Sociodemographic and Clinical Factors Associated with the Diagnosis, Initial Treatment Selection and Survival of Prostate Cancer in Florida

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1 University of Miami Scholarly Repository Open Access Dissertations Electronic Theses and Dissertations Sociodemographic and Clinical Factors Associated with the Diagnosis, Initial Treatment Selection and Survival of Prostate Cancer in Florida Donald Le Roy Parris Jr University of Miami, Follow this and additional works at: Recommended Citation Parris, Donald Le Roy Jr, "Sociodemographic and Clinical Factors Associated with the Diagnosis, Initial Treatment Selection and Survival of Prostate Cancer in Florida" (2013). Open Access Dissertations This Embargoed is brought to you for free and open access by the Electronic Theses and Dissertations at Scholarly Repository. It has been accepted for inclusion in Open Access Dissertations by an authorized administrator of Scholarly Repository. For more information, please contact

2 UNIVERSITY OF MIAMI SOCIODEMOGRAPHIC AND CLINICAL FACTORS ASSOCIATED WITH THE DIAGNOSIS, INITIAL TREATMENT SELECTION AND SURVIVAL OF PROSTATE CANCER IN FLORIDA By Donald L. Parris, Jr. A DISSERTATION Submitted to the Faculty of the University of Miami in partial fulfillment of the requirements for the degree of Doctor of Philosophy Coral Gables, Florida June 2013

3 NOTE: All Master s and Ph.D. students must include this page in the front matter. Substitute your name in place of Anne S. Smith Donald L. Parris, Jr. All Rights Reserved

4 UNIVERSITY OF MIAMI A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy SOCIODEMOGRAPHIC AND CLINICAL FACTORS ASSOCIATED WITH THE DIAGNOSIS, INITIAL TREATMENT SELECTION AND SURVIVAL OF PROSTATE CANCER IN FLORIDA Donald L. Parris, Jr. Approved: Tulay Koru Sengul, Ph.D. Assistant Professor Public Health Sciences M. Brian Blake, Ph.D. Dean of the Graduate School Noella A. Dietz, Ph.D. Research Assistant Professor Public Health Sciences David J. Lee, Ph.D. Professor Public Health Sciences Edward J. Trapido, Sc.D., FACE Professor and Wendell Gauthier Chair Cancer Epidemiology Louisiana State University School of Public Health

5 PARRIS, JR. DONALD L. (Ph.D., Epidemiology) Sociodemographic and Clinical Factors Associated (June 2013) with the Diagnosis, Initial Treatment Selection and Survival of Prostate Cancer in Florida Abstract of a dissertation at the University of Miami. Dissertation supervised by Dr. Tulay Koru-Sengul. No. of pages in text. (221) Prostate cancer is the most common solid malignancy diagnosed in American men. One in six men in the US is expected to be diagnosed with prostate cancer in his lifetime. Despite the large number of men diagnosed every year with prostate cancer, there is no consensus concerning the best and most appropriate treatment. Moreover, most men with early stage prostate cancer have a life expectancy comparable to similarly aged men without prostate cancer and will die with prostate cancer rather than from prostate cancer. Without evidence supporting one treatment over another, it is important to understand the factors that inform treatment selection and what impact they may have on survival. The Florida Cancer Data System was used to obtain data from men (n=118,533) diagnosed with prostate cancer between the years 2001 and US Census data was used to construct area-based socioeconomic measures of education and socioeconomic status. Census tract based Rural-Urban Commuting Area Codes were used to determine urban vs. rural residence. Approximately 73% of the men in this study were Non-Hispanic White, 13% Non-Hispanic Black, 11% Hispanic and 3% were categorized as Non-Hispanic Other or Unknown race/ethnicity. Most (81%) men were diagnosed with localized (early stage)

6 prostate cancer. Roughly 10% of the cases were Regional/Distant (advanced stage) and 9% were not staged. Men diagnosed with localized prostate cancer received surgery (34%), radiation (32%), hormonal therapy (22%) or watchful waiting (12%) as their initial treatment. Binary logistic regression models were fitted to predict late stage prostate cancer. For men diagnosed with localized prostate cancer, polytomous logistic regression models were fitted to predict initial treatment. Lastly, survival analysis was conducted using the Kaplan-Meier method and Cox proportional hazard regression models were used to model time-to-event outcome (overall survival/all-cause mortality). The regression models were adjusted for age at diagnosis, insurance, education, marital and smoking status, SES, urban/rural residence, initial treatment and tumor grade. The results of the analyses show that sociodemographic factors were associated with stage of cancer diagnosis, initial treatment selection, and overall survival for men with prostate cancer. Non-Hispanic Black men were more likely to present with late stage disease (OR=1.16; 95% CI: 1.09, 1.23) as were men who were not married (OR=1.24; 95% CI: 1.18, 1.30) and who currently smoked (OR=1.36; 95% CI: 1.28, 1.46). Variation existed in the initial treatments for men diagnosed with localized prostate cancer. Non-Hispanic Black (OR=0.66; 95% CI: 0.61, 0.71) and Hispanic men (OR=0.85; 95% CI: 0.79, 0.92) were less likely to receive surgery or radiation therapy, respectively. Non-Hispanic Black men with the lowest socioeconomic status were less likely to receive radiation therapy over watchful waiting (OR=0.66; 95% CI: 0.50, 0.87). Compared to men with private insurance, men with no insurance were more likely

7 to receive watchful waiting over surgery (OR=2.04; 95% CI: 1.75, 2.38), radiation therapy (OR=2.32; 95% CI: 1.96, 2.7) or hormonal therapy (OR=1.43; 95% CI: 1.22, 1.69). Men with public insurance were less likely to receive surgery (OR=0.88; 95% CI: 0.84, 0.93) and more likely to received hormonal therapy (OR=1.18; 95% CI: 1.12, 1.25) over watchful waiting. Lastly, men with less than a high school education were more likely to select watchful waiting (OR=1.11; 95% CI: 1.01, 1.2) over surgery. Cox proportional hazard regression models showed differences in overall survival based upon initial treatment and sociodemographic factors. Although surgery had better 5-year survival rates than watchful waiting (82% vs. 76%), there was no statistically significant difference in overall mortality risk between the two treatments. All of the sociodemographic predictors of interest (race/ethnicity, socioeconomic status, education level, smoking status and marital status) were significant predictors of overall mortality risk for men diagnosed with localize prostate cancer. With an eye toward reducing healthcare costs and improving efforts to eliminate health disparities, it is imperative that we understand what external factors affect screening behaviors for early detection, initial treatment selection, and survival for men diagnosed with prostate cancer.

8 DEDICATION I dedicate this dissertation to my loving family: my parents, who have always supported me in whatever I chose to do and have been my number one fan; my brothers and sister, who are my sources of strength and inspiration; my nieces and nephews, who keep me laughing and smiling; and to Mitch, for his patience, love and understanding over the past 16 years. Lastly, I dedicate this dissertation to my grandmother, Dolores Mira Dominguez, whose love and kindness I will always cherish. iii

9 ACKNOWLEDGEMENTS I d like to thank Dr. Tulay Koru-Sengul for the guidance and mentoring she has provided to me over the course of my dissertation journey. To my entire dissertation committee, Drs. Tulay Koru-Sengul, Noella A. Dietz, Edward J. Trapido and David J. Lee, thank you for the support, patience, understanding and exceptional feedback. Without you, this work would not be possible! I d also like to thank Dr. Fernando Bianco for serving as my clinical advisor on this endeavor. Thank you to my fellow students and to the staff and faculty of the Department of Public Health Sciences. Special thanks to Dr. Susan Golembeski and Linda Siebert for providing flexibility at work to allow me to achieve this goal. And to my co-workers along the way at Moffitt/M2Gen and Baptist Health South Florida, your support and encouragement kept me going! Lastly, I would like to thank Dr. Jill MacKinnon, whose public health surveillance course sparked my interest in working with cancer registry data. iv

10 TABLE OF CONTENTS Page LIST OF FIGURES LIST OF TABLES. viii ix Chapters 1 INTRODUCTION AND STUDY AIMS Overview of Chapters 7 2 PROSTATE CANCER... 8 Epidemiology of Prostate Cancer Prostate Cancer Defined Risk Factors Prostate Cancer Screening and Diagnosis Screening Guidelines Biopsy, Gleason Score and Cancer Staging 19 Prostate Cancer Treatment Adverse Treatment Effects Comparative Effectiveness of Prostate Cancer Treatments Prostate Cancer Health Disparities Differentiation and Consolidation Theory METHODS 36 Study Design Setting and Time Period Data Sources.. 37 Florida Cancer Data System US Census Area Based Socioeconomic Measures. 40 Missing Data.. 41 Human Subjects Protection Participants Eligibility Criteria Specific Aim 1 Sociodemographic and Clinical Factors Specific Aim 2 Stage of Prostate Cancer at Diagnosis Specific Aim 3 Initial Treatment for Localized Prostate Cancer Specific Aim 4 Survival Analysis of Localized Prostate Cancer. 48 Study Variables.. 49 Stage at Diagnosis Initial Treatment.. 50 Vital Status.. 51 Age at Diagnosis v

11 Race/Ethnicity Marital Status Tobacco Use Insurance Status Education Level Socioeconomic Status Urban/Rural Classification.. 55 Grade at Diagnosis Length of Time of Follow-up.. 57 Statistical Analysis Methods Specific Aim 1 Sociodemographic and Clinical Factors Specific Aim 2 Stage of Prostate Cancer at Diagnosis Specific Aim 3 Initial Treatment for Localized Prostate Cancer. 60 Specific Aim 4 Survival Analysis of Localized Prostate Cancer RESULTS Specific Aim 1 Sociodemographic and Clinical Factors Specific Aim 2 Stage of Prostate Cancer at Diagnosis Hypothesis 2-1 Effect of Race/Ethnicity Hypothesis 2-2 Effect of Marital Status Hypothesis 2-3 Effect of Smoking Status Specific Aim 3 Initial Treatment for Localized Prostate Cancer Hypothesis 3-1 Effect of Race/Ethnicity Hypothesis 3-2 Effect of Socioeconomic Status Hypothesis 3-3 Effect of Education Level Hypothesis 3-4 Effect of Insurance Status Specific Aim 4 Survival Analysis of Localized Prostate Cancer Hypothesis 4-1 Effect of Initial Treatment Hypothesis 4-2 Effect of Race/Ethnicity Hypothesis 4-3 Effect of Socioeconomic Status Hypothesis 4-4 Effect of Education Level Hypothesis 4-5 Effect of Smoking Status Hypothesis 4-6 Effect of Marital Status DISCUSSION Prostate Cancer in the State of Florida.. 92 Stage of Prostate Cancer at Diagnosis Initial Treatment for Localized Prostate Cancer Overall Survival for Men Diagnosed with Localized Prostate Cancer 103 Conclusions STRENGTHS AND LIMITATIONS FUTURE DIRECTIONS vi

12 FIGURES TABLES 131 REFERENCES APPENDIX A FLORIDA CANCER DATA SYSTEM DATA FIELDS. 214 APPENDIX B FLORIDA DEPARTMENT OF HEALTH INSTITUTIONAL REVIEW BOARD EXEMPT RULING LETTER APPENDIX C RURAL URBAN COMMUTING AREA CODES APPENDIX D SUMMARY OF KEY VARIABLES OF INTEREST vii

13 LIST OF FIGURES Figures Page 1 Prostate Gland Stages of Prostate Cancer Gleason Pattern Results of Applying Eligibility Criteria by Specific Aim Sample Sizes by Hypothesis Specific Aim Sample Sizes by Hypothesis Specific Aim Sample Sizes by Hypothesis Specific Aim Survival Curves-Overall and by Initial Treatment for Men Diagnosed with Localized Prostate Cancer, Florida Survival Curves-Overall and by Race/Ethnicity for Men Diagnosed with Localized Prostate Cancer, Florida Survival Curves-Overall and by Socioeconomic Level for Men Diagnosed with Localized Prostate Cancer, Florida Survival Curves-Overall and by Education Level for Men Diagnosed with Localized Prostate Cancer, Florida Survival Curves-Overall and by Smoking Status for Men Diagnosed with Localized Prostate Cancer, Florida Survival Curves-Overall and by Marital Status for Men Diagnosed with Localized Prostate Cancer, Florida viii

14 LIST OF TABLES Tables Page 1 Sociodemographic and Clinical Characteristics of Men Diagnosed with Prostate Cancer by Year, Florida Sociodemographic Characteristics by Stage of Diagnosis of Prostate Cancer Diagnosis, Florida Univariate and Multivariate Logistic Models: Effect of Race/Ethnicity on Stage (Late vs. Early) at Diagnosis of Prostate Cancer, Florida Univariate and Multivariate Logistic Models: Effect of Marital Status on Stage (Late vs. Early) at Diagnosis of Prostate Cancer, Florida Univariate Logistic Models: Effect of Marital Status on Stage (Late vs. Early) at Diagnosis of Prostate Cancer by Race/Ethnicity, Florida Multivariate Logistic Models: Effect of Marital Status on Stage (Late vs. Early) at Diagnosis of Prostate Cancer by Race/Ethnicity, Florida Univariate and Multivariate Logistic Models: Effect of Smoking Status on Stage (Late vs. Early) at Diagnosis of Prostate Cancer, Florida Univariate Logistic Models: Effect of Smoking Status on Stage (Late vs. Early) at Diagnosis of Prostate Cancer by Race/Ethnicity, Florida Multivariate Logistic Models: Effect of Smoking Status on Stage (Late vs. Early) at Diagnosis of Prostate Cancer by Race/Ethnicity, Florida Sociodemographic and Clinical Characteristics by Initial Treatment of Localized Prostate Cancer, Florida Univariate Multinomial Logistic Models: Effect of Race/Ethnicity on Initial Treatment Selection for Localized Prostate Cancer, Florida ix

15 12 Multivariate Multinomial Logistic Models: Effect of Race/Ethnicity on Initial Treatment Selection for Localized Prostate Cancer, Florida Univariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer, Florida Univariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer of Non-Hispanic White Men, Florida Univariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer of Non-Hispanic Black Men, Florida Univariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer of Hispanic Men, Florida Multivariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer, Florida Multivariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer of Non-Hispanic White Men, Florida Multivariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer of Non-Hispanic Black Men, Florida Multivariate Multinomial Logistic Models: Effect of Socioeconomic Status on Initial Treatment Selection for Localized Prostate Cancer of Hispanic Men, Florida Univariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer, Florida Univariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic White Men, Florida x

16 23 Univariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic Black Men, Florida Univariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer of Hispanic Men, Florida Multivariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer, Florida Multivariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic White Men, Florida Multivariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic Black Men, Florida Multivariate Multinomial Logistic Models: Effect of Education Level on Initial Treatment Selection for Localized Prostate Cancer of Hispanic Men, Florida Univariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer, Florida Univariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic White Men, Florida Univariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic Black Men, Florida Univariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer of Hispanic Men, Florida Multivariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer, Florida xi

17 34 Multivariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic White Men, Florida Multivariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer of Non- Hispanic Black Men, Florida Multivariate Multinomial Logistic Models: Effect of Insurance Status on Initial Treatment Selection for Localized Prostate Cancer of Hispanic Men, Florida Sociodemographic and Clinical Characteristics of Men Diagnosed with Localized Prostate Cancer, Florida Mean Overall Survival and Overall Survival Rates at Time (years) after Diagnosis of Localized Prostate Cancer by Initial Treatment, Florida Length of Follow-up Time after Diagnosis of Localized Prostate Cancer by Initial Treatment, Florida Univariate & Multivariate Cox Proportional Hazard Models: Effect of Initial Treatment on Overall Survival of Prostate Cancer, Florida Univariate Cox Proportional Hazard Models: Effect of Initial Treatment on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Multivariate Cox Proportional Hazard Models: Effect of Initial Treatment on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Mean Overall Survival and Overall Survival Rates at Time (years) after Diagnosis of Localized Prostate Cancer by Race/Ethnicity, Florida Length of Follow-up Time after Diagnosis of Localized Prostate Cancer by Race/Ethnicity, Florida Univariate & Multivariate Cox Proportional Hazard Models: Effect of Race/Ethnicity on Overall Survival of Prostate Cancer, Florida xii

18 46 Mean Overall Survival and Overall Survival Rates at Time (years) after Diagnosis of Localized Prostate Cancer by Socioeconomic Status, Florida Length of Follow-up Time after Diagnosis of Localized Prostate Cancer by Socioeconomic Status, Florida Univariate & Multivariate Cox Proportional Hazard Models: Effect of Socioeconomic Status on Overall Survival of Prostate Cancer, Florida Univariate Cox Proportional Hazard Models: Effect of Socioeconomic Status on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Multivariate Cox Proportional Hazard Models: Effect of Socioeconomic Status on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Mean Overall Survival and Overall Survival Rates at Time (years) after Diagnosis of Localized Prostate Cancer by Education Level, Florida Length of Follow-up Time after Diagnosis of Localized Prostate Cancer by Education Level, Florida Univariate & Multivariate Cox Proportional Hazard Models: Effect of Education Level on Overall Survival of Prostate Cancer, Florida Univariate Cox Proportional Hazard Models: Effect of Education Level on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Multivariate Cox Proportional Hazard Models: Effect of Education Level on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Mean Overall Survival and Overall Survival Rates at Time (years) after Diagnosis of Localized Prostate Cancer by Smoking Status, Florida Length of Follow-up Time after Diagnosis of Localized Prostate Cancer by Smoking Status, Florida xiii

19 58 Univariate & Multivariate Cox Proportional Hazard Models: Effect of Smoking Status on Overall Survival of Prostate Cancer, Florida Univariate Cox Proportional Hazard Models: Effect of Smoking Status on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Multivariate Cox Proportional Hazard Models: Effect of Smoking Status on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Mean Overall Survival and Overall Survival Rates at Time (years) after Diagnosis of Localized Prostate Cancer by Marital Status, Florida Length of Follow-up Time after Diagnosis of Localized Prostate Cancer by Marital Status, Florida Univariate & Multivariate Cox Proportional Hazard Models: Effect of Marital Status on Overall Survival of Prostate Cancer, Florida Univariate Cox Proportional Hazard Models: Effect of Marital Status on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida Multivariate Cox Proportional Hazard Models: Effect of Marital Status on Overall Survival of Prostate Cancer by Race/Ethnicity, Florida xiv

20 CHAPTER 1 INTRODUCTION AND STUDY AIMS Cancer is a major burden of disease in the US and the worldwide. One in four deaths in the US is from cancer [1]. In 2010, medical costs associated with cancer were estimated at $124.6 billion, with the highest costs associated with breast cancer ($16.5 billion), followed by colorectal cancer ($14 billion), lymphoma ($12 billion), lung cancer ($12 billion), and prostate cancer ($12 billion) [2]. If cancer incidence, survival rates, and costs remain stable, and the U.S. population ages at the rate predicted by the U.S. Census Bureau, direct cancer care expenditures will reach $158 billion by 2020 [3]. The impact of cancer extends beyond the costs of treatment and lost lives. The cancer survivor, in addition to family members, friends, and caregivers, may face social, emotional, and physical challenges related to diagnosis and treatment. Some types of cancer, such as breast, colorectal, and cervical can be found before they cause symptoms through routine screening. For colorectal and cervical cancer, screening finds abnormal cells that can turn into cancer and by removing these cells physicians can prevent the cancer from ever occurring. Screening also can be used to find cancer at its earliest stage when treatment is most effective and chances for survival are highest. For other types of cancer, screening methods are being evaluated for their ability to reduce the number of cancer deaths (e.g., lung cancer screening). Prostate cancer is a cancer that has a screening mechanism for early detection and diagnosis; however, the relationship between prostate cancer screening, diagnosis, and treatment is less clear. 1

21 2 One in six men in the US is expected to be diagnosed with prostate cancer in his lifetime [1]. Despite the large number of men diagnosed every year with prostate cancer, there is no consensus concerning the best and most appropriate treatment [3, 4]. Moreover, most men with early stage prostate cancer have a life expectancy comparable to similarly aged men without prostate cancer and will die with prostate cancer rather than from prostate cancer [1]. Without evidence favoring one treatment over another, it is important to understand the factors that inform treatment selection and what impact this may have on survival. There are several treatment approaches for localized prostate cancer; these include radical prostatectomy, radiation therapy, and active surveillance [5]. Newly diagnosed patients are faced with a myriad of decisions regarding their diagnosis and treatment. Patients must consider treatment selection type, despite a lack of scientific evidence to unequivocally support one treatment over another. Patients are informed of their risk for potential side effects from being treated for prostate cancer as well (e.g., erectile dysfunction, urinary incontinence) [5]. Further, at the time of diagnosis, patients are receiving recommendations from physicians, family members, or friends which can be contradictory to one another. Finally, there also are economic consequences to consider as costs to treat prostate cancer differ by modality. For instance, a man diagnosed with prostate cancer has to take into account the much greater costs of definitive therapy (e.g., surgery, radiation therapy) over conservative management (active surveillance) perhaps knowing that one treatment strategy may not be better than the other [5]. To further complicate the matter, extant research shows that regional differences exist in the diagnosis and treatment of prostate cancer [6, 7]. Harlan et al. examined

22 3 cancer registry data from six regions in the US Connecticut, New Mexico, Seattle, Utah, Los Angeles, and Atlanta [7]. They found that men living in Atlanta or Connecticut were more likely to receive radiation therapy than men living in the other four areas. Additionally, men in Utah received aggressive therapy more frequently than men in Los Angeles [7]. The geographic variation in treatment of men diagnosed with prostate cancer suggests a lack of consensus among physicians in selecting one treatment over another. It appears geographic location also is a factor in men s treatment for prostate cancer; therefore, regionally specific information is relevant in understanding the factors related to prostate cancer treatment. Using data from the Florida Cancer Data System (FCDS), the goal of this dissertation is to provide state level estimates on the factors affecting prostate cancer treatment and survival. Specifically, I will examine factors associated with initial treatment selection and how those factors influence overall survival for men diagnosed with localized prostate cancer in Florida. The objective of this research is to identify if sociodemographic and tumor characteristics predict initial treatment selection and overall survival. Prior research in other areas has identified demographic and clinical factors associated with the treatment of prostate cancer, including age at diagnosis, comorbidity, tumor characteristics, and race/ethnicity [7]. Furthermore, results from the Center for Disease Control (CDC) National Program of Cancer Registries (NPCR) Patterns of Care Study, which used cancer registry data from seven states (California, Colorado, Illinois, Louisiana, New York, Rhode Island, and South Carolina), showed state of residence was a significant predictor of both treatment selection and overall survival.

