Reformulating provider profiling by grouping providers treating similar patients prior to evaluating performance.

Standard approaches to comparing health providers' performance rely on hierarchical logistic regression models that adjust for patient characteristics at admission. Estimates from these models may be misleading when providers treat different patient populations and the models are misspecified. To address this limitation, we propose a novel profiling approach that identifies groups of providers treating similar populations of patients and then evaluates providers' performance within each group. The groups of providers are identified using a Bayesian multilevel finite mixture of general location models. To compare the performance of our proposed profiling approach to standard methods, we use patient-level data from 119 skilled nursing facilities in Massachusetts. We use simulated and observed outcome data to explore the performance of these profiling methods in different settings. In simulations, our proposed method classifies providers to groups with similar patients' admission characteristics. In addition, in the presence of limited overlap in patient characteristics across providers and misspecifications of the outcome model, the provider-level estimates obtained using our approach identified providers that under- and overperformed compared to the standard regression-based approaches more accurately.

Multiple imputation procedures for estimating causal effects with multiple treatments with application to the comparison of healthcare providers.

Choosing between multiple healthcare providers requires us to simultaneously compare the expected outcomes under each provider. This comparison is complex because the composition of patients treated by each provider may differ. Similar issues arise when simultaneously comparing the adverse effects of interventions using non-randomized data. To simultaneously estimate the effects of multiple providers/interventions we propose procedures that explicitly impute the set of potential outcomes for each subject. The procedures are based on different specifications of the generalized additive models (GAM) and the Bayesian additive regression trees (BART). We compare the performance of the proposed procedures to previously proposed matching and weighting procedures using an extensive simulation study for continuous outcomes. Our simulations show that when the distributions of the covariates across treatment groups have adequate overlap, the multiple imputation procedures based on separate BART or GAM models in each treatment group are generally superior to weighting based methods and have similar and sometimes better performance than matching on the logit of the generalized propensity score. Another advantage of these multiple imputation procedures is the ability to provide point and interval estimates to a wide range of causal effect estimands. We apply the proposed procedures to comparing multiple nursing homes in Massachusetts for readmission outcomes. The proposed approach can be applied to other causal effects applications with multiple treatments.

Racial/Ethnic Differences in 30-Day Mortality for Heart Failure and Pneumonia in the Veterans Health Administration Using Claims-based, Clinical, and Social Risk-adjustment Variables.

BACKGROUND: Prior studies have identified lower mortality in Black Veterans compared with White Veterans after hospitalization for common medical conditions, but these studies adjusted for comorbid conditions identified in administrative claims. OBJECTIVES: The objectives of this study were to compare mortality for non-Hispanic White (hereafter, "White"), non-Hispanic Black (hereafter, "Black"), and Hispanic Veterans hospitalized for heart failure (HF) and pneumonia and determine whether observed mortality differences varied according to whether claims-based comorbid conditions and/or clinical variables were included in risk-adjustment models. RESEARCH DESIGN: This was an observational study. SUBJECTS: The study cohort included 143,520 admissions for HF and 127,782 admissions for pneumonia for Veterans hospitalized in 132 Veterans Health Administration (VA) Medical Centers between January 2009 and September 2015. MEASURES: The primary independent variable was racial/ethnic group (ie, Black, Hispanic, and non-Hispanic White), and the outcome was all-cause mortality 30 days following admission. To compare mortality by race/ethnicity, we used logistic regression models that included different combinations of claims-based, clinical, and sociodemographic variables. For each model, we estimated the average marginal effect (AME) for Black and Hispanic Veterans relative to White Veterans. RESULTS: Among the 143,520 (127,782) hospitalizations for HF (pneumonia), the average patient age was 71.6 (70.9) years and 98.4% (97.1%) were male. The unadjusted 30-day mortality rates for HF (pneumonia) were 7.2% (11.0%) for White, 4.1% (10.4%) for Black and 8.4% (16.9%) for Hispanic Veterans. Relative to White Veterans, when only claims-based variables were used for risk adjustment, the AME (95% confidence interval) for the HF [pneumonia] cohort was -2.17 (-2.45, -1.89) [0.08 (-0.41, 0.58)] for Black Veterans and 1.32 (0.49, 2.15) [4.51 (3.65, 5.38)] for Hispanic Veterans. When clinical variables were incorporated in addition to claims-based ones, the AME, relative to White Veterans, for the HF [pneumonia] cohort was -1.57 (-1.88, -1.27) [-0.83 (-1.31, -0.36)] for Black Veterans and 1.50 (0.71, 2.30) [3.30 (2.49, 4.11)] for Hispanic Veterans. CONCLUSIONS: Compared with White Veterans, Black Veterans had lower mortality, and Hispanic Veterans had higher mortality for HF and pneumonia. The inclusion of clinical variables into risk-adjustment models impacted the magnitude of racial/ethnic differences in mortality following hospitalization. Future studies examining racial/ethnic disparities should consider including clinical variables for risk adjustment.

