) age was 64.4 (13.0) years. A total of 9.09% identified as Black, 15.17% as Hispanic, 70.56% as White, and 5.17% as other or missing race. In adjusted analyses, Black (-1.8 [95% CI, -2.6 to 1.1] percentage points [pp]) and Hispanic (-3.1 [95% CI, -3.7 to -2.4] pp) patients were less likely than White patients to present to PCI-capable hospitals (P < .001 for both). Among patients initially presenting to PCI-capable hospitals, Black patients were less likely to receive PCI than White patients (-8.6 [95% CI, -9.5 to -7.7] pp; P < .001). Among patients initially presenting to non-PCI-capable hospitals, Black (-4.0 [95% CI, -6.4 to -1.5] pp; P = .001) and Hispanic (-4.2 [95% CI, -6.3 to -2.0] pp; P < .001) patients were less likely to be transferred than White patients. Among transferred patients, Black patients were less likely to undergo PCI at the receiving hospital than White patients (-13.3 [95% CI, -16.6 to -9.9] pp; P < .001). Conclusions and Relevance:In this cross-sectional study examining racial and ethnic disparities in receipt of PCI for patients with STEMI, racial and ethnic disparities persisted throughout the care process. The largest magnitude of disparity was PCI receipt if transferred, but the disparity with the largest impact was PCI receipt when initially presenting to PCI-capable hospitals.

To build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science.

Interventions (including behavioral, biobehavioral, biomedical, and social-structural interventions) hold tremendous potential not only to improve public health overall but also to reduce health disparities and promote health equity. In this study, we introduce one way in which interventions can be optimized for health equity in a principled fashion using the multiphase optimization strategy (MOST). Specifically, we define intervention equitability as the extent to which the health benefits provided by an intervention are distributed evenly versus concentrated among those who are already advantaged, and we suggest that, if intervention equitability is acknowledged to be a priority, then equitability should be a key criterion that is balanced with other criteria (effectiveness overall, as well as affordability, scalability, and/or efficiency) in intervention optimization. Using a hypothetical case study and simulated data, we show how MOST can be applied to achieve a strategic balance that incorporates equitability. We also show how the composition of an optimized intervention can differ when equitability is considered versus when it is not. We conclude with a vision for next steps to build on this initial foray into optimizing interventions for equitability.

ObjectiveTo examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences.Data Sources and Study SettingED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018).Study DesignLogistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means.Data Collection/Extraction MethodsWe examined 1,265,588 adult ED patients transferred from 187 hospitals.Principal FindingsBlack patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer.ConclusionsLarge population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

Background: The autistic population is rapidly increasing; meanwhile, autistic adults face disproportionate risks for adverseCOVID-19 outcomes. Limited research indicates that autistic individuals have been accepting of initial vaccination, but researchhas yet to document this population's perceptions and acceptance of COVID-19 boosters. Objective: This study aims to identify person-level and community characteristics associated with COVID-19 vaccination andbooster acceptance among autistic adults, along with self-reported reasons for their stated preferences. Understanding thisinformation is crucial in supporting this vulnerable population given evolving booster guidelines and the ending of the publichealth emergency for the COVID-19 pandemic. Methods: Data are from a survey conducted in Pennsylvania from April 11 to September 12, 2022. Demographic characteristics,COVID-19 experiences, and COVID-19 vaccine decisions were compared across vaccination status groups. Chi-square analysesand 1-way ANOVA were conducted to test for significant differences. Vaccination reasons were ranked by frequency; co-occurrencewas identified using phi coefficient correlation plots. Results: Most autistic adults (193/266, 72.6%) intended to receive or received the vaccine and booster, 15% (40/266) did notreceive or intend to receive any vaccine, and 12.4% (33/266) received or intended to receive the initial dose but were hesitant toaccept booster doses. Reasons for vaccine acceptance or hesitancy varied by demographic factors and COVID-19 experiences.The most significant were previously contracting COVID-19, desire to access information about COVID-19, and discomfort withothers not wearing a mask (all P=.001). County-level factors, including population density (P=.02) and percentage of the countythat voted for President Biden (P=.001) were also significantly associated with differing vaccination acceptance levels. Reasonsfor accepting the initial COVID-19 vaccine differed among those who were or were not hesitant to accept a booster. Those whoaccepted a booster were more likely to endorse protecting others and trusting the vaccine as the basis for their acceptance, whereasthose who were hesitant about the booster indicated that their initial vaccine acceptance came from encouragement from someonethey trusted. Among the minority of those hesitant to any vaccination, believing that the vaccine was unsafe and would makethem feel unwell were the most often reported reasons Conclusions: Intention to receive or receiving the COVID-19 vaccination and booster was higher among autistic adults thanthe population that received vaccines in Pennsylvania. Autistic individuals who accepted vaccines prioritized protecting others,while autistic individuals who were vaccine hesitant had safety concerns about vaccines. These findings inform public healthopportunities and strategies to further increase vaccination and booster rates among generally accepting autistic adults, to bettersupport the already strained autism services and support system landscape. Vaccination uptake could be improved by leveragingpassive information diffusion to combat vaccination misinformation among those not actively seeking COVID-19 informationto better alleviate safety concerns

