Development and Validation of a Psychiatric Case-Mix System

Geographic disparities in telemedicine mental health use by applying three way ANOVA on Medicaid claims population data

BACKGROUND

Utilization of telemedicine care for vulnerable and low income populations, especially individuals with mental health conditions, is not well understood. The goal is to describe the utilization and regional disparities of telehealth care by mental health status in Texas. Texas Medicaid claims data were analyzed from September 1, 2012, to August 31, 2018 for Medicaid patients enrolled due to a disability.

METHODS

We analyzed the growth in telemedicine care based on urban, suburban, and rural, and mental health status. We used t-tests to test for differences in sociodemographic characteristics across patients and performed a three-way Analyses of Variance (ANOVA) to evaluate whether the growth rates from 2013 to 2018 were different based on geography and patient type. We then estimated patient level multivariable ordinary least square regression models to estimate the relationship between the use of telemedicine and patient characteristics in 2013 and separately in 2018. Outcome was a binary variable of telemedicine use or not. Independent variables of interest include geography, age, gender, race, ethnicity, plan type, Medicare eligibility, diagnosed mental health condition, and ECI score.

RESULTS

Overall, Medicaid patients with a telemedicine visit grew at 81%, with rural patients growing the fastest (181%). Patients with a telemedicine visit for a mental health condition grew by 77%. Telemedicine patients with mental health diagnoses tended to have 2 to 3 more visits per year compared to non-telemedicine patients with mental health diagnoses. In 2013, multivariable regressions display that urban and suburban patients, those that had a mental health diagnosis were more likely to use telemedicine, while patients that were younger, women, Hispanics, and those dual eligible were less likely to use telemedicine. By 2018, urban and suburban patients were less likely to use telemedicine.

CONCLUSIONS

Growth in telemedicine care was strong in urban and rural areas between 2013 and 2018 even before the COVID-19 pandemic. Those with a mental health condition who received telemedicine care had a higher number of total mental health visits compared to those without telemedicine care. These findings hold across all geographic groups and suggest that mental health telemedicine visits did not substitute for face-to-face mental health visits.

Development and validation of a prognostic index for mental health and substance use disorder burden

OBJECTIVE

To develop an accessible index which quantifies MHSUD burden among patients of Veterans Affairs hospitals.

METHOD

We used 21 disorder categories provided by the diagnostic and statistical manual (DSM) to characterize diagnoses among primary care (PC) patients. For each patient, we generated counts of unique disorder categories present during the PC encounter or in the year prior. We used these counts to generate multiple indexes, which we compared in a 60% training sample of our population. Using model fit statistics generated from ordered multinomial logistic regressions, we identified the subset of DSM categories which, structured as index, were most predictive of MHSUD hospitalization and death. We validated and fine-tuned the form of the selected index in the full population using measures of calibration and discrimination.

RESULTS

In model development, the index (I-6) which best fit the data (R2 = 0.191) included the following six disorder categories: substance use, depressive, psychotic, bipolar, trauma, and personality. When applied in the full population and weighted by disorder severity, this index demonstrated good predictive discrimination for MHSUD death (C = 0.66) and hospitalization (C = 0.88) and was well calibrated in comparisons of observed versus predicted outcomes.

CONCLUSIONS

We recommend the I-6 as a parsimonious and effective tool for MHSUD burden risk adjustment.

Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression

BACKGROUND

A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods.

METHODS

This retrospective cohort study involved 71,682 commercially insured older adults aged 55-64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM).

RESULTS

Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM.

CONCLUSIONS

Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

Healthcare utilization among patients with psychiatric hospitalization admitted through the emergency department (ED): A claims-based study

OBJECTIVE

To characterize the US national level healthcare utilization patterns of patients with commercial insurance plans before and after a psychiatric hospitalization admitted through the emergency department (ED) using insurance claims data.

METHOD

We identified 34,250 patients from multiple commercial health insurance providers across the US who meet our eligibility criteria. We summarized their healthcare encounters and used logistic regression models to study the patterns of healthcare utilization including prior visits, outpatient follow-ups, and hospital- or ED-readmissions.

