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Suicide is a leading cause of death for adolescents and young adults, or transitional age youth, and suicidal thoughts and behaviors (STBs) are among the top predictors of suicide. This study aimed to compare the effectiveness of low versus high frequency mental health (MH) specialist visits at preventing suicidal thoughts and behaviors (STBs) among adolescents and transitional age youth receiving combined psychotherapy and psychotropic medication treatment in a culturally-diverse community health system in the United States. Between 2010-2020, we identified 4,888 individuals (12-25 years) with MH disorders who received at least one outpatient psychotherapy visit and one psychotropic medication order during their index treatment month. In the 10-12 months following the index month, unadjusted STBs were higher (3.20 %; 95 % CI 0.49 %, 5.89 %; p < 0.001) in youth with consistently high frequency MH specialist visits (4.40 %) relative to low (1.20 %). After balancing time-varying covariates, STBs were lower, but not statistically different in the consistently high relative to low group (Diff=-1.93 %; 95 % CI -4.56 %, 0.70 %; p = 0.150). Such adjustment for confounding by mental health severity indicators may strengthen evaluations of community-based treatment in diverse populations, where conventional randomization is impossible., Competing Interests: Declaration of competing interest has provided paid statistical consultancy for the University of California, Los Angeles. 〈one anonymized author〉 has received funding from NIA T32AG51108 and the National Institute of Mental Health (Grant No. P50 MH126283, T32 MH019733) since the beginning of this work. In the past 36 months, 〈one anonymized author〉 has received funding from the Manton foundation., (Copyright © 2024. Published by Elsevier B.V.)

During the initial year of the COVID-19 pandemic, a disproportionate share of COVID-19-related deaths occurred among nursing home residents. Initial estimates of all-cause mortality rates also spiked in early and late 2020 before falling to near or below historical rates by early 2021. During the first 3 years of the pandemic, the US nursing home resident population also decreased by 18% (239 000 fewer residents) compared with pre-pandemic levels. After accounting for these population changes, the all-cause nursing home mortality rate has remained above pre-pandemic levels through the middle of 2023. The peak was in December 2020 at 5692 deaths per 100 000 residents, which was 19% higher than estimates not accounting for the population decrease., Competing Interests: Conflicts of interest Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials., (© The Author(s) 2024. Published by Oxford University Press on behalf of Project HOPE - The People-To-People Health Foundation, Inc.)

Objective: The authors sought to update and expand the evidence on the quality of health care and disparities in care among Medicaid beneficiaries with schizophrenia., Methods: Adult beneficiaries of New York State Medicaid with schizophrenia receiving care during 2016-2019 were identified. Composite quality scores were derived from item response theory models by using evidence-based indicators of the quality of mental and general medical health care. Risk-adjusted racial-ethnic differences in quality were estimated and summarized as percentiles relative to White beneficiaries' mean quality scores., Results: The study included 71,013 beneficiaries; 42.8% were Black, 22.9% Latinx, 27.4% White, and 6.9% other race-ethnicity. Overall, 68.8% had a mental health follow-up within 30 days of discharge, and 90.2% had no preventable hospitalizations for chronic obstructive pulmonary disease or asthma. Among beneficiaries receiving antipsychotic medications, medication adherence was adequate for 43.7%. Fourteen indicators for mental and general medical health care quality yielded three composites: two for mental health care (pharmacological and ambulatory) and one for acute mental and general medical health care. Mean quality of pharmacological mental health care for Black and Latinx beneficiaries was lower than for White beneficiaries (39th and 44th percentile, respectively). For Black beneficiaries, mean quality of ambulatory mental health care was also lower (46th percentile). In New York City, Black beneficiaries received lower-quality care in all domains. The only meaningful group difference in the quality of acute mental and general medical health care indicated higher-quality care for individuals with other race-ethnicity., Conclusions: Disparities in the quality of Medicaid-financed health care persist, particularly for Black beneficiaries. Regional differences merit further attention., Competing Interests: The authors report no financial relationships with commercial interests.

This study aims to examine 20-year temporal trends in all-cause mortality among psychiatric patients and investigating impacts of risk factors on the time trends based on 218,703 Finnish adults with mental disorders who were discharged from 87 psychiatric hospitals between 1 Jan 1995 and 31 Dec 2014. The age-period-cohort analysis of Poisson model with random hospital effects estimated temporal trends in death rate and associated factors at individual, healthcare system, and society levels, following the WHO multilevel intervention framework model for six major psychiatric diagnosis. The adjusted annual mortality declined by 2.2 % annually (RR: 0.978 [95 % CI 0.976-0.980]) for all individuals, and by 2.8 % after adjusting for all risk factors, with varied decreasing rate between 2.0 % and 3.6 % by diagnosis. Individual level factors accounted for the declining rate by 54.5 % for all patients, with the highest impact on patients with personality disorders, followed by patients with affective disorders and patients with schizophrenia. Identified declining trends and associated factors which are preventable and modifiable for individuals with specific psychiatric diagnosis may lead to develop targeted service and intervention strategies in bringing down mortality further for the population., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier B.V.)

Background: Exposure to second-generation antipsychotics (SGAs) carries a risk of type 2 diabetes, but questions remain about the diabetogenic effects of SGAs., Aims: To assess the diabetes risk associated with two frequently used SGAs., Method: This was a retrospective cohort study of adults with schizophrenia, bipolar I disorder or severe major depressive disorder (MDD) exposed during 2008-2013 to continuous monotherapy with aripiprazole or olanzapine for up to 24 months, with no pre-period exposure to other antipsychotics. Newly diagnosed type 2 diabetes was quantified with targeted minimum loss-based estimation; risk was summarised as the restricted mean survival time (RMST), the average number of diabetes-free months. Sensitivity analyses were used to evaluate potential confounding by indication., Results: Aripiprazole-treated patients had fewer diabetes-free months compared with olanzapine-treated patients. RMSTs were longer in olanzapine-treated patients, by 0.25 months [95% CI: 0.14, 0.36], 0.16 months [0.02, 0.31] and 0.22 months [0.01, 0.44] among patients with schizophrenia, bipolar I disorder and severe MDD, respectively. Although some sensitivity analyses suggest a risk of unobserved confounding, E-values indicate that this risk is not severe., Conclusions: Using robust methods and accounting for exposure duration effects, we found a slightly higher risk of type 2 diabetes associated with aripiprazole compared with olanzapine monotherapy regardless of diagnosis. If this result was subject to unmeasured selection despite our methods, it would suggest clinician success in identifying olanzapine candidates with low diabetes risk. Confirmatory research is needed, but this insight suggests a potentially larger role for olanzapine in the treatment of well-selected patients, particularly for those with schizophrenia, given the drug's effectiveness advantage among them.

