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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. See related Commentary by Berry et al. in this issue. [ABSTRACT FROM AUTHOR]

Objective: To investigate risk-adjusted, 30-day postdischarge heart failure mortality and readmission rates stratified by hospital teaching intensity.Data Sources and Study Setting: A total of 709 221 Medicare fee-for-service beneficiaries discharged from 3135 US hospitals between 1/1/2013 and 11/30/2014 with a principal diagnosis of heart failure.Study Design: Hospitals were classified as Council of Teaching Hospitals and Health Systems (COTH) major teaching hospitals, non-COTH teaching hospitals, and nonteaching hospitals. Hospital teaching status was linked with MedPAR patient data and FY2016 Hospital Readmission Reduction Program penalties. Index hospitalization survival probabilities were estimated with hierarchical logistic regression and used to stratify index hospitalization survivors into severity deciles. Decile-specific models were estimated for 30-day postdischarge readmission and mortality. Thirty-day postdischarge outcomes were estimated by teaching intensity and penalty categories.Principal Findings: Averaged across deciles, adjusted 30-day COTH hospital readmission rates were, on a relative scale ([COTH minus nonteaching] ÷ nonteaching), 1.63 percent higher (95% CI: 0.89 percent, 2.25 percent) than at nonteaching hospitals, but their average adjusted 30-day postdischarge mortality rates were 11.55 percent lower (95% CI: -13.78 percent, -9.37 percent). Penalized COTH hospitals had the highest readmission rates of all categories (23.99 percent [95% CI: 23.50 percent, 24.49 percent]) but the lowest 30-day postdischarge mortality (8.30 percent [95% CI: 7.99 percent, 8.57 percent] vs 9.84 percent [95% CI: 9.69 percent, 9.99 percent] for nonpenalized, nonteaching hospitals).Conclusions: Heart failure readmission penalties disproportionately impact major teaching hospitals and inadequately credit their better postdischarge survival. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background/Objectives: Gabapentinoids are commonly prescribed to relieve pain. The development of edema, an established adverse effect of gabapentinoids, may lead to a potentially harmful prescribing cascade whereby individuals are subsequently prescribed diuretics and exposed to diuretic‐induced adverse events. The frequency of this prescribing cascade is unknown. Our objective was to measure the association between new dispensing of a gabapentinoid and the subsequent dispensing of a diuretic in older adults with new low back pain. Design Population‐based cohort study. Setting: Ontario, Canada. Participants: A total of 260,344 community‐dwelling adults aged 66 years or older, newly diagnosed with low back pain between April 1, 2011, and March 31, 2019. Measurements Exposure status was assigned using dispensed medications in the 1 week after low back pain diagnosis. Older adults newly dispensed a gabapentinoid (N = 7867) were compared with older adults who were not newly dispensed a gabapentinoid (N = 252,477). Hazard ratios (HRs) with 95% confidence intervals (CIs) for dispensing of a diuretic within 90 days of follow‐up among older adults prescribed gabapentin relative to those who were not. Results: Older adults newly dispensed a gabapentinoid had a higher risk of being subsequently dispensed a diuretic within 90 days compared with older adults who were not prescribed a gabapentinoid (2.0% vs. 1.3%). After covariate adjustment, new gabapentinoid users had a higher rate of being dispensed a diuretic compared with those not prescribed a gabapentinoid (HR: 1.44, 95% CI: 1.23, 1.70). The rate of diuretic prescription among new gabapentinoid users increased with increasing gabapentinoid dosages. Conclusions: We have demonstrated the presence of a potentially inappropriate and harmful prescribing cascade. Given the widespread use of gabapentinoids, the population‐based scale of this problem may be substantial. Increased awareness of this prescribing cascade is required to reduce the unnecessary use of diuretics and the exposure of patients to additional adverse drug events. [ABSTRACT FROM AUTHOR]

Background/Objectives: No data exist regarding the validity of International Classification of Disease (ICD)‐10 dementia diagnoses against a clinician‐adjudicated reference standard within Medicare claims data. We examined the accuracy of claims‐based diagnoses with respect to expert clinician adjudication using a novel database with individual‐level linkages between electronic health record (EHR) and claims. Design: In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims‐based definitions of dementia against the reference standard. Setting: Mass‐General‐Brigham Healthcare (MGB), Massachusetts, USA. Participants: From an eligible population of 40,690 fee‐for‐service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. Intervention: None. Measurements: We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration‐in‐the‐large [CITL] and calibration slope) of three ICD‐10 claims‐based definitions of dementia against clinician‐adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10‐fold cross‐validation (CV). Results: Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non‐Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best‐performing definition demonstrated excellent accuracy (CV‐AUC 0.94; 95% CI 0.92–0.96) and was well‐calibrated to the reference standard of clinician‐adjudicated dementia (CV‐CITL <0.001, CV‐slope 0.97). Conclusion: This study is the first to validate ICD‐10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real‐world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD‐9 definition of dementia. [ABSTRACT FROM AUTHOR]