RESULTS

Suicidal ideation was highly prevalent at the time of the index event (29.88%). Almost half of the patients (48.28%) had healthcare encounters with the same primary diagnosis one year before admission, about 5% had outpatient follow-ups or were readmitted to the hospital or ED 7 days post discharge. The post 30-day follow-ups and readmission rates were slightly higher. In general, older patients were less likely to have prior visits, follow-ups, or readmissions, and patients with SUDs, specifically alcohol dependence, opioid dependence/abuse, and stimulant dependence, were more likely to have outpatient follow-ups.

CONCLUSION

Patterns of patients' prior visits, follow-ups, and readmissions varied by demographics and psychiatric comorbidity. Additional studies are needed to further explain the spatial variations of utilization patterns.

Case-Mix Classification for Mental Health Care in Community Settings: A Scoping Review

As mental health care transitions from facility-based care to community-based services, methods to classify patients in terms of their expected health care resource use are an essential tool to balance the health care needs and equitable allocation of health care resources. This study performed a scoping review to summarize the nature, extent, and range of research on case-mix classifications used to predict mental health care resource use in community settings. This study identified 17 eligible studies with 32 case-mix classification systems published since the 1980s. Most of these studies came from the USA Veterans Affairs and Medicare systems, and the most recent studies came from Australia. There were a wide variety of choices of input variables and measures of resource use. However, much of the variance in observed resource use was not accounted for by these case-mix systems. The research activity specific to case-mix classification for community mental health care was modest. More consideration should be given to the appropriateness of the input variables, resource use measure, and evaluation of predictive performance. Future research should take advantage of testing case-mix systems developed in other settings for community mental health care settings, if possible.

Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance

IMPORTANCE

Policymakers and consumers are eager to compare hospitals on performance metrics, such as surgical complications or unplanned readmissions, measured from administrative data. Fair comparisons depend on risk adjustment algorithms that control for differences in case mix.

OBJECTIVE

To examine whether the Medicare Advantage risk adjustment system version 21 (V21) adequately risk adjusts performance metrics for Veterans Affairs (VA) hospitals.

DESIGN, SETTING, AND PARTICIPANTS

This cohort analysis of administrative data from all 5.5 million veterans who received VA care or VA-purchased care in 2012 was performed from September 8, 2015, to October 22, 2018. Data analysis was performed from January 22, 2016, to October 22, 2018.

EXPOSURES

A patient's risk as measured by the V21 model.

MAIN OUTCOMES AND MEASURES

The main outcome was total cost, and the key independent variable was the V21 risk score.

RESULTS

Of the 5 472 629 VA patients (mean [SD] age, 63.0 [16.1] years; 5 118 908 [93.5%] male), the V21 model identified 694 706 as having a mental health or substance use condition. In contrast, a separate classification system for psychiatric comorbidities identified another 1 266 938 patients with a mental health condition. The V21 model missed depression not otherwise specified (396 062 [31.3%]), posttraumatic stress disorder (345 338 [27.3%]), and anxiety (129 808 [10.2%]). Overall, the V21 model underestimated the cost of care by $2314 (6.7%) for every person with a mental health diagnosis.

CONCLUSIONS AND RELEVANCE

The findings suggest that current aspirations to engender competition by comparing hospital systems may not be appropriate or fair for safety-net hospitals, including the VA hospitals, which treat patients with complex psychiatric illness. Without better risk scores, which is technically possible, outcome comparisons may potentially mislead consumers and policymakers and possibly aggravate inequities in access for such vulnerable populations.

Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

OBJECTIVE

Many healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories.

STUDY DESIGN

Observational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization.

METHODS

Using VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups.

RESULTS

The best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services.

CONCLUSIONS

On average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.

Differences in Risk Scores of Veterans Receiving Community Care Purchased by the Veterans Health Administration

OBJECTIVE

To assess differences in risk (measured by expected costs associated with sociodemographic and clinical profiles) between Veterans receiving outpatient services through two community care (CC) programs: the Fee program ("Fee") and the Veterans Choice Program ("Choice").

