Development and validation of a psychiatric case-mix system.

BACKGROUND: Although difficulties in applying risk-adjustment measures to mental health populations are increasingly evident, a model designed specifically for patients with psychiatric disorders has never been developed. OBJECTIVE: Our objective was to develop and validate a case-mix classification system, the "PsyCMS," for predicting concurrent and future mental health (MH) and substance abuse (SA) healthcare costs and utilization. SUBJECTS: Subjects included 914,225 veterans who used Veterans Administration (VA) healthcare services during fiscal year 1999 (FY99) with any MH/SA diagnosis (International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] codes 290.00-312.99, 316.00-316.99). METHODS: We derived diagnostic categories from ICD-CM codes using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition definitions, clinical input, and empiric analyses. Weighted least-squares regression models were developed for concurrent (FY99) and prospective (FY00) MH/SA costs and utilization. We compared the predictive ability of the PsyCMS with several case-mix systems, including adjusted clinical groups, diagnostic cost groups, and the chronic illness and disability payment system. Model performance was evaluated using R-squares and mean absolute prediction errors (MAPEs). RESULTS: Patients with MH/SA diagnoses comprised 29.6% of individuals seen in the VA during FY99. The PsyCMS accounted for a distinct proportion of the variance in concurrent and prospective MH/SA costs (R=0.11 and 0.06, respectively), outpatient MH/SA utilization (R=0.25 and 0.07), and inpatient MH/SA utilization (R=0.13 and 0.05). The PsyCMS performed better than other case-mix systems examined with slightly higher R-squares and lower MAPEs. CONCLUSIONS: The PsyCMS has clinically meaningful categories, demonstrates good predictive ability for modeling concurrent and prospective MH/SA costs and utilization, and thus represents a useful method for predicting mental health costs and utilization.

Age differences in symptoms of depression and anxiety: examining behavioral medicine outpatients.

This study examined whether symptoms of depression and concomitant anxiety differed between older and younger medical outpatients referred to a behavioral medicine clinic. In a sample of 178 male veterans aged 21-83 years, older adults (> or = 60 years) reported lower overall depressive symptoms on the Beck Depression Inventory (BDI) and anxiety symptoms on the State-Trait Anxiety Inventory than did younger adults ( < 60 years). Depressive symptoms were highly prevalent. Among older adults, 60.0% scored 10 or higher on BDI and 33.8% scored 16 or higher. Among younger adults, 70.8% scored 10 or higher on BDI, and 48.7% scored 16 or higher. The age difference in overall depressive symptoms was driven by cognitive-affective symptoms. While older adults had lower cognitive-affective symptoms than did younger adults, the two groups did not differ on somatic-performance symptoms. these results suggest the importance of assessing cognitive-affective depressive symptoms in both older and younger male medical outpatients.

Diagnostic cost groups (DCGs) and concurrent utilization among patients with substance abuse disorders.

OBJECTIVE: To assess the performance of Diagnostic Cost Groups (DCGs) in explaining variation in concurrent utilization for a defined subgroup, patients with substance abuse (SA) disorders, within the Department of Veterans Affairs (VA). DATA SOURCES: A 60 percent random sample of veterans who used health care services during Fiscal Year (FY) 1997 was obtained from VA administrative databases. Patients with SA disorders (13.3 percent) were identified from primary and secondary ICD-9-CM diagnosis codes. STUDY DESIGN: Concurrent risk adjustment models were fitted and tested using the DCG/HCC model. Three outcome measures were defined: (1) "service days" (the sum of a patient's inpatient and outpatient visit days), (2) mental health/substance abuse (MH/SA) service days, and (3) ambulatory provider encounters. To improve model performance, we ran three DCG/HCC models with additional indicators for patients with SA disorders. DATA COLLECTION: To create a single file of veterans who used health care services in FY 1997, we merged records from all VA inpatient and outpatient files. PRINCIPAL FINDINGS: Adding indicators for patients with mild/moderate SA disorders did not appreciably improve the R-squares for any of the outcome measures. When indicators were added for patients with severe SA who were in the most costly category, the explanatory ability of the models was modestly improved for all three outcomes. CONCLUSIONS: Modifying the DCG/HCC model with additional markers for SA modestly improved homogeneity and model prediction. Because considerable variation still remained after modeling, we conclude that health care systems should evaluate "off-the-shelf" risk adjustment systems before applying them to their own populations.