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.

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.

ESTIMATING TREATMENT EFFECTS ON HEALTHCARE COSTS UNDER EXOGENEITY: IS THERE A 'MAGIC BULLET'?

Methods for estimating average treatment effects, under the assumption of no unmeasured confounders, include regression models; propensity score adjustments using stratification, weighting, or matching; and doubly robust estimators (a combination of both). Researchers continue to debate about the best estimator for outcomes such as health care cost data, as they are usually characterized by an asymmetric distribution and heterogeneous treatment effects,. Challenges in finding the right specifications for regression models are well documented in the literature. Propensity score estimators are proposed as alternatives to overcoming these challenges. Using simulations, we find that in moderate size samples (n= 5000), balancing on propensity scores that are estimated from saturated specifications can balance the covariate means across treatment arms but fails to balance higher-order moments and covariances amongst covariates. Therefore, unlike regression model, even if a formal model for outcomes is not required, propensity score estimators can be inefficient at best and biased at worst for health care cost data. Our simulation study, designed to take a 'proof by contradiction' approach, proves that no one estimator can be considered the best under all data generating processes for outcomes such as costs. The inverse-propensity weighted estimator is most likely to be unbiased under alternate data generating processes but is prone to bias under misspecification of the propensity score model and is inefficient compared to an unbiased regression estimator. Our results show that there are no 'magic bullets' when it comes to estimating treatment effects in health care costs. Care should be taken before naively applying any one estimator to estimate average treatment effects in these data. We illustrate the performance of alternative methods in a cost dataset on breast cancer treatment.

Reliance on Veterans Affairs outpatient care by Medicare-eligible veterans.

OBJECTIVE: To examine longitudinal changes in Medicare-eligible veterans' reliance on the Department of Veterans Affairs (VA) healthcare system for primary and specialty care over 4 years. METHODS: We merged VA administrative and Medicare claims data to examine outpatient use during fiscal years (FY) 2001 to 2004 by 15,520 Medicare-eligible veterans who used VA primary care in FY2000. Reliance on VA outpatient care was defined as the proportion of total (VA/Medicare) visits received in VA for primary or specialty care. RESULTS: Of 869,000 primary and specialty care visits in the study period, 39% occurred within VA and 77% were specialty care. Reliance on VA primary care was substantially higher than specialty care (66% vs. 50% in FY2001; P<0.001). Reliance on VA primary and specialty care decreased over time (57% vs. 31% in FY2004; P<0.001). Significant shifts occurred at both extremes of VA reliance. From FY2001 to FY2004, the proportion of patients in the top decile of reliance on VA primary care decreased from 39% to 31%, whereas the proportion in the bottom decile doubled from 8% to 18%. Similarly, the proportion of patients in the top decile of reliance on VA specialty care decreased from 24% to 13%, whereas the proportion in the bottom decile doubled from 22% to 47%. CONCLUSIONS: Reliance on VA primary and specialty care among VA primary care patients decreased substantially over time, particularly for specialty care. Increasing use of non-VA services may complicate VA's implementation of patient-centered medical home models and performance measurement.

Estimating lifetime or episode-of-illness costs under censoring.

Many analyses of healthcare costs involve use of data with varying periods of observation and right censoring of cases before death or at the end of the episode of illness. The prominence of observations with no expenditure for some short periods of observation and the extreme skewness typical of these data raise concerns about the robustness of estimators based on inverse probability weighting (IPW) with the survival from censoring probabilities. These estimators also cannot distinguish between the effects of covariates on survival and intensity of utilization, which jointly determine costs. In this paper, we propose a new estimator that extends the class of two-part models to deal with random right censoring and for continuous death and censoring times. Our model also addresses issues about the time to death in these analyses and separates the survival effects from the intensity effects. Using simulations, we compare our proposed estimator to the inverse probability estimator, which shows bias when censoring is large and covariates affect survival. We find our estimator to be unbiased and also more efficient for these designs. We apply our method and compare it with the IPW method using data from the Medicare-SEER files on prostate cancer.

Effect of full-service partnerships on homelessness, use and costs of mental health services, and quality of life among adults with serious mental illness.