Objective: To evaluate the effectiveness of a clinical decision support system (CDSS) in improving the use of guideline accordant antihypertensive treatment in primary care settings in China., Design: Pragmatic, open label, cluster randomised trial., Setting: 94 primary care practices in four urban regions of China between August 2019 and July 2022: Luoyang (central China), Jining (east China), and Shenzhen (south China, including two regions)., Participants: 94 practices were randomised (46 to CDSS, 48 to usual care). 12 137 participants with hypertension who used up to two classes of antihypertensives and had a systolic blood pressure <180 mm Hg and diastolic blood pressure <110 mm Hg were included., Interventions: Primary care practices were randomised to use an electronic health record based CDSS, which recommended a specific guideline accordant regimen for initiation, titration, or switching of antihypertensive (the intervention), or to use the same electronic health record without CDSS and provide treatment as usual (control)., Main Outcome Measures: The primary outcome was the proportion of hypertension related visits during which an appropriate (guideline accordant) treatment was provided. Secondary outcomes were the average reduction in systolic blood pressure and proportion of participants with controlled blood pressure (<140/90 mm Hg) at the last scheduled follow-up. Safety outcomes were patient reported antihypertensive treatment related events, including syncope, injurious fall, symptomatic hypotension or systolic blood pressure <90 mm Hg, and bradycardia., Results: 5755 participants with 23 113 visits in the intervention group and 6382 participants with 27 868 visits in the control group were included. Mean age was 61 (standard deviation 13) years and 42.5% were women. During a median 11.6 months of follow-up, the proportion of visits at which appropriate treatment was given was higher in the intervention group than in the control group (77.8% (17 975/23 113) v 62.2% (17 328/27 868); absolute difference 15.2 percentage points (95% confidence interval (CI) 10.7 to 19.8); P<0.001; odds ratio 2.17 (95% CI 1.75 to 2.69); P<0.001). Compared with participants in the control group, those in the intervention group had a 1.6 mm Hg (95% CI -2.7 to -0.5) greater reduction in systolic blood pressure (-1.5 mm Hg v 0.3 mm Hg; P=0.006) and a 4.4 percentage point (95% CI -0.7 to 9.5) improvement in blood pressure control rate (69.0% (3415/4952) v 64.6% (3778/5845); P=0.07). Patient reported antihypertensive treatment related adverse effects were rare in both groups., Conclusions: Use of a CDSS in primary care in China improved the provision of guideline accordant antihypertensive treatment and led to a modest reduction in blood pressure. The CDSS offers a promising approach to delivering better care for hypertension, both safely and efficiently., Trial Registration: ClinicalTrials.gov NCT03636334., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: support from the Chinese Academy of Medical Sciences and the Sanming Project of Medicine in Shenzhen; in the past three years, HMK received options for Element Science and Identifeye and payments from F-Prime for advisory roles, and he is a cofounder of, and holds equity in, Hugo Health, Refactor Health, and Ensight-AI, and he is associated with research contracts through Yale University from Janssen, Kenvue, and Pfizer; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing individual-level data, coupled with a lack of uncertainty quantification from machine learning predictions, make it challenging to achieve valid inferences in multi-source environments. In this paper, we consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations, and show that one can incorporate machine learning prediction algorithms in the estimation of nuisance functions while still achieving parametric rates of convergence to nominal coverage probabilities. Moreover, when conditional outcome invariance is violated, we propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction. We highlight the robustness and efficiency of our proposals for a variety of conformal scores and data-generating mechanisms via extensive synthetic experiments. Hospital length of stay prediction intervals for pediatric patients undergoing a high-risk cardiac surgical procedure between 2016-2022 in the U.S. illustrate the utility of our methodology.

Objective: The authors sought to examine trends in stimulant initiation and follow-up care for attention-deficit hyperactivity disorder (ADHD) via telemedicine., Methods: This retrospective longitudinal study used national, deidentified commercial health insurance outpatient claims among children (ages 2-17 years; N=535,629) and adults (ages 18-64 years; N=2,116,160) from January 2019 through April 2022. Regression analyses were used to examine risk for stimulant initiation, whether initiation occurred via telemedicine or in-person care, and receipt of a follow-up visit., Results: The mean monthly adjusted number of stimulant initiations per 100,000 enrollees was similar for children before and during the COVID-19 pandemic (prepandemic, 57 initiations; during pandemic, 56 initiations) but increased for adults (prepandemic, 27 initiations; during pandemic, 33 initiations). Initiations via telemedicine peaked at 53%-57% in April 2020 and dropped to about 14% among children and 28% among adults in April 2022. Telemedicine initiations were significantly more common among psychiatrists than among other prescribers (OR=3.70, 95% CI=3.38-4.06 [children]; OR=3.02, 95% CI=2.87-3.17 [adults]) and less common for rural residents (OR=0.57, 95% CI=0.40-0.82 [children]; OR=0.75, 95% CI=0.61-0.92 [adults]). Follow-up care was significantly more common among individuals whose care was initiated via telemedicine than among those receiving in-person care (OR=1.09, 95% CI=1.00-1.19 [children]; OR=1.61, 95% CI=1.53-1.69 [adults])., Conclusions: Many stimulant treatments were initiated via telemedicine. Proposed rules to prohibit controlled substance prescribing without an in-person evaluation would require significant changes in current practice, potentially limiting access to stimulant medications for ADHD., Competing Interests: Dr. Mehrotra has received fees related to telemedicine from Black Opal Ventures, Commonwealth of Massachusetts, NORC, Pew Charitable Trust, and Sanofi. The other authors report no financial relationships with commercial interests.

Background: The impact of quality improvement initiatives program (QIP) on coronary artery bypass grafting surgery (CABG) remains scarce, despite improved outcomes in other surgical areas. This study aims to evaluate the impact of a package of QIP on mortality rates among patients undergoing CABG., Materials and Methods: This prospective cohort study utilized data from the multicenter database Registro Paulista de Cirurgia Cardiovascular II (REPLICCAR II), spanning from July 2017 to June 2019. Data from 4018 isolated CABG adult patients were collected and analyzed in three phases: before-implementation, implementation, and after-implementation of the intervention (which comprised QIP training for the hospital team). Propensity Score Matching was used to balance the groups of 2170 patients each for a comparative analysis of the following outcomes: reoperation, deep sternal wound infection/mediastinitis ≤30 days, cerebrovascular accident, acute kidney injury, ventilation time >24 h, length of stay <6 days, length of stay >14 days, morbidity and mortality, and operative mortality. A multiple regression model was constructed to predict mortality outcomes., Results: Following implementation, there was a significant reduction of operative mortality (61.7%, P =0.046), as well as deep sternal wound infection/mediastinitis ( P <0.001), sepsis ( P =0.002), ventilation time in hours ( P <0.001), prolonged ventilation time ( P =0.009), postoperative peak blood glucose ( P <0.001), total length of hospital stay ( P <0.001). Additionally, there was a greater use of arterial grafts, including internal thoracic ( P <0.001) and radial ( P =0.038), along with a higher rate of skeletonized dissection of the internal thoracic artery., Conclusions: QIP was associated with a 61.7% reduction in operative mortality following CABG. Although not all complications exhibited a decline, the reduction in mortality suggests a possible decrease in failure to rescue during the after-implementation period., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39 000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on 3-year diabetes risk or death, we find a safety benefit of moving from a second-generation drug considered among the safest of the second-generation drugs to an infrequently prescribed first-generation drug known for having low cardiometabolic risk., (© 2024 John Wiley & Sons, Ltd.)