Background The autistic population is rapidly increasing; meanwhile, autistic adults face disproportionate risks for adverse COVID-19 outcomes. Limited research indicates that autistic individuals have been accepting of initial vaccination, but research has yet to document this population’s perceptions and acceptance of COVID-19 boosters. Objective This study aims to identify person-level and community characteristics associated with COVID-19 vaccination and booster acceptance among autistic adults, along with self-reported reasons for their stated preferences. Understanding this information is crucial in supporting this vulnerable population given evolving booster guidelines and the ending of the public health emergency for the COVID-19 pandemic. Methods Data are from a survey conducted in Pennsylvania from April 11 to September 12, 2022. Demographic characteristics, COVID-19 experiences, and COVID-19 vaccine decisions were compared across vaccination status groups. Chi-square analyses and 1-way ANOVA were conducted to test for significant differences. Vaccination reasons were ranked by frequency; co-occurrence was identified using phi coefficient correlation plots. Results Most autistic adults (193/266, 72.6%) intended to receive or received the vaccine and booster, 15% (40/266) did not receive or intend to receive any vaccine, and 12.4% (33/266) received or intended to receive the initial dose but were hesitant to accept booster doses. Reasons for vaccine acceptance or hesitancy varied by demographic factors and COVID-19 experiences. The most significant were previously contracting COVID-19, desire to access information about COVID-19, and discomfort with others not wearing a mask (all P=.001). County-level factors, including population density (P=.02) and percentage of the county that voted for President Biden (P=.001) were also significantly associated with differing vaccination acceptance levels. Reasons for accepting the initial COVID-19 vaccine differed among those who were or were not hesitant to accept a booster. Those who accepted a booster were more likely to endorse protecting others and trusting the vaccine as the basis for their acceptance, whereas those who were hesitant about the booster indicated that their initial vaccine acceptance came from encouragement from someone they trusted. Among the minority of those hesitant to any vaccination, believing that the vaccine was unsafe and would make them feel unwell were the most often reported reasons. Conclusions Intention to receive or receiving the COVID-19 vaccination and booster was higher among autistic adults than the population that received vaccines in Pennsylvania. Autistic individuals who accepted vaccines prioritized protecting others, while autistic individuals who were vaccine hesitant had safety concerns about vaccines. These findings inform public health opportunities and strategies to further increase vaccination and booster rates among generally accepting autistic adults, to better support the already strained autism services and support system landscape. Vaccination uptake could be improved by leveraging passive information diffusion to combat vaccination misinformation among those not actively seeking COVID-19 information to better alleviate safety concerns.

In the United States, patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL) who have failed at least two lines of systemic therapy may be eligible for Chimeric Antigen Receptor T-cell (CAR-T) therapy. Investigating lines of therapy to evaluate eligibility for CAR-T can be difficult for researchers using real-world data, including electronic health records and claims data. This is because the heterogeneity of treatment regimens, many of which do not precisely align with treatment guidelines, make identifying switches in lines of therapy challenging. We explore whether unsupervised machine learning may be useful for identifying switches in treatment lines for DLBCL patients.