23 4 The findings from this study revealed that regional differences exist in how clinicians and patients select treatment options [6]. Therefore, the information generated from my research can be used to better understand the treatment selection and overall survival for Florida residents diagnosed with localized prostate cancer. This new knowledge can be used to educate patients and clinicians with an aim of reducing disparities in prostate cancer diagnosis, treatment, and survival. Using the data from the FCDS to investigate factors associated with treatment selection for prostate cancer and overall survival, the following specific aims and hypotheses are proposed: Specific Aim 1 Describe initial treatment selection, sociodemographic characteristics, and tumor characteristics (stage and grade) at the time of diagnosis of men in Florida diagnosed with prostate cancer from Specific Aim 2 Identify sociodemographic characteristics associated with tumor stage at time of diagnosis for Florida residents diagnosed with prostate cancer from Hypothesis 2-1: Non-Hispanic Black men are more likely to be diagnosed with advanced/late stage disease than Non-Hispanic white and Hispanic men.

24 5 Hypothesis 2-2: Men who are not married are more likely to be diagnosed with advanced/late stage disease than men who are married. Hypothesis 2-3: Current smokers are more likely to be diagnosed with advanced/late stage disease than non-smokers. Specific Aim 3 Identify the sociodemographic and tumor characteristics associated with initial treatment selection by Florida residents diagnosed with localized prostate cancer from Hypothesis 3-1: Non-Hispanic White and Hispanic men are more likely to choose surgery than Non-Hispanic Black men. Hypothesis 3-2: Men of higher socioeconomic status are more likely to choose surgery than men of lower socioeconomic status. Hypothesis 3-3: Men with higher education levels are more likely to choose surgery than men with lower levels of education. Hypothesis 3-4: Men with private insurance are more likely to choose surgery than men with public or no insurance.

25 6 Specific Aim 4 Examine the relationship between initial treatment selection, sociodemographic, and tumor characteristics on overall survival for Florida residents diagnosed with localized prostate cancer from Hypothesis 4-1: There will be no difference in survival curves and risk of mortality between initial treatments. Hypothesis 4-2: Non-Hispanic White and Hispanic men will have higher survival rates and a lower risk of mortality compared to Non-Hispanic Black men. Hypothesis 4-3: Men of high socioeconomic status will have higher survival rates and a lower risk of mortality than men with the lowest level of socioeconomic status. Hypothesis 4-4: Men with higher levels of education will have highest survival rates and a lower risk of mortality than men with lower levels of education. Hypothesis 4-5: Never smokers will have higher survival rates and a lower risk of mortality than current smokers. Hypothesis 4-6: Men who are married will have higher survival rates and lower risk of mortality than men who are not married.

26 7 Unique to this study, I analytically target factors to predict initial treatment selection and overall survival of prostate cancer for men in Florida, thereby identifying those men who are at greatest risk for both the under- and over-treatment of their cancer. In using this innovative analytical approach, I anticipate the following outcomes: (1) specific factors would be identified that predict initial treatment selection and overall survival; and (2) this information could be used to increase awareness among physicians and men in the state regarding prostate cancer treatment selection by providing more information about health disparities in prostate cancer. Due to this research, while contributing to the field of cancer epidemiology, the results of this study are expected to have an important positive impact on the health of men at-risk for and men who have prostate cancer by providing regionally specific information to patients and providers. Overview of Chapters This dissertation is organized as follows. In Chapter 2, I provide an overview of prostate cancer. Chapter 3 is the methodological section where I describe the sources of data, study variables, and statistical analysis plan. In Chapters 4, 5 and 6, I present the results of the analyses, discussion of their implications, and a review of the study s limitations. The dissertation ends with the presentation of future research directions in Chapter 7.

27 CHAPTER 2 PROSTATE CANCER In order to examine prostate cancer and treatment selection, I begin with an overview of prostate cancer. In this chapter, I outline the epidemiology of prostate cancer, how it is defined by the medical community, factors that make up screening, diagnosing, and treating the disease, as well as provide readers with some of the larger problems surrounding treatment. That is, I also explore issues related to health disparities as it pertains to prostate cancer. Finally, I provide a theoretical basis for initial treatment decision making for prostate cancer. Epidemiology of Prostate Cancer Prostate cancer is the most common solid malignancy diagnosed in American men [1]. Approximately 1 in 6 men will receive a diagnosis of prostate cancer and 1 in 34 will die from it [1, 5]. In 2013, prostate cancer will be diagnosed in an estimated 238,590 men and approximately 29,720 men will die of the disease [1]. The median age at diagnosis for prostate cancer is 68 years, making it the highest age-specific incidence of any cancer [5]. Black men have the highest incidence rate for prostate cancer in the US (228.7 cases per 100,000) and are more than twice as likely as White men to die of the disease (53.1 deaths vs deaths per 100,000 respectively) [1]. In Florida, it is estimated that 17,330 cases of prostate cancer will be diagnosed in 2013, making it second in the nation behind California (23,740 total cases diagnosed) [1]. In Florida, Black men have a 8

28 9 48% higher incidence of prostate cancer when compared to White men [8]. Hispanic men, however, have similar rates to White men [9]. Reasons for this discrepancy in incidence are outlined in the following sub-sections of this chapter. Further, with the introduction of the prostate-specific antigen (PSA) test in the mid-1980s, prostate cancer incidence rates increased dramatically for all men. After the introduction of the PSA test, prostate cancer incidence rates increased by about 12% per year, and this increase peaked in 1992 [10]. After 1992, the rate subsequently declined, at about 10% per year for the next three years and then appeared to stabilize from 1995 to 2005 [10, 11] at about 150 cases per 100,000 men. In 2011, Kohler et al. reported a stable trend of prostate cancer incidence from 1998 to 2007; however, demographic and clinical factors were not examined in that study [12]. With the widespread use of the PSA test, the mean age at diagnosis dropped substantially, from 72.2 years between to 67.2 years between 2004 and 2005 [13]. Studies using Surveillance, Epidemiology, and End Results Program (SEER) data have shown that the distribution of prostate cancer stage and grade also has dramatically changed, with the majority of tumors being localized and moderately differentiated [14, 15]. In other words, the PSA has helped in diagnosing prostate cancer that is at a much earlier stage and less aggressive. Currently, age at diagnosis, cancer stage, and grade are among the most important factors used to determine the prostate cancer treatment modality such as prostatectomy, radiation, or active surveillance [16]. Since most of screen-detected prostate cancers are low risk and are unlikely to cause death, medical experts have agreed that active surveillance, a way to monitor disease periodically rather than treat it immediately, has

29 10 emerged as a viable treatment option for patients with low risk prostate cancer. As I describe in this chapter, below, diagnosis, grade, and cancer stage are only a few of the determinants for what treatment options men are given for their prostate cancer. Prostate Cancer Defined The prostate gland is about the size of a walnut. It sits under the bladder and in front of the rectum. The urethra runs directly through the prostate. The rectum sits just behind the prostate and the bladder (Figure 1). Sitting just above the prostate are the seminal vesicles two smaller glands that secrete about 60% of the substances that make up semen [16]. Running alongside and attached to the sides of the prostate are the nerves that control erectile function. The prostate is not essential for life, but it is important for reproduction. The prostate gland s primary function is to secrete a slightly alkaline fluid that forms part of the seminal fluid [17]. During male orgasm the muscular glands of the prostate help to propel the prostate fluid and sperm that was produced in the testicles into the urethra. The prostate gland supplies substances that facilitate fertilization and sperm transit and survival [16, 17]. Enzymes like PSA are actually used to loosen up semen to help sperm reach the egg during intercourse. The prostate typically grows during adolescence under the control of the male hormone testosterone and its byproduct dihydrotestosterone (DHT) [16, 17]. Prostate cancer is a malignant tumor that consists of cells from the prostate gland. Generally, the tumor grows slowly and remains confined to the gland for many years. During this time, the tumor produces little or no symptoms or outward signs. However,

30 11 all prostate cancers do not behave in the same manner. There are aggressive types of prostate cancer that grow and spread rapidly. Aggressive prostate cancer can cause a significant shortening of men s life expectancy. To measure prostate cancer aggressiveness, medical experts rely on the Gleason score (discussed in detail in the Prostate Cancer Screening and Detection section of this chapter), which is calculated by a trained pathologist observing prostate biopsy specimens under the microscope. If prostate cancer advances, however, it can spread beyond the prostate into the surrounding tissues (Figure 2). Moreover, the cancer also can metastasize throughout other areas of the body such as the bladder, rectum, and bones [16]. Outward symptoms and signs, therefore, are more often associated with advanced prostate cancer. In general, only a small percentage of prostate cancers are aggressive, while the vast majority are slow growing [17]. Risk Factors Identified risk factors for prostate cancer include age, family history, obesity, hormones, race/ethnicity, and diet. Increasing age is the best established risk factor for diagnosis of prostate cancer. An analysis from the Cancer of the Prostate Strategic Urologic Research Endeavor registry [18] found that while the likelihood of high-risk disease increased with increasing age (26% of men 75 years), cancer-specific survival differences for age categories were greatly influenced by treatment decisions, which are themselves driven by age to a greater extent than disease risk [19, 20]. Family history also has been consistently associated with prostate cancer in epidemiologic studies [20]. The impact of family history as a risk factor for development

31 12 of prostate cancer varies with the degree of relatedness and number of relatives affected. For instance, if a man has a father diagnosed with prostate cancer, his relative risk for the disease is greater than if it were some other type of family member, such as an uncle or cousin. Further, a recent large study on familial prostate cancer confirmed that one s risk is directly related to number of relatives and patient s age. The investigators found that the highest relative risk was in men younger than 65 years of age with three affected brothers (HR 23.3; 95% CI: 5.81, 93.11) and lowest in men years of age with an affected father only (HR 1.8; 95% CI: 1.68, 1.90) [22]. Hence, family history as a predictor for one s risk of prostate cancer is dependent on both factors. There are some contradictory research findings on the predictors of prostate cancer risk and obesity. The findings of general population studies of obesity and prostate cancer incidence have been inconsistent [23 25], whereas studies of prostate cancer mortality have found consistent associations between obesity and mortality [26, 27]. A recent meta-analysis found a modest increase in prostate cancer risk with increasing body mass index (BMI) (RR=1.05; 95% CI: 1.0, 1.08) per 5 unit increase in BMI [23]. However, when analyses were conducted by grade and stage of disease at diagnosis, obesity was found to be associated with high-grade prostate cancer and not associated with, or perhaps even protective for, localized or low-grade disease [23, 28]. It is possible that the complex relationship between obesity and health care use and screening may affect the validity and interpretation of results from studies on obesity and prostate cancer risk [29 33]. Moreover, because of comorbidities related to obesity, obese men may have greater contact with the health care system, thereby receiving more intense prostate cancer screening [29, 34]. Yet, obesity also is associated with poverty and lower

32 13 educational attainment, which are associated with worse access to health care [35 39]. As I previously stated, the research on the relationship between obesity and prostate cancer is equivocal. Another risk factor for prostate cancer is hormones, especially androgens, which are required for growth, maintenance, and function of the prostate [40]. The progression of prostate cancer from a subclinical form to a clinically important form may result, in part, from altered hormone metabolism [40]. That is, the growth of cancer may be related to an abnormality in hormone levels. There are other components that affect prostate cancer as well. There are multiple issues surrounding how race/ethnicity related differences affect prostate cancer; these include differences in environmental exposure, detection, and genetics. African American men have the highest rates of prostate cancer in the U.S., with an estimated incidence of cases per 100,000 expected in 2013, as compared with White (141.0 cases), Hispanic (124.9 cases), American Indian (98.8 cases), or Asian (77.2 cases) populations [1]. Furthermore, African American men are more likely to present with prostate cancer at a younger age, with higher grade or stage of disease [41], and are at greater risk for prostate cancer death [1, 41-43]. These race/ethnic related differences may be due to a variety of factors, some sociodemographic and some biologic. The 2002 Institute of Medicine report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, found evidence that racial and ethnic minorities tend to receive lower-quality health care than whites, even when access-related variables, such as patients insurance status and income, are controlled [44]. Evidence of a genetic component to the high incidence and mortality

33 14 rate in African American men comes from epidemiologic studies of men with similar genetic backgrounds. For example, men in Nigeria and Ghana also have a high incidence of prostate cancer, as do men of African descent in the Caribbean islands and in the United Kingdom [45]. Finally, descriptive epidemiologic studies of migrants, geographic variations, and temporal studies suggest that dietary factors also may contribute to prostate cancer development [46, 47]. For example, incidence in the US is 83.8 cases per 100,000 men, compared with 22.7 in Japan and 22.4 in the Republic of Korea [44]. Furthermore, migration studies show that prostate cancer incidence is higher among Japanese immigrants to the United States than in Japan [49, 50]. These data suggest that the American lifestyle (diet, exercise, etc.) may be a contributing factor to the difference between countries. Although not identified as a risk factor for prostate cancer, recent studies have demonstrated a correlation between smoking and an increased risk of prostate cancer mortality [51]. The latest review by the US Surgeon General found the evidence probable that smoking contributes to a higher prostate cancer mortality rate [52]. A review of the literature shows an approximate 30% increase in risk of fatal prostate cancer when comparing current smokers with never smokers [53]. Several studies reported that smoking is associated with more aggressive disease at diagnosis, defined as a higher stage or tumor grade [54-56]. Likewise, there is a relationship between smoking and disease progression after diagnosis, defined as biochemical recurrence [56-58], metastasis [59], and hormone-refractory prostate cancer [60]. Consequently, smoking may increase the risk of aggressive prostate cancer and prostate cancer mortality.

34 15 In sum, prostate cancer, the most common malignancy among men, affects men differently. Risk factors, both sociodemographic and biological may explain this variation. Prostate Cancer Screening and Diagnosis Prostate cancer is primarily a disease of elderly men, with approximately 90% of men with prostate cancer having the disease confined to the prostate gland (clinically localized disease) [1]. Clinically localized prostate cancer generally causes no symptoms. It is commonly thought that the slowing of the urinary stream, arising at night to urinate, and increased urinary frequency are symptoms of prostate cancer; however, they actually are associated with aging and often are unrelated to the presence of prostate cancer. Because of the lack of symptoms for the disease, particularly in its early stage, early detection tests have been developed to identify prostate cancer while it remains confined to the prostate. The two most commonly used tests are the serum prostatespecific antigen (PSA) level test and the digital rectal examination (DRE) [61, 62]. Prostate-specific antigen, or PSA, is a protein produced by cells of the prostate gland. The PSA test measures the level of PSA in a man s blood. For this test, a blood sample is sent to a laboratory for analysis. The results are usually reported as nanograms of PSA per milliliter (ng/ml) of blood. The blood level PSA is often elevated in men with prostate cancer. The PSA test was originally approved by the FDA in 1986 to monitor the progression of prostate cancer in men who had already been diagnosed with the disease [16,17]. In 1994, the FDA approved the use of the PSA test in conjunction with a digital rectal exam (DRE) to test asymptomatic men for prostate cancer [17]. Men who

35 16 report prostate symptoms often undergo PSA testing (along with a DRE) to help doctors determine the nature of the problem. Both tests should be used as the standard of care to determine if there is prostate cancer in men at risk for the disease. In addition to prostate cancer, a number of benign (not cancerous) conditions can cause a man s PSA level to rise. The most frequent benign prostate conditions are prostatitis (inflammation of the prostate) and benign prostatic hyperplasia (BPH) (enlargement of the prostate) [16, 17]. Inflammation of the prostate often is caused by bacterial infections, while enlargement of the prostate is not fully understood but may be caused by hormonal changes. There is no evidence linking prostatitis or BPH to prostate cancer, but it is possible for a man to have one or both of these conditions and develop prostate cancer. Further, there is no specific normal versus abnormal level for the PSA in the blood [63]. Most physicians consider PSA levels of 4.0 ng/ml or lower as normal. Men with PSA levels above 4.0 ng/ml are recommended to get a prostate biopsy to determine whether prostate cancer is present or not. However, the accuracy of this cut point is unclear. Recent studies have shown that some men with PSA levels below 4.0 ng/ml have prostate cancer, while many men with higher PSA levels do not have prostate cancer [64]. Further, various other factors can cause a man s PSA level to fluctuate. While prostate biopsies and prostate surgery can definitively determine if cancer is present, they also can increase a man s PSA level. Conversely, some drugs, including finasteride and dutasteride, which are used to treat BPH, lower a man s PSA level [16, 17] which has led to the debate of using these drugs to prevent prostate cancer. PSA levels also may vary somewhat across testing

36 17 laboratories [63]. So despite the existence of a blood test to screen men for early detection of prostate cancer, a number of difficulties exist with this test. Another complicating factor is that studies to establish the normal range of the PSA level have been conducted primarily in populations of white men [64]. Whereas expert opinions vary, there is no clear consensus regarding the optimal PSA threshold recommended for prostate biopsy for men of any other racial or ethnic group. With these qualifications in mind, physicians generally watch a man s PSA level with the expectation that the higher a man s PSA level, the more likely it is that he has prostate cancer. Moreover, a continuous rise in a man s PSA level over time also may be a sign of prostate cancer [16, 17, 63, 65]. The PSA doubling time (PSADT) is used as predictive marker for assessing disease outcome [65]. Screening Guidelines Due to the widespread use of the PSA test to screen for prostate cancer, since the early 2000 s, the lifetime risk of being detected with prostate cancer in the US has nearly doubled to 20% [61]. During this same time period, however, the risk of dying has remained at approximately 3% suggesting that there may be considerable over-detection and over-treatment of prostate cancer [66]. The US government commissioned a task force to examine these issues. Findings from the US Preventive Services Task Force (USPSTF) show current evidence is inadequate to determine whether treatment for prostate cancer detected by screening improves health outcomes in men relative to clinical detection [66]. Moreover, the USPSTF found insufficient evidence to assess the balance of benefits and harms of PSA screening in men [66].

37 18 In 2013, the American Urological Association (AUA) changed their guidelines regarding screening [67]. The AUA recommends against PSA screening in men under age 40 years. For this age group, there is a low prevalence of clinically detectable prostate cancer and no evidence demonstrating the benefit of screening. Additionally, the AUA does not recommend routine screening in men between ages of 40 to 54 years who are at average risk. Recommendations regarding prostate cancer screening for men younger than age 55 years who are at higher risk (e.g. positive family history or African American race), should be individualized [67]. It appears that the greatest benefit of screening is for men ages 55 to 69 years, according to the AUA [67]. For men ages 55 to 69 years, the decision to undergo PSA screening involves weighing the benefits of preventing prostate cancer mortality in 1 man for every 1,000 men screened over a decade against the known potential harms associated with screening (and treatment) [67]. For this reason, the AUA strongly recommends shared decision-making for these men who are considering PSA screening, and proceed based on a man s values and preferences. The recommendation for this age group relies on the man s ability to come to a decision on what is best for him based upon his preferences and input from his physician and social support network. For those men who do wish to be screened, to reduce the harms of screening, the AUA recommends a routine screening interval of two years or more. Compared to annual screening, a two year screening interval will preserve the majority of the benefits gained from screening and reduce the hazards of screening, such as over diagnosis and false positives [67]. Additionally, rescreening intervals can be individualized according to the baseline PSA level.