The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a group-specific effect that differs from the population average. In fact, it is possible that there is underlying systematic effect heterogeneity that is obscured by focusing on the population mean estimand. In this context, understanding which covariates contribute to this treatment effect heterogeneity (TEH) and how these covariates determine the differential treatment effect (TE) is an important consideration. Towards such an understanding, this paper briefly reviews three approaches used in making causal inferences and conducts a simulation study to compare these approaches according to their performance in an exploratory evaluation of TEH when the heterogeneous subgroups are not known a priori. Performance metrics include the detection of any heterogeneity, the identification and characterization of heterogeneous subgroups, and unconfounded estimation of the TE within subgroups. The methods are then deployed in a comparative effectiveness evaluation of drug-eluting versus bare-metal stents among 54 099 Medicare beneficiaries in the continental United States admitted to a hospital with acute myocardial infarction in 2008. [ABSTRACT FROM AUTHOR]

Background: Telomeres cap and protect DNA but shorten with each somatic cell division. Aging and environmental and lifestyle factors contribute to the speed of telomere attrition. Current evidence suggests a link between relative telomere length (RTL) and depression but the directionality of the relationship remains unclear. We prospectively examined associations between RTL and subsequent depressive symptom trajectories.Methods: Among 8,801 women of the Nurses' Health Study, depressive symptoms were measured every 4 years from 1992 to 2012; group-based trajectories of symptoms were identified using latent class growth-curve analysis. Multinomial logistic models were used to relate midlife RTLs to the probabilities of assignment to subsequent depressive symptom trajectory groups.Results: We identified four depressive symptom trajectory groups: minimal depressive symptoms (62%), worsening depressive symptoms (14%), improving depressive symptoms (19%), and persistent-severe depressive symptoms (5%). Longer midlife RTLs were related to significantly lower odds of being in the worsening symptoms trajectory versus minimal trajectory but not to other trajectories. In comparison with being in the minimal symptoms group, the multivariable-adjusted odds ratio of being in the worsening depressive symptoms group was 0.78 (95% confidence interval, 0.62-0.97; p = 0.02), for every standard deviation increase in baseline RTL.Conclusions: In this large prospective study of generally healthy women, longer telomeres at midlife were associated with significantly lower risk of a subsequent trajectory of worsening mood symptoms over 20 years. The results raise the possibility of telomere shortening as a novel contributing factor to late-life depression. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the association between hospital safety culture and 30-day risk-adjusted mortality for Medicare patients with acute myocardial infarction (AMI) in a large, diverse hospital cohort.Subjects: The final analytic cohort consisted of 19,357 Medicare AMI discharges (MedPAR data) linked to 257 AHRQ Hospital Survey on Patient Safety Culture surveys from 171 hospitals between 2008 and 2013.Study Design: Observational, cross-sectional study using hierarchical logistic models to estimate the association between hospital safety scores and 30-day risk-adjusted patient mortality. Odds ratios of 30-day, all-cause mortality, adjusting for patient covariates, hospital characteristics (size and teaching status), and several different types of safety culture scores (composite, average, and overall) were determined.Principal Findings: No significant association was found between any measure of hospital safety culture and adjusted AMI mortality.Conclusions: In a large cross-sectional study from a diverse hospital cohort, AHRQ safety culture scores were not associated with AMI mortality. Our study adds to a growing body of investigations that have failed to conclusively demonstrate a safety culture-outcome association in health care, at least with widely used national survey instruments. [ABSTRACT FROM AUTHOR]