CONTEXT: Chronically homeless adults with severe mental illness are heavy users of costly inpatient and emergency psychiatric services. Full-service partnerships (FSPs) provide housing and engage clients in treatment. OBJECTIVE: To examine changes in recovery outcomes, mental health service use and costs, and quality of life associated with participation in FSPs. DESIGN: A quasi-experimental, difference-in-difference design with a propensity score-matched control group was used to compare mental health service use and costs of FSP with public mental health services. Recovery outcomes were compared before and after services use, and quality of life was compared cross-sectionally. SETTING: San Diego County, California, from October 2005 through June 2008. PARTICIPANTS: Two hundred nine FSP clients and 154 clients receiving public mental health services. MAIN OUTCOME MEASURES: Recovery outcomes (housing, financial support, and employment), mental health service use (use of outpatient, inpatient, emergency, and justice system services), and mental health services and housing costs from the perspective of the public mental health system. RESULTS: Among FSP participants, the mean number of days spent homeless per year declined 129 days from 191 to 62 days; the probability of receiving inpatient, emergency, and justice system services declined by 14, 32, and 17 percentage points, respectively; and outpatient mental health visits increased by 78 visits (P < .001 each). Outpatient costs increased by $9180; inpatient costs declined by $6882; emergency service costs declined by $1721; jail mental health services costs declined by $1641; and housing costs increased by $3180 (P < .003 each). Quality of life was greater among FSP clients than among homeless clients receiving services in outpatient programs. CONCLUSIONS: Participation in an FSP was associated with substantial increases in outpatient services and days spent in housing. Reductions in costs of inpatient/emergency and justice system services offset 82% of the cost of the FSP.

Measuring racial/ethnic disparities across the distribution of health care expenditures.

OBJECTIVE: To assess whether black-white and Hispanic-white disparities increase or abate in the upper quantiles of total health care expenditure, conditional on covariates. DATA SOURCE: Nationally representative adult population of non-Hispanic whites, African Americans, and Hispanics from the 2001-2005 Medical Expenditure Panel Surveys. STUDY DESIGN: We examine unadjusted racial/ethnic differences across the distribution of expenditures. We apply quantile regression to measure disparities at the median, 75th, 90th, and 95th quantiles, testing for differences over the distribution of health care expenditures and across income and education categories. We test the sensitivity of the results to comparisons based only on health status and estimate a two-part model to ensure that results are not driven by an extremely skewed distribution of expenditures with a large zero mass. PRINCIPAL FINDINGS: Black-white and Hispanic-white disparities diminish in the upper quantiles of expenditure, but expenditures for blacks and Hispanics remain significantly lower than for whites throughout the distribution. For most education and income categories, disparities exist at the median and decline, but remain significant even with increased education and income. CONCLUSIONS: Blacks and Hispanics receive significantly disparate care at high expenditure levels, suggesting prioritization of improved access to quality care among minorities with critical health issues.

Issues for the next generation of health care cost analyses.

BACKGROUND: Given the characteristics of health care expenditure/cost data-a mass of observations at zero, and skewed positive expenditures, various alternative estimators have been developed that can address the analytical issues these characteristics raise. The field continues to develop new approaches and to evaluate the performance of the existing ones. OBJECTIVES: We discuss the strengths and limitations in existing methods for estimation and for model specification and checking. We suggest some areas that need fuller development or a better understanding of how the estimation approach performs when the outcome exhibits the skewness and heavy right tails that are typical of health care data. We also address various other aspects of cost analysis that include dealing with induced censoring, estimating casual effects, and generating reliable predictions that may apply to many studies. RESULTS: No current method is optimal or dominant for all cost applications. Many of the diagnostics used in choosing among alternatives have limitations that need more careful study. Several avenues in modeling cost data remain unexplored. CONCLUSIONS: Taken together, we hope that this essay would serve as a guide to the choice among methods and to the next generation of methodological research in this field.

A cost analysis of San Diego County's REACH program for homeless persons.