Background: Individuals with psychotic symptoms experience substantial morbidity and have shortened life expectancies; early treatment may mitigate the worst effects. Understanding care preceding a first psychotic disorder diagnosis is critical to inform early detection and intervention., Study Design: In this observational cohort study using comprehensive information from the Massachusetts All-Payer Claims Database, we identified the first psychotic disorder diagnosis in 2016, excluding those with historical psychotic disorder diagnoses in the prior 48 months among those continuous enrollment data. We reviewed visits, medications, and hospitalizations 2012-2016. We used logistic regression to examine characteristics associated with pre-diagnosis antipsychotic use., Study Results: There were 2505 individuals aged 15-35 years (146 per 100 000 similarly aged individuals in the database) with a new psychotic disorder diagnosis in 2016. Most (97%) had at least one outpatient visit in the preceding 48 months; 89% had a prior mental health diagnosis unrelated to psychosis (eg, anxiety [60%], depression [60%]). Many received psychotropic medications (77%), including antipsychotic medications (46%), and 68% had a visit for injury or trauma during the preceding 48 months. Characteristics associated with filling an antipsychotic medication before the psychotic disorder diagnosis included male sex and Medicaid insurance at psychosis diagnosis., Conclusions: In this insured population of Massachusetts residents with a new psychotic disorder diagnosis, nearly all had some healthcare utilization, visits for injury or trauma were common, and nearly half filled an antipsychotic medication in the preceding 48 months. These patterns of care could represent either pre-disease signals, delays, or both in receiving a formal diagnosis., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Contemporary data collection strategies, storage capabilities, and modern statistical methodology have made retrospective analyses of observational databases commonplace. Such databases afford opportunities to learn about the effectiveness and risks of interventions or health behaviors that generally cannot be randomized. In this issue of NEJM Evidence , Cho et al. 1 assemble survey data and cohort data from four countries to quantify the association between age-sex-specific smoking cessation and mortality. The authors conclude that smoking cessation at any age is associated with lower excess overall mortality risk and lower death from diseases made more common by smoking. It is difficult to argue with this conclusion - to question the magnitude of the associations is not.

Importance: Individuals of racial and ethnic minority groups may be less likely to use telemedicine in part due to lack of access to technology (ie, digital divide). To date, some studies have found less telemedicine use by individuals of racial and ethnic minority groups compared with White individuals, and others have found the opposite. What explains these different findings is unclear., Objective: To quantify racial and ethnic differences in the receipt of telemedicine and total visits with and without accounting for demographic and clinical characteristics and geography., Design, Setting, and Participants: This cross-sectional study included individuals who were continuously enrolled in traditional Medicare from March 2020 to February 2022 or until death., Exposure: Race and ethnicity, which was categorized as Black non-Hispanic, Hispanic, White non-Hispanic, other (defined as American Indian/Pacific Islander, Alaska Native, and Asian), and unknown/missing., Main Outcomes and Measures: Total telemedicine visits (audio-video or audio); total visits (telemedicine or in-person) per individual during the study period. Multivariable models were used that sequentially adjusted for demographic and clinical characteristics and geographic area to examine their association with differences in telemedicine and total visit utilization by documented race and ethnicity., Results: In this national sample of 14 305 819 individuals, 7.4% reported that they were Black, 5.6% Hispanic, and 4.2% other race. In unadjusted results, compared with White individuals, Black individuals, Hispanic individuals, and individuals of other racial groups had 16.7 (95% CI, 16.1-17.3), 32.9 (95% CI, 32.3-33.6), and 20.9 (95% CI, 20.2-21.7) more telemedicine visits per 100 beneficiaries, respectively. After adjustment for clinical and demographic characteristics and geography, compared with White individuals, Black individuals, Hispanic individuals, and individuals of other racial groups had 7.9 (95% CI, -8.5 to -7.3), 13.2 (95% CI, -13.9 to -12.6), and 9.2 (95% CI, -10.0 to -8.5) fewer telemedicine visits per 100 beneficiaries, respectively. In unadjusted and fully adjusted models, and in 2019 and the second year of the COVID-19 pandemic, Black individuals, Hispanic individuals, and individuals of other racial groups continued to have fewer total visits than White individuals., Conclusions and Relevance: The results of this cross-sectional study of US Medicare enrollees suggest that although nationally, Black individuals, Hispanic individuals, and individuals of other racial groups received more telemedicine visits during the pandemic and disproportionately lived in geographic regions with higher telemedicine use, after controlling for geographic region, Black individuals, Hispanic individuals, and individuals of other racial groups received fewer telemedicine visits than White individuals.

Background: Following the identification of a late mortality signal, the Food and Drug Administration (FDA) convened an advisory panel that concluded that additional clinical study data are needed to comprehensively evaluate the late mortality signal observed with the use of drug-coated balloons (DCB) and drug-eluting stent (DES). The objective of this review is to (1) identify and summarize the existing clinical and cohort studies assessing paclitaxel-coated DCBs and DESs, (2) describe and determine the quality of the available data sources for the evaluation of these devices, and (3) present methodologies that can be leveraged for proper signal discernment within available data sources., Methods: Studies and data sources were identified through comprehensive searches. original research studies, clinical trials, comparative studies, multicenter studies, and observational cohort studies written in the English language and published from January 2007 to November 2021, with a follow-up longer than 36 months, were included in the review. Data quality of available data sources identified was assessed in three groupings. Moreover, accepted data-driven methodologies that may help circumvent the limitations of the extracted studies and data sources were extracted and described., Results: There were 39 studies and data sources identified. This included 19 randomized clinical trials, nine single-arm studies, eight registries, three administrative claims, and electronic health records. Methodologies focusing on the use of existing premarket clinical data, the incorporation of all contributed patient time, the use of aggregated data, approaches for individual-level data, machine learning and artificial intelligence approaches, Bayesian approaches, and the combination of various datasets were summarized., Conclusion: Despite the multitude of available studies over the course of eleven years following the first clinical trial, the FDA-convened advisory panel found them insufficient for comprehensively assessing the late-mortality signal. High-quality data sources with the capabilities of employing advanced statistical methodologies are needed to detect potential safety signals in a timely manner and allow regulatory bodies to act quickly when a safety signal is detected., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Gressler, Avila-Tang, Mao, Avalos-Pacheco, Shaya, Torosyan, Liebeskind, Kinard, Mack, Normand, Ritchey and Marinac-Dabic.)

