Article Abstract

Article Abstract

Online ISSN: 1099-176X Print ISSN: 1091-4358
The Journal of Mental Health Policy and Economics
Volume 9, Issue 3, 2006. Pages: 123-132

Published Online: 12 September 2006

Geographic Variation in Alcohol, Drug, and Mental Health Services Utilization: What are the Sources of the Variation?

Mark J. Edlund,*¹ Thomas R. Belin,² Lingqi Tang³

¹MD, PhD, Research Health Scientist, Center for Mental Health Care and Outcomes Research, Central Arkansas Veterans Healthcare System; Assistant Professor, Dept. of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR, USA
²PhD, Professor, Dept. of Biostatistics, UCLA School of Public Health, and Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
³PhD, Principal Statistician, Center for Health Services Research, NPI - Semel Institute for Neuroscience, UCLA, Los Angeles, CA, USA

* Correspondence to: Mark Edlund, M.D., Ph.D., 2200 Fort Roots Drive, Bldg 58, North Little Rock, AR 72114, USA
Tel.: +1-501-257 1712
Fax: +1-501-257 1718
E-mail: edlundmarkj@uams.edu

Source of Funding: This work was directly supported by a grant from the Robert Wood Johnson Foundation (044199) and a VA HSR&D Career Development Award to Dr. Edlund (RCO 03-036) and NIMH grant R01-MH-60213 (Dr. Belin).

Abstract

Geographic variation in health services utilization for medical, surgical, and psychiatric conditions may signal problems in quality. We utilized data from a nationally representative survey of alcohol, drug and mental disorders (ADM) and treatment to investigate the extent to which geographic variation in treatment rates for ADM disorders was due to variation in case-mix across sites. The amount of the variation in treatment rates explained by geographic area in unadjusted fixed effects and random intercepts models was statistically significant with R² statistics ranging from 1% to 2% and intra-cluster correlations (ICCs) ranging from 0.009 to 0.043. Considerably more variation was explained in analyses that adjusted for individuals’ ADM disorders, physical health, and socioeconomic status, with R² statistics from 10% to 19%. In random intercept models ICCs were decreased 20 to 100% in adjusted models. There may be only modest potential for improving quality by reducing geographic variation.

Background: Studies have documented geographic variation in health services utilization over a range of medical, surgical, and psychiatric conditions. These geographic differences are of concern to policy makers, as they may represent either excessive levels of unnecessary care or inappropriately low utilization of necessary services. However, the sources of geographic variation are not well understood, and variation may not represent a quality problem, to the extent that geographic variation is due to sampling variability or variation in case-mix across sites.

Aims of the Study: Our aim was to determine the extent to which geographic variation in assessment and treatment rates for alcohol, drug, and mental disorders (ADM) was due to variation in case-mix across sites and to quantify the amount of geographic variation after case-mix adjustment.

Methods: We analyzed data from Healthcare for Communities, a nationally representative telephone survey of ADM disorders and treatment. We utilized fixed effects and random intercept models to analyze whether individuals received a brief primary care ADM assessment, any primary care ADM treatment, any specialty ADM treatment, or any ADM treatment. Using the coefficient of variation and intra-class correlation (ICC) as summaries, we simulated reference distributions for the amount of variability in ADM assessment and treatment rates expected due to variation in case mix across 60 geographic areas. We compared this with the actual variation among our 60 sites, and the variation that remained after adjusting for ADM disorders, physical health, and socioeconomic characteristics.

Results: The amount of the variation in assessment and treatment rates explained by geographic area in unadjusted fixed effects and random intercepts models was statistically significant with R² statistics ranging from 1% to 2% in fixed effects models and ICC's ranging from 0.009 to 0.043. Considerably more variation was explained in analyses that included individual level characteristics such as ADM disorders, physical health, and socioeconomic status, with R²statistics from 10% to 19%. In random intercept models the ICC's were decreased 20 to 100% in models that adjusted for ADM disorders, physical health, and socioeconomic status.

Discussion: We found significant variation in ADM assessment and treatment rates across geographic sites. However, the magnitude of geographic variation was relatively modest, with 0.9 to 4.3% of the total variation in ADM assessment and treatment occurring at the geographic level. Further, it appears that a moderate amount of this geographic variation may be due to differences in case mix across sites.

Implications for Health Policy: Although geographic variation in assessment and treatment rates can signal variable quality, there may be only modest potential for improving quality by reducing geographic variation. Further, the success of efforts to decrease geographic variation by focusing on provider behavior may be limited by the extent to which systematic variation is explained by individual characteristics.

Implications for Further Research: Future work on geographic differences in mental health care as well as in health services more generally should pay particular attention to adjusting for individual differences in morbidity, which strongly predict treatment utilization.

Received 24 October 2005; accepted 31 May 2006