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

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.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

Lix LM,Yogendran MS,Leslie WD,Shaw SY,Baumgartner R,Bowman C,Metge C,Gumel A,Hux J,James RC

doi

10.1016/j.jclinepi.2008.02.002

subject

Has Abstract

pub_date

2008-12-01 00:00:00

pages

1250-1260

issue

12

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(08)00050-4

journal_volume

61

pub_type

杂志文章

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