An analysis of the effects of organization and financing on the utilization and costs of public mental health services in san diego county.
BACKGROUND
Research has shown that how mental health services are organized and financed affects the types and amounts of services delivered. Most of these studies have considered differences between fee-for-service (FFS) providers and capitated provider groups. To our knowledge, no published study has compared utilization and costs under the contractual relationships that are used extensively by San Diego County's public mental health system.


OBJECTIVE
To analyze the relationship between financing and service delivery in a large community public mental health system among three outpatient provider types: county providers, county-contracted providers, and FFS providers.


METHODS
We examined outpatient services, hospitalizations, use of emergency services, and costs for persons treated for schizophrenia, bipolar disorder, or major depression in San Diego County in fiscal years 2002-2003 and 2003-2004 (N=20,562 person-years). We assigned clients to one of the three provider types based on where they received a majority of their medication management services. Costs were determined by detailed examination of cost reports, and were linked to individual level encounter data. Multivariate models were used to adjust for a number of socio-demographic and clinical characteristics expected to affect service use. Standardized estimates by provider types were calculated. Sensitivity and instrumental variables analyses were conducted.


RESULTS
Clients of county-contracted providers received slightly more than two additional outpatient services per year than clients of other providers (22.1 vs. 19.2 and 19.8 for clients of county and FFS providers, respectively). Clients of FFS providers were more likely to be hospitalized (26.1%) than clients of county or county-contracted providers (10.2% and 10.6%). Total costs were higher among clients of county and FFS providers (USD 5,957 and USD 5,878) than they were among clients of county-contracted providers (USD 4,976). Results were robust in sensitivity and instrumental variables analyses.


CONCLUSIONS
We compared mental health services among AOAMHS clients receiving services from county, county-contracted, or FFS providers by assigning clients to a provider based on where they receiving the majority of their outpatient medication management, and analyzing their use of outpatient and inpatient / emergency services and outpatient and total costs. We found that clients of county-contracted providers had the highest number of outpatient visits, the lowest probability of admission to a psychiatric facility or use of emergency services, and the lowest total costs including outpatient costs, inpatient and emergency costs, and medications. Limitations included a lack of detailed information on illness severity; limited information on visits by clients to FFS emergency rooms or admission of indigent clients to FFS hospitals; and a lack of information on related social costs including those incurred by the justice system.


CONCLUSIONS
The explicit consideration of the organizational and financing structures of existing and potential new programs, and the structured evaluation of implemented programs, may inform the adoption of the most cost-effective programs. Quality-based contracting may be an effective method for improving outcomes and reducing costs.
Gilmer
Todd
T
Department of Family and Preventive Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0622, USA. tgilmer@ucsd.edu
D000328:Adult; D000554:Ambulatory Care Facilities / Q000706:statistics & numerical data; D002140:California; D003156:Community Mental Health Services / Q000191:economics* / Q000458:organization & administration / Q000706:statistics & numerical data*; D003365:Costs and Cost Analysis; D005260:Female; D006279:Health Maintenance Organizations; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000628:therapy; D008875:Middle Aged; D015999:Multivariate Analysis; D017150:Public Sector; D012923:Social Class; D014600:Utilization Review / Q000379:methods