Background: Prescribing cascades occur when a drug adverse event is misinterpreted as a new medical condition and a second, potentially unnecessary drug, is prescribed to treat the adverse event. The population-level consequences of prescribing cascades remain unknown., Methods: This population-based cohort study used linked health administrative databases in Ontario, Canada. The study included community-dwelling adults, 66 years of age or older with hypertension and no history of heart failure (HF) or diuretic use in the prior year, newly dispensed a calcium channel blocker (CCB). Individuals subsequently dispensed a diuretic within 90 days of incident CCB dispensing were classified as the prescribing cascade group, and compared to those not dispensed a diuretic, classified as the non-prescribing cascade group. Those with and without a prescribing cascade were matched one-to-one on the propensity score and sex. The primary outcome was a serious adverse event (SAE), which was the composite of emergency room visits and hospitalizations in the 90-day follow-up period. We estimated hazard ratios (HRs) with 95% confidence intervals (CI) for SAE using an Andersen-Gill recurrent events regression model., Results: Among 39,347 older adults with hypertension and no history of HF who were newly dispensed a CCB, 1881 (4.8%) had a new diuretic dispensed within 90 days after CCB initiation. Compared to the non-prescribing cascade group, those in the prescribing cascade group had higher rates of SAEs (HR: 1.21, 95% CI: 1.02-1.43)., Conclusions: The CCB-diuretic prescribing cascade was associated with an increased rate of SAEs, suggesting harm beyond prescribing a second drug therapy. Our study raises awareness of the downstream impact of the CCB-diuretic prescribing cascade at a population level and provides an opportunity for clinicians who identify this prescribing cascade to review their patients' medications to determine if they can be optimized., (© 2023 The Authors. Journal of the American Geriatrics Society published by Wiley Periodicals LLC on behalf of The American Geriatrics Society.)

Background: Type 2 myocardial infarction (T2MI) and type 1 myocardial infarction (T1MI) differ with respect to demographics, comorbidities, treatments, and clinical outcomes. Reliable quality and outcomes assessment depends on the ability to distinguish between T1MI and T2MI in administrative claims data. As such, we aimed to develop a classification algorithm to distinguish between T1MI and T2MI that could be applied to claims data., Methods: Using data for beneficiaries in a Medicare accountable care organization contract in a large health care system in New England, we examined the distribution of MI diagnosis codes between 2018 to 2021 and the patterns of care and coding for beneficiaries with a hospital discharge diagnosis International Classification of Diseases , Tenth Revision code for T2MI, compared with those for T1MI. We then assessed the probability that each hospitalization was for a T2MI versus T1MI and examined care occurring in 2017 before the introduction of the T2MI code., Results: After application of inclusion and exclusion criteria, 7759 hospitalizations for myocardial infarction remained (46.5% T1MI and 53.5% T2MI; mean age, 79±10.3 years; 47% female). In the classification algorithm, female gender (odds ratio, 1.26 [95% CI, 1.11-1.44]), Black race relative to White race (odds ratio, 2.48 [95% CI, 1.76-3.48]), and diagnoses of COVID-19 (odds ratio, 1.74 [95% CI, 1.11-2.71]) or hypertensive emergency (odds ratio, 1.46 [95% CI, 1.00-2.14]) were associated with higher odds of the hospitalization being for T2MI versus T1MI. When applied to the testing sample, the C-statistic of the full model was 0.83. Comparison of classified T2MI and observed T2MI suggest the possibility of substantial misclassification both before and after the T2MI code., Conclusions: A simple classification algorithm appears to be able to differentiate between hospitalizations for T1MI and T2MI before and after the T2MI code was introduced. This could facilitate more accurate longitudinal assessments of acute myocardial infarction quality and outcomes., Competing Interests: Disclosures Dr McCarthy reports consulting for Abbott Laboratories and Roche Diagnostic. The other authors report no conflicts.

Importance: Process-based quality measures are generally intended to promote evidence-based practices that have been proven to improve outcomes. However, due to lack of standardized implementation of diagnostic codes in dentistry, assessing the association between process and oral health outcomes has been challenging., Objective: To estimate the association of adhering to dental quality measures with patient oral health outcomes., Design, Setting, and Participants: Using a target trial emulation, a causal inference framework, this retrospective cohort study estimated the difference in the risk of developing tooth decay between US children who adhered to process-based dental quality measures (receiving topical fluoride and sealant [treated groups]) and those who did not (control groups). Electronic health records of US children and adolescents aged 0 to 18 years from January 1, 2014, to December 31, 2020, were used. To emulate random treatment assignment based on baseline confounders, coarsened exact matching was used to produce covariate balance between the treated and control groups. A time-to-event regression model produced effect estimates, adjusting for time-varying covariates. Near-far matching was used to account for unmeasured confounders as a sensitivity analysis. Data were analyzed from May 1 to August 7, 2023., Exposures: Adherence to dental quality measures., Main Outcomes and Measures: Incidence of tooth decay., Results: Among 69 212 US children aged between 0 and 18 years (mean [SD] age, 10.2 [5.0] years; 49.5% male, 50.4% female, and 0.1% unknown or transgender), 1930 (2.8%) were Asian, 2038 (2.9%) were Black, 8667 (12.5%) were Hispanic, 33 632 (48.6%) were White, and 22 945 (33.2%) were multiracial, other, or missing racial and ethnic group identification. Relative to control individuals, treated individuals were more likely to be at elevated risk of caries (fluoride measure: 16 453 [76.5%] vs 15 236 [39.8%]; sealant measure: 2264 [54.6%] vs 997 [44.0%]) and have regular dental visits (fluoride measure: 21 498 [100%] vs 13 741 [35.9%]; sealant measure: 1623 [39.2%] vs 871 [38.4%]). Adherence to quality measures was associated with reduced risk of tooth decay with adjusted hazard ratios of 0.82 (95% CI, 0.78- 0.86) for fluoride and 0.86 (95% CI, 0.76-0.97) for sealant in the matched cohort. Benefits of adhering to quality measures were greater among children at elevated vs low risk and with public vs commercial insurance for both measures., Conclusions: In this cohort study, adhering to dental quality measures was associated with reduced risk of tooth decay, and benefits were greater among children at elevated risk and with public insurance. These findings provide insights in facilitating targeted application of quality measures or developing more tailored quality improvement initiatives.