BackgroundRisk-stratified follow-up guidelines that account for the absolute risk and timing of recurrence may improve the quality and efficiency of breast cancer follow-up. The objective of this study was to assess the relationship of anatomic stage and receptor status with timing of the first recurrence for patients with local-regional breast cancer and generate risk-stratified follow-up recommendations. MethodsThe authors conducted a secondary analysis of 8007 patients with stage I-III breast cancer who enrolled in nine Alliance legacy clinical trials from 1997 to 2013 (ClinicalTrials.gov identifier NCT02171078). Patients who received standard-of-care therapy were included. Patients who were missing stage or receptor status were excluded. The primary outcome was days from the earliest treatment start date to the date of first recurrence. The primary explanatory variable was anatomic stage. The analysis was stratified by receptor type. Cox proportional-hazards regression models produced cumulative probabilities of recurrence. A dynamic programming algorithm approach was used to optimize the timing of follow-up intervals based on the timing of recurrence events. ResultsThe time to first recurrence varied significantly between receptor types (p < .0001). Within each receptor type, stage influenced the time to recurrence (p < .0001). The risk of recurrence was highest and occurred earliest for estrogen receptor (ER)-negative/progesterone receptor (PR)-negative/Her2neu-negative tumors (stage III; 5-year probability of recurrence, 45.5%). The risk of recurrence was lower for ER-positive/PR-positive/Her2neu-positive tumors (stage III; 5-year probability of recurrence, 15.3%), with recurrences distributed over time. Model-generated follow-up recommendations by stage and receptor type were created. ConclusionsThis study supports considering both anatomic stage and receptor status in follow-up recommendations. The implementation of risk-stratified guidelines based on these data has the potential to improve the quality and efficiency of follow-up.

Background: Comparisons of lobectomy versus total thyroidectomy for papillary thyroid cancer have not addressed significant threats to valid inference from observational data. The purpose of this study was to compare survival after lobectomy versus total thyroidectomy for papillary thyroid cancer while addressing bias from unmeasured confounding. Methods: This retrospective cohort study included 84,300 patients treated with lobectomy or total thyroidectomy for papillary thyroid cancer in the National Cancer Database from 2004 to 2017. The primary outcome was overall survival evaluated by flexible parametric survival models and inverse probability weighting on the propensity score. Bias from unobserved confounding was assessed using two-way deterministic sensitivity analysis and 2-stage least squares regression. Results: The median age of treated patients was 48 years (interquartile range, 37-59), 78% were women, and 76% were white. We found no statistically significant differences in overall survival or 5-and 10-year survival between patients treated with lobectomy or total thyroidectomy. Additionally, we found no statistically significant difference in survival by subgroups, including tumor size (<4 cm or & GE;4 cm), age (<65 or & GE;65), or estimated risk of mortality. Sensitivity analyses suggested that an unmeasured confounder would need to have an extremely large effect to change the primary finding. Conclusion: This is the first study to compare lobectomy and total thyroidectomy outcomes while adjusting for and quantifying the potential effects of unmeasured confounding variables on observational data. The findings suggest that total thyroidectomy is unlikely to offer a survival advantage over lo-bectomy regardless of tumor size, patient age, or overall risk of death. Published by Elsevier Inc.