OBJECTIVE: This study examined mental health service utilization and costs associated with the California Assembly Bill 2034 housing-first program for homeless persons in San Diego County: Reaching Out and Engaging to Achieve Consumer Health (REACH). METHODS: Encounter data were used to identify REACH clients and a control group that was matched by propensity score. Mental health services costs for case management, outpatient services, inpatient and emergency services, criminal justice system services, and total services were summarized for two-year periods before and after clients initiated REACH. Incremental costs of the program were calculated as the difference in cost among clients in the REACH group, from pre- to postintervention, less the difference in cost among those in the control group from pre- to postintervention. RESULTS: A total of 177 REACH clients and 161 clients in a control group matched by propensity score were identified. Among REACH participants, case management costs increased by $6,403 (p<.001) from pre- to postintervention, inpatient plus emergency services costs declined by $6,103 (p=.034), and costs for mental health services provided by the criminal justice system declined by $570 (p=.020) compared with the control group. The standardized difference-in-difference estimate of the total costs between REACH clients and the control group was not significant. CONCLUSIONS: Participation in REACH was associated with substantial increases in outpatient services as well as cost offsets in inpatient and emergency services and criminal justice system services. The net cost of services, $417 over two years, was substantially lower than the total cost of services ($20,241).

Attrition in the RAND Health Insurance Experiment: a response to Nyman.

In a prior article in this journal, John Nyman argues that the effect on health care use and spending found in the RAND Health Insurance Experiment is an artifact of greater voluntary attrition in the cost-sharing plans relative to the free care plan. Specifically, he speculates that those in the cost-sharing plans, when faced with a hospitalization, withdrew. His argument is implausible because (1) families facing a hospitalization would be worse off financially by withdrawing; (2) a large number of observational studies find a similar effect of cost sharing on use; (3) those who left did not differ in their utilization prior to leaving; (4) if there had been no attrition and cost sharing did not reduce hospitalization rates, each adult in each family that withdrew would have had to have been hospitalized once each year for the duration of time they would otherwise have been in the experiment, an implausibly high rate; (5) there are benign explanations for the higher attrition in the cost-sharing plans. Finally, we obtained follow-up health-status data on the great majority of those who left prematurely. We found the health-status findings were insensitive to the inclusion of the attrition cases.

Optimal health insurance for prevention and treatment.

This paper reexamines the efficiency-based arguments for optimal health insurance, extending the classic analysis to consider optimal coverage for prevention and treatment separately. Our paper considers the tradeoff between individuals' risk reduction on the one hand, and both ex ante and ex post moral hazard on the other. We demonstrate that it is always desirable to offer at least some insurance coverage for preventive care if individual consumers ignore the impact of their preventive care on the health premium. Using a utility-based framework, we reconfirm the conventional tradeoff between risk avoidance (by risk sharing) and moral hazard for insuring treatment goods. Uncompensated losses that reduce effective income provide a new efficiency-based argument for more generous insurance coverage for prevention and treatment of health conditions. The optimal coinsurance rates for prevention and for treatment are not identical.

The impact of welfare reform on insurance coverage before pregnancy and the timing of prenatal care initiation.

OBJECTIVE: This study investigates the impact of welfare reform on insurance coverage before pregnancy and on first-trimester initiation of prenatal care (PNC) among pregnant women eligible for Medicaid under welfare-related eligibility criteria. DATA SOURCES: We used pooled data from the Pregnancy Risk Assessment Monitoring System for eight states (AL, FL, ME, NY, OK, SC, WA, and WV) from 1996 through 1999. STUDY DESIGN: We estimated a two-part logistic model of insurance coverage before pregnancy and first-trimester PNC initiation. The impact of welfare reform on insurance coverage before pregnancy was measured by marginal effects computed from coefficients of an interaction term for the postreform period and welfare-related eligibility and on PNC initiation by the same interaction term and the coefficients of insurance coverage adjusted for potential simultaneous equation bias. We compared the estimates from this model with results from simple logistic, ordinary least squares, and two-stage least squares models. PRINCIPAL FINDINGS: Welfare reform had a significant negative impact on Medicaid coverage before pregnancy among welfare-related Medicaid eligibles. This drop resulted in a small decline in their first-trimester PNC initiation. Enrollment in Medicaid before pregnancy was independent of the decision to initiate PNC, and estimates of the effect of a reduction in Medicaid coverage before pregnancy on PNC initiation were consistent over the single- and two-stage models. Effects of private coverage were mixed. Welfare reform had no impact on first-trimester PNC beyond that from reduced Medicaid coverage in the pooled regression but separate state-specific regressions suggest additional effects from time and income constraints induced by welfare reform may have occurred in some states. CONCLUSIONS: Welfare reform had significant adverse effects on insurance coverage and first-trimester PNC initiation among our nation's poorest women of childbearing age. Improved outreach and insurance options for these women are needed to meet national health goals.