Meta-analysis of individual participant data (IPD) is increasingly utilised to improve the estimation of treatment effects, particularly among different participant subgroups. An important concern in IPD meta-analysis relates to partially or completely missing outcomes for some studies, a problem exacerbated when interest is on multiple discrete and continuous outcomes. When leveraging information from incomplete correlated outcomes across studies, the fully observed outcomes may provide important information about the incompleteness of the other outcomes. In this paper, we compare two models for handling incomplete continuous and binary outcomes in IPD meta-analysis: a joint hierarchical model and a sequence of full conditional mixed models. We illustrate how these approaches incorporate the correlation across the multiple outcomes and the between-study heterogeneity when addressing the missing data. Simulations characterise the performance of the methods across a range of scenarios which differ according to the proportion and type of missingness, strength of correlation between outcomes and the number of studies. The joint model provided confidence interval coverage consistently closer to nominal levels and lower mean squared error compared with the fully conditional approach across the scenarios considered. Methods are illustrated in a meta-analysis of randomised controlled trials comparing the effectiveness of implantable cardioverter-defibrillator devices alone to implantable cardioverter-defibrillator combined with cardiac resynchronisation therapy for treating patients with chronic heart failure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Objectives: To examine dose‐related prescribing and short‐term serious events associated with initiation of cholinesterase inhibitor (ChEI) therapy. Design: Retrospective, population‐based cohort study. Setting: Ontario, Canada. Participants: Women (n=47,829) and men (n=32,503) aged 66 and older who initiated a ChEI between April 1, 2010, and June 30, 2016. Measurements: All‐cause serious events (emergency department (ED) visits, inpatient hospitalizations, death) within 30 days of ChEI initiation. Multivariable Cox proportional hazards models were used to estimate adjusted rates of serious events. Results: Overall, 4.8% of older adults were dispensed a lower‐than‐recommended ChEI starting dose, 87.9% a recommended dose, and 7.3% a higher‐than‐recommended starting dose. Eight thousand six hundred seventy‐one (10.8%) individuals experienced a serious event within 30 days of initiating therapy, primarily ED visits (8,540, 10.6%). Relative to those initiated on a recommended starting dose, those initiated on a higher dose had a significantly increased rate of serious events (women adjusted hazard ratio (aHR) 1.50, 95% confidence interval (CI) =1.38–1.63; men aHR 1.31, 95% CI=1.19–1.45). Similar patterns were found for ED visits and inpatient hospitalizations but not death. The relative effect of higher‐than‐recommended starting dose dispensed vs. recommended starting dose dispensed was greater in women than it was in men: the number needed to harm was 22 (95% confidence interval (CI)=18–29) for women and 36 (95% CI= 26–61) for men. Conclusion: Serious events immediately after initiation of ChEIs were associated with starting ChEI dose. This association was stronger in women. [ABSTRACT FROM AUTHOR]

Abstract: High‐dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high‐dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high‐dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem. [ABSTRACT FROM AUTHOR]

Objectives To evaluate the incidence and characteristics of nursing home (NH) use after implantable cardioverter-defibrillator (ICD) implantation. Design Cohort study. Setting Medicare beneficiaries in the National Cardiovascular Data Registry- ICD Registry. Participants Individuals aged 65 and older receiving ICDs between January 1, 2006, and March 31, 2010 (N = 192,483). Measurements Proportion of ICD recipients discharged to NHs directly after device placement, cumulative incidence of long-term NH admission, and factors associated with immediate discharge to a NH and time to long-term NH admission. Results Over 4 years, 40.6% of the cohort died, and 35,939 (18.7%) experienced at least one NH admission, including 4.0% directly discharged to a NH after ICD implantation and 2.8% admitted to long-term NH care during follow-up. The cumulative incidence of long-term NH admission, accounting for the competing risk of death, was 1.7% at 1 year, 3.8% at 3 years, and 4.6% at 4 years; 20.1% of individuals admitted to a NH died there. Factors most strongly associated with direct NH discharge and time to long-term NH care were older age (adjusted odds ratio (AOR) = 2.09, 95% confidence interval ( CI) = 2.01-2.17 per 10-year increment; adjusted hazard ratio (AHR) = 1.88, 95% CI = 1.80-1.97, respectively), dementia (AOR = 2.60, 95% CI = 2.25-3.01; AHR = 2.50, 95% CI = 2.14-2.93, respectively), and Medicare Part A claim for NH stay in prior 6 months (AOR = 3.96, 95% CI = 3.70-4.25; AHR = 2.88, 95% CI = 2.65-3.14, respectively). Conclusion Nearly one in five individuals are admitted to NHs over a median of 1.6 years of follow-up after ICD implantation. Understanding these outcomes may help inform the clinical care of these individuals. [ABSTRACT FROM AUTHOR]

Objective: To determine whether (a) quality in schizophrenia care varies by race/ethnicity and over time and (b) these patterns differ across counties within states.Data Sources: Medicaid claims data from California, Florida, New York, and North Carolina during 2002-2008.Study Design: We studied black, Latino, and white Medicaid beneficiaries with schizophrenia. Hierarchical regression models, by state, quantified person and county effects of race/ethnicity and year on a composite quality measure, adjusting for person-level characteristics.Principal Findings: Overall, our cohort included 164,014 person-years (41-61 percent non-whites), corresponding to 98,400 beneficiaries. Relative to whites, quality was lower for blacks in every state and also lower for Latinos except in North Carolina. Temporal improvements were observed in California and North Carolina only. Within each state, counties differed in quality and disparities. Between-county variation in the black disparity was larger than between-county variation in the Latino disparity in California, and smaller in North Carolina; Latino disparities did not vary by county in Florida. In every state, counties differed in annual changes in quality; by 2008, no county had narrowed the initial disparities.Conclusions: For Medicaid beneficiaries living in the same state, quality and disparities in schizophrenia care are influenced by county of residence for reasons beyond patients' characteristics. [ABSTRACT FROM AUTHOR]

