This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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351
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Health Policy Statistics Section
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Abstract - #308775 |
Title:
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A Multiple Imputation Model for Ascertaining Chronic Disease Cases in Administrative Data: Application to Osteoporosis
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Author(s):
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Lisa M. Lix*+ and Heather J. Prior and Chel Hee Lee and Marina Yogendran
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Companies:
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University of Saskatchewan and Manitoba Centre for Health Policy and University of Saskatchewan and Manitoba Centre for Health Policy
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Address:
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107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada
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Keywords:
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misclassification ;
missing data ;
administrative health databases ;
chronic disease ;
bias
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Abstract:
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Previous research shows that administrative health databases, including physician and hospital data, have low sensitivity for ascertaining cases of many chronic diseases. A measurement error (ME) model with multiple imputation (MI) was used to correct for under-ascertainment bias in physician billing claims from Manitoba, Canada when estimating osteoporosis prevalence. The validation data were bone mineral density tests from fiscal years 1999/00 to 2000/01. The unadjusted osteoporosis prevalence estimate was 3.3% (95% confidence interval [CI]=3.2, 3.3). A model that included covariates of patient sociodemographic, drug treatment, fracture, and physician characteristics resulted in good discriminative performance (c-statistic = 0.81; 95% CI=0.80, 0.83) and the estimate of prevalence using MI (13.4%; 95% CI=12.2, 14.6) was comparable to a previously published Canadian estimate (15.8%).
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