Development and estimation of a pediatric chronic disease score using automated pharmacy data.
BACKGROUND
Although risk assessment models for specific adult populations such as the elderly have been developed, little work has focused on developing pediatric-specific models. The lack of pediatric models may result in incorrect estimates of relative disease severity among children, in reduced reimbursement for health plans and providers, and in inadequate health care for chronically ill children.


OBJECTIVE
To develop and to evaluate a pediatric risk assessment model using automated pharmacy data.


METHODS
Retrospective, case-cohort study using automated data.


METHODS
All children continuously enrolled in Group Health Cooperative of Puget Sound during 1992 and 1993.


METHODS
The Pediatric Chronic Disease Score (PCDS), an algorithm that classified children into chronic disease categories by prescription drug fills, was compared with the ICD-9-CM-based Ambulatory Care Groups (ACG) model and a demographic model for prediction of total, ambulatory, or primary care costs and primary care visits. Forecast models were estimated using linear regression and they were evaluated with R2, mean prediction error, mean squared prediction error, and Mincer-Zarnowitz tests.


RESULTS
The pharmacy-based PCDS performed significantly better on each of the four forecasting accuracy tests than did a demographic model (eg, R2s averaging fourfold higher). Compared with the ACG model, the PCDS model performed similarly on mean squared prediction error tests; however, the ACG generally had higher validation R2 values.


CONCLUSIONS
A pharmacy-based pediatric risk assessment model performs better than a demographic model and represents a viable alternative to ICD-9-CM-based models. Further research is necessary to determine if children must be considered separately from adults when conducting population-based risk assessments.
Fishman
P A
PA
Center for Health Studies, Group Health Cooperative of Puget Sound, Seattle, WA 98101, USA.
Shay
D K
DK
D000293:Adolescent; D000465:Algorithms; D015982:Bias; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000145:classification* / Q000188:drug therapy* / Q000191:economics; D016303:Clinical Pharmacy Information Systems / Q000706:statistics & numerical data*; D003953:Diagnosis-Related Groups / Q000145:classification; D011307:Drug Prescriptions / Q000191:economics / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting; D006279:Health Maintenance Organizations / Q000191:economics / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D007223:Infant; D016014:Linear Models; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D020379:Risk Adjustment; D012720:Severity of Illness Index; D014861:Washington