Modeling events requires accounting for differential follow-up duration, especially when combining randomized and observational studies. Although events occur at any point over a follow-up period and censoring occurs throughout, most applied researchers use odds ratios as association measures, assuming follow-up duration is similar across treatment groups. We derive the bias of the rate ratio when incorrectly assuming equal follow-up duration in the single study binary treatment setting. Simulations illustrate bias, efficiency, and coverage and demonstrate that bias and coverage worsen rapidly as the ratio of follow-up duration between arms moves away from one. Combining study rate ratios with hierarchical Poisson regression models, we examine bias and coverage for the overall rate ratio via simulation in three cases: when average arm-specific follow-up duration is available for all studies, some studies, and no study. In the null case, bias and coverage are poor when the study average follow-up is used and improve even if some arm-specific follow-up information is available. As the rate ratio gets further from the null, bias and coverage remain poor. We investigate the effectiveness of cardiac resynchronization therapy devices compared with those with cardioverter-defibrillator capacity where three of eight studies report arm-specific follow-up duration. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective: To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally.Data Source: Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database.Study Design: Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-standardized in-hospital mortality rate (RSMR), and assessed hospital characteristics associated with the distinct patterns using multinomial logistic regression.Principal Findings: During 2005-2011, mean hospital RSC decreased from $12,003 to $10,782, while mean hospital RSMR declined from 3.9 to 3.2 percent. We identified five distinct hospital patterns: highest cost and low mortality (3.2 percent of the hospitals), high cost and low mortality (20.4 percent), medium cost and low mortality (34.6 percent), medium cost and high mortality (6.2 percent), and low cost and low mortality (35.6 percent). Longer hospital stay and greater use of intensive care unit and surgical procedures were associated with phenotypes with higher costs or greater mortality.Conclusions: Hospitals vary substantially in the joint longitudinal patterns of cost and mortality, suggesting marked difference in value of care. Understanding determinants of the variation will inform strategies for improving the value of hospital care. [ABSTRACT FROM AUTHOR]

Objective To examine racial/ethnic disparities in quality of schizophrenia care and assess the size of observed disparities across states and over time. Data Sources Medicaid claims data from CA, FL, NY, and NC. Study Design Observational repeated cross-sectional panel cohort study of white, black, and Latino fee-for-service adult beneficiaries with schizophrenia. Main outcome was the relationship of race/ethnicity and year with a composite measure of quality of schizophrenia care derived from 14 evidence-based quality indicators. Principal Findings Quality was assessed for 325,373 twelve-month person-episodes between 2002 and 2008, corresponding to 123,496 Medicaid beneficiaries. In 2002, quality was lowest for blacks in all states. With the exception of FL, quality was lower for Latinos than whites. In CA, blacks had about 43 percent of the individual indicators met compared to 58 percent for whites. Quality improved annually for all groups in CA, NY, and NC. While in CA the improvement was slightly larger for Latinos, in FL quality improved for blacks but declined for Latinos and whites. Conclusions Quality of schizophrenia care is poor and racial/ethnic disparities exist among Medicaid beneficiaries from four states. The size of the disparities varied across the states, and most of the initial disparities were unchanged by 2008. [ABSTRACT FROM AUTHOR]