38 19 The AUA does not recommend routine PSA screening in men over age 70 years or any man with less than a 10 to 15 year life expectancy. However, with increases in life expectancy, some men over age 70 years who are in excellent health may benefit from prostate cancer screening [67]. While the PSA test and DRE can be used to screen men for signs of prostate cancer, only a biopsy can confirm if cancer is present. Biopsy, Gleason Score, and Cancer Staging At the time of biopsy, several small cores of tissue are removed from the prostate and then are examined by a pathologist to determine if cancer is present [67]. The Gleason score of the tumor and clinical stage are clinical variables that correlate with the pathological extent of the disease. Physicians will examine the PSA level, Gleason score, and clinical stage to make a prognosis. These three measures are used in validated monograms to estimate the probability of the progression of disease outside the prostate [5]. Specifically, the Gleason score is the grading system used for prostate cancer [68]. It is a score based on the review of pathological specimens of the prostate. A grade ranging from 1 to 5 is given to the two most frequent patterns of cells as viewed by the pathologist under a microscope and summed for a score. This score has a range of 2 to 10, with 2 signifying a cancer that acts and closely resembles normal prostate cells and 10 signifying a cancer that does not. The Gleason score is based exclusively on the architectural pattern of the glands of the prostate tumor. It evaluates how effectively the cells of any particular cancer are able to structure themselves into glands resembling those of the normal prostate. The

39 20 ability of a tumor to mimic normal gland architecture is called differentiation. A tumor whose structure is nearly normal (well differentiated) will probably have a biological behavior relatively close to normal [16, 17, 68]. The Gleason grading system goes from very well differentiated (Grade 1) to very poorly differentiated (Grade 5). A conceptual diagram (Figure 3) was created by Dr. Donald Gleason to show the continuum of deteriorating cancer cell architecture; the four dividing lines along this continuum, which he discovered, are able to identify patients with significantly different prognosis [68]. This diagram was derived from a study that included 2,900 patients. Listed below are the descriptions of each Gleason pattern: Gleason Grades 1 and 2 - These grades closely resemble a normal prostate. They are the least important grades because they seldom occur in the general population and because they confer a prognostic benefit which is only slightly better than grade 3 [68]; Gleason Grade 3 - The most common grade and also is considered well differentiated (like Grades 1 and 2) [68]; Gleason Grade 4 The most important grade because it is fairly common and because of the fact that if a lot of it is present, patient prognosis is usually (but not always) worsened by a considerable degree [68];

40 21 Gleason Grade 5 - Gleason Grade 5 is an important grade because it usually predicts another significant step towards poor prognosis. Its overall importance for the general population is reduced by the fact that it is less common than grade 4, and it is seldom seen in men whose prostate cancer is diagnosed early in its development [68]. A staging system is a standard way for the cancer care team to describe how far a cancer has spread. The most widely used staging system for prostate cancer is the American Joint Committee on Cancer (AJCC) TNM system [69]. There are four primary stages of prostate cancer (Figure 2). In Stage I, cancer is found only in the prostate. In Stage II, cancer is more advanced than in Stage I, but has not spread outside of the prostate. Stage II is divided into Stage IIA and Stage IIB. Stage IIA signifies an intermediate level of risk, while Stage IIB signifies a high risk of recurrence. In Stage III, cancer has spread beyond the outer layer of the prostate and may have spread to the seminal vesicles. Stage IV, also known as metastatic cancer, has spread beyond the seminal vesicles to nearby tissue or organs, such as the rectum, bladder, or pelvic wall. Cancer also may have spread to nearby lymph nodes or to distant parts of the body and often spreads to the bones [16, 17, [70]. The combination of Gleason score and tumor staging provide a patient and their clinician with an initial roadmap from which to plan treatment strategies. These pieces of information are critical when it comes to the discussion of prostate cancer treatment.

41 22 Prostate Cancer Treatment Prostate cancer treatment primarily consists of the following treatments: watchful waiting, active surveillance, radical prostatectomy, or radiation therapy. Adding to the complexity of the treatment decision making process, patients also are faced with choosing within treatment types; that is, what type of surgery to have (open vs. laparoscopic/robotic), what type of radiotherapy to have (conformal vs. intensity modulated or type of brachytherapy isotope), or whether to have a combination of therapies. Less used treatments for prostate cancer include hormonal therapy (androgen deprivation therapy) and cryotherapy. Depending on the Gleason score, the stage at diagnosis, and the health of the patient, the physician and the patient will discuss treatment options. Watchful waiting is based on the premise that some patients will not benefit from definitive treatment of the primary prostate cancer [71]. The decision is made at the outset to forgo definitive treatment and instead to provide palliative treatment for local or metastatic progression, if and when it occurs. Options for local palliative treatment could include transurethral resection of the prostate or other procedures for the management of urinary tract obstruction, and hormonal therapy or radiotherapy for palliation of metastatic lesions. In contrast, active surveillance is based on the premise some patients may benefit from treatment of their primary prostate cancer. A program of active surveillance has two goals: (1) to provide definitive treatment for men with localized cancers that are likely to progress; and (2) to reduce the risk of treatment-related complications for men

42 23 with cancers that are not likely to progress [71]. Active surveillance and watchful waiting are coded in the same manner in cancer registry data [72]. Radical prostatectomy is a surgical procedure in which the entire prostate gland is removed and attached seminal vesicles, plus the ampulla of the vas deferens are removed [71]. Radical prostatectomy may be performed using a retropubic or perineal incision or by using a laparoscopic or robotic assisted technique. Depending on tumor characteristics and the patient's sexual function, either nerve-sparing (to preserve erectile function) or non-nerve-sparing radical prostatectomies are performed [73]. Pelvic lymphadenectomy can be performed concurrently with radical prostatectomy and is generally reserved for patients with higher risk of nodal involvement [62]. Because the entire prostate gland is removed with radical prostatectomy, the major potential benefit of this procedure is a cancer cure in patients in whom the prostate cancer is truly localized. In cases where the prostate cancer is of a high grade, when the tumor has spread outside of the prostate gland, or when the tumor is not completely excised, removing the prostate may not ensure that all the cancer is eliminated, putting the patient at risk for recurrence. Radiation (radiotherapy) treatments include external beam radiotherapy and interstitial prostate brachytherapy. External beam radiation therapy (EBRT) is one of the oldest techniques used to treat prostate cancer [5]. With external radiation, electrons of high-energy electromagnetic waves are accelerated so that they hit a metallic target and yield photons. The photons interact with surrounding tissues producing high-speed electrons that are able to split water molecules. The split water molecules yield a hydroxyradical, which causes damage to the DNA of rapidly dividing cells, including

43 24 tumor cells. Normal cells do not experience as much of a deadly result as tumor cells because normal cells are not dividing as rapidly. Further, normal cells have the ability to repair the damaged DNA [71]. Patients with clinically localized prostate cancer are considered candidates for interstitial prostate brachytherapy, but practitioners differ with respect to which risk groups are offered this approach. Some practitioners will use this treatment option for low-risk disease only, while others will treat both low and intermediate-risk patients [74]. Prior to initiating therapy, a transrectal ultrasound-based volume study is performed to assess prostate volume and to determine the number of needles and corresponding radioactive seeds, the isotope, and the isotope strength necessary for the procedure. The radioactive needles are implanted via a transperineal approach under guidance of transrectal ultrasound or magnetic resonance imaging [74]. One of the most important factors in predicting the effectiveness of an implant is implant quality. An excellent implant is defined as one in which 90% or more of the prostate gland volume receives at least 100% of the prescription dose [75]. Because of the variability of patient characteristics, Gleason score, and stage at diagnosis, there is no one standard for care for prostate cancer. Most decisions on treatment are a shared experience between the physician and patient. Most men with prostate cancer live for many years after diagnosis and may never suffer morbidity or mortality attributable to prostate cancer. Therefore, the short-term and long-term adverse consequences of therapy are of great importance. Short- and long-term adverse consequences also are part of the conversation about treatment options between the physician and patient.

44 25 Adverse Treatment Effects Adverse effects of radical prostatectomy include immediate postoperative complications and long-term urinary and sexual complications [16, 17]. External beam or interstitial radiation therapy in men, with localized prostate cancer, may lead to urinary, gastrointestinal, and sexual complications [16, 17]. Though there have been improvements in surgical and radiation techniques to reduce the incidence of many of these complications, these complications still exist for some men who undergo radical prostatectomy [76]. Hormone treatment typically consists of androgen deprivation therapy, and consequences of such therapy may include vasomotor flushing, anemia, and bone density loss. Numerous clinical trials have studied the role of bone antiresorptive therapy for prevention of bone density loss and fractures [76]. Long-term consequences of androgen deprivation therapy may include adverse body composition changes (e.g., decrease lean body mass and increase in fat mass) and increased risk of insulin resistance, diabetes, and cardiovascular disease [16,17]. Due to the lack of conclusive evidence of a definitive choice as best therapy, men and their physicians may select treatments based on other criteria. The following referenced studies are important in that they illustrate that treatment decisions are often based upon factors not related to the perceived efficacy of a particular treatment. Moreover, these studies used population based data from cancer registries to conduct their analyses of which data from Florida was not included. For instance, the Prostate Cancer Outcomes Study which found that age, race/ethnicity, marital status, number living in the home, education level, insurance coverage, and geographic region were related to treatment selection. Additionally, data show Hispanic men received radical

45 26 prostatectomy more often than Non-Hispanic Whites or Blacks, while radical prostatectomy was received less frequently by patients with lower education levels and incomes [7]. The CDC-NPCR Patterns of Care Study found that older age, Black race/ethnicity, being unmarried, having public insurance, co-morbid conditions, and state of residence were associated with conservative treatment (hormone therapy, watchful waiting). Overall survival was related to younger age at diagnosis, being married, Gleason score under 8, radical prostatectomy, and state of residence [6]. Additionally, comorbidity was only associated with risk of death within the first three years of diagnosis [6]. In total, treatment decisions for localized (early stage) prostate cancer can be particularly challenging in light of the presence of potential adverse treatment effects. As previously mentioned, the decision is complicated by the fact that there is equivocal evidence comparing treatments stating which one is most appropriate based on the patient s diagnosis. Further, various treatments have side effects that differ which also factor into treatment decisions. Thus, patients are inundated with large amounts of information from which pertinent risks and benefits must be assessed. The decision making process can easily become overwhelming for the patient since the decision is typically a new type of experience for the patient. Patients often feel some urgency to make a decision quickly, but because it is an emotional time, patients may not be able to clearly make a treatment decision [77]. Further, patients may not be able to make the decision by themselves and may rely heavily on the advice of their physician.

46 27 Comparative Effectiveness of Prostate Cancer Treatments Systematic reviews have provided inadequate information for assessing the comparative effectiveness of treatments and any associated harms [4]. Of the limited research that has been done to compare treatments, the results have been inconclusive at best. Here, I briefly summarize some of those studies. Two randomized controlled trials of men with prostate cancer were compared. The two treatment procedures were watchful waiting and radical prostatectomy. Nearly all of the cases were detected by methods other than a PSA test [4]. In the Scandinavian Prostate Cancer Group Study No. 4, surgery reduced the incidence of all cause deaths, disease-specific deaths, and distant metastases compared with the watchful waiting group [78]. A second study did not yield statistically significant results, although it did report a median survival of 10.6 years for radical prostatectomy compared to 8 years for watchful waiting. One limitation to this study was that it was under powered to detect moderately large treatment differences [79]. Next, a small trial compared radical prostatectomy to EBRT, where results indicated that radical prostatectomy was more effective in preventing progression, recurrence, or distant metastases relative to EBRT [80]. The most promising results have come from the recently completed Prostate Cancer Intervention Versus Observation Trial (PIVOT) [3]. This is a randomized trial that compared all-cause mortality and prostate specific cancer mortality among men assigned to surgery or observation (watchful waiting, active surveillance). The results of this trial found that among men with localized prostate cancer detected during the early era of PSA testing, radical prostatectomy did not significantly reduce all-cause or prostate-cancer specific mortality compared with the observation group [3].

47 28 Study participants were followed up for a minimum of 12 or more years. Median follow-up was 10 years. At 10 years, 171 of the 364 men (47.0%) assigned to the radical prostatectomy group died, compared with 183 of the 367 (49.9%) men assigned to observation (HR=0.88; 95% CI: 0.71, 1.08) [3]. Men assigned to the radical prostatectomy group had 21 (5.8%) die from prostate cancer or treatment, as compared with 31 men (8.4%) assigned to observation (HR=0.63; 95% CI: 0.36, 1.09) [3]. The effect of treatment on all-cause and prostate-cancer mortality did not differ according to age, race/ethnicity, coexisting conditions, self-reported performance status, or histological features of the tumor. Moreover, radical prostatectomy was associated with reduced all-cause mortality among men with a PSA value greater than 10 ng/ml and possibly among those with intermediate-risk or high-risk. Adverse events within 30 days after surgery occurred in 21.4% of men, including one death [3]. Studies also have examined the cost-effectiveness of treatment options. When examining the cost-effectiveness of treatments, Cooperberg et al. conducted the most comprehensive cost-effectiveness analysis to date for localized prostate cancer [81]. The researchers found that radiation therapy methods were consistently more expensive than surgical methods. Costs ranged from $19,901 (robot-assisted prostatectomy for low-risk disease) to $50,276 (combined radiation therapy for high-risk disease) [81]. Further, the study found other minor differences in quality-adjusted life years across treatment options. Overall, radiation therapy was consistently more expensive than surgery, and some alternatives, e.g. intensity-modulated radiation therapy for low-risk disease, were both more expensive and less effective than competing alternatives [81].

48 29 As one would anticipate, a cost analysis of prostate cancer treatment versus conservative management (watchful waiting/active surveillance) found patients receiving combinations of active treatments have the highest additional costs over conservative management (at $63,500), followed by $48,550 for intensity-modulated radiation therapy, $37,500 for primary androgen deprivation therapy, and $28,600 for brachytherapy [82]. Radical prostatectomy ($15,200) and external beam radiation therapy ($18,900) were associated with the lowest costs. The study authors went on to argue that the population model used in the study estimated that US health expenditures could be lowered by: 1) using initial conservative management over all active treatment ($ billion annual savings); 2) shifting patients receiving intensity-modulated radiation therapy to conservative management ($ million); 3) foregoing primary androgen deprivation therapy ($555 million); 4) reducing the use of adjuvant androgen deprivation in addition to local therapies ($630 million); and 5) using single treatments rather than combination local treatments ($ million). This study found that all active treatments are associated with higher longer-term costs than conservative management. Substantial savings, representing up to 30% of total costs, could be realized by adopting conservative management strategies, including active surveillance, for initial management of men with localized prostate cancer [82]. All in all, comparative effectiveness research of prostate cancer treatments has much to be desired. Recent randomized trials have shown no superior benefits of one treatment over another nor do they elucidate which treatments may work better in different settings or for certain populations. Moreover, the cost differential between

49 30 treatments is significant. Cost savings could be achieved with taking an initial conservative management treatment approach. Prostate Cancer Health Disparities Health disparities are population-specific differences in the presence of disease, health outcomes, quality of health care, and access to health care services that exist across racial and ethnic groups. Many factors contribute to racial, ethnic, and socioeconomic health disparities, including inadequate access to care, poor quality of care, community features (such as poverty and violence), and personal behaviors. These factors are often associated with underserved racial and ethnic minority groups, individuals who have experienced economic obstacles, those with disabilities, and individuals living within medically underserved communities. Consequently, individuals living in both urban and rural areas may experience health disparities. As with other cancers, disparities exist among men diagnosed with and treated for prostate cancer. In prostate cancer, much of the interest in disparities has focused on the differences between Black and White men as Black men have the highest incidence of prostate cancer in the US [1]. Furthermore, African American men are more likely to present at a younger age, with higher grade or stage of disease [41], and are at greater risk for prostate cancer death [1, 41-43]. Racial/ethnic disparities in prostate cancer are well documented in tumor grade, stage, and survival [83-87]. As a result of the uniquely high prostate cancer risk seen among Black men, many studies have focused on men of sub-saharan African ancestry to possibly explain why Black men are at risk for the disease [45, 88-93], although there

50 31 also has been considerable interest in why Asian men have comparatively low prostate cancer incidence and mortality [48-50]. These studies suggest genetics and dietary factors may play a role in explaining the disparities. However, there are other reasons for the existence of health disparities in prostate cancer diagnosis, treatment, and outcomes. These are discussed below. As previously mentioned, prostate cancer is a disease primarily of older men. From , the median age at diagnosis for prostate cancer was 66 years of age [88]. The distribution of ages at diagnoses was: none diagnosed either under age 20 or between to 20 and 34; 0.6% between 35 and 44; 9.6% between 45 and 54; 32.3% between 55 and 64; 35.8% between 65 and 74; 17.7% between 75 and 84; and 4.0% 85+ years of age [88]. Marital status, one of the most important forms of social support for men, has been shown to have a positive impact on health [95]. Men who are married enjoy longer overall survival and lower mortality for many major causes of death compared to those who were never married, separated, widowed, or divorced [96-98]. Goodwin et al. were the first to demonstrate the favorable effect of being married on overall survival in patients afflicted with cancer [99]. Similarly in prostate cancer, sociodemographic factors, such as that of marital status, may further add to the explanatory power of conventional clinical variables in predicting treatment outcomes and/or survival [ ]. Environmental factors and lifestyle variables are also thought to influence the development of prostate cancer [105]. Studies have found an association between indicators of poverty and the risk of prostate cancer; however, most observations are

51 32 based predominantly on Caucasian populations and therefore may not apply to individuals with different lifestyles and racial/ethnic backgrounds [105]. Researchers also have looked to ecological studies to explain differences in diagnosis and treatment outcomes. Ecological studies suggest that racial disparities in prostate cancer may be explained, in part, by differences in socioeconomic factors of the environment in which a person resides [106, 107]. That is, these studies suggest that individuals living in resource rich communities or with greater access to care may have better outcomes. However, those studies did not account for an individual s lifestyle or behavior. In fact, one study evaluating racial disparities in prostate cancer incidence looked at both individual and area-based sociodemographic characteristics. This study found no association between poverty and poor education and increased prostate cancer incidence among African Americans in Virginia [108]. Furthermore, a recent study by Fedewa et al. found that insurance status was strongly associated with disease severity among prostate cancer patients [87]. Uninsured and Medicaid-insured patients had elevated PSA levels, higher odds of advanced Gleason score (Uninsured OR= 1.97; 95% CI: 1.82, 2.12; Medicaid OR=1.67; 95% CI: 1.55, 1.79), and advanced tumor stage (Uninsured OR=1.85; 95% CI: 1.69, 2.03; Medicaid OR=1.49; 95% CI: 1.35, 1.63) compared with privately insured patients [87]. Black (OR=1.19; 95% CI: 1.15, 1.23), Hispanic (OR=1.16; 95% CI: 1.10, 1.23), and Asian patients (OR= 1.22; 95% CI: 1.24, 1.43) had higher odds of advanced Gleason score and similar odds of advanced stage of disease relative to Whites [87]. The authors concluded that insurance status is strongly associated with disease severity and may be related to

52 33 lack of access to preventive services such as PSA screening and barriers to medical evaluation. Similarly, Albano et al. examined the effect of education level on cancer mortality [109]. Educational attainment was strongly and inversely associated with mortality from all cancers combined in Black and White men and in White women. That is, Black and White men and White women with higher education levels had lower risks of mortality. Additionally, among the most important and novel findings of the study, Black men who completed 12 or fewer years of education had a prostate cancer death rate that was more than double that of Black men with more schooling (10.5 versus 4.8 per 100,000 men; RR=2.17, 95% CI: 1.82, 2.58) [109]. For this reason, the researchers concluded that cancer death rates differ by level of education. Likewise, geographical disparities in prostate cancer treatment and mortality exist [108]. Studies have shown that there are rural versus urban differences in prostate cancer treatment and mortality [110, 111]. Men in rural areas had significantly higher prostate cancer mortality rates than their more affluent and urbanized counterparts [110]. One hypothesis is that men living in rural areas have greater difficulty accessing care [110, 111]. The rural versus urban pattern held when demographic characteristics were introduced. Black men had a 22% higher mortality rate in rural areas than in the mosturbanized area [111]. Prostate cancer health disparities manifest themselves from a variety of different factors both sociodemographic and genetic. Identifying men at a higher risk for prostate cancer and may be useful in targeting interventions and tracking cancer disparities.

53 34 Differentiation and Consolidation Theory (Diff-Con) I summarize here a theory of how men may or may not choose one treatment option over another. Because the medical model for health care of prostate cancer is to have a shared decision-making process with the physician and patient, the patient has a large say in how his treatment should progress. Svenson s Differentiation and Consolidation (Diff-Con) theory provides a model for which sociodemographic factors and tumor characteristics play a role in the decision-making process for newly diagnosed patients with prostate cancer [112, 113]. An examination of prostate cancer diagnosis and treatment, I argue, should be viewed from a theoretical framework that incorporates decision-making preferences. One relatively comprehensive psychological theory of decision-making that can be applied to prostate cancer treatment selection is Diff-Con [112, 113]. Diff-Con is unique among psychological theories because it includes both pre- and post-decision processes as being important to the decision making process. The main goal of decision making, according to Diff-Con, is to create an alternative that is sufficiently superior in comparison to its competitor(s) through restructuring and applying one or several decision rules [112]. The restructuring processes, called differentiation, are derived from a number of different rules contingent on the situation and the person in the situation. A key element of Diff-Con is the assumption that sufficient differentiation protects the decision maker from external (i.e. poor outcomes) and internal (i.e. changes of one s own values) threats to the preference for the chosen alternative. In other words, Diff-Con allows the decision maker to weigh his/her options and make a decision that he or she feels is better than all of the alternative options. Additionally, it also assumes that

54 35 people want to minimize their effort and minimize the potential for post-decision regret and/or cognitive dissonance [112]. Therefore, Diff-Con provides a model which can account for external variables to factor into the decision-making process and not have the decision maker rely solely on clinical data, which may or may not be the best alternative choice [112, 113]. Sociodemographic factors and tumor characteristics, such as stage and grade of cancer at diagnosis, each factor into the decision-making and play an equal role in that process. As newly diagnosed patients with prostate cancer attempt to make sense of their situation, Diff-Con allows the patient to choose an option that he or she likes best and then cognitively defend that option as the best option, regardless. Diff-Con then plays a role in the differentiation processes that is exacting to a particular patient and to his or her situation. If this theory is accurate, men s choice of treatment option, in collaboration with his physician, will not necessarily reflect an outcome based exclusively on clinical data. Rather, this theory is significant in that it will ensure that a patient s treatment choice cannot be challenged by contradictory data pointing to another treatment option. I predict that men who have been diagnosed with prostate cancer will have treatment outcomes that are variable.