OBJECTIVE:Optimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial. METHOD:We define various sets of hypothetical decision-maker preferences, and we apply DAIVE to identify optimized interventions appropriate to each case. RESULTS:We demonstrate how DAIVE can be used to make decisions about the composition of optimized interventions and how the choice of optimized intervention can differ according to decision-maker preferences and objectives. CONCLUSIONS:We offer recommendations for intervention scientists who want to apply DAIVE to select optimized interventions based on data from their own factorial optimization trials. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Objective: We sought to evaluate local/regional recurrence rates after breast-conserving surgery in a cohort of patients enrolled in legacy trials of the Alliance for Clinical Trials in Oncology and to evaluate variation in recurrence rates by receptor subtype. Background: Multiple randomized controlled trials have demonstrated equivalent survival between breast conservation and mastectomy, albeit with higher local/regional recurrence rates after breast conservation. However, absolute rates of local/regional recurrence have been declining with multi-modality treatment. Methods: Data from 5 Alliance for Clinical Trials in Oncology legacy trials that enrolled women diagnosed with breast cancer between 1997 and 2010 were included. Women who underwent breast-conserving surgery and standard systemic therapies (n=4,404) were included. Five-year rates of local/regional recurrence were estimated from Kaplan-Meier curves. Patients were censored at the time of distant recurrence (if recorded as the first recurrence), death, or last follow-up. Multivariable Cox proportional hazards models were used to identify factors associated with time to local/regional recurrence, including patient age, tumor size, lymph node status, and receptor subtype. Results: Overall 5-year recurrence was 4.6% (95% CI=4.0-5.4%). Five-year recurrence rates were lowest in those with ER+ or PR+ tumors (Her2+ 3.4% [95% CI 2.0-5.7%], Her2- 4.0% [95% CI 3.2-4.9%]) and highest in the triple-negative subtype (7.1% [95% CI 5.4-9.3%]). On multivariable analysis, increasing nodal involvement and triple-negative subtype were positively associated with recurrence ( P <0.0001). Conclusions: Rates of local/regional recurrence after breast conservation in women with breast cancer enrolled in legacy trials of the Alliance for Clinical Trials in Oncology are significantly lower than historic estimates. This data can better inform patient discussions and surgical decision-making.

Background: The COVID-19 pandemic has resulted in a continuum of changes in communities that have impacted the lives and health of millions of autistic people. Method: To identify community participation changes during COVID-19, we conducted a twotimepoint (2018 and 2022) longitudinal quantitative study involving 116 autistic adults in Pennsylvania to investigate the impact of the pandemic on their community participation. Community participation was measured by the Temple University Community Participation Measure, and the impact of the pandemic was measured by a series of factors related to the COVID-19 pandemic (e.g., healthcare access, transportation, safety, etc.). Results: Results of paired sample t-tests did not show changes in participants' total days of participation over the last 30 days, the total number of activities, or percentages of all activities participants considered important and participated in (i.e., breadth ratio) between the timepoints. However, the percentage of activities that were important to participants and in which they reported engaging as much as they wanted to (i.e., sufficiency ratio) reduced significantly. When examining participation outcomes and COVID-19 impact, we found that multiple participation outcomes (i.e., number of activities, breadth ratio, and sufficiency ratio) were negatively associated with the COVID-19 impact. Conclusion: Results suggest that the COVID-19 impacts on autistic adults are variable, with those reporting a more significant impact also reporting a significantly lower level of participation. These findings emphasize the importance of individualized planning to support autistic adults to maintain or regain participation in their preferred activities during the pandemic and beyond.

Policy Points Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities. No conceptual framework currently exists that offers a patient-centered, population-based perspective for the structure of hospital transfer networks. The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism. CONTEXT Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we propose a conceptual framework, the hospital transfer network equity-quality model (NET-EQUITY). NET-EQUITY can help optimize population outcomes, decrease disparities, and enhance planning by supporting a framework for understanding emergency department transfers. METHODS To develop the NET-EQUITY framework, we synthesized work on health systems and quality of health care (Donabedian, the Institute of Medicine, Ferlie, and Shortell) and the research framework of the National Institute on Minority Health and Health Disparities with legal and empirical research. FINDINGS The central thesis of our framework is that the structure of hospital transfer networks influences patient outcomes, as defined by the Institute of Medicine, which includes equity. The structure of hospital transfer networks is shaped by internal and external factors. The four main external factors are the regulatory, economic environment, provider, and sociocultural and physical/built environment. These environments all implicate issues of equity that are important to understand to foster an equitable population-based system of emergency care. The framework highlights external and internal factors that determine the structure of hospital transfer networks, including structural racism and inequity. CONCLUSIONS The NET-EQUITY framework provides a patient-centered, equity-focused framework for understanding the health of populations and how the structure of hospital transfer networks can influence the quality of care that patients receive.