142nd APHA Annual Meeting and Exposition

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Estimating and Testing Significance of Change in Prevalence of a Trait When Some Change may be Attributable to Regression to the Mean

142nd APHA Annual Meeting and Exposition (November 15 - November 19, 2014): http://www.apha.org/events-and-meetings/annual
Monday, November 17, 2014

William Johnson, PhD , Biostatistics & Epidemiology Core, Pennington Biomedical Research Center, Baton Rouge, LA
Robbie Beyl, PhD , Biostatistics & Epidemiology Core, Pennington Biomedical Research Center, Baton Rouge, LA
Jeff Burton, PhD , Biostatistics & Epidemiology Core, Pennington Biomedical Research Center, Baton Rouge, LA
When the prevalence of a disease-related trait rises to a level that causes public health concern, a typical course of action is to investigate population level interventions aimed at reducing the amount of existing disease in the population. Suppose a characteristic of interest [e.g., body mass index (BMI)] is measured on a continuous scale and the collection of all values of the trait (BMI’s) in the population has a distribution that is symmetric and bell shaped so that it is well approximated by a normal distribution. Further, suppose there is a specific value (cut-point) such that values that exceed that cut-point are considered to be unhealthy in the context of disease or risk of disease. The aim is to estimate and test significance of change in prevalence of unhealthy status following intervention when a portion of the change may be attributable to that intervention with a second portion attributable to regression to the mean. Key considerations are the nature of the underlying unrestricted bivariate distribution of pre- and post-intervention responses, the strength of correlation between these observed responses, and the choice of cut-point used to define the unhealthy status. The motivation for analytical methods discussed is a weight loss intervention for lowering the prevalence of obesity in adults where individuals with BMI ≥ 30 mg/kg2 are classified as obese.

Learning Areas:

Biostatistics, economics
Chronic disease management and prevention
Conduct evaluation related to programs, research, and other areas of practice
Public health or related research

Learning Objectives:
Differentiate effect of intervention (to reduce prevalence of trait) and effect of regression to the mean on prevalence of the specified trait where participants are defined as having the trait only if the outcome variable is above (or below) a specified cutpoint.

Keyword(s): Biostatistics, Obesity

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am Professor and Director of Biostatistics at Pennington Biomedical Research Center, Louisiana State University; I am also Director of Biostatistics for two center grants. I have been Co-Investigator/Biostatistician on multiple federally funded grants focusing on obesity, diabetes, aging, physical activity research. I am a well-published (200+) expert on the theory and methods of statistical analysis for biomedical research with applications in the basic, clinical and population sciences.
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.