55 CHAPTER 3 METHODS This chapter is organized into three sections. The first section outlines the dissertation s study design, setting, time period, and population examined. The second section provides information regarding the various data sources used in the analyses, namely the Florida Cancer Data System (FCDS) and the Year 2000 US Census, as well as the construction of the study variables used in the analyses. Additionally, a summary of the regulatory process followed for this research is described. The third section provides a detailed analysis plan for each of the specific aims and hypotheses for this study. Study Design This dissertation used two separate study designs to achieve its aims. First, a cross-sectional design was used to describe the sociodemographic (age, race/ethnicity, marital status, tobacco use, type of insurance, education level, socioeconomic status, and urban/rural residence) and tumor characteristics (tumor grade and stage) of men diagnosed with prostate cancer in the state of Florida (Specific Aim 1). These characteristics were examined to see if they were associated with prostate cancer stage at diagnosis (Specific Aim 2) and the initial treatment selected for localized prostate cancer (Specific Aim 3). Second, a retrospective cohort design was used to assess the impact of initial treatment selection on overall survival (all-cause mortality) for men diagnosed with localized prostate cancer (Specific Aim 4). 36

56 37 Setting and Time Period The research presented here is comprised of secondary data analyses from the FCDS data. As such, all data from the FCDS were collected previously at their reporting facilities across the state of Florida, according to legislative mandate. The majority of the study s aims and analyses used data from January 1, 2001 to December 31, 2009 (Specific Aims 1 to 3). For the survival analysis (Specific Aim 4), I used a shorter time span to examine prostate cancer diagnosis. The data points for Specific Aim 4 are from January 1, 2001 to December 21, The rationale for selecting these particular time periods is as follows: (1) January 1, 2001 was selected as the start date because SEER Summary Stage 2000, the variable used for staging cancer in the FCDS, went into use in 2001; (2) the US Census data from the year 2000 was used to derive area-based socioeconomic measures for the study population since the 2010 Census was not yet fielded; and (3) the study time period end date of December 31, 2009 was selected to allow for a minimum follow-up time period of five years for each patient (Specific Aim 4) and to gain a decade s worth of descriptive information related to prostate cancer for men in Florida (Specific Aims 1 to 3). Below, I describe each data source. Data Sources Florida Cancer Data System The primary data source for this study was the FCDS. I requested the data from the Florida Department of Health Bureau of Epidemiology. The resulting dataset consists of 437,368 prostate cancer cases diagnosed in Florida from

57 38 The FCDS is a legislatively mandated, population-based central cancer registry for Florida and is the single largest population-based cancer incidence registry in the nation. Over 150,000 cases are collected from patient medical records annually. Cancer cases are submitted by hospitals, freestanding ambulatory surgical facilities, radiation therapy facilities, private physicians, and death certificates. Ninety-six percent of all records in the FCDS database are histologically confirmed and the estimated overall completeness is greater than 95%, as determined by external quality control audits. Data collected and coded by the FCDS are in accordance with national standards as set forth by the North American Association of Central Cancer Registries (NAACCR). The FCDS uses the International Classification of Diseases Oncology, 3rd edition (ICD-O-3) to code primary site and morphology [114]. The FCDS is part of the Centers for Disease Control and Prevention National Program of Cancer Registries (CDC-NPCR) and is nationally certified by the NAACCR at the highest level, Gold Certification. Gold certification is conferred on central cancer registries that exceed all standards for completeness, timeliness, and quality [114]. The FCDS contains a wealth of information related to the demographics of cancer patients. Items include age at diagnosis, race/ethnicity, marital status, residence (census tract), and other information such as tobacco use and type of insurance. Additionally, the FCDS has prostate tumor characteristics (date of diagnosis, stage of diagnosis, grade of tumor). See Appendix A for a list of available data fields from the FCDS.

58 US Census The US Constitution, Article I, Section 2 mandates that an apportionment of representatives among the states, for the House of Representatives, be carried out every 10 years (decennially). Apportionment is the process of dividing the 435 seats in the US House of Representatives among the 50 states [115]. The US Census is used for this purpose. In addition to apportionment, the decennial census results are used to distribute federal, state, local, and tribal funds; draw state legislative districts; and evaluate the success of programs or identify populations in need of services [116]. For the administration of the 2000 US Census, two forms were used: a short form and a long form. The short form was sent to every household and the long form was sent to only a limited number of households (about one in every six houses nationwide) [115, 116]. The short form collected data on race/ethnicity, Hispanic ethnicity, and household relationship for each person living in the household. The long form collected additional information such as marital status, place of birth, citizenship, school enrollment, educational attainment, occupation, and income. The data collected by the US Census then are aggregated by various geographic subdivisions such as state, county, census tract, and city. For this study, the geographic subdivision of census tract level was used. Census tracts are small statistical subdivisions (averaging about 4,000 persons) of counties that generally have stable boundaries and, when first established, were designed to have relatively homogeneous demographic characteristics [115, 116]. Census tract boundaries are delineated with the intention of being stable over many decades, so they generally follow relatively permanent visible features.

59 40 While the FCDS contains a wealth of demographic and clinical information, it does not contain socioeconomic information like income level or education. In an effort to include these variables in the study, I use data from the 2000 US Census to construct the following area-based socioeconomic measures (ABSMs): (1) socioeconomic status (based upon percent of census tract below poverty) and (2) education level (based upon education level attainment of census tract). The construction of these variables is described in the Analysis Variables section, below. Area-Based Socioeconomic Measures The extent to which neighborhood data are informative about, and relevant to, the health of their inhabitants is of important interest to epidemiologists and health services researchers. The absence of socioeconomic data in most US public health databases has increased the use of area-based socioeconomic measures (ABSMs) as a way to mitigate this deficiency. Strengths of using ABSMs for research include: 1) they can easily be appended to any database with addresses or other geographical identifiers such as a census tract, 2) they provide data for determining contextual and compositional neighborhood effects on health, in addition to effects that are due to individual-level socioeconomic position, and 3) they can be applied equally to all persons in the geographic subdivision, regardless of age, gender, and employment status [ ]. However, there are limitations to using the ABSM, they include: 1) they can be misconstrued as only a "proxy" for individuallevel socioeconomic data (rather than seen as complementary), 2) they reflect socioeconomic context at the time of case ascertainment, not necessarily during the

60 41 relevant period being analyzed, and 3) they can be outdated given the decennial nature of the US Census [ ]. For this research, ABSMs will be calculated for socioeconomic status, education level, and urban/rural residence. Socioeconomic status will be derived from the percent of the population in the census tract living below poverty, taken from the 2000 US Census. Krieger et al. provides public use data files containing percent poverty level data from the 2000 US Census for each census tract. These files are available on the Public Health Disparities Geocoding Project website [121]. Education level will be derived from the 2000 US Census data from the census tract s educational level attainment. The US Department of Agriculture s Economic Research Service Rural-Urban Commuting Area (RUCA) codes are a new census tract-based classification scheme that utilizes the standard Bureau of Census Urbanized Area and Urban Cluster definitions in combination with work commuting information to characterize all of the nation's census tracts regarding their rural and urban status and relationships [122, 123]. They are based upon population density, urbanization, and daily commuting. For this study, urban/rural classification will be determined by RUCA codes. Missing Data This study used large, publicly available population-based datasets. Inherent to these datasets are missing or incomplete data. Reasons for missing data include incomplete or missing records (labs, imaging studies, medical records, pathology reports), poor documentation of certain patient characteristics (e.g., tobacco use), and patients lost to follow up. Described below are the methods used to handle missing data.

61 42 Dates of diagnosis, treatment, and last contact were used in the analyses or to compute other variables such as length of follow-up. If a date consisted of only a year, then June 15 was assigned as the month and date. If a date consisted of only a month and year, then 15 was assigned as the day. If dates were missing, the subject was not included in the analysis for which that date was necessary. For example, if a subject had a date of diagnosis, but no date of last contact, then the subject would be excluded from any survival analyses. Further, subjects were excluded from Specific Aims 2, 3 and 4 if they had no staging information as these aims rely on stage as a grouping variable. Additionally, if a subject s census tract was missing, they were excluded from Specific Aims 2, 3 and 4 as their sociodemographic profiles would be incomplete (e.g., missing education level, socioeconomic status, and urban/rural residence classification). Lastly, in any analyses in which the primary predictor variable under consideration had unknown or missing data, those cases were excluded from the analysis. For example, when examining the effect of marital status on stage of prostate cancer presentation, those subjects whose marital status was unknown were excluded from the analysis. The predictor variables which were used for adjustment purposes in the regression models include an unknown/missing value as a valid category to account for the missingness that occurred in that particular variable. The handling of missing data in applying the eligibility criteria and for the analyses in each specific aim is explained in detail below in the Participants Eligibility Criteria and Statistical Analysis Methods sections, respectively.

62 43 Human Subjects Protection This dissertation research was approved by the Florida Department of Health Bureau of Epidemiology and uses publicly available data from the FCDS. Certain categories of research are designated as exempt from federal regulations related to the use of human subjects [124]. This study (Protocol ID# H12045) was reviewed by the Florida Department of Health Institutional Review Board (IRB) and ruled as exempt. The research falls under exemption category 4 - research involving the collection or study of existing data, documents, records, pathological specimens or diagnostic specimens, if these sources are publicly available or the information is recorded by the investigator in such a manner that the subjects cannot be identified directly or through identifiers linked to the subjects [124]. The study also was filed with the University of Miami IRB (Protocol ID# ) which has an affiliation agreement with the Florida Department of Health IRB. As a result, the University IRB also considered this research exempt from review. See Appendix B for the Florida Department of Health IRB Exemption Letter. Participants Eligibility Criteria The primary data source for this dissertation research was the FCDS; as such the study subjects were limited to Florida residents. The eligibility criteria for this dissertation research and the FCDS variables and codes used to select the study subjects are described for each specific aim below. The criteria, along with the resulting sample sizes, are summarized by specific aim in Figures 4-7. The FCDS variables and codes used in this dissertation are presented and summarized in table format in Appendix C.

63 44 Specific Aim 1 Sociodemographic and Clinical Factors For inclusion in the aim that described the sociodemographic and clinical characteristics of men diagnosed with prostate cancer, subjects were selected from a dataset consisting of 437,368 Florida residents diagnosed with prostate cancer from , using the following eligibility criteria: Inclusion criteria Florida residents o County at time of Diagnosis (NAACCR Item #90) < 998; this variable is based upon the subjects address at time of diagnosis. Date of diagnosis between January 1, 2001 and December 31, 2009 o Date of Diagnosis (NAACCR Item #390) is the date reported by the reporting facility of the diagnosis of prostate cancer. Age between 18 and 89 o Age at Diagnosis (NAACCR Item #230); this variable is the difference in years between the date of diagnosis and the subject s date of birth. Adenocarcinoma of the prostate o Histology/behavior codes (NAACCR Items #522 and 523) = 8140/3 8141/3, 8143/3, 8147/3, 8211/3, 8251/3, 8255/3, 8260/3, 8261/3, 8262/3, 8263/3, 8310/3, 8322/3, 8323/3, 8480/3,8481/3, or 8550/3; these codes are based upon the SEER ICD-O-3 histology and behavior codes. A behavior code of 3 is a malignant cancer.

64 45 First primary prostate cancer o Primary Site (NAACCR Item #400) = C619 and Sequence Number (NAACCR #560) =0,1; these codes are based upon the SEER ICD-O-3 codes. Exclusion criteria Duplicate case o FCDS Duplicate Code (NAACCR Item #2220)=Y. In-situ stage o SEER Summary Stage 2000 (NAACCR Item #759) =0; based upon the SEER ICD-O-3 codes. Since approximately 95% of all prostate cancers are adenocarcinomas [1], we restricted our dataset to adenocarcinoma only. In restricting the dataset to adenocarcinomas, when comparing treatment options, I am able to make comparisons using the same cancer type and do not have to account for variability in cancer type. Additionally, I was only interested in the relationship between the sociodemographic and clinical characteristics to primary prostate cancers. The dataset created for Specific Aim 1 serves as the foundation for all additional datasets created for the other study aims. Specific Aim 2 Stage of Prostate Cancer at Diagnosis The sample used for the analysis of the association between the sociodemographic characteristics and stage of cancer presentation was a subset of those subjects in the

65 46 Specific Aim 1 dataset. To create the analysis dataset for Specific Aim 2, the following exclusionary criteria were applied to the Specific Aim 1 dataset: Exclusion criteria Unstaged cancer cases o SEER Summary Stage 2000 (NAACCR Item #759) =9; based upon the SEER ICD-O-3 codes. Missing census tract o Census Tract 2000 (NAACCR Item #130) = missing; this variable is calculated by the FCDS through geocoding of the address at time of diagnosis to the 2000 US Census Tract. Unknown relationship of case to reporting facility o Class of Case (NAACCR Item #610) = 99; this variable is recorded by the reporting facility. Diagnoses established by an autopsy or by death certificate only o Class of Case (NAACCR Item #610) = 38 (autopsy) or 49 (death certificate). Subjects who had missing census tract information were excluded as there would be no way of determining their area-based measures of socioeconomic status, education level, and urban/rural classification. Additionally, any unstaged cancers, those diagnosed via an autopsy or death certificate and those reported by a facility with an unknown

66 47 relationship to the case (research subject) were excluded since the information contained in these subjects records were minimal. Specific Aim 3 Initial Treatment for Localized Prostate Cancer A subset of those subjects in Specific Aim 2 was used to examine the association between sociodemographic and clinical characteristics and initial treatment selection for localized prostate cancer. The following eligibility criteria were applied to the Specific Aim 2 to create the analysis dataset for Specific Aim 3: Inclusion criteria Local prostate cancer cases o SEER Summary Stage 200 (NAACCR Item #759) =1; based upon the Exclusion criteria SEER ICD-O-3 codes. First course of treatment at another facility other than the treatment facility o Class of Case (NAACCR Item #610) = 21, 30, 31, 33 or 37. Cases treated initially by chemotherapy, immunotherapy or other o Initial Treatment (derived variable see Study Variable section below) = chemotherapy, immunotherapy, or other. As the aim of this study is to examine the sociodemographic and clinical characteristics associated with initial treatment selection for localized prostate cancer therapy, only localized cases are examined here. Additionally, we excluded cases treated

67 48 elsewhere because these subjects had minimal treatment data in their records. Lastly, chemotherapy and immunotherapy are rarely used as the sole treatment of localized prostate cancer and therefore excluded from these analyses. Specific Aim 4 Survival Analysis of Localized Prostate Cancer The subjects included in this specific aim were a subset of those subjects in the Specific Aim 3 dataset who were diagnosed with prostate cancer between January 1, 2001 and December 31, To create the dataset for Specific Aim 4, the following eligibility criteria were applied to the Specific Aim 3 dataset: Inclusion Criteria Year of diagnosis between January 1, 2001 and December 31, 2004 o Date of Diagnosis (NAACCR Item #390) between 1/1/2001 and 12/31/2004. Exclusion Criteria Died or lost-to-follow-up within 6-months of diagnosis o Length of follow-up time (derived variable see Study Variable section below) <183 days. Invalid date of last contact (e.g., the data of last contact is prior to the date of diagnosis) o Date of Last Contact (NAACCR Item #1750). The rationale for the above criteria was based on the creation of the analysis variables (described in the Analysis Variables section below) for this specific aim. Initial treatment

68 49 was a derived hierarchical variable that consisted of the treatments given within the first six months after diagnosis; therefore, treatment information would be incomplete for a subject who died or was lost-to-follow-up during this time period. The follow-up time variable was based on the date of diagnosis and date of last contact (negative days of follow-up are not possible). Study Variables The proposed research uses data collected as part of the FCDS and the US Census. The key measures and variables can be grouped into two categories: 1) selection criteria and 2) analysis. This section will focus on the variables used for analysis purposes as the selection criteria variables are previously described in the Participants Eligibility Criteria section above. The outcome variables of stage at diagnosis, initial treatment, and survival are listed first. The FCDS variables and codes used in this dissertation are presented and summarized in table format in Appendix C. Stage at Diagnosis Stage of Diagnosis is based upon the FCDS variable SEER Summary Stage 2000 (NAACCR Item# 759) and has the following categories: 0=in-situ, 1= localized, 2= regional - direct extension only, 3= regional - regional lymph nodes only, 4= regional - direct extension and regional lymph nodes, 5= Regional - NOS, 7= Distant, 8= not applicable, or 9= unstaged (6 is not used as a category). The stage of diagnosis was recoded into: in situ, localized, regional/distant not applicable, and unstaged. For this

69 50 research, only localized and regional/distant cases were used. Late stage is the reference category of this variable when used in the regression models. Initial Treatment Initial treatment is a derived variable based upon the following fields in the FCDS: RX Date for Surgery, Radiation, Chemo, Hormone, Immunotherapy, and Other (NAACCR Items# 1200, 1210, 1220, 1230, 1240, and 1250 respectively). It is defined as treatment received in the first 6-months after diagnosis and based upon the dates listed above and the method created by Harlan et al. [6]. The variable initial treatment has the possible values of surgery, radiation, hormone, and watchful waiting. For the purposes of this study, I will use the term watchful waiting as it was not possible to distinguish between watchful waiting and active surveillance in the cancer registry data. Watchful waiting is the reference category for this variable when used in the regression models. Initial treatment is a hierarchical variable created to quantify treatment, ranging from the most aggressive therapy to the least aggressive. Men who received surgery were assigned to the surgery category, whether or not they received any other adjuvant therapy, such as radiation or hormonal therapy. Those who received radiation therapy were categorized as having radiation therapy, whether they also received hormonal therapy. Men who were included in the hormone category consisted of those who received only hormonal therapy. Men who had no reported therapy within 6-months of their diagnosis were placed into the watchful waiting group (active surveillance). Therapies given 6-months after diagnosis were excluded as I was interested in examining factors related to initial treatment. Additionally, men who only received chemotherapy

70 51 (n=57), immunotherapy (n=13), or other therapy (n=52) were excluded from the analyses as these therapies are rarely used as the sole therapy for localized prostate cancer. Vital Status Vital status is taken from the FCDS variable Last Contact Flag (NAACCR Item #1751) and has two values, 1=alive or 0=dead. This is the vital status of the subject on the date the reporting facility has a record of the subject being either alive or dead. For the survival analysis portion of this research, vital status was recoded into the variable event. The values for event are 0=alive, 1=dead. If the vital status of a subject was dead, then the date of last contact is equal to the date of death. 5-Year Mortality As a descriptive variable, 5-year mortality status was calculated based upon the Date of Last Contact (NAACCR Item #1750) and the Last Contact Flag. If a subject died within five years of their diagnosis of prostate cancer, their 5-year mortality status would classified as dead, while all others would be considered alive. Age at Diagnosis The source of this variable is the FCDS variable Age at Diagnosis (NAACCR Item #230) and is the difference in years between the date of diagnosis and the subject s date of birth.

71 52 Race/Ethnicity This is a derived variable based upon a combination of the FCDS variables Race1 (NAACCR Item #160) and Spanish/Hispanic Origin (NAACCR Item #190). The possible values for the Race1 variable were: 1=White, 2=Black, 3=Other, and 9=Unknown. The possible values for the Spanish/Hispanic Origin variable were: 0=non- Hispanic, 1=Mexican, 2=Puerto Rican, 3=Cuban, 4=South/Central American, 5=Other, 6=Hispanic NOS, 7=Spanish surname only, 8=Dominican Republic, and 9=Unknown whether Spanish or not. A subject was considered non-hispanic if their Spanish/Hispanic Origin was either non-hispanic or Unknown whether Spanish or not; all others were considered Hispanic. The race/ethnicity variable was recoded into the following possible values: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Other, Hispanic, and Unknown. Non-Hispanic White is the reference category for this variable when used in the regression models. Marital Status Marital status is a derived variable based upon the FCDS variable Marital Status at DX (NAACCR Item #150). This is the marital status the subject had at the time of diagnosis. Marital Status at DX had the following possible values: 1=single, 2=married, 3=separated, 4=divorced, 5=widowed, 6= unmarried or domestic partner, or 7=unknown. The variable marital status was recoded into three categories: married, single (single, separated, divorced, widowed, unmarried, or domestic partner), or unknown. For the regression models, married is the reference category.

