Organizational and client determinants of cost in outpatient substance abuse treatment.
BACKGROUND
Understanding variation in the cost of outpatient substance abuse treatment is important for improving the delivery and financing of care. Studies that examine how the cost of treatment relates to treatment program and client characteristics can provide important data about variables that affect unit costs of treatment. Such analyses can inform those who are responsible for setting appropriate reimbursement rates and can give important cost data to program directors responsible for delivering cost-effective treatment.


OBJECTIVE
The aim of this study is to describe the results from cost function analyses of outpatient substance abuse treatment programs sampled in the Alcohol and Drug Services Study (ADSS). The ADSS is a national study conducted in the late 1990s to collect organizational, client, and cost data of the specialty sector.


METHODS
The authors examined how organizational and client characteristics affect the cost per episode and the cost per enrollment day of outpatient care. The analysis incorporates organizational variables such ownership, average length of stay, and visits per enrollment day, as well as client characteristics such as gender, age, and primary drug of choice. For further applicability for current treatment policy, the ADSS cost data were inflated from 1997 to 2005 dollars. Mixed model regressions using log-log and log-linear relationships were developed.


RESULTS
Several organizational characteristics have statistically significant coefficients in the model estimating cost per episode, including log of point prevalence (-0.53, p<.01), log of average length of stay (0.73, p<.01), log of visits per enrollment day (0.45, p<.01), log of labor index (0.50, p<.01), proportion of counselor time spent in direct counseling (-0.52, p<.01), and location outside a metropolitan area (-0.19. p<.05). None of the client variables are statistically significant in this model. The analysis of cost per enrollment day indicates diseconomies of scope for programs that provide a broader array of ancillary services.


CONCLUSIONS
Findings suggest there exist increasing returns to scale in outpatient substance abuse treatment. Mergers of substance abuse treatment programs may be economically beneficial. Other major determinants of cost include the average length of stay, wage rates, visits per enrollment day, and direct client contact time. Increased efficiency may enable programs to control costs in these areas. In addition, many of the patterns identified in the model represent the way in which outpatient substance abuse treatment facilities are reimbursed for services. As these patterns become more specified for client conditions, client factors may become statistically significant in determining costs. The potential problem of endogeneity is addressed. Limitations of the study include possible inaccuracies in non-personnel cost data, changes in the treatment system unaccounted for in the model, and limited market area information with regard to input prices.


CONCLUSIONS
If further research indicates economies of scale, policymakers might consider supporting the merging of treatment programs. Also, further research into the optimal-mix of ancillary and treatment services would provide useful data for treatment programs seeking to balance resource constraints while providing important clinical and support activities. Lastly, research is needed to understand the relationship between treatment costs and service reimbursement.
Beaston-Blaakman
Aaron
A
Family Health International, PO Box 13950, Research Triangle Park, NC 27709, USA, ablaakman@fhi.org
Shepard
Donald
D
Horgan
Constance
C
Ritter
Grant
G
D000553:Ambulatory Care / Q000191:economics* / Q000458:organization & administration / Q000706:statistics & numerical data; D001294:Attitude to Health; D017050:Episode of Care; D017048:Health Care Costs; D006291:Health Policy; D006302:Health Services Research; D006801:Humans; D008605:Mental Health Services / Q000191:economics* / Q000458:organization & administration / Q000706:statistics & numerical data; D017059:Models, Econometric; D011050:Policy Making; D016320:Substance Abuse Treatment Centers / Q000191:economics* / Q000458:organization & administration / Q000706:statistics & numerical data; D019966:Substance-Related Disorders / Q000191:economics* / Q000628:therapy; D014481:United States