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UNDERSTANDING THE EFFECT OF SOCIOECONOMIC GRADIENT WITHIN RACIAL/ETHNIC GROUPS ON BREAST, COLORECTAL AND PROSTATE CANCER OUTCOMES by PATRICIA ALVAREZ VALVERDE B.A., University of California, Berkeley, 1990 M.P.H., University of North Carolina, Chapel Hill, 2006 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Health Services Research Program 2014 © 2014 PATRICIA ALVAREZ VALVERDE ALL RIGHTS RESERVED This thesis for the Doctor of Philosophy degree by Patricia Alvarez Valverde has been approved for the Health Services Research Program By Mary Plomondon, Chair Tim Byers, Advisor Peter Raich Anna Barón Betsy Risendal Date: 5/8/2014 ii Valverde, Patricia Alvarez (Ph.D., Health Services Research) Understanding the Effect of Socioeconomic Gradient within Racial/Ethnic Groups on Breast, Colorectal and Prostate Cancer Outcomes Thesis directed by Professor Tim Byers. ABSTRACT The association between race/ethnicity and socioeconomic status is complex and varies across and within racial and ethnic groups with regards to cancer outcomes. This study evaluates the combined effects of race/ethnicity and poverty level on mortality after accounting for demographic, tumor, treatment and comorbidity characteristics. We used the SEER-Medicare database for patients diagnosed with breast, colorectal or prostate cancers in 1992-2000 with five year follow-up. The unadjusted analysis of racial and ethnic groups found that both African Americans and Latinos compared to Whites experienced excess mortality. The unadjusted analysis of poverty levels identified that all poverty levels compared to areas with the lowest levels of poverty were at increased risk for cancer-specific and all-cause mortality. After adjusting for patient demographic characteristics, co-morbidities, tumor characteristics and treatment, and poverty level, African Americans remained at increased risk for breast (HR 1.18, 95% CI 1.06-1.31), colorectal (HR 1.19, 95% CI 1.10-1.29) and prostate (HR 1.19, 95% CI 1.09-1.30) cancer specific mortality and colorectal all-cause mortality (HR 1.10, 95% CI 1.04-1.16). Asians consistently exhibited fully adjusted decreased risk for all mortality outcomes whereas Latinos varied between decreased risk, increased risk and no difference from Whites depending on cancer. An interaction between race/ethnicity and poverty was found in breast all-cause mortality (p=0.021). iii Where an interaction was demonstrated, the association between increasing poverty level and increasing mortality risk among African Americans did not remain significant. In addition, Latinos’ positive association between poverty and mortality was only found for prostate cancer-specific mortality. However, Whites and Asians consistently showed a positive association with mortality as poverty levels increased across cancers and outcomes. A positive association between poverty and cancer-specific and all-cause mortality for breast, prostate and colorectal cancers was found among specific racial/ethnic groups. An interaction effect between poverty and race/ethnicity was identified in breast all-cause mortality. For particular racial/ethnic groups such as African Americans and Latinos, decreased poverty does not lessen mortality risk. The form and content of this abstract are approved. I recommend its publication. Approved: Tim Byers iv DEDICATION I dedicate this work to the three most important men and young woman in my life. First to my father, Max, who was taken from me way too soon. My father was excited when I was going to pursue a master’s degree at the time of his death. I think he would be surprised, proud and thrilled that I am now obtaining a doctoral degree. This is also dedicated to my husband Salvador and my son Felix, who have watched me and supported my through this long and difficult endeavor. They never doubted that I could finish or that it would be worthwhile and both sacrificed in their own way for my accomplishment. My son has shown wisdom and patience beyond his years. Lastly, this is dedicated to my daughter Paloma who is already strong, smart and confident. I hope that she will learn to not give up on her dreams whether academic, artistic or personal. v ACKNOWLEDGEMENTS I would like to thank the support, guidance and patience of my committee. Drs. Byers, Plomondon, Baron, Raich and Risendal, each provided countless hours in helping me through this process. They never gave up on me even when I started to wonder if it would ever end. I would also like to acknowledge the incredible support of my sisters Nancy and Sue, for their words of comfort and assistance. My friends, Paty, Edith, mis ahijadas Tiara and Maya, and many others have encouraged me these past years. I will make up for all those parties, events and activities I missed! I thank my co-workers Andi, Kathie, Liz, Herminia and Jesus and many others who showed patience and compassion for me during my very stressful times. vi TABLE OF CONTENTS CHAPTER I: OVERVIEW .................................................................................................................... 1 Introduction ......................................................................................................................... 1 Problem Statement .............................................................................................................. 3 Breast, Colorectal, Prostate Cancer Disparities .................................................................. 4 Socioeconomic Cancer Disparities ..................................................................................... 5 The Interaction of Socioeconomic Status and Race/Ethnicity and Outcomes .................... 7 Conceptual Framework ....................................................................................................... 8 Problem Statement/ Hypothesis .......................................................................................... 9 II: LITERATURE REVIEW............................................................................................. 10 Overview ........................................................................................................................... 10 Race/Ethnicity and Cancer ................................................................................................ 11 Socioeconomic Status Influence on Cancer ...................................................................... 30 Delays in Cancer Care ...................................................................................................... 37 Comorbidities and Cancer................................................................................................. 40 Insurance Factors and Cancer ........................................................................................... 43 Place of Birth and Cancer ................................................................................................. 44 Summary of Findings ........................................................................................................ 45 III: RESEARCH PLAN .................................................................................................... 47 Research Aims .................................................................................................................. 47 Data Source ....................................................................................................................... 48 Measurement of Variables ................................................................................................ 49 Inclusion and Exclusion Criteria ....................................................................................... 57 Analytic Plan ..................................................................................................................... 59 Manuscript Plans ............................................................................................................... 64 IV: RESULTS ................................................................................................................... 66 Breast Cancer Results ....................................................................................................... 66 Colorectal Cancer Results ................................................................................................. 88 Prostate Cancer Results................................................................................................... 114 vii Overall Results ................................................................................................................ 136 V: DISCUSSION ............................................................................................................ 143 Discussion ....................................................................................................................... 143 Summary of Principal Implications ................................................................................ 146 Comparison of Findings to Literature ............................................................................. 158 Limitations ...................................................................................................................... 169 Future Research .............................................................................................................. 170 Conclusion ...................................................................................................................... 171 REFERENCES ............................................................................................................... 172 APPENDIX A. Literature Search Strategy .......................................................................................... 192 viii LIST OF TABLES TABLE 1: Percent (%) of Population within 11 SEER Areas, 1990 ............................................... 6 2: Health Behaviors (%) by Racial/Ethnic Group 2009.................................................... 20 3: Colorectal Cancer Stage Distribution (%) by Race/Ethnicity ...................................... 23 4: Age adjusted mortality rate per 100,000 by Race/Ethnicity, US .................................. 26 5: Relative Risk of Cancer Death for those with Lowest Education vs. Highest Level ... 36 6: Distribution of Breast Cancer Cases by Potential SES Variables ................................ 50 7: Distribution of Colorectal Cancer Cases by Potential SES Variables .......................... 51 8: Distribution of Prostate Cancer Cases by Potential SES Variables .............................. 52 9: Breast Cancer Cases by Race/Ethnicity ........................................................................ 53 10: Colorectal Cases by Race/Ethnicity............................................................................ 54 11: Prostate Cancer Cases by Race/Ethnicity ................................................................... 54 12: Tumor and Treatment Variables ................................................................................. 55 13: Inclusion and Exclusion Criteria List ......................................................................... 57 14: Breast Cancer Eligibility............................................................................................. 59 15: Colorectal Cancer Eligibility ...................................................................................... 59 16: Prostate Cancer Eligibility .......................................................................................... 59 17: Demographic Characteristics of Breast Cancer Cases by Poverty Level ................... 68 18: Clinical Characteristics of Breast Cancer Cases by Poverty Level ............................ 69 19: Demographic Characteristics of Breast Cancer Cases by Race/Ethnicity .................. 72 20: Clinical Characteristics of Breast Cancer Cases by Race/Ethnicity ........................... 73 21: Odds of Stages II/III/IV by Race/ethnicity and Poverty Level, Breast Cancer .......... 76 ix 22: Breast Cancer-Specific 5-Year Mortality Cox Proportional Hazard HR (95% CI) ... 84 23: All-Cause 5-Year Mortality HR (95% CI), Breast Cancer ......................................... 89 24: Demographic Characteristics of Colorectal Cancer Cases by Poverty Level ............. 93 25: Clinical Characteristics of Colorectal Cancer Cases by Poverty Level ...................... 95 26: Demographic Characteristics of Colorectal Cancer Cases by Race/Ethnicity ........... 99 27: Clinical Characteristics of Colorectal Cancer Cases by Race/Ethnicity................... 101 28: Odds of Stages II/III/IV by Race/ethnicity & Poverty Level, CRC ......................... 105 29: CRC-Specific 5-Year Mortality Cox Proportional Hazard HR (95% CI) ................ 111 30: All-Cause 5-Year Mortality Cox Proportional Hazard HR (95% CI), CRC ............ 115 31: Demographic Characteristics of Prostate Cancer Cases by Poverty Level .............. 119 32: Clinical Characteristics of Prostate Cancer Cases by Poverty Level ........................ 121 33: Demographic Characteristics of Prostate Cancer Cases by Race/Ethnicity ............. 124 34: Clinical Characteristics of Prostate Cancer Cases by Race/Ethnicity ...................... 125 35: Odds of Stages II/III/IV by Race/ethnicity and Poverty Level, Prostate Cancer .... 127 36: Prostate Cancer-Specific 5-Year Mortality HR (95% CI) ........................................ 133 37: All-Cause 5-Year Mortality Prostate Cancer HR (95% CI) ..................................... 137 38: Summary of Significant Mortality Hazard Ratios .................................................... 140 39 Summary of Findings for Breast, Colorectal and Prostate Cancer Cases .................. 140 x LIST OF FIGURES FIGURE 1: Health Disparity Factors ................................................................................................. 9 2: Cox Model for Time-Dependent Variables .................................................................. 63 3: Breast Cancer-Specific 5-Year Survival ....................................................................... 78 4: Breast Cancer-Specific 5-Year Survival by Race/Ethnic Group and Poverty Level .... 79 5: Breast Cancer Overall 5-Year Survival ........................................................................ 80 6: Breast Cancer Overall 5-Year Survival by Race/Ethnicity and Poverty ...................... 80 7: Colorectal Cancer-Specific 5-Year Survival .............................................................. 106 8: CRC-Specific 5-Year Survival By Race/Ethnicity and Poverty Level ....................... 107 9: CRC Overall 5-Year Survival ..................................................................................... 107 10: CRC Overall 5-Year Survival by Racial/Ethnic Group and Poverty Level ............. 108 11: Prostate Cancer-Specific 5-Year Survival ................................................................ 129 12: Prostate Cancer-Specific 5-Year Survival by Racial/Ethnic Group and Poverty Level ........................................................................................................... 129 13: Overall 5-Year Survival for Prostate Cancer ............................................................ 130 14: Overall 5-Year Survival by Racial/Ethnic Group and Poverty Level in Prostate Cancer ........................................................................................................... 130 xi CHAPTER I OVERVIEW Introduction The 2010 US Census revealed an increase in proportion of the US population by persons of color and individuals born outside of the US with considerable estimated future growth. Immigrants have grown from almost 5% of the US population in 1970 to over 12% by 20091. Of US adults 65 and older, 12.5% were born outside of the US. Over the next 40 years, the population distribution of the US will change considerably2. By 2050, less than 53% may be non-Latino White, while the US population is projected to be made up of 16% African American, 23% Latinos, 10% Asian/Pacific Islander (PI) and about 1% American Indian Alaskan Native (AIAN). Immigrants from Mexico, China and other Latin American and Asian countries will continue to change the current US demographic landscape1. Given this dramatic shift, there is urgency to understand both the risk and protective factors experienced by racial and ethnic groups related to breast, colorectal and prostate cancers. Predictive influences vary by racial/ethnic group and by cancer outcome measured. To eliminate disparities, policy makers will need to understand the modifiable factors related to stage at diagnosis, incidence rate, survival and mortality. In addition, grasping the complexities and paradoxes of immigrant cancer outcomes will be necessary to support improving cancer outcomes. In 2011, approximately 612,580 adults in the US will be diagnosed with and 122,620 will die from colorectal, prostate and female breast cancers3. These three cancer sites are among the top four sources of new cases and cancer deaths for both men and women3. Prostate and colorectal cancers constitute 38% of new cases for men and 19% of 1 cancer deaths. Among women, breast and colorectal cases are 39% of all new cancers and 24% of cancer deaths. Racial and ethnic disparities will be discussed in the distribution of risk factors, incidence of disease, stage at diagnosis, treatment utilization and ultimately survival and mortality for these cancer sites. Breast Cancer is the most common cancer among women and second leading cause of death3. A woman’s lifetime risk for breast cancer is 12.15%. In the United States, approximately 230,480 new cases of breast cancer will be diagnosed in 2011. The age-standardized incidence rate of breast cancer is 120.7 per 100,000. The 5-year relative survival of breast cancer is 90% yet in 2011, an estimated 39,970 breast cancer deaths will occur. The age-adjusted mortality rate is 24.0 per 100,000 for women over 65 years of age. The risk for breast cancer increases with age. Colorectal cancer (CRC) risk also increases with age3. CRC is the third most common cancer among men and women and third leading cause of cancer death for each sex. In 2011, an estimated 101,340 colon cancer cases and 39,870 rectal cancer cases are expected to be diagnosed. A man’s lifetime risk for colorectal cancer is 5.30% and a woman’s risk is 4.97%. CRC afflicts 57.1 men and 42.4 women per 100,000. The 5- year relative survival for CRC is 65% for all stages combined. In 2011, over 25,000 men and 24,000 women may die from cancers of the colon and rectum. The colorectal mortality rate is 21.2 and 14.9 per 100,000 for men and women respectively. Prostate cancer is more common than both breast and CRC3. It is the leading cause of new cases of cancer for men and the second cause of death conferring a 16.22% lifetime risk of developing the cancer among all men. The risk for prostate cancer increases by age, in a magnitude greater than that of breast cancer and CRC. The incident 2 rate for prostate cancer is almost three times greater than CRC incidence at 153.5 per 100,000 men. Men’s 5-year all stage relative survival is 99% but an estimated 33,720 will still die from prostate cancer in 2011. Given the high survival from the disease, the prostate cancer age-adjusted mortality rate is closer to that of CRC with 24.7 per 100,000 men. Taken together, these three cancers impact millions of Americans each year through new diagnosis, deaths and living with the physical, emotional and economic consequences of the disease. The American Cancer Society has estimated that 43% of cancer deaths (36,720) among men and 30% of deaths (23,650) among women 25-64 years of age could be averted if the disparities related to education are eliminated3. Given an unequal burden of disease exists among the subgroups in the US, eliminating cancer disparities among these key cancers is imperative. This research study will examine the combined effect of race/ethnicity and socioeconomic status on three important cancers impacting US society-breast, colorectal and prostate cancers. Problem Statement The research study proposed herein seeks to add to the body of knowledge of the contributors to cancer disparities in the United States, particularly among those over 65 years of age. We propose to further elucidate the role of socioeconomic status (SES) across racial and ethnic groups in effecting disparate cancer outcomes by investigating different elements of three broad categories: 1). Patient factors; 2). Structural barriers/system factors and; 3) clinical factors4. Patient factors include tumor characteristics, racial/ethnic membership, place of birth, age, neighborhood level of socioeconomic status and comorbidities. Structural barriers and system influences are 3 timeliness or delays of care for different segments of the care trajectory and insurance and cost burden. Clinical factors will involve measurement of type of treatment received and adherence to guideline therapy. By further isolating potential contributors of different survival, we may take action to eliminate cancer disparities. Three outcome measures will be used to evaluate SES and racial/ethnic variations. First, the proportion of cases diagnosed at an early stage is an important intermediate indicator of access to care, use of screening exams and adoption of health behaviors for cancer prevention often used5, 6, 7. Programs and policies that encourage and provide for the use of cancer screening and diagnostic tests seek to increase the diagnosis of early stage disease8. Stage of diagnosis also is a predictor of cancer survival with higher survival among early stage diagnosed cancers. The second outcome measure is cancerspecific survival measured at 5 years6. Finally, overall 5-year survival will be examined. Survival is impacted by the quality of treatment services, co-morbidities, tumor characteristics and other factors. Stage of disease and cancer survival are key indicators for cancer prevention and control. Breast, Colorectal, Prostate Cancer Disparities Understanding racial/ethnic differences in cancer outcomes cannot be done without expanding our conceptual framework beyond race. In fact, Dr. Brawley, Oncologist and Chief Medical Officer of the American Cancer Society suggests, “It is more scientific to think of race as a surrogate for area of geographic origin, socioeconomic status and culture, all of which have correlations with disease risk.”9 As a result, we must seek to improve our ability to capture these elements in disparity research. 4 Socioeconomic Cancer Disparities A recent report on socioeconomic inequalities in all-cause mortality rates suggests a growing disparity by socioeconomic status in the United States10. Socioeconomic status, insurance and race/ethnicity are inexorably linked in US society. Socioeconomic status refers to an individual’s relative position in society. That position is created by the interweaving of income, education, resource access and economic opportunities. Socioeconomic position consists of “the social and economic factors that influence what position individuals and groups hold within the structure of society, i.e. social and economic factors are the best indicators of location in the social structure that may have influences on health.”11 There are two approaches to measuring SES: compositional and contextual. Compositional measurements focus on “the socioeconomic and behavioral characteristics of individual and their associated health outcomes”12 and require information about the individual. The person’s income, education level, and occupation are examples of compositional SES measures. The contextual approach 3“examines the social and economic conditions that affect all individuals who share a particular social environment.”12 Measurements of the number of people in poverty or with a college education, describe the context in which the cancer patient lives, socializes and interacts in our society. Individual and area-level measures do not always correlate. Both compositional and contextual SES measures are each independently associated with poor health outcomes. In essence, both are important and provide key information about the individual’s socioeconomic situation13. Individuals from minority communities are more likely to be poor and live in poor communities. Impoverished families are less likely to 5 have health insurance. Health insurance enables access to cancer screening, diagnostic procedures and cancer treatment. Evidence suggests that race, ethnicity, insurance status, and SES are each important predictors of cancer survival. Medicare, a universal health insurance program for disabled individuals and those 65 year and older, does not remove the entire cost burden from its participants. As a result, cost barriers which may impact timeliness of care and treatment decisions among the most vulnerable although attenuated are not necessarily eliminated. It is clear that levels of SES are not equally distributed within racial/ethnic groups within the SEER registry patient population18 (Table 1). In addition, the population of adults 65 years and older and people with at least a high school diploma differ by area poverty level. Table 1: Percent (%) of Population within 11 SEER Areas, 1990 % Census Tract Population Below Poverty Level 10%-19.99% poverty 22.9 ≥ 20% poverty 72.55 20.41 7.04 African American American Indian, Alaskan Native (AIAN) Asian/ Pacific Islander (PI) Latino 24.96 25.58 49.46 29.42 23.03 47.54 60.22 23.75 16.03 28.31 31.06 Age group 65+ 11.42 11.48 31.06 9.02 85.03 73.53 Population Group Total Population White High School diploma or more Source: Singh et al 200318 < 10% poverty 59.28 6 17.82 56.31 Investigating the race/ethnicity and SES relationship utilizing a linked dataset of cancer registries (Surveillance, Epidemiology, and End Results, SEER) and Medicare claims provide unique opportunities. The advantage of Medicare-SEER data is the comprehensive claims information for treatment data and the removal of insurance as a confounder. Yet, few investigations move beyond White-African American comparisons and a dearth of studies exist on the socioeconomic gradient within racial/ethnic minority subpopulations even with this large population-based cancer database14. The socioeconomic gradient refers to improving health as income, education, wealth or occupation level progresses. A gradual impact on health by higher social class has been noted over the centuries. The landmark study, The Whitehall Study which began in 1967, was a longitudinal study of 18,000 men in the British civil service. They found that men in the lowest occupation grade were more likely to die prematurely than men at higher occupational levels. This study established the evidence of a social gradient across many health conditions15. The Interaction of Socioeconomic Status and Race/Ethnicity on Cancer Outcomes It is assumed that with improved education, the person can obtain a higher paying job resulting in living in a nicer community with improved schools. However, research has shown that improvements in education do not result in the same work, income and housing improvements among all racial and ethnic groups12, 16, 17. There continue to be disparities in pay by gender and race for similar occupations and educational level. The gains in education and income are different among groups. Thus, social epidemiologists justify the use of investigating how disease impacts racial/ethnic groups within varying levels of social class. 