Regional variation in length of stay for stroke inpatient rehabilitation in traditional Medicare and Medicare Advantage.

Regional variation in health care use threatens efficient and equitable resource allocation. Within the Medicare program, variation in care delivery may differ between centrally administered traditional Medicare (TM) and privately managed Medicare Advantage (MA) plans, which rely on different strategies to control care utilization. As MA enrollment grows, it is particularly important for program design and long-term health care equity to understand regional variation between TM and MA plans. This study examined regional variation in length of stay (LOS) for stroke inpatient rehabilitation between TM and MA plans in 2019 and how that changed in 2020, the first year of the COVID-19 pandemic. Results showed that MA plans had larger across-region variations than TM (SD = 0.26 vs 0.24 days; 11% relative difference). In 2020, across-region variation for MA further increased, but the trend for TM stayed relatively stable. Market competition among all inpatient rehabilitation facilities (IRFs) within a region was associated with a moderate increase in within-region variation of LOS (elasticity = 0.46). Policies reducing administrative variation across MA plans or increasing regional market competition among IRFs can mitigate regional variation in health care use.

Analyzing health outcomes measured as bounded counts.

This paper assesses analytical strategies that respect the bounded-count nature of health outcomes encountered often in empirical applications. Absent in the literature is a comprehensive discussion and critique of strategies for analyzing and understanding such data. The paper's goal is to provide an in-depth consideration of prominent issues arising in and strategies for undertaking such analyses, emphasizing the merits and limitations of various analytical tools empirical researchers may contemplate. Three main topics are covered. First, bounded-count health outcomes' measurement properties are reviewed and their implications assessed. Second, issues arising when bounded-count outcomes are the objects of concern in evaluations are described. Third, the (conditional) probability and moment structures of bounded-count outcomes are derived and corresponding specification and estimation strategies presented with particular attention to partial effects. Many questions may be asked of such data in health research and a researcher's choice of analytical method is often consequential.

Using measures of race to make clinical predictions: Decision making, patient health, and fairness.

The use of race measures in clinical prediction models is contentious. We seek to inform the discourse by evaluating the inclusion of race in probabilistic predictions of illness that support clinical decision making. Adopting a static utilitarian framework to formalize social welfare, we show that patients of all races benefit when clinical decisions are jointly guided by patient race and other observable covariates. Similar conclusions emerge when the model is extended to a two-period setting where prevention activities target systemic drivers of disease. We also discuss non-utilitarian concepts that have been proposed to guide allocation of health care resources.

The Relative Importance of Hospital Discharge and Patient Composition in Changing Post-Acute Care Utilization and Outcomes Among Medicare Beneficiaries.

Background: The COVID-19 pandemic changed care delivery. But the mechanisms of changes were less understood. Objectives: Examine the extent to which the volume and pattern of hospital discharge and patient composition contributed to the changes in post-acute care (PAC) utilization and outcomes during the pandemic. Research design: Retrospective cohort study. Medicare claims data on hospital discharges in a large healthcare system from March 2018 to December 2020. Subjects: Medicare fee-for-service beneficiaries, 65 years or older, hospitalized for non-COVID diagnoses. Measures: Hospital discharges to Home Health Agencies (HHA), Skilled Nursing Facilities (SNF), and Inpatient Rehabilitation Facilities (IRF) versus home. Thirty- and ninety-day mortality and readmission rates. Outcomes were compared before and during the pandemic with and without adjustment for patient characteristics and/or interactions with the pandemic onset. Results: During the pandemic, hospital discharges declined by 27%. Patients were more likely to be discharged to HHA (+4.6%, 95% CI [3.2%, 6.0%]) and less likely to be discharged to either SNF (-3.9%, CI [-5.2%, -2.7%]) or to home (-2.8% CI [-4.4%, -1.3%]). Thirty- and ninety-day mortality rates were significantly higher by 2% to 3% points post-pandemic. Readmission were not significantly different. Up to 15% of the changes in discharge patterns and 5% in mortality rates were attributable to patient characteristics. Conclusions: Shift in discharge locations were the main driver of changes in PAC utilization during the pandemic. Changes in patient characteristics explained only a small portion of changes in discharge patterns and were mainly channeled through general impacts rather than differentiated responses to the pandemic.