72 53 Tobacco Use Tobacco use is a derived variable based upon the FCDS Tobacco Use variable (NAACCR Item #2220), with the following possible values: 0= none- never smoked, 2= light less than 1 pack/day, 3= moderate- 1-2 pack/day, 4= heavy- more than 2 pack/day, 5= cigarettes NOS, 6= cigar or pipe, 7= smokes other than tobacco, 8= snuff/chew, or 9= unknown. The tobacco use item was recoded into the following: never smoker (none, never smoked), current smoker (light less than 1 pack/day; moderate, 1-2 pack/day; heavy, more than 2 pack/day; cigarettes NOS), former smoker (history of smoking), other (cigar or pipe; smokes other than tobacco; snuff/chew), and unknown. Never smoker is the reference category of this variable when used in regression models. Insurance Status Insurance status was derived from the FCDS variable Primary Payor at DX (NAACCR Item #630), with the following possible values: 1= not insured, 2= self-pay, 10= insurance NOS, 20= private insurance: managed Care HMO PPO, 21= private insurance: Fee-for-Service, 31= Medicaid, 35= Medicaid administered through a managed care plan, 60= Medicare/Medicare NOS, 61= Medicare with supplement NOS, 62= Medicare administered through a Managed Care plan, 63=Medicare with private supplement, 64= Medicare with Medicaid eligibility, 65= TRICARE, 66= Military, 67= Veterans Affairs, 68= Indian/Public Health Service, or 99= Unknown. Insurance status was recoded into none (not insured, self-pay), private (insurance NOS, Private Insurance: managed care HMO PPO, private insurance: fee-for-service, Medicare with private supplement), public (Medicare, TRICARE, Military, Veterans Affairs, Indian/Public

73 54 Health Service), or unknown. Private insurance is the reference category of this variable when used in regression models. Education Level Education level consists of three categories: less than high school, high school graduate, and more than high school. It is an ABSM calculated variable using data from the US Census. The data from the Population 25 years and over (2000 US Census SF3 Items P P037018, Table Number 3) were downloaded from the American FactFinder website [125]. For each census tract, education level was coded as the percent of the population having less than a high school education, having a high school degree, and more than a high school education (any education after completing high school). To calculate this variable, I took the category with the highest percentage and applied that to the entire census tract. For example, if a census tract had 10% of the population with less than high school, 40% with a high school degree, and the remaining 50% with more than high school, all subjects residing in that census tract would be coded as having more than a high school education. More than high school is the reference category of this variable when used in regression models. Socioeconomic Status The variable socioeconomic status is a 2000 US Census derived ABSM variable consisting of four categories: lowest, low, medium, and high. It is based upon the percentage of the population of the census tract below the federal poverty level. The

74 55 cutoffs for each of the categories are as follows: lowest=greater than 20% below poverty, low=10% to 19.9% below poverty, medium=5% to 9.9% below poverty, and high=less than 5% below poverty. High SES is the reference category of this variable when used in regression models. This classification of socioeconomic status is based upon the work of Krieger et al. [ ] and is based upon US Census data. To facilitate the use of this method, the poverty data is made these available as comma-delimited text files for each year of the decennial Census (1980, 1990, and 2000) on the Public Health Disparities Geocoding Project website [121]. The data consists of two fields per record. The first field is the 11-digit area key (i.e. the geocode), which uniquely identifies the census tract: Digits 1-2 = State code, Digits 3-5 = County code, Digits 6-11 = Census Tract code (often used with a decimal point: xxxx.xx), and Digit 12 = Block group code. The second field is the percent of people in the census tract living below the federally defined poverty line. Urban/Rural Classification Urban/rural classification is an ABSM derived variable from the US Department of Agriculture s Economic Research Service Rural-Urban Commuting Area (RUCA) codes [122, 123] and has the following possible values: urban, large town, small town, or rural. Urban is the reference category of this variable when used in regression models. RUCAs have been used mainly to aggregate geocodes into four categories (Categorization A). This is generally a useful aggregation used for many health related projects [122, 123]. These codes are based upon measures of population density,

75 56 urbanization, and daily commuting to identify urban cores and adjacent territory that is economically integrated with those cores [122, 123]. The codes are presented in detail in Appendix D. Urban is defined as a census tract having a RUCA code of: 1, 1.0, 1.1, 2, 2.0, 2.1, 3, 3.0, 4.1, 5.1, 7.1, 8.1, or Large town codes include: 4, 4.0, 4.2, 5, 5.0, 5.2, 6, 6.0, or 6.1. Small town is defined by codes: 7, 7.0, 7.2, 7.3, 7.4, 8, 8.0, 8.2, 8.3, 8.4, 9, 9.0, 9.1, or 9.2. And lastly, rural is defined as codes: 10, 10.0, 10.2, 10.3, 10.4, 10.5, or 10.6 [122, 123]. Grade at Diagnosis Tumor grade at diagnosis is taken from the FCDS variable Grade (NAACCR Item #440) and has the following possible values: 1= Grade I - well differentiated, 2= Grade II - moderately differentiated, 3= Grade III- poorly differentiated, 4= Grade IV undifferentiated, and 9= cell type not determined, not stated or not applicable. The FCDS does not capture Gleason Score, which is the grading scale for prostate cancer. However, the FCDS Grade item is based upon the Gleason Score as follows: Gleason scores of 2, 3, or 4 are coded as Grade I - well differentiated; 5 and 6 as Grade II - moderately differentiated; 7, 8, 9, and 10 as Grade III- poorly differentiated. For this study, tumor grade is reported as well differentiated, moderately differentiated, and poor/undifferentiated. Well differentiated is the reference category of this variable when used in regression models.

76 57 Length of Follow-up Time Length of follow-up time is a calculated variable and is the difference in the number of days between the Date of Diagnosis (NAACCR Item #390) and Date of Last Contact (NAACCR Item #1750). To convert days into months, the total number of days was divided by To convert into years, the total number of days was divided by Statistical Analysis Methods The statistical analyses in this dissertation included descriptive statistics such as frequencies and percentages for categorical variables, mean, median, minimum, maximum, 25%th and 75%th percentile for continuous variables. Median survival, as well as survival rates for 1-, 2-, 3-, and 5-years, were calculated by Kaplan-Meier method for the clinical outcome of overall survival. Univariate and multivariate logistic regression models for both binary and polytomous outcomes were fit. These logistic regression models resulted in odds ratios (OR) and 95% confidence intervals (95% CI) for the predictors in the models in relation to the binary and polytomous outcomes. Univariate and multivariate Cox proportional hazard regression models for timeto-event outcome and overall survival were fit. These Cox regression models resulted in hazard ratios (HR) and 95% confidence intervals (95% CI) for the predictors in the models in relation to the survival outcome. All statistical analyses were performed using SAS Version 9.2 and all of the hypothesis tests for the various statistical tests were twosided. The Type-I error rate was set at 5% (an alpha of 0.05) and p-values less than 0.05 were considered statistically significant. Additionally, as one of the objectives, this study

77 58 was to provide more information regarding prostate cancer health disparities in the state, hence all analyses were stratified by race/ethnicity for the subgroup analyses. The detailed methods for the analyses are described below for each specific aim. Specific Aim 1 The goal of Specific Aim 1 was to describe the sociodemographic characteristics and tumor characteristics (stage and grade) at time of diagnosis of Florida residents diagnosed with prostate cancer from 2001 to For Specific Aim 1, descriptive statistics (counts and percentages, ranges, medians, means, and standard deviations, as appropriate) were used to summarize the sociodemographic and tumor and treatment related variables. Specific Aim 2 The goal of Specific Aim 2 was to identify the sociodemographic characteristics associated with stage of prostate cancer at time of diagnosis of Florida residents who were diagnosed during the years 2001 to I hypothesized: Hypothesis 2-1: Non-Hispanic Black men are more likely to be diagnosed with advanced/late stage disease than Non-Hispanic white and Hispanic men. Hypothesis 2-2: Men who are not married are more likely to be diagnosed with advanced/late stage disease than men who are married.

78 59 Hypothesis 2-3: Current smokers are more likely to be diagnosed with advanced/late stage disease than non-smokers. The hypotheses were investigated by examining the relationship between sociodemographic variables and stage of prostate cancer at time of diagnosis. As such, those subjects whose cancer stage was unknown ( unstaged ) were excluded from the analyses (n=11,082). Additionally, those subjects with a missing census tract or whose diagnosis came from an autopsy or death certificate were also excluded (n=6,347) (Figure 5). Univariate and multivariate logistic regression models were fitted to stage of prostate cancer presentation, late (regional/distant) as reference group vs. early (local), with race/ethnicity, marital status, or tobacco use as the main predictors, but further adjusted by sociodemographic characteristics (described for each hypothesis below). Unadjusted as well as adjusted odds ratios (OR) and corresponding 95% confidence intervals (95% CI) were reported to identify the significant predictors of late-stage prostate cancer. The logistic regression models followed the equation, below [126], where the natural log odds of late stage prostate cancer is represented on the left side of the equation by ˆ ln (1 ˆ ) and the independent sociodemographic variables are represented by X 1 to X k on the right. ˆ ln b0 bx 1 1 bx 2 2 bx k (1 ˆ ) k

79 60 Race/ethnicity was the primary variable of interest for hypothesis 2-1. For the analysis, the reference category for the independent variable race/ethnicity was Non- Hispanic White. The multivariate logistic regression model was adjusted for age at diagnosis, marital status, tobacco use, type of insurance, education level, socioeconomic status, and urban/rural residence. Subjects whose race/ethnicity was unknown were excluded from the analyses (n=2,192). Marital status was the primary variable of interest for hypothesis 2-2. For the analysis, the reference category for the independent variable marital status was married. The multivariate logistic regression model was adjusted for age at diagnosis, race/ethnicity, tobacco use, type of insurance, education level, socioeconomic status, and urban/rural residence. Subjects whose marital status was unknown were excluded from the analyses (n=3,462). Tobacco use (cigarette smoking) was the primary variable of interest for hypothesis 2-3. For the analysis, the reference category for the independent variable tobacco use was Never Smoker. The multivariate logistic regression model was adjusted for age at diagnosis, race/ethnicity, marital status, type of insurance, education level, socioeconomic status, and urban/rural residence. Subjects whose tobacco use was other or unknown were excluded from the above analyses (n=23,983). Specific Aim 3 The goal for Specific Aim 3 was to identify the sociodemographic and tumor characteristics associated with initial treatment selected by Florida residents who were diagnosed with localized prostate cancer from My hypotheses were:

80 61 Hypothesis 3-1: Non-Hispanic White and Hispanic men are more likely to choose surgery than Non-Hispanic Black men. Hypothesis 3-2: Men of higher socioeconomic status are more likely to choose surgery than men of lower socioeconomic status. Hypothesis 3-3: Men with higher education levels are more likely to choose surgery than men with lower levels of education. Hypothesis 3-4: Men with private insurance are more likely to choose surgery than men with public or no insurance. The hypotheses were investigated by examining the relationship between sociodemographic and clinical variables and the initial treatment selected for men diagnosed with localized prostate cancer. As such, those subjects whose cancer stage was advanced (late stage) were excluded from the analyses (n=10,578). Additionally, those subjects with missing census tract information and thus socioeconomic, education, and urban/rural classification, also were excluded (n=6,315). Lastly, those individuals treated at a location other than the reporting facility, or with a treatment other than the standard treatments for localized prostate cancer, also were excluded (n=5,464) (Figure 6). Univariate and multivariate multinomial logistic regression models were fitted to initial treatment (surgery, radiation, hormonal, or watchful waiting (as reference)) with

81 62 race/ethnicity, socioeconomic status, education level, and insurance as the main predictors. The models were adjusted for sociodemographic and clinical characteristics (described by hypothesis below). Unadjusted and adjusted ORs and 95% CIs were reported to identify the significant predictors of initial treatment selection. The multinomial logistic regression models followed the equation below [126], where the natural log odds of selecting a treatment compared to the natural log odds of selecting watchful waiting is represented on the left side of the equation by and the independent variables represented by X i on the right. Race/ethnicity was the primary variable of interest for hypothesis 3-1. For the analysis, the reference category for the independent variable race/ethnicity was Non- Hispanic White. The multivariate multinomial logistic regression model was adjusted for age at diagnosis, marital status, tobacco use, type of insurance, education level, socioeconomic status, urban/rural residence, and tumor grade. Subjects whose race/ethnicity was unknown were excluded from the analyses (n=1,839). Socioeconomic status was the primary variable of interest for hypothesis 3-2. For the analysis, the reference category for the independent variable socioeconomic status was High. The multivariate multinomial logistic regression model was adjusted for age at diagnosis, race/ethnicity, marital status, tobacco use, type of insurance, education level, urban/rural residence, and tumor grade. Education level was the primary variable of interest for hypothesis 3-3. For the analyses, the reference category for the independent variable education level was More than High School. The multivariate multinomial logistic regression model was adjusted

82 63 for age at diagnosis, race/ethnicity, marital status, tobacco use, type of insurance, socioeconomic status, urban/rural residence, and tumor grade. Insurance type was the primary variable of interest for hypothesis 3-4. For the analyses, the reference category for the independent variable insurance was Private insurance. The multivariate multinomial logistic regression model was adjusted for age at diagnosis, race/ethnicity, marital status, tobacco use, education level, socioeconomic status, urban/rural residence, and tumor grade. Subjects whose insurance type was unknown were excluded from the analyses (n=1,888). Specific Aim 4 For Aim 4, I examine the relationship of initial treatment selection and sociodemographic and tumor characteristics on overall survival for Florida residents diagnosed with localized prostate cancer from Hypothesis 4-1: There will be no difference in survival curves and risk of mortality between initial treatments. Hypothesis 4-2: Non-Hispanic White and Hispanic men will have higher survival rates and a lower risk of mortality compared to Non-Hispanic Black men. Hypothesis 4-3: Men of high socioeconomic status will have higher survival rates and a lower risk of mortality than men with the lowest level of socioeconomic status.

83 64 Hypothesis 4-4: Men with higher levels of education will have highest survival rates and a lower risk of mortality than men with lower levels of education. Hypothesis 4-5: Never smokers will have higher survival rates and a lower risk of mortality than current smokers. Hypothesis 4-6: Men who are married will have higher survival rates and lower risk of mortality than men who are not married. Specific Aim 4 was explored by the use of survival analysis and the use of Cox proportional hazard regression models. These analyses were conducted on a subset of men who were diagnosed with localized prostate cancer between the time period of January 1, 2001 to December 31, Men who did not survive or were lost-to-followup within six months of diagnosis (during which time the initial treatment was determined) were excluded from the analysis (n=8,519) as were those with incorrect or missing dates of last contact (n=54) (Figure 7). First, overall survival (all-cause mortality) using the Kaplan-Meier method was used to calculate survival curves for each of the following: initial treatment, race/ethnicity, socioeconomic status, education level, smoking status, and marital status (Hypotheses 4-1 to 4-6). The log-rank test was used to compare the survival curves. The analyses were also stratified by race/ethnicity. The Kaplan-Meier product limit formula for the survival curves is [127]: source

84 65 where is the survival probability at failure (death) time t (j-1). The formula for the log-rank test for several groups is: where is the test statistic and O i are the observed failures (deaths) and E i are the expected failures (deaths) [127]. Next, Cox proportional hazard regression models were fitted to estimate risk of death from all causes (overall survival) as the main predictor, but further adjusted for sociodemographic and clinical characteristics (described below by specific aim). Unadjusted and adjusted hazard ratios (HR) and 95% confidence intervals (95% CI) were reported to identify significant predictors for overall survival. The formula for the Cox proportional hazards model [127] is below where represents the hazard at time t with the set of predictor variables X. Initial treatment was the primary variable of interest for Hypothesis 4-1. For the analyses, the reference category for the main predictor initial treatment was watchful waiting. The multivariate hazards model was adjusted for age at diagnosis, race/ethnicity, marital status, tobacco use, type of insurance, socioeconomic status education level, urban/rural residence, and tumor grade. Race/ethnicity was the primary variable of interest for Hypothesis 4-2. For the analyses, the reference category for the main predictor race/ethnicity was Non-Hispanic

85 66 White. The multivariate hazards model was adjusted for age at diagnosis, initial treatment, marital status, tobacco use, type of insurance, socioeconomic status, education level, urban/rural residence, and tumor grade. Subjects whose race/ethnicity was unknown were excluded from the analysis (n=545). Socioeconomic status was the primary variable of interest for Hypothesis 4-3. For the analyses, the reference category for the main predictor socioeconomic status was High. The multivariate hazards model was adjusted for age at diagnosis, race/ethnicity, initial treatment, marital status, tobacco use, type of insurance, education level, urban/rural residence, and tumor grade. Education level was the primary variable of interest for Hypothesis 4-4. For the analyses, the reference category for the main predictor education was More than High School. The multivariate hazards model was adjusted for age at diagnosis, race/ethnicity, initial treatment, marital status, tobacco use, type of insurance, socioeconomic status, urban/rural residence, and tumor grade. Tobacco use (cigarette smoking) was the primary variable of interest for Hypothesis 4-5. For the analyses, the reference category for the main predictor tobacco use status was Never Smoker. The multivariate hazards model was adjusted for age at diagnosis, race/ethnicity, initial treatment, marital status, education level, type of insurance, socioeconomic status, urban/rural residence, and tumor grade. Subjects whose tobacco use was other or unknown were excluded from the above analyses (n=5,063). Marital status was the primary variable of interest for Hypothesis 4-6. For the analyses, the reference category for the main predictor tobacco use status was Married.

86 67 The multivariate hazards model was adjusted for age at diagnosis, race/ethnicity, initial treatment, tobacco use, education level, type of insurance, socioeconomic status, urban/rural residence, and tumor grade. Subjects whose marital status use was unknown were excluded from the above analyses (n=827).

87 CHAPTER 4 RESULTS A dataset containing 318,835 men diagnosed with prostate cancer in the state of Florida from 1981 to 2010 served as the source of the data for this dissertation. Eligibility criteria were applied to this dataset for the analyses conducted for each specific aim (Figure 4). This chapter describes the study s findings by specific aim. Specific Aim 1 Sociodemographic and Clinical Factors From the time period of January 1, 2001 to December 31, 2009, a total of 118,533 men were diagnosed with prostate cancer in Florida. Age at diagnosis and race/ethnicity data were taken from the FCDS. The mean age at diagnosis was 67.7 years (SD=9.1). The minimum age was 32 years and maximum 89. The median age was 68 years and the lower quartile was 62 years with an upper quartile of 74. Approximately 73% of men were Non-Hispanic White, 13% Non-Hispanic Black, 11% Hispanic, and 1% Other race/ethnicity. A total of 2,529 men (2%) had missing race/ethnicity data and were categorized as Unknown. These data are summarized in Table 1. Sociodemographic Factors Marital status, tobacco use, and insurance type came from the FCDS, while education level, socioeconomic status, and urban/rural classification were ABSMs created using US Census Tract level data. Seventy-four percent of men were married and had more than a high school education (51%). Most men had insurance (95%), with 44% 68

88 69 having private insurance and 51% having public insurance such as Medicare. Almost 40% of men were considered to live in areas with the lowest and low socioeconomic status, while 81% lived in an urban area. Close to 36% of the men had never smoked, with 10% being current smokers. Tobacco use data was missing for approximately 24% of the men in this sample. Clinical Factors Stage and grade of prostate cancer at diagnosis came from the FCDS. Localized (early stage) prostate cancer accounted for 81% of the cases. Approximately 10% of the cases were Regional/Distant (advanced stage) and 9% were not staged. The percent of patients with missing staging data dramatically decreased from 2001 (15%) to 2009 (5%). The majority of cases were considered Grade II- Moderately Differentiated (54%). Grade II prostate cancer from the FCDS is equivalent to cancers with a Gleason score of 5 and 6 [128]. Specific Aim 2 Stage of Prostate Cancer Diagnosis Table 2 shows the distribution of sociodemographic characteristics between early (localized) and late (advanced) stage prostate cancer. A total of 101,104 men diagnosed with localized or regional/distant stage prostate cancer were included in the analysis for Specific Aim 2 (Figure 4). Early stage prostate cancer consisted of 90% of the cases and the mean age at diagnosis was 67.1 years (SD=8.9). The minimum age was 32 years and maximum 89. The median age was 67 years with the lower quartile at 61 years and upper quartile at 74 years. Late stage prostate cancer was diagnosed more frequently in

89 70 younger (<60) and older men (80+) (25.4% vs. 19.4% and 10.4% vs. 7.4%, respectively). The majority of men was Non-Hispanic White (72%) and married (75%). About 37% of men never smoked, 30% were former smokers, and 10% current smokers. Tobacco use information was missing for 22% of subjects. The sample sizes and criteria for each hypothesis for Specific Aim 2 are found in Figure 5. Hypothesis 2-1 Effect of Race/Ethnicity Patients with missing race/ethnicity information (n=2,192) were excluded from the analysis of the effect of race/ethnicity on prostate cancer presentation. A total of 98,912 subjects remained (Figure 5). Race/ethnicity was associated with late stage cancer presentation. When examining the effect of race/ethnicity on stage of prostate cancer at diagnosis, in the univariate model, Non-Hispanic Blacks were 38% more likely than Non-Hispanic Whites to be diagnosed with late stage prostate cancer (OR=1.38; 95% CI: 1.31, 1.46). However, race/ethnicity was not a significant predictor of late stage disease for Hispanic men and Non-Hispanic men of Other race/ethnicity. All of the sociodemographic variables of interest (age at diagnosis, marital status, tobacco use, insurance status, socioeconomic status, education level, and urban/rural status) were statistically significant at the univariate level and were included in the multivariate model (Table 3). When controlling for sociodemographic factors, the association between Non-Hispanic Black race/ethnicity and late stage prostate cancer remained as Non-Hispanic Blacks were 16% more likely to be diagnosed with late stage prostate cancer (OR=1.16; 95% CI: 1.09, 1.23) (Table 3).

