Affordability of Forensic Assertive Community Treatment Programs: A Return-on-Investment Analysis.

OBJECTIVE: In this study, the authors assessed return on investment (ROI) associated with a forensic assertive community treatment (FACT) program. METHODS: A retrospective secondary data analysis of a randomized controlled trial comprising 70 legal-involved patients with severe mental illness was conducted in Rochester, New York. Patients were randomly assigned to receive either FACT or outpatient psychiatric treatment including intensive case management. Unit of service costs associated with psychiatric emergency department visits, psychiatric inpatient days, and days in jail were obtained from records of New York State Medicaid and the Department of Corrections. The total dollar value difference between the two trial arms calculated on a per-patient-per-year (PPPY) basis constituted the return from the FACT intervention. The FACT investment cost was defined by the total additional PPPY cost associated with FACT implementation relative to the control group. ROI was calculated by dividing the return by the investment cost. RESULTS: The estimated return from FACT was $27,588 PPPY (in 2019 dollars; 95% confidence interval [CI]=$3,262-$51,913), which was driven largely by reductions in psychiatric inpatient days, and the estimated investment cost was $18,440 PPPY (95% CI=$15,215-$21,665), implying an ROI of 1.50 (95% CI=0.35-2.97) for FACT. CONCLUSIONS: The Rochester FACT program was associated with approximately $1.50 return for every $1 spent on its implementation, even without considering potential returns from other sources, including reductions in acute medical care, crime-related damages, and public safety costs. ROI estimates were highly dependent on context-specific factors, particularly Medicaid reimbursement rates for assertive community treatment and hospital stays.

Impact of non-cardiovascular disease burden on thirty-day hospital readmission in heart failure patients.

BACKGROUND: Little is known about the impact of non-cardiovascular disease (CVD) burden on 30- -day readmission in heart failure (HF) patients. The aim of the study was to assess the role of non-CVD burden on 30-day readmission in HF patients. \ METHODS: We analyzed the effect of non-CVD burden by frequency of ICD-9 code categories on readmis-sions of patients discharged with a primary diagnosis of HF. We first modeled the probability of readmis-sion within 30 days as a function of demographic and clinical covariates in a randomly selected training dataset of the total cohort. Variable selection was carried out using a bootstrap LASSO procedure with 1000 bootstrap samples, the final model was tested on a validation dataset. Adjusted odds ratios and confidence intervals were reported in the validation dataset. RESULTS: There were a total of 6228 HF hospitalizations, 1523 (24%) with readmission within 30 days of discharge. The strongest predictor for 30-day readmissions was any hospital admission in the prior year (p < 0.001). Cardiovascular risk factors did not enter the final model. However, digestive system diseases increased the risk for readmission by 17% for each diagnosis (p = 0.046), while respiratory diseases and genitourinary diseases showed a trend toward a higher risk of readmission (p = 0.07 and p = 0.09, respectively). Non-CVDs out-competed cardiovascular covariates previously reported to predict readmission. CONCLUSIONS: In patients with HF hospitalization, prior admissions predicted 30-day readmission. Diseases of the digestive system also increase 30-day readmission rates. Assessment of non-CVD burden in HF patients could serve as an important risk marker for 30-day readmissions.

Ethnic Disparities in Medicare Part D Satisfaction and Intention to Switch Plans.

Medicare Part D has been successful in providing affordable prescription drug coverage with relatively high levels of beneficiary reported satisfaction. We use nationally representative survey data to examine whether racial/ethnic disparities exist in reported Part D satisfaction and plan evaluations. Compared to non-Hispanic White Medicare beneficiaries, Hispanic beneficiaries are considerably more likely to report to switch to a new plan in the next year and, among beneficiaries auto-enrolled in a Part D plan, are less likely to be very satisfied with the currently enrolled plan. The findings of ethnic disparities in both Medicare Part D plan satisfaction and the intent to switch plans call for future quality and equity improvement efforts to address these disparities.

The Care Span: Lower Hispanic participation in Medicare Part D may reflect program barriers.

