Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases.

Published: July 10, 2008
Category: Bibliography > Papers
Authors: Baumgartner R, Bowman C, Gumel A, Hux J, James RC, Leslie WD, Lix LM, Metge C, Shaw SY, Yogendran MS
Countries: Canada
Language: null
Types: Care Management
Settings: Academic

J Clin Epidemiol 61:1250-1260.

Manitoba Centre for Health Policy, University of Manitoba, Winnipeg, MB, Canada

OBJECTIVES: The aim was to construct and validate algorithms for osteoporosis case ascertainment from administrative databases and to estimate the population prevalence of osteoporosis for these algorithms.

STUDY DESIGN AND SETTING: Artificial neural networks, classification trees, and logistic regression were applied to hospital, physician, and pharmacy data from Manitoba, Canada. Discriminative performance and calibration (i.e., error) were compared for algorithms defined from different sets of diagnosis, prescription drug, comorbidity, and demographic variables. Algorithms were validated against a regional bone mineral density testing program.

RESULTS: Discriminative performance and calibration were poorer and sensitivity was generally lower for algorithms based on diagnosis codes alone than for algorithms based on an expanded set of data features that included osteoporosis prescriptions and age. Validation measures were similar for neural networks and classification trees, but prevalence estimates were lower for the former model.

CONCLUSION: Multiple features of administrative data generally resulted in improved sensitivity of osteoporosis case-detection algorithm without loss of specificity. However, prevalence estimates using an expanded set of features were still slightly lower than estimates from a population-based study with primary data collection. The classification methods developed in this study can be extended to other chronic diseases for which there may be multiple markers in administrative data.

Comment in J Clin Epidemiol. 2010 Aug;63(8):938-9; author reply 939.

PMID: 18619800

High-Impact Chronic Conditions,Population Markers,Targeted Program,Canada,Aged,Algorithms,Bone Density,Databases,Factual,Drug Prescriptions/statistics & numerical data,Drug Utilization/statistics & numerical data,Epidemiologic Methods,Gender,Forms and Records Control,Manitoba/epidemiology,Middle Aged,Neural Networks (Computer),Socioeconomic Factors

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