Embracing Uncertainty: The Value of Partial Identification in Public Health and Clinical Research.

INTRODUCTION: This paper describes the methodology of partial identification and its applicability to empirical research in preventive medicine and public health. METHODS: The authors summarize findings from the methodologic literature on partial identification. The analysis was conducted in 2020-2021. RESULTS: The applicability of partial identification methods is demonstrated using 3 empirical examples drawn from published literature. CONCLUSIONS: Partial identification methods are likely to be of considerable interest to clinicians and others engaged in preventive medicine and public health research.

Discovering treatment effectiveness via median treatment effects-Applications to COVID-19 clinical trials.

Comparing median outcomes to gauge treatment effectiveness is widespread practice in clinical and other investigations. While common, such difference-in-median characterizations of effectiveness are but one way to summarize how outcome distributions compare. This paper explores properties of median treatment effects (TEs) as indicators of treatment effectiveness. The paper's main focus is on decisionmaking based on median TEs and it proceeds by considering two paths a decisionmaker might follow. Along one, decisions are based on point-identified differences in medians alongside partially identified median differences; along the other decisions are based on point-identified differences in medians in conjunction with other point-identified parameters. On both paths familiar difference-in-median measures play some role yet in both the traditional standards are augmented with information that will often be relevant in assessing treatments' effectiveness. Implementing either approach is straightforward. In addition to its analytical results the paper considers several policy contexts in which such considerations arise. While the paper is framed by recently reported findings on treatments for COVID-19 and uses several such studies to explore empirically some properties of median-treatment-effect measures of effectiveness, its results should be broadly applicable.

Likelihood of hospital readmission in Medicare Advantage and Fee-For-Service within same hospital.

OBJECTIVE: To assess the extent to which all-cause 30-day readmission rate varies by Medicare program within the same hospitals. STUDY DESIGN: We used conditional logistic regression clustered by hospital and generalized estimating equations to compare the odds of unplanned all-cause 30-day readmission between Medicare Fee-for-Service (FFS) and Medicare Advantage (MA). DATA COLLECTION: Wisconsin Health Information Organization collects claims data from various payers including private insurance, Medicare, and Medicaid, twice a year. PRINCIPAL FINDINGS: For 62 of 66 hospitals, hospital-level readmission rates for MA were lower than those for Medicare FFS. The odds of 30-day readmission in MA were 0.92 times lower than Medicare FFS within the same hospital (odds ratio, 0.93; 95 percent confidence interval, 0.89-0.98). The adjusted overall readmission rates of Medicare FFS and MA were 14.9 percent and 11.9 percent, respectively. CONCLUSION: These findings provide additional evidence of potential variations in readmission risk by payer and support the need for improved monitoring systems in hospitals that incorporate payer-specific data. Further research is needed to delineate specific care delivery factors that contribute to differential readmission risk by payer source.

Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects.

While many results from the treatment-effect and related literatures are familiar and have been applied productively in health economics evaluations, other potentially useful results from those literatures have had little influence on health economics practice. With the intent of demonstrating the value and use of some of these results in health economics applications, this paper focuses on one particular class of parameters that describe probabilities that one outcome is larger or smaller than other outcomes ("inequality probabilities"). While the properties of such parameters have been exposited in the technical literature, they have scarcely been considered in informing practical questions in health evaluations. This paper shows how such probabilities can be used informatively, and describes how they might be identified or bounded informatively given standard sampling assumptions and information only on marginal distributions of outcomes. The logic of these results and the empirical implementation thereof-sampling, estimation, and inference-are straightforward. Derivations are provided and several health-related applications are presented.

Considering mean and inequality health outcomes together: the population health performance index.