7 SEER published a monograph providing a comprehensive illustration of SES and race/ethnicity interaction across cancer sites and cancer outcomes18. This analysis reviewed cases during the years 1975-1999 and found an increasing disparity in allcancer mortality among men in high poverty areas and a slight increased mortality difference is found among women. However, the SEER analysis was not able to control for insurance status, tumor characteristics, complete and appropriate treatment received, and comorbidities- each of which are independently associated with cancer outcomes. As a result, the race/ethnicity-SES interaction has not been thoroughly explored using the Medicare-SEER dataset. Analysis using Medicare-SEER data often simply adjust for SES, or use weak SES measurement, rendering interaction analysis flawed. Furthermore, little has been done on the relationship of delays in care with race/ethnicity and SES. The level of comorbidity and cost burden within our racial/ethnic groups at varying SES levels needs further investigation. Utilizing place of birth data in this elderly cohort of cancer cases may improve the characterization of Latino and Asian cancer outcomes in particular. Conceptual Framework This research is guided by a conceptual framework initiated by Dr. Harold Freeman and modified by Ward et al. The framework19 describes the factors influencing health disparities. These factors (social, economic and cultural) are described to some extent at both the individual and neighborhood level. Clinical trajectories shown in the box will be examined through clinical data and measure delays. Furthermore, acknowledging that all these factors could potentially differ by immigrant status, the analysis by place of birth will provide additional refinement of this framework. 8 Figure 1: Health Disparity Factors Adapted from Ward et al: Adapted from Freeman HP and Institute of Medicine (CACancer J Clin 2004;54:78–93 Problem Statement/ Hypothesis The relationship between race/ethnicity and socioeconomic status is complex and varies across and within racial and ethnic groups. Socioeconomic status modifies the effect of race/ethnicity on cancer outcomes such as stage of disease at diagnosis, survival and risk of death; such that lower SES racial/ethnic minority groups fare worse than more affluent racial/ethnic groups and non-minority poor groups. 9 CHAPTER II LITERATURE REVIEW Overview In order to understand the need for a closer investigation of the interplay of race/ethnicity with socioeconomic status, a review of the literature was conducted. Initially, considerable research studies focused on the racial/ethnic disparities in cancer outcomes with minimal consideration of the impact of social class. Studies on racial/ethnic disparities have added socioeconomic status (SES) as a potential confounding variable. Simultaneously, research on the independent impact of SES on cancer outcomes developed with substantial discussion around appropriate and feasible measurement of SES. The two fields of research merged as SES was found to explain varying levels of the racial/ethnic differences in cancer outcomes, most prominently in the SEER monograph of 200318. In addition, some investigations have tried to better characterize broad racial/ethnic categories by immigration status, insurance status and other descriptors. This literature review will exam the published articles on racial/ethnic disparities in cancer including risk, incidence, stage at diagnosis, treatment uptake, survival and mortality for breast, colorectal and prostate cancers focusing on the elderly cancer patient when possible. Then, the literature concentrates on the relationship between socioeconomic status and cancer. A review of the literature that assesses the interaction of race/ethnicity and SES is conducted. Lastly, research publications that examine additional factors are appraised and include delays in care, comorbidities, insurance 10 factors such as cost-sharing and Medicaid/Medicare dual eligibles, and finally the role of place of birth on cancer. The PubMed database of the US National Library of Medicine, National Institute of Health, was searched for relevant peer-reviewed articles. A variety of key word combinations and filters were used and the search was limited to English language articles. Articles were searched from 1990 until current unless considered a sentinel publication. The list of search strategies and number of citations obtained are listed in the appendix. Race/Ethnicity and Cancer Breast Cancer A review of studies comparing predictors of survival and mortality by race/ethnicity was performed. Many studies only compared the experiences of White and African American women. Whenever possible, information for post-menopausal women is presented. In addition, age-adjusted odds are provided before adjustment for other covariates including socioeconomic status which will be discussed in subsequent sections. Risk factors Breast cancer risk factors include age at menarche, first birth and menopause, parity, age, family history of breast or ovarian cancer, personal history of in situ or atypical hyperplasia biopsy results, BRCA1/BRCA2 genetic mutation, post-menopausal obesity, hormone replacement therapy (HRT) and alcohol use20-22. These and additional factors such as socioeconomic status, physical activity and routine use of mammography vary in prevalence by race/ethnicity20. 11 In the Women’s Health Initiative, a large cohort of 156,570 postmenopausal women, breast cancer risk factors explained most of the differences between Whites and all groups except African Americans who continued with unexplained lower risk for invasive cancer20. Similarly, Hines et al investigated White -Latino differences and observed that very little of postmenopausal Latino breast cancers are attributed to risk factors which explain the majority of White women’s cancer of the same cohort. They suggest that estrogen-related factors which may be genetic, biological, environmental and lifestyle, could contribute to the higher White breast cancer incidence compared to Latinos23. Health behaviors are important risk factors for breast cancer given the rise in obesity rates in the US and accounting for almost 20% of breast cancer risk24. Nationally, obesity prevalence differs by subpopulation. Obesity among premenopausal women confers increased risk for ER-negative tumors and decreased risk for ER-positive tumors. However, obesity in postmenopausal women increases risk for ER positive tumors25. The increased adjusted odds of obesity in Mexican American women compared to Whites were 1.31 (95% CI: 1.11-1.55) and for African American women, 2.01 (95% CI: 1.241.83). The obesity prevalence for White, African American and Mexican American women is 28.7%, 52.7% and 37.8% respectively26. Almost half of White women, 49.7%, engage in no physical activity compared to 64.5% of African American women and 69.8% of Mexican American women aged 65 years and older. Poor diet shows less racial/ethnic variation with 61.1% of elderly White women eating a poor diet, 57.6% of African American women and 60.7% in Mexican American women. In one national 12 study, women ≥65 years of age reported current smoking rates of 8.7%, 8.4% and 9.5% of White, African American and Mexican American women respectively27. Continued disparities in mammography use exist. Recommendations from the US Preventive Task Force have changed the initial age of mammography screening from age 40 to individual and clinician preference of screening prior to age 50 and a shift from annual to biennial mammographic screening28. In the Women’s Health Study, White and Asian women had the highest proportion of mammography within 2 years (82.1% and 80.8%) while African American women (75.7%), AIAN women (71.6%) and Latino women (67.5%) observed progressively decreasing use (p<.001)20. Among Medicare beneficiaries, African American and Latino women were more likely to be inadequately screened than Asian and White women29. Risk factor prevalence differs in the underlying population and helps us to understand potential influences that create persistent differing incidence and stage of disease proportions. However, there are limited studies able to adequately measure the known risk factors for breast cancer. This is primarily due to the limitations of large datasets such as cancer registry data. Studies with significant risk factor information conduct interviews to collect the data missing from medical records or cancer registries. One of the studies, enrolled a Latino group with a large proportion of immigrants (41%) without an adequate measure of acculturation20. Yet, these groups were analyzed together even though risk factors, health behaviors and health care access differ between US born and foreign born Latinos30. 13 Incidence In 2006, Non-Latino White women had the highest age-adjusted incidence rate of breast cancer (141.1 per 100,000) followed by African American (119.4 per 100,000), Asians (96.6 per 100,000), Latinos (89.9 per 100,000) and AIAN women (54.8 per 100,000)31. When stratified by age group, a higher incidence rate among African American women younger than 40 years old emerges compared to White women32. The incidence rate ratio (IRR) was 1.16 (95% CI: 1.10-1.23) for the African American-White comparison. For all other age groups, White women showed higher incidence rates with African American-White IRRs ranging from 0.93 to 0.83 32. Studies using the Surveillance, Epidemiology, and End Result (SEER) Program data have reported that the incidence of breast cancer began to decrease around 1999 and more sharply in 200233 and continued through 200534. Race/Ethnic and stage-stratified analysis shows different levels of reduction in the incidence rate purportedly due to the plateauing of mammography uptake and the reduction in use of hormone replacement therapy (HRT) 24, 33-36. White women experienced increasing incidence of invasive cancer which stabilized then significantly decreased in 200435. Asian women’s decreased use of HRT resulted in a more gradual lowering of invasive cancer incidence while Latino women showed little change and African American women no change in invasive cancer incidence rates. In situ incidence rates decreased only for White women during 20012004. There is a link between the decreased use in HRT and the reduction in incidence of estrogen positive, progesterone positive and lobular and small tumors24, 33, 35, 37. A state survey indicated a 7% decrease in HRT use among White women, 11% decrease in 14 Asian women, 5% drop among Latinos and no change in African American use35. The changes in estrogen-receptor (ER) positive and progestin-receptor (PR) positive, lobular and small tumors among White and Asian women illustrate the impact of HRT discontinuation on cancer subtype incidence. More recent national surveys present a reduction in estrogen plus progestin therapy use in White women ages 60-69 from 12.9% to 3% over an 8 year period38. Although African American women had experienced low uptake of HRT, use decreased from 3.2% to 1.1% over the same period. Latino women dropped from 7.4% to 2.6% use by 2008. Race/ethnic-specific incidence rates that are further stratified by stage, time period and tumor subtypes reveal important areas of investigation and intervention. Given that each racial/ethnic group is a heterogeneous group with numerous subgroups related to country and region of origin, geographic location, neighborhood characteristics and language, additional characterization is required to disentangle multi-faceted exposures and influences. Otherwise, race and ethnicity continue to be a proxy for unmeasured differences found in heterogeneous social groups. Stage at diagnosis and tumor characteristics Stage at diagnosis, the strongest predictor of breast cancer survival, does not follow the racial/ethnic distribution of incidence29. In most age groups, all minority groups except Asian women tend to be diagnosed at later stage of disease than White women39-42. Even within an insured group of breast cancer patients with similar screening access and rates, Latino women 50 years and older had an age adjusted odds of stage IV disease of 3.41 (95% CI, 1.53-7.54) compared to White women43. Latino women also 15 had odds of 2.25 (95% CI, 1.3-3.9) for poorly differentiated tumors and 2.04 (95% CI, 1.04-4.00) for tumors 5cm and greater compared to White women. Differences in tumor characteristics which impact survival have been identified20, 29 . Using SEER data, African American women experienced significantly fewer ER+/PR+ tumors than White women (p<.0001) more ER-/PR+ tumors (p<.001) and ER/PR- tumors (p<.001) than White women regardless of age group32. Increased odds of ER-/PR- tumors was also found among Latino women, Asian and AIAN in addition to African American women when compared to White women ranging from 1.2 to 2.4 higher odds. However, African American women still experienced the highest increased odds of ER-/PR- tumors of all groups (OR 2.4, 95% CI 2.3-2.5)41. The SEER-Medicare data show a significant difference in distribution of tumor grade among White, African American, Asian/Pacific Islander and Latino women (p<.001)29. Together, tumor markers, stage, size and lymph node status were found to contribute 29% of mortality reduction for all stages from baseline model to full adjustment. A major strength of the Medicare-SEER studies is the inclusion of Latino, Asian and AIAN groups in the analysis. Furthermore, stage and tumor characteristics may differ by subgroups due to different diet, screening behaviors and genetic background which are unmeasured in registry analysis. Treatment Many studies have reviewed treatment pattern differences which contribute to survival. African American21, Mexican American40 and Puerto Rican40 women have a higher likelihood of receiving inadequate treatment than White women. Most Asian subgroups showed higher likelihood of appropriate treatment than White women40. In the 16 clinical trial setting, similar responses to treatment between African American and White women resulted in comparable disease-free survival but did not eliminate racial differences in mortality44. The studies reviewed do not consistently have information regarding hormone receptor status. Thus, it is difficult to know whether chemotherapy is indicated without this information and what appears a treatment disparity may reflect the underlying difference in tumor types. Although treatment delays were mentioned as a possible explanation for increased mortality risk, delay was not measured29. Survival and mortality Survival is largely driven by stage at diagnosis, tumor biology and treatments received37. The 5-year cause specific survival rate during 2001-2007 was 88.8% for white women, 77.5% for African American women, 90.3% for Asian/Pacific Islander, 85.6% for AIAN and 83.8% among Latino women with breast cancer. Across all stages, white women experienced significantly greater survival than African American women (all stages 90.1% vs. 78.3%, p<.0001)32. Localized cancers exhibited significant survival differences between White and African American women (99.6% vs. 94.7% p<.0001) and regional as well (84.1% vs. 71.2% p<.0001). Survival among white and African American women with distant disease was significantly different (26.0% vs. 15.7% p<.0001). Under the controlled environment of clinical trial protocols, statistically significant survival disadvantage for African American women both pre and postmenopausal was still observed45. The decline in breast cancer mortality since 1990 is primarily due to the combination of increased mammography screening and improved breast cancer 17 treatments46. The mortality rates during 2003-2007 for white, African American, Asian/Pacific Islander, AIAN and Latino were 23.9, 32.4, 12.2, 17.6, and 15.3 deaths per 100,000 respectively. Excess risk among African American women compared to White women for allcause and breast cancer specific mortality has been exhibited in Medicare-SEER47, SEER40 and case-control studies20 persisting after adjusting for multiple covariates. For example, African American women experience a fully-adjusted 35% all-cause excess risk and 83% excess risk for breast cancer specific mortality compared to White women47. Additional subgroups noted higher mortality risk compared to White women including AIAN, Hawaiian, Latino overall and Mexican women after full adjustment40. Further subgroup analysis on mortality risk detected additional differences when compared to white women41. After adjusting for age and registry, Pacific Islander, AIAN, Mexican, Puerto Rican, Cuban, other Latinos and Filipino, Hawaiian and Samoan subgroups exhibited statistically significantly increased mortality risk when compared to white women. Japanese women had statistically significant less mortality risk than white women. Many large registry studies continue to conduct African American and white comparisons while combining other groups47, 48, excluding all other racial/ethnic groups42, 45 or adding other groups to White21. As seen, Latinos and Asians experience cancer survival and mortality differently with some Asian groups mirroring or bettering White outcomes. The addition of the ‘other’ category provides little information given the varying direction and magnitude of risk for cancer outcomes. It is clear that the 18 differences of breast cancer tumor types and survival and mortality in African American women are not fully understood given the excess risk that remains unexplained. Colorectal Cancer Risk factors Established risk factors for colorectal cancers are obesity, lack of exercise, smoking, Type II diabetes, postmenopausal hormones, and nonsteroidal antiinflammatory drugs (NSAIDs) and most importantly diet (fat, alcohol and red meat consumption)49, 50 . When comparing individuals with the highest level of a risk factor to those with the lowest levels, risk varies from 20% increase for diabetes to 10% for alcohol consumption51. Diet is responsible for as little as 12%52 and up to 90%53 of the risk for CRC. Diet and lifestyle have been estimated to have an estimated Population Attributable Risk of 70% (33%-92%) for CRC 54. The Health Professionals Follow-Up study provided an opportunity to study the impact of 6 modifiable risk factors for colon cancers. Measurements of obesity, physical activity, alcohol, smoking, red meat and folate consumption were completed on the nearly all white 47,927 men aged 40-75 years. They observed that a third to half of the risk of colon cancer would be avoidable by a reduction of risk factors matching the lowest 20% risk score group54. They also conclude that distal colon cancers are more preventable by lowering risk factors than cancers of the proximal colon. A study of African American-White diet and CRC risk concluded that the effect of particular food groups on CRC varied by race/ethnicity52. Among Whites, increased red meat and refined carbohydrates conferred increased risk while among African Americans, high dairy intake doubled CRC risk. 19 Physical activity is another modifiable risk factor for colorectal cancer. Those at the highest levels of physical activity exhibit a 50% lower incidence in colon cancer53. Yet, even moderate level of physical activity will provide risk reduction benefits55. A meta-analysis of 52 studies showed that physically active adults observed a 20%-30% reduced risk of colon cancer when compared to those less active55. Alcohol and smoking are risk factors for both colon and rectal cancers. Smoking is estimated to account for 15-20% of colorectal cancers55 and 7,000 to 9,000 colorectal cancer deaths each year in the United States53. Smoking may be associated with an even greater proportion of incident rectal cancers and rectal cancer deaths55. The prevalence of current smoking and other risk factors vary by race/ethnic group (Table 2)56. Similarly, alcohol has been implicated as a risk factor for both cancers but a more recent study has suggested a particularly deleterious effect on rectal cancer risk55. Both intake of NSAID and HRT reduce risk for colorectal cancer with potential side effects, thus limiting their risk reduction potential55. Table 2: Health Behaviors (%) by Racial/Ethnic Group 2009 Racial/Ethnic Gender Group Risk Factor Obesity Physical inactivity Current Smoking White Men 24.5 32.6 30.4 Women 19.8 33.6 33.5 African Men 23.9 37.6 41.8 American Women 19.2 50 52.6 Latino Men 19 35.8 NA Women 9.8 45.2 Asian Men 16.9 NA Women 7.5 AIAN Men 29.7 Women NA Source: Cancer Prevention and Early Detection 2011, ACS56 20 Heavy Alcohol consumption 32.5 16.7 NA 27.2 5.8 NA Colorectal cancer (CRC) screening via colonoscopy has the opportunity to reduce the incidence and mortality of colorectal cancer for the average risk adult 50 years and older57, 58. Colonoscopy and other endoscopic procedures can remove pre-cancerous adenomas thus preventing cancer from forming33. Sigmoidoscopy does not reach the proximal colon region, an area where African Americans are purported to have an increased incidence of colonic disease50. The current CRC screening recommendations from the US Preventive Services Task Force include fecal occult blood testing, sigmoidoscopy and colonoscopy to detect early stage cancer and adenomatous polyps. Yet, rates of any type of CRC screening vary in the US due to the numerous barriers facing screening adherence59. Medicare began providing coverage of 80% of the Medicare-allowed cost for colonoscopy screening in mid-2001, an expansion from the 1998 coverage of fecal occult blood tests (FOBT), sigmoidoscopy, barium enema and colonoscopy for high risk individuals. The levels of CRC screening are gradually increasing for each racial/ethnic group with an overall 17% increase of screening use from 2000 to 200560. In 2005, 41.1% of African Americans were current with CRC screening guidelines compared to 48.3% of Whites, 35.9% of Latinos, 32.3% of AIAN and 37.4% of Asians61. A two state study of Medicare beneficiary reported decreased odds among African Americans of receiving a physician recommendation for any CRC test compared to Whites (OR 0.48, 0.37-0.63)59. Incidence Overall CRC incidence rates have been declining since 1985 and stabilized during 1995-1998 and then began to decrease again. By 1990, African American women under 80 years old displayed higher age-adjusted incidence rates then White women, unlike 21 African American62,63 men whose rates are higher than White men for age groups under 65. Through 2004, African American men and women had the smallest decline in incidence, followed by Latinos with the largest decline by Whites63.The age-adjusted male incidence rates per 100,000 for 2003-2007 are 56.8 for White, 68.3 for African Americans, 42.8 among Asians/PI, 43.2 for AIAN and 49.2 in Latinos and 57.2 overall51. For women, the rates are 41.9 among whites, 51.6 for African American, 32.5 Asian/Pacific Islander (PI), 34.4 American Indian/Alaskan Native (AIAIN) and 34.8 among Latinos. Stage at diagnosis and tumor characteristics Like breast cancer, CRC screening and risk factors prevalence is different among racial/ethnic groups, as well as polyp presentation and stage of diagnosis. A review of colonoscopy reports of 46,726 patients observed64 a higher total number of tumors among African Americans compared to whites (0.9% vs. 0.6%, p=0.02) and mean number of tumors (1.13 vs. 1.02, p=0.006). African Americans were more likely than whites to have colon tumors (adjusted OR 1.78, 95% CI, 1.14-2.77). African Americans were also significantly more likely to have proximal tumors then whites after adjusting for confounders (OR 4.37, 1.16-16.42). The distribution of stage of disease by racial/ethnic group over 1999-2006 is shown in Table 3. African Americans are consistently diagnosed at later staged disease for both colon and rectal cancers alike50. The multi-ethnic cohort of 157,000 participants of the Women’s Health Initiative was utilized to identify racial differences in colorectal incidence and mortality64. The highest incidence rates were among African American women, followed by white, AIAN, Asian/PI, and Latino women. All minority groups 22 within the WHI reported less CRC screening by FOBT or endoscopy. The age adjusted risk for invasive cancer was highest among African American women (HR 1.16, 0.991.34) and lowest among Latinos (HR 0.73, 0.54-0.97) with White women as the referent group. The risk factors exhibiting the largest impact on risk for invasive cancer were BMI, lack of physical activity, smoking and alcohol consumption. White and Asian women were more likely to have higher levels of physical activity and white women consumed more alcohol than other groups. African American, Latino, and AIAN women all reported higher BMI levels. Table 3: Colorectal Cancer Stage Distribution (%) by Race/Ethnicity White African American Local 42 36 37 40 Regional 35 34 36 37 Distant 19 25 22 19 Unstaged 4 5 5 4 Local 40 36 37 38 Regional 36 34 36 41 Distant 19 24 21 18 Unstaged 5 6 5 3 Source: ACS, Colorectal Cancer Facts & Figures 2011-201351 Women Men Stage Race/Ethnicity Latino Asian/PI AIAN 36 37 24 3 40 39 17 3 Treatment Treatment use is a key predictor of survival. For example, chemotherapy following surgery for stage III colon cancer can reduce disease recurrence by 34% and improve overall survival by 24%65. Despite the benefits, over 40% of elderly stage III colon cancer patients do not receive adjuvant chemotherapy66. Regardless of medical oncology consult, African Americans have lower uptake of chemotherapy than Whites66-68. Among Medicare beneficiaries, some studies have observed different rates of medical oncology consult68 whereas others have similar visit 23 rates67. In a study with similar consult rates, younger African American elderly (66-70) still had a 20% less chemotherapy uptake than their white counterparts (p<.001)67. Fifty percent of the difference was explained by patient, physician, hospital and environmental factors. A slightly larger study with more recent years of diagnosis by Luo et al. found that elderly African Americans with stage III colon cancers were less likely to see a medical oncologist than White patients (OR=0.934, 95% CI 0.89-0.98). A slightly larger study estimated that surgeon characteristics accounted for 21% of the variability in consultation with a medical oncologist by this group of elderly Medicare patients68. In addition, the availability of specialists modestly mediates the use of colon cancer treatments171. A systematic review furthered this investigation by examining 22 studies of predictors of chemotherapy receipt among stage III colon cancer patients65. Seven out of 9 studies found significant differences in African American-white or non-white-white comparisons of chemotherapy use. The gap in chemotherapy receipt did not close from 1997 to 2002 between African Americans and White Medicare patients66. Among rectal cancer patients, African Americans have a similar difference in chemotherapy and radiation therapy use compared to White patients according to some studies. Morris et al found no differences in consultations with a medical oncologist or radiation oncologist between African American and White Medicare patients, but fewer African Americans saw both types of specialists. A difference of 23.7% in chemotherapy use was identified among African Americans with no comorbidities. In other words, those who were the best candidates for chemotherapy and the most to gain, were the least likely to be treated. An analysis of Latino rectal cancer patients compared to White patients revealed that Latinos were significantly more likely to receive neoadjuvant 24 therapy but less likely to have sphincter-sparing surgery than whites. The authors conclude that the lower receipt of surgery could be an indication of poorer overall quality of care among Latinos69. Yet, other studies including the Patterns of Care and a statespecific study found no racial difference in adjuvant chemotherapy use70, 71 but other treatment differences were identified71. It is unclear whether treatment utilization differences stem from patient preference, provider preference or bias, level of comorbidities, or cost factors among others influences. The SEER studies cannot adjust for insurance status or comorbidities and the Medicare studies do not adjust for beneficiary cost burden. In addition, in claims-based or registry data, patient preference is not collected and a fuller description of medical conditions which make particular treatment untenable may be unavailable. Survival and mortality The different distribution of disease stage at diagnosis among African Americans with colorectal cancers coupled with CRC screening use and treatment patterns greatly contribute to the continued disparity in survival 50, 72. Across population-based cancer registry, Veteran Affairs system, and Medical center studies, increased risk of death and shorter survival was consistent when comparing African American colorectal cancer outcomes to their White counterparts. These disparities persist even after controlling for various tumor, clinical and socio-demographic factors 58, 64, 72. Furthermore, the WhiteAfrican American difference in CRC mortality is greater among those over 65 than in the younger age group50. The age-adjusted mortality rates are lower for women across all race/ethnic groups (table 4). 