COVID-19 has had a disproportionate impact on the most disadvantaged members of society, including minorities and those with disabling chronic illnesses such as schizophrenia. We examined the pandemic's impacts among New York State's Medicaid beneficiaries with schizophrenia in the immediate post-pandemic surge period, with a focus on equity of access to critical healthcare. We compared changes in utilization of key behavioral health outpatient services and inpatient services for life-threatening conditions between the pre-pandemic and surge periods for White and non-White beneficiaries. We found racial and ethnic differences across all outcomes, with most differences stable over time. The exception was pneumonia admissions-while no differences existed in the pre-pandemic period, Black and Latinx beneficiaries were less likely than Whites to be hospitalized in the surge period despite minorities' heavier COVID-19 disease burden. The emergence of racial and ethnic differences in access to scarce life-preserving healthcare may hold lessons for future crises., (© 2023. RAND Corporation, under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Individuals with schizophrenia exposed to second-generation antipsychotics (SGA) have an increased risk for diabetes, with aripiprazole purportedly a safer drug. Less is known about the drugs' mortality risk or whether serious mental illness (SMI) diagnosis or race/ethnicity modify these effects., Methods: Authors created a retrospective cohort of non-elderly adults with SMI initiating monotherapy with an SGA (olanzapine, quetiapine, risperidone, and ziprasidone, aripiprazole) or haloperidol during 2008-2013. Three-year diabetes incidence or all-cause death risk differences were estimated between each drug and aripiprazole, the comparator, as well as effects within SMI diagnosis and race/ethnicity. Sensitivity analyses evaluated potential confounding by indication., Results: 38 762 adults, 65% White and 55% with schizophrenia, initiated monotherapy, with haloperidol least (6%) and quetiapine most (26·5%) frequent. Three-year mortality was 5% and diabetes incidence 9.3%. Compared with aripiprazole, haloperidol and olanzapine reduced diabetes risk by 1.9 (95% CI 1.2-2.6) percentage points, or a 18.6 percentage point reduction relative to aripiprazole users' unadjusted risk (10.2%), with risperidone having a smaller advantage. Relative to aripiprazole users' unadjusted risk (3.4%), all antipsychotics increased mortality risk by 1.1-2.2 percentage points, representing 32.4-64.7 percentage point increases. Findings within diagnosis and race/ethnicity were generally consistent with overall findings. Only quetiapine's higher mortality risk held in sensitivity analyses., Conclusions: Haloperidol's, olanzapine's, and risperidone's lower diabetes risks relative to aripiprazole were not robust in sensitivity analyses but quetiapine's higher mortality risk proved robust. Findings expand the evidence on antipsychotics' risks, suggesting a need for caution in the use of quetiapine among individuals with SMI.

Importance: During the COVID-19 pandemic, a large fraction of mental health care was provided via telemedicine. The implications of this shift in care for use of mental health service and quality of care have not been characterized., Objective: To compare changes in care patterns and quality during the first year of the pandemic among Medicare beneficiaries with serious mental illness (schizophrenia or bipolar I disorder) cared for at practices with higher vs lower telemedicine use., Design, Setting, and Participants: In this cohort study, Medicare fee-for-service beneficiaries with schizophrenia or bipolar I disorder were attributed to specialty mental health practices that delivered the majority of their mental health care in 2019. Practices were categorized into 3 groups based on the proportion of telemental health visits provided during the first year of the pandemic (March 2020-February 2021): lowest use (0%-49%), middle use (50%-89%), or highest use (90%-100%). Across the 3 groups of practices, differential changes in patient outcomes were calculated from the year before the pandemic started to the year after. These changes were also compared with differential changes from a 2-year prepandemic period. Analyses were conducted in November 2022., Exposure: Practice-level use of telemedicine during the first year of the COVID-19 pandemic., Main Outcomes and Measures: The primary outcome was the total number of mental health visits (telemedicine plus in-person) per person. Secondary outcomes included the number of acute hospital and emergency department encounters, all-cause mortality, and quality outcomes, including adherence to antipsychotic and mood-stabilizing medications (as measured by the number of months of medication fills) and 7- and 30-day outpatient follow-up rates after discharge for a mental health hospitalization., Results: The pandemic cohort included 120 050 Medicare beneficiaries (mean [SD] age, 56.5 [14.5] years; 66 638 females [55.5%]) with serious mental illness. Compared with prepandemic changes and relative to patients receiving care at practices with the lowest telemedicine use: patients receiving care at practices in the middle and highest telemedicine use groups had 1.11 (95% CI, 0.45-1.76) and 1.94 (95% CI, 1.28-2.59) more mental health visits per patient per year (or 7.5% [95% CI, 3.0%-11.9%] and 13.0% [95% CI, 8.6%-17.4%] more mental health visits per year, respectively). Among patients of practices with middle and highest telemedicine use, changes in adherence to antipsychotic and mood-stabilizing medications were -0.4% (95% CI, -1.3% to 0.5%) and -0.1% (95% CI, -1.0% to 0.8%), and hospital and emergency department use for any reason changed by 2.4% (95% CI, -1.5% to 6.2%) and 2.8% (95% CI, -1.2% to 6.8%), respectively. There were no significant differential changes in postdischarge follow-up or mortality rates according to the level of telemedicine use., Conclusions and Relevance: In this cohort study of Medicare beneficiaries with serious mental illness, patients receiving care from practices that had a higher level of telemedicine use during the COVID-19 pandemic had more mental health visits per year compared with prepandemic levels, with no differential changes in other observed quality metrics over the same period.

Background: The American Rescue Plan Act of 2021 awarded $500 million toward scaling "strike teams" to mitigate the impact of Coronavirus Disease 2019 (COVID-19) within nursing homes. The Massachusetts Nursing Facility Accountability and Support Package (NFASP) piloted one such model during the first weeks of the pandemic, providing nursing homes financial, administrative, and educational support. For a subset of nursing homes deemed high-risk, the state offered supplemental, in-person technical infection control support., Methods: Using state death certificate data and federal nursing home occupancy data, we examined longitudinal all-cause mortality per 100,000 residents and changes in occupancy across NFASP participants and subgroups that varied in their receipt of the supplemental intervention., Results: Nursing home mortality peaked in the weeks preceding the NFASP, with a steeper increase among those receiving the supplemental intervention. There were contemporaneous declines in weekly occupancy. The potential for temporal confounding and differential selection across NFASP subgroups precluded estimation of causal effects of the intervention on mortality., Conclusions: We offer policy and design suggestions for future strike team iterations that could inform the allocation of state and federal funding. We recommend expanded data collection infrastructure and, ideally, randomized assignment to intervention subgroups to support causal inference as strike team models are scaled under the direction of state and federal agencies., (© 2023 The American Geriatrics Society.)