DATA SOURCES/STUDY SETTING

Administrative data from VHA's Corporate Data Warehouse in fiscal years (FY) 2014-2015.

STUDY DESIGN

We compared the clinical characteristics of Veterans across three groups (Fee only, Choice only, and Fee & Choice). We classified Veterans into risk groups based on Nosos risk scores and examined the relationship between type of outpatient utilization and risk within each CC group. We also examined changes in utilization of VHA and CC in FY14-FY15. We used chi-square tests, t tests, and ANOVAs to identify significant differences between CC groups.

PRINCIPAL FINDINGS

Of the 1,400,977 Veterans using CC in FY15, 91.4 percent were Fee-only users, 4.4 percent Choice-only users, and 4.2 percent Fee & Choice users. Mean concurrent risk scores were higher for Fee only and Fee & Choice (1.9, SD = 2.7; 1.8, SD = 2.2) compared to Choice-only users (1.0, SD = 1.2) (p < .0001). Most CC users were "dual users" of both VHA and CC in FY14-FY15.

CONCLUSIONS

As care transitions from VHA to CC, VHA should consider how best to coordinate care with community providers to reduce duplication of efforts, improve handoffs, and achieve the best outcomes for Veterans.

Impact of Social Determinants of Health on Medical Conditions Among Transgender Veterans

INTRODUCTION

Transgender individuals experience pronounced disparities in health (e.g., mood disorders, suicide risk) and in the prevalence of social determinants of housing instability, financial strain, and violence. The objectives of this study were to understand the prevalence of social determinants among transgender veterans and assess their associations with medical conditions.

METHODS

This project was a records review using administrative data from the U.S. Department of Veterans Affairs databases for 1997-2014. Transgender veterans (N=6,308) were defined as patients with any of four ICD-9 diagnosis codes associated with transgender status. Social determinants were operationalized using ICD-9 codes and Department of Veterans Affairs clinical screens indicating violence, housing instability, or financial strain. Multiple logistic regression was used to assess the associations of social determinants with medical conditions: mood disorder, post-traumatic stress disorder, alcohol abuse disorder, illicit drug abuse disorder, tobacco use disorder, suicidal risk, HIV, and hepatitis C.

RESULTS

After adjusting for sociodemographic variables, housing instability and financial strain were significantly associated with all medical conditions except for HIV, and violence was significantly associated with all medical conditions except for tobacco use disorder and HIV. There was a dose response-like relationship between the increasing number of forms of social determinants being associated with increasing odds for medical conditions.

CONCLUSIONS

Social determinants are prevalent factors in transgender patients' lives, exhibiting strong associations with medical conditions. Documenting social determinants in electronic health records can help providers to identify and address these factors in treatment goals.

Risk Adjustment Tools for Learning Health Systems: A Comparison of DxCG and CMS-HCC V21

OBJECTIVE

To compare risk scores computed by DxCG (Verisk) and Centers for Medicare and Medicaid Services (CMS) V21.

RESEARCH DESIGN

Analysis of administrative data from the Department of Veterans Affairs (VA) for fiscal years 2010 and 2011.

STUDY DESIGN

We regressed total annual VA costs on predicted risk scores. Model fit was judged by R-squared, root mean squared error, mean absolute error, and Hosmer-Lemeshow goodness-of-fit tests. Recalibrated models were tested using split samples with pharmacy data.

DATA COLLECTION

We created six analytical files: a random sample (n = 2 million), high cost users (n = 261,487), users over age 75 (n = 644,524), mental health and substance use users (n = 830,832), multimorbid users (n = 817,951), and low-risk users (n = 78,032).

PRINCIPAL FINDINGS

The DxCG Medicaid with pharmacy risk score yielded substantial gains in fit over the V21 model. Recalibrating the V21 model using VA pharmacy data-generated risk scores with similar fit statistics to the DxCG risk scores.