This study aimed to refine a dimensional scale for measuring psychosocial adjustment in African youth using item response theory (IRT). A 60-item scale derived from qualitative data was administered to 667 war-affected adolescents (55% female). Exploratory factor analysis (EFA) determined the dimensionality of items based on goodness-of-fit indices. Items with loadings less than 0.4 were dropped. Confirmatory factor analysis (CFA) was used to confirm the scale's dimensionality found under the EFA. Item discrimination and difficulty were estimated using a graded response model for each subscale using weighted least squares means and variances. Predictive validity was examined through correlations between IRT scores ( θ) for each subscale and ratings of functional impairment. All models were assessed using goodness-of-fit and comparative fit indices. Fisher's Information curves examined item precision at different underlying ranges of each trait. Original scale items were optimized and reconfigured into an empirically-robust 41-item scale, the African Youth Psychosocial Assessment (AYPA). Refined subscales assess internalizing and externalizing problems, prosocial attitudes/behaviors and somatic complaints without medical cause. The AYPA is a refined dimensional assessment of emotional and behavioral problems in African youth with good psychometric properties. Validation studies in other cultures are recommended. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The evaluation, comparison, and public report of health care provider performance is essential to improving the quality of health care. Hospitals, as one type of provider, are often classified into quality tiers (e.g., top or suboptimal) based on their performance data for various purposes. However, potential misclassification might lead to detrimental effects for both consumers and payers. Although such risk has been highlighted by applied health services researchers, a systematic investigation of statistical approaches has been lacking. We assess and compare the expected accuracy of several commonly used classification methods: unadjusted hospital-level averages, shrinkage estimators under a random-effects model accommodating between-hospital variation, and two others based on posterior probabilities. Assuming that performance data follow a classic one-way random-effects model with unequal sample size per hospital, we derive accuracy formulae for these classification approaches and gain insight into how the misclassification might be affected by various factors such as reliability of the data, hospital-level sample size distribution, and cutoff values between quality tiers. The case of binary performance data is also explored using Monte Carlo simulation strategies. We apply the methods to real data and discuss the practical implications. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives To understand how drug therapy differently affects older women and men. Design Population-based, retrospective cohort study. Setting Ontario, Canada. Participants Twenty-one thousand five hundred twenty-six older adults (13,760 women, 7,766 men) with dementia newly started on oral atypical antipsychotic therapy between April 1, 2007, and March 1, 2010. Measurements Numbers and rates of serious events. Serious events were defined as a hospital admission or death within 30 days of treatment initiation. Unadjusted and adjusted odds ratios of women and men were compared in the full cohort and in strata based on setting of care, age, Charlson Comorbidity Index ( CCI), and antipsychotic dose. Results Of 21,526 older adults with a median age of 84, 1,889 (8.8%) had a serious event (1,044 women, 7.6%; 845 men, 10.9%). Of these, 363 women (2.6%) and 355 men (4.6%) died. Men were more likely than women to be hospitalized or die during the 30-day follow-up period (adjusted odds ratio = 1.47, 95% confidence interval = 1.33-1.62) and consistently more likely to experience a serious event in each stratum. A gradient of risk according to drug dose was found for the development of a serious event in women and men. Conclusion The risk of developing a serious event shortly after the initiation of antipsychotic therapy was high in women and men with dementia but was consistently higher in older men. This pattern remained the same in strata based on setting of care, age, CCI, and antipsychotic dose. [ABSTRACT FROM AUTHOR]

In this commentary, we argue that although randomization has many benefits, not all questions we seek to answer fit into a randomized setting. Our argument utilizes the clinical setting of carotid atherosclerosis management where specific clinical questions are answered by using a variety of comparative effectiveness designs. Observational studies should not be ruled out when designing studies to address questions of comparative effectiveness. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives. In disparities models, researchers adjust for differences in ''clinical need,'' including indicators of comorbidities. We reconsider this practice, assessing (1) if and how having a comorbidity changes the likelihood of recognition and treatment of mental illness; and (2) differences in mental health care disparities estimates with and without adjustment for comorbidities. Data. Longitudinal data from 2000 to 2007 Medical Expenditure Panel Survey (n511,083) split into pre and postperiods for white, Latino, and black adults with probable need for mental health care. Study Design. First, we tested a crowd-out effect (comorbidities decrease initiation of mental health care after a primary care provider [PCP] visit) using logistic regression models and an exposure effect (comorbidities cause more PCP visits, increasing initiation of mental health care) using instrumental variable methods. Second, we assessed the impact of adjustment for comorbidities on disparity estimates. Principal Findings. We found no evidence of a crowd-out effect but strong evidence for an exposure effect. Number of postperiod visits positively predicted initiation of mental health care. Adjusting for racial/ethnic differences in comorbidities increased black-white disparities and decreased Latino-white disparities. Conclusions. Positive exposure findings suggest that intensive follow-up programs shown to reduce disparities in chronic-care management may have additional indirect effects on reducing mental health care disparities. [ABSTRACT FROM AUTHOR]