25 Table 4: Age adjusted mortality rate per 100,000 by Race/Ethnicity, US American Indian and African White Alaskan American Native (AIAN) 20.6 30.5 13.2 19.2 Male 14.4 21.0 9.9 12.9 Female Source: ACS, Colorectal Cancer Facts & Figures 2011-2013 Asian/ Pacific Islander (PI) Latino 15.6 10.5 African Americans experienced poorer survival for both rectal and colon cancers compared to Whites, Latinos, Chinese and non-Chinese Asians73, 71. The 5-year colon cancer-specific survival for these groups was 64% (African American), 70% (Whites)73, 70% (Latinos), 73% (Chinese) and 72% (non-Chinese Asians), p<.0001. The 5-year rectal cancer-specific survival for each groups was 65%, 71%, 69%, 73% and 73% respectively (p<.0001). Five year overall survival was similarly lowest among African Americans for rectal and colon cancer. The risk of death for African American colon cancer patients was 15% higher than that of white patients after accounting for stage and treatment. Some Asian subgroups had significantly less risk of colon and rectal cancer death than Whites. The 16% higher risk among Latinos was explained by stage and surgery while the non-Chinese Asians continued to exhibit lower risk which compared to whites. Up to a third of increased risk of death among African American men and women has been attributed to stage of disease, anatomical site and tumor grade74. There is the opportunity to significantly reduce the burden of colorectal cancer through the increasing use of colonoscopy screening tests and improved access and acceptance of effective treatment. Furthering our understanding of predictors of these differences in treatment utilization resulting in survival and mortality differences is imperative. Colorectal cancer treatment is complex and expensive. There is little 26 information about the impact of cost and insurance coverage on treatment patterns for colorectal cancer. Prostate Cancer Risk factors Age is the primary risk factor for prostate cancer and incidence and mortality rates increase faster with age than for any other major cancer75. By the age of 80, anywhere between 60% to 70% of men have evidence of prostate cancer upon autopsy75. There have been contradictory results related to the role of high fat and red meat consumption and obesity, physical inactivity and smoking on prostate cancer risk75-80. There is conflicting evidence as to the efficacy of the prostate specific antigen (PSA) screening test and the digital rectal exam (DRE) for early detection leading to mortality reduction81, 82. As a result, neither the American Cancer Society nor the US Preventive Task Force recommends the routine use of the PSA or DRE to detect prostate cancers. Instead, men should discuss their individual risk with their health care provider and decide for or against screening accordingly (informed decision-making). White men 50 years and older have reported higher use of the PSA test (46.6%) compared to African American men (38.6%), Latino men (32.7%), Asian men (34.7%) and AIAN men (9.7%) in 200856 yet another study found similar use among men of various racial/ethnic groups 50-79 years of age84 . Latino men tended to perceive higher risk for prostate cancer while African American men perceived lower risk compared to men of the same age. Perception of low or similar risk to their peers predicted low PSA test use83. 27 Incidence The increase in the absolute number of prostate cancers diagnosed is directly related to the use of the prostate specific antigen blood test as a screening tool85. Yet, African American men have low rates of PSA testing, and the highest prostate cancer incidence rates3. In the mid-1980’s the use of diagnostic PSA’s caused an increase in incidence rates with 100% increases for both white and African American men from 1986 to early 1990’s82. Localized and regional stage prostate cancer incidence rates increased while distant disease rates decreased simultaneously. White male age-adjusted incidence of prostate cancer is an estimated 143.8 per 100,000, 230.0 for African Americans, 81.0 for Asian/PI, 101.5 for AIAN and 128.0 for Latino males3. Stage at diagnosis and tumor characteristics African American men are diagnosed at a younger age, higher tumor stage and grade than white men86, 87. Nearly 89% of prostate cancers of White men and only 85% of African American cases are diagnosed at localized stage86. Treatment There is concern that the over diagnosis of prostate cancer leads to unneeded treatment of the disease. In fact, more than one third of those diagnosed may be unnecessarily treated for localized prostate cancer85. There is evidence that African American men are more likely to be treated with radiation88 or active surveillance and less prostatectomy89 and hormone therapy90 compared to White and Latino men even when facing aggressive disease91. Latino men are less likely to receive surgery and more likely to use hormone therapy and active surveillance89 compared to white men88. Yet, 52% of those who opt for active surveillance have disease progression within 5 years of 28 diagnoses and require additional curative treatment92. Active surveillance entails medical monitoring which is initiated later and occurs less frequently among African American and Latino men compared to White men92. While some studies have shown improved survival for those who undergo prostatectomy compared to active surveillance and radiation among surgical candidates90, 93-95, evidence for the most efficacious treatment in relation to side effects remains lacking96. Survival and mortality Prostate cancer mortality declined in the early 1990’s but it is unknown if it is due to the use of the PSA test or better reporting of prostate cancer deaths24, 82. The decline in mortality occurred for both African American and White men at differencing rates. Some of the decline may be attributed to the decrease in distant disease incidence82. The ageadjusted mortality rate per 100,000 for the 2003-2007 period is 22.8 for White men, 54.2 for African Americans, 10.6 for Asian/PI, 20.0 for AIAN men and 18.8 for Latino men3. Many studies show an increased fully adjusted risk of death among African American men90, 97, 98 while others have been able to attribute the excess risk to tumor aggressiveness, treatment types and patient factors98. Meta-Analysis of prostate cancer studies have resulted in differing conclusions regarding White-African American survival differences. A 2008 meta-analysis observed worse overall survival for African American men compared to White men (risk ratio 1.35, 1.23-1.48) not fully explained by comorbidities, PSA testing or treatment access. A more recent analysis found increased unadjusted mortality among African American men compared to White men (HR=1.47, 1.31-1.65)99. There was a non-significant 7% increased risk of death after full adjustment. Asian and Latino men both have decreased risk of death compared to White men3, 98. The 29 lack of consensus on risk factors for aggressive disease, screening test and treatment modalities creates challenges for public health efforts around informed decision-making. Socioeconomic Status Influence on Cancer Socioeconomic status has been typically handled two different ways in the research literature. Some investigators treat SES as a confounder and use SES as an adjustment variable to help explain racial/ethnic differences. Others believe that SES mediates the association of race/ethnicity and cancer outcomes. Regardless, calls for increased investigation into the interplay of race/ethnicity and socioeconomic status continue22. It is worthwhile to conduct interaction analysis with Medicare-SEER data for the following reasons: 1). Minimize insurance as confounder; 2). Utilize more complete treatment information; 3). Focus on one age group; 4). Use five year follow up information; 5). The ability to review racial/ethnic subgroups such as Latinos and Asians. Socioeconomic Status as Adjustment/Confounding Variable Socioeconomic status has been measured in a variety of ways including percent of census tract at or below poverty, median household income, education level, percent of census tract without a high school diploma, composite measure, indexes (Townsend, deprivation). Cancer risk factors such as obesity and smoking vary by SES level. For example, obesity has an inverse relationship with both individual level of education and income. BMI decreases with increasing levels of income and education26. Smoking prevalence peaks among those with a GED and then decreases as education increases56. Lower educational levels and increased poverty are all linked to lower levels of mammography, colorectal and prostate cancer screening use56, 100. SES and cancer risk differ by cancer site. For example, those living in high SES areas or with high individual 30 income and educational attainment, experience increased breast cancer risk101, but slightly decreased colorectal cancer risk18 and risk for prostate cancer decreases102. When stratified by stage, low SES and low educational levels are associated with more advanced stage of disease at diagnosis for breast5, prostate5, 103 and to a lesser extent, colorectal18 cancers in some analysis and not in others5. Freeman H et al suggests that the main contributors of late stage presentation of African American colorectal cancer patients at a safety-net hospital are the combined effects of poverty, access and education104. Treatment differences by SES level have been observed for all three cancers5, particularly among the non-elderly. Education level and obesity but not race, were associated with increased modification of chemotherapy among breast cancer patients105. Areas of low educational levels showed less adjuvant therapy70, 106 for stage III colon cancer and less sphincter-sparing surgery107 for rectal cancer when compared to areas of higher education. Area level education accounted for 17% of the disparity in chemotherapy use among 66-70 year old African Americans and Whites, 33% among 7175 year olds and 67% of the disparity among 76-80 year olds67. For prostate cancer, the receipt of prostatectomy has been associated with increased levels of income94, 108. In many breast cancer survival studies, a difference in distribution of tumor, clinical and treatment factors by race/ethnic group area was observed but also within levels of socioeconomic measures47. A great deal of research has been devoted to understanding the White-African American difference in CRC cancer mortality. SES consistently explains a considerable portion or all of the survival difference. SES as measured by poverty, income or 31 education has accounted for much of the excess risk of all-cause 109, 111 and cancerspecific73, 109, 111 death among African Americans compared to Whites. Marcella et al observed increased colorectal cancer mortality risk among those in areas with low educational status compared to higher educational achievement. In the model, the excess risk for African Americans was reduced one third when education and employment were added but still remained 19% greater than whites. Furthermore, it was observed that SES effects are greatest for regional stage of disease. At this stage, SES accounted for 26% of the excess risk of mortality among African Americans. SES had a larger observed magnitude of effect on those younger than 60 years. Similarly, Le et al found increasing 1, 5 and 10 year CRC –specific and overall survival with increased level of SES for both colon and rectal cancers (both p<.0001). When SES was introduced into the rectal cancer specific mortality model, it resulted in a reduction in the hazard ratio from 1.19 to 1.11 (1.02-1.20) for African Americans remaining significant. When controlling for other factors, risk of death increased as SES decreased. Stage, treatment and SES were the most important factors in explaining the White-African American colorectal cancer mortality risk differences. The existence of prostate cancer racial differences has been controversial with many studies showing that adjustment for SES reduces African American-White differences to non-significance108, 110. Yet, others show persistent racial differences in prostate cancer specific mortality112, 108 and overall mortality108 after accounting for SES. Four articles47, 73, 111, 112 have been recently published on race/ethnicity, SES and prostate, colorectal and breast cancer in Medicare-SEER participants. In most of the studies, SES and race/ethnicity interaction was tested with insignificant results. Thus, 32 SES was treated as a confounding variable. One prime issue has been the measurement of SES in the publications. These studies have constructed the census tract poverty variable as the following: high SES being areas with less than 3.9% at or below poverty, second quartile 3.92%-7.21%, third quartile is 7.22%-13.08% and the lowest SES level is ≥13.09%. As seen in data from the SEER monograph on SES (table 1), 72.55% of Whites, 28% of Latinos and 24.96% of African Americans are in SES census tracts with less than 10% of poverty. Thus, there are potentially insufficient cases in the SES quartiles particularly for the African American group to reflect a gradient. In addition, the use of ≥ 20% of area has been employed to define poverty area in numerous studies and is associated with various health outcomes. This warrants further work focused on the interplay of SES both within and across racial/ethnic groups. Race/Ethnicity Stratified by SES There are several examples of research which has stratified by SES within racial/ethnic groups primarily focusing on cancer incidence. In a study of breast cancer incidence among White women by county level poverty, incidence is most pronounced among the 55 to 80 year old113 age group. Among African American, there is less of an ascent of incidence by county poverty across the ages. The greatest racial difference in breast cancer incidence has been shown to be in low SES areas and decreased as SES increased36, 114, 115. This effect was pronounced in Latino and Asian groups suggesting that increasing SES may be responsible for introducing risk factors with the largest effect on demographic population with lowest overall risk of breast cancer. There was no narrowing of the disparity among African American women as SES increased or only a minimal decrease in disparity observed. SES may modify the relationship between 33 race/ethnicity and breast cancer risk36 with the SES effect decreasing at higher income levels114. The varying use of screening tests by both minority groups and SES groups further impact the differences in incidence and stage of disease. Some argue that screening tests are implemented into the community following the SES gradient, further exacerbating the already existing outcome inequalities116. In prostate cancer, a positive trend with SES and incidence for all racial/ethnic groups was found after the widespread introduction of PSA testing94, 115, 117, 118. For example, within Latinos, the highest SES group had an incidence rate 80% higher than the lowest SES group. Across all race/ethnic groups, the increased risk of prostate cancer was higher among those in the highest SES level compared to lowest both among those with localized and aggressive disease117. Latinos 75-85 years old at the higher SES levels were found to have increased incidence rates, higher than their White peers. CRC incidence did not exhibit the same positive trend as prostate and breast. Higher CRC incidence was observed among high SES levels in Whites but conversely among lower SES in Latinos and Asian/PI groups118 while the opposite was observed in another analysis115. Most studies have found an inverse SES gradient in stage at diagnosis with sufficient sample sizes to stratify for breast39, colorectal50 and prostate116 cancers. More affluent cases are diagnosed at earlier stages than lower SES cases. Protective factors for stage of prostate cancer in one analysis were census areas with higher portions of whitecollar jobs and county resources as measured by an index116. For grade of tumor, the protective influence of income held only for White men, not for African Americans. 34 County resources were also found to have an inverse relationship with tumor grade. Authors believe this may be a result of unequal distribution of county resources. The effect of SES on treatment selection has been shown to differ by racial/ethnic group21, 89. Among men with prostate cancer, those with incomes of less than $40,000 or lived in low educational level areas were more likely to use active surveillance89. Within each racial/ethnic group, there was a difference in magnitude of effect of education and income on active surveillance use. For example, among Latino men, the areas with mid and lowest educational levels observed odds of active surveillance of 2.6 (1.5-4.4) and 2.1 (1.5-3.0) respectively. African Americans observed odds of 1.6 (middle level) and 1.3 (lowest level) and among Whites 1.1 and 1.2 respectively. Use of colorectal treatment differences was reduced 5.3% for African Americans and 7.3% for Latinos by controlling for SES171. An early study of prostate cancer survival119 identified the interrelationship of race/ethnicity and SES on survival. A dose-response relationship between SES and survival across prostate cancer stages was observed. Both race and SES were independent predictors of survival and SES but when both were included, SES was a significant predictor. The authors suggest that differences in African American-white survival are due to distribution of SES between the racial groups. Simply using the educational level indicated on death certificates estimated colorectal death rates that were higher for African Americans than white males across all educational levels, a trend mostly mirrored by African American women and white women. The relative risks related to education within racial groups for colorectal cancer were stronger than those associated with race within educational groups. The opposite 35 was true for prostate and breast cancers where relative risks associated with race within the lowest educational levels compared to the highest educational was higher in magnitude than the relative risks by level of education within racial groups. Relative risks of cancer death by education level within racial/ethnic groups show large disparities within the racial groups which are otherwise hidden in non-stratified analysis (Table 5). Table 5: Relative Risk of Cancer Death for those with Lowest Education vs. Highest Level Race/Ethnicity All Races Gender Breast CRC Prostate 2.18 (2.00-2.39) 1.66 (1.44-1.93) Men 1.34 (1.26-1.43) 2.06 (1.86-2.29) Women 2.17 (1.66-2.87) 1.51 (1.03-2.22) African Men American 1.22 (1.03-1.44) 1.42 (1.11-1.83) Women 2.18 (1.97-2.41) 1.48 (1.25-1.75) White Men 1.36 (1.26-1.46) 2.20 (1.95-2.48) Women 1.41 (0.67-2.96) 1.61 (0.36-7.2) Latino Men 0.65 (0.41-1.03) 1.11 (0.47-2.60) Women Source: National Center for Health Statistics Citation: Siegal et al. 2011 These studies establish the gradual and differing impact of SES within race/ethnic groups. They have used education119, a composite SES score116, occupation and poverty115, and poverty alone21. Only one21 of these studies was completed with the elderly cohort of Medicare-SEER. Some studies suggest that SES influence on survival and mortality decreases with age5, 74 which could be further tested using Medicare-SEER cases. In the current group of studies, unmeasured predictors such as insurance, treatment, access to care, comorbidities and delays may overestimate or attenuate the influence of SES within racial/ethnic groups. Each of these studies has limitations including missing some or all treatment data113, 117, not enough power for interaction analysis119, limited to one region115, combining all minority groups into one94, adding non-African American minorities to 36 White21, missing screening or service use information114, 118 , lack of adjustment for comorbidities113 , incomplete tumor information116 , and race/ethnicity absent from the analysis120. We do not, as a result, fully understand their interplay due to the limited number of studies that stratify by SES. Delays in Cancer Care Most of the literature on delays in care has been published based on the Canadian and European health care systems. The national health care systems set target timeframes in which a patient and providers should manage diagnostic through treatment timeliness. Thus, it is difficult to extrapolate comparable statistics for date of pathologic diagnosis to start of treatment, data readily available in cancer registry datasets. Non-US studies typically measure times from symptom onset to provider referral to treatment initiation121-128. Diagnosis date occurs during the timeframe but is not necessarily singled out. Researchers have investigated determinants of diagnosis delay129 and treatment delay. Health care system issues, the clinical indication for the diagnostic procedures and family income were predictors of delay in one Canadian study129. Social class and ethnic group were predictors of different types of delays in CRC, prostate and breast cancer cases in a UK study121. There have been observed increased risk for delays among African American130133 and Latino130 breast cancer patients and lower SES patients132 in the initiation of treatment when compared to White patients. Even among Medicare beneficiaries, African Americans had 86% increased risk and Latinos a 66% increased risk of a delay of 60 days and longer when compared to White women after controlling for potential confounders. 