Antipsychotics and the risk of diabetes and death among adults with serious mental illnesses.

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.

Racial-Ethnic Disparities in Quality of Care Among Medicaid Beneficiaries With Schizophrenia.

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.

Statistical Approaches for Quantifying the Quality of Neurosurgical Care.

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.

Reevaluating Congenital Heart Surgery Center Performance Using Operative Mortality.

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.

Mortality Prediction After Cardiac Surgery in Children: An STS Congenital Heart Surgery Database Analysis.

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.

Risk of weight gain for specific antipsychotic drugs: a meta-analysis.

People with schizophrenia are at considerably higher risk of cardiometabolic morbidity than the general population. Second-generation antipsychotic drugs contribute to that risk partly through their weight gain effects, exacerbating an already high burden of disease. While standard 'as-randomized' analyses of clinical trials provide valuable information, they ignore adherence patterns across treatment arms, confounding estimates of realized treatment exposure on outcome. We assess the effect of specific second-generation antipsychotics on weight gain, defined as at least a 7% increase in weight from randomization, using a Bayesian hierarchical model network meta-analysis with individual patient level data. Our data consisted of 14 randomized clinical trials contributing 5923 subjects (mean age = 39 [SD = 12]) assessing various combinations of olanzapine (n = 533), paliperidone (n = 3482), risperidone (n = 540), and placebo (n = 1368). The median time from randomization to dropout or trial completion was 6 weeks (range: 0-60 weeks). The unadjusted probability of weight gain in the placebo group was 4.8% across trials. For each 10 g chlorpromazine equivalent dose increase in olanzapine, the odds of weight gain increased by 5 (95% credible interval: 1.4, 5.3); the effect of risperidone (odds ratio = 1.6 [0.25, 9.1]) was estimated with considerable uncertainty but no different from paliperidone (odds ratio = 1.3 [1.2, 1.5]).