In clinical trials multiple outcomes are often used to assess treatment interventions. This paper presents an evaluation of likelihood-based methods for jointly testing treatment effects in clinical trials with multiple continuous outcomes. Specifically, we compare the power of joint tests of treatment effects obtained from joint models for the multiple outcomes with univariate tests based on modeling the outcomes separately. We also consider the power and bias of tests when data are missing, a common feature of many trials, especially in psychiatry. Our results suggest that joint tests capitalize on the correlation of multiple outcomes and are more powerful than standard univariate methods, especially when outcomes are missing completely at random. When outcomes are missing at random, test procedures based on correctly specified joint models are unbiased, while standard univariate procedures are not. Results of a simulation study are reported, and the methods are illustrated in an example from the Clinical Antipsychotic Trials of Intervention Effectiveness for schizophrenia. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective: To investigate the feasibility, reliability, and validity of comprehensively assessing physician-level performance in ambulatory practice.Data Sources/study Setting: Ambulatory-based general internists in 13 states participated in the assessment.Study Design: We assessed physician-level performance, adjusted for patient factors, on 46 individual measures, an overall composite measure, and composite measures for chronic, acute, and preventive care. Between- versus within-physician variation was quantified by intraclass correlation coefficients (ICC). External validity was assessed by correlating performance on a certification exam.Data Collection/extraction Methods: Medical records for 236 physicians were audited for seven chronic and four acute care conditions, and six age- and gender-appropriate preventive services.Principal Findings: Performance on the individual and composite measures varied substantially within (range 5-86 percent compliance on 46 measures) and between physicians (ICC range 0.12-0.88). Reliabilities for the composite measures were robust: 0.88 for chronic care and 0.87 for preventive services. Higher certification exam scores were associated with better performance on the overall (r = 0.19; p<.01), chronic care (r = 0.14, p = .04), and preventive services composites (r = 0.17, p = .01).Conclusions: Our results suggest that reliable and valid comprehensive assessment of the quality of chronic and preventive care can be achieved by creating composite measures and by sampling feasible numbers of patients for each condition. [ABSTRACT FROM AUTHOR]

The assumption that comparative effectiveness research will provide timely, relevant evidence rests on changing the current framework for assembling evidence. In this commentary, we provide the background of how coverage decisions for new medical technologies are currently made in the United States. We focus on the statistical issues regarding how to use the ensemble of information for inferring comparative effectiveness. It is clear a paradigm shift in how clinical information is integrated in real-world settings to establish effectiveness is required. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Increasingly multiple outcomes are collected in order to characterize treatment effectiveness or to evaluate the impact of large policy initiatives. Often the multiple outcomes are non-commensurate, e.g. measured on different scales. The common approach to inference is to model each outcome separately ignoring the potential correlation among the responses. We describe and contrast several full likelihood and quasi-likelihood multivariate methods for non-commensurate outcomes. We present a new multivariate model to analyze binary and continuous correlated outcomes using a latent variable. We study the efficiency gains of the multivariate methods relative to the univariate approach. For complete data, all approaches yield consistent parameter estimates. When the mean structure of all outcomes depends on the same set of covariates, efficiency gains by adopting a multivariate approach are negligible. In contrast, when the mean outcomes depend on different covariate sets, large efficiency gains are realized. Three real examples illustrate the different approaches. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Developing clinically meaningful summary measures of health-care quality is key to inferring quality of care. Current summary measures use a number of different approaches to weight their individual measures but rarely use weights based on clinical 'importance'. Such an approach would help to focus quality improvement efforts on areas likely to have the largest impact on health outcomes. Using coronary artery bypass graft (CABG) surgery as a case study, we weight and combine 11 process, complication, and survival measures to summarize differences in quality-adjusted life expectancy 1 year following surgery for a sample of hospitals. We use a fully Bayesian analysis to estimate 1-year survival outcomes using a hierarchical exponential survival model. We then estimate the expected utility of the year following surgery for each patient using complication probabilities fitted from hierarchical models and utility values from the literature. We estimate quality-adjusted life years (QALYs) for each hospital as the utility-weighted average 1-year survival probability and then estimate 'incremental QALYs' by taking the difference in QALYs for each hospital relative to a comparison group that reflects the average performance of all hospitals in the state. We illustrate our framework by estimating incremental QALYs for 14 hospitals performing CABG surgery in Massachusetts in 2003 and find that a composite measure based on QALYs can change the classification of quality outliers relative to conventional mortality measures. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Health plans have begun to combine data on the quality and cost of medical providers in an attempt to identify and reward those that offer the greatest 'value.' The analytical methods used to combine these measures in the context of provider profiling have not been rigorously studied. We propose three methods to measure and compare the value of hospital care following acute myocardial infarction by combining a single measure of quality, in-hospital survival, and the cost of an episode of acute care. To illustrate these methods, we use administrative data for heart attack patients treated at 69 acute care hospitals in Massachusetts in fiscal year 2003. In the first method we reproduce a common approach to value profiling by modeling the two case mix-standardized outcomes independently. In the second approach, survival is regressed on patient risk factors and the average cost of care at each hospital. The third method models survival and cost for each hospital jointly and combines the outcomes on a common scale using a cost-effectiveness framework. For each method we use the resulting parameter estimates or functions of the estimates to compute posterior tail probabilities, representing the probability of being classified in the upper or lower quartile of the statewide distribution. Hospitals estimated to have the highest and lowest value according to each method are compared for consistency, and the advantages and disadvantages of each approach are discussed. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Both the private and public sectors have begun giving financial incentives to healthcare providers, such as hospitals, delivering superior 'quality of care'. Quality of care is assessed through a set of disease-specific measures that characterize the performance of healthcare providers. These measures are then combined into a unidimensional composite score. Most of the programs that reward superior performance use raw averages of the measures as the composite score. The scores based on raw averages fail to take into account typical characteristics of data used for performance evaluation, such as within-patient and within-hospital correlations, variable number of measures available in different hospitals, and missing data. In this paper, we contrast two different versions of composites based on raw average scores with a model-based score constructed using a latent variable model. We also present two methods to identify hospitals with superior performance. The methods are illustrated using national data collected to evaluate quality of care delivered by the U.S. acute care hospitals. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To study the relationship between initiating therapy with an antipsychotic medication and a subsequent new diagnosis of a drug-induced movement disorder other than parkinsonism in older adults with dementia. Retrospective, population-based cohort study. Ontario, Canada. Ontario residents aged 66 and older with a diagnosis of dementia newly started on treatment with typical or atypical antipsychotic therapy. Estimated relative risk of developing a drug-induced movement other than parkinsonism in the 1-year follow-up period after starting therapy with an antipsychotic medication. From April 1, 1997, to March 31, 2001, 21, 835 older adults with dementia who were newly started on antipsychotic medications were identified. Nine thousand seven hundred ninety subjects were started on atypical antipsychotics and 12,045 subjects started on typical antipsychotics. Demographic characteristics were similar between the groups. There were 5.24 cases of tardive dyskinesia (TD) or other drug-induced movement disorder per 100 person-years on therapy with a typical antipsychotic and 5.19 cases per 100 person-years on therapy with an atypical antipsychotic. The risk of developing drug-induced movement disorder while being treated with an atypical agent was not statistically different from that with a typical antipsychotic (relative risk=0.99, 95% confidence interval=0.86–1.15; P<.93). Older adults with dementia who are treated with typical or atypical antipsychotic therapy are at risk for developing TD and other drug-induced movement disorders. [ABSTRACT FROM AUTHOR]