37 Five studies133-135, 139, 140 have measured the impact of delays on Medicare beneficiaries. One of the five studies used data from 1993-1996 for the test to admission segment of care139. Delays of at least 43 days was related to a slight increase in risk of death (HR 1.2, 1.1-1.3) while controlling for age, stage, race, income, gender, admission type and hospital volume. The test to admission wait time differed by race, income and comorbidity level among other factors measured. The second study analyzed odds of experiencing breast cancer diagnosis and treatment delays but did not estimate the impact on survival133. African American women were more likely to be delayed during each treatment segment. African American women had an 117% increased odds of treatment delay compared to white Women and all women with three or more comorbid conditions observed a decrease in odds of delay by half. A third study134 reviewed the surgery to Chemotherapy. Stage I/II Breast cancer cases delayed 3 months or more had increased risk of death than those beginning within 1 month of surgery (HR= 1.69, 1.31-2.19). While only 10% delayed chemotherapy initiation, this delay increased overall mortality by 46% and cancer specific mortality by 69%. No difference in delays between African American and white women were identified but was hampered by small number of African American and limited follow-up time. In addition, researchers used the date of last surgery in order to account for surgical complications. This also means that potential delays between procedures were not included and total delay could be underestimated. A fourth Medicare-SEER study investigated rectal cancer delays. The all-cause mortality was 1.65 (1.43-1.9) for those initiating chemotherapy late, and a hazard ratio of 1.91 for cancer specific mortality in one study of rectal cancer patients135. Among stage III colon cancer cases, a delay of 3 38 months was associated with a 50% increase in colon-specific survival and a 60% increase in overall survival when compared to those initiating chemotherapy within 1 month after surgery140 in the final Medicare-SEER study. Delays of 2-3 months increased overall mortality by 40% compared to initiation within 1 month. Race and SES were not associated with treatment delay or independent predictors of poorer survival among the colon and rectal cases analyzed. The Medicare-SEER studies were primarily interested in predictors of delay and impact of delay on outcomes. How delays in care contribute to racial/ethnic cancer outcomes is not investigated. The publications mentioned included a racial/ethnic category of White-African American or White, African American and other. Thus, little is known of delays among other racial/ethnic Medicare beneficiary groups. In a study of the National Cancer Database, Latino and African American women were both identified as at increased risk for 60 day and 90 day delays in chemotherapy after adjustment136. Decreasing levels of education was also associated with increased risk of delay. A smaller study focusing on stage I/II breast cancer patients found that initiation of chemotherapy of more than 12 weeks after surgery was associated with a decrease in survival (HR=1.6, 1.2-2.3). No measures for SES or race/ethnicity137 were included in the model. Most recently, a meta-analysis on the survival effect of timing of chemotherapy after CRC surgery provides support for causality138. Ten studies were pooled and the hazard ratio for overall survival per 4 week of delay was 1.14 (1.10-1.17). An example is provided of a patient initiating adjuvant chemotherapy at 8 weeks rather than 4 weeks would experience a 14% increased risk, or delayed to 12 weeks, a 30% increased risk. Thus, if delays in the initiation of chemotherapy are an independent predictor of mortality 39 and potentially on the causal pathway, it should be considered in future cancer disparities research. This would require measurement of delays and inclusion as a confounding variable, an effort that only occasionally occurs in disparities research42. Comorbidities and Cancer Delays may be partly related to specific comorbidities and severity of comorbid conditions134, 140. Particular comorbidities can facilitate access to medical services and therefore cancer screenings and treatment133, while others appear to act as barriers to obtaining care. For example, men with hypertension have higher likelihood of PSA testing100. Comorbid conditions associated with lower PSA testing include End-Stage renal Disease, dementia, chronic liver disease, congestive heart failure, myocardial infarction, delirium, psychosis, stroke, obesity and cancer other than prostate. Increased contact with the healthcare system through medical care for chronic conditions confer lower risk of diagnosis of late stage breast cancer141. Chronic conditions associated with lower odds of advanced breast cancer diagnosis are cardiovascular disease, musculoskeletal disorders, gastrointestinal disease, benign breast disease and genital/urinary disorders (7-24% lower odds). Chronic conditions which increased odds for advanced stage diagnosis were diabetes, endocrine disease, psychiatric disorders and hematologic disease (11-20% increase). It is unclear what aspects of having a chronic condition facilitates or hinders cancer screening and treatment. It is possible that insurance, cost and system barriers play a role. Also, personal beliefs and health behaviors may be exacerbated by a chronic condition. Lack of social and emotional support may augment underlying mental health conditions. 40 The presence of particular comorbid conditions and severity seem to influence the appropriateness of cancer treatment options21, 94 . Both individual studies and metaanalysis have provided evidence that studying the specific comorbid conditions in addition to comorbidity indexes or counts, provide additional information on particular influences of treatment choices and potential disparities. Men with coronary heart failure, dementia and chronic obstructive pulmonary disease were significantly associated with active surveillance as the treatment modality for prostate cancer89. A meta-analysis found that diabetes impacted prostate cancer treatment choices and called for more studies on how diabetes, a common medical condition among elderly men, impacts prostate cancer outcomes. They found a pooled hazard ratio for prostate cancer mortality of 1.57 (1.122.2) for men with type 2 diabetes but there was a lack of studies that measured the effect of diabetes on prostate-specific or overall survival. Another analysis suggests that African Americans tend to be more hypertensive and prescribed medication which may minimize potential protective factors for prostate cancer, offering a contributing factor to prostate cancer outcome differences90. The effect of comorbidity index on survival was smaller in magnitude among African American men than White men. The White-African American differences in survival decreased with increasing levels of comorbidities. When comparing risk of death among African American men with a particular comorbid condition to White men with the same condition, four were significant: coronary heart failure (1.92, 1.34-2.74), uncomplicated diabetes (1.55, 1.15-2.11), hypertension (1.44, 1.10-1.90) and valvular disease (1.58, 1.05-2.38). Colorectal cancer patients with any mental health condition had significant increased odds of receiving no treatment and no chemotherapy when controlling for 41 possible confounders142. In addition, those with preexisting mental conditions experienced lower overall and colon specific survival than those with no mental condition diagnosis. Psychotic disorder and dementia had the highest proportion of unknown stage or diagnosed at autopsy and the poorest overall and disease specific survival. Race/ethnicity and neighborhood income were included in the model with other variables so we do not know whether these factors acted differently and to what extent they contributed or lessened the differences. Among stage III colon cancer cases without chemotherapy, the presence of comorbidities was a predictor of survival70. However, this study was only able to measure overall survival, not disease-specific survival. Among breast cancer patients, specific comorbid conditions provide increased risk of death even among early stage patients when compared to those without comorbidities143. Adjusted relative hazards for breast cancer specific mortality was 1.24 for cardiovascular disease, 1.13 for previous cancer, 1.13 for COPD and 1.10 for diabetes144. An additional analysis observed that each of 13 comorbid conditions contributed to decreased overall survival and increased mortality143. The prevalence of comorbid conditions vary by race/ethnic groups and have an inverse relationship with SES. Yet, there is stark information about differences in specific comorbidities and comorbidity levels by SES within racial/ethnic group and its effect on survival and mortality. It is standard practice to include comorbid index in disparity research, but expanding the types of medical diseases to include mental health and less severe but common conditions is atypical. 42 Insurance Factors and Cancer Although Medicare is a universal health coverage program for the elderly and disabled, participants have varying levels of costs requirements. Low income beneficiaries can apply for Medicaid coverage to pay for a portion of the Medicare cost sharing requirements. However, an estimated 60% of potentially eligible Medicare beneficiaries do not enroll in Medicaid145. Other options include MediGap policies or supplemental insurance. Medicare cost sharing includes premiums, a deductible, copayments and coinsurance. Elderly in the lowest income areas tend to have the lowest participation in the coverage plans that provide the most protection from out of pocket expenses146, 147 except for the 25% on Medicaid. As a result, Medicare beneficiary in low SES areas tend to spend a larger proportion of their income on medical expenses than their higher income peers ranging from a third to half of their income145, 148. Low income Medicare beneficiaries without Medicaid or low cost sharing supplemental insurance, may factor in costs when deciding on high cost treatment such as chemotherapy and prescriptions67, 149. Fee-for-service Medicare and Medigap policies were similar in average out of pocket expenses. However, those with at least some type of supplemental insurance pay considerably less than “original” or fee-for-service Medicare ranging from $9 less to $1,754 less for those with Medicaid150. The poor and near-poor elderly are able to apply for state assistance for help with premiums and non-covered Medicare services. These individuals with both Medicare and Medicaid are called ‘dual eligibles’ and differ from the Medicare population in health status, cost burden and health behaviors such as mammography151 and other preventive152 and maintenance screenings153. The Medicare-SEER dataset includes a flag for those 43 covered by Medicaid which has a range of sensitivity of 74.2%-80% and positive predictive power of 88%-95% when compared to State Medicaid files154. Few studies use the Medicare Buy-In flag for dual eligible appropriately. The variable can provided additional information regarding individual level SES when used cautiously and strategically. To be used as the single indicator of SES it must be linked to state Medicaid to capture the unidentified dual eligible participants in the Medicare data135 or with refinement. No articles found on race/ethnic and SES disparities have attempted to measure Medicare beneficiary cost burden from the claims data even though it may influence treatment decisions145. It is unclear if cost burden is an independent predictor of survival, or simply a mediator for treatment choices and adherence. Place of Birth and Cancer Although immigrants tend to have lower incomes than their US born counterparts, they bring with them a set of health behaviors and practices from their home country. This includes diet, activity and health service use that both can act as protection155 for certain cancers, but also serve as a barrier to early detection and treatment. The length of residency in the US then attenuates the impact of native behaviors and utilization practices through the adoption of both health seeking behavior including cancer screenings, but also diet patterns, smoking and lifestyles that can increase risk for cancer and chronic conditions. Immigrants in the US more than 10 years have slightly to moderately lower breast, colorectal and prostate cancer screening test use compared to those born in the US but higher than those more recently arrived156. Potential risk factors for delays in care were more frequent among foreign born Latinos than US born in the California Health Interview Survey. For example, The foreign born Latinos had 44 significantly less educational levels, three quarters did not speak English well, were heavier, and more reported no moderate/vigorous activity level in the past month. Yet, foreign born Latino and Asian women in California had lower breast cancer incidence rates157-159 than US born Latinos and Asians but were more likely to be diagnosed with advanced disease157, 160 . Foreign birth conferred reduced risk of overall risk of death for Latino breast cancer patients but not for Asian women158, 159. Those living in low SES and high concentrated Latino neighborhoods had poorer overall and breast cancer specific survival. Measuring place of birth is challenging since most hospitals and registries do not collect place of birth data. Therefore, deceased cancer cases are 10 times more likely to have place of birth data recorded161. Particular groups with high proportion of immigrants such as Southeast Asians are more likely to have place of birth recorded in the cancer registry than other ethnic groups (Latinos or Japanese). As a result, some researchers include an unknown category and use area-level descriptors of neighborhoods to identify potential immigrants and low acculturated individuals. However, place of birth information from death certificates are highly sensitive and have high positive predictive value162. Because of these challenges, place of birth is rarely investigated in MedicareSEER data even as a potential confounder. Summary of Findings The body of evidence shows that unmeasured factors captured by race/ethnicity categories independently predict poorer outcomes for some, and positive outcomes for others. Similarly, socioeconomic status regardless of measure used, is independently associated with a positive relationship for stage and survival and an inverse relationship 45 for incidence. Both race/ethnicity and socioeconomic status categories are attempts to capture differences within groups that are historical, entrenched, resource-related, reflect risk profile and to some extent genetic and biological differences, and culture. Research that attempts to measure elements that may factor into race/ethnicity and SES disparities that are commonly missed will help to explain some of the protection and deleterious effects of membership in these groups. Stratifying racial/ethnic groups by SES within the Medicare-SEER database will be novel as is the exploration into the role of delays, the use of nativity, Medicaid status and beneficiary cost-burden and an expanded review of comorbidities. These efforts have not been undertaken in the cohort of Medicare beneficiaries previously or to a very limited extent. Limitations Medicare-SEER data does not include any individual level indicator of SES aside from Medicare Buy in (Medicaid eligibility). Census tract area level variables, although critical, do not serve as a proxy for individual SES. In addition, 20% of census tract data, the smallest geographical level available, is missing. No zip code level data is missing in this dataset. Substantial data (40%) of detailed place of birth data is missing which is a major limitation of nativity analysis. However, despite these limitations, a concerted effort to disentangle both SES and race/ethnicity including delays in care and place of birth in the analysis, will add important information to the effort to reduce cancer disparities. 46 CHAPTER III RESEARCH PLAN Research Aims Hypothesis 1 The impact of socioeconomic status (SES) on various factors (stage, treatment utilization and survival) differs between and within elderly racial/ethnic minorities; low SES provides greater magnitude of effect on breast, prostate and colorectal cancer outcomes among racial/ethnic minority groups. Research Aim Examine the relationship of socioeconomic status (SES) with tumor characteristics and treatment utilization within racial/ethnic groups on cause-specific and overall survival among the Medicare beneficiaries. Objective 1: Describe the relationship between SES and race/ethnicity, while accounting for contributing factors on stage of disease and five year overall and cancer-specific survival. Objective 2: Examine the extent to which place of birth, patient cost burden and additional comorbidities further account for differences in cancer outcomes within racial/ethnicity groups and SES levels. Hypothesis 2 Greater delays in cancer care account for cause-specific survival differences among socioeconomic and racial/ethnic groups. 47 Research Aim Investigate differences in delays of care within socioeconomic and racial/ethnic strata and their impact on cause-specific survival among breast and colorectal cancer cases. Objective 1: Assess and describe differences in the timing from diagnosis to first surgery with curative intent within socioeconomic and racial/ethnic groups. Data Source Population-Based SEER-Medicare Linked Dataset The Medicare-SEER dataset is a linked set of records between the National Cancer Institute’s SEER program, a network of population-based tumor registries, matched to Medicare beneficiary files. The Medicare files consist of claims data related to hospital, physician, outpatient medical services and durable medical equipment payments. The SEER-Medicare dataset contains data on over 2.4 million individuals with cancer. This linked dataset provides robust information including treatment, demographic and medical utilization data. During the years 1992-2000, 71,769 colorectal cancer cases, 70,060 breast cancer cases, and 109,772 prostate cancer cases are included in the dataset. The current group of 17 SEER registries cover approximately 26% of the total US population and 23% of white, 23% of African American and 40% of Latino populations relative to the 2000 census. Census tract and zip code level area-based information has been geocoded to the majority of cases in the dataset. 48 Data from twelve SEER registries will be used for this analysis. All 12 registries have contributed data to SEER since 1992 or earlier. The registries included are: Atlanta, Connecticut, Detroit, Hawaii, Iowa, New Mexico, San Francisco-Oakland, Seattle-Puget Sound, Utah, Los Angeles, San Jose-Monterey, and Rural Georgia cancer registry. Measurement of Variables Area-level Socioeconomic Status This study will utilize primarily percent of adults below the poverty level, but will also consider percent of adults without a high school degree as potential SES indicators. Utilizing census block SES variables have been used in many national studies when individual level SES data is unavailable. Ideally both individual and area-level SES measures would be used but are lacking in cancer registry datasets. The three factors commonly measured are education, poverty and median annual household income. One education census variable in the Medicare-SEER dataset is the percent of adults aged >= 25 years old with less than 12 years of education. Poverty is estimated by the percent of individuals in the census tract living below the poverty line. Areas with at least 20% of its residents living at or below the federal poverty level are considered a ‘poverty area’163. Areas with more than 40% of residents in poverty are categorized as ‘extreme poverty’. Income is defined as the median annual household income for the census tract. It is suggested that income may be a weak measure of socioeconomic position for those over 65 years of age since there is a weaker association of income and health among that age group with the reduction of earned income164 whereas percent of poverty is a robust measure of SES17, 163, 165, 166. 49 The education variable will be categorized into tertiles based on the distribution of the data. Each analysis will be run using each SES variable to identify the variable with the strongest SES gradient. It is predicted that poverty level will be the most appropriate measure. The final breast cancer sample was categorized both by socioeconomic status as defined by percent of census tract residents at or below poverty and by racial/ethnic group (table 6). The area-level poverty categories were divided into four groups. High poverty was defined as 20% of those in the census tract at or below poverty (5,856), middle poverty includes those between 10% and 19.9%, low poverty is 5-9.9% and very low poverty is 0-4.9% of those in the census tract at or below poverty level. There were 2,304 cases that lacked poverty information but included data in the education variable. The eligible cases are described demographically in table by level of area socioeconomic status. Table 6: Distribution of Breast Cancer Cases by Potential SES Variables Definition Source Mean Median Range Missing Categorical Poverty % of persons at or below poverty level Census Tract 9.61% 6.85% 0-87.17 2,304 >=20% (5,856) 10%- < 20% (12,463) 0%-<10% (37,256) >=20% (5,856) 10-19.9% (12,463) 5-9.9% (17,511) 0-4.9% (19,745) Missing (2,304) 50 Education % of persons without a high school diploma Census Tract 17.43% 14.84% 0-87.38 186 >19.23% (20,424) >10.44%-19.23% (19,805) 0%-10.44% (19,758) >= 40% (3,307) 25-39.9% (8,574) 15-24.9% (16,611) 0-14.9%(29,201) Missing (186) The CRC cases were divided into four poverty groups (table 7). High poverty was defined as 20% of those in the census tract at or below poverty (6,106), middle poverty includes those between 10% and 19.9% (12,113), low poverty is 5-9.9% (16,228) and very low poverty is 0-4.9% of those in the census tract at or below poverty level (17,347). There were 2,026 cases with missing poverty information. Table 7: Distribution of Colorectal Cancer Cases by Potential SES Variables Definition Source Mean Median Range Missing Categorical Poverty % of persons at or below poverty level Census Tract 9.61% 6.85% 0-87.17 2,304 >=20% (5,856) 10%- < 20% (12,463) 0%-<10% (37,256) Education % of persons without a high school diploma Census Tract 18.64% 16.01% 0-89.08 146 >19.23% (20,764) >10.44%-19.23% (18,007) 0%-10.44% (14,903) >=20% (6,106) 10-19.9% (12,113) 5-9.9% (16,228) 0-4.9% (17,347) Missing (2,026) >= 40% (3,673) 25-39.9% (8,920) 15-24.9% (16,369) 0-14.9% (24,712) Missing (146) The distribution of the prostate cancer cases by SES variables is provided in table 8. The majority of cases can be found in the highest SES level, in areas where 0-4.9% of the people are at or below poverty. The second greatest proportion of cases are in the next highest SES level (5-9.9%) followed by the second lowest SES level (10-19.9%). The smallest group is that of areas with highest proportion of people living in poverty. There are 2,795 prostate cancer cases living in census tracts that are missing the poverty information. 51 Table 8: Distribution of Prostate Cancer Cases by Potential SES Variables Definition Source Mean Median Range Missing Categorical Poverty % of persons at or below poverty level Census Tract 10.2% 6.96% 0-100 2,795 0%-<10% (62,635) 10%- < 20% (20,670) >=20% (12,337) Missing (2,795) Education % of persons without a high school diploma Census Tract 18.3% 15.47% 0-100 262 0%-10.44% (30,348) >10.44%-19.23% (31,209) >19.23% (36,618) Missing (262) 0-4.9% (34,326) 5-9.9% (28,309) 10-19.9% (20,670) >=20% (12,337) Missing (2,795) 0-14.9% (47,368) 15-24.9% (28,070) 25-39.9% (15,615) >= 40% (7,122) Missing (262) Definition and Measurement of Race and Ethnicity Mutually exclusive categories will be developed to delineate five racial/ethnic groups: White, African American, Latino (any race), AIAN, Asian/PI. For the interaction analysis, groups may be collapsed to ensure adequate numbers. AIAN and others may be merged into an “other” category or dropped from the analysis. Agreement of race coding in SEER cancer registry data and self-report was found to be excellent (κ=.90), and adequate for Latino ethnicity (κ=.61)167. It has been estimated that SEER data underreports AIAN by 66%. The racial/ethnic groups were derived using all available information from several SEER and Medicare variables. Among the breast cancer group, White women made up 87.1% of the sample followed by 6.2% of the group identified as African American women and 3.2% were Latinas. Native American women (0.2%) and unknown or other 52 0.01% were collapsed into one group. Asian women initially identified by subgroups but then collapsed into an overall Asian group of 3.3% of the overall sample. The subgroups included Chinese (0.7%), Japanese (1.4%). Filipino (0.5%), Hawaiian (0.3%) and Asian, not otherwise specified (0.4%). Table 9: Breast Cancer Cases by Race/Ethnicity White African American Latina Asian Chinese Japanese Filipino Hawaiian AIAN Other Number of cases (%) 50,383 (87.1%) 3,562 (6.2%) 1,870 (3.2%) 254 (0.4%) 416 (0.7%) 832 (1.4%) 273 (0.5%) 158 (0.3%) 129 (0.2%) 2 (.01%) 13.1% of Asian These groups are combined 1,933 21.5% of Asian 3.3% 43% of Asian 14.1% of Asian 8.2% of Asian These groups are combined 131, 0.2% Among the colorectal cancer cases, White cases made up 84.2% of the total followed by 7.0% who were identified as African American, 3.6% who were Latinos and Native American cases made up only 0.2% of all cases. Asian cases were initially identified by subgroups but then collapsed into an overall Asian group of 5.0% of the overall sample. The subgroups included Chinese (1.3%), Japanese (2.1%). Filipino (0.7%), Hawaiian (0.2%) and Asian, not otherwise specified (0.7%). The prostate cancer cases were slightly more diverse than the breast and colorectal sample. Ten percent of all cases were African American, four percent were Latino or Asian. Within the Asian category, Filipinos were more heavily represented in this sample, more so than the breast and colorectal groups. 