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A Bayesian approach to assess heart disease mortality among persons with diabetes in the presence of missing data

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Author Info
Betsy Cadwell ()
James Boyle
Edward Tierney
Theodore Thompson
Abstract

Some states’ death certificate form includes a diabetes yes/no check box that enables policy makers to investigate the change in heart disease mortality rates by diabetes status. Because the check boxes are sometimes unmarked, a method accounting for missing data is needed when estimating heart disease mortality rates by diabetes status. Using North Dakota’s data (1992–2003), we generate the posterior distribution of diabetes status to estimate diabetes status among those with heart disease and an unmarked check box using Monte Carlo methods. Combining this estimate with the number of death certificates with known diabetes status provides a numerator for heart disease mortality rates. Denominators for rates were estimated from the North Dakota Behavioral Risk Factor Surveillance System. Accounting for missing data, age-adjusted heart disease mortality rates (per 1,000) among women with diabetes were 8.6 during 1992–1998 and 6.7 during 1999–2003. Among men with diabetes, rates were 13.0 during 1992–1998 and 10.0 during 1999–2003. The Bayesian approach accounted for the uncertainty due to missing diabetes status as well as the uncertainty in estimating the populations with diabetes. Copyright Springer Science+Business Media, LLC 2007

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File URL: http://hdl.handle.net/10.1007/s10729-007-9016-9
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Publisher Info
Article provided by Springer in its journal Health Care Management Science.

Volume (Year): 10 (2007)
Issue (Month): 3 (September)
Pages: 231-238
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Handle: RePEc:kap:hcarem:v:10:y:2007:i:3:p:231-238

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Web page: http://www.springerlink.com/link.asp?id=101767

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Related research
Keywords: Missing data; Bayesian methods; Random walk Metropolis-Hastings; Diabetes mortality; Death certificate data; Diabetes check box;

References listed on IDEAS
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  1. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639. [Downloadable!] (restricted)
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This page was last updated on 2009-12-22.


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