90 71 Hypothesis 2-2 Effect of Marital Status When examining the association of marital status on stage of prostate cancer at diagnosis, men with unknown marital status (n=3,462) were excluded from the analysis. A total of 97,642 subjects remained (Figure 5). In the univariate analysis, marital status was associated with late stage prostate cancer presentation. Compared to married men, those who were not married were 35% more likely to be diagnosed with late state prostate cancer (OR=1.35; 95% CI: 1.29, 1.41) (Table 4). All of the sociodemographic variables of interest (race/ethnicity, age at diagnosis, tobacco use, insurance status, socioeconomic status, education level, and urban/rural status) were statistically significant at the univariate level and were included in the multivariate model (Table 4). When controlling for sociodemographic factors, the association between marital status and late stage prostate cancer remained as men who were not married were 24% more likely to be diagnosed with late stage prostate cancer than married men (OR=1.24; 95% CI: 1.18, 1.30) (Table 4). The analyses stratified by race consisted of 94,860 men. Men of unknown race/ethnicity and those who were Non-Hispanic Other were excluded (Table 5). In the stratified univariate analysis, the association between marital status and stage of prostate cancer at diagnosis was strongest among Non-Hispanic Black men who were not married (OR=1.65; 95% CI: 1.49, 1.84). This compares to an increase of the likelihood of late stage prostate cancer among not married, Non-Hispanic White men (OR=1.22; 95% CI: 1.15, 1.30) and Hispanic men (OR=1.47; 95% CI: 1.28, 1.68). All of the sociodemographic variables of interest, except education level, were statistically significant at the univariate level for each race/ethnicity category (Table 5). Education

91 72 level was not a significant predictor of late stage prostate cancer for Non-Hispanic Black men; however, it still was included in the multivariate models for all race/ethnicities. In the multivariate analyses stratified by race/ethnicity, the association between marital status and late stage prostate cancer diagnosis remained (Table 6). Non-Hispanic White men who were not married were 16% more likely to have late stage prostate cancer than men who were married of their same race/ethnicity (OR=1.16; 95% CI: 1.10, 1.24). This association was also true for Non-Hispanic Black and Hispanic men who were 50% and 38% more likely to be diagnosed with late stage prostate cancer than married men of the same race/ethnicity (OR=1.50; 95% CI: 1.34, 1.67; OR=1.38; 95% CI: 1.20, 1.58, respectively). Hypothesis 2-3 Effect of Smoking Status To examine the relationship of smoking status to stage of prostate cancer at diagnosis, the tobacco use variable was used. Those men who had missing (unknown) (n=22,074) or other tobacco use (n=1,909) were excluded from the analyses as I was interested only in the effect of cigarette smoking on stage of diagnosis (Figure 5). A total of 77,121 subjects remained. In the univariate analysis, current smokers were almost 1.5 times more likely to be diagnosed with late stage prostate cancer compared to nonsmokers (OR=1.49; 95% CI: 1.39, 1.58); however, former smokers were 10% less likely than non-smokers to be diagnosed with late stage prostate cancer (OR=0.90; 95% CI: 0.85, 0.94) (Table 7). All of the sociodemographic variables of interest (race/ethnicity, age at diagnosis, marital status, insurance status, socioeconomic status, education level, and urban/rural

92 73 status) were statistically significant at the univariate level and were included in the multivariate models (Table 7). When controlling for sociodemographic factors, the association between smoking status and late stage prostate cancer remained as men who smoked were 36% more likely to be diagnosed with late stage prostate cancer than nonsmokers (OR=1.36; 95% CI: 1.28, 1.46). Additionally, former smokers were 7% less likely than non-smokers to present with late stage cancer (OR=0.93; 95% CI: 0.88, 0.98) (Table 7). The analyses stratified by race/ethnicity consisted of 75,075 men who were Non- Hispanic White, Non-Hispanic Black, and Hispanic (Table 8). In the stratified univariate analysis, Non-Hispanic White, Non-Hispanic Black, and Hispanic men who smoked were 22% (OR=1.22; 95% CI: 1.15, 1.30), 65% (OR=1.65; 95% CI: 1.49, 1.84) and 47% (OR=1.47; 95% CI: 1.28, 1.68) more likely to be diagnosed with late stage prostate cancer than non-smokers of the same race/ethnicity, respectively (Table 8). All of the sociodemographic variables of interest, except education level, were statistically significant at the univariate level for each race/ethnic category. Education level was not a significant predictor of late stage prostate cancer for Non-Hispanic Blacks; however, it was still included in the multivariate models for all race/ethnicities. In the multivariate analyses stratified by race/ethnicity, the association between cigarette smoking and late stage prostate cancer diagnosis remained and was most pronounced in Non-Hispanic Black men (Table 9). Non-Hispanic Black men who smoked were 44% more likely to have late stage prostate cancer than non-smokers of their same race/ethnicity (OR=1.44; 95% CI: 1.24, 1.67). Non-Hispanic White and Hispanic men who smoked were 35% (OR=1.35; 95% CI: 1.24, 1.46) and 39%

93 74 (OR=1.39; 95% CI: 1.15, 1.68) more likely to be diagnosed with late stage prostate cancer than non-smokers of the same race/ethnicity respectively. Specific Aim 3 Initial Treatment for Localized Prostate Cancer A total of 85,062 men diagnosed with localized prostate cancer from were used in the analyses for Specific Aim 3 (Figure 4). The distribution of tumor grade for these men was as follows: 2% Grade I- Well Differentiated, 62% Grade II Moderately Differentiated, 32.3% Grades III/IV Poor/Undifferentiated, and 4% unknown. Surgery was the most frequently selected initial treatment (34%) followed by radiation therapy (32%), hormonal therapy (22%), and watchful waiting (12%). Surgery was the treatment option selected most by younger men as more than half of the men who received surgery did so before the age of 64 (Table 10). The mean age at diagnosis was 67.2 years (SD=8.7). The mean age at diagnosis was 67.2 years (SD=8.7). The minimum age was 32 years and maximum 89. The median age was 68 years with the lower quartile at 61 years and upper quartile at 73 years. Almost 75% of the men were Non-Hispanic White. Approximately 12% of subjects were from the lowest socioeconomic status, 15% had less than a high school education, and 50% had public insurance. The sample sizes and criteria for each hypothesis for Specific Aim 3 are found in Figure 6.

94 75 Hypothesis 3-1 Effect of Race/Ethnicity A total of 83,223 men with valid race/ethnicity data were included in the analysis to determine if there is an association between race/ethnicity and initial treatment selected for localized prostate cancer (n=1,839 were excluded) (Figure 6). Race/ethnicity was associated with initial treatment selection. In the univariate multinomial logistic regression models that examined the likelihood of selecting surgery, radiation, or hormonal therapy over watchful waiting, Non-Hispanic Black men were significantly less likely to select surgery (OR= 0.77, 95% CI: 0.72,0.82), radiation (OR=0.71; 95% CI: 0.66,0.76), or hormonal therapy (OR=0.78; 95% CI: 0.72,0.83) compared to Non- Hispanic Whites (Table 11). There was no significant difference in the selection of surgery over watchful waiting when comparing Hispanic men to Non-Hispanic White men. However, Hispanic men were less likely to choose radiation therapy over watchful waiting (OR=0.78; 95% CI: 0.72, 0.84) and more likely to choose hormonal therapy over watchful waiting (OR=1.22; 95% CI: 1.13, 1.31) compared to Non-Hispanic White men. All of the sociodemographic and clinical characteristics of interest (age at diagnosis, marital status, tobacco use, insurance status, socioeconomic status, education level, urban/rural status, and tumor grade) were statistically significant at the univariate level and were included in the multivariate models (Table 11). When controlling for sociodemographic and clinical characteristics, the associations remained (Table 12) as those in the univariate analyses, however, most of the effect sizes were smaller in the adjusted models compared to the unadjusted models. However, an exception was among Non-Hispanic Black men who, in the adjusted model, were 34% less likely to choose surgery over watchful waiting when compared to Non-Hispanic White men (OR=0.66;

95 76 95% CI: 0.61, 0.71) compared to 23% in the unadjusted model (OR=0.77; 95% CI:0.72, 0.82). Hypothesis 3-2 Effect Socioeconomic Status A total of 85,062 men were included in the analysis of socioeconomic status and initial treatment selection for localized prostate cancer (Figure 6). In the univariate analysis, lowest socioeconomic status (20% or more of the census tract living below poverty) was associated with initial treatment selection when comparing surgery and radiation therapy to watchful waiting. Men with the lowest socioeconomic status were 30% less likely (OR=0.70; 95% CI: 0.65, 0.75) to select surgery or radiation therapy over watchful waiting when compared to men of high socioeconomic status (less than 5% of the census tract living below poverty). Additionally, men of low (10% to 19.9%% of the census tract living below poverty) and medium (5% to 9.9% of the census tract living below poverty) socioeconomic status were more likely to select hormonal therapy over watchful waiting when compared to men of high socioeconomic status (OR=1.08; 95% CI: 1.01, 1.16 and OR=1.12; 95% CI: 1.05, 1.20, respectively) (Table 13). There were no significant differences in the selection of surgery or radiation therapy over watchful waiting when comparing men with low and medium socioeconomic status to men with high socioeconomic status. The results of the univariate analyses stratified by race are presented in Tables 14 to 16. In the stratified univariate analysis, among Non-Hispanic Black men, those with the lowest socioeconomic status were almost half as likely as those with high socioeconomic status to select surgery over watchful waiting (OR= 0.55; 95% CI: 0.45, 0.69). Among Non-Hispanic White men, those with lowest socioeconomic status were

96 77 16% less likely to choose either surgery or radiation over watchful waiting (OR= 0.84 (0.74, 0.94) and (OR= 0.84 (0.74, 0.94), respectively. Lastly, among Hispanic men, those with lowest socioeconomic status were less likely to choose surgery (OR=0.64; 95% CI: 0.51, 0.82) and more likely to choose hormonal therapy (OR=1.34; 95% CI: 1.04, 1.73) over watchful waiting when compared to men of high socioeconomic status. All of the sociodemographic and clinical characteristics of interest (age at diagnosis, race/ethnicity, marital status, tobacco use, insurance status, education level, urban/rural status, and tumor grade) were statistically significant at the univariate level and were included in the multivariate models. When controlling for sociodemographic and clinical characteristics, the associations remained; however, with slightly smaller effect sizes (Tables 17 to 20). The change in effect size was seen most among Non- Hispanic Black men. Non-Hispanic Black men with the lowest socioeconomic status were 55% less likely than those with high socioeconomic status to choose surgery over watchful waiting in the unadjusted model compared to 16% less likely in the adjusted model (OR=0.84; 95% CI: 0.63, 1.11). Hypothesis 3-3 Effect of Education Level A total of 85,062 men were included in the analysis of education level and initial treatment selection for localized prostate cancer (Figure 6). In the univariate analysis, having less than a high school education was associated with initial treatment selection when comparing surgery and radiation therapy to watchful waiting. Men with less than a high school education were about 24% less likely to select surgery (OR=0.76; 95% CI: 0.71, 0.81) or radiation therapy (OR=0.76; 95% CI: 0.71, 0.81) over watchful waiting

97 78 when compared to men with more than a high school education (Table 21). There were no significant differences among high school graduates compared to those with more than a high school education. The results of the univariate analyses stratified by race/ethnicity are presented in Tables 22 to 24. In the stratified univariate analysis, among Non-Hispanic Black men, those with less than a high school education were approximately 40% less likely as those with more than a high school education to select surgery over watchful waiting (OR=0.59; 95% CI: 0.51, 0.68). Among Non-Hispanic White men, those with less than a high school education were less likely to choose any treatment over watchful waiting when compared to those with more than a high school education (Table 22). Lastly, among Hispanic men, those with less than a high school education were 1.4 times more likely to choose hormonal therapy (OR=1.44; 95% CI: 1.24, 1.67) over watchful waiting when compared to men with more than a high school education. Additionally, they were about 14% less likely to choose surgery (OR=0.86; 95% CI: 0.74, 1.00) or 15% less likely to choose radiation therapy over watchful waiting (OR=0.85; 95% CI: 0.74, 0.98). All of the sociodemographic and clinical characteristics of interest (age at diagnosis, race/ethnicity, marital status, tobacco use, insurance status, urban/rural status, and tumor grade) were statistically significant at the univariate level and were included in the multivariate models. When controlling for sociodemographic and clinical characteristics, most of the associations from the univariate analysis did not remain significant (Tables 25 to 28). Among Non-Hispanic Blacks, the association between less than a high school education and initial treatment was no longer statistically significant (Table 27). Among Non-Hispanic White and Hispanic men, having a less than high

98 79 school education only remained significantly associated with hormonal therapy compared to watchful waiting (Non-Hispanic White OR=0.81; 95% CI: 0.71, 0.93 and Hispanic men OR=1.38; 95% CI: 1.15, 1.66). Hypothesis 3-4 Effect of Insurance Status A total of 83,674 men were included in the analysis of insurance status and initial treatment selection for localized prostate cancer (1,388 men were excluded who had unknown insurance status) (Figure 6). In the univariate analysis, compared to those with private insurance, men with no insurance were less likely to select surgery (OR=0.47; 95% CI: 0.40, 0.54), radiation therapy (OR=0.37; 95% CI: 0.32, 0.44) or hormonal therapy (OR=0.63; 95% CI: 0.54, 0.74) over watchful waiting (Table 29). Those with public insurance were less likely to select surgery (OR=0.53; 95% CI: 0.51, 0.56) and more likely to select hormonal therapy (OR= 1.42; 95% CI: 1.35, 1.49) compared to those with private insurance. There were no significant differences in the selection of radiation therapy over watchful waiting when comparing men with public insurance to those with private insurance. The results of the univariate analyses stratified by race/ethnicity are presented in Tables 30 to 32. In the stratified univariate analysis, among Non-Hispanic White men, those with no insurance were 40% less likely to receive surgery (OR=0.61; 95% CI: 0.48, 0.78) and about half as likely to receive radiation therapy (OR=0.52; 95% CI: 0.40, 0.68) as those with private insurance. Those with public insurance were less likely to receive surgery (OR=0.55; 95% CI: 0.52, 0.58), and more likely to receive hormonal therapy (OR=1.51; 95% CI: 1.42, 1.61).

99 80 Among Non-Hispanic Black men, compared to men with private insurance, those with no insurance were 65% less likely to receive surgery (OR=0.35; 95% CI: 0.27, 0.46), 66% less likely to receive radiation therapy (OR=0.34; 95% CI: 0.25, 0.45), and half as likely to receive hormonal therapy (OR=0.50; 95% CI: 0.37,0.67). Those with public insurance were 66% less likely to select surgery (OR=0.44; 95% CI: 0.39, 0.50) and 19% less likely to selection radiation therapy (OR=0.81; 95% CI: 0.71, 0.92) over watchful waiting when compared to those with insurance. Lastly, among Hispanic men, those with no insurance were 59% less likely to have surgery (OR=0.41; 95% CI: 0.32, 0.54), 72% less likely to have radiation therapy (OR=0.28; 95% CI: 0.20, 0.39) and 64% less likely to have hormonal therapy (OR=0.44; 95% CI: 0.32, 0.60), over watchful waiting when compared men with private insurance, respectively. Those with public insurance followed a similar pattern as Non-Hispanic White men. Hispanic men with public insurance were less likely to have selected surgery (OR=0.49; 95% CI: 0.42, 0.56) and more likely to have selected hormonal therapy (OR=1.19; 95% CI: 1.03, 1.38) over watchful waiting compared to those with private insurance. All of the sociodemographic and clinical characteristics of interest (age at diagnosis, race/ethnicity, marital status, tobacco use, socioeconomic status, education level, urban/rural status, and tumor grade) were statistically significant at the univariate level and were included in the multivariate models. When controlling for sociodemographic and clinical characteristics, the associations between having no insurance or public insurance and initial treatment selection remained (Tables 33 to 36).

100 81 Specific Aim 4 Survival Analysis of Localized Prostate Cancer Survival analysis was conducted on 28,298 subjects who were diagnosed with localized prostate cancer from (Figure 4). The mean age at diagnosis for these men was 68.4 years (SD=8.4). The minimum age was 32 years and maximum 89. The median age was 69 years with the lower quartile at 63 years and upper quartile at 74 years. Table 37 displays the sociodemographic and clinical characteristics of this group. Most men were Non-Hispanic White (75%) and married (77%). A little over a third had private insurance (34%) and 34% were former smokers. Over half had more than a high school education (56%). Most lived in an urban environment (94%) and 12% were in the lowest socioeconomic status (lived in a census tract with greater than 20% of the population living below poverty). Most selected radiation (32%) and hormonal therapy (32%) as their initial treatment. Surgery was selected by 25% of the subjects and watchful waiting was selected by 12%. Approximately 71% had Grade II Moderately Differentiated tumors. The sample sizes and criteria for each hypothesis for Specific Aim 4 are found in Figure 6. Hypothesis 4-1 Effect of Initial Treatment on Survival Rates Survival analysis was performed to examine the effect of initial treatment on mortality. Table 38 displays the mean and median follow-up times by initial treatment for subjects included in the analyses. Follow-up times for all groups had a range of 6.1 months to 138 months (11.5 years). Median follow-up time for surgery was 55.2 months (4.6 years) compared to 47.4 months (4.0 years) for watchful waiting.

101 82 Table 39 lists the mean survival (median survival was not attained by any treatment) and 1-, 2-, 3-, and 5-year survival rates for each treatment. Estimated survival rates for all men (regardless of treatment) at 1-, 2-, 3-, and 5-years were 99%, 95%, 91%, and 80%, respectively. There were differences in the survival rates between treatments (log-rank test, p<0.001) (Figure 8). The 5-year survival rates for surgery, radiation therapy, hormonal therapy and watchful waiting were 82%, 81%, 78 % and 76%, respectively. Initial treatment selection, along with all the sociodemographic and clinical factors of interest (age at diagnosis, race/ethnicity, marital status, tobacco use, socioeconomic status, insurance status, education level, urban/rural status, and tumor grade) were found to be significantly associated with survival at the univariate level and were included in the multivariate Cox proportional regression model (Table 40). There was no difference between risk of death between surgery and watchful waiting in the multivariate model. There were differences in survival in the multivariate models between those receiving radiation or hormonal therapy when compared to watchful waiting. The hazard ratios (HR) and 95% confidence intervals (95% CI) for radiation and hormonal therapy were HR=0.83 (95% CI: 0.77, 0.89) and HR=0.86 (95% CI: 0.80, 0.93), respectively. There were differences in survival among race/ethnicities when examining the effect of initial treatment (Tables 41 and 42). Univariate and multivariate Cox regression models stratified by race/ethnicity were fitted with the same sociodemographic and clinical characteristics. Initial treatment selection, along with the other variables of

102 83 interest, was found to be significantly associated with survival within each race/ethnicity category. In the multivariate analysis stratified by race/ethnicity, among Non-Hispanic White men, radiation and hormonal therapy had a survival advantage when compared to watchful waiting (HR=0.83; 95% CI: 0.76, 0.90 and HR=0.89; 95% CI: 0.81, 0.97, respectively). Hormonal therapy was the only treatment with a survival advantage (HR=0.77; 95% CI: 0.62, 0.95) among Non-Hispanic Black when compared to watchful waiting. Among Hispanics, only radiation therapy had a survival advantage (HR=0.80; 95% CI: 0.61, 1.04) over watchful waiting. Hypothesis 4-2 Effect of Race/Ethnicity Survival analysis was performed to examine the effect race/ethnicity on mortality of patients diagnosed with localized prostate cancer. Those with unknown race/ethnicity data were excluded from the analysis (n=545) (Figure 7). Table 43 displays the mean and median follow-up times by race/ethnicity for subjects included in the analyses. Follow-up times for all groups had a range of 6.1 months to 138 months (11.5 years). Median follow-up time for Non-Hispanic White men was 54.0 months (4.5 years) compared to 46 months (3.8 years) for Non-Hispanic Blacks. Table 44 lists the mean survival (median survival was not attained by any race/ethnicity) and 1-, 2-, 3-, and 5-year survival rates for each race/ethnicity category. Estimated survival rates for all men (regardless of treatment) at 1-, 2-, 3-, and 5-years were 99%, 95%, 91% and 80%, respectively. There were differences in the survival rates between treatments (log-rank test, p<0.001) (Figure 9). The 5-year survival rates varied