Background: Hospitals with high mortality and readmission rates for patients with heart failure (HF) might also perform poorly in other quality concepts. We sought to evaluate the association between hospital performance on mortality and readmission with hospital performance rates of safety adverse events., Methods: This cross-sectional study linked the 2009 to 2019 patient-level adverse events data from the Medicare Patient Safety Monitoring System, a randomly selected medical records-abstracted patient safety database, to the 2005 to 2016 hospital-level HF-specific 30-day all-cause mortality and readmissions data from the United States Centers for Medicare & Medicaid Services. Hospitals were classified to one of 3 performance categories based on their risk-standardized 30-day all-cause mortality and readmission rates: better (both in <25th percentile), worse (both >75th percentile), and average (otherwise). Our main outcome was the occurrence (yes/no) of one or more adverse events during hospitalization. A mixed-effect model was fit to assess the relationship between a patient's risk of having adverse events and hospital performance categories, adjusted for patient and hospital characteristics., Results: The study included 39 597 patients with HF from 3108 hospitals, of which 252 hospitals (8.1%) and 215 (6.9%) were in the better and worse categories, respectively. The rate of patients with one or more adverse events during a hospitalization was 12.5% (95% CI, 12.1-12.8). Compared with patients admitted to better hospitals, patients admitted to worse hospitals had a higher risk of one or more hospital-acquired adverse events (adjusted risk ratio, 1.24 [95% CI, 1.06-1.44])., Conclusions: Patients admitted with HF to hospitals with high 30-day all-cause mortality and readmission rates had a higher risk of in-hospital adverse events. There may be common quality issues among these 3 measure concepts in these hospitals that produce poor performance for patients with HF., Competing Interests: Disclosures In the past 3 years, Harlan Krumholz received expenses and/or personal fees from UnitedHealth, Element Science, Eyedentifeye, and F-Prime. He is a co-founder of Refactor Health and HugoHealth, and is associated with contracts, through Yale New Haven Hospital, from the Centers for Medicare & Medicaid Services and through Yale University from the Food and Drug Administration, Johnson & Johnson, Google, and Pfizer. Dr Normand, S. Eckenrode, and J. Mathew work under contract with the Centers for Medicare & Medicaid Services to develop and maintain performance measures outside this submitted work. Dr Wang had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. He is the founder of Boston Deep Data LLC. Dr Metersky has worked on various quality improvement and patient safety projects with the Centers for Medicare & Medicaid Services and the Agency for Healthcare Research and Quality. His employer has received remuneration for this work. The other authors report no conflicts.

Importance: While large oral health disparities remain by race and ethnicity among children, the associations of race, ethnicity, and mediating factors with oral health outcomes are poorly characterized. Identifying the pathways that explain these disparities would be critical to inform policies to effectively reduce them., Objective: To measure racial and ethnic disparities in the risk of developing tooth decay and quantify relative contributions of factors mediating the observed disparities among US children., Design, Setting, and Participants: This retrospective cohort study used electronic health records of US children from 2014 to 2020 to measure racial and ethnic disparities in the risk of tooth decay. Elastic net regularization was used to select variables to be included in the model among medical conditions, dental procedure types, and individual- and community-level socioeconomic factors. Data were analyzed from January 9 to April 28, 2023., Exposures: Race and ethnicity of children., Main Outcomes and Measures: The main outcome was diagnosis of tooth decay in either deciduous or permanent teeth, defined as at least 1 decayed, filled, or missing tooth due to caries. An Anderson-Gill model, a time-to-event model for recurrent tooth decay events with time-varying covariates, stratified by age groups (0-5, 6-10, and 11-18 years) was estimated. A nonlinear multiple additive regression tree-based mediation analysis quantified the relative contributions of factors underlying the observed racial and ethnic disparities., Results: Among 61 083 children and adolescents aged 0 to 18 years at baseline (mean [SD] age, 9.9 [4.6] years; 30 773 [50.4%] female), 2654 Black individuals (4.3%), 11 213 Hispanic individuals (18.4%), 42 815 White individuals (70.1%), and 4401 individuals who identified as another race (eg, American Indian, Asian, and Hawaiian and Pacific Islander) (7.2%) were identified. Larger racial and ethnic disparities were observed among children aged 0 to 5 years compared with other age groups (Hispanic children: adjusted hazard ratio [aHR], 1.47; 95% CI, 1.40-1.54; Black children: aHR, 1.30; 95% CI, 1.19-1.42; other race children: aHR, 1.39; 95% CI, 1.29-1.49), compared with White children. For children aged 6 to 10 years, higher risk of tooth decay was observed for Black children (aHR, 1.09; 95% CI, 1.01-1.19) and Hispanic children (aHR, 1.12; 95% CI, 1.07-1.18) compared with White children. For adolescents aged 11 to 18 years, a higher risk of tooth decay was observed only in Black adolescents (aHR, 1.17; 95% CI, 1.06-1.30). A mediation analysis revealed that the association of race and ethnicity with time to first tooth decay became negligible, except for Hispanic and children of other race aged 0 to 5 years, suggesting that mediators explained most of the observed disparities. Insurance type explained the largest proportion of the disparity, ranging from 23.4% (95% CI, 19.8%-30.2%) to 78.9% (95% CI, 59.0%-114.1%), followed by dental procedures (receipt of topical fluoride and restorative procedures) and community-level factors (education attainment and Area Deprivation Index)., Conclusions: In this retrospective cohort study, large proportions of disparities in time to first tooth decay associated with race and ethnicity were explained by insurance type and dental procedure types among children and adolescents. These findings can be applied to develop targeted strategies to reduce oral health disparities.

Background: Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes., Methods: Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV)., Results: Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)]., Conclusions: A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Clinical prediction models for deep sternal wound infections (DSWI) after coronary artery bypass graft (CABG) surgery exist, although they have a poor impact in external validation studies. We developed and validated a new predictive model for 30-day DSWI after CABG (REPINF) and compared it with the Society of Thoracic Surgeons model (STS). The REPINF model was created through a multicenter cohort of adults undergoing CABG surgery (REPLICCAR II Study) database, using least absolute shrinkage and selection operator (LASSO) logistic regression, internally and externally validated comparing discrimination, calibration in-the-large (CL), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), trained between the new model and the STS PredDeep, a validated model for DSWI after cardiac surgery. In the validation data, c-index = 0.83 (95% CI 0.72-0.95). Compared to the STS PredDeep, predictions improved by 6.5% (IDI). However, both STS and REPINF had limited calibration. Different populations require independent scoring systems to achieve the best predictive effect. The external validation of REPINF across multiple centers is an important quality improvement tool to generalize the model and to guide healthcare professionals in the prevention of DSWI after CABG surgery., (© 2022. The Author(s).)

