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.

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.

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.

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.

The association between motor vehicle injuries and health-related quality of life: a longitudinal study of a population-based sample in the United States.

PURPOSE: As many as 3 million US residents are injured in traffic-related incidents every year leaving many victims with disabling conditions. To date, limited numbers of studies have examined the effects of traffic-related injuries on self-reported health. This study aims to examine the association between health-related quality of life (HRQOL) and traffic-related injuries longitudinally in a nationally representative sample of US adult population. METHODS/APPROACH: This is a longitudinal study of adult participants (age ≥18) from seven panels (2000-2007) of the Medical Expenditure Panel Survey. The dependent variables included the physical and mental components of the SF-12, a measure of self-reported health. The outcome was assessed twice during the follow-up period: round 2 (~4-5 months into the study) and round 4 (~18 months into the study) for 62,298 individuals. Two methods estimate the association between traffic-related injuries and HRQOL: a within person change using paired tests and a between person change using multivariable regression adjusting for age, sex, income and educational level. RESULTS: Nine hundred and ninety-three participants reported traffic-related injuries during the follow-up period. Compared to their pre-crash HRQOL, these participants lost 2.7 of the physical component score while their mental component did not change. Adjusted results showed significant deficits in the physical component (-2.84, p value = <.001) but not the mental component (-0.07, p value = .83) of HRQOL after controlling for potential confounders. CONCLUSION: Traffic injuries were significantly associated with the physical component of HRQOL. These findings highlight the individual and societal burden associated with motor vehicle crash-related disability in the United States.

The effectiveness-efficiency trade-off in health care: The case of hospitals in Lombardy, Italy.

We study the presence and the magnitudes of trade-offs between health outcomes and hospitals' efficiency using a data set from Lombardy, Italy, for the period 2008-2011. Our goal is to analyze whether the pressures for cost containment may affect hospital performance in terms of population health status. Unlike previous work in this area, we analyze hospitals at the ward level so comparisons can be made across more homogeneous treatments. We focus on two different health outcomes: mortality and readmission rates. We find that there is a trade-off between mortality rates and efficiency, as more efficient hospitals have higher mortality rates. We also find, however, that more efficient hospitals have lower readmission rates. Moreover, we show that focusing the analysis at the ward level is essential, since there is evidence of higher mortality rates in general medicine and surgery, while in oncology mortality is lower in more efficient hospitals. Furthermore, we find that consideration of spatial processes is important since mortality rates are higher for hospitals subject to high degree of horizontal competition, but lower for those hospitals having strong competition but high efficiency. This implies that the interplay of efficient resource allocation and hospital competition is important for the sustainability and effectiveness of regional health care systems.

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.

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.

Understanding socioeconomic and racial differences in adult lung function.

RATIONALE: The contribution of socioeconomic factors to racial differences in the distribution of lung function is not well understood. OBJECTIVES: We investigated the contribution of socioeconomic factors to racial differences in FEV1 using statistical tools that allow for examination across the population distribution of FEV1. METHODS: We compared FEV1 for white and African-American participants (aged 20-80 yr) in NHANES III with greater than or equal to two acceptable maneuvers to a restricted sample following the routine exclusion criteria used to derive population reference equations. Ordinary least squares and quantile regression analyses using spirometric, anthropometric, and socioeconomic data (high school completion) were performed separately by sex for both data sets. MEASUREMENTS AND MAIN RESULTS: In the entire sample with acceptable spirometry (n » 9,658), high school completion was associated with a mean 69.13-ml increase in FEV1 for males (P , 0.05) and a mean 50.75-ml increase in FEV1 for females (P , 0.01). In quantile regression analysis, we observed a significant racial difference in the association of high school completion with FEV1 among both sexes that varied across the distribution; college completion was associated with an additional increase in FEV1 for white males (70.36-250.76 ml) and white females (57.87-317.77 ml). Routine exclusion criteria differentially excluded individuals by age, race, and education. In the restricted sample (n » 2,638), the association with high school completion was not significant. CONCLUSIONS: High school completion is associated with racially patterned improvements in the FEV1 of adults in the general population. The application of routine exclusion criteria leads to underestimates of the role of high school completion on FEV1.

