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Quality measurement plays an increasing role in U.S. health care. Measures inform quality improvement efforts, public reporting of variations in quality of care across providers and hospitals, and high‐stakes financial decisions. To be meaningful in these contexts, measures should be reliable and not heavily impacted by chance variations in sampling or measurement. Several different methods are used in practice by measure developers and endorsers to evaluate reliability; however, there is uncertainty and debate over differences between these methods and their interpretations. We review methods currently used in practice, pointing out differences that can lead to disparate reliability estimates. We compare estimates from 14 different methods in the case of two sets of mental health quality measures within a large health system. We find that estimates can differ substantially and that these discrepancies widen when sample size is reduced. [ABSTRACT FROM AUTHOR]

Objective: To illustrate the importance of a multidimensional view of disparities in quality of antidepressant medication management (AMM), as well as discriminating "within‐facility" disparities from disparities that exist between facilities. Data Sources and Study Setting: We used data from the Veterans Health Administration's (VA) Corporate Data Warehouse (CDW) which contains clinical and administrative data from VA facilities nationally. Study Design: CDW data were used to measure five indicators of AMM quality, including the HEDIS Effective Acute‐Phase and Effective Continuation‐Phase measures. Mixed effects regression models were used to examine differences in quality indicators between racial/ethnic groups, controlling for other demographic and clinical factors. An adaptation of the Kitagawa‐Blinder‐Oaxaca (KBO) method was used to decompose mean differences in treatment quality between racial and ethnic groups into within‐ and between‐facility effects. Data Extraction Methods: Demographic, clinical, and health service utilization data were extracted for patients in fiscal year 2017 with a diagnosis of depression and a new start of an antidepressant medication. Principal Findings: The decomposition of the overall differences between White and Black patients on receiving an initial 90‐day prescription (46.7% vs. 32.7%), Effective Acute‐Phase (79.7% vs. 66.8%), and Effective Continuation‐Phase (64.0% vs. 49.6%) HEDIS measures revealed that most of the overall effects were "within‐facility," meaning that Black patients are less likely to meet these measures regardless of where they are treated. Although the overall magnitude of disparities between White and Hispanic patients on these three measures was very similar (46.7% vs. 32.7%; 79.7% vs. 69.2%; 64.0% vs. 53.6%), the differences were more attributable to Hispanic patients being treated in facilities with overall lower performance on these measures. Conclusions: Discriminating within‐ and between‐facility disparities and taking a multidimensional view of quality are essential to informing efforts to address disparities in AMM quality. [ABSTRACT FROM AUTHOR]

Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability that reflects how likely their data were to have been generated from the assumed model. The individual-level probabilities are simultaneously estimated and used to weight each individual's contribution in parameter estimation. We examine the utility of our approach for Gaussian mixture models and linear factor models through several simulation studies, drawing contrasts with the EM algorithm. We demonstrate that our method yields inferences more robust to population heterogeneity or other model misspecifications than EM does. We hope that the proposed approach can be incorporated into the model-building process to improve population-level estimates and to shed light on subsets of the population that demand further attention. [ABSTRACT FROM AUTHOR]

IR-806 is a near-infrared cyaninedye that undergoes a two-stepassembly process in aqueous solutions. The final assemblies orientationallyorder into a liquid crystal at a very low concentration (∼0.6wt % at room temperature). While the first step of the assembly processis continuous as the dye concentration or temperature is varied (isodesmic),the second step is more abrupt (cooperative). Because the absorptionspectrum of IR-806 changes dramatically during the assembly process,careful equilibrium and kinetic absorption experiments are utilizedto examine the details of the cooperative second step. These experimentsinvolve changes in both concentration and temperature, allowing aclose thermodynamic analysis of the assembly process. Both equilibriumand kinetic investigations reveal that the assembly process is highlycooperative and can be described by multiple models (for example,nucleation and growth) in the highly cooperative limit. The enthalpyassociated with the growth process and the activation energy of therate-limiting step during disassembly are determined. These findingshave significant implications for the structure of the assembliesthat form the liquid crystal phase in IR-806. [ABSTRACT FROM AUTHOR]

Objective: We characterized the quality of statistical methods for studies of racial and ethnic disparities in the surgical-relevant literature during 2021-2022., Background: Hundreds of scientific papers are published each year describing racial and ethnic disparities in surgical access, quality, and outcomes. The content and design quality of this literature has never been systematically reviewed., Methods: We searched for 2021-2022 studies focused on describing racial and/or ethnic disparities in surgical or perioperative access, process quality, or outcomes. Identified studies were characterized in terms of three methodological criteria: 1) adjustment for variables related to both race/ethnicity and outcomes, including social determinants of health (SDOH); 2) accounting for clustering of patients within hospitals or other subunits ("providers") and; 3) distinguishing within- and between-provider effects., Results: We identified 224 papers describing racial and/or ethnic differences. Of the 38 single institution studies, 24 (63.2%) adjusted for at least one SDOH variable. Of the 186 multisite studies, 113 (60.8%) adjusted for at least one SDOH variable, and 43 (23.1%) accounted for clustering of patients within providers using appropriate statistical methods. Only 10 (5.4%) of multi-institution studies made efforts to examine how much of overall disparities were driven by within versus between provider effects., Conclusions: Most recently published papers on racial and ethnic disparities in the surgical literature do not meet these important statistical design criteria and therefore may risk inaccuracy in the estimation of group differences in surgical access, quality, and outcomes. The most potent leverage points for these improvements are changes to journal publication guidelines and policies., Competing Interests: None of the authors have relevant conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)