BACKGROUND: The purpose was to develop and test a population health measure that combines mean health outcomes and inequalities into a single GDP-like metric to help policymakers measure population health performance on both dimensions in one metric. METHODS: The Population Health Performance Index is a weighted average of a mean index and an inequality index according to the user's inequality aversion. We deploy this methodology for two combinations of health outcome and disparity domain: infant mortality by race and unhealthy days by education. RESULTS: The PHPI is bounded between 0 and 1, and is comprised of a weighted average of two separate indices: a mean index and an inequality index, with 1 representing the ideal state of no ill health and no inequality and 0 representing the worst state in the U.S. PHPI values across states (neutral 50:50 weighting) vary between 0.60 (Massachusetts) to 0.17 (Delaware) for infant mortality by race and between 0.65 (North Dakota) to 0.00 (West Virginia) for unhealthy days by education. For some states, the choice of inequality aversion significantly impacts their PHPI value and state rank. CONCLUSIONS: Mean and inequality health outcomes can be combined into a single Population Health Performance Index for use by public and private policy makers, like the GDP is used as a summary metric to measure economic output. The index can allow for varying degrees of inequality aversion, an individual's or jurisdiction's value choice that can substantially impact the value of this new summary population health metric.

Substance Use Screening, Brief Intervention, and Referral to Treatment Among Medicaid Patients in Wisconsin: Impacts on Healthcare Utilization and Costs.

Unhealthy substance use in the USA results in significant mortality and morbidity. This study measured the effectiveness of paraprofessional-administered substance use screening, brief intervention, and referral to treatment (SBIRT) services on subsequent healthcare utilization and costs. The pre-post with comparison group study design used a population-based sample of Medicaid patients 18-64 years receiving healthcare services from 33 clinics in Wisconsin. Substance use screens were completed by 7367 Medicaid beneficiaries, who were compared to 6751 randomly selected treatment-as-usual Medicaid patients. Compared to unscreened patients, those screened changed their utilization over the 24-month follow-up period by 0.143 outpatient days per member per month (PMPM) (p < 0.001), -0.036 inpatient days PMPM (p < 0.05), -0.001 inpatient admissions PMPM (non-significant), and -0.004 emergency department days PMPM (non-significant). The best estimate of net annual savings is $391 per Medicaid adult beneficiary (2014 dollars). SBIRT was associated with significantly greater outpatient visits and significant reductions in inpatient days among working-age Medicaid beneficiaries in Wisconsin.

Marginal Effects in Multivariate Probit Models.

Estimation of marginal or partial effects of covariates x on various conditional parameters or functionals is often a main target of applied microeconometric analysis. In the specific context of probit models, estimation of partial effects involving outcome probabilities will often be of interest. Such estimation is straightforward in univariate models, and results covering the case of quadrant probability marginal effects in bivariate probit models for jointly distributed outcomes y have previously been described in the literature. This paper's goals are to extend Greene's results to encompass the general M≥2 multivariate probit (MVP) context for arbitrary orthant probabilities and to extended these results to models that condition on subvectors of y and to multivariate ordered probit data structures. It is suggested that such partial effects are broadly useful in situations wherein multivariate outcomes are of concern.

Estimation of Multivariate Probit Models via Bivariate Probit.

This paper suggests the utility of estimating multivariate probit (MVP) models using a chain of bivariate probit estimators. The proposed approach is based on Stata's biprobit and suest procedures and is driven by a Mata function. Two potential advantages over Stata's mvprobit procedure are suggested: significant reductions in computation time; and essentially unlimited dimensionality of the outcome set. The time savings arise because the proposed approach does not rely simulation methods; the dimension advantage arises because only pairs of outcomes are considered at each estimation stage. Importantly, the proposed approach provides a consistent estimator of all the MVP model's parameters under the same assumptions required for consistent estimation via mvprobit, and simulation exercises reported below suggest no loss of estimator precision relative to mvprobit.