CONCLUSIONS

Although the CMS V21 and DxCG prospective risk scores were similar, the DxCG model with pharmacy data offered improved fit over V21. However, health care systems, such as the VA, can recalibrate the V21 model with additional variables to develop a tailored risk score that compares favorably to the DxCG models.

The epidemiology of substance use disorders in US Veterans: A systematic review and analysis of assessment methods

BACKGROUND

Substance use disorders (SUDs), which encompass alcohol and drug use disorders (AUDs, DUDs), constitute a major public health challenge among US veterans. SUDs are among the most common and costly of all health conditions among veterans.

OBJECTIVES

This study sought to examine the epidemiology of SUDs among US veterans, compare the prevalence of SUDs in studies using diagnostic and administrative criteria assessment methods, and summarize trends in the prevalence of SUDs reported in studies sampling US veterans over time.

METHODS

Comprehensive electronic database searches were conducted. A total of 3,490 studies were identified. We analyzed studies sampling US veterans and reporting prevalence, distribution, and examining AUDs and DUDs.

RESULTS

Of the studies identified, 72 met inclusion criteria. The studies were published between 1995 and 2013. Studies using diagnostic criteria reported higher prevalence of AUDs (32% vs. 10%) and DUDs (20% vs. 5%) than administrative criteria, respectively. Regardless of assessment method, both the lifetime and past year prevalence of AUDs in studies sampling US veterans has declined gradually over time.

CONCLUSION

The prevalence of SUDs reported in studies sampling US veterans are affected by assessment method. Given the significant public health problems of SUDs among US veterans, improved guidelines for clinical screening using validated diagnostic criteria to assess AUDs and DUDs in US veteran populations are needed.

SCIENTIFIC SIGNIFICANCE

These findings may inform VA and other healthcare systems in prevention, diagnosis, and intervention for SUDs among US veterans.

Postdischarge Behavioral Health Treatment and 6-Month Reattempt Rate for Veterans Hospitalized for Suicide Attempt

BACKGROUND

The highest risk for suicide occurs immediately after psychiatric discharge. The U.S. Department of Veteran's Affairs' (VA) suicide prevention program emphasizes suicide surveillance and frequent contact after suicide attempt.

OBJECTIVES

To describe the 6-month reattempt rate and appointment characteristics for veterans after VA hospitalization for suicide attempt.

DESIGN

This retrospective observational study identified veterans hospitalized for suicide attempt (N = 504). Comparisons of patient characteristics and treatment delivery were conducted between veteran groups.

RESULTS

The sample (N = 504) was predominantly White (82%) and male (91%), with a median age of 50 years. The 6-month reattempt rate was 6%. Timing of first appointment was earlier in the reattempt group (n = 20) versus the no-reattempt group (n = 467). Appointment intensity, especially telephone appointments, was greater in the reattempt group.

CONCLUSION

The effect of postdischarge treatment on preventing suicide cannot be determined by evaluating only treatment timing and intensity. Future studies should measure the treatment quality and clinical severity.

Testosterone treatment and mortality in men with low testosterone levels

CONTEXT

Low testosterone levels in men have been associated with increased mortality. However, the influence of testosterone treatment on mortality in men with low testosterone levels is not known.

OBJECTIVE

The objective of the study was to examine the association between testosterone treatment and mortality in men with low testosterone levels.

DESIGN

This was an observational study of mortality in testosterone-treated compared with untreated men, assessed with time-varying, adjusted Cox proportional hazards regression models. Effect modification by age, diabetes, and coronary heart disease was tested a priori.

SETTING

The study was conducted with a clinical database that included seven Northwest Veterans Affairs medical centers.

PATIENTS

Patients included a cohort of 1031 male veterans, aged older than 40 yr, with low total testosterone [≤250 ng/dl (8.7 nmol/liter)] and no history of prostate cancer, assessed between January 2001 and December 2002 and followed up through the end of 2005.

MAIN OUTCOME MEASURE

Total mortality in testosterone-treated compared with untreated men was measured.

