Building multi-level models to examine the relationship between obesity and pros

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PROJECT SUMMARY (See instructions): Prostate cancer is a common, complex disease with strong ethnic disparities and only a few confirmed and non-modifiable risk factors. African-Americans are at highest risk for developing prostate cancer and often present with advanced disease. Obesity, a potentially modifiable risk factor, has been shown to increase the risk of advanced disease at diagnosis and treatment (biochemical) failure. Obesity is also most prevalent in African-American populations. However, there has been controversy regarding obesity's role in prostate cancer progression, as all studies have not shown associations. Although linked to obesity and advanced cancer, patient- and neighborhood-level factors have not been studied as modifiers of obesity effects in prostate cancer patients. The goal of this project is to examine prostate cancer disparities and to build multilevel models of obesity and prostate cancer outcomes. Our general hypothesis is that patient- and neighborhood-level factors that often differ by race/ethnicity modify the effects of obesity on prostate cancer outcomes. We propose the following specific aims: Aim 1: To determine associations between racial disparities (measured by the relative measure of disparity. Slope Index of Inequality, and the Relative Index of Inequality mean and ratio) in prostate cancer severity (Kattan Scores, tumor characteristics, and age at diagnosis) and demographic/behavioral modifiers (body mass index (BMI), pack years, years of education, and age); Aim 2: To determine the relationship between obesity and prostate cancer outcomes by neighborhood characteristics (socioeconomic status, stress, demographic composition, and built environment). We will use a prospective, longitudinal case-case study design to examine the relationship between patient- and neighborhood-level characteristics and disparities in prostate cancer severity. These outcomes include pathological factors, age at diagnosis, tumor stage at diagnosis, PSA at diagnosis, and risk of treatment failure. Measures of disparity will be used to determine how racial disparities vary with obesity, smoking, education and age. Multivariate models will be used to examine the effects of obesity on prostate cancer outcomes, determining the influence of patient and neighborhood factors. We will be able to distinguish the effects of obesity from other patient-level variables and to examine such effects in neighborhood context. Determining significant modification by patient and neighborhood variables may suggest novel pathways for prostate cancer progression, identify groups of patients at highest risk for poor outcomes, and provide strategies for effective intervention to decrease disparities.