Race and preference-based health-related quality of life measures in the United States.

BACKGROUND: Health-related quality of life instruments (HRQoL) are widely used to produce measures that summarize population health and to inform decision-making and health policy. Although the literature about the relationship between health and race in the United States is quite extensive, there is a lack of studies that comprehensively examine the relationship between race and preference-based HRQoL. Given the widespread use of these measures, it becomes important to understand the extent of the race differences in HRQoL scores and factors associated with any such differences. METHODS: We examined the differences in HRQoL, between blacks and whites and associated factors, using the summary scores of the SF-6D, EQ-5D, QWB-SA, HUI2, HUI3, administered by telephone to a nationally representative sample of 3,578 black and white US adults between the ages of 35 and 89 in the National Health Measurement Study (NHMS). RESULTS: Black women had substantially lower HRQoL than white women. The difference was largely explained by sociodemographic and socioeconomic variables. Black men did not differ significantly from white men, except for the EQ-5D. HRQoL among black men was higher at higher income levels, while the HRQoL of black women was especially low compared to other groups at high income levels.

For-profit hospital status and rehospitalizations at different hospitals: an analysis of Medicare data.

BACKGROUND: About one quarter of rehospitalized Medicare patients are admitted to hospitals different from their original hospital. The extent to which this practice is related to for-profit hospital status and affects payments and mortality is unknown. OBJECTIVE: To describe and examine predictors of and payments for rehospitalization at a different hospital among Medicare patients rehospitalized within 30 days at for-profit and nonprofit or public hospitals. DESIGN: Cohort study of patients discharged and rehospitalized from January 2005 to November 2006. SETTING: Medicare fee-for-service hospitals throughout the United States. PARTICIPANTS: A 5% random national sample of Medicare patients with acute care rehospitalizations within 30 days of discharge (n = 74,564). MEASUREMENTS: 30-day rehospitalizations at different hospitals and total payments or mortality over the subsequent 30 days. Multivariate logistic and quantile regression models included index hospital for-profit status, discharge counts, geographic region, rural-urban commuting area, and teaching status; patient sociodemographic characteristics, disability status, and comorbid conditions; and a measure of risk adjustment. RESULTS: 16 622 patients (22%) in the sample were rehospitalized at a different hospital. Factors associated with increased risk for rehospitalization at a different hospital included index hospitalization at a for-profit, major medical school-affiliated, or low-volume hospital and having a Medicare-defined disability. Compared with patients rehospitalized at the same hospital, patients rehospitalized at different hospitals had higher adjusted 30-day total payments (median additional cost, $1308 per patient; P < 0.001) but no statistically significant differences in 30-day mortality, regardless of index hospital for-profit status. LIMITATION: The database lacked detailed clinical information about patients and did not include information about specific provider practice motivations or the role of patient choice in hospitalization venues. CONCLUSION: Rehospitalizations at different hospitals are common among Medicare patients, are more likely among those initially hospitalized at a for-profit hospital, and are related to increased overall payments without improved mortality. PRIMARY FUNDING SOURCE: University of Wisconsin Hartford Center of Excellence in Geriatrics, National Institutes of Health.

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.

Understanding the production of population health and the role of paying for population health.

This article considers 2 related themes that address population health outcomes and the contributions to those outcomes by time, place, individual behaviors and choices, and activities of various social sectors. First, what does it mean to "produce" population health, and how can the production of health be understood empirically? Second, through what processes can incentives be modified to improve population health? Among the issues that arise are understanding the mechanisms through which paying for population health works and how the health-producing incentives materialize in various sectors, especially those whose primary functions are not generally viewed as fostering better population health.

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.