Methodology for causal inference based on propensity scores has been developed and popularized in the last two decades. However, the majority of the methodology has concentrated on binary treatments. Only recently have these methods been extended to settings with multi-valued treatments. We propose a number of discrete choice models for estimating the propensity scores. The models differ in terms of flexibility with respect to potential correlation between treatments, and, in turn, the accuracy of the estimated propensity scores. We present the effects of discrete choice models used on performance of the causal estimators through a Monte Carlo study. We also illustrate the use of discrete choice models to estimate the effect of antipsychotic drug use on the risk of diabetes in a cohort of adults with schizophrenia. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To describe physician practices, ranging from solo and two-physician practices to large medical groups, in three geographically diverse parts of the country with strong managed care presences.Surveys of medical practices in three managed care markets conducted in 2000–2001.We administered questionnaires to all medical practices affiliated with two large health plans in Boston, MA, and Portland, OR, and to all practices providing primary care for cardiovascular disease patients admitted to five large hospitals in Minneapolis, MN. We offer data on how physician practices are structured under managed care in these geographically diverse regions of the country with a focus on the structural characteristics, financial arrangements, and care management strategies adopted by practices.A two-staged survey consisting of an initial telephone survey that was undertaken using CATI (computerized assisted telephone interviewing) techniques followed by written modules triggered by specific responses to the telephone survey.We interviewed 468 practices encompassing 668 distinct sites of care (overall response rate 72 percent). Practices had an average of 13.9 member physicians (range: 1–125). Most (80.1 percent) medium- (four to nine physicians) and large-size (10 or more physicians) groups regularly scheduled meetings to discuss resource utilization and referrals. Almost 90 percent of the practices reported that these meetings occurred at least once per month. The predominant method for paying practices was via fee-for-service payments. Most other payments were in the form of capitation. Overall, 75 percent of physician practices compensated physicians based on productivity, but there was substantial variation related to practice size. Nonetheless, of the practices that did not use straight productivity methods (45 percent of medium-sized practices and 54 percent of large practices), most used arrangements consisting of combinations of salary and productivity formulas.We found diversity in the characteristics and capabilities of medical practices in these three markets with high managed care involvement. Financial practices of most practices are geared towards rewarding productivity, and care management practices and capabilities such as electronic medical records remain underdeveloped. [ABSTRACT FROM AUTHOR]