RESULTS

Testosterone treatment was initiated in 398 men (39%) during routine clinical care. The mortality in testosterone-treated men was 10.3% compared with 20.7% in untreated men (P<0.0001) with a mortality rate of 3.4 deaths per 100 person-years for testosterone-treated men and 5.7 deaths per 100 person-years in men not treated with testosterone. After multivariable adjustment including age, body mass index, testosterone level, medical morbidity, diabetes, and coronary heart disease, testosterone treatment was associated with decreased risk of death (hazard ratio 0.61; 95% confidence interval 0.42-0.88; P = 0.008). No significant effect modification was found by age, diabetes, or coronary heart disease.

CONCLUSIONS

In an observational cohort of men with low testosterone levels, testosterone treatment was associated with decreased mortality compared with no testosterone treatment. These results should be interpreted cautiously because residual confounding may still be a source of bias. Large, randomized clinical trials are needed to better characterize the health effects of testosterone treatment in older men with low testosterone levels.

Comparing methods for identifying future high-cost mental health cases in Medicaid

OBJECTIVE

This article examines methods for identifying future high-cost cases of Medicaid-covered mental health care services.

METHODS

Florida Medicaid claims data are used to compare methods based on prior cost, and concurrent and prospective diagnosis-based models. Individuals with prior year expenditures in the top decile or with predicted expenditures in the top decile from the diagnosis-based models were expected to be high-cost individuals.

RESULTS

Individuals in the top decile of prior year costs averaged $13,684 (U.S. dollars) in costs in the following year with 50% remaining in the top decile of spending. Individuals classified as high cost by diagnosis-based models averaged $10,935 to $10,974, with 34% meeting the criteria for a high-cost case in the following year.

CONCLUSION

In contrast to research on high-costs cases for physical health care, prior cost was superior to diagnosis-based models at identifying future high cases for mental health care.

Development of a Medicaid behavioral health case-mix model

Many Medicaid programs have either fully or partially carved out mental health services. The evaluation of carved out plans requires a case-mix model that accounts for differing health status across Medicaid managed care plans. This article develops a diagnosis-based case-mix adjustment system specific to Medicaid behavioral health care. Several different model specifications are compared that use untransformed, square root transformed, and log-transformed expenditures.

Gender differences in veterans health administration mental health service use: effects of age and psychiatric diagnosis

PURPOSE

The objective of this study was to compare gender differences in mental health disease burden and outpatient mental health utilization among veterans utilizing Veterans Health Administration (VHA) mental health services in fiscal year 1999 (FY99), after the first Gulf War and significant restructuring of VHA services.

METHODS

We used logistic regression to examine the relationships among gender, age, diagnostic groups, and utilization of mental health and specialty mental health services in a national sample of veterans. The sample included 782,789 veterans with at least 1 outpatient visit in the VHA in FY99 associated with a mental health or substance abuse (SA) diagnosis. Subgroup analyses were performed for 4 diagnostic categories: 1) posttraumatic stress disorder (PTSD), 2) SA disorders, 3) bipolar and psychotic disorders, and 4) mood and anxiety disorders.

MAIN FINDINGS

Younger women veterans (<35 years old) were significantly less likely and older women (> or =35) more likely to use any mental health services in comparison with their male counterparts. Similar findings were observed for younger women diagnosed with SA or mood and anxiety disorders, but not among veterans with PTSD or bipolar and psychotic disorders, among whom no there were no gender or age differences. In the case of specialized services for SA or PTSD, women younger than 55 with SA or PTSD were significantly less likely to use services than men.

CONCLUSION

Women veterans underutilized specialty mental health services in relation to men but receipt of mental health care overall in FY99 varied by age and diagnosis. Examining gender differences alone, without taking other factors into account, may not provide an adequate picture of women veterans' current mental health service needs.

Service implications of providing intensive monitoring during high-risk periods for suicide among VA patients with depression

OBJECTIVES

Department of Veterans Affairs (VA) patients in depression treatment have high suicide rates after psychiatric hospitalization, antidepressant starts, and dosage changes. Policy makers have recommended closer monitoring during these periods to reduce suicide. This study assessed the frequency of high-risk periods in clinical settings, the levels of monitoring provided during these periods, and the estimated costs of providing monitoring consistent with the most stringent Food and Drug Administration recommendation for treatment periods after antidepressant change (seven visits in the first 12 weeks).

