220150 Markov Modeling Techniques for Selecting Morbidity

Tuesday, November 9, 2010 : 10:50 AM - 11:10 AM

Hamisu Salihu, MD, PhD , Epidemiology and Biostatistics, University of South Florida, Tampa, FL
Alfred Mbah, PhD , Department of Epidemiology and Biostatistics, University of South Florida, Tampa, FL
Heather Clayton, MPH , Department of Community & Family Health, University of South Florida, College of Public Health, Tampa, FL
Background - Researchers have struggled to develop algorithms to classify important morbidities for inclusion in analyses of public health data. While some morbidities may be quite common, others are relatively rare, and criteria for their inclusion in analyses of morbidity are often not straight forward. To assist with the selection process, an objective methodology by which to determine inclusion criteria for morbidity is warranted. Methods Using data from the Florida Linked birth certificate and hospital inpatient discharge data (1998-2006), we developed a 3 stage hierarchical model to establish a cut-off point for selecting proportion of morbidities for inclusion in statistical analyses. The Bayesian approach was used for implementation of the model and because of the intractability of the posterior distribution, the Markov Chain Monte Carlo (MCMC) method was used to simulate direct draws from the posterior distribution. The median of the draws of the proportion of morbidities from the posterior distribution was used to establish a cut-off point for morbidity selection. The WinBUGS framework (version 1.4) was used for the analyses. Results Of the 28 infant morbidities included in our Markov modeling technique, 7 morbidities were selected for epidemiologic analyses based on the mean of the pooled morbidity curve (mean = 0.05). Each of these morbidities had a prevalence of 5% or greater in our study population. Conclusion The MCMC method which is mostly useful when the posterior distribution is intractable provides a medium for obtaining objective cut-off values for selection of pregnancy-related morbidity of major impact.

Learning Areas:
Biostatistics, economics
Public health or related research

Learning Objectives:
Descrube a novel technique for selecting morbidities for inclusion in epidemiologic analyses, particularly in situations where there are several potential morbidities (e.g. infant morbidity, maternal morbidity) for consideration. Discuss the utility of modern computing techniques for use in complex epidemiological problems. Describe how the popular WinBUGS modeling framework can be used for selecting morbidities for inclusion in epidemiologic analyses.

Keywords: Data/Surveillance, Infant Health

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified to be an abstract author as I am a doctorally trained statistician, and I developed the methodology to be presented.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.