53 Table 10: Colorectal Cases by Race/Ethnicity White African American Latina Asian Chinese Japanese Filipino Hawaiian AIAN Number of cases (%) 45,306 (84.2%) 3,771 (7.0%) 1,934 (3.6%) 390 (0.7%) 714 (1.3%) 1,112 (2.1%) 362 (0.7%) 127 (0.2%) 104 (0.2%) 14.4% of Asian 26.4% of Asian 41.1% of Asian 13.4% of Asian 4.7% of Asian These ethnic groups are combined 2,705, 5.0% Table 11: Prostate Cancer Cases by Race/Ethnicity White African American Latina Asian Chinese Japanese Filipino Hawaiian AIAN Other Number of cases (%) 80,117 (81.4%) 9,958 (10.1%) 4,051 (4.1) 583 (0.6%) 850 (0.9%) 1,457 (1.5%) 947 (1.0%) 228 (0.2%) 228 (0.23%) 18 (0.02%) 14.3% of Asian These groups are combined 4,065, 20.9% of Asian 4.1% 35.8% of Asian 23.3% of Asian 5.6% of Asian These groups are combined 246, 0.002% Comorbidities The Deyo168 adaptation of the Charlson comorbidity index169 will be used to capture comorbid conditions and a summary score for each cancer case. Medicare claims are scanned for eighteen conditions beginning one year prior to the cancer diagnosis through one month after the diagnosis. The physician, inpatient and outpatient claims are used to find the comorbid condition. When using the physician and outpatient claims to more efficiently and effectively identify comorbid conditions, the condition must be 54 found twice more than 30 days apart to be counted. The comorbid conditions are then weighted to create a summary score. Additional conditions will be assessed as an expanded comorbidity list. Mental health, substance abuse, obesity, hypertension and anemia will be collected and items from the Charlson Comorbidity index separated. Patient Characteristics Variables that will be examined in the analysis include sex (colorectal), marital status, rural/urban place of residence, and age at diagnosis. Diagnosis related variables will depend on the cancer under study and are listed in the table below. Table 12: Tumor and Treatment Variables Breast Tumor Variables Prostate Colon Rectum Stage at diagnosis Stage at diagnosis Stage at diagnosis Stage at diagnosis Tumor grade Site/location Tumor grade Partial colectomy: cecectomy, Partial resection of transverse colon & flexures, Ileocolectomy, Enterocolectomy, Partial/subtotal colectomy, Laser surgery, polypectomy, Cryosurgery, fulguration Total/ hemicolectomy: Hemicolectomy or greater, All right/left &portion of transverse Surgical resection: curative intentwith/without sphincter preservation. Low anterior resection (LAR), abdominoperineal resection (APR) Tumor grade Tumor grade Hormone receptor status Treatment Variables Breast conserving Surgery: surgery: prostatectomy Segmental mastectomy, Lumpectomy, Quadrantectomy, Tylectomy, Wedge resection, Nipple resection, Excisional biopsy Mastectomy: Subcutaneous, Total Modified radical, Radical Extended radical mastectomy Hormone therapy: Androgen deprivation, Orchiectomy 55 Breast Prostate Radiation Not indicated Indicated & received Indicated & not received Chemotherapy Not indicated Indicated & received Indicated & not received Radiation: External beam Interstitial Chemotherapy Colon colectomy, total colectomy Radiation Not indicated Indicated & received Indicated & not received Rectum Chemotherapy Not indicated Indicated & received Indicated & not received Chemotherapy Not indicated Indicated & received Indicated & not received Radiation Not indicated Indicated & received Indicated & not received Watchful waiting (no known treatment on record) Other Potential Factors/ Precision Variables Analysis will include adjustment for SEER region to account for regional differences in patterns of care. The diagnosis years in this dataset will be grouped together (1992-1994, 1995-1997, 1998-2000) for each cancer site to reflect period of common treatment guidelines. Sensitivity analysis will be conducted to gauge differences resulting in grouped year of diagnosis compared to individual diagnosis year. The SEER variable for urban and rural will be collapsed into two levels. Exploratory Variables The Medicaid Supplemental Insurance Buy-In variable will be used in the secondary analysis. The Medicaid variable provides insight into income and asset status of those identified with Medicaid. Thus, it is the only individual-level variable with income information. Place of birth information will be used to create a variable 56 categorizing a case as an immigrant, non-immigrant or unknown status. This variable will be used for an exploratory analysis of associations with place of birth and impact on excess risk of death. Delay in initiating treatment from the time of diagnosis will be determined for colorectal and breast cancer cases. The first curative treatment will be determined and days from diagnosis will be calculated. Cases that receive neo-adjuvant therapy will not be considered as experiencing delay. Those who have not initiated treatment within 8 weeks, or 56 days of diagnosis will be considered as experiencing delays. Inclusion and Exclusion Criteria The criteria for inclusion and exclusion for the three cancer samples is shown in table 13. A final analytic sample of 57,879 eligible invasive breast cancer cases diagnosed between 1992 and 2000 was identified from the Medicare-SEER dataset. A total of 278,770 breast cases were excluded from the analysis due falling within the exclusion criteria. Of the initial group, male and non- invasive case (2,634), 65 years of age and younger (145,132), diagnosed prior to 1992 or after 2000 (93,664), participating in an HMO during the year prior to and 1 year post diagnosis (20,667), without both Medicare Parts A and B one year prior to and 1 year post diagnosis (10,994), diagnosed at autopsy (1), not diagnosed within the 12 SEER registries (5,678) were excluded (table 14).. A total of 98,437 invasive prostate cancers were identified from an initial sample pool of 423,908 prostate cancer cases (table 16). Over 100,000 cases were eliminated due to age or non-invasive status. Another 140,000 cases were excluded due to year of diagnosis, over 34,000 due to enrollment in an HMO, and 22,000 because of lack of 57 Medicare parts A and B. The remaining 8,000 excluded cases were from registries outside of the 12 selected registries. The final analytic sample was 53,820 eligible invasive colorectal cancer cases diagnosed between 1992 and 2000 identified from the Medicare-SEER dataset (table 15). A total of 284,529 colorectal cancer cases were excluded from the analysis due falling within the exclusion criteria of non- invasive case and 65 years of age and younger (67,949), diagnosed prior to 1992 or after 2000 (106,485), participating in an HMO during the year prior to and 1 year post diagnosis (22,638), without both Medicare Parts A and B one year prior to and 1 year post diagnosis (28,290), not diagnosed within the 12 SEER registries (5,347). The final sample was categorized both by socioeconomic status as defined by percent of census tract residents at or below poverty and by racial/ethnic group. Table 13: Inclusion and Exclusion Criteria List Site Breast Inclusion ≥ 66 years old Part A and Part B continuously during 1 year prior to diagnosis through 5 year follow-up period. Woman Within 12 SEER registries since 1992 Colorectal ≥ 66 years old Part A and Part B continuously during 1 year prior to diagnosis through 5 year follow-up period. Within 12 SEER registries since 1992 Prostate ≥ 66 years old Part A and Part B continuously during 1 year prior to diagnosis through 5 year follow-up period. Within 12 SEER registries since 1992 58 Exclusion < 66 years old Does not have both part A and B during 1 year prior and during 5 year follow-up period. Enrollment in HMO during 1 year prior and 1 year follow-up period. Diagnosis at autopsy < 66 years old Does not have both part A and B during 1 year prior and during 5 year follow-up period. Enrollment in HMO during 1 year prior and 1 year follow-up period. Diagnosis from autopsy Male < 66 years old Does not have both part A and B during 1 year prior and during 5 year follow-up period. Enrollment in HMO during 1 year prior and 1 year follow-up period. Diagnosis from autopsy Table 14: Breast Cancer Eligibility Initial number of breast cancer cases Exclude males and non-invasive cases Exclude those under 66 years old Exclude diagnosed before 1992 and after 2000 Exclude those in HMO 1 year prior to diagnosis through 1 year after diagnosis Exclude those without Parts A & B one year prior to diagnosis through 1 year post diagnosis Exclude diagnosed at autopsy Exclude registries other than 12 registries 336,649 334,015 188,883 95,219 74,552 63,558 63,557 57,879 A total of 98,437 invasive prostate cancers were identified from an initial sample pool of 423,908 prostate cancer cases (table 16). Over 100,000 cases were eliminated due to age or non-invasive status. Another 140,000 cases were excluded due to year of diagnosis, over 34,000 due to enrollment in an HMO, and 22,000 because of lack of Medicare parts A and B. The remaining 8,000 excluded cases were from registries outside of the 12 selected registries. Table 15: Colorectal Cancer Eligibility Initial number of CRC cases Exclude those under 66 years old and non-invasive cases Exclude diagnosed before 1992 and after 2000 Exclude those in HMO 1 year prior to diagnosis through 1 year after diagnosis Exclude those without Parts A & B one year prior to diagnosis through 1 year post diagnosis Exclude registries other than 12 registries 284,529 216,580 110,095 87,457 59,167 53,820 Analytic Plan The research project utilized descriptive data analysis followed by univariate and bivariate analysis to inform the more detailed analysis. Descriptive statistics of the characteristics of the study participants were completed including means, frequencies, distributions and proportion of patient and disease characteristics. Univariate analysis 59 was done with each factor of interest related to the outcomes, stage at diagnosis and cause-specific and overall survival, including age, race/ethnicity, co-morbidities, SES, treatment factors and tumor characteristics. The full analysis was conducted beginning with an investigation of the SES-race/ethnicity interaction for each cancer site. Next, an exploration of the impact of delays in care, immigrant status and Medicaid Buy-in on excess or decreased risk of death among racial/ethnic groups was conducted. Table 16: Prostate Cancer Eligibility Initial number of prostate cases Exclude those under 66 years old and non-invasive Exclude diagnosed before 1992 and after 2000 Exclude those in HMO 1 year prior to diagnosis through 1 year after diagnosis Exclude those without Parts A & B one year prior to diagnosis through 1 year post diagnosis Exclude diagnosed at autopsy Exclude registries other than 12 registries 423,908 307,595 162,994 128,290 106,925 106,924 98,437 Aim 1: Race/ethnicity and SES on Stage at Diagnosis A separate analysis for each cancer site was completed. Each cancer site had a slightly different set of variables included as potential confounders or modifiers. Bivariate analysis was used to examine the relationship between factors. Nominal variables were analyzed using a 2-sided t test. Categorical variables were compared using the Chi-square test. Variable associations examined included age and tumor characteristics and treatment factors with race/ethnicity and SES. We examined the association of race/ethnicity and SES and stage at diagnosis while controlling for other factors using ordinal regression for the breast and colorectal sample and logistic regression for the prostate cancer sample. Race/ethnicity and SES was examined independently and then in an interaction term. 60 Unadjusted survival analysis was conducted using the Kaplan Meier estimate for cancer-specific and overall survival. The lifetest procedure was utilized in SAS (v. 9.3 SAS Institute, Inc. Cary NC) to estimate probably of surviving up to 5 years. Trend tests were completed for the poverty categories and poverty categorized within each racial/ethnic group. Significant differences between racial/ethnic 5 year survival rates were identified using the Log-rank test to compare survival curves as well as racial/group differences within each poverty level. The Kaplan-Meier method was used to estimate the 5-year cause specific survival probability for each specific cancer since diagnosis. For the overall survival analysis, any death was considered an event while cancer specific survival censored deaths due to causes other than the specific cancer under investigation. Censoring occurs because the event of interest happens, the study follow-up period ends before the event has occurred or the person is lost to follow-up. Event-free survival was calculated using the KaplanMeier product limit estimator for levels of socioeconomic status and race/ethnicity groups. The risk of death was estimated using the Cox Proportional Hazard model. Cox proportional hazard regression models estimated the instantaneous risk of death at a given time (time t) and measure the independent influence of SES, race/ethnicity and other covariates on hazard risk. Separate models were generated for each race/ethnicity and SES level. Proportional hazard and linearity were tested to assure assumptions for Cox Proportional Hazards are met by graphical inspection. Interactions between SES and race/ethnicity were tested by modeling the interaction term. Twotailed tests with a 0.05 level of significance was used throughout. Wald test for interaction was employed for testing the significance of the race-poverty interaction term. 61 Aim 2: Analysis of Delays in the Initiation of Treatment Baseline characteristics including demographic and clinical descriptors were compared by delay status using Chi Square test for categorical variables and Wilcoxon test for continuous variables. Univariate and multivariate Cox regression models were employed to investigate the association between the delays to the initiation of treatment and SES/racial/ethnic groups and patient and clinical factors similar to the analysis in research aim 1. An exploratory analysis was completed by adding delay, immigrant and Medicaid Buy-in to the aforementioned Cox proportional hazard regression models. The influence of the group of variables on reducing or enhancing racial/ethnic and SES mortality risk differences was observed. The treatment delay analysis required the use of a time-varying covariate in the Cox proportional hazard model. A time independent variable remains constant over time whereas a time-varying variable may change for a given subject over time. Time varying variables do not meet the proportional hazard assumption and require an extended Cox model170. The proportionality assumption is that the hazard ratio comparing any two factors of predictors is constant over time. Thus, the hazards can be graphed against time and if they cross or are far from parallel, then the assumption is not met. Time-varying variables may be considered ‘internal’ variables if the main cause of time delays is due to patient preference and patient actions. However, if the main cause of increased time is the infrastructure (i.e. appointment system) of the treating institution, availability of specialists and tests, then, the time variable would be an ‘external’ or ‘ancillary’ variable. This study assumes that this time varying variable is ancillary in that increased delays are largely a result of institutional and provider-level barriers more than 62 patient activation factors170. However, there are likely both internal and ancillary forces acting on all time-varying variables. The Cox model used for time-dependent covariates is shown below. Figure 2: Cox Model for Time-Dependent Variables170 ( ( )) ( ) [∑ X(t)= X1, X2, …..Xp1, Time independent Power Calculationsvariable 1 ∑ ( )] X1(t), X2(t)……Xp2(t)) Time varying variable Power Calculations A power calculation was conducted to assure the ability to minimize type I and type II errors. A power calculation to test race-poverty interaction effects in a Cox Proportional Hazards Regression was completed using R software (version 3.0.1). For each cancer site, White and African American proportion of events (cancer-specific and all-cause deaths) were compared in high poverty and very low poverty cells. Among the 55,392 breast cancer cases, there is 92% power to detect an HR of 1.5 for breast cancer specific mortality and 90% power for an HR of 1.3 for all-cause mortality. The 50,377 CRC cases provide 95% power for a detectable HR of 1.4 in CRC-specific mortality and 96% power for HR 1.3 in all-cause mortality. Similarly, the 93,999 prostate allow for 94% power for a 1.5 HR in prostate cancer-specific mortality and 88% power for 1.2 HR for all-cause mortality. A power calculation for the main effects analysis of race/ethnicity and poverty on mortality was conducted to assure the ability to minimize type I and type II errors. Power was set at 0.95, Beta at 0.05 and two-sided alpha at 0.01. The breast cancer sample size 63 is 63,000 and the standard deviation of the predictor variable is 0.13. The event rate was calculated at 0.30 and the R² was set at 0.30. There is sufficient power to detect a hazard ratio of 1.32 in the main effects analysis. Clinical vs. Statistical Significance Given the large sample size afforded by the Medicare SEER dataset, the power has been set at 95% and alpha is set at 0.05. In addition, statistically significant associations between variables will be reviewed for clinical feasibility rather than relying solely on the statistical test in order to report and investigate further. Time-Varying Covariates All variables will be considered fixed variables with the exception of time from diagnosis to first treatment. Diagnosis to first treatment will measure the number of weeks from diagnosis to the date of the initiation of treatment. Age will be considered a fixed variable and categorize age groups. Missing Data Missing data will be handled using two different methodologies. Census tract missing data will be imputed by zip code level data when available. Patient and clinical missing data will be categorized as missing and analyzed as a separate level of the variable. Manuscript Plans Manuscripts will be developed according to the research aims delineated. A manuscript for each cancer site (breast, colorectal and prostate) will describe the interplay of SES and race/ethnicity. The results will include influence on stage at diagnosis, overall survival, disease-specific survival and hazard risk of all-cause mortality and disease- 64 specific mortality. The results from the exploratory analysis will be included in the initial papers on race/ethnic and SES interaction. 65 CHAPTER IV RESULTS Breast Cancer Results Poverty Level Groups The eligible cases are described demographically and clinically in tables 17-18 by area poverty level. Women in Very Low poverty areas were more likely to be younger, White, have stage I tumors, well differentiated tumors, have had breast conserving surgery and either not needing chemotherapy or receiving adjuvant chemotherapy if required. In addition, women in very low poverty areas displayed less comorbidities than those in other poverty levels. Similar to very low poverty cases, women in the low poverty areas also tended to be white, diagnosed with stage I disease, have well or moderately differentiated tumors, and were more likely to have ER/PR positive tumors. Women in low poverty showed high proportions of cases where radiation was required and received, or the converse, not received when not required. Middle poverty cases were primarily white, had the second largest proportion of Medicaid supplemental insurance, ER/PR positive tumors, and had a mastectomy. They also displayed high proportions of cases receiving chemotherapy regardless of indication. Women in the highest poverty areas were more likely to be African American, an immigrant, and have Medicaid supplemental insurance. In addition, women in the poorest areas had greater proportions of Stage III or IV tumors, poorly/undifferentiated or unknown tumor grades, ER/PR negative or unknown tumors, and receipt of mastectomy. Women in these poor areas were also less likely to receive radiation therapy regardless of indication. Poorer women displayed higher levels of Charlson comorbidity index scores 66 as well as diagnosed hypertension, and chronic conditions. The poorest women exhibited the greatest delays to first treatment. Women in the missing poverty category were primarily white, diagnosed with Stage I disease, received mastectomies and did not require chemotherapy or radiation therapy. They also exhibited a score of 0 on the Charlson comorbidity index yet had the second highest proportions of hepatitis, arthritis and depression. Racial/Ethnic Groups Patient and clinical characteristics are shown by racial/ethnic group (tables 1920). White women were more likely than other groups to be in the very low poverty areas, be diagnosed with stage I disease (except Asian women), have breast-conserving surgery, not requiring or receiving chemotherapy, and either receiving required radiation or not needing radiation. White women were also more likely to score 0 in the Charlson comorbidity index and generally had low proportions of chronic conditions. African American women were more likely to have Medicaid, be staged at II or higher or have unstaged/missing tumors, poor or undifferentiated graded tumors and ER/PR negative or unknown status tumors. African American women had the largest proportion of cases in the high poverty areas, cases with no surgery, and not receiving chemotherapy or radiation even when indicated. African American women were more likely to have scored higher (1 or greater) on the Charlson comorbidity index and greater proportions of cases with chronic conditions including hypertension, anemia, hepatitis and arthritis. African American women were more likely to experience delays to first treatment than any other racial/ethnic group. 67 Table 17: Demographic Characteristics of Breast Cancer Cases by Poverty Level 68 Variable High poverty N (%) Middle poverty N (%) Low Poverty N (%) Poverty Missing N (%) Total N (%) 17,511 Very Low Poverty N (%) 19,745 Total Patients Number of deaths (5-year) All-cause Breast specific Age mean (sd) 66-69 70-74 75-79 80+ Race/Ethnicity White African American Latina Asian AIAN / Other Marital Status Unmarried Married Unknown Immigrant Yes No Unknown Medicaid Buyin Yes Geography Rural/Less Urban Urban/Metro 5,856 12,463 1,988 789 (39.7) 76.5 (6.9) 1,166 (19.9) 1,561 (26.7) 1,429 (24.4) 1,700 (29.0) P value 2,320 57,879 3,649 1,294 (35.5) 76.7 (6.9) 2,354 (18.9) 3,372 (27.1) 2,932 (24.1) 3,805 (28.0) 4,737 1,690 (35.7) 76.4 (6.7) 3,432 (19.6) 4,757 (27.2) 4,291 (24.5) 5,031 (28.7) 4,991 1,810 (36.3) 75.9 (6.6) 4,258 (21.6) 5,693 (28.8) 4,703 (23.8) 5,091 (25.8) 621 197 (31.7) 76.0 (6.5) 481 (20.9) 641 (27.8) 590 (25.6) 592 (25.7) 15,986 5,780 (36.2) 76.3 (6.7) 11,691 (20.2) 16,024 (27.7) 13,945 (24.1) 16,219 (28.0) 3,012 (51.4) 1,984 (33.9) 560 (9.6) 260 (4.4) 40 (0.7) 10,531 (84.5) 838 (6.7) 553 (4.4) 509 (4.1) 32 (0.3) 16,047 (91.6) 437 (2.5) 416 (2.4) 581 (3.3) 30 (0.2) 18,652 (94.5) 226 (1.1) 292 (1.5) 552 (2.8) 23 (0.1) 2,141 (92.9) 77 (3.3) 49 (2.1) 31 (1.4) 6 (0.3) 50,383 (87.1) 3,562 (6.2) 1,870 (3.2) 1,933 (3.3) 131 (0.2) P<.000 1 3,850 (65.7) 1,790 (30.6) 216 (3.7) 7,413 (59.5) 4,693 (37.7) 357 (2.9) 9,577 (54.7) 7,446 (42.5) 488 (2.8) 9,870 (50.0) 9,313 (47.2) 562 (2.9) 1,202 (52.2) 984 (42.7) 118 (5.1) 31,912 (55.1) 24,226 (41.9) 1,741 (3.0) P<.000 1 430 (7.3) 3,353 (57.3) 2,073 (35.4) 728 (5.8) 7,267 (58.3) 4,468 (35.9) 1,020 (5.8) 9,961 (56.9) 6,530 (37.3) 1,123 (5.7) 11,099 (56.2) 7,523 (38.1) 136 (5.9) 1,658 (72.0) 510 (22.1) 3,437 (5.9) 33,338 (57.6) 21,104 (36.5) 1,674 (28.6) 1,781 (14.3) 1,418 (8.1) 804 (4.1) 176 (7.6) 5,853 (10.1) P<.000 1 P<.000 1 504 (8.6) 5,352 (91.4) 2,216 (17.8) 10,247 (82.2) 1,775 (10.1) 15,736 (89.9) 185 (0.9) 19,560 (99.1) 317 (13.8) 1,987 (86.2) 4,997 (8.6) 52,882 (91.4) P<.000 1 P=.001 7 P<.000 1 P<.000 1 Table 18: Clinical Characteristics of Breast Cancer Cases by Poverty Level Variable High poverty (N=5,856) N (%) Middle poverty (N=12,463) N (%) Low Poverty (N=17,511) N (%) Very Low Poverty (N=19,745) N (%) Poverty Missing (N=2,320) N (%) Total (n=57,879) N (%) P value 2 (0.03) 2,627 (44.9) 2,166 (37.0) 425 (7.3) 200 (3.4) 436 (7.5) 15 (0.12) 6,558 (52.6) 4,093 (32.8) 700 (5.6) 335 (2.7) 762 (6.1) 19 (0.11) 9,417 (53.8) 5,605 (32.0) 913 (5.2) 487 (2.8) 1,070 (6.1) 22 (0.11) 10,798 (54.7) 6,229 (31.6) 929 (4.7) 517 (2.6) 1,250 (6.3) 4 (0.17) 1,289 (56.0) 654 (28.4) 92 (4.0) 43 (1.9) 222 (9.6) 62 (0.1) 30,689 (53.0) 18,747 (32.4) 3,059 (5.3) 1,582 (2.7) 3,740 (6.5) 829 (14.1) 1,937 (33.1) 1,654 (28.2) 1,442 (24.6) 1,953 (15.7) 4,402 (35.3) 3,453 (27.7) 2,655 (21.3) 2,952 (16.9) 6,282 (35.9) 4,629 (26.4) 3,648 (20.8) 3,461 (17.5) 7,295 (37.0) 4,890 (24.8) 4,099 (20.8) 380 (16.5) 820 (35.6) 538 (23.4) 566 (24.6) 9,569 (16.5) 20,736 (35.8) 15,164 (26.2) 12,410 (21.4) 3,556 (60.7) 732 (12.5) 23 (0.4) 1,545 (26.4) 8,465 (67.9) 1,333 (10.7) 65 (0.5) 2,600 (20.9) 12,123 (69.2) 1,795 (10.3) 64 (0.4) 3,529 (20.2) 13,518 (68.5) 2,024 (10.3) 69 (0.4) 4,134 (20.9) 1,475 (64.0) 211 (9.2) 13 (0.6) 605 (26.3) 39,137 (67.6) 6,095 (10.5) 234 (0.4) 12,413 (21.5) 2,224 (38.0) 3,356 (57.3) 276 (4.7) 4,952 (43.6) 7,110 (57.1) 401 (3.1) 7,631 (43.6) 9,338 (53.3) 542 (3.1) 9,540 (48.3) 9,691 (49.1) 514 (2.6) 847 (36.8) 1,379 (59.9) 78 (3.4) 25,194 (43.5) 30,874 (53.3) 1,811 (3.1) P< .0001 454 (7.8) 859 (14.7) 876 (7.0) 1,312 (10.5) 1,199 (6.9) 1,763 (10.1) 1,351 (6.8) 1,773 (9.0) 114 (5.0) 185 (8.0) 3,994 (6.9) 5,892 (10.2) P<.0001 4,003 (68.4) 9,017 (72.4) 12,857 (73.4) 14,609 (74.0) 1,797 (78.0) 42,283 (73.1) Cancer Stage 69 Stage 0 Stage I Stage II Stage III Stage IV Unknown/unstaged Tumor grade Well Moderate Poor/Undifferentiated Unknown Hormone receptor Status -ER/PR Positive Negative Borderline Unknown Surgery Type BCS Mastectomy None/Unknown Chemotherapy Indicated & recvd Indicated & not recvd Not indic & not recvd/ unk P< .0001 P<.0001 P<.0001 70 Variable High poverty (N=5,856) N (%) Low Poverty (N=17,511) N (%) 540 (9.2) Middle poverty (N=12,463) N (%) 1,258 (10.1) Poverty Missing (N=2,320) N (%) 208 (9.0) Total (n=57,879) N (%) 1,692 (9.7) Very Low Poverty (N=19,745) N (%) 2,012 (10.2) Recvd & indic unk Radiation Indicated & recvd Indicated & not recvd Not indic & not recvd/ unk Recvd & indic unk Charlson Comorbidity Index 0 1 2+ Specific Comorbidities (Yes) Hypertension Anemia Hepatitis Arthritis Anxiety Depression Bipolar Schizophrenia Delay to First Treatment >= 8 weeks Yes 1,950 (33.3) 757 (12.9) P value 4,370 (35.1) 1,166 (9.4) 6,894 (39.4) 1,637 (9.4) 8,578 (43.4) 1,564 (8.9) 706 (30.6) 183 (7.9) 22,630 (39.1) 5,278 (9.1) 2,800 (47.8) 349 (6.0) 8,144 (46.5) 909 (5.2) 8,355 (47.7) 739 (4.2) 8,144 (46.5) 909 (5.2) 1,307 (56.7) 108 (4.7) 26,866 (46.4) 3,105 (5.4) 3,695 (63.1) 1,284 (21.9) 887 (15.0) 8,648 (69.4) 2,482 (19.9) 1,333 (10.7) 12,538 (71.6) 3,430 (19.6) 1,543 (8.8) 14,383 (72.8) 3,699 (18.7) 1,663 (8.4) 1,658 (72.0) 443 (19.2) 203 (8.8) 40,922 (70.7) 11,338 (19.6) 5,619 (9.7) P<.0001 3,736 (63.8) 1,268 (22.4) 174 (3.1) 877 (15.5) 212 (3.7) 297 (5.2) 31 (0.6) 96 (1.7) 7,037 (56.5) 1,907 (15.7) 273 (2.3) 1,340 (11.1) 402 (3.3) 724 (6.0) 69 (0.6) 98 (0.8) 9,558 (54.6) 2,599 (15.2) 386 (2.3) 1,830 (10.7) 568 (3.3) 959 (5.6) 99 (0.6) 111 (0.7) 10,529 (53.3) 3,045 (15.9) 435 (2.3) 2,056 (10.7) 531 (2.8) 933 (4.9) 110 (0.6) 112 (0.6) 1,242 (53.9) 400 (17.9) 54 (2.4) 250 (11.2) 74 (3.3) 128 (5.7) 322 (0.6) 18 (0.8) 32,102 (55.5) 9,219 (16.4) 1,322 (2.4) 6,353 (11.3) 1,787 (3.2) 3,041 (5.4) 322 (0.6) 435 (0.8) P<.0001 P<.0001 P<.01 P<.0001 P<.001 P<.001 NS P<.0001 1,295 (22.6) 1,952 (15.9) 2,657 (15.4) 3,226 (16.6) 344 (15.3) 9,474 16.7) <.0001 5,710 (9.9) P< 0001 Latinas were more likely to live in either middle or high poverty areas, be immigrants (second to Asian women), and the most likely to have Medicaid supplemental insurance. Similar to African American women, a greater proportion of Latinas were diagnosed later than stage I disease or had unstaged/missing tumors. Latinas were more likely to receive chemotherapy when indicated. In addition, Latinas experienced high proportions of chronic conditions and mental health conditions including hypertension, anxiety and depression. Asian women were most likely to live in either very low or low poverty areas, be an immigrant, diagnosed at stage I, had differentiated tumor grade and with ER/PR positive tumors. Asian women tended to receive radiation regardless of indication. Native American and other racial/ethnic women had high proportions of women living in high poverty areas and were more likely to have moderately differentiated tumors, high proportion of ER/PR negative tumors, had a mastectomy. Native American women had high proportions of scores of 2 or greater on the Charlson comorbidity index and the second highest proportion of cases experiencing delays to initiation of treatment. Stage at Diagnosis Ordinal Regression The association between diagnosis at stage II, III or IV and racial/ethnic group and socioeconomic status was investigated using ordinal logistic regression (table 21). African American women experienced increased adjusted odds of diagnosis at stages II, III or IV when compared to white women (OR 1.70, 95% CI 1.58-1.83). The increased odds continued, although was reduced after accounting for poverty level (OR 1.52, 95% CI 1.41-1.65). 