103 84 by race/ethnic group (Non-Hispanic White (80%), Non-Hispanic Black (74%), Non- Hispanic Other (84%), and Hispanic men (83%), respectively). Race/ethnicity, along with all the sociodemographic and clinical factors of interest (age at diagnosis, initial treatment, marital status, tobacco use, socioeconomic status, insurance status, education level, urban/rural status, and tumor grade) were found to be significantly associated with survival at the univariate level and were included in the multivariate Cox regression model (Table 45). There was no difference between risk of death between Non-Hispanic Other and Non-Hispanic White men in the univariate and multivariate models. In the multivariate model, Non-Hispanic Black men had a 35% higher risk of death than Non-Hispanic White men (HR=1.35; 95% CI: 1.24, 1.47). Hispanic men had a 15% lower risk of death than Non-Hispanic White men (HR=0.15; 95% CI: 0.78, 0.93). Hypothesis 4-3 Effect of Socioeconomic Status Survival analysis was performed to examine the effect of socioeconomic status on mortality. Table 46 displays the mean and median follow-up times by socioeconomic status for subjects included in the analyses. Follow-up times for all groups had a range of 6.1 months to 138 months (11.5 years). Median follow-up time for those with a high socioeconomic status was 55.5 months (4.6 years) compared to 50.5 months (4.2 years) for those with the lowest socioeconomic status. Table 47 lists the mean survival (median survival was not attained by any socioeconomic status) and 1-, 2-, 3-, and 5-year survival rates for each socioeconomic status. Estimated survival rates for all men (regardless of socioeconomic status) at 1-, 2-,

104 85 3-, and 5-years were 99%, 95%, 91% and 80%, respectively. There were differences in the survival rates between levels of socioeconomic status (log-rank test, p<0.001) (Figure 10). The 5-year survival rates for men in high, medium, low and lowest socioeconomic status were 84%, 81%, 78% and 73%, respectively. Socioeconomic status, along with all the sociodemographic and clinical factors of interest (age at diagnosis, initial treatment, race/ethnicity, marital status, tobacco use, insurance status, education level, urban/rural status, and tumor grade) were found to be significantly associated with survival at the univariate level and were included in the multivariate Cox regression model (Table 48). Socioeconomic status followed a doseresponse relationship in both the univariate and multivariate models. In the multivariate model, when compared to men with a high socioeconomic status, those with lowest socioeconomic status had a 42% greater risk of death (HR=1.42; 95% CI: 1.28, 1.58) followed by a 25% greater risk of death for those with low socioeconomic status (HR=1.25; 95% CI: 1.16, 1.35) and 13% greater risk of death for those with a medium socioeconomic status (HR=1.13; 95% CI: 1.06, 1.21). There were differences in survival among race/ethnicities when examining the effect of socioeconomic status (Tables 49 and 50). Univariate and multivariate Cox regression models stratified by race/ethnicity were fitted with the same sociodemographic and clinical characteristics. Socioeconomic status, along with the other variables of interest, was found to be significantly associated with survival within each race/ethnic group. In the multivariate analysis stratified by race, socioeconomic status had the highest impact among Non-Hispanic Black men, where those in the lowest

105 86 socioeconomic class had a 52% greater risk of death than men of the same/race ethnicity who were in the highest socioeconomic status (HR=1.56; 95% CI: 1.06, 2.30). This compares to a 41% greater risk of death among Non-Hispanic White men (HR=1.41; 95% CI: 1.23, 2.61), and 29% greater risk among Hispanic men (HR=1.29; 95% CI: 0.93, 1.79). Hypothesis 4-4 Effect of Education Level Survival analysis was performed to examine the effect of education level on mortality. Table 51 displays the mean and median follow-up times by education level for subjects included in the analyses. Follow-up times for all groups had a range of 6.1 months to 138 months (11.5 years). Median follow-up time for those with more than a high school education level was 50.2 months (4.2 years) compared to 55.5 months (4.6 years) for those with the less than a high school education. Table 52 lists the mean survival (median survival was not attained by any education level) and 1-, 2-, 3-, and 5-year survival rates for each education level. Estimated survival rates for all men (regardless of education level) at 1-, 2-, 3-, and 5- years were 99%, 95%, 91% and 80%, respectively. There were differences in the survival rates between education levels (log-rank test, p<0.001) (Figure 11). The 5-year survival rates for men with more than a high school education, high school education, and less than a high school education were 82%, 78%, and 76%, respectively. Education level, along with all the sociodemographic and clinical factors of interest (age at diagnosis, initial treatment, race/ethnicity, marital status, tobacco use, insurance status, socioeconomic status, urban/rural status, and tumor grade) were found

106 87 to be significantly associated with survival at the univariate level and were included in the multivariate Cox regression model (Table 53). Although significantly associated with mortality in the univariate model, only high school education level was associated with mortality in the multivariate model (HR=1.11; 95% CI: 1.05, 1.17). There were differences in survival among race/ethnicity groups when examining the effect of education level (Tables 54 and 55). Univariate and multivariate Cox regression models stratified by race/ethnicity were fitted with the same sociodemographic and clinical characteristics. Education level, along with the other variables of interest, was found to be significantly associated with survival within each race/ethnicity. In the multivariate analysis stratified by race/ethnicity, education level was not significantly associated with mortality among Non-Hispanic Black or Hispanic men. Among Non-Hispanic White men, those with a high school education had a 10% greater risk of death than those with more than a high school education. Hypothesis 4-5 Effect of Smoking Status Survival analysis was performed to examine the effect of smoking status on mortality. Those men who had missing (unknown) (n=4,456) or other tobacco use (n=607) were excluded from the analyses as I was interested only in the effect of cigarette smoking on stage of diagnosis (Figure 7). Table 56 displays the mean and median follow-up times by smoking status for subjects included in the analyses. Followup times for all groups had a range of 6.1 months to 138 months (11.5 years). Median follow-up time for those who never smoked was 50.2 months (4.2 years) compared to 55.5 months (4.6 years) for those who were former smokers.

107 88 Table 57 lists the mean survival (median survival was not attained by any smoking status) and 1-, 2-, 3-, and 5-year survival rates for each smoking status. Estimated survival rates for all men (regardless of smoking status) at 1-, 2-, 3-, and 5- years were 99%, 95%, 91% and 80%, respectively. There were differences in the survival rates between smoking statuses (log-rank test, p<0.001) (Figure 12). The 5-year survival rates for men who were current, former, and never smokers were 75%, 79%, and 80%, respectively. Tobacco use (cigarette smoking status), along with all the sociodemographic and clinical factors of interest (age at diagnosis, initial treatment, race/ethnicity, marital status, socioeconomic status, insurance status, education level, urban/rural status, and tumor grade) were found to be significantly associated with survival at the univariate level and were included in the multivariate Cox regression model (Table 58). Smoking status followed a dose-response relationship in both the univariate and multivariate models. In the multivariate model, when compared to men who never smoked, current smokers had a 75% greater risk of death (HR=1.75; 95% CI: 1.62, 1.89) followed by a 17% greater risk of death for those who were former smokers (HR=1.17; 95% CI: 1.10, 1.23). There were differences in survival among race/ethnicities when examining the effect of smoking status (Tables 59 and 60). Univariate and multivariate Cox regression models stratified by race/ethnicity were fitted with the same sociodemographic and clinical characteristics. Smoking status, along with the other variables of interest, was found to be significantly associated with survival within each race/ethnicity category.

108 89 In the multivariate analysis stratified by race/ethnicity, cigarette smoking had the most impact among Non-Hispanic White men, where current smokers were almost two times at greater risk of death than those who were never smokers (HR=1.90; 95% CI: 1.74, 2.08). Non-Hispanic Black and Hispanic men who were current smokers had almost a 40% greater risk of death than never smokers of their own race/ethnicity HR=1.39; 95% CI: 1.13, 1.70 and HR=1.38; 95% CI: 1.08, 1.75, respectively). Former smokers who were Non-Hispanic White, had a 19% greater risk of death than never smokers (HR=1.19; 95% CI: 1.12, 1.27). This association was not statistically significant among Non-Hispanic Blacks and Hispanics. Hypothesis 4-6 Effect of Marital Status Survival analysis was performed to examine the effect of marital status on mortality. Men with unknown marital status (n=827) were excluded from the analyses (Figure 7). Table 61 displays the mean and median follow-up times by marital status for patients included in the analyses. Follow-up times for all groups had a range of 6.1 months to 138 months (11.5 years). Median follow-up time for those who were married was 55.5 months (4.6 years) compared to 53.7 months (4.5 years) for those who were not married. Table 62 lists the mean survival (median survival was not attained by any marital status) and 1-, 2-, 3-, and 5-year survival rates for each marital status. Estimated survival rates for all men (regardless of marital status) at 1-, 2-, 3-, and 5-years were 99%, 95%, 91% and 80%, respectively. There were differences in the survival rates between marital

109 90 statuses (log-rank test, p<0.001) (Figure 13). The 5-year survival rates for men who were married and not married were 82%, and 74%, respectively. Marital status, along with all the sociodemographic and clinical factors of interest (age at diagnosis, initial treatment, race/ethnicity, marital status, tobacco use, insurance status, socioeconomic status, education level, urban/rural status, and tumor grade) were found to be significantly associated with survival at the univariate level and were included in the multivariate Cox regression model (Table 63). In the multivariate model, when compared to married men, those who were not married had a 34% greater risk of death (HR=1.34: 95% CI: 1.27, 1.41). There were differences in survival among race/ethnicities when examining the effect of marital status (Tables 64 and 65). Univariate and multivariate Cox regression models stratified by race/ethnicity were fitted with the same sociodemographic and clinical characteristics. Marital status, along with the other variables of interest, was found to be significantly associated with survival within each race/ethnicity group. In the multivariate analysis stratified by race/ethnicity, marital status had the highest impact on mortality among Hispanic men, where those who were not married had a 53% greater risk of death than men of the same/race ethnicity who were married (HR=1.53; 95% CI: 1.29, 1.81). This compares to a 45% greater risk of death among Non-Hispanic Black men (HR=1.45; 95% CI: 1.25, 1.67) and 43% greater risk among Non-Hispanic White men (HR=1.43; 95% CI: 1.35, 1.53). Results show that the majority of the hypotheses were born out. Treatment options and survival for men diagnosed with prostate cancer were dependent on factors unrelated to clinical data. While the vast majority of outcomes were in the expected

110 91 direction, there were a few exceptions. For instance, although, 97% of the men in this study had some form of insurance, there were still differences between men with public (Medicare) and private insurance. This suggests that financial considerations may come into play for the patient, provider or both when deciding on a treatment. Future research is warranted to fully understand the relationship between nonclinical factors and prostate cancer outcomes.

111 CHAPTER 5 DISCUSSION This study was designed to provide information on men in Florida regarding the impact of sociodemographic and clinical characteristics on prostate cancer diagnosis, initial treatment selection, and survival. In this chapter, I discuss the dissertation s four specific aims as they relate to published literature that is currently available. I end this chapter with a summary of the study s main conclusions. To note, when discussing results from other studies, I report the race/ethnicity categories as they are used in the study that is referenced. Prostate Cancer in the State of Florida In this dissertation, I focused on the experience of Florida men diagnosed with prostate cancer from 2001 to The study population was mainly Non-Hispanic White men (73%), with an average age at diagnosis of 68 years. Approximately 35% of men were diagnosed with prostate cancer under the age of 64, 41% were diagnosed between 65 and 74 years, and 24% at 75 or older. At the time of diagnosis, most men were married (74%), had insurance (95%), and an education level of high school degree or higher (85%). The majority of men presented with localized or early stage prostate cancer (81%), while 9% were unstaged. For men with early stage or localized prostate cancer, they had tumors that were considered Grade II Moderately Differentiated (Gleason Score of 5 or 6) (54%). These data are consistent with other population based prostate 92

112 93 cancer studies in the US [6, 7, 8, 129] and with reports on prostate cancer from the state of Florida [8, 130]. Stage of Prostate Cancer Diagnosis Prostate cancer diagnosis is dependent upon when and if a man is screened for this disease. Some men are more or less likely to get screened for prostate cancer. A number of factors have been cited for why some men are less likely to get screened. These include the lack of access to health care, low socioeconomic status, inadequate knowledge about prostate cancer/screening, fear of being diagnosed with the disease, a failure of patient-provider communication, distrust of the medical profession, and an aversion to digital rectal exams. In particular, studies have identified the above reasons as possible barriers to prostate cancer screening in Black men [131]. Lower screening rates among Black men may explain the greater likelihood of Black men presenting with advanced disease [132]. I found an association between Non-Hispanic Black men and late stage prostate cancer when compared to Non-Hispanic White men. Unlike other studies, where Black men were twice as likely to be diagnosed with late stage prostate cancer [ ] when compared to White men, I found Non-Hispanic Black men were 38% more likely to be diagnosed with late stage prostate cancer. The likelihood of a late stage diagnosis decreased to 16% after adjusting for sociodemographic characteristics, suggesting that environmental or behavioral factors such as socioeconomic status, education level, smoking status, and insurance status may explain some of the disparities between Non- Hispanic Black and Non-Hispanic White men and stage of prostate cancer presentation.

113 94 Differences in outcomes between my study and others may be explained, in part, by the fact that other studies were conducted during the era before and shortly after widespread PSA testing, when men were more likely to be diagnosed with late stage prostate cancer. My study includes the time period of when PSA screening had been widely accepted when men were more likely to be diagnosed with early stage prostate cancer. Additionally, Florida cancer registry data were not included in the other studies; this effect cannot be underestimated since Florida has a uniquely diverse population comprised mainly of older residents. To bolster my findings, a study conducted by Oliver et al. examined race/ethnicity and the likelihood of a man presenting with localized prostate cancer at diagnosis. This study was conducted among men in four cancer registries from Southeastern states (Florida, Georgia, Kentucky, and Maryland) [140]. After adjusting for sociodemographic characteristics, Florida was the only state in which Black men were less likely to present with localized prostate cancer (OR=0.79; 95% CI: 0.73, 0.85); rather, Black men were more likely to present with late stage prostate cancer (equivalent OR=1.27). Unlike my study, Oliver et al. used data from Florida from the time period of 1990 to 2003; I present more recent data with longer follow-up. In a second study to examine stage of diagnosis and race/ethnicity, data from the Surveillance Epidemiology and End Results (SEER)- Medicare examined a similar time period ( ) and found that Black men had a higher odds of late versus early stage cancer at diagnosis than their White counterparts (OR=1.24; 95% CI: 1.13, 1.37) [141]. Aside from race/ethnicity, other dynamics have been shown to affect screening and diagnosis of prostate cancer. Past studies have shown social support networks have

114 95 been beneficial on health and longevity [96]. A number of scholars have examined the relationship between social support and men s health. In fact, marriage is considered to be one of the most important forms of social support for men [95]. As I predicted, after adjusting for sociodemographic characteristics, I found that men who were not married had a 24% increase in the likelihood of being diagnosed with late stage prostate cancer. These results are consistent with other studies; for example, a study by Hoffman et al. showed men who were not married were 25% more likely to be diagnosed with late stage prostate cancer, also after adjusting for sociodemographics [135]. Moreover, a recent study by Tyson et al. found married men were diagnosed with earlier clinical stages of prostate cancer than men who were not married (41% vs. 37%, p < ) [142]. And, in another recent study comparing marital status and stage of prostate cancer at time of radical prostatectomy, Abdollah et al. reported that separated, divorced, and widowed men had more advanced stage cancer than men who were married (OR=1.10; 95% CI: 1.04, 1.16) [143]. However, in their study, there were no significant differences between never married men and married men. In this dissertation I also examined the relationship between marital status and late stage cancer presentation within race/ethnicity categories. In the sample used for this analysis, overall 75% of men were married. Among Non-Hispanic White men, 80% were married compared to 67% of Non-Hispanic Black men. The effect of marital status on stage of disease was most noticeable among Non-Hispanic Black men; that is, unmarried men were 50% more likely to be diagnosed with late stage prostate cancer, whereas, late stage diagnosis was least among unmarried Non-Hispanic White men (16%). Findings from this research underscores the degree to which marital status (and thus social

115 96 support) impacts men s health, specifically how it affects prostate cancer stage of diagnosis. Further, married men are more likely to get screened for prostate cancer [144, 145], a factor related to stage of prostate cancer diagnosis. More research is necessary to elucidate the relationship between marital status, screening behaviors, and stage of prostate cancer at diagnosis. A third predictor of late stage disease focused on men s smoking status. I predicted current smokers to be more likely to be diagnosed with advanced/late stage disease than non-smokers. Smoking is an important risk factor for many cancers, including prostate. In fact, the latest review by the US Surgeon General found the evidence probable that smoking contributes to a higher prostate cancer mortality rate [52]. The increase in mortality can be achieved through four biologic mechanisms that are affected by cigarette smoking and are most commonly considered when explaining how smoking could cause or accelerate the course of prostate cancer; these include the elevated levels of cadmium, how tobacco affects male hormones, genetic mutations from smoking, or reduced immune function from smoking [146]. In a meta-analysis of 24 cohort studies, data showed an association between smoking and prostate cancer incidence and mortality [147]. Further, several studies reported that smoking is associated with more aggressive disease at diagnosis [54-56]. While examining the association of smoking and late stage prostate cancer at diagnosis, I found that smokers had higher odds of late stage cancer than never smokers. In the adjusted model, current smokers were 36% more likely than never smokers to have a late stage prostate cancer diagnosis. Interestingly, former smokers were 7% less likely than never smokers to have a late state prostate cancer diagnosis. The relationship

116 97 between smoking and late stage prostate cancer was similar within race/ethnicity categories, as well. One possible explanation for the differences in findings between smokers, former smoker and never smokers may have to do with screening behaviors. It may be that smokers are less likely to be screened for prostate cancer than never smokers. Furthermore, former smokers may be more likely to be screened for prostate cancer than smoker and never smokers. These results are consistent with other studies examining smoking and prostate cancer outcomes. Compared with never smokers, about 15% of current smokers had late stage cancer compared to 8% of former smokers (p<0.001) [148]. Moreover, in a study by Rolison et al., non-smokers were almost twice as likely as smokers to undergo prostate cancer screening [149]. They also found that quitters (former smokers) were 2.4 times more likely than smokers to undergo prostate cancer screening. Further, Byrne et al. found similar results using data from the Florida Behavioral Surveillance System and the Florida Tobacco Callback Survey; they found smokers were 40% less likely to have ever been screened for prostate cancer after controlling for sociodemographic factors [150]. Therefore, the relationship between smoking and late stage prostate cancer at diagnosis may, in part, be explained by screening behaviors. However, additional research is necessary to better understand the biological mechanisms of smoking that potentially affect prostate cancer. Further investigation is also warranted to better understand the screening and health behaviors of smokers as they relate to prostate cancer.

117 98 Initial Treatment for Localized Prostate Cancer As there is no definitive answer to which treatment for localized prostate cancer is best, the choice of initial treatment can be a difficult process for patients and physicians. As such, we found variation in the distribution of initial treatment for men diagnosed with localized prostate cancer. The rates of surgery and radiation therapy in this study were comparable to those of the Center for Disease Control and Prevention s National Program of Cancer Registries (CDC-NPCR) Patterns of Care Study conducted by Schymura et al. [6]. However, the CDC-NPCR study reported a higher proportion of men receiving watchful waiting (19%) compared to the men in this study (12%) and a lower proportion of men receiving hormonal therapy (10% vs. 22%, respectively) [6]. Part of the difference may reflect geographical variations and temporal trends in treatment patterns. The CDC-NPCR study consisted of men from seven states across the US in 1997 (California, Colorado, Illinois, Louisiana, New York, Rhode Island, and South Carolina). Differences in treatment patterns across states have been reported in other studies [7, 129, 151, 152]. Therefore, while this study had similar findings for surgery and radiation therapy, other forms of treatment varied. I argue that treatment selection is based upon factors other than clinical considerations. When examining the association of race/ethnicity on initial treatment selection, I found that compared to Non-Hispanic White men, Non-Hispanic Black men were less likely to receive surgery or radiation therapy. These results are consistent with studies that have shown Black men are more likely to be treated conservatively and less likely to undergo surgery [6, 7, 151, 152]. Hispanic men in my study were more likely to receive

118 99 surgery and hormonal therapy when compared to Non-Hispanic White men. These results are also consistent with the study by Schymura et al. [6]. Race-related differences in prostate cancer treatment selection remain poorly understood. Although sociodemographic and clinical characteristics were controlled for in my analyses, differences in treatment selection between race/ethnicities still existed. Possible reasons for the differences in treatment patterns include patient preferences, access to care issues (poor access, lack of specialists), and attitudes towards the health system [7]. Further research is warranted to help understand why these differences exist. Another factor to be considered when examining treatment selection is socioeconomic status. Unlike other studies [6, 7] where no association existed between socioeconomic status and initial treatment selection, in these analyses stratified by race/ethnicity, Non-Hispanic Black men in the lowest socioeconomic category were almost one and a half times more likely to receive watchful waiting over radiation therapy when compared to men of the same race/ethnicity with high socioeconomic status. This association existed even after adjusting for other sociodemographic characteristics such as insurance status, education level, and urban/rural status. Interestingly, this finding did not exist for Non-Hispanic White or Hispanic men with lower socioeconomic status. The results from my analysis suggest that among Non- Hispanic Black men, the impact of being in the lowest socioeconomic status plays a role in the initial treatment decision making process as they were less likely to choose one of the most expensive forms of treatment (e.g., radiation therapy) [82]. A greater understanding of the impact of socioeconomic status among Non-Hispanic Black men as it relates to localized prostate cancer treatment selection is needed.