Estimating causal effects in psychiatric clinical trials is often complicated by treatment non-compliance and missing outcomes. While new estimators have recently been proposed to address these problems, they do not allow for inclusion of continuous covariates. We propose estimators that adjust for continuous covariates in addition to non-compliance and missing data. Using simulations, we compare mean squared errors for the new estimators with those of previously established estimators. We then illustrate our findings in a study examining the efficacy of clozapine versus haloperidol in the treatment of refractory schizophrenia. For data with continuous or binary outcomes in the presence of non-compliance, non-ignorable missing data, and a covariate effect, the new estimators generally performed better than the previously established estimators. In the clozapine trial, the new estimators gave point and interval estimates similar to established estimators. We recommend the new estimators as they are unbiased even when outcomes are not missing at random and they are more efficient than established estimators in the presence of covariate effects under the widest variety of circumstances. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective. To compare process of care and outcome after acute myocardial infarction, for patients with and without mental illness, cared for in the Veterans Health Administration (VA) health care system. Data Sources/Setting. Primary clinical data from 81 VA hospitals. Study Design. This was a retrospective cohort study of 4,340 veterans discharged with clinically confirmed acute myocardial infarction. Of these, 859 (19.8 percent) met the definition of mental illness.Measures were age–adjusted in–hospital and 90–day cardiac procedure use; age–adjusted relative risks (RR) of use of thrombolytic therapy, betablockers, angiotensin converting enzyme (ACE) inhibitors, or aspirin at discharge; risk adjusted 30–day and one–year mortality. Results. Patients with mental illness were marginally less likely than those without mental illness to undergo in–hospital angiography (age–adjusted RR 0.90 [95 percent confidence interval: 0.83, 0.98]), but there was no significant difference in the age adjusted RR of coronary artery bypass graft surgery in the 90 days after admission (0.85 [0.69,1.05]), or in the receipt of medications of known benefit. For example, ideal candidates with and without mental illness were equally likely to receive beta–blockers at the time of discharge (age–adjusted RR 0.92 [0.82, 1.02]). The risk–adjusted odds ratio (OR) for death in patients with mental illness versus those without mental illness within 30 days was 1.00 (0.75,1.32), and for death within one year was 1.25 (1.00,1.53). Conclusions. Veterans Health Administration patients with mental illness were marginally less likely than those without mental illness to receive diagnostic angiography, and no less likely to receive revascularization or medications of known benefit after acute myocardial infarction. Mortality at one year may have been higher, although this finding did not reach statistical significance. These findings are consistent with other studies showing reduced health care disparities in the VA for other vulnerable groups, and suggest that an integrated health care system with few financial barriers to health care access may attenuate some health care disparities. Further work should address how health care organizational features might narrow disparities in health care for vulnerable groups. [ABSTRACT FROM AUTHOR]

Objective. To examine whether availability of cardiac services at the admitting hospital affects case–selection for angiography and one–year survival following angiography, within groups of patients who have similar clinical need for angiography according to published criteria. Study Setting. Elderly Medicare beneficiaries (37,788) discharged with a diagnosis of acutemyocardial infarction (AMI) from hospitals in seven U.S. states between February 1994 and July 1995.We focused on patients who were eligible to receive angiography 12 or more hours after symptom onset. Data Collection. Data were abstracted from patient’s medical records, Medicare National Claims Standard Analytic Files, Health Care Financing Administration (HCFA) Provider of Service File and Health Insurance Master File. Methods. Admitting hospitals were classified as offering no cardiac services, angiography only, or revascularization. Case–selection differences across these three types of hospitals were examined by comparing relative risk of receiving angiography for various patient and hospital characteristics. Relative differences in one–year survival rate, comparing patients who received angiography to those who did not, were estimated within each hospital type and clinical need category (necessary, appropriate, or uncertain) after matching on propensity to receive angiography. Principal Findings. Compared to patients for whom angiography was deemed necessary, the relative risk of receiving angiography among those for whom it was deemed of uncertain benefit was 0.58, 0.79, and 0.92 (p–value of homogeneity test < 0.001) at hospitals offering no cardiac services, angiography only, and revascularization, respectively. There was no significant difference in survival following angiography across hospital types, overall as well as within clinical need categories. Conclusions. Despite increased case selection at hospitals with on–site cardiac services, there was no evidence of increase in the survival rate associated with angiography use at these hospitals. [ABSTRACT FROM AUTHOR]

A change in payment mechanism for inpatient care from per diem to per episode creates two incentives -- a marginal and an average price effect -- to change length of stay. The decrease in marginal price per day to zero should reduce the length of stay, while an increase in average price per inpatient stay should increase the length of stay. This study uses data from a natural experiment to estimate both marginal and average price elasticities, and to test whether the length of stay falls after the introduction of prospective payment in a sample of 8509 severely mentally ill patients. We estimate that the marginal price elasticity is zero, but the average price elasticity is between 0.16 and 0.20. The results were generally robust for short- and long stayers, and for persons admitted early and late after the change in payment mechanism . The model controlled for hospital fixed effects and individual random effects.