71 Table 19: Demographic Characteristics of Breast Cancer Cases by Race/Ethnicity 72 Variable White No. (%) Latina No. (%) Asian No. (%) AIAN/ Other No. (%) All No. (%) 50,383 African American No. (%) 3,562 Total Patients Number of deaths (5 year) All-cause Breast specific Age mean (sd) 66-69 70-74 75-79 80+ % below poverty High Poverty Middle Poverty Low Poverty Very Low Poverty Missing Marital Status Unmarried Married Unknown Immigrant Yes No Unknown Medicaid Buyin Yes Rural/Less Urban Urban/Metro P value 1,870 1,933 131 57,879 13,786 4,831 (35.0) 76.4 (6.7) 9,805 (19.5) 13,793 (27.4) 12,260 (24.3) 14,525 (28.8) 1,280 575 (44.9) 75.9 (6.9) 807 (22.7) 1,021 (28.7) 830 (23.3) 904 (25.4) 494 205 (41.5) 75.2 (6.5) 480 (25.7) 552 (29.5) 405 (21.7) 433 (23.2) 380 150 (39.5) 74.1 (5.9) 569 (29.4) 624 (32.2) 417 (21.6) 323 (16.7) 46 19 (58.7) 75.9 (6.6) 30 (22.9) 34 (26.0) 33 (25.2) 34 (26.0) 15,986 5,780 (36.2) 76.3 (6.7) 11,691 (20.2) 16,024 (27.7) 13,945 (24.1) 16,219 (28.0) P<.0001 3,012 (6.0) 10,531 (20.9) 16,047 (31.9) 18,652 (37.0) 2,141 (4.3) 1,984 (55.7) 838 (23.5) 437 (12.3) 226 (6.3) 77 (2.2) 560 (30.0) 553 (30.0) 416 (22.3) 292 (15.6) 49 (2.6) 260 (13.5) 509 (26.3) 581 (30.1) 552 (28.6) 31 (1.6) 40 (30.5) 32 (24.4) 30 (22.9) 23 (17.6) 6 (4.6) 5,856 (10.1) 12,463 (21.5) 17,511 (30.3) 19,745 (34.1) 2,304 (4.0) P<.0001 27,326 (54.2) 21,588 (42.9) 1,469 (2.9) 2,501 (70.2) 902 (25.3) 159 (4.5) 1,107 (59.2) 703 (37.6) 60 (3.2) 906 (46.9) 979 (50.7) 48 (2.5) 72 (55.0) 54 (41.2) 5 (3.8) 31,912 (55.1) 24,226 (41.9) 1,741 (3.0) P<.0001 2,441 (4.8) 29,408 (58.4) 18,534 (36.8) 34 (1.0) 2,158 (60.6) 1,370 (38.5) 437 (23.4) 766 (41.0) 667 (35.7) 525 (27.2) 910 (47.1) 498 (25.8) 0 96 (73.3) 35 (26.7) 3,437 (5.9) 33,338 (57.6) 21,104 (36.5) 3,389 (6.7) 4,765 (9.5) 45,618 (90.5) 1,235 (34.7) 63 (1.8) 3,499 (98.2) 721 (38.6) 145 (7.8) 1,725 (92.3) 465 (24.1) 3 (0.2) 1,930 (99.8) 43 (32.8) 21 (16.0) 110 (84.0) 5,853 (10.1) 4,997 (8.6) 52,882 (91.4) P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 Table 20: Clinical Characteristics of Breast Cancer Cases by Race/Ethnicity Variable White (n=50,383) No. (%) African American (n=3,562) No. (%) Latina (n=1,870) No. (%) Asian (n=1,933) No. (%) AIAN/ Other (n=131) No. (%) All (n=57,879) No. (%) 56 (0.1) 27,196 (54.0) 16,072 (31.9) 2,508 (5.0) 1,322 (2.6) 3,229 (6.4) 2 (.1) 1,456 (40.8) 1,328 (37.3) 319 (9.0) 140 (3.9) 317 (8.9) 3 (0.2) 870 (46.5) 719 (38.3) 127 (6.8) 64 (3.4) 90 (4.8) 1 (0.1) 1,103 (57.1) 587 (30.4) 94 (4.9) 54 (2.8) 94 (4.9) 0 (0) 64 (48.9) 44 (33.6) 11 (8.3) 2 (1.5) 10 (7.6) 62 (0.1) 30,689 (53.0) 18,747 (32.4) 3,059 (5.3) 1,582 (2.7) 3,740 (6.5) 8,497 (16.9) 18,255 (36.2) 12,982 (25.8) 10,649 (21.1) 426 (12.0) 1,040 (29.2) 1,119 (31.4) 977 (27.4) 295 (15.8) 649 (34.7) 526 (28.1) 400 (21.4) 337 (17.4) 738 (38.2) 506 (26.2) 352 (18.2) 14 (10.7) 54 (41.2) 31 (23.7) 32 (24.4) 9,569 (16.5) 20,736 (35.8) 15,164 (26.2) 12,410 (21.4) 34,592 (68.7) 5,088 (10.1) 204 (0.4) 10,499 (20.8) 1,928 (54.1) 559 (15.7) 17 (0.5) 1,058 (29.7) 1,191 (63.7) 201 (10.8) 6 (0.3) 472 (25.2) 1,343 (69.5) 229 (11.9) 6 (0.3) 355 (18.4) 83 (63.4) 18 (13.7) 1 (0.8) 29 (22.1) 39,137 (67.6) 6,095 (10.5) 234 (0.4) 12,413 (21.5) 22,170 (44.0) 26,717 (53.0) 1,496 (3.0) 1,483 (41.6) 1,878 (52.7) 201 (5.6) 751 (40.2) 1,061 (56.7) 58 (3.1) 744 (38.5) 1,135 (58.7) 54 (2.8) 46 (35.1) 83 (63.4) 2 (1.5) 25,194 (43.5) 30,874 (53.3) 1,811 (3.1) 3,341 (6.6) 4,881 (9.7) 37,220 (73.9) 4,941 (9.8) 329 (9.2) 582 (16.3) 2,291 (64.3) 360 (10.1) 175 (9.4) 246 (13.2) 1,273 (68.1) 176 (9.4) 139 (7.2) 164 (8.5) 1,409 (72.9) 221 (11.4) 10 (7.6) 19 (14.5) 90 (68.7) 12 (9.2) 3,994 (6.9) 5,892 (10.2) 42,283 (73.1) 5,710 (9.9) P value Cancer Stage 73 Stage 0 Stage I Stage II Stage III Stage IV Unstaged/unk Tumor grade Well Moderate Poor/Undiff Unknown ER/PR Positive Negative Borderline Unknown Surgery Type BCS Mastectomy None/Unknown Chemotherapy Indicated & recvd Indicated & not recvd Not indic/not recvd/unk Recvd & indic unk Radiation P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 74 Variable White (n=50,383) No. (%) Latina (n=1,870) No. (%) Asian (n=1,933) No. (%) AIAN/ Other (n=131) No. (%) All (n=57,879) No. (%) 19,802 (39.3) 4,365 (8.7) 23,537 (46.7) 2,679 (5.3) African American (n=3,562) No. (%) 1,312 (36.8) 509 (14.3) 1,549 (43.5) 192 (5.4) Indicated & recvd Indicated & not recvd Not indic/not recvd/unk Recvd & indic unk Charlson Comorbidity Index 0 1 2+ Specific Comorbidities (Yes) Hypertension Anemia Hepatitis Arthritis Anxiety Depression Bipolar Schizophrenia Delay to First Treatment >= 8 wks Yes P value 722 (38.6) 240 (12.8) 804 (43.0) 104 (5.6) 755 (39.1) 146 (7.6) 912 (47.2) 120 (6.2) 39 (29.8) 18 (13.7) 64 (48.9) 10 (6.2) 22,630 (39.1) 5,278 (9.1) 26,866 (46.4) 3,105 (5.4) P<.0001 36,209 (71.9) 9,679 (19.2) 4,495 (8.9) 2,065 (58.0) 860 (24.1) 637 (17.9) 1,239 (66.3) 371 (19.8) 260 (13.9) 1,331 (68.9) 398 (20.6) 204 (10.6) 78 (59.5) 30 (22.9) 23 (17.6) 40,922 (70.7) 11,338 (19.6) 5,619 (9.7) P<.0001 1,097 (2.2) 7,754 (15.8) 1,097 (2.2) 5,243 (10.7) 1,564 (3.2) 2,757 (5.6) 290 (0.6) 322 (0.7) 111 (3.3) 943 (27.6) 111 (3.3) 680 (19.9) 87 (2.6) 114 (3.3) 14 (0.4) 68 (2.0) 58 (3.3) 269 (15.1) 58 (3.3) 244 (13.7) 87 (4.9) 113 (6.4) 10 (0.6) 21 (1.2) 54 (2.9) 235 (12.8) 54 (2.9) 172 (9.4) 43 (2.3) 52 (2.8) 8 (0.4) 24 (1.3) 2 (1.6) 18 (14.0) 2 (1.6) 14 (10.9) 6 (4.7) 5 (3.9) 0 (0) 0 (0) 1,322 (2.4) 9,219 (16.4) 1,322 (2.4) 6,353 (11.3) 1,787 (3.2) 3,041 (5.4) 322 (0.6) 435 (0.8) P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 7,743 (15.6) 1,044 (30.2) 354 (19.3) 306 (16.3) 27 (20.8) 9,474 (16.7) P<.0001 Latinas also exhibited excess risk of diagnosis at stages II-IV compared to white women which was only slightly attenuated by the addition of poverty level (OR 1.37, 95% CI 1.24-1.51). Asian women showed no difference in odds of later stage at diagnosis when compared to white women both before and after adding poverty level to the model. Each poverty level displayed increased adjusted odds of diagnosis at stages II-IV compared to cases in the very low poverty areas. The largest magnitude of risk was among women in the highest poverty areas (OR 1.48, 95% CI 1.39-1.57) which was reduced after including race/ethnicity by 14.9% to OR 1.26 (95% CI 1.18-1.35). Women in low and middle poverty areas also showed increased odds of diagnosis at stages II-IV even after accounting for race/ethnicity (OR 1.06 and 1.07 respectively). Increased risk among African American women and those in high poverty were the most impacted by the inclusion of poverty or race/ethnicity. All other racial/ethnic groups and poverty levels were minimally affected. African American women, regardless of poverty level, exhibited increased odds of diagnosis at stages II-IV when compared to white women in the same poverty level. The magnitude of increased adjusted odds ranged from a low of OR 1.44 (95% CI 1.281.62) among African American women in high poverty to a high of OR 1.95 (95% CI 1.61-2.35) for African American women in low poverty areas. Latinas also display increased adjusted odds of diagnosis at higher stages than stage I when compared to white women in the same poverty levels ranging from OR 1.53 in the high poverty group to OR 1.31 and OR 1.32 in the middle and low poverty groups. 75 Table 21: Odds of Stages II/III/IV and Race/ethnicity and Poverty Level among Breast Cancer Cases Stages II, II, IV N (%) Race/Ethnicity White African American Latina Asian Odds Ratio (95% confidence interval) (n=51,882) Main Effects Full Model Main Effects Full Model With Poverty 19,172 (42.5) 1,760 (55.2) 887 (51.3) 725 (40.1) 1.0 (referent) 1.70 (1.58-1.83) 1.43 (1.30-1.57) 1.03 (0.92-1.16) Main Effects Full Model 1.0 (referent) 1.52 (1.41-1.65) 1.37 (1.24-1.51) 1.01 (0.90-1.13) Main Effects Full Model With Race/Ethnicity 7,664 (41.5) 6,997 (42.7) 5,114 (43.9) 2,769 (51.5) 1.0 (referent) 1.07 (1.03-1.12) 1.12 (1.07-1.18) 1.48 (1.39-1.57) Interaction Full Model 1.0 (referent) 1.06 (1.01-1.10) 1.07 (1.02-1.12) 1.26 (1.18-1.35) 7,231 (41.5) 107 (51.2) 128 (45.9) 198 (38.0) 1.0 (referent) 1.46 (1.12-1.89) 1.24 (0.99-1.57) 0.99 (0.82-1.20) 6,358 (42.3) 224 (55.9) 197 (49.8) 218 (39.1) 1.0 (referent) 1.95 (1.61-2.35) 1.32 (1.09-1.60) 1.00 (0.84-1.20) 4,240 (43.0) 412 (53.2) 261 (49.7) 201 (41.4) 1.0 (referent) 1.51 (1.31-1.75) 1.31 (1.10-1.55) 1.09 (0.90-1.32) 1,343 (47.9) 1,017 (56.5) 301 (56.8) 108 (44.1) 1.0 (referent) 1.44 (1.28-1.62) 1.53 (1.28-1.83) 0.92 (0.71-1.19) 7,231 (41.5) 6,358 (42.3) 1.0 (referent) 1.05 (1.00-1.10) Poverty Very Low Low Middle High Very Low Poverty White African American Latina Asian Low Poverty White African American Latina Asian Middle Poverty White African American Latina Asian High Poverty White African American Latina Asian White Very Low Poverty Low Poverty 76 Stages II, II, IV N (%) Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latina Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty Odds Ratio (95% confidence interval) (n=51,882) 4,240 (43.0) 1,343 (47.9) 1.07 (1.01-1.13) 1.28 (1.18-1.38) 107 (51.2) 224 (55.9) 412 (53.2) 1,017 (56.5) 1.0 (referent) 1.40 (1.02-1.93) 1.11 (0.83-1.49) 1.26 (0.96-1.66) 128 (45.9) 197 (49.8) 261 (49.7) 301 (56.8) 1.0 (referent) 1.11 (0.83-1.50) 1.12 (0.85-1.49) 1.57 (1.19-2.09) 198 (38.0) 218 (39.1) 201 (41.4) 108 (44.1) 1.0 (referent) 1.06 (0.83-1.35) 1.17 (0.91-1.50) 1.18 (0.87-1.60) Compared to white women, Latinas and African Americans experienced increased odds of stage II or greater diagnosis at each SES level. Asian women showed no difference in risk when compared to white women. The high poverty level consistently exhibited increased odds of stage II or greater disease when compared to those at the very low poverty level within each racial/ethnic group. Both White women and Latinas in high poverty areas experienced statistically significant increased odds of later stage I diagnosis compared to White woman and Latinas in the very low poverty areas (OR 1.28 and OR 1.57 respectively). Breast Cancer-Specific Survival The breast cancer specific survival for both poverty and racial/ethnic groups are displayed in figure 3. Significant differences exist between the racial/ethnic groups (p<0.0001) and a gradient by poverty level (test for trend p<0.0001). Asian women have 77 the highest five-year breast cancer specific survival (91.5%) followed by women in the very low poverty group (89.6%), White women (89.1%), those in low (89.1%), middle (88.1%) poverty groups, Latinas (87.3%), women in high poverty areas (84.7%) with the lowest survival among African American women (81.1%). Survival within socioeconomic and racial/ethnic groups (Figure 4) demonstrates the consistently low survival rates among African American women regardless of poverty level (80.7-82.2%), persistently high survival among white (86.6-89.9%) and Asian women (89.5-92.8%) across poverty level. Latina survival varies with the lowest survival for women in the middle poverty group (84.5%) and the highest survival for those in the very low and low poverty groups (90.4%). Only white women exhibited significant trend by poverty level (p<0.0001) while all poverty levels had significantly different survival by race/ethnicity (p<0.0001 to p=0.0003). Figure 3: Breast Cancer-Specific 5-Year Survival Five-Year Survival (%) Breast Cancer Specific Survival 94.0 92.0 90.0 88.0 86.0 84.0 82.0 80.0 78.0 76.0 74.0 Series1 Very Low Low Middle High 89.6 89.1 88.1 84.7 African American Latina 81.1 Poverty Group 87.3 White Asian 89.1 91.5 Racial/Ethnic Group 78 Figure 4: Breast Cancer-Specific 5-Year Survival by Race/Ethnic Group and Poverty Level Breast Cancer Specific Survival By Racial/Ethnic Group and Socioeconomic Status 92.5 91.5 94.0 90.1 92.0 89.6 89.5 90.4 89.2 88.8 White Five-Year Survival (%) 90.0 86.3 87.1 88.0 86.0 Asian 83.7 84.0 82.0 African American Latina 91.5 80.7 82.2 80.9 80.0 81.0 Asian 78.0 White 76.0 Latina 74.0 Very Low Race/ Ethnicity African American Low Poverty Group Middle High Five-Year Overall Survival Significant overall survival differences between racial/ethnic groups (p<0.0001) and poverty levels (p<0.0001) were identified (figure 5). The five-year overall survival analysis demonstrates a stronger poverty gradient for survival with increasing survival as SES increases (Figure 6, p<0.0001). The differences between racial/ethnic groups are significant (P<0.0001) showing a 20% difference in survival between African American (62.6%) and Asian women (83.3%) in the very low poverty group. White and Asian women clearly displayed incremental decreases in survival as poverty level increases (trend test p<0.0001 and p=0.0001 respectively). 79 Figure 5: Breast Cancer Overall 5-Year Survival Five-Year Survival (%) Breast Cancer Overall Survival 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 Series1 Very Low Low Middle High 73.4 71.6 69.1 64.7 African American Latina 62.0 Poverty Group 71.6 White Asian 71.2 79.7 Racial/Ethnic Group Figure 6: Breast Cancer Overall 5-Year Survival by Race/Ethnicity and Poverty Breast Cancer Overall Survival By Racial/Ethnic Group and Socioeconomic Status 83.3 90.0 73.0 80.0 Five-Year Survival (%) 70.0 73.1 80.9 78.1 71.3 61.5 60.0 74.2 69.5 White 71.365.1 66.6 66.3 62.6 77.2 African American Latina 61.1 50.0 40.0 30.0 20.0 Asian White 10.0 Latina 0.0 Very Low Low Poverty Group African American Middle High 80 Race/ Ethnicity Breast Cancer-Specific Mortality Breast cancer-specific mortality main effects analysis (models A-C with poverty/race) A series of models were employed with each model including key sets of variables of interest. All model covariates met the proportional hazards assumptions. The crude model revealed statistically significant differences of risk of death by racial/ethnic group (table 22). As each group of variables were added to the model, the considerable differences between African Americans, Latinas and Asian women disappeared and the confidence intervals overlapped. African American women displayed significant excess hazard for breast cancer-specific mortality when compared to white women HR 1.81 (95% CI 1.66-1.97). African American women experienced a reduction (28.6%) but not elimination of excess hazard once cancer and treatment characteristics were added to the model. This increased risk continued even after accounting for poverty level (HR 1.18, 95% CI 1.06-1.31) and controlling for demographic, comorbidity, cancer and treatment characteristics. Latinas exhibited an increased mortality hazard of HR 1.20 (95% CI 1.03-1.38) which was removed once treatment and tumor variables were included. The addition of comorbidity variables and poverty had little impact on Latinas or Asian mortality hazards. Asian women displayed an unadjusted decreased hazard (HR 0.78 (95% CI 0.66-0.92) which became non-significant once demographic variables were included in the model. The mortality hazard for Asian women was not changed by the addition of treatment and tumor characteristics to the model. Each poverty level exhibited excess crude mortality hazards when compared to very low poverty levels and were considerably different. Women in high poverty areas 81 experienced an unadjusted risk of death 1.55 times greater (95% CI 1.43-1.69) than women in the lowest poverty level. Once adjustment for treatment and cancer characteristics occurred, only high poverty level remained significantly different. The addition of race/ethnicity only slightly attenuated the increased hazard for the high poverty level when compared to very low poverty (HR 1.13, 95% CI 1.02-1.24). Breast cancer-specific mortality interaction analysis (models A-C) The unadjusted model did not display significant interaction effect (p=.178). However, after final adjustment, the trend toward significant interaction is evident (p=.077). African American women at all levels of poverty had significantly greater crude mortality hazard when compared to white women (HRs 1.44-1.85). The excess mortality hazard among African American women in the low and high poverty groups were reduced sharply (by 37% & 23%) once treatment and cancer variables were included in the model. Only Latinas in the middle poverty level displayed excess hazard reduced from 1.49 to 1.28 (95% CI 1.01-1.62) compared to Whites after controlling for treatment and cancer characteristics, the only remaining example of increased mortality risk for Latinas when compared to White women. Latinas within high poverty groups compared to white women experienced decreased mortality hazard in the full model (HR 0.73, 95% CI 0.55-0.95), even lower risk than the main effects results. White women in high poverty areas compared to very low poverty areas exhibited significantly increased adjusted mortality hazard (HR 1.18, 95% CI 1.05-1.33). Latinas exhibited 61% increased mortality hazard when comparing those in the middle poverty group to very low poverty group, a contrast to from the main effects hazard although the confidence intervals overlap. African American women at the low, middle and highest 82 poverty levels displayed no significant differences when compared to African Americans in the lowest poverty group. In the final model, the African American point estimates suggested protection although were non-significant, a departure from the estimates in the main effects model by poverty group. In the interaction analysis, significant differences between racial/ethnic groups existed although the main effects analysis displayed overlapping confidence intervals in the final model. For example, among women in the high poverty levels, there are significant differences in the risk of death between African American women and Latinas given the non-overlapping confidence intervals. Furthermore, Latinas in the middle and in the high poverty groups have significantly different hazard ratios in the final model from the crude model hazard ratios. Breast cancer-specific mortality supplemental analysis (model D) The addition of Medicaid supplemental insurance, immigrant and delays variables to the model increased Latinas’ decreased mortality hazard (HR 0.85, 95% CI 0.73-1.00) yet had little impact on the excess mortality hazard for African American women (HR 1.17, 95% CI 1.05-1.30). The supplemental variables also enhanced the protectiveness experienced by Latinas in the high poverty level when compared to white women (HR 0.65, 95% CI 0.50-0.86). Latinas in the middle poverty group experienced excess mortality hazard which became non-significant once the supplemental variables were added (HR 1.20, 95% CI 0.94-1.52). African American women in the very low poverty and middle poverty levels continued to exhibit a 57% and 22% increased hazard of mortality respectively. 83 Table 22: Breast Cancer-Specific 5-Year Mortality Cox Proportional Hazard HR (95% CI) Breast-Specific Mortality Crude model SES/ Race/ethnicity Model A Model A + B Model A + B + C Variables A: Variables B: Age, marital Comorbidities, status, era of Chronic condition, diagnosis, SEER Mental Health registry condition Main Effects Model Variables C: Stage, grade, ER/PR, surgery, radiation, chemotherapy 1.0 0.95 (0.82-1.10) 1.25 (1.14-1.37) 0.90 (0.74-1.10) Secondary Analysis Model A+B+C+D Variables D: Medicaid, immigrant, delay Model ABC With Poverty Model ABCD With Poverty 1.0 0.92 (0.80-1.07) 1.18 (1.06-1.31) 0.89 (0.73-1.09) Model ABC With Race 1.0 0.85 (0.73-1.00) 1.17 (1.05-1.30) 0.84 (0.69-1.02) Model ABCD With Race 1.0 1.04 (0.97-1.12) 1.05 (0.97-1.13) 1.13 (1.02-1.24) 1.0 1.01 (0.94-1.08) 1.01 (0.94-1.10) 1.06 (0.96-1.17) 1.0 1.15 (1.00-1.32) 1.81 (1.66-1.97) 0.78 (0.66-0.92) 1.0 1.20 (1.03-1.38) 1.78 (1.63-1.96) 0.90 (0.74-1.10) 1.0 1.18 (1.02-1.36) 1.75 (1.60-1.92) 0.90 (0.74-1.09) Poverty Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.07 (1.00-1.14) 1.17 (1.09-1.25) 1.55 (1.43-1.69) 1.0 1.09 (1.02-1.17) 1.17 (1.08-1.26) 1.50 (1.37-1.63) 1.0 1.0 1.05 (0.98-1.13) 1.09 (1.01-1.16) 1.07 (0.99-1.15) 1.16 (1.07-1.25) 1.47 (1.35-1.61) 1.19 (1.09-1.30) Interaction Model 1.0 0.90 (0.60-1.35) 1.85 (1.33-2.58) 0.72 (0.52-0.99) 1.0 0.99 (0.66-1.48) 1.84 (1.32-2.56) 0.82 (0.58-1.15) 1.0 1.17 (0.85-1.61) 1.80 (1.29-2.51) 0.98 (0.65-1.46) 1.0 0.82 (0.55-1.23) 1.52 (1.09-2.12) 0.87 (0.62-1.23) 1.0 0.85 (0.57-1.28) 1.57 (1.12-2.18) 0.80 (0.57-1.13) 1.0 0.88 (0.63-1.22) 1.68 (1.32-2.14) 0.80 (0.60-1.08) 1.0 0.91 (0.66-1.27) 1.69 (1.32-2.15) 0.92 (0.67-1.26) 1.0 0.90 (0.65-1.26) 1.66 (1.30-2.12) 0.92 (0.67-1.26) 1.0 0.84 (0.60-1.17) 1.05 (0.82-1.34) 0.93 (0.68-1.27) 1.0 0.74 (0.53-1.03) 1.05 (0.82-1.35) 0.89 (0.65-1.22) 84 Race/Ethnicity White Latina African American Asian Very Low Poverty White Latina African American Asian Low Poverty White Latina African American Asian Middle Poverty Breast-Specific Mortality 85 White Latina African American Asian High Poverty White Latina African American Asian White Very Low Poverty Low Poverty Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latina Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty Type III χ2 p-value Crude model Model A Model A + B Model A + B + C 1.0 1.43 (1.14-1.80) 1.72 (1.44-2.06) 0.75 (0.54-1.03) 1.0 1.52 (1.20-1.92) 1.74 (1.45-2.09) 0.86 (0.61-1.20) 1.0 1.49 (1.18-1.89) 1.72 (1.43-2.07) 0.86 (0.61-1.20) 1.0 1.28 (1.01-1.62) 1.25 (1.04-1.51) 0.82 (0.58-1.16) Secondary Analysis Model A+B+C+D 1.0 1.20 (0.94-1.52) 1.22 (1.01-1.47) 0.79 (0.56-1.11) 1.0 0.92 (0.70-1.20) 1.44 (1.24-1.67) 0.77 (0.51-1.16) 1.0 1.01 (0.77-1.32) 1.46 (1.25-1.70) 0.91 (0.60-1.38) 1.0 1.00 (0.76-1.30) 1.45 (1.24-1.69) 0.91 (0.60-1.37) 1.0 0.73 (0.55-0.95) 1.12 (0.96-1.31) 0.95 (0.63-1.44) 1.0 0.65 (0.50-0.86) 1.10 (0.94-1.29) 0.87 (0.57-1.31) 1.0 1.06 (0.99-1.14) 1.11 (1.02-1.19) 1.38 (1.23-1.55) 1.0 1.07 (0.99-1.15) 1.08 (1.00-1.18) 1.30 (1.16-1.47) 1.0 1.06 (0.99-1.14) 1.08 (0.99-1.17) 1.29 (1.15-1.45) 1.0 1.05 (0.98-1.13) 1.03 (0.95-1.12) 1.18 (1.05-1.33) 1.0 1.02 (0.95-1.10) 1.00 (0.92-1.08) 1.12 (0.99-1.26) 1.0 0.96 (0.64-1.44) 1.03 (0.71-1.48) 1.07 (0.76-1.51) 1.0 0.98 (0.65-1.47) 1.03 (0.71-1.48) 1.04 (0.73-1.46) 1.0 0.98 (0.65-1.47) 1.03 (0.71-1.48) 1.04 (0.74-1.47) 1.0 0.72 (0.48-1.08) 0.85 (0.59-1.23) 0.87 (0.61-1.22) 1.0 0.69 (0.46-1.03) 0.78 (0.54-1.12) 0.79 (0.56-1.11) 1.0 1.04 (0.62-1.74) 1.76 (1.11-2.78) 1.40 (0.88-2.25) 1.0 0.99 (0.59-1.65) 1.67 (1.05-2.65) 1.34 (0.83-2.14) 1.0 0.98 (0.59-1.65) 1.64 (1.04-2.60) 1.32 (0.82-2.11) 1.0 1.08 (0.64-1.81) 1.61 (1.02-2.55) 1.04 (0.65-1.68) 1.0 1.00 (0.60-1.68) 1.40 (0.88-2.22) 0.88 (0.53-1.48) 1.0 1.19 (0.77-1.83) 1.15 (0.74-1.80) 1.48 (0.89-2.45) 1.0 1.0 1.0 1.20 (0.78-1.85) 1.20 (0.78-1.85) 1.12 (0.73-1.73) 1.14 (0.73-1.78) 1.14 (0.73-1.78) 0.98 (0.62-1.53) 1.45 (0.88-2.42) 1.44 (0.87-2.40) 1.29 (0.78-2.14) Wald Test for Interaction (9 df) 1.0 1.14 (0.74-1.75) 0.98 (0.63-1.54) 1.21 (0.73-2.01) 12.67 0.178 11.88 0.220 11.41 0.249 15.56 0.077 17.25 0.045 All-Cause Mortality Breast cancer all-cause mortality main effects analysis (models A-C with poverty/race) A series of models with the same sets of covariates in the cancer-specific analysis were employed. All covariates met the proportional hazards assumptions. The all-cause mortality hazard ratios are shown for breast cancer cases in table 23. African American women exhibited increased all-cause mortality when compared to White women in the crude model (HR 1.42, 95% CI 1.34-1.51). The remaining excess risk greatly decreased once treatment and tumor factors were added and eliminated when poverty was included (HR 1.02 95% CI 0.95-1.09). Latinas and Asian women both experienced decreased mortality hazard in the fully adjusted model. The addition of treatment and tumor factors further moved Latinas’ hazard ratio into significantly decreased risk which continued even with poverty (HR 0.83, 95% CI 0.76-0.91). Asian women’s lower risk of death was attenuated by demographic factors (model A) but remained significant after full adjustment HR 0.81 (95% CI 0.72-0.92). All poverty levels displayed increased mortality hazard except low poverty once comorbidity factors were included. Treatment and cancer variables provided the greatest influence on the mortality hazards for women in the high poverty level when compared to very low poverty. Breast cancer all-cause mortality interaction analysis (models A-C) The crude interaction model was highly suggestive of interaction between race/ethnicity and poverty (p=.05). After full adjustment, the interaction strengthened (p=.02). When compared to White women, African American women exhibited excess unadjusted mortality hazards ranging from 1.17 in high poverty to 1.47 in very low 86 poverty. After adjustment, African American women at all poverty levels no longer experienced statistically significant excess mortality hazard although point estimates with opposing directions appeared. The hazard ratio was 1.18 (95% CI 0.95-1.48) within the very low poverty level and protective (HR 0.87, 95% CI 0.73-1.03) in the low poverty group but the confidence intervals overlapped. The risk estimates differed from the main effects model, but were not statistically different Initially, when compared to white women in the very low poverty group, white women at each level of poverty showed unadjusted excess mortality hazard. After adjustment, only women in the high poverty maintained excess mortality risk (HR 1.15, 95% CI 1.07-1.23). Asian women also experienced increased mortality hazard at all poverty levels when compared to Asian women in the very low poverty group. After full adjustment, Asian women in the high poverty level remained with a 45% excess of risk of death. Both African American women and Latinas exhibited point estimates that differed from the direction of main effects estimates of increased risk in the middle and high poverty levels. Latinas in the low and high poverty levels compared to the very low poverty group suggested reduced risk of death. African American women in the low poverty group compared to very low poverty also trended toward reduced risk. The difference between African Americans and Latinas are strongest within the high poverty group. In contrast, African Americans and Asians differed greater within the very low poverty group. Breast cancer all-cause mortality supplemental analysis (model D) The addition of supplemental variables to the model strengthened the protective hazard ratio for Latinas and Asian women and moved African American women to 87 reduced risk compared to white women. Women in the high poverty levels continued to show excess hazard (HR 1.07, 95% CI 1.01-1.14). In the interaction model, White women in the high poverty group displayed increased mortality hazard (HR 1.09, 95% CI 1.02-1.17) while Latinas in the high poverty group experienced decreased mortality hazard (HR 0.70, 95% CI 0.53-0.92) when compared to those in the very low poverty level. Colorectal Cancer Results Poverty Group The distribution of colorectal cancer cases by poverty level is shown in tables 2425. The very low poverty level CRC cases tended to be white, male, and have unknown immigrant status. They were also more likely to be diagnosed at stage I at diagnosis and with well differentiated tumors, treated with chemotherapy and radiation, received guideline therapy and scored 0 on the Charlson Comorbidity Index. The greatest proportions of adenocarcinomas and male cases were among those in the very low poverty level. Cases in the low poverty level also tended to be white, diagnosed at stage II, III or regional, with moderately differentiated and adenocarcinoma tumors, and received surgery treatment. Middle poverty level CRC cases were white but had the