METHODS

Monitoring visits were identified in the 12-week period after antidepressant starts and dosage changes and after discharge from psychiatric hospitalization for 100,000 randomly selected VA patients in depression treatment between April 1, 1999, and September 30, 2004. Incremental costs of providing intensive monitoring were estimated by using VA Health Economics Resource Center average cost data.

RESULTS

Patients averaged less than one high-risk period each year. They completed an average of 2.4 monitoring visits during the 12-week period after antidepressant treatment events and 4.9 visits after psychiatric hospitalization. Providing intensive monitoring would cost an additional $408-$537 for each high-risk period after antidepressant treatment events and $313-$341 for each high-risk period after psychiatric hospitalization. During fiscal year 2004 providing intensive monitoring during all high-risk periods would have cost an additional $183-$270 million. Providing intensive monitoring only after psychiatric hospitalizations would have cost an additional $15-$17 million.

CONCLUSIONS

Providing intensive monitoring for VA patients in depression treatment during all high-risk periods for suicide would require substantial services reorganization and incremental expenditures. Modest expenditures would support intensive monitoring during the highest-risk period that follows psychiatric hospitalization.

Expenditures in mental illness and substance use disorders among veteran clinic users with diabetes

Few studies have looked at the health-care expenditures of diabetes patients based on the type of co-occurring conditions of mental illness (MI) or substance use disorders (SUD). Our study analyzes the health-care expenditures associated with various diagnostic clusters of co-occurring drug, alcohol, tobacco use, and mental illness in veterans with diabetes. We merged Veteran Health Administration and Medicare fee-for-service claims database (fiscal years 1999 and 2000) for analysis (N = 390,253) using generalized linear models; SUD/MI were identified using International Classification of Diseases, 9th edition codes. The total average expenditures (fiscal year 2000) were lowest ($6,185) in the "No MI and No SUD" and highest ($19,801) for individuals with schizophrenia/other psychoses and alcohol/drug use. High expenditures were associated with both SUD and MI conditions in diabetes patients, and veterans with alcohol/drug use had the highest expenditures across all groups of MI. These findings reinforce the need to target groups with multiple comorbidities specifically those with serious mental illnesses and alcohol/drug use for interventions to reduce health-care expenditures.

Use of VA and Medicare services by dually eligible veterans with psychiatric problems

OBJECTIVE

To examine how service accessibility measured by geographic distance affects service sector choices for veterans who are dually eligible for veterans affairs (VA) and Medicare services and who are diagnosed with mental health and/or substance abuse (MH/SA) disorders.

DATA SOURCES

Primary VA data sources were the Patient Treatment (acute care), Extended Care (long-term care), and Outpatient Clinic files. VA cost data were obtained from (1) inpatient and outpatient cost files developed by the VA Health Economics and Resource Center and (2) outpatient VA Decision Support System files. Medicare data sources were the denominator, Medicare Provider Analysis Review (MEDPAR), Provider-of-Service, Outpatient Standard Analytic and Physician/Supplier Standard Analytic files. Additional sources included the Area Resource File and Census Bureau data.

STUDY DESIGN

We identified dually eligible veterans who had either an inpatient or outpatient MH/SA diagnosis in the VA system during fiscal year (FY)'99. We then estimated one- and two-part regression models to explain the effects of geographic distance on both VA and Medicare total and MH/SA costs.

PRINCIPAL FINDINGS

Results provide evidence for substitution between the VA and Medicare, demonstrating that poorer geographic access to VA inpatient and outpatient clinics decreased VA expenditures but increased Medicare expenditures, while poorer access to Medicare-certified general and psychiatric hospitals decreased Medicare expenditures but increased VA expenditures.

CONCLUSIONS

As geographic distance to VA medical facility increases, Medicare plays an increasingly important role in providing mental health services to veterans.