119 100 Similar socioeconomic findings were seen in a study conducted in the United Kingdom (UK), a country with a socialized health care system. Lyratzopoulus et al. found that after controlling for clinical and sociodemographic variables men from lower socioeconomic groups were significantly less likely than those from higher groups to be treated with surgery or radiation therapy [153]. The UK study only focused on men in the UK who were nearly all White, removing race/ethnicity as a factor in their analysis. Studies in the US examining race and socioeconomic factors in prostate cancer survival have suggested that differences in treatment patterns based on socioeconomic status are a significant contributor to the survival disadvantage seen in Black men [154, 155]. The initial treatment selection process can be an overwhelming process for some patients. Patients obtain prostate cancer information from many sources, including their physicians, family, friends, books, pamphlets, the Internet, and medical journals [156]. The ability to access and comprehend medical information may be influenced by education level. Educational level may have an influence on the complex interaction between patients and physicians. Low health literacy has been correlated with poor prostate cancer knowledge and may impair a patient s understanding of shared decision making [157]. Previous studies have suggested an influence of patient educational level on treatment decisions for patients with newly diagnosed prostate cancer [158]. I found that after controlling for sociodemographic and clinical characteristics, having less than a high school education level (when compared to men with more than high school) was significantly associated with initial treatment selection. Less educated men were less likely to select surgery over watchful waiting. Having less than a high school education was a significant factor within race/ethnicity as well; however, it

120 101 affected the choice of treatment modality differently. For example, Non-Hispanic White men who had less than a high school education were less likely to select hormonal therapy over watchful waiting, whereas Hispanic men with less than a high school education were more likely to select hormonal therapy over watchful waiting. Among Non-Hispanic Black men, those with a high school education were more likely to choose radiation therapy over watchful waiting when compared to men with more than a high school education. Most studies examining the relationship of prostate cancer initial treatment selection and education level, in the context of socioeconomic status, use proxy measures of income or insurance status [159]. In one of the few studies to examine the effect of education level on initial treatment selection for prostate cancer, Kane et al. found that after controlling for sociodemographic characteristics (e.g., socioeconomic status, insurance, and comorbidity), education level was a significant predictor only among older men (greater than 75 years of age) [160]. In their study, those patients older than 75 years with a higher education level received more aggressive treatment than did those with less education. Understanding the specific effect of one factor, such as education, is difficult given its complex relationship with race/ethnicity, income, educational level, and insurance status. Although we adjusted for these effects, our results indicate that education level remains a poorly understood factor in the initial treatment selection process. In predicting primary treatment, educational level appears to be less influential than clinical variables such as stage, grade, and pretreatment symptoms [159, 160]. Since clinical factors have such a strong influence on initial treatment options recommended by

121 102 a physician, perhaps a patient's educational level becomes less important in determining treatment. Additional research is needed to further understand how education relates to treatment selection. As previously mentioned, insurance status is typically examined in the context of socioeconomic status. In this study, I examined the effect of insurance status on initial treatment selection. As one would expect, I found that compared to men with private insurance, men with no insurance were less likely to choose surgery, radiation therapy, or hormonal therapy over watchful waiting. Additionally, men with public insurance were less likely to select surgery and more likely to select hormonal therapy. My findings are consistent with past studies that examined insurance and prostate cancer initial treatment. For instance, a study conducted by Cooperberg et al. found that men with private insurance were more likely to have surgery [161], while the study by Schymura et al., found that men with public insurance were more likely to receive conservative treatment (watchful waiting or hormone therapy) [6]. Yet, some studies have not found an association between initial therapy and type of insurance. A study by Harlan et al. is a case in point [7]. Additionally, as shown in my study, when the analyses are stratified by race/ethnicity, the relationship between insurance status and initial treatment selection was most evident among Non-Hispanic Black and Hispanic men. Men with public or no insurance were more likely to receive conservative treatment. The majority of men in my study (97%) had some form of insurance. Fifty percent had public insurance, with 47% having private insurance. Differences in initial treatments based on insurance type suggest insurance may play a role in the patient s

122 103 decision making process and/or the physician s selection of treatment. Future research should examine how the patient-physician decision-making process actually works. Overall Survival for Men Diagnosed with of Localized Prostate Cancer Very few studies have examined survival for men diagnosed with localized prostate cancer in the context of controlling for sociodemographic and clinical characteristics. To the best of my knowledge, this is the first such study, using data from the Florida Cancer Data System. This study provides an analysis of survival using overall mortality; that is, an examination of death from any underlying cause for prostate cancer patients. When examining the impact of initial treatment on overall survival, we found significant differences in the 5-year survival rates between treatments. Approximately 82% of men who had surgery were alive at five years compared to 76% of men who received watchful waiting (an approximate 7% difference). However, when I controlled for sociodemographic and clinical characteristics, I found the risk of overall mortality between patients who had surgery versus watchful waiting was no longer statistically significant. I found that men who were treated with radiation and hormonal therapy had a lower overall mortality risk than those treated with watchful waiting. In the analysis stratified by race/ethnicity, I found a similar pattern within each race/ethnic category. Yet, among Non-Hispanic Black men, those who were treated with hormonal therapy had a lower overall mortality risk compared to watchful waiting than men treated with surgery or radiation therapy. These differences may be explained by comorbidities

123 104 present at the time of diagnosis which can influence treatment selection and outcomes. As I did not have comorbidity data, this association cannot be verified. Further, the recently published results of the PIVOT trial, a randomized controlled trial comparing prostatectomy to observation (watchful waiting and active surveillance) reported that all-cause mortality did not differ significantly between the two groups [3]. My results are consistent with the observational studies that examined overall mortality for men diagnosed with prostate cancer [162, 163]. However, my results differed slightly from those of the CDC-NPCR Patterns of Care Study, which did not include data from Florida [4]. In the CDC-NPCR study, Schymura et al. reported that 5-year overall survival rates for men treated with surgery were 94% and 75% for watchful waiting (relative difference of 20%). However, it is difficult to do a direct comparison of my data with the Schymura et al. study since we did not have comordibity data or PSA levels in our multivariate models. Schymura s et al. study used surgery as the referent treatment and reported a 2.3 fold increase in the risk of overall mortality among men treated with watchful waiting compared to radical prostatectomy (equivalent HR of 0.43 comparing surgery to watchful waiting) [6]. Therefore, the differences in results may be explained by regional differences in demographic and treatment patterns and differences in methodologies used to calculate mortality risk. The process of selecting a treatment for prostate cancer can be complex and overwhelming. The results of our analysis show that there were differences in overall survival based upon initial treatment selection. However, radiation and hormonal therapy had a modest decrease in overall mortality risk when compared to watchful waiting. These differences may be explained by other factors not controlled for in our analysis

124 105 such as comorbidities, access to care, and patient or health system characteristics. It may be that these factors influence which patients select and receive a particular type of therapy. Further, racial/ethnic disparities existed in overall survival after diagnosis of prostate cancer. I found that 5-year overall survival rates were significantly different between race/ethnicities, with the largest difference between Hispanic men (83%) and Non-Hispanic Black men (74%). This pattern held when I examined the overall mortality risk while controlling for sociodemographic and clinical characteristics. Non-Hispanic Black men had a 35% increased risk when compared to Non-Hispanic White men for overall mortality risk. Hispanic men had a 15% reduced overall mortality risk. These results are consistent with studies in the published literature, such as the systematic review and meta-analysis conducted by Evans et al. [164]. In that study, Non- Hispanic Black men diagnosed with localized prostate cancer had a 35% increased risk of overall mortality when compared to Non-Hispanic White men. My results, however, differed from White et al. who used cancer registry data from the state of Texas. They reported 5-year overall survival rates of 79% for White men, 70% for Black men, and 74% for Hispanic men diagnosed with localized prostate cancer [165]. The studies by Evans et al. and White et al. also found that Non-Hispanic Black men had a higher risk of prostate specific mortality than Non-Hispanic White men, while Hispanic men had the lowest risk [164, 165]. The racial/ethnic differences observed in my analyses are consistent with findings from other population based studies that examined overall mortality among men diagnosed with prostate cancer [ ]. Differences in overall mortality also may be

125 106 attributed to characteristics not controlled for in our study (diet, exercise, comorbidity) as they reflect the expected mortality patterns of the general population [167]. Racial/ethnic health disparities in prostate cancer may be explained, in part, by genetics and dietary factors [48-50], as well as differences in prostate cancer treatment. Further investigation is needed into why these disparities exist. As with most diseases, socioeconomic status plays a large role in health outcomes. Higher socioeconomic status often is associated with lower rates of mortality [168]. This also is true when it comes to mortality rates for prostate cancer [ ]. The lower mortality rates observed in prostate cancer patients appear to be correlated with higher levels of socioeconomic status and are likely attributed to variables linked to better health through being able to afford optimal medical services. The optimal use of health services includes efforts to have early detection and treatment regimens, acquire pertinent health information and education, and avoid high risk health behaviors [169, 170]. When looking at all-cause mortality among men with prostate cancer and its relationship to socioeconomic status, as one would anticipate, I found higher levels of socioeconomic status resulted in higher 5-year survival rates. The survival rates ranged from 84% for men with high socioeconomic status to 73% for men with the lowest socioeconomic status. Moreover, overall mortality risk followed a similar pattern with men of lowest socioeconomic status having the highest risk of mortality (42%). In the analyses stratified by race/ethnicity, the impact of socioeconomic status was greatest among Non-Hispanic Black men and smallest among Hispanic men. These results are consistent with findings from studies that examine the association of socioeconomic

126 107 status on overall and prostate cancer specific mortality [ ]. Past research shows that for men diagnosed with prostate cancer (localized and regional stage), higher levels of socioeconomic status was associated with longer overall and cancer-specific survival [169]. Socioeconomic status, therefore, is a complex construct and is often correlated with race/ethnicity, educational level, place of residence, and income level, all of which have been associated with prostate cancer screening, treatment, and outcomes. By understanding the relationship between socioeconomic status and prostate cancer screening and outcomes, researchers could help identify populations that should be targeted for intervention programs to increase screening and early detection as well as inform individuals about treatment and outcomes. The impact of education level on mortality has been documented extensively [170, 171]. Experts estimate that for each additional year of education, one can lower the probability of dying within the next 10 years by approximately one to three percentage points [171]. Specific to prostate cancer, when looking at the association between education level and all-cause mortality, I found that men with higher levels of education have higher survival rates at 5-years (82% for men with some college/college, 78% high school degree, and 76% less than high school degree). Nonetheless, we found that the overall mortality risk for men with less than high school was not significantly different than that for men with more than a high school education. Further, in the analysis stratified by race/ethnicity, education was only a significant predictor of overall mortality for Non-Hispanic White men with a high school education.

127 108 These results are consistent with prior research focusing on 5-year overall survival rates among men with prostate cancer. Five-year overall survival rates were positively associated with higher levels of education [155]. Conversely, there is a negative association between education level and risk of prostate cancer specific mortality [155]. In other words, men with higher education levels were less likely to die of prostate cancer. For example, Black men who completed 12 or fewer years of education had a prostate cancer death rate that was more than double that of Black men with more schooling (10.5 vs. 4.8 per 100,000 men; RR = 2.17; 95% CI: 1.82, 2.58) [109]. I found an association between education level and overall mortality risk among men diagnosed with prostate cancer that is consistent with mortality risk among the general population. It is important for health care practitioners to be aware of their patient s education level, health literacy, and ability to comprehend treatment related information. Further investigation is warranted to examine the effect of education level on prostate cancer specific mortality in Florida and to identify factors that can be used to create interventions to reduce health related education level disparities. As with most cancers, tobacco use often is associated with poor treatment outcomes and survival. A review of the literature on prostate cancer and cigarette smoking showed an approximate 30% increase in risk of fatal prostate cancer when comparing current smokers with never smokers [53]. Further, the Health Professionals Follow-up Study shows men diagnosed with prostate cancer had an absolute crude rate for all-cause mortality for never smokers and current smokers at 27.3 and 53.0 per 1,000 person years, respectively [51].

128 109 In my study, when examining smoking and all-cause mortality among men with prostate cancer, the 5-year survival rate for current smokers was 75% compared to 82% of never smokers. Former smokers had a 79% 5-year survival rate. Smokers also had a 75% higher overall mortality risk than never smokers. In the analyses stratified by race/ethnicity, the effect of smoking was highest among Non-Hispanic White men where smokers had a 90% increase in overall mortality risk compared to never smokers. The effects of smoking on overall mortality are well documented. However, there are not many population based studies that examine cigarette smoking and its relationship to mortality among men with prostate cancer, while controlling for sociodemographic and clinical factors. Further research is warranted to further explain the relationship of smoking with prostate cancer specific mortality. Regardless of the cancer diagnosis, men who smoke should quit; people who quit smoking tend to live longer than those individuals who continue to smoke, regardless of age [172]. Men who are married enjoy longer overall survival and lower mortality for many major causes of death compared to those who were never married, separated, widowed, or divorced [95-97]. Being married also has been demonstrated to have a favorable effect on overall survival in patients afflicted with cancer [98]. In my study, being married was associated with overall mortality among men diagnosed with prostate cancer. The 5-year survival rate for married men was 82% compared to 74% for men who were not married. Additionally, men who were not married had a 34% increase in overall mortality risk when compare to married men. In the analyses stratified by race/ethnicity, the effect of marriage was strongest among Hispanic men; those that were not married had a 40% increase in overall mortality risk. My study is consistent with findings from the CDC-

129 110 NPCR Patterns of Care Study [6]. The impact of being married on health is well documented [96-98] and marriage is considered the most important form of social support for men [95]. It is, therefore, important to establish, strengthen, or maintain social support networks for men diagnosed with prostate cancer. Targeting men with low levels of social support who are diagnosed with prostate cancer may improve their overall health and survival. Conclusion In summary, I found sociodemographic factors associated with stage of cancer diagnosis, initial treatment selection, and overall survival for men with prostate cancer. Non-Hispanic Black men were more likely to present with late stage disease as were men who were not married and who currently smoked. Variation existed in the initial treatments selected for men diagnosed with localized prostate cancer. Non-Hispanic Black and Hispanic men were less likely to receive definitive treatment (surgery, radiation therapy) and Non-Hispanic Black men with the lowest socioeconomic status were less likely to receive radiation therapy over watchful waiting. Lastly, having less than a high school education and having public or no insurance were associated with being less likely to select surgery over watchful waiting. Differences existed in overall survival based upon initial treatment and sociodemographic factors. In spite of this, results of the survival analyses should be interpreted with caution as they were not risk adjusted and focus on all-cause mortality. With that said, they do provide valuable information regarding health disparities among

130 111 men diagnosed with prostate cancer in the state of Florida. With an eye toward reducing healthcare costs and improving efforts to eliminate health disparities, it is imperative that we understand what external factors affect screening behaviors for early detection, initial treatment selection, and survival for men diagnosed with prostate cancer.

131 CHAPTER 6 STRENGTHS AND LIMITATIONS To the author s knowledge, this study is the first to provide a comprehensive examination of the impact of sociodemographic and clinical factors related to the stage at diagnosis, initial treatment selection, and overall survival for men diagnosed with prostate cancer in the state of Florida. These factors include age, race/ethnicity, education level, socioeconomic status, medical insurance coverage, smoking status, and tumor grade. Further, all of the analyses presented in this dissertation also were stratified by race/ethnicity providing a unique insight into the effects of sociodemographic and clinical factors within different race/ethnicity groups. Data from the Florida Cancer Data System (FCDS) was used for this study. It is certified at the highest level (Gold Certification), placing it among the best for completeness, timeliness, and quality of data. One unique aspect about Florida is that it is comprised of a culturally diverse population to create a rich environment from which to conduct research comparing racial/ethnic groups. Additionally, due to its large proportion of elderly residents, Florida is second in the US for the number of prostate cancer cases diagnosed, a factor that has contributed to the large sample size of this study. For this research, I was limited to the data contained in the public file made available for research purposes by the FCDS. Further, the FCDS does not collect information related to a cancer patient s comorbidities. Studies looking at cancer morbidity and mortality therefore are limited by this omission. Clinical and lab values 112

132 113 (such as PSA level) typically found in a patient s chart also are not collected by the FCDS. These data are necessary for adjusting for burden of disease, an important factor related to initial treatment selection and survival. This dissertation primarily used a cross-sectional study design, making it impossible to establish any causal relationship between the stage of diagnosis and sociodemographic factors, or between initial treatment selection and a patient s sociodemographic and clinical factors. Further, with the absence of longitudinal data, I am unable to examine individual changes over time. However, the cross-sectional data does allow me to address what happens to observed differences when statistical controls are introduced. The use of area-based social measures meant applying a census tract block group level value of education or socioeconomic status to an individual. In this context, the ecological fallacy, where I am assuming that all members of a group are exhibiting characteristics of the group at large, is present. Additionally, data regarding tobacco use were missing for approximately 22% of the study population. It is not known if the data were missing at random or if there was a systematic bias that contributed to the high percentage of missing tobacco use data. Therefore, my conclusions are tentative since I recognize that I may have biased results using these variables. Additionally, I was not able to account for the facility at which treatment occurred and control for any clustering that may have occurred as a result of treatment preferences at one facility versus another. Moreover, patients treated in the same facility also may share certain characteristics, including outcomes. I was unable to classify the facility

133 114 (e.g., academic medical center, community hospital, low volume, high volume, etc.) to compare patients and their outcomes by facility characteristics. Finally, disease specific or actual cause of death was not available to the researchers; therefore, all-cause mortality was only modeled in the survival analyses. The impact of sociodemographic variables, clinical factors, and initial treatment could not be analyzed in relation to prostate-cancer specific mortality. With these caveats in mind, I highlight some significant findings and comment on their potential significance. I found that sociodemographic factors explained the differences in initial treatment selection for men diagnosed with localized prostate cancer. Disparities in race/ethnicity, socioeconomic status, and education level accounted for the variation in treatments received. Although, 97% of the men in this study had some form of insurance, there were still differences between men with public and private insurance. This suggests that financial considerations may come into play for the patient, provider or both when deciding on a treatment. Lastly, I found that social support (marital status) is an important predictor of prostate cancer stage and overall survival. Interventions designed to establish, strengthen, or maintain social support networks for men with diagnosed with prostate cancer may lead to improvements in survival and overall health.

134 CHAPTER 7 FUTURE DIRECTIONS As this research has provided information that is unique and novel, it is just the beginning for the possible types of research that can be done to better understand factors associated with prostate cancer diagnosis, treatment, and outcomes. I found marital status (not being married) was significantly associated with late stage prostate cancer diagnoses. While marriage is a complex union involving social, behavioral, and economic considerations, in this instance, it may be acting as a proxy for the underlying concept of social support. Is it the act of being married that influences men s prostate cancer diagnosis or treatment or having a support network or system that is affecting the psychological and physiological health of men? I would hypothesize the latter. Future studies examining this phenomenon will want to attempt to directly measure social support and its effects on men s prostate cancer diagnosis, either qualitatively or quantitatively. As there is no gold standard for the treatment of prostate cancer, I would propose that future studies specifically targeting men s decision-making will be better able to identify this process and how a treatment is selected. This could be done in several ways, either through survey research or focus groups. Future studies should collect data from the patient, physician, and family in order to get a comprehensive perspective on this issue. Factors related to the detection and treatment of malignant diseases, such as the availability of screening programs and access to health care facilities differ between 115

135 urban and rural communities [173]. Moreover, prostate cancer mortality has been shown to be greater in rural areas of the United States [174]. Understanding the variation in the diagnosis and treatment of prostate cancer by patient residence is therefore important. Although not examined in my dissertation, I would propose a study that would examine urban and rural differences in prostate cancer stage at diagnosis and initial treatment selection. To improve our understanding of the factors associated with treatment outcomes for prostate cancer, it is imperative to that the clinical data be as robust as possible. With advances in technology and data linkages, the future possibilities for conducting research in this area will be significant. The FCDS already is being linked with Medicare data. This dataset will provide researchers with a rich data source from which to gather information on the treating facility and type, patient comorbidities, complications, and other treatment related data not currently collected by cancer registries. Studies have demonstrated a genetic link between prostate cancer and men of West African ancestry [45]. It is important to recognize the diversity present within the Non-Hispanic Black population of Florida and in this study. Additionally, considerable cultural, social and genetic heterogeneity exists among Florida Hispanics [175]. Therefore, a prospective study examining genetic data from prostate cancer specimens, clinical data, and patient reported behavioral outcomes such as quality of life for each treatment would go a long way toward identifying and understanding the confounding elements associated with prostate cancer diagnosis, treatments and outcomes. The results of such a study would provide important information to patients and physicians during the initial treatment selection process. 116

136 117 Lastly, given the controversy with prostate cancer screening guidelines and the recent changes in recommendations from both the US Preventative Services Task Force and American Urological Association, a study comparing prostate cancer screening rates, stage at diagnosis, and mortality rates both before and after the guideline changes would be essential to determine if and to what degree there were any resulting harms or benefits.

137 Figure 1. Prostate Gland 118

138 Figure 2. Stages of Prostate Cancer 119

139 Figure 3. Gleason Score 120