Background: The Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) provides observed-to-expected (O/E) operative mortality ratios to more than 100 congenital heart centers in North America. We compared the current approach for estimating O/E ratios to approaches incorporating information on diagnosis as moderators of procedures, other unused risk factors, and additional variation in confidence interval construction to characterize center performance., Methods: Bayesian additive regression trees (BART) and lasso models linked operative mortality to diagnosis-procedure categories, procedure-specific risk factors, and syndromes/abnormalities. Bootstrapping accounted for variation in the STS-CHSD (STS bootstrap) and lasso CIs. We compared O/E estimates, interquartile range of CI widths, and concordance of center performance categorizations (worse-than-, as-, or better-than-expected mortality) of the new approaches to the STS-CHSD., Results: In 110 surgical centers including 98,822 surgical operative encounters, there were 2818 (2.85%) operative mortalities (center range, 0.37%-10%). Compared with the STS-CHSD, BART- and lasso-estimated O/E ratios varied more and had narrower confidence intervals (interquartile range of confidence interval: STS-CHSD = 1.11, STS bootstrap = 0.98; lasso = 0.80; BART = 0.96). Concordance of performance categorization with the STS-CHSD ranged from 84% (lasso) to 91% (STS Bootstrap); more than 70% of discordant centers improved categories. Discordant centers had smaller volumes, fewer operative mortalities, and treated more patients with congenital lung abnormalities., Conclusions: Relative to the STS-CHSD, up to 16% of hospitals changed performance categories, most improving performance. Given the significance of quality reports for congenital heart centers, inclusion of additional risk factors and unaddressed variation should be considered., (Copyright © 2022 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: The Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (CHSD) provides risk-adjusted operative mortality rates to approximately 120 North American congenital heart centers. Optimal case-mix adjustment methods for operative mortality risk prediction in this population remain unclear., Methods: A panel created diagnosis-procedure combinations of encounters in the CHSD. Models for operative mortality using the new diagnosis-procedure categories, procedure-specific risk factors, and syndromes or abnormalities included in the CHSD were estimated using Bayesian additive regression trees and least absolute shrinkage and selector operator (lasso) models. Performance of the new models was compared with the current STS CHSD risk model., Results: Of 98 825 operative encounters (69 063 training; 29 762 testing), 2818 (2.85%) STS-defined operative mortalities were observed. Differences in sensitivity, specificity, and true and false positive predicted values were negligible across models. Calibration for mortality predictions at the higher end of risk from the lasso and Bayesian additive regression trees models was better than predictions from the STS CHSD model, likely because of the new models' inclusion of diagnosis-palliative procedure variables affecting <1% of patients overall but accounting for 27% of mortalities. Model discrimination varied across models for high-risk procedures, hospital volume, and hospitals., Conclusions: Overall performance of the new models did not differ meaningfully from the STS CHSD risk model. Adding procedure-specific risk factors and allowing diagnosis to modify predicted risk for palliative operations may augment model performance for very high-risk surgical procedures. Given the importance of risk adjustment in estimating hospital quality, a comparative assessment of surgical program quality evaluations using the different models is warranted., (Copyright © 2022 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with high mortality can have profound effects; a hospital's seemingly good performance for readmission may be an artifact of it having poor performance for mortality. in this paper we propose novel multivariate hospital-level performance measures for readmission and mortality that derive from framing the analysis as one of cluster-correlated semi-competing risks data. We also consider a number of profiling-related goals, including the identification of extreme performers and a bivariate classification of whether the hospital has higher-/lower-than-expected readmission and mortality rates via a Bayesian decision-theoretic approach that characterizes hospitals on the basis of minimizing the posterior expected loss for an appropriate loss function. in some settings, particularly if the number of hospitals is large, the computational burden may be prohibitive. To resolve this, we propose a series of analysis strategies that will be useful in practice. Throughout, the methods are illustrated with data from CMS on N = 17,685 patients diagnosed with pancreatic cancer between 2000-2012 at one of J = 264 hospitals in California.

In 2020 Medicare reintroduced Alzheimer's disease and related dementias (ADRD) Hierarchical Condition Categories (HCCs) to risk-adjust Medicare Advantage and accountable care organization (ACO) payments. The potential for Medicare spending increases from this policy change are not well understood because the baseline accuracy of ADRD HCCs is uncertain. Using linked 2016-18 claims and electronic health record data from a large ACO, we evaluated the accuracy of claims-based ADRD HCCs against a reference standard of clinician-adjudicated disease. An estimated 7.5 percent of beneficiaries had clinician-adjudicated ADRD. Among those with ADRD HCCs, 34 percent did not have clinician-adjudicated disease. The false-negative and false-positive rates were 22.7 percent and 3.2 percent, respectively. Medicare spending for those with false-negative ADRD HCCs exceeded that of true positives by $14,619 per beneficiary. If, after the reintroduction of risk adjustment for ADRD, all false negatives were coded as having ADRD, expenditure benchmarks for beneficiaries with ADRD would increase by 9 percent. Monitoring ADRD coding could become challenging in the setting of concurrent incentives to decrease false-negative rates and increase false-positive rates.

Objectives: The 2021 American Rescue Plan Act (ARPA) increased the availability and magnitude of premium tax credits (PTCs) for consumers purchasing individual marketplace plans in 2021-2022. Millions currently purchase PTC-ineligible plans off of the marketplace. We estimate the proportion of off-marketplace enrollees who would be eligible for the expanded PTCs under ARPA, calculate PTC amounts for eligible enrollees, and examine factors influencing plan choice that could inform outreach efforts., Study Design: We analyzed data from a survey of a random sample of off-marketplace enrollees in California in 2017 (n = 829)., Methods: Using survey data including self-reported income, household size, and employment status combined with 2021 benchmark premium data from Covered California, we estimate eligibility for PTCs and potential PTC amounts under ARPA among off-marketplace enrollees. We adjust for both survey design weights and poststratification weights., Results: Among off-marketplace enrollees, we estimate that approximately 12% are potentially ineligible for PTCs because they reported incomes less than 100% of the poverty level or because they had access to employer-sponsored coverage for their family through themselves or their partner. The median annual PTC in 2021 for eligible off-marketplace enrollees was $311 but varied greatly by age, family or individual plan, and household income (5%-95% range, $0-$14,836). In 2017, 69% of off-marketplace enrollees were unaware that they had to enroll in marketplace plans to receive PTCs, and 51% received enrollment assistance from insurance brokers., Conclusions: These findings suggest the need for targeted outreach to encourage off-marketplace enrollees to switch to marketplace plans.

Importance: Investing in health care staffs' education can change the scope of action and improve care. The effectiveness of staff education remains inconclusive., Objective: To examine whether an evidence-based educational intervention for nurses decreases the use of seclusion rooms in psychiatric hospitals compared with usual practice., Design, Setting, and Participants: In this pragmatic, 2-arm parallel, stratified cluster randomized clinical trial, 28 wards in 15 psychiatric hospitals in Finland were screened for eligibility and randomly allocated (1:1). Nurses joined on either intervention (n = 13) or usual practice (n = 15) wards. The intervention was performed from May 1, 2016, to October 31, 2017. The follow-up data for January 1 to December 31, 2017, were collected from hospital registers in 2018. Data analysis was performed October 27, 2021., Interventions: Evidence-based education delivered during 18 months, including 8 months of active education, followed by a 10-month maintenance period., Main Outcomes and Measures: The primary outcome was the occurrence of patient seclusion (events per total number of patients)., Results: Of 28 psychiatric hospital wards screened (437 beds and 648 nurses), 27 wards completed the study. A total of 8349 patients were receiving care in the study wards, with 53% male patients and a mean (SD) age of 40.6 (5.7) years. The overall number of seclusions was 1209 (14.5%) in 2015 and 1349 (16.5%) in 2017. In the intervention group, the occurrence rate of seclusion at the ward level decreased by 5.3% from 629 seclusions among 4163 patients (15.1%) to 585 seclusions among 4089 patients (14.3%) compared with a 34.7% increase from 580 seclusions among 4186 patients (13.9%) to 764 seclusions among 4092 patients (18.7%) in the usual practice group. The adjusted rate ratio was 0.86 (95% CI, 0.40-1.82) in 2015 and 0.66 (95% CI, 0.31-1.41) in 2017 (P = .003). However, the number of forced injections increased in the intervention group from 317 events among 4163 patients (7.6%) in 2015 to 486 events among 4089 patients (11.9%) in 2017 compared with an increase in the usual practice group from 414 events among 4186 patients (9.9%) in 2015 to 481 events among 4092 patients (11.8%) in 2017. Seven adverse events were reported., Conclusions and Relevance: In this randomized clinical trial, the educational intervention had a limited effect on the change of occurrence rate of patient seclusion, whereas the use of forced injections increased. More studies are needed to better understand the reasons for these findings., Trial Registration: ClinicalTrials.gov Identifier: NCT02724748.

