Risk adjustment using administrative data-based and survey-derived methods for explaining physician utilization.
OBJECTIVE
The objective of this study was to evaluate an administrative data-based risk adjustment method for predicting physician utilization and the contribution of survey-derived indicators of health status. The results of this study will support the use of administrative data for planning, reimbursement, and assessing equity of physician utilization.


METHODS
The Ontario portion of the 2000-2001 Canadian Community Health Survey was linked with administrative physician claims data from 2002-2003 and 2003-2004. Explanatory models of family physician (FP) and specialist physician (SP) utilization were run using demographic information and The Johns Hopkins University Adjusted Clinical Groups (ACG) Case-mix System. Survey-based measures of health status were then added to the models. The coefficient of determination, R, indicated the models' explanatory power.


RESULTS
The study sample consisted of 25,558 individuals aged 20 to 79 years representing approximately 7.8 million people. Over the 2 years of study period, 82.5% of the study population had a FP visit with a median of 6 visits and 53.2% had a SP visit with a median of 1 visit. The R values based on administrative data alone were 33% and 21% for the frequency of FP and SP visits and 16% and 35% for having one or more visit to an FPs and SPs, respectively. The addition of the survey-based measures to the administrative data-based models produced less than a 2% increase in explanatory power for any outcome.


CONCLUSIONS
Administrative data-based measures of morbidity burden are valid and useful indicators of future physician utilization. The survey-derived measures used in this study did not contribute significantly to models on the basis of administrative data-based measures. These findings support the future use of administrative data-based data and Adjusted Clinical Groups for planning, reimbursement, and research.
Sibley
Lyn M
LM
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. lyn.sibley@utoronto.ca
Moineddin
Rahim
R
Agha
Mohammad M
MM
Glazier
Richard H
RH
D000328:Adult; D000368:Aged; D005194:Family Practice; D005260:Female; D005544:Forecasting; D015276:Health Care Rationing; D019538:Health Care Surveys / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods / Q000706:statistics & numerical data; D006305:Health Status Indicators; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009017:Morbidity; D009864:Ontario / Q000453:epidemiology; D011320:Primary Health Care / Q000706:statistics & numerical data*; D011446:Prospective Studies; D020379:Risk Adjustment / Q000379:methods*