Despite the successes of Medicare's Part D prescription drug program, an estimated 12.5 percent of Americans ages sixty-five and older do not have prescription drug coverage. It is possible that some who remain without coverage do so for rational economic reasons. However, barriers to insurance uptake, such as the program's complexity, may exist for certain elderly people. Racial and ethnic minorities may be particularly susceptible to these barriers. To investigate the role that race and ethnicity may play in Medicare Part D participation, we analyzed data from the 2011 National Health and Aging Trends Study. We found that Hispanics were 35 percent less likely than non-Hispanic whites to have coverage, after individual predictors of prescription drug demand were controlled for. There was no statistically significant difference in Part D coverage between non-Hispanic blacks and non-Hispanic whites. Results of a stratified analysis suggest that the difference between Hispanics and non-Hispanic whites in Part D coverage may be driven by ethnic disparities among those eligible for the low-income Part D subsidy but not automatically enrolled in it. Further research is needed to identify both the exact mechanisms underlying the observed differential uptake in the rapidly growing elderly Hispanic population and potential policy-based solutions.

Continuity of care and the risk of preventable hospitalization in older adults.

IMPORTANCE: Preventable hospitalizations are common among older adults for reasons that are not well understood. OBJECTIVE: To determine whether Medicare patients with ambulatory visit patterns indicating higher continuity of care have a lower risk of preventable hospitalization. DESIGN: Retrospective cohort study. SETTING: Ambulatory visits and hospital admissions. PARTICIPANTS: Continuously enrolled fee-for-service Medicare beneficiaries older than 65 years with at least 4 ambulatory visits in 2008. EXPOSURES: The concentration of patient visits with physicians measured for up to 24 months using the continuity of care score and usual provider continuity score on a scale from 0 to 1. MAIN OUTCOMES AND MEASURES: Index occurrence of any 1 of 13 preventable hospital admissions, censoring patients at the end of their 24-month follow-up period if no preventable hospital admissions occurred, or if they died. RESULTS: Of the 3,276,635 eligible patients, 12.6% had a preventable hospitalization during their 2-year observation period, most commonly for congestive heart failure (25%), bacterial pneumonia (22.7%), urinary infection (14.9%), or chronic obstructive pulmonary disease (12.5%). After adjustment for patient baseline characteristics and market-level factors, a 0.1 increase in continuity of care according to either continuity metric was associated with about a 2% lower rate of preventable hospitalization (continuity of care score hazard ratio [HR], 0.98 [95% CI, 0.98-0.99; usual provider continuity score HR, 0.98 [95% CI, 0.98-0.98). Continuity of care was not related to mortality rates. CONCLUSIONS AND RELEVANCE: Among fee-for-service Medicare beneficiaries older than 65 years, higher continuity of ambulatory care is associated with a lower rate of preventable hospitalization.

What is the best way to estimate hospital quality outcomes? A simulation approach.

OBJECTIVE: To test the accuracy of alternative estimators of hospital mortality quality using a Monte Carlo simulation experiment. DATA SOURCES: Data are simulated to create an admission-level analytic dataset. The simulated data are validated by comparing distributional parameters (e.g., mean and standard deviation of 30-day mortality rate, hospital sample size) with the same parameters observed in Medicare data for acute myocardial infarction (AMI) inpatient admissions. STUDY DESIGN: We perform a Monte Carlo simulation experiment in which true quality is known to test the accuracy of the Observed-over-Expected estimator, the Risk Standardized Mortality Rate (RSMR), the Dimick and Staiger (DS) estimator, the Hierarchical Poisson estimator, and the Moving Average estimator using hospital 30-day mortality for AMI as the outcome. Estimator accuracy is evaluated for all hospitals and for small, medium, and large hospitals. DATA EXTRACTION METHODS: Data are simulated. PRINCIPAL FINDINGS: Significant and substantial variation is observed in the accuracy of the tested outcome estimators. The DS estimator is the most accurate for all hospitals and for small hospitals using both accuracy criteria (root mean squared error and proportion of hospitals correctly classified into quintiles). CONCLUSIONS: The mortality estimator currently in use by Medicare for public quality reporting, the RSMR, has been shown to be less accurate than the DS estimator, although the magnitude of the difference is not large. Pending testing and validation of our findings using current hospital data, CMS should reconsider the decision to publicly report mortality rates using the RSMR.

A flexible two-part random effects model for correlated medical costs.

