The Drivers of Discretionary Utilization: Clinical History Versus Physician Supply.

PURPOSE: Because the effect of physician supply on utilization remains controversial, literature based on non-Medicare populations is sparse, and a physician supply expansion is under way, the potential for physician-induced demand across diverse populations is important to understand. A substantial proportion of gastrointestinal endoscopies may be inappropriate. The authors analyzed the impact of physician supply, practice patterns, and clinical history on esophagogastroduodenoscopy (EGD, defined as discretionary) among patients hospitalized with lower gastrointestinal bleeding (LGIB). METHOD: Among 34,344 patients hospitalized for LGIB from 2004 to 2009, 43.1% and 21.3% had a colonoscopy or EGD, respectively, during the index hospitalization or within 6 months after. Linking to the Dartmouth Atlas via patients' hospital referral region, gastroenterologist density and hospital care intensity (HCI) index were ascertained. Adjusting for age, gender, comorbidities, and race/education indicators, the association of gastroenterologist density, HCI index, and history of upper gastrointestinal disease with EGD was estimated using logistic regression. RESULTS: EGD was not associated with gastroenterologist density or HCI index, but was associated with a history of upper gastrointestinal disease (OR 2.30; 95% CI 2.17-2.43), peptic ulcer disease (OR 4.82; 95% CI 4.26-5.45), and liver disease (OR 1.34; 95% CI 1.18-1.54). CONCLUSIONS: Among patients hospitalized with LGIB, large variation in gastroenterologist density did not predict EGD, but relevant clinical history did, with association strengths commensurate with risk for upper gastrointestinal bleeding. In the scenario studied, no evidence was found that specialty physician supply increases will result in more discretionary care within commercially insured populations.

Optimal health insurance for multiple goods and time periods.

We examine the efficiency-based arguments for second-best optimal health insurance with multiple treatment goods and multiple time periods. Correlated shocks across health care goods and over time interact with complementarity and substitutability to affect optimal cost sharing. Health care goods that are substitutes or have positively correlated demand shocks should have lower optimal patient cost sharing. Positive serial correlations of demand shocks and uncompensated losses that are positively correlated with covered health services also reduce optimal cost sharing. Our results rationalize covering pharmaceuticals and outpatient spending more fully than is implied by static, one good, or one period models.

The causal effects of home care use on institutional long-term care utilization and expenditures.

Limited evidence exists on whether expanding home care saves money overall or how much institutional long-term care can be reduced. This paper estimates the causal effect of Medicaid-financed home care services on the costs and utilization of institutional long-term care using Medicaid claims data. A unique instrumental variable was applied to address the potential bias caused by omitted variables or reverse effect of institutional care use. We find that the use of Medicaid-financed home care services significantly reduced but only partially offset utilization and Medicaid expenditures on nursing facility services. A $1000 increase in Medicaid home care expenditures avoided 2.75 days in nursing facilities and reduced annual Medicaid nursing facility costs by $351 among people over age 65 when selection bias is addressed. Failure to address selection biases would misestimate the substitution and offset effects.

Measuring overfitting in nonlinear models: a new method and an application to health expenditures.

When fitting an econometric model, it is well known that we pick up part of the idiosyncratic characteristics of the data along with the systematic relationship between dependent and explanatory variables. This phenomenon is known as overfitting and generally occurs when a model is excessively complex relative to the amount of data available. Overfitting is a major threat to regression analysis in terms of both inference and prediction. We start by showing that the Copas measure becomes confounded by shrinkage or expansion arising from in-sample bias when applied to the untransformed scale of nonlinear models, which is typically the scale of interest when assessing behaviors or analyzing policies. We then propose a new measure of overfitting that is both expressed on the scale of interest and immune to this problem. We also show how to measure the respective contributions of in-sample bias and overfitting to the overall predictive bias when applying an estimated model to new data. We finally illustrate the properties of our new measure through both a simulation study and a real-data illustration based on inpatient healthcare expenditure data, which shows that the distinctions can be important.

Is personality associated with health care use by older adults?

CONTEXT: The patterns of health care utilization in the United States pose well-established challenges for public policy. Although economic and sociological research has resulted in considerable knowledge about what influences the use of health services, the psychological literature in this area is underdeveloped. Importantly, it is not known whether personality traits are associated with older adults' use of acute and long-term care services. METHODS: Data were collected from 1,074 community-dwelling seniors participating in a Medicare demonstration. First they completed a self-report questionnaire measuring the "Big Five" personality traits: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. During the next two years, the participants maintained daily journals of their use of health care services. We used regression models based on the Andersen behavioral model of health care utilization to test for associations. FINDINGS: Our hypothesis that higher Neuroticism would be associated with greater health care use was confirmed for three services-probability of any emergency department (ED) use, likelihood of any custodial nursing home use, and more skilled nursing facility (SNF) days for SNF users-but was disconfirmed for hospital days for those hospitalized. Higher Openness to Experience was associated with a greater likelihood of custodial home care use, and higher Agreeableness and lower Conscientiousness with a higher probability of custodial nursing home use. For users, lower Openness was associated with more ED visits and SNF days, and lower Conscientiousness with more ED visits. For many traits with significant associations, the predicted use was 16 to 30 percent greater for people high (low) versus low (high) in specific traits. CONCLUSIONS: Personality traits are associated with Medicare beneficiaries' use of many expensive health care services, findings that have implications for health services research and policy. Accordingly, person-centered interventions, population-based translational effectiveness programs, and other personalized approaches that leverage the profound advances in personality psychology in recent decades should be considered.