second largest proportions of African Americans, Latinos, Asians and Native Americans. Middle poverty level cases were more likely to be diagnosed at stage II, poorly differentiated and adenocarcinoma tumors compared to other poverty groups. Those in the highest poverty level were more likely to be non-white, immigrant, and receiving Medicaid supplemental insurance than other poverty levels. High poverty 88 Table 23: All-Cause 5-Year Mortality Cox Proportional Hazard HR (95% CI) for Breast Cancer All-Cause Mortality Breast Cases Crude model SES/ Race/ethnicity Model A Model A + B Model A + B + C Variables A: Variables B: Age, marital Comorbidities, status, era of Chronic condition, diagnosis, SEER Mental Health registry condition Main Effects Model Variables C: Stage, grade, ER/PR, surgery, radiation, chemotherapy Secondary Analysis Model A+B+C+D Variables D: Medicaid, immigrant, delay Model ABC With Poverty Model ABCD With Poverty 1.0 0.97 (0.88-1.06) 1.42 (1.34-1.51) 0.69 (0.62-0.77) 1.0 1.03 (0.94-1.14) 1.42 (1.34-1.51) 0.84 (0.75-0.95) 1.0 0.96 (0.88-1.05) 1.30 (1.22-1.38) 0.82 (0.73-0.93) 1.0 0.83 (0.73-0.93) 1.08 (1.02-1.15) 0.85 (0.78-0.94) 1.0 0.83 (0.76-0.91) 1.02 (0.95-1.09) 0.81 (0.72-0.92) Model ABC With Race 1.0 0.77 (0.70-0.84) 0.99 (0.93-1.06) 0.75 (0.66-0.85) Model ABCD With Race Poverty Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.09 (1.05-1.13) 1.20 (1.15-1.25) 1.43 (1.36-1.51) 1.0 1.06 (1.02-1.11) 1.14 (1.09-1.20) 1.35 (1.28-1.43) 1.0 1.0 1.03 (0.99-1.08) 1.05 (1.01-1.09) 1.10 (1.05-1.15) 1.06 (1.01-1.11) 1.26 (1.19-1.33) 1.13 (1.07-1.19) Interaction Model 1.0 1.03 (0.99-1.08) 1.06 (1.01-1.11) 1.13 (1.07-1.20) 1.0 1.01 (0.96-1.05) 1.02 (0.98-1.07) 1.07 (1.01-1.14) 1.0 1.01 (0.81-1.27) 1.47 (1.18-1.84) 0.58 (0.47-0.72) 1.0 1.16 (0.92-1.45) 1.46 (1.17-1.82) 0.70 (0.56-0.88) 1.0 1.12 (0.89-1.41) 1.31 (1.05-1.64) 0.69 (0.55-0.87) 1.0 1.01 (0.81-1.27) 1.18 (0.95-1.48) 0.71 (0.57-0.89) 1.0 1.01 (0.80-1.27) 1.17 (0.94-1.46) 0.65 (0.52-0.81) 1.0 1.01 (0.81-1.27) 1.20 (1.01-1.42) 0.66 (0.55-0.80) 1.0 0.80 (0.65-0.99) 1.23 (1.03-1.45) 0.83 (0.68-1.01) 1.0 0.77 (0.63-0.95) 1.10 (0.93-1.31) 0.80 (0.66-0.98) 1.0 0.72 (0.58-0.89) 0.87 (0.73-1.03) 0.81 (0.66-0.99) 1.0 0.64 (0.52-0.79) 0.85 (0.71-1.00) 0.74 (0.61-0.91) 89 Race/Ethnicity White Latina African American Asian Very Low Poverty White Latina African American Asian Low Poverty White Latina African American Asian Middle Poverty 90 All-Cause Mortality Breast Cases Crude model Model A Model A + B Model A + B + C White Latina African American Asian High Poverty White Latina African American Asian White Very Low Poverty Low Poverty Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latina Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.05 (0.90-1.23) 1.31 (1.16-1.47) 0.72 (0.60-0.87) 1.0 1.13 (0.97-1.32) 1.36 (1.21-1.54) 0.87 (0.71-1.06) 1.0 1.05 (0.89-1.22) 1.27 (1.12-1.43) 0.87 (0.71-1.06) 1.0 0.96 (0.82-1.13) 1.08 (0.96-1.22) 0.86 (0.70-1.05) Secondary Analysis Model A+B+C+D 1.0 0.91 (0.78-1.07) 1.04 (0.92-1.17) 0.82 (0.67-1.00) 1.0 0.77 (0.65-0.91) 1.17 (1.06-1.28) 0.74 (0.58-0.94) 1.0 0.90 (0.76-1.08) 1.22 (1.11-1.34) 0.88 (0.69-1.14) 1.0 0.83 (0.70-0.99) 1.15 (1.05-1.27) 0.87 (0.68-1.12) 1.0 0.72 (0.60-0.85) 1.01 (0.91-1.11) 0.89 (0.70-1.15) 1.0 0.64 (0.54-0.76) 0.98 (0.88-1.08) 0.80 (0.62-1.04) 1.0 1.09 (1.05-1.14) 1.18 (1.12-1.23) 1.40 (1.31-1.50) 1.0 1.06 (1.02-1.11) 1.10 (1.05-1.16) 1.27 (1.19-1.37) 1.0 1.05 (1.01-1.10) 1.07 (1.02-1.13) 1.21 (1.13-1.30) 1.0 1.04 (1.00-1.09) 1.05 (1.00-1.10) 1.15 (1.07-1.23) 1.0 1.02 (0.97-1.06) 1.01 (0.96-1.06) 1.09 (1.02-1.17) 1.0 0.89 (0.67-1.17) 1.04 (0.81-1.33) 1.10 (0.88-1.39) 1.0 0.89 (0.68-1.18) 1.04 (0.81-1.33) 1.07 (0.84-1.34) 1.0 0.88 (0.67-1.16) 1.04 (0.81-1.33) 1.07 (0.85-1.35) 1.0 0.76 (0.58-1.01) 0.96 (0.75-1.23) 0.98 (0.78-1.24) 1.0 0.73 (0.56-0.97) 0.90 (0.70-1.15) 0.91 (0.72-1.15) 1.0 0.83 (0.61-1.12) 1.22 (0.93-1.60) 1.06 (0.81-1.40) 1.0 0.74 (0.54-1.00) 1.08 (0.82-1.42) 1.00 (0.75-1.32) 1.0 0.73 (0.54-0.99) 1.00 (0.76-1.32) 0.91 (0.69-1.20) 1.0 0.74 (0.55-1.01) 1.00 (0.76-1.31) 0.81 (0.62-1.08) 1.0 0.64 (0.47-0.87) 0.92 (0.70-1.20) 0.70 (0.53-0.92) 1.0 1.25 (0.94-1.65) 1.45 (1.09-1.92) 1.77 (1.28-2.43) 1.0 1.0 1.0 1.25 (0.94-1.66) 1.22 (0.92-1.61) 1.19 (0.89-1.57) 1.27 (0.96-1.68) 1.36 (1.03-1.80) 1.34 (1.01-1.78) 1.60 (1.16-2.20) 1.53 (1.11-2.11) 1.45 (1.05-2.00) Wald Test for Interaction (9 df) 1.0 1.17 (0.88-1.55) 1.28 (0.96-1.69) 1.36 (0.98-1.87) Type III χ2 p-value 17.203 0.0456 15.504 0.078 14.750 0.098 19.517 0.021 26.197 0.002 cases were more significantly associated with stage II, IV and unstaged status at diagnosis and moderately differentiated or unknown grade and histology status. These cases received considerably less surgery, chemotherapy and guideline treatment than others. In addition, individuals in high poverty were scored higher (1 and 2+) for the Charlson Comorbidity Index and had the highest proportions of chronic conditions. The CRC cases in the missing poverty group were considerably more white, female and diagnosed at the localized historical stage than other groups. They also had the greatest proportion of well differentiated and mucinous adenocarcinoma. Those in the missing poverty group experienced the highest levels of most mental health conditions. Race/Ethnic Groups White CRC cases were largely in the very low and low poverty groups, and diagnosed at stage I, II or localized stages compared to other racial/ethnic groups (tables 26-27). White men and women tended to be diagnosed with poorly and undifferentiated tumor grades and mucinous adenocarcinoma histologies. Whites provided the largest share of rectal tumors and received guideline therapy. African Americans had the greatest proportion of cases in the highest poverty level and rectosigmoid cancers. African Americans had high proportions of stage IV and unstaged, unknown grade and mucinous adenocarcinoma histology cancers. Yet, African Americans were the least likely to receive guideline treatment compared to other racial/ethnic groups. In addition, African Americans had high levels of 1 and 2+ scores on the Charlson comorbidity index and the highest proportion of hypertension, anemia and arthritis and experienced the most treatment delay. 91 Latinos were primarily represented in the high and middle poverty groups, were the second largest proportion of immigrants and the highest proportion of cases with supplemental Medicaid. Latinos were diagnosed with stage IV and distant disease at greater proportions than others. Latinos were more likely to receive surgery, chemotherapy and radiation treatment than others. Additionally, Latinas has the highest proportion of mental health conditions. Asian men and women were largely in the very low, low and middle poverty groups, the only racial ethnic group with more male cases than female and the highest proportion of immigrants and second greatest proportion of cases with supplemental Medicaid coverage. Asian cases were mostly represented among moderately differentiated, adenocarcinoma and left-sided tumors. AIANs were likely to be in the high and middle poverty groups, most likely to be female, and non-immigrant. AIANs has the highest proportions of stage II and III cases, yet also well differentiated tumor grade compared to other racial/ethnic groups. Stage at Diagnosis Ordinal Regression All racial/ethnic groups were at increased odds of diagnosis at stages II-IV when compared to white women (table 28). Latinos were at 1.19 greater odds of later stage at diagnosis (95% CI 1.09-1.31) even after accounting for poverty level. Asians had the second greatest magnitude of risk of later stage disease (OR 1.15, 95% CI 1.05-1.26), followed by African Americans (OR 1.12, 95% CI 1.04-1.20) after adjusting for poverty. The high poverty level when compared to those in the very low poverty group was at increased odds for diagnosis at stages II-IV (OR 1.10, 95% CI 1.03-1.16). When accounting for race/ethnicity, the increased odds of later stage disease for higher poverty cases became non-significant. 92 Table 24: Demographic Characteristics of Colorectal Cancer Cases by Poverty Level 93 Variable High poverty N (%) Low Poverty N (%) 6,106 Middle poverty N (%) 12,113 16,228 Very Low Poverty N (%) 17,347 Poverty Missing N (%) 2,026 Total Patients Number of deaths (5 year) All-cause CRC specific Age mean (sd) 66-69 70-74 75-79 80+ Race/Ethnicity White African American Latino Asian AIAN Gender Male Female Marital Status Unmarried Married Unknown Immigrant Yes No Unknown Medicaid Buyin Yes 2,998 1,557 (51.9) 77.4 (7.0) 1,016 (16.6) 1,494 (24.5) 1,494 (24.5) 2,102 (34.4) Total N (%) 53,820 5,470 2,821 (51.6) 77.6 (6.8) 1,861 (15.4) 2,958 (24.4) 2,978 (24.6) 4,316 (35.6) 6,960 3,551 (51.0) 77.4 (6.8) 2,567 (15.8) 3,994 (24.6) 4,030 (24.8) 5,637 (34.7) 7,166 3,682 (51.4) 76.9 (6.7) 3,015 (17.4) 4,534 (26.1) 4,363 (25.2) 5,435 (31.3) 921 379 (41.2) 77.8 (6.7) 294 (14.5) 467 (23.1) 488 (24.1) 777 (38.4) 23,515 11,990 (51.0) 77.3 (6.8) 8,753 (16.3) 13,447 (25.0) 13,353 (24.8) 18,267 (33.9) 2,878 (47.1) 2,068 (33.9) 619 (10.1) 508 (8.3 33 (0.5) 9,847 (81.3) 953 (7.9) 588 (4.9) 691 (5.7) 34 (0.3) 14,555 (89.7) 441 (2.7) 408 (2.5) 806 (5.0) 18 (0.1) 16,126 (93.0) 239 (1.4) 293 (1.7) 673 (3.9) 16 (0.1) 1,900 (93.8) 70 (3.5) 26 (1.3) 27 (1.3) 3 (0.2) 45,306 (84.2) 3,771 (7.0) 1,934 (3.6) 2,705 (5.0) 104 (0.2) 2,687 (44.0) 3,419 (56.0) 5,529 (45.7) 6,584 (54.4) 7,529 (46.4) 8,699 (53.6) 8,508 (49.1) 8,839 (51.0) 842 (41.6) 8,839 (51.0) 25,095 (46.6) 28,725 (58.4) 3,391 (55.5) 2,499 (40.9) 216 (3.5) 638 (10.5) 3,744 (61.3) 1,724 (28.2) 5,753 (47.5) 6,053 (50.0) 307 (2.5) 981 (8.1) 7,583 (62.6) 3,549 (29.3) 7,152 (44.1) 8,702 (53.6) 374 (2.3) 1,191 (7.3) 10,155 (62.6) 4,882 (30.1) 6,753 (38.9) 10,160 (58.6) 434 (2.5) 1,321 (7.6) 10,774 (62.1) 5,252 (30.3) 909 (44.9) 1,032 (50.9) 85 (4.2) 137 (6.8) 1,526 (75.3) 363 (17.9) 23,958 (44.5) 28,446 (52.9) 1,416 (2.6) 4,268 (7.9) 33,782 (62.8) 15,770 (29.3) 1,672 (27.4) 1,642 (13.6) 1,293 (8.0) 650 (3.8) 138 (6.8) 5,395 (10.0) P value P< .0001 P< .0001 P< .0001 P< .0001 P< .0001 P< .0001 P< .0001 Variable Geography Rural/Less Urban Urban/Metro High poverty N (%) Middle poverty N (%) Low Poverty N (%) Very Low Poverty N (%) Poverty Missing N (%) Total N (%) P value 59 (1.0) 6,047 (99.0) 490 (4.1) 11,623 (96.0) 341 (2.1) 15,887 (97.9) 14 (0.08) 17,333 (99.9) 73 (3.6) 1,953 (96.4) 73 (3.6) 52,843 (98.2) P< .0001 94 Table 25: Clinical Characteristics of Colorectal Cancer Cases by Poverty Level Variable High poverty (N=6,106) N (%) Middle poverty (N=12,113) N (%) Low Poverty (N=16,228) N (%) Very Low Poverty (N=17,347) N (%) Poverty Missing (N=2,026) N (%) Total (n=53,820) N (%) P value 178 (2.9) 1,621 (26.6) 2,040 (33.4) 1,401 (22.9) 503 (8.2) 363 (5.9) 287 (2.4) 3,461 (28.6) 4,189 (34.6) 2,776 (22.9) 921 (7.6) 479 (4.0) 369 (2.3) 4,675 (28.8) 5,621 (34.6) 3,762 (23.2) 1,188 (7.3) 613 (3.8) 483 (2.8) 5,122 (29.5) 5,828 (33.6) 3,933 (22.7) 1,318 (7.6) 663 (3.8) 51 (2.5) 632 (31.2) 700 (34.6) 414 (20.4) 131 (6.5) 98 (4.8) 1,368 (2.5) 15,511 (28.8) 18,378 (34.2) 12,286 (22.8) 4,061 (7.6) 2,216 (4.1) P<.0001 2,714 (44.5) 2,451 (40.1) 593 (9.7) 348 (5.7) 5,579 (46.4) 5,007 (41.3) 1,078 (8.9) 449 (3.7) 7,466 (46.0) 6,816 (42.0) 1,374 (8.5) 572 (3.5) 8,243 (47.5) 6,950 (40.1) 1,523 (8.8) 631 (3.6) 1,003 (49.5) 782 (38.6) 146 (7.2) 95 (4.7) 25,005 (46.5) 22,006 (40.9) 4,714 (8.8) 2,095 (3.9) P<.0001 616 (10.1) 3,894 (63.8) 850 (13.9) 26 (0.4) 720 (11.8) 1,202 (9.9) 7,573 (62.5) 2,079 (17.2) 94 (0.8) 1,165 (9.6) 1,471 (9.1) 10,284 (63.4) 2,722 (16.8) 104 (0.6) 1,647 (10.2) 1,783 (10.3) 10,879 (62.7) 2,588 (14.9) 78 (0.5) 2,019 (11.6) 221 (10.9) 1,230 (60.7) 330 (16.3) 8 (0.4) 237 (11.7) 5,293 (9.8) 33,860 (62.9) 8,569 (15.9) 310 (0.6) 5,788 (10.8) P<.0001 5,211 (85.3) 651 (10.7) 10,435 (86.2) 1,294 (10.7) 13,989 (86.2) 1,736 (10.7) 15,054 (86.8) 1,726 (10.0) 1,728 (85.3) 244 (12.0) 46,417 (86.2) 5,651 (10.5) Cancer Stage 95 Stage 0 Stage I Stage II Stage III Stage IV Unknown/unstaged Historic Stage Localized Regional Distant Unstaged/unk Tumor grade Well Moderate Poor Undifferentiated Unknown Histology Adenocarcinoma Mucinous adenocarcinoma Other/unkown Tumor Location Proximal/Transverse Descending Sigmoid P=.001 244 (4.0) 384 (3.2) 503 (3.1) 567 (3.3) 54 (2.7) 1,752 (3.3) 2,714 (44.5) 304 (5.0) 1,388 (22.7) 5,520 (45.6) 495 (4.1) 2,721 (22.5) 7,494 (46.2) 701 (4.3) 3,570 (22.0) 7,840 (45.2) 720 (4.2) 3,951 (22.8) 976 (48.2) 84 (4.2) 439 (21.7) 24,544 (45.6) 2,304 (4.3) 12,069 (22.4) Variable High poverty (N=6,106) N (%) Colon NOS/unk Rectosigmoid Rectum Colon Left-sided Right Sided Other/Unknown Treatment Surgery Yes Chemotherapy Yes Radiation Yes 96 Guideline Treatment Yes No Unknown Charlson Comorbidity Index 0 1 2+ Specific Comorbidities (Yes) Hypertension Anemia Hepatitis Arthritis Anxiety Depression Bipolar Low Poverty (N=16,228) N (%) 532 (8.7) 1,040 (17.0) 128 (2.1) Middle poverty (N=12,113) N (%) 1,047 (8.6) 2,124 (17.5) 206 (1.7) Poverty Missing (N=2,026) N (%) 155 (7.7) 330 (16.3) 42 (2.1) Total (n=53,820) N (%) P value 1,437 (8.9) 2,767 (17.1) 259 (1.6) Very Low Poverty (N=17,347) N (%) 1,574 (9.1) 3,000 (17.3) 262 (1.5) 897 (1.7) 4,745(8.8) 9,261 (17.2) P=.01 1,948 (43.0) 2,537 (56.0) 83 (0.9) 3,677 (41.1) 5,182 (58.0) 83 (0.9) 4,859 (40.4) 7,053 (58.7) 112 (0.9) 5,294 (41.5) 7,390 (57.9) 89 (0.7) 608 (39.5) 925 (60.0) 8 (0.5) 16,386 (41.2) 23,087 (58.0) 341 (0.9) P=.006 5,313 (87.0) 1,818 (29.8) 833 (13.6) 10,761 (88.8) 3,887 (32.1) 1,628 (13.4) 14,495 (89.3) 5,451 (33.6) 2,263 (14.0) 15,491 (89.3) 6,098 (35.2) 2,517 (14.5) 1,808 (89.2) 610 (30.1) 230 (11.4) 47,868 (88.9) 17,864 (33.2) 7,471 (13.9) P<.0001 P<.0001 P=.0008 4,034 (66.1) 1,531 (25.1) 541 (8.9) 8,636 (71.3) 2,711 (22.4) 766 (6.3) 11,786 (72.6) 3,460 (21.3) 982 (6.1) 12,743 (73.5) 3,458 (19.9) 1,146 (6.6) 1,460 (72.1) 417 (20.6) 149 (7.4) 38,659 (71.8) 11,577 (21.5) 3,584 (6.7) P<.0001 3,612 (59.2) 1,446 (23.7) 1,048 (17.2) 7,651 (63.2) 2,776 (22.9) 1,686 (13.9) 10,510 (64.8) 3,582 (22.1) 2,136 (13.2) 11,446 (66.0) 3,859 (22.3) 2,042 (11.8) 1,294 (63.9) 487 (24.0) 245 (12.1) 34,513 (64.1) 12,150 (22.6) 7,157 (13.3) P<.0001 3,652 (59.8) 1,927 (31.6) 158 (2.6) 745 (12.2) 148 (2.4) 262 (4.3) 30 (0.5) 6,383 (52.7) 3,257 (26.9) 235 (1.9) 1,047 (8.6) 316 (2.6) 547 (4.5) 47 (0.4) 8,548 (52.7) 4,331 (26.7) 315 (1.9) 1,366 (8.4) 402 (2.5) 666 (4.1) 60 (0.4) 9,046 (52.2) 4,602 (26.5) 309 (1.8) 1,481 (8.5) 308 (1.8) 645 (3.7) 56 (0.3) 1,041 (51.4) 601 (30.0) 28 (1.4) 162 (8.0) 52 (2.6) 117 (5.8) 10 (0.5) 28,670 (53.3) 14,718 (27.4) 1,045 (1.9) 4,801 (8.9) 1,226 (2.3) 2,237 (4.2) 203 (0.4) P<.0001 P<.0001 P=.0008 P<.0001 P<.0001 P<.0001 NS Variable High poverty (N=6,106) N (%) Low Poverty (N=16,228) N (%) 50 (0.8) Middle poverty (N=12,113) N (%) 73 (0.6) Schizophrenia Delay to First Treatment (Yes) >= 8 weeks Poverty Missing (N=2,026) N (%) 7 (0.4) Total (n=53,820) N (%) P value 62 (0.4) Very Low Poverty (N=17,347) N (%) 64 (0.4) 256 (0.5) P<.0001 943 (16.4) 1,451 (12.6) 1,966 (12.7) 2,289 (13.8) 272 (14.1) 6,921 (13.5) P<.0001 97 African Americans when compared to Whites showed inconsistent increased odds of diagnosis at later stages compared to white women in the same poverty levels ranging from an odds of 0.80 (95% CI 0.63-1.03) very low poverty to 1.22 (95% CI 1.02-1.46) in low poverty. Latinos’ increased odds ranged from 1.07 odds (95% CI 0.89-1.30) in low poverty to 1.34 odds (95% CI 1.13-1.58) among the high. Asians trended toward increased odds among all poverty groups. Only African Americans displayed significant increased odds by poverty level. Both the high and low poverty groups within African Americans experience an excess risk of 50% or more while those in the middle poverty group had 1.38 times the odds of African Americans in the very low poverty level. CRC Cancer-Specific Survival Colorectal cancer specific 5-year survival was computed for each poverty level and racial/ethnic group (Figure 7). Men and women in the very low poverty level experienced the highest 5-year survival (75.6%) followed by Asians (75.3%), low poverty (75.1%), Whites (74.9%), middle poverty (73%), high poverty (70.1%), Latinos (69.9%) and African Americans (68%). African Americans and Latinos displayed survival gradients (p=0.0012 & p<0.0001 respectively). Figure 8 exhibited the lowest survival among African Americans in the high poverty group (66.2%) and Latinos in the very low poverty category (66.4%). The highest survival was among Asians in the very low poverty group (77.9%). Five-Year Overall Survival Overall survival (figure 9) followed a fairly similar pattern with Asians displaying the highest survival (59.4%), the very low poverty group (57.2%), low poverty (55.5%), Whites (55.2%), middle poverty (53.3%), Latinos (52.9%), high poverty level (49.3%) 98 Table 26: Demographic Characteristics of Colorectal Cancer Cases by Race/Ethnicity 99 Variable White No. (%) Latino No. (%) Asian No. (%) AIAN No. (%) All No. (%) 45,306 African American No. (%) 3,771 Total Patients Number of deaths (5 year) All-cause CRC specific Age mean (sd) 66-69 70-74 75-79 80+ % below poverty High Poverty Middle Poverty Low Poverty Very Low Poverty Missing Gender Male Female Marital Status Unmarried Married Unknown Immigrant Yes No Unknown P value 1,934 2,705 104 53,820 19,644 9,848 (50.1) 77.5 (6.8) 7,024 (15.5) 11,084 (24.5) 11,308 (25.0) 15,890 (35.1) 1,890 1,024 (54.2) 76.4 (6.7) 726 (19.3) 1,034 (27.4) 938 (24.9) 1,073 (28.5) 875 504 (57.6) 76.1 (6.8) 437 (22.6) 525 (27.2) 423 (21.9) 549 (28.4) 1,055 586 (55.6) 76.1 (6.5) 543 (20.1) 771 (28.5) 656 (24.3) 735 (27.2) 51 28 (54.9) 75.3 (6.2) 23 (22.1) 33 (31.7) 28 (26.9) 20 (19.2) 23,515 P< 11,990 (51.0) .0001 77.3 (6.8) 8,753 (16.3) 13,447 (25.0) P< 13,353 (24.8) .0001 18,267 (33.9) 2,878 (6.4) 9,847 (21,7) 14,555 (32.1) 16,126 (35.6) 1,900 (4.2) 2,068 (54.8) 953 (25.3) 441 (11.7) 239 (6.3) 70 (1.9) 619 (32.0) 588 (30.4) 408 (21.1) 293 (15.2) 26 (1.3) 508 (18.8) 691 (25.6) 806 (29.8) 673 (24.9) 27 (1.0) 33 (31.7) 34 (32.7) 18 (17.3) 16 (15.4) 3 (2.9) 6,106 (11.4) 12,113 (22.5) 16,228 (30.2) 17,347 (32.2) P< 2,026 (3.8) .0001 21,070 (46.5) 24,236 (53.5) 1,583 (42.0) 2,188 (58.0) 931 (48.1) 1,003 (51.9) 1,473 (54.5) 1,232 (45.6) 38 (36.5) 66 (63.5) 25,095 (46.6) P< 28,725 (53.4) .0001 19,836 (43.8) 24,308 (53.7) 1,162 (2.6) 2,827 (6.2) 29,165 (64.4) 13,314 (29.4) 2,223 (59.0) 1,414 (37.5) 134 (3.6) 25 (0.7) 2,582 (68.5) 1,164 (30.9) 897 (46.4) 970 (50.2) 67 (3.5) 493 (25.5) 803 (41.5) 638 (33.0) 956 (35.3) 1,699 (62.8) 50 (1.9) 919 (34.0) 1,153 (42.6) 633 (23.4) 46 (44.2) 55 (52.9) 3 (2.9) 4 (3.9) 79 (76.0) 21 (20.2) 23,958 (44.5) 28,446 (52.9) 1,416 (2.6) 4,268 (7.9) 33,782 (62.8) 15,770 (29.3) P< .0001 P< .0001 Variable White No. (%) Medicaid Buyin Yes 2,807 (6.2) Urban/Rural Rural/Less Urban 951 (2.1) Urban/Metro 44,355 (97.9) African American No. (%) Latino No. (%) Asian No. (%) AIAN No. (%) All No. (%) P value 1,048 (27.8) 663 (34.3) 853 (31.5) 24 (23.1) 5,395 (10.0) P< .0001 12 (0.3) 3,759 (99.7) 13 (0.7) 1,921 (99.3) 1 (0.04) 2,704 (99.9) 0 (0) 104 (100) 997 (1.8) 52,843 (98.2) P< .0001 100 Table 27: Clinical Characteristics of Colorectal Cancer Cases by Race/Ethnicity Variable White (n=45,306) No. (%) African American (n=3,771) No. (%) Latino (n=1,934) No. (%) Asian (n=2,705) No. (%) AIAN (n=104) No. (%) All (n=53,820) No. (%) 1,115 (2.5) 13,202 (29.1) 15,667 (34.6) 10,224 (22.6) 3,301 (7.3) 1,797 (4.0) 118 (3.1) 1,022 (27.1) 1,141 (30.3) 895 (23.7) 348 (9.2) 247 (6.6) 53 (2.7) 489 (25.3) 690 (35.7) 448 (23.2) 190 (9.8) 64 (3.3) 81 (3.0) 779 (28.8) 838 (31.0) 691 (25.6) 215 (8.0) 101 (3.7) 1 (1.0) 19 (18.3) 42 (40.4) 28 (26.9) 7 (6.7) 7 (6.7) 1,368 (2.5) 15,511 (28.8) 18,378 (34.2) 12,286 (22.8) 4,061 (7.6) 2,216 (4.1) 21,191 (46.8) 18,573 (41.0) 3,848 (8.5) 1,694 (3.7) 1,674 (44.4) 1,460 (38.7) 399 (10.6) 238 (6.3) 846 (43.7) 807 (41.7) 223 (11.5) 58 (3.0) 1,258 (46.5) 1,117 (41.3) 232 (8.6) 98 (3.6) 36 (34.6) 49 (47.1) 12 (11.5) 7 (6.7) 25,005 (46.5) 22,006 (40.9) 4,714 (8.8) 2,095 (3.9) 4,476 (9.9) 28,335 (62.5) 7,452 (16.5) 278 (0.6) 4,765 (10.5) 411 (10.9) 2,402 (63.7) 424 (11.2) 20 (0.5) 514 (13.6) 210 (10.9) 1,236 (63.9) 274 (14.2) 5 (0.3) 209 (10.8) 180 (6.7) 1,821 (67.3) 406 (15.0) 7 (0.3) 291 (10.8) 16 (15.4) 66 (63.5) 13 (12.5) 0 (0) 9 (8.7) 5,293 (9.8) 33,860 (62.9) 8,569 (15.9) 310 (0.6) 5,788 (10.8) 39,023 (86.1) 4,858 (10.7) 3,189 (84.6) 404 (10.7) 1,674 (86.6) 201 (10.4) 2,441 (90.2) 180 (6.7) 90 (86.5) 8 (7.7) 46,417 (86.2) 5,651 (10.5) 1,425 (3.2) 178 (4.7) 59 (3.1) 84 (3.1) 6 (5.8) 1,752 (3.3) 20,856 (46.0) 1,892 (4.2) 10,033 (22.1) 1,878 (49.8) 214 (5.7) 809 (21.5) 835 (43.2) 63 (3.3) 419 (21.7) 923 (34.1) 134 (5.0) 787 (29.1) 52 (50.0) 1 (1.0) 21 (20.2) 24,544 (45.6) 2,304 (4.3) 12,069 (22.4) P value Cancer Stage 101 Stage 0 Stage I Stage II Stage III Stage IV Unstaged/unk Historic Stage Localized Regional Distant Unstaged/unk Tumor grade Well Moderate Poor Undifferentiated Unknown Histology Adenocarcinoma Mucinous adenocarcinoma Other/unknown Tumor Location Proximal/Transverse Descending Sigmoid P< .0001 P< .0001 P< .0001 P< .0001 P< .0001 Variable White (n=45,306) No. (%) Colon NOS/unknown Rectosigmoid Rectum Colon Left-sided Right Sided Other/Unknown Treatment Surgery Yes Chemotherapy Yes Radiation Yes 102 Guideline Treatment Yes No Unknown Charlson Comorbidity Index 0 1 2+ Specific Comorbidities (Yes) Hypertension Anemia Hepatitis Arthritis Anxiety Depression Bipolar Latino (n=1,934) No. (%) Asian (n=2,705) No. (%) AIAN (n=104) No. (%) All (n=53,820) No. (%) 3,983 (8.8) 3,983 (8.8) 7,810 (17.2) African American (n=3,771) No. (%) 289 (7.7) 494 (13.1) 87 (2.3) P value 190 (9.8) 392 (20.3) 35 (1.8) 273 (10.1) 548 (20.3) 40 (1.5) 10 (9.6) 17 (16.4) 3 (2.9) 897 (1.7) 4,745 (8.8) 9,261 (17.2) 13,574 (40.5) 19,659 (58.7) 280 (0.8) 1,228 (41.1) 1,732 (58.0) 28 (0.9) 560 (41.4) 777 (57.5) 15 (1.1) 999 (53.0) 868 (46.1) 17 (0.9) 25 (32.5) 51 (66.2) 1 (1.3) 16,386 (41.2) 23,087 (58.0) 341 (0.9) P< .0001 40,464 (89.3) 15,065 (33.3) 6,308 (13.9) 3,225 (85.5) 1,128 (29.9) 434 (11.5) 1,728 (89.4) 705 (36.5) 327 (16.9) 2,362 (87.3) 935 (34.6) 391 (14.5) 89 (85.6) 31 (29.8) 11 (10.6) 47,868 (88.9) 17,864 (33.2) 7,471 (13.9) P<.0001 P<.0001 P<.0001 32,935 (72.7) 9,459 (20.9) 2,912 (6.4) 2,459 (65.2) 947 (25.1) 365 (9.7) 1,355 (70.1) 462 (23.9) 117 (6.1) 1,846 (68.2) 677 (25.0) 182 (6.7) 38,659 (71.8) 11,577 (21.5) 3,584 (6.7) 38,659 (71.8) 11,577 (21.5) 3,584 (6.7) P<.0001 29,367 (64.8) 10,148 (22.4) 5,791 (12.8) 2,163 (57.4) 925 (24.5) 683 (18.1) 1,208 (62.5) 425 (22.0) 301 (15.6) 1,717 (63.5) 628 (23.2) 360 (13.3) 58 (55.8) 24 (23.1) 22 (22.2) 34,513 (64.1) 12,150 (22.6) 7,157 (13.3) P<.0001 23,449 (51.8) 12,160 (26.8) 800 (1.8) 3,875 (8.6) 1,048 (2.3) 1,975 (4.4) 172 (0.4) 2,644 (70.1) 1,400 (37.1) 104 (2.8) 538 (14.3) 78 (2.1) 120 (3.2) 17 (0.5) 960 (49.6) 542 (28.0) 63 (3.3) 190 (9.8) 62 (3.2) 90 (4.7) 10 (0.5) 1,567 (57.9) 590 (21.8) 77 (2.9) 186 (6.9) 36 (1.3) 48 (1.8) 4 (0.2) 50 (48.1) 26 (25.0) 1 (1.0) 12 (11.5) 2 (1.9) 4 (3.9) 0 (0) 28,670 (53.3) 14,718 (27.4) 1,045 (1.9) 4,801 (8.9) 1,226 (2.3) 2,237 (4.2) 203 (0.4) P<.0001 P<.0001 P<.0001 P<.0001 P=.0006 P<.0001 NS Variable White (n=45,306) No. (%) Latino (n=1,934) No. (%) Asian (n=2,705) No. (%) AIAN (n=104) No. (%) All (n=53,820) No. (%) P value 197 (0.4) African American (n=3,771) No. (%) 43 (1.1) Schizophrenia Delay to First Treatment (Yes) >= 8 weeks 10 (0.5) 5 (0.2) 1 (1.0) 256 (0.5) P<.0001 7,931 (17.5) 994 (26.4) 355 (18.4) 536 (19.8) 20 (19.2) 9,836 (18.3) P<.0001 103 and African Americans (48.1%). African Americans and Whites experienced a survival gradient by poverty level (p=0.0003 and p<0.0001 respectively). The highest survival was among Asians in the very low poverty category (65.2%) and African Americans in the high poverty group (45.7%), an almost 20% difference (figure 10). CRC Specific Mortality CRC cancer-specific mortality main effects analysis (models A-C with poverty/race) Crude and adjusted models were developed and all covariates met the proportional hazards assumptions. Both Latinos and African Americans exhibited unadjusted increased risk of death compared to Whites (table 29). African Americans experienced an unadjusted hazard ratio of 1.33 (95% CI 1.25-1.42) which was reduced to an excess mortality risk of 19% after full adjustment. The increase among Latinos became non-significant once tumor and treatment factors were included and further reduced after accounting for poverty (HR 1.07, 95% CI 0.97-1.18). Asians exhibited no risk difference from Whites. Both high and middle poverty groups displayed both crude and adjusted increased risk of death. The addition of race/ethnicity influenced the high poverty level mortality hazards to a greater extent than tumor and treatment factors. CRC cancer-specific mortality interaction analysis (models A-C) No interaction effect was found both in the crude (p=.286) and adjusted (p=.429) models. Of those within the very low poverty category, Latinos, not African Americans, experienced excess risk (HR 1.26, 95% CI 1.02-1.57) both in crude and adjusted models compared to Whites. 