The implementation of managed behavioral healthcare in Colorado and the effects on older Medicaid beneficiaries.
BACKGROUND
One of five persons over the age of 65 experiences a diagnosable form of mental illness. Yet their access to and use of specialty services are the lowest among all age groups. It is unclear how managed behavioral healthcare has affected this problematic situation. The Colorado Medicaid Mental Health Capitation Pilot Program, implemented in 1995, provided an opportunity to investigate the impact of managed behavioral healthcare on older Medicaid beneficiaries.


OBJECTIVE
This study compared two capitated administrative models of Medicaid mental health service delivery to a traditional fee-for-service model, and specifically focused on how these models shaped service use and expenditure patterns for Medicaid beneficiaries over the age of 65.


METHODS
This study employed a quasi-experimental, pre-post design with a non-equivalent comparison group that reflects the implementation of capitation financing in some parts of Colorado and not others. A difference in difference specification was used to identify the effects of capitation under two administrative models relative to areas remaining under fee-for-service reimbursement. Logistic and Ordinary Least Squares regression were used to estimate service use and (logged) expenditures per repeat and total number of service users. Generalized corrections for heteroskedasticity and repeated observations were applied. Probabilities and average user expenditures were derived from regression results with a fixed case-mix and compared to actuals.


RESULTS
The analyses indicated that one of the capitated administrative models increased the total number of older beneficiaries who used services while the total number of service users decreased in the other capitated models. Both capitated models reduced repeat use and expenditures for specialty mental health services relative to the traditional FFS model.


CONCLUSIONS
Capitation had the expected effect of reducing the duration and intensity of treatment. Clear differences between the two capitated administrative models emerged that appeared consistent with their management philosophies. Measured effects were limited to services covered by capitation and may have been influenced by the observational design. Overall results were somewhat different from those pertaining to younger populations studied in Colorado.


CONCLUSIONS
While capitation clearly reduced total expenditures for older beneficiaries, its influence on specific treatment process measures such as user expenditures, repeat users and total users may vary considerably across treatment systems. Notably, capitation may result in increases or decreases in total users within a specific sub-population such as elders.


CONCLUSIONS
This analysis provides critical information for those state mental health and Medicaid agencies that are expanding the application of managed behavioral healthcare within a demographic environment where the population of older adults with mental illnesses is increasing. Financing, organization and their impact on specific treatment populations need to be considered in developing and applying managed behavioral health care.


CONCLUSIONS
The differential effects on elders by administrative models needs further explication and should be measured against clinical and social outcomes as well as the effect of other sources of financing and service substitution.
Kaskie
Brian
B
Department of Health Management and Policy, The University of Iowa, 200 Hawkins Drive, E206 GH, Iowa City, IA 52242, USA. brian-kaskie@uiowa.edu
Wallace
Neal
N
Kang
Soo
S
Bloom
Joan
J
D000368:Aged; D000369:Aged, 80 and over; D002204:Capitation Fee; D003120:Colorado; D018588:Fee-for-Service Plans; D005260:Female; D005102:Health Expenditures; D006279:Health Maintenance Organizations / Q000458:organization & administration* / Q000706:statistics & numerical data; D006302:Health Services Research; D006801:Humans; D008297:Male; D008484:Medicaid / Q000458:organization & administration*; D001523:Mental Disorders / Q000628:therapy*; D008605:Mental Health Services / Q000458:organization & administration* / Q000706:statistics & numerical data*; D017146:Models, Organizational; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D010865:Pilot Projects