Geographic variation in alcohol, drug, and mental health services utilization: what are the sources of the 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.


OBJECTIVE
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 R2 statistics 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.


CONCLUSIONS
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.


CONCLUSIONS
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.


CONCLUSIONS
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.
Edlund
Mark J
MJ
Research Health Scientist, Center for Mental Health Care and Outcomes Research, Central Arkansas Veterans Healthcare System, North Little Rock, AR, USA. edlundmarkj@uams.edu
Belin
Thomas R
TR
Tang
Lingqi
L
D000293:Adolescent; D000328:Adult; D000368:Aged; D005260:Female; D019538:Health Care Surveys; D006801:Humans; D008297:Male; D008605:Mental Health Services / Q000706:statistics & numerical data*; D008875:Middle Aged; D020379:Risk Adjustment; D017062:Small-Area Analysis; D016320:Substance Abuse Treatment Centers / Q000706:statistics & numerical data*; D014481:United States