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


METHODS
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.


CONCLUSIONS
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.
Lix
Lisa M
LM
Manitoba Centre for Health Policy, University of Manitoba, Canada; Department of Community Health Sciences, University of Manitoba, Canada. Electronic address: lisa_lix@cpe.umanitoba.ca.
Yogendran
Marina S
MS
Manitoba Centre for Health Policy, University of Manitoba, Canada.
Leslie
William D
WD
Department of Medicine, University of Manitoba, Canada.
Shaw
Souradet Y
SY
Department of Community Health Sciences, University of Manitoba, Canada.
Baumgartner
Richard
R
Institute for Biodiagnostics, National Research Council, Winnipeg, Canada.
Bowman
Christopher
C
Department of Electrical and Computer Engineering, University of Manitoba, Canada; Institute for Biodiagnostics, National Research Council, Winnipeg, Canada.
Metge
Colleen
C
Manitoba Centre for Health Policy, University of Manitoba, Canada; Faculty of Pharmacy, University of Manitoba, Canada.
Gumel
Abba
A
Department of Mathematics, University of Manitoba, Canada.
Hux
Janet
J
Institute for Clinical Evaluative Sciences, Toronto, Canada.
James
Robert C
RC
Private Scholar, Salt Spring Island, British Columbia, Canada.
D000368:Aged; D000465:Algorithms; D015519:Bone Density; D016208:Databases, Factual; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D004363:Drug Utilization / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D005260:Female; D005568:Forms and Records Control; D006801:Humans; D008350:Manitoba / Q000453:epidemiology; D008875:Middle Aged; D016571:Neural Networks, Computer; D015663:Osteoporosis, Postmenopausal / Q000175:diagnosis* / Q000453:epidemiology*; D012959:Socioeconomic Factors