Mortality Trends for Veterans Hospitalized With Heart Failure and Pneumonia Using Claims-Based vs Clinical Risk-Adjustment Variables.

Importance: Prior studies have reported declines in mortality for patients admitted to Veterans Health Administration (VA) and non-VA hospitals using claims-based risk adjustment. These apparent mortality reductions may be influenced by changes in coding practices. Objective: To compare trends in the VA for 30-day mortality following hospitalization for heart failure (HF) and pneumonia using claims-based and clinical risk-adjustment models. Design, Setting, and Participants: This observational time-trend study analyzed admissions to a VA Medical Center with a principal diagnosis of HF, pneumonia, or sepsis/respiratory failure (RF) with a secondary diagnosis of pneumonia. Exclusion criteria included having less than 12 months of VA enrollment, being discharged alive within 24 hours, leaving against medical advice, and hospice utilization. Exposures: Admission to a VA hospital from January 2009 through September 2015. Main Outcomes and Measures: The primary outcome was 30-day, all-cause mortality. All models included age and sex. Claims-based covariates included 22 (30) comorbidities for HF (pneumonia). Clinical covariates included vital signs, laboratory values, and ejection fraction. Results: Among the 146 924 HF admissions, the mean (SD) age was 71.6 (11.4) years and 144 502 (98.4%) were men; among the 131 325 admissions for pneumonia, the mean (SD) age was 70.8 (12.3) years and 127 491 (97.1%) were men. Unadjusted 30-day mortality rates were 6.45% (HF) and 11.22% (pneumonia). Claims-based models showed an increased predicted risk of 30-day mortality over time (0.019 percentage points per quarter for HF [95% CI, 0.015 to 0.023]; 0.053 percentage points per quarter for pneumonia [95% CI, 0.043 to 0.063]). Clinical models showed declines or no change in predicted risk (-0.014 percentage points per quarter for HF [95% CI, -0.020 to -0.008]; -0.004 percentage points per quarter for pneumonia [95% CI, -0.017 to 0.008]). Claims-based risk adjustment yielded declines in 30-day mortality of 0.051 percentage points per quarter for HF (95% CI, -0.074 to -0.027) and 0.084 percentage points per quarter for pneumonia (95% CI, -0.111 to -0.056). Models adjusting for clinical covariates attenuated or eliminated these changes for HF (-0.017 percentage points per quarter; 95% CI, -0.039 to 0.006) and for pneumonia (-0.026 percentage points per quarter; 95% CI, -0.052 to 0.001). Compared with the claims-based models, the clinical models for HF and pneumonia more accurately differentiated between patients who died after 30 days and those who did not. Conclusions and Relevance: Among HF and pneumonia hospitalizations, adjusting for clinical covariates attenuated declines in mortality rates identified using claims-based models. Assessments of temporal trends in 30-day mortality using claims-based risk adjustment should be interpreted with caution.