Cost-effectiveness of systematic depression treatment among people with diabetes mellitus.

CONTEXT: Depression co-occurring with diabetes mellitus is associated with higher health services costs, suggesting that more effective depression treatment might reduce use of other medical services. OBJECTIVE: To evaluate the incremental cost and cost-effectiveness of a systematic depression treatment program among outpatients with diabetes. DESIGN: Randomized controlled trial comparing systematic depression treatment program with care as usual. SETTING: Primary care clinics of group-model prepaid health plan. PATIENTS: A 2-stage screening process identified 329 adults with diabetes and current depressive disorder. INTERVENTION: Specialized nurses delivered a 12-month, stepped-care depression treatment program beginning with either problem-solving treatment psychotherapy or a structured antidepressant pharmacotherapy program. Subsequent treatment (combining psychotherapy and medication, adjustments to medication, and specialty referral) was adjusted according to clinical response. MAIN OUTCOME MEASURES: Depressive symptoms were assessed by blinded telephone assessments at 3, 6, 12, and 24 months. Health service costs were assessed using health plan accounting records. RESULTS: Over 24 months, patients assigned to the intervention accumulated a mean of 61 additional days free of depression (95% confidence interval [CI], 11 to 82 days) and had outpatient health services costs that averaged $314 less (95% CI, $1007 less to $379 more) compared with patients continuing in usual care. When an additional day free of depression is valued at $10, the net economic benefit of the intervention is $952 per patient treated (95% CI, $244 to $1660). CONCLUSIONS: For adults with diabetes, systematic depression treatment significantly increases time free of depression and appears to have significant economic benefits from the health plan perspective. Depression screening and systematic depression treatment should become routine components of diabetes care.

Using the SF-12 health status measure to improve predictions of medical expenditures.

BACKGROUND: Relatively few studies have used self-reported health status in models to predict medical expenditures, and many of these have used the SF-36. OBJECTIVES: We sought to examine the ability of the briefer SF-12 measure of health status to predict medical expenditures in a nationally representative sample. METHODS: We used data from the 2000-2001 panel of the Medical Expenditure Panel Study. Respondents (n = 5542) completed the SF-12 in a questionnaire. Interviews obtained data on demographics and selected chronic conditions. Data on expenditures incurred subsequent to the interview were obtained in part from provider records. We examined different regression model specifications and compared different statistical estimation techniques. RESULTS: Adding the SF-12 to a regression model improved the prediction of subsequent medical expenditures. In a model with only age and gender, adding the SF-12 increased R from 0.06 to 0.13. The coefficients for the Physical Component Summary (PCS) and the Mental Component Summary (MCS) of the SF-12 for this model were -0.045 (P < 0.01) and -0.012 (P < 0.01), respectively. In a model including demographic characteristics, chronic conditions, and previous expenditures, adding the SF-12 increased the R from 0.26 to 0.29. The coefficients for the PCS and the MCS for this model were -0.025 (P < 0.001) and -0.005 (P = 0.15), respectively. A single general health status question performed almost as well as the full SF-12. Models estimated using ordinary least squares had undesirable properties. In terms of R, a generalized linear model (GLM) with a Poisson variance function was consistently superior to a GLM with a gamma variance function. CONCLUSIONS: Information on self-reported health status is useful in predicting medical expenditures. The extent to which the SF-12 adds predictive power over a comprehensive array of diagnostic data remains to be examined.