Risk adjustment in claims-based research: the search for efficient approaches.
Claims-based indices of comorbidity and severity, as well as other measures derived from routinely collected administrative data, are developed and tested. The extent to which risk adjustments using claims can be improved by adding information from one well-known measure based on chart review and patient examination (the American Society of Anesthesiologists' (ASA) Physical Status score) is also examined. Readmissions and mortality after three common surgical procedures are the outcomes studied using multiple logistic regression. Claims-based measures of comorbidity, derived both from hospital discharge abstracts at the time of surgery and from hospitalizations in the 6 months before surgery, provided reasonably good predictions of postsurgical readmissions and mortality. In the most complete logistic regression models, the Somers' Dyx measure of fit (a rank correlation coefficient) ranged from 0.23 to 0.38 for readmissions and from 0.46 to 0.72 for mortality. In 5 out of 6 cases, these predictions were not improved by including the prospectively-collected ASA Physical Status score. Such difficulties in improving risk adjustment by more intensive data collection are discussed in terms of their research implications.
Roos
L L
LL
Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Canada.
Sharp
S M
SM
Cohen
M M
MM
Wajda
A
A
D015897:Comorbidity; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D007348:Insurance, Health; D008137:Longitudinal Studies; D008297:Male; D008350:Manitoba; D008499:Medical Records; D010359:Patient Readmission; D011183:Postoperative Complications / Q000401:mortality; D012044:Regression Analysis; D012307:Risk Factors; D012720:Severity of Illness Index