Importance: It is known that hospitalized patients who experience adverse events are at greater risk of readmission; however, it is unknown whether patients admitted to hospitals with higher risk-standardized readmission rates had a higher risk of in-hospital adverse events., Objective: To evaluate whether patients with pneumonia admitted to hospitals with higher risk-standardized readmission rates had a higher risk of adverse events., Design, Setting, and Participants: This cross-sectional study linked patient-level adverse events data from the Medicare Patient Safety Monitoring System (MPSMS), a randomly selected medical record abstracted database, to the hospital-level pneumonia-specific all-cause readmissions data from the Centers for Medicare & Medicaid Services. Patients with pneumonia discharged from July 1, 2010, through December 31, 2019, in the MPSMS data were included. Hospital performance on readmissions was determined by the risk-standardized 30-day all-cause readmission rate. Mixed-effects models were used to examine the association between adverse events and hospital performance on readmissions, adjusted for patient and hospital characteristics. Analysis was completed from October 2019 through July 2020 for data from 2010 to 2017 and from March through April 2022 for data from 2018 to 2019., Exposures: Patients hospitalized for pneumonia., Main Outcomes and Measures: Adverse events were measured by the rate of occurrence of hospital-acquired events and the number of events per 1000 discharges., Results: The sample included 46 047 patients with pneumonia, with a median (IQR) age of 71 (58-82) years, with 23 943 (52.0%) women, 5305 (11.5%) Black individuals, 37 763 (82.0%) White individuals, and 2979 (6.5%) individuals identifying as another race, across 2590 hospitals. The median hospital-specific risk-standardized readmission rate was 17.0% (95% CI, 16.3%-17.7%), the occurrence rate of adverse events was 2.6% (95% CI, 2.54%-2.65%), and the number of adverse events per 1000 discharges was 157.3 (95% CI, 152.3-162.5). An increase by 1 IQR in the readmission rate was associated with a relative 13% higher patient risk of adverse events (adjusted odds ratio, 1.13; 95% CI, 1.08-1.17) and 5.0 (95% CI, 2.8-7.2) more adverse events per 1000 discharges at the patient and hospital levels, respectively., Conclusions and Relevance: Patients with pneumonia admitted to hospitals with high all-cause readmission rates were more likely to develop adverse events during the index hospitalization. This finding strengthens the evidence that readmission rates reflect the quality of hospital care for pneumonia.

Background: Quantifying quality of health care can provide valuable information to patients, providers, and policy makers. However, the observational nature of measuring quality complicates assessments., Methods: We describe a conceptual model for defining quality and its implications about the data collected, how to make inferences about quality, and the assumptions required to provide statistically valid estimates. Twenty-one binary or polytomous quality measures collected from 101,051 adult Medicaid beneficiaries aged 18-64 years with schizophrenia from 5 U.S. states show methodology. A categorical principal components analysis establishes dimensionality of quality, and item response theory models characterize the relationship between each quality measure and a unidimensional quality construct. Latent regression models estimate racial/ethnic and geographic quality disparities., Results: More than 90% of beneficiaries filled at least 1 antipsychotic prescription and 19% were hospitalized for schizophrenia during a 12-month observational period in our multistate cohort with approximately 2/3 nonwhite beneficiaries. Four quality constructs emerged: inpatient, emergency room, pharmacologic/ambulatory, and ambulatory only. Using a 2-parameter logistic model, pharmacologic/ambulatory care quality varied from -2.35 to 1.26 (higher = better quality). Black and Latinx beneficiaries had lower pharmacologic/ambulatory quality compared with whites. Race/ethnicity modified the association of state and pharmacologic/ambulatory care quality in latent regression modeling. Average quality ranged from -0.28 (95% confidence interval, -2.15 to 1.04) for blacks in New Jersey to 0.46 [95% confidence interval, -0.89 to 1.40] for whites in Michigan., Conclusions: By combining multiple quality measures using item response theory models, a composite measure can be estimated that has more statistical power to detect differences among subjects than the observed mean per subject., (Copyright © 2022 Elsevier Inc. All rights reserved.)

This cohort study evaluates the ascertainment of Alzheimer disease and related dementia using diagnostic codes in various health care settings., Competing Interests: Conflict of Interest Disclosures: Dr Festa reported grants from the National Institutes of Health (NIH; CTSA TL1 TR001864), VA Office of Academic Affiliations through the VA/National Clinician Scholars Program, and Yale University during the conduct of the study. Dr Blacker reported grants from NIH during the conduct of the study. Dr Newhouse reported grants from the National Institute of Aging during the conduct of the study. Dr Hsu reported grants from NIH during the conduct of the study as well as personal fees from Cambridge Health Alliance, Delta Health Alliance, Alta Med, Columbia University, University of Southern California, RWJF, and Community Servings outside the submitted work. No other disclosures were reported., (Copyright 2022 Festa N et al. JAMA Health Forum.)

Objective: To examine whether quality of dental care varies by age and over time and whether community-level characteristics explain these patterns., Data Source: Deidentified medical and dental claims from a commercial insurer from January 2015 to December 2019., Study Design: A retrospective cohort study. The primary outcome was a composite quality score, derived from seven dental quality measures (DQMs), with higher values corresponding to better quality. Hierarchical regression models identified person- and zip code-level factors associated with the quality., Data Collection/extraction Methods: Continuously enrolled US dental insurance beneficiaries younger than 21 years of age., Principal Findings: Quality was assessed for 4.88 million person-years covering 1.31 million persons. Overall quality slightly improved over time, mostly driven by substantial improvements among children aged 0-5 years by 0.153 points/year (95% confidence interval [CI]:0.151, 0.156). Quality was poorest and declined over time among adolescents with only 20.5% of DQMs met as compared to 42.6% among aged 0-5 years in 2019. Dental professional shortage, median household income, percentages of African Americans, unemployed, and less-educated populations at the zip code level were associated with the composite score., Conclusion: Quality of dental care among adolescents remains low, and place of residence influenced the quality. Increasing the supply of dentists and oral health promotion strategies targeting adolescents and low-performing localities should be explored., (© 2021 Health Research and Educational Trust.)