Measuring disparities across the distribution of mental health care expenditures.

BACKGROUND: Previous mental health care disparities studies predominantly compare mean mental health care use across racial/ethnic groups, leaving policymakers with little information on disparities among those with a higher level of expenditures. AIMS OF THE STUDY: To identify racial/ethnic disparities among individuals at varying quantiles of mental health care expenditures. To assess whether disparities in the upper quantiles of expenditure differ by insurance status, income and education. METHODS: Data were analyzed from a nationally representative sample of white, black and Latino adults 18 years and older (n=83,878). Our dependent variable was total mental health care expenditure. We measured disparities in any mental health care expenditures, disparities in mental health care expenditure at the 95th, 97.5 th, and 99 th expenditure quantiles of the full population using quantile regression, and at the 50 th, 75 th, and 95 th quantiles for positive users. In the full population, we tested interaction coefficients between race/ethnicity and income, insurance, and education levels to determine whether racial/ethnic disparities in the upper quantiles differed by income, insurance and education. RESULTS: Significant Black-white and Latino-white disparities were identified in any mental health care expenditures. In the full population, moving up the quantiles of mental health care expenditures, Black-White and Latino-White disparities were reduced but remained statistically significant. No statistically significant disparities were found in analyses of positive users only. The magnitude of black-white disparities was smaller among those enrolled in public insurance programs compared to the privately insured and uninsured in the 97.5 th and 99 th quantiles. Disparities persist in the upper quantiles among those in higher income categories and after excluding psychiatric inpatient and emergency department (ED) visits. DISCUSSION: Disparities exist in any mental health care and among those that use the most mental health care resources, but much of disparities seem to be driven by lack of access. The data do not allow us to disentangle whether disparities were related to white respondent's overuse or underuse as compared to minority groups. The cross-sectional data allow us to make only associational claims about the role of insurance, income, and education in disparities. With these limitations in mind, we identified a persistence of disparities in overall expenditures even among those in the highest income categories, after controlling for mental health status and observable sociodemographic characteristics. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: Interventions are needed to equalize resource allocation to racial/ethnic minority patients regardless of their income, with emphasis on outreach interventions to address the disparities in access that are responsible for the no/low expenditures for even Latinos at higher levels of illness severity. IMPLICATIONS FOR HEALTH POLICIES: Increased policy efforts are needed to reduce the gap in health insurance for Latinos and improve outreach programs to enroll those in need into mental health care services. IMPLICATIONS FOR FURTHER RESEARCH: Future studies that conclusively disentangle overuse and appropriate use in these populations are warranted.

Beta-binomial regression and bimodal utilization.

OBJECTIVE: To illustrate how the analysis of bimodal U-shaped distributed utilization can be modeled with beta-binomial regression, which is rarely used in health services research. DATA SOURCES/STUDY SETTING: Veterans Affairs (VA) administrative data and Medicare claims in 2001-2004 for 11,123 Medicare-eligible VA primary care users in 2000. STUDY DESIGN: We compared means and distributions of VA reliance (the proportion of all VA/Medicare primary care visits occurring in VA) predicted from beta-binomial, binomial, and ordinary least-squares (OLS) models. PRINCIPAL FINDINGS: Beta-binomial model fits the bimodal distribution of VA reliance better than binomial and OLS models due to the nondependence on normality and the greater flexibility in shape parameters. CONCLUSIONS: Increased awareness of beta-binomial regression may help analysts apply appropriate methods to outcomes with bimodal or U-shaped distributions.

Comparing VA and private sector healthcare costs for end-stage renal disease.

BACKGROUND: Healthcare for end-stage renal disease (ESRD) is intensive, expensive, and provided in both the public and private sector. Using a societal perspective, we examined healthcare costs and health outcomes for Department of Veterans Affairs (VA) ESRD patients comparing those who received hemodialysis care at VA versus private sector facilities. METHODS: Dialysis patients were recruited from 8 VA medical centers from 2001 through 2003 and followed for 12 months in a prospective cohort study. Patient demographics, clinical characteristics, quality of life, healthcare use, and cost data were collected. Healthcare data included utilization (VA), claims (Medicare), and patient self-report. Costs included VA calculated costs, Medicare dialysis facility reports and reimbursement rates, and patient self-report. Multivariable regression was used to compare costs between patients receiving dialysis at VA versus private sector facilities. RESULTS: The cohort comprised 334 patients: 170 patients in the VA dialysis group and 164 patients in the private sector group. The VA dialysis group had more comorbidities at baseline, outpatient and emergency visits, prescriptions, and longer hospital stays; they also had more conservative anemia management and lower baseline urea reduction ratio (67% vs. 72%; P<0.001), although levels were consistent with guidelines (Kt/V≥1.2). In adjusted analysis, the VA dialysis group had $36,431 higher costs than those in the private sector dialysis group (P<0.001). CONCLUSIONS: Continued research addressing costs and effectiveness of care across public and private sector settings is critical in informing health policy options for patients with complex chronic illnesses such as ESRD.