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Showing content from https://pubmed.ncbi.nlm.nih.gov/18619800/ below:

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

. 2008 Dec;61(12):1250-1260. doi: 10.1016/j.jclinepi.2008.02.002. Epub 2008 Jul 10. Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases

Affiliations

¹ 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.
² Manitoba Centre for Health Policy, University of Manitoba, Canada.
³ Department of Medicine, University of Manitoba, Canada.
⁴ Department of Community Health Sciences, University of Manitoba, Canada.
⁵ Institute for Biodiagnostics, National Research Council, Winnipeg, Canada.
⁶ Department of Electrical and Computer Engineering, University of Manitoba, Canada; Institute for Biodiagnostics, National Research Council, Winnipeg, Canada.
⁷ Manitoba Centre for Health Policy, University of Manitoba, Canada; Faculty of Pharmacy, University of Manitoba, Canada.
⁸ Department of Mathematics, University of Manitoba, Canada.
⁹ Institute for Clinical Evaluative Sciences, Toronto, Canada.
¹⁰ Private Scholar, Salt Spring Island, British Columbia, Canada.

PMID: 18619800
DOI: 10.1016/j.jclinepi.2008.02.002

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

Lisa M Lix et al. J Clin Epidemiol. 2008 Dec.

. 2008 Dec;61(12):1250-1260. doi: 10.1016/j.jclinepi.2008.02.002. Epub 2008 Jul 10. Affiliations

¹ 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.
² Manitoba Centre for Health Policy, University of Manitoba, Canada.
³ Department of Medicine, University of Manitoba, Canada.
⁴ Department of Community Health Sciences, University of Manitoba, Canada.
⁵ Institute for Biodiagnostics, National Research Council, Winnipeg, Canada.
⁶ Department of Electrical and Computer Engineering, University of Manitoba, Canada; Institute for Biodiagnostics, National Research Council, Winnipeg, Canada.
⁷ Manitoba Centre for Health Policy, University of Manitoba, Canada; Faculty of Pharmacy, University of Manitoba, Canada.
⁸ Department of Mathematics, University of Manitoba, Canada.
⁹ Institute for Clinical Evaluative Sciences, Toronto, Canada.
¹⁰ Private Scholar, Salt Spring Island, British Columbia, Canada.

PMID: 18619800
DOI: 10.1016/j.jclinepi.2008.02.002

Item in Clipboard

Abstract

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.

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Use of Brier score to assess binary predictions.
Rufibach K. Rufibach K. J Clin Epidemiol. 2010 Aug;63(8):938-9; author reply 939. doi: 10.1016/j.jclinepi.2009.11.009. Epub 2010 Mar 1. J Clin Epidemiol. 2010. PMID: 20189763 No abstract available.