In this paper, we propose a flexible "two-part" random effects model (Olsen and Schafer, 2001; Tooze et al., 2002) for correlated medical cost data. Typically, medical cost data are right-skewed, involve a substantial proportion of zero values, and may exhibit heteroscedasticity. In many cases, such data are also obtained in hierarchical form, e.g., on patients served by the same physician. The proposed model specification therefore consists of two generalized linear mixed models (GLMM), linked together by correlated random effects. Respectively, and conditionally on the random effects and covariates, we model the odds of cost being positive (Part I) using a GLMM with a logistic link and the mean cost (Part II) given that costs were actually incurred using a generalized gamma regression model with random effects and a scale parameter that is allowed to depend on covariates (cf., Manning et al., 2005). The class of generalized gamma distributions is very flexible and includes the lognormal, gamma, inverse gamma and Weibull distributions as special cases. We demonstrate how to carry out estimation using the Gaussian quadrature techniques conveniently implemented in SAS Proc NLMIXED. The proposed model is used to analyze pharmacy cost data on 56,245 adult patients clustered within 239 physicians in a mid-western U.S. managed care organization.

Conditional GEE for recurrent event gap times.

This paper deals with the analysis of recurrent event data subject to censored observation. Using a suitable adaptation of generalized estimating equations for longitudinal data, we propose a straightforward methodology for estimating the parameters indexing the conditional means and variances of the process interevent (i.e. gap) times. The proposed methodology permits the use of both time-fixed and time-varying covariates, as well as transformations of the gap times, creating a flexible and useful class of methods for analyzing gap-time data. Censoring is dealt with by imposing a parametric assumption on the censored gap times, and extensive simulation results demonstrate the relative robustness of parameter estimates even when this parametric assumption is incorrect. A suitable large-sample theory is developed. Finally, we use our methods to analyze data from a randomized trial of asthma prevention in young children.

Quantifying the physician contribution to managed care pharmacy expenses: a random effects approach.

BACKGROUND: Despite the availability of more sophisticated techniques, few alternatives to ordinary least squares (OLS) regression have been utilized to profile physician prescribing in managed care. It is not known to what extent the modest R values derived from OLS models reflect incomplete risk adjustment or widely varying physician prescribing patterns. OBJECTIVES: To quantify the role of interphysician variability relative to overall variability in managed care pharmacy expenses, and to examine the extent to which different statistical approaches generate meaningful differences in profile results. RESEARCH DESIGN: Comparison of three basic statistical modeling approaches: OLS, fixed effects regression, and random effects (ie, hierarchical) regression models. SETTING: Two managed care populations that differed more than 2-fold in per member pharmacy expenditures in 1999, one from the Midwestern United States, the other from three Western States. MAIN OUTCOME MEASURES: The intraclass correlation coefficient (ICC, the proportion of variability in expenses attributable to differences among physicians) and the range of projected expenses attributed to each physician's prescribing style. RESULTS: The ICCs were small for aggregated pharmacy expenditures, 0.04 or less in both populations. As determined by OLS, the most costly physician contributed 94,399 U.S. dollars in excess expenses to the organization whereas the most parsimonious saved 89,940 U.S. dollars. When derived from random effects models, the range in performance was 63% of that derived from OLS. CONCLUSIONS: In the populations studied, systematic prescribing differences among physicians were small relative to the overall variability in pharmacy expenses, suggesting other factors were more likely driving these costs. Random effects models generated smaller estimates of the individual physicians' contribution to costs, sometimes considerably, relative to those derived from OLS and fixed effects approaches.

Flexible hazard regression modeling for medical cost data.

The modeling of lifetime (i.e. cumulative) medical cost data in the presence of censored follow-up is complicated by induced informative censoring, rendering standard survival analysis tools invalid. With few exceptions, recently proposed nonparametric estimators for such data do not extend easily to handle covariate information. We propose to model the hazard function for lifetime cost endpoints using an adaptation of the HARE methodology (Kooperberg, Stone, and Truong, Journal of the American Statistical Association, 1995, 90, 78-94). Linear splines and their tensor products are used to adaptively build a model that incorporates covariates and covariate-by-cost interactions without restrictive parametric assumptions. The informative censoring problem is handled using inverse probability of censoring weighted estimating equations. The proposed method is illustrated using simulation and also with data on the cost of dialysis for patients with end-stage renal disease.