104 Table 28: Odds of Stages II/III/IV by Race/ethnicity & Poverty Level among Colorectal Cancer Cases Stages II, II, IV N (%) Race/Ethnicity White African American Latino Asian Odds Ratio (95% confidence interval) (n=48,266) Main Effects Full Main Effects Full Model Model With Poverty 28,018 (69.0) 2,343 (70.0) 1,313 (73.2) 1,731 (69.2) 1.0 (referent) 1.14 (1.06-1.22) 1.20 (1.10-1.32) 1.16 (1.05-1.27) Main Effects Full Model 1.0 (referent) 1.12 (1.04-1.20) 1.19 (1.09-1.31) 1.15 (1.05-1.26) Main Effects Full Model With Race/Ethnicity 11,067 (68.4) 10,557 (69.3) 7,859 (69.5) 3,922 (70.9) 2.0 (referent) 1.03 (0.99-1.08) 1.04 (1.00-1.09) 1.10 (1.03-1.16) Interaction Full Model 1.0 (referent) 1.03 (0.99-1.07) 1.03 (0.98-1.08) 1.05 (0.98-1.12) 10,293 (68.4) 136 (61.8) 199 (72.9) 439 (68.8) 1.0 (referent) 0.80 (0.63-1.03) 1.19 (0.96-1.49) 1.17 (1.00-1.37) 9,470 (69.2) 293 (71.8) 270 (71.2) 524 (70.0) 1.0 (referent) 1.22 (1.02-1.46) 1.07 (0.89-1.30) 1.18 (1.02-1.37) 6,413 (69.4) 600 (68.7) 400 (71.6) 446 (69.4) 1.0 (referent) 1.08 (0.95-1.23) 1.17 (1.00-1.37) 1.16 (1.00-1.36) 1,842 (69.8) 1,314 (71.2) 444 (76.3) 322 (68.4) 1.0 (referent) 1.19 (1.06-1.33) 1.34 (1.13-1.58) 1.10 (0.91-1.32) 10,293 (68.4) 9,470 (69.2) 1.0 (referent) 1.02 (0.98-1.07) Poverty Very Low Low Middle High Very Low Poverty White African American Latino Asian Low Poverty White African American Latino Asian Middle Poverty White African American Latino Asian High Poverty White African American Latino Asian White Very Low Poverty Low Poverty 105 Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latino Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty Stages II, II, IV N (%) 6,413 (69.4) 1,842 (69.8) Odds Ratio (95% confidence interval) (n=48,266) 1.03 (0.98-1.08) 1.02 (0.94-1.10) 136 (61.8) 293 (71.8) 600 (68.7) 1,314 (71.2) 1.0 (referent) 1.55 (1.15-2.09) 1.38 (1.05-1.81) 1.50 (1.16-1.94) 199 (72.9) 270 (71.2) 400 (71.6) 444 (76.3) 1.0 (referent) 0.92 (0.69-1.22) 1.01 (0.77-1.31) 1.14 (0.87-1.48) 439 (68.8) 524 (70.0) 446 (69.4) 322 (68.4) 1.0 (referent) 1.04 (0.85-1.26) 1.03 (0.84-1.25) 0.96 (0.77-1.19) Figure 7: Colorectal Cancer-Specific 5-Year Survival Five-Year Survival (%) CRC-Specific Survival by Poverty and Race/Ethnic Groups 78 76 74 72 70 68 66 64 Series1 Very Low Low Middle High 75.6 75.1 73 70.1 Poverty Group African American Latino 68.0 69.9 White Asian 74.9 75.3 Racial/Ethnic Group 106 Figure 8: Colorectal Cancer-Specific 5-Year Survival By Race/Ethnicity and Poverty Level Colorectal Cancer Specific Survival By Racial/Ethnic Group77.9 and Socioeconomic Status 75.7 Five-Year Survival (%) 78.0 75.3 76.2 75.1 Latino 74.4 76.0 African American 75.9 73.4 73.0 71.2 74.0 72.0 69.4 70.0 69.0 66.4 69.0 69.4 68.0 66.0 66.2 64.0 Asian White 62.0 Latino 60.0 Very Low African American Low Middle Poverty High Race/ Ethnicity Figure 9: CRC Overall 5-Year Survival CRC Overall Survival by Poverty and Race/Ethnic Groups Five-Year Survival (%) 70 60 50 40 30 20 10 0 Series1 Very Low Low Middle High 57.2 55.5 53.3 49.3 Poverty Group African American Latino 48.1 52.9 White Asian 55.2 59.4 Racial/Ethnic Group 107 Figure 10: CRC Overall 5-Year Survival by Racial/Ethnic Group and Poverty Level CRC Overall Survival By Racial/Ethnic Group and Socioeconomic Status 65.2 70.0 57.9 56.9 56.6 58.3 60.0 50.6 40.0 30.0 20.0 Five Year Survival (%) 50.9 50.0 55.5 African American Latino 59.0 54.5 53.3 52.4 White 50.4 52.2 49.6 45.7 Asian 10.0 White Latino 0.0 Very Low Low Poverty Group Race/ Ethnicity African American Middle High Excess mortality was displayed by African Americans in the low (22%), middle (15%) and high (25%) poverty levels compared to whites in the same poverty levels. Comparing high poverty to very low level resulted in significant increased mortality within Asians after controlling for all variables (HR 1.32, 95% CI 1.03-1.69). Within the African American group, those in high poverty compared to very low poverty began with increased risk (HR 1.47, 95% CI 1.11-1.96) but the addition of the treatment and cancer characteristics to the model eliminated the statistical significance (HR 1.26, 95% CI 0.941.67). Asians exhibited a potential reduction in risk of cases in the low poverty level compared to very low poverty group (HR 0.79, 95% CI 0.59-1.06). Within the middle 108 poverty level, Asians showed decreased risk compared to Whites (HR 0.85, 95% CI 0.711.01) and increased risk in high poverty (HR 1.14, 95% CI 0.93-1.39). CRC cancer-specific mortality supplemental analysis (model D) African Americans showed a slight increase in excess mortality hazard after the addition of the supplemental variables (HR 1.20, 95% CI 1.11-1.30). The middle poverty group also maintained significant increased mortality (HR 1.09, 95% CI 1.03-1.15) while the high poverty risk was attenuated. The excess mortality of African Americans in the low, middle and high poverty levels compared to Whites, showed slight enhanced risk by the addition of the supplemental variables. Colorectal Cancer All Cause Specific Mortality CRC cancer all-cause mortality main effects analysis (models A-C with poverty/race) A series of models with the same sets of covariates in the cancer-specific analysis were employed. All covariates met the proportional hazards assumptions. African Americans were found to be at a 10% elevated risk for mortality after accounting for demographic, comorbidities, treatment and cancer characteristics and poverty levels when compared to Whites down from a crude risk of 24% (table 30). In contrast, Asians were at decreased mortality hazard in the full model (HR 0.86, 95% CI 0.80-0.93). Latinos showed no differences compared to whites after full adjustments were made. The adjusted risk estimates are significant difference between African Americans and Asians with non-overlapping confidence intervals. Initially, each poverty level displayed a gradient of 5% (low poverty), 13% (middle poverty) and 27% (high poverty) increased risk for mortality which decreased to 4%, 11% and 17% for the low, middle and high 109 poverty groups respectively after adjustment. High and low poverty groups displayed considerably different risk estimates. CRC cancer all-cause interaction analysis (models A-C) The test for interaction was non-significant for both the crude (p=.21) and adjusted models (p=.808). African Americans had substantial greater mortality hazard compared to Whites only in the high poverty category (HR 1.11, 95% CI 1.02-1.20). Latinos showed no difference to whites at any poverty level except for very low poverty, although the adjusted hazard ratio was not statistically significant (HR 1.13, 95% CI 0.96-1.34). Poverty levels within Latinos displayed different risk estimates from the main effects model particularly among the low poverty group (HR 0.85, 95% CI 0.68-1.07). Only Whites exhibited statistically significant risk gradient by poverty level nearly copying the main effects estimates. African Americans and Asians suggested a risk gradient but were non-significant. Unlike the main effects analysis, Asians and Latinos were considerably different within the very low poverty group. CRC cancer all-cause supplemental analysis (model D) The addition of the supplemental variables had little impact on the point estimates and hazard ratios for all-cause mortality. The variables enhanced protection for Asians and attenuated elevated risk among other race/ethnic and poverty groups. 110 Table 29: Colorectal Cancer-Specific 5-Year Mortality Cox Proportional Hazard HR (95% CI) CRC-Specific Mortality Crude model SES/ Race/ethnicity Model A Model A + B Model A + B + C Variables A: Age, gender, marital status, era of diagnosis, SEER registry Variables B: Comorbidities, Chronic condition, Mental Health condition Variables C: Stage, grade, histology surgery, radiation, chemo, guideline treatment Main Effects Model Secondary Analysis Model A+B+C+D Variables D: Medicaid, immigrant, delay Model ABC & Poverty Model ABCD & Poverty 111 Race/Ethnicity White Latino African American Asian 1.0 1.23 (1.12-1.34) 1.33 (1.25-1.42) 0.98 (0.90-1.06) 1.0 1.23 (1.11-1.35) 1.32 (1.24-1.42) 1.05 (0.95-1.16) 1.0 1.23 (1.12-1.35) 1.32 (1.24-1.42) 1.05 (0.95-1.16) 1.0 1.10 (1.00-1.21) 1.26 (1.18-1.35) 0.97 (0.88-1.08) 1.0 1.07 (0.97-1.18) 1.19 (1.10-1.29) 0.95 (0.86-1.06) Model ABC & Race 1.0 1.03 (0.94-1.14) 1.20 (1.11-1.30) 0.91 (0.82-1.02) Model ABCD & Race Poverty Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.04 (0.99-1.09) 1.13 (1.07-1.18) 1.28 (1.20-1.35) 1.0 1.05 (1.00-1.11) 1.14 (1.08-1.20) 1.26 (1.18-1.34) 1.0 1.0 1.05 (1.00-1.11) 1.03 (0.99-1.08) 1.14 (1.08-1.20) 1.12 (1.07-1.19) 1.26 (1.18-1.34) 1.20 (1.13-1.28) Interaction Model 1.0 1.03 (0.98-1.08) 1.10 (1.04-1.16) 1.12 (1.05-1.20) 1.0 1.01 (0.96-1.06) 1.09 (1.03-1.15) 1.07 (1.00-1.15) 1.0 1.46 (1.17-1.81) 1.02 (0.77-1.35) 0.92 (0.78-1.10) 1.0 1.44 (1.16-1.79) 1.03 (0.78-1.36) 0.99 (0.82-1.19) 1.0 1.44 (1.16-1.79) 1.03 (0.78-1.36) 0.99 (0.82-1.20) Very Low Poverty White Latino African American Asian Low Poverty 1.0 1.26 (1.02-1.57) 1.08 (0.82-1.43) 0.94 (0.78-1.13) 1.0 1.21 (0.97-1.50) 1.13 (0.86-1.50) 0.92 (0.76-1.11) CRC-Specific Mortality 112 White Latino African American Asian Middle Poverty White Latino African American Asian High Poverty White Latino African American Asian White Very Low Poverty Low Poverty Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latino Very Low Poverty Low Poverty Middle Poverty High Poverty Crude model Model A Model A + B Model A + B + C 1.0 1.03 (0.83-1.27) 1.26 (1.04-1.51) 1.01 (0.87-1.17) 1.0 1.03 (0.83-1.27) 1.26 (1.04-1.51) 1.06 (0.90-1.25) 1.0 1.03 (0.83-1.27) 1.26 (1.04-1.54) 1.07 (0.90-1.26) 1.0 0.97 (0.78-1.20) 1.22 (1.01-1.47) 0.95 (0.81-1.12) Secondary Analysis Model A+B+C+D 1.0 0.89 (0.72-1.10) 1.21 (1.01-1.46) 0.90 (0.76-1.06) 1.0 1.15 (0.98-1.35) 1.17 (1.03-1.34) 0.89 (0.75-1.05) 1.0 1.16 (0.98-1.37) 1.16 (1.02-1.33) 0.95 (0.79-1.13) 1.0 1.16 (0.98-1.36) 1.16 (1.02-1.33) 0.95 (0.79-1.13) 1.0 1.06 (0.89-1.25) 1.15 (1.01-1.32) 0.85 (0.71-1.01) 1.0 1.07 (0.91-1.27) 1.17 (1.02-1.34) 0.79 (0.66-0.95) 1.0 1.18 (1.00-1.40) 1.32 (1.18-1.47) 1.03 (0.85-1.25) 1.0 1.24 (1.04-1.47) 1.33 (1.19-1.49) 1.10 (0.90-1.34) 1.0 1.24 (1.04-1.47) 1.33 (1.19-1.49) 1.10 (0.91-1.34) 1.0 1.08 (0.91-1.28) 1.25 (1.11-1.40) 1.14 (0.93-1.39) 1.0 1.03 (0.87-1.23) 1.26 (1.12-1.41) 1.14 (0.94-1.40) 1.0 1.03 (0.99-1.09) 1.12 (1.06-1.18) 1.14 (1.05-1.24) 1.0 1.04 (0.99-1.10) 1.12 (1.06-1.19) 1.11 (1.02-1.21) 1.0 1.04 (0.99-1.10) 1.12 (1.06-1.19) 1.11 (1.02-1.21) 1.0 1.03 (0.98-1.08) 1.11 (1.05-1.18) 1.09 (1.00-1.19) 1.0 1.02 (0.97-1.07) 1.10 (1.04-1.17) 1.04 (0.95-1.13) 1.0 1.27 (0.92-1.77) 1.28 (0.95-1.73) 1.47 (1.11-1.96) 1.0 1.27 (0.92-1.77) 1.27 (0.94-1.71) 1.44 (1.08-1.92) 1.0 1.27 (0.91-1.77) 1.27 (0.94-1.71) 1.44 (1.08-1.91) 1.0 1.16 (0.84-1.62) 1.19 (0.88-1.61) 1.26 (0.94-1.67) 1.0 1.09 (0.78-1.51) 1.13 (0.84-1.53) 1.15 (0.86-1.54) 1.0 0.73 (0.54-0.99) 0.88 (0.68-1.15) 0.93 (0.71-1.20) 1.0 0.75 (0.55-1.01) 0.90 (0.69-1.18) 0.96 (0.73-1.25) 1.0 0.74 (0.55-1.00) 0.90 (0.69-1.18) 0.96 (0.73-1.25) 1.0 0.79 (0.59-1.06) 0.93 (0.71-1.22) 0.93 (0.71-1.21) 1.0 0.75 (0.55-1.01) 0.98 (0.75-1.28) 0.89 (0.68-1.16) 112 CRC-Specific Mortality Asian Very Low Poverty Low Poverty Middle Poverty High Poverty Type III χ2 p-value Crude model 1.0 1.13 (0.90-1.41) 1.08 (0.85-1.36) 1.27 (0.99-1.62) 10.852 0.286 Model A Model A + B + C Secondary Analysis Model A+B+C+D 1.0 1.0 1.0 1.12 (0.90-1.40) 1.12 (0.90-1.40) 1.04 (0.83-1.31) 1.07 (0.85-1.36) 1.07 (0.85-1.36) 1.01 (0.80-1.27) 1.24 (0.97-1.59) 1.24 (0.97-1.59) 1.32 (1.03-1.69) Wald Test for Interaction (4 df) 1.0 0.99 (0.79-1.24) 0.95 (0.75-1.20) 1.29 (1.01-1.66) 10.477 0.313 Model A + B 10.483 0.313 113 113 9.091 0.429 12.889 0.168 Prostate Cancer Results Poverty The prostate cancer case distribution by poverty level is illustrated in tables 3132. Men with prostate cancer in the very low poverty category were more likely than other poverty levels to be white, unknown immigrant status, diagnosed with localized/regional stage, Gleason scores of 5-7, received radiation and/or radical prostatectomy. Those in very low poverty had the lowest Charlson comorbidity index among poverty groups. The low poverty group was predominately White, diagnosed at the localized/regional stage, received surgery, hormone therapy, and chemotherapy. Of the surgery types, low poverty cases obtained radical prostatectomy in greater proportions than other poverty levels. Middle poverty cases were White and with higher proportions of non-White ethnic groups than low and very low poverty groups. The Middle poverty level tended to have a Gleason score of 2-4, have treatments of surgery, hormone therapy and chemotherapy. The high poverty level is primarily non-white and more immigrant and with supplemental Medicaid insurance than others. High poverty cases had the highest proportion of distant diagnosed cases, cases with Gleason scores of 8-10, watchful waiting treatment choice, and of those choosing surgery, they received other types of surgery than prostatectomy. In addition, high poverty prostate cancer cases tended to have higher Charlson comorbidity index scores and a greater presence of chronic conditions than other poverty levels. Racial/Ethnic Groups White prostate cancer cases were represented in the very low poverty level, had tumors with Gleason scores of 5-7, and received radiation at higher rates than others 114 Table 30: All-Cause 5-Year Mortality Cox Proportional Hazard HR (95% CI) Colorectal Cancer All-Cause Mortality CRC Cases SES/ Race/ethnicity Crude model Model A Model A + B Model A + B + C Variables A: Age, gender, marital status, era of diagnosis, SEER registry Variables B: Comorbidities, Chronic condition, Mental Health condition Variables C: Stage, grade, histology surgery, radiation, chemo, guideline treatment Main Effects Model Secondary Analysis Model A+B+C+D Variables D: Medicaid, immigrant, delay Model ABC & Poverty Model ABCD & Poverty 115 Race/Ethnicity White Latino African American Asian 1.0 1.08 (1.00-1.15) 1.24 (1.19-1.31) 0.88 (0.83-0.94) 1.0 1.11 (1.03-1.19) 1.25 (1.18-1.31) 0.94 (0.88-1.02) 1.0 1.08 (1.01-1.16) 1.21 (1.15-1.27) 0.93 (0.86-1.00) 1.0 1.01 (0.94-1.09) 1.17 (1.11-1.23) 0.88 (0.82-0.95) 1.0 0.98 (0.91-1.06) 1.10 (1.04-1.16) 0.86 (0.80-0.93) Model ABC & Race 1.0 0.97 (0.90-1.04) 1.09 (1.03-1.15) 0.84 (0.78-0.91) Model ABCD & Race Poverty Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.05 (1.02-1.09) 1.13 (1.09-1.17) 1.27 (1.22-1.33) 1.0 1.07 (1.03-1.11) 1.15 (1.10-1.19) 1.25 (1.19-1.31) 1.0 1.0 1.05 (1.02-1.09) 1.04 (1.01-1.08) 1.12 (1.08-1.16) 1.11 (1.07-1.15) 1.21 (1.15-1.26) 1.17 (1.11-1.22) Interaction Model 1.0 1.04 (1.00-1.08) 1.10 (1.06-1.15) 1.14 (1.08-1.20) 1.0 1.02 (0.99-1.06) 1.09 (1.05-1.13) 1.10 (1.04-1.15) 1.0 1.24 (1.04-1.47) 1.01 (0.83-1.24) 0.79 (0.69-0.90) 1.0 1.23 (1.04-1.46) 1.06 (0.87-1.30) 0.87 (0.75-1.00) 1.0 1.22 (1.03-1.45) 1.02 (0.83-1.24) 0.86 (0.75-1.00) Very Low Poverty White Latino African American Asian 115 1.0 1.13 (0.96-1.34) 1.06 (0.86-1.29) 0.84 (0.72-0.96) 1.0 1.12 (0.94-1.32) 1.07 (0.88-1.31) 0.82 (0.71-0.94) 116 All-Cause Mortality CRC Cases Low Poverty White Latino African American Asian Middle Poverty White Latino African American Asian High Poverty White Latino African American Asian White Very Low Poverty Low Poverty Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latino Very Low Poverty Low Poverty Middle Poverty Crude model Model A Model A + B Model A + B + C Secondary Analysis Model A+B+C+D 1.0 0.95 (0.81-1.11) 1.13 (0.99-1.30) 0.93 (0.83-1.04) 1.0 0.99 (0.85-1.16) 1.17 (1.02-1.35) 0.99 (0.88-1.12) 1.0 0.96 (0.82-1.13) 1.16 (1.01-1.34) 0.98 (0.86-1.10) 1.0 0.96 (0.82-1.13) 1.12 (0.97-1.28) 0.93 (0.79-1.09) 1.0 0.88 (0.75-1.03) 1.10 (0.96-1.27) 0.88 (0.78-0.99) 1.0 1.02 (0.90-1.15) 1.11 (1.01-1.22) 0.85 (0.75-0.96) 1.0 1.06 (0.94-1.21) 1.11 (1.00-1.22) 0.89 (0.79-1.02) 1.0 1.03 (0.91-1.17) 1.08 (0.98-1.19) 0.90 (0.79-1.02) 1.0 0.97 (0.85-1.10) 1.08 (0.98-1.19) 0.83 (0.73-0.94) 1.0 1.00 (0.88-1.13) 1.07 (0.97-1.19) 0.79 (0.69-0.90) 1.0 0.97 (0.85-1.10) 1.16 (1.07-1.25) 0.86 (0.74-0.99) 1.0 1.06 (0.93-1.21) 1.18 (1.08-1.28) 0.89 (0.77-1.03) 1.0 1.04 (0.91-1.19) 1.16 (1.07-1.26) 0.87 (0.75-1.01) 1.0 0.96 (0.85-1.10) 1.11 (1.02-1.20) 0.87 (0.75-1.01) 1.0 0.94 (0.82-1.07) 1.09 (1.01-1.19) 0.87 (0.75-1.01) 1.0 1.05 (1.01-1.09) 1.13 (1.09-1.17) 1.23 (1.16-1.30) 1.0 1.06 (1.02-1.10) 1.13 (1.09-1.18) 1.18 (1.11-1.26) 1.0 1.05 (1.01-1.08) 1.11 (1.07-1.16) 1.15 (1.09-1.22) 1.0 1.04 (1.00-1.08) 1.11 (1.07-1.16) 1.14 (1.07-1.21) 1.0 1.03 (0.98-1.06) 1.09 (1.05-1.14) 1.10 (1.03-1.16) 1.0 1.18 (0.92-1.50) 1.24 (0.99-1.54) 1.40 (1.14-1.72) 1.0 1.17 (0.92-1.49) 1.18 (0.95-1.47) 1.31 (1.07-1.62) 1.0 1.19 (0.94-1.52) 1.18 (0.95-1.47) 1.32 (1.07-1.62) 1.0 1.10 (0.86-1.40) 1.13 (0.91-1.41) 1.19 (0.97-1.47) 1.0 1.05 (0.83-1.34) 1.09 (0.88-1.36) 1.12 (0.91-1.38) 1.0 0.80 (0.64-1.01) 0.93 (0.76-1.14) 1.0 0.85 (0.68-1.07) 0.98 (0.79-1.21) 1.0 0.82 (0.66-1.03) 0.93 (0.76-1.15) 1.0 0.85 (0.68-1.07) 0.95 (0.77-1.17) 1.0 0.80 (0.64-1.01) 0.97 (0.79-1.20) 116 All-Cause Mortality CRC Cases High Poverty Crude model Model A Model A + B Model A + B + C 0.96 (0.78-1.18) 1.02 (0.83-1.25) 0.98 (0.80-1.20) 0.97 (0.79-1.19) Asian Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.24 (1.04-1.47) 1.22 (1.02-1.46) 1.33 (1.10-1.61) 1.0 1.0 1.0 1.21 (1.02-1.44) 1.18 (1.00-1.40) 1.13 (0.95-1.34) 1.17 (0.98-1.40) 1.16 (0.97-1.38) 1.10 (0.92-1.31) 1.22 (1.01-1.48) 1.17 (0.96-1.41) 1.19 (0.98-1.43) Wald Test for Interaction (4 df) Type III χ2 p-value 12.053 0.210 8.439 0.491 9.690 0.376 117 117 5.298 0.808 Secondary Analysis Model A+B+C+D 0.92 (0.75-1.14) 1.0 1.10 (0.93-1.31) 1.05 (0.88-1.26) 1.17 (0.97-1.42) 7.103 0.626 (tables 33-34). African American cases were over-represented in the high poverty category and diagnosed at distant or unknown stage and unknown Gleason score. Additionally, African Americans had greater representation using watchful waiting, no surgery, and receiving neither surgery nor radiation. African American had higher Charlson Comorbidity index scores and proportions of chronic conditions such as hypertension, anemia and arthritis. Latinos were also largely in the high poverty group, an immigrant, receiving supplemental Medicaid insurance, and diagnosed with Gleason score 2-4 tumors. Latinos were more likely to receive radical prostatectomy and other surgery types and lower utilization of radiation than others and had greater representation in mental health conditions. Asian prostate cancers tended to be more in the low poverty groups than other racial/ethnic groups. Asians also had the highest proportion of immigrants and the second largest with Medicaid. Asian cases were the second leading group with localized/regional stage, the highest proportion of Gleason scores of 8-10 tumors. Asians were most likely to receive hormone therapy, and chemotherapy, had the second highest proportion of hypertension and highest percent of anemia of all groups. AIANs were mostly among the high poverty level, those receiving supplemental Medicaid, diagnosed with localized/regional cancer stage or at distant stage, and treated with watchful waiting. Stage at Diagnosis Ordinal Regression African Americans displayed a considerable excess of risk for diagnosis at distant stage (OR 1.78, 95% CI 1.63-1.95) when compared to Whites which was reduced with the addition of poverty (OR 1.53, 95% CI 1.38-1.69) (table 35). Latinos initially had an 18% increased risk of distant stage diagnosis which decreased once poverty was 118 Table 31: Demographic Characteristics of Prostate Cancer Cases by Poverty Level Variable 119 Total Patients Number of deaths (5 year) All-cause Prostate specific Age mean (sd) 66-69 70-74 75-79 80+ Race/Ethnicity White African American Latina Asian AIAN / Other Marital Status Unmarried Married Unknown Immigrant Yes No Unknown Medicaid Buyin Yes High poverty N (%) 12,337 Middle poverty N (%) 20,670 Low Poverty N (%) 28,309 Very Low Poverty N (%) 34,326 Poverty Missing N (%) 2,759 4,092 1,163 (28.4) 75.1 (6.0) 98,437 6,062 1,595 (26.3) 75.3 (6.1) 7,348 1,951 (26.7) 75.1 (5.9) 7,903 2,057 (26.0) 74.5 (5.6) 827 185 (22.3) 75.9 (6.0) 26,232 6,951 (26.5) 75.0 (5.9) P=.003 2,832 (23.0) 3,849 (31.2) 3,114 (25.2) 2,542 (20.6) 4,527 (21.9) 6,515 (31.5) 5,115 (24.8) 4,513 (21.8) 6,204 (21.9) 9,368 (33.1) 7,074 (25.0) 5,663 (20.0) 8,267 (24.1) 11,713 (34.1) 8,545 (24.9) 5,801 (16.9) 510 (18.3) 851 (30.5) 764 (27.3) 670 (24.0) 22,340 (22.7) 32,296 (32.8) 24,612 (25.0) 19,189 (19.5) P< .0001 4,702 (38.1) 5,534 (44.9) 1,376 (11.2) 620 (5.0) 105 (0.9) 15,958 (77.2) 2,387 (11.6) 1,204 (5.8) 1,055 (5.1) 66 (0.3) 24,997 (88.3) 1,232 (4.4) 798 (2.8) 1,236 (4.4) 46 (0.2) 31,935 (93.0) 662 (1.9) 593 (1.7) 1,111 (3.2) 25 (0.1) 2,525 (90.3) 143 (5.1) 80 (2.9) 43 (1.5) 4 (0.1) 80,117 (81.4) 9,958 (10.1) 4,051 (4.1) 4,065 (4.1) 246 (0.3) 4,215 (34.2) 7,031 (57.0) 1,091 (8.8) 919 (7.5) 7,105 (57.6) 4,313 (35.0) 4,713 (22.8) 14,402 (69.7) 1,555 (7.5) 1,353 (6.6) 11,915 (57.6) 7,402 (35.8) 5,446 (19.2) 20,562 (72.6) 2,301 (8.1) 1,718 (6.1) 15,832 (55.9) 10,759 (38.0) 5,402 (15.7) 25,785 (75.1) 3,139 (9.1) 1,964 (5.7) 18,693 (54.5) 13,669 (39.8) 555 (19.9) 1,796 (64.3) 444 (15.9) 179 (6.4) 1,843 (65.9) 773 (27.7) 20,331 (20.7) 69,576 (70.7) 8,530 (8.7) 6,133 (6.2) 55,388 (56.3) 36,916 (37.5) 2,306 (18.7) 1,712 (8.3) 1,060 (3.7) 553 (1.6) 133 (4.8) 5,764 (5.9) 119 Total N (%) P value P< .0001 P< .0001 P< .0001 P< .0001 Geography Rural/Less Urban Urban/Metro 130 (1.1) 12,207 (91.4) 799 (3.9) 19,871 (96.1) 516 (1.8) 27,793 (98.2) 120 120 35 (0.10) 34,291 (99.9) 103 (3.7) 2,692 (96.3) 1,583 (1.6) 96,854 (98.4) P< .0001 Table 32: Clinical Characteristics of Prostate Cancer Cases by Poverty Level Variable High poverty (N=12,337) Middle poverty (N=20,670) Low Poverty (N=28,309) Very Low Poverty (N=34,326) Poverty Missing (N=2,759) Total (n=98,437) P value 214 (1.7) 8,033 (65.1) 1,080 (8.8) 3,010 (24.4) 298 (1.4) 14,316 (69.3) 1,524 (7.4) 4,532 (21.9) 409 (1.4) 20,037 (70.8) 1,772 (6.3) 6,091 (21.5) 481 (1.4) 23,996 (70.0) 1,786 (5.2) 8,063 (23.5) 46 (1.7) 1,658 (59.3) 159 (5.7) 932 (33.4) 1,448 (1.5) 68,040 (69.1) 6,321 (6.4) 22,628 (23.0) P< .0001 1,545 (12.5) 6,666 (54.0) 2,903 (23.5) 1,223 (9.9) 2,688 (13.0) 11,707 (56.6) 4,629 (22.4) 1,646 (8.0) 3,496 (12.4) 16,633 (58.8) 6,034 (21.3) 2,146 (7.6) 4,049 (11.8) 20,797 (60.6) 6,865 (20.0) 2,615 (7.6) 363 (13.0) 1,505 (53.9) 553 (19.8) 374 (13.4) 12,141 (12.3 57,308 (58.2) 20,984 (21.3) 8,004 (8.1) P<.0001 3,511 (28.5) 3,979 (32.3) 3,439 (27.9) 2,297 (18.6) 3,516 (28.5) 6,391 (30.9) 6,975 (33.7) 6,013 (29.1) 4,058 (19.6) 5,019 (24.3) 8,692 (30.7) 10,096 (35.7) 8,265 (29.2) 5,588 (19.7) 6,439 (22.8) 10,189 (29.7) 14,073 (41.0) 9,799 (28.6) 6,368 (18.6) 6,998 (20.4) 710 (25.4) 932 (33.6) 743 (26.7) 504 (18.0) 775 (27.7) 29,493 (30.0) 36,055 (36.6) 28,259 (28.7) 18,815 (19.1) 22,747 (23.1) P<.0001 P<.0001 P= .003 P=.0002 P<.0001 1,994 (16.2) 4,219 (20.4) 6,204 (21.9) 7,458 (21.7) 427 (15.3) 20,302 (20.6) 1,517 (12.3) 8,826 (71.5) 2,172 (10.5) 14,279 (69.1) 2,488 (8.8) 19,617 (69.3) 2,731 (8.0) 24,137 (70.3) 283 (10.1) 2,085 (74.6) 9,191 (9.3) 68,944 (70.0) P<.0001 336 (4.2) 3,209 (40.0) 576 (4.0) 5,036 (35.2) 787 (3.9) 6,713 (33.5) 996 (4.2) 7,366 (30.7) 59 (3.6) 637 (38.4) 2,754 (4.1) 22,961 (33.8) NS P<.0001 8,213 (66.6) 2,616 (21.2) 14,996 (72.6) 3,852 (18.6) 21,118 (74.6) 4,969 (17.6) 25,969 (75.7) 5,906 (17.2) 2,097 (75.0) 494 (17.7) 72,393 (73.5) 17,837 (18.1) Cancer Stage 121 In Situ Localized/Regional Distant Unknown/unstaged Gleason Score Score 2-4 Score 5-7 Score 8-10 Unknown Treatment(s) Received (Yes) Any Surgery Type Any Radiation Type Hormone Therapy Chemotherapy Watchful Waiting Only Surgery Type Radical Prostatectomy Other Surgery/Cryo/Thermo None Local/Regional Stage Both Surgery /rad Neither Surgery/Rad Charlson Comorbidity Index 0 1 121 Variable 2+ Specific Comorbidities (Yes) Hypertension Anemia Hepatitis Arthritis Anxiety Depression Bipolar Schizophrenia High poverty (N=12,337) 1,508 (12.2) Middle poverty (N=20,670) 1,822 (8.8) Low Poverty (N=28,309) Poverty Missing (N=2,759) 204 (7.3) Total (n=98,437) P value 2,222 (7.9) Very Low Poverty (N=34,326) 2,451 (7.1) 8,207 (8.3) P<.0001 6,542 (53.0) 2,200 (17.8) 209 (1.7) 1,278 (10.4) 158 (1.3) 274 (2.2) 42 (0.3) 70 (0.6) 9,148 (44.3) 2,591 (12.5) 239 (1.2) 1,351 (6.5) 270 (1.3) 540 (2.6) 50 (0.2) 57 (0.3) 12,365 (43.7) 3,621 (12.8) 264 (0.9) 1,765 (6.2) 386 (1.4) 725 (2.6) 74 (0.3) 52 (0.2) 15,487 (45.1) 4,549 (13.3) 346 (1.0) 2,145 (6.3) 373 (1.1) 794 (2.3) 95 (0.3) 46 (0.1) 1,187 (42.5) 385 (13.8) 23 (0.8) 180 (6.4) 39 (1.4) 67 (2.4) 8 (0.3) 8 (0.3) 44,729 (45.4) 13,346 (13.6) 1,081 (1.1) 6,719 (6.8) 1,226 (1.3) 2,400 (2.4) 269 (0.3) 233 (0.2) P<.0001 P<.0001 P<.0001 P<.0001 P=.02 NS NS P<.0001 122 122 included. All poverty levels were at greater risk for distant stage diagnosis when compared to very low poverty even after the accounting for race/ethnicity. Those in the high poverty category were at greatest risk with 1.40 (95% CI 1.261.55) greater adjusted odds of diagnosis at distant stage than those in the very low poverty group. African Americans in the low, middle and high poverty levels experienced higher odds (ORs 1.43-1.69) of distant stage disease compared to Whites. Asians displayed a 42% increased risk of late stage diagnosis compared to whites only in the high poverty group. Within White, African American and Asian prostate cases, significant excess risk of distant stage disease was found by poverty level. The magnitude of odds ranged from 1.29 for Whites in the high poverty group, 1.87 among Asians in high poverty, to 211 for African Americans in the high poverty group. Prostate Cancer-Specific Survival Prostate-specific five year survival is illustrated in figure 10. The highest survival was among those in the very low poverty level (93.1%), followed by Asians (92.8%), Whites (92.2%), low poverty (92.1%), middle poverty (91.0%), Latinos (90.6%), African Americans (89.0%), and the high poverty level (88.9%). Prostate cancer survival within poverty level by race/ethnicities is shown in figure 11. Asians in the very low poverty level experienced the highest survival of 94.3% and African Americans in the high poverty level had the lowest (89.2%). All racial/ethnic groups displayed significant tests for trend by poverty level (p=0.002 to p<0.0001). Only the middle and high poverty levels had significantly different poverty levels by racial/ethnic group (p=0.02 and p=0.0004 respectively). 123 Table 33: Demographic Characteristics of Prostate Cancer Cases by Race/Ethnicity Variable White No. (%) Total Patients Number of deaths (5 year) All-cause Prostate specific Age mean (sd) 124 66-69 70-74 75-79 80+ % below poverty High Poverty Middle Poverty Low Poverty Very Low Poverty Missing Marital Status Unmarried Married Unknown Immigrant Yes No Unknown Medicaid Buyin Yes Urban/Rural Rural/Less Urban Urban/Metro Latino No. (%) Asian No. (%) AIAN/ Other No. (%) All No. (%) 80,117 African American No. (%) 9,958 4,051 4,065 246 98,437 20,876 5,409 (25.9) 75.0 (5.8) 3,198 930 (29.1) 74.4 (5.8) 1,089 330 (30.3) 74.7 (6.2) 987 256 (25.9) 75.8 (6.0) 82 26 (31.7) 75.2 (6.9) 26,232 6,951 (26.5) 75.0 (5.9) 17,845 (22.3) 26,251 (32.8) 20,301 (25.3) 15,720 (19.6) 2,603 (26.1) 3,381 (34.0) 2,317 (23.3) 1,657 (16.6) 1,056 (26.1) 1,334 (32.9) 866 (21.4) 795 (19.6) 767 (18.9) 1,258 (31.0) 1,078 (26.5) 962 (23.7) 69 (28.1) 72 (29.3) 50 (20.3) 55 (22.4) 22,340 (22.7) 32,296 (32.8) 24,612 (25.0) 19,189 (19.5) 4,702 (5.9) 15,958 (19.9) 24,997 (31.2) 31,935 (39.9) 2,525 (3.2) 5,534 (55.6) 2,387 (24.0) 1,232 (12.4) 662 (6.7) 143 (1.4) 1,376 (34.0) 1,204 (29.7) 798 (19.7) 593 (14.6) 80 (2.0) 620 (15.3) 1,055 (26.0) 1,236 (30.4) 1,111 (27.3) 43 (1.1) 105 (42.7) 66 (26.8) 46 (18.7) 25 (10.2) 4 (1.6) 12,337 (12.5) 20,670 (21.0) 28,309 (28.8) 34,326 (34.9) 2,795 (2.8) 15,013 (18.7) 58,146 (72.6) 6,958 (8.7) 3,721 (4.6) 45,994 (57.4) 30,402 (38.0) 3,609 (36.2) 5,408 (54.3) 941 (9.5) 116 (1.2) 6,197 (62.2) 3,645 (36.6) 994 (24.5) 2,754 (68.0) 303 (7.5) 1,023 (25.3) 1,371 (33.8) 1,657 (40.9) 658 (16.2) 3,108 (76.5) 299 (7.4) 1,264 (31.1) 1,663 (40.9) 1,138 (28.0) 57 (23.2) 160 (65.0) 29 (11.8) 9 (3.7) 163 (66.3) 74 (30.1) 20,331 (20.7) 69,576 (70.7) 8,530 (8.7) 6,133 (6.2) 55,388 (56.3) 36,916 (37.5) 1,986 (2.5) 1,435 (14.4) 1,219 (30.1) 1,068 (26.3) 56 (22.8) 5,764 (5.9) P<.0001 1,535 (1.9) 78,582 (98.1) 16 (0.2) 9,942 (99.8) 29 (0.7) 4,022 (99.3) 2 (0.1) 4,063 (99.9) 1 (0.4) 245 (99.6) 1,583 (1.6) 96,854 (98.4) P<.0001 124 P value P<.0001 P< .0001 P< .0001 P<.0001 P<.0001 Table 34: Clinical Characteristics of Prostate Cancer Cases by Race/Ethnicity Variable White (n=80,117) No. (%) African American (n=9,958) No. (%) Latino (n=4,051) No. (%) Asian (n=4,065) No. (%) AIAN/ Other (n=246) All (n=98,437) No. (%) P value 1,205 (1.5) 55,605 (69.4) 4,772 (6.0) 18,535 (23.1) 134 (1.4) 6,506 (65.3) 866 (8.7) 2,452 (24.6) 70 (1.7) 2,854 (70.5) 313 (7.7) 814 (20.1) 38 (0.9) 2,898 (71.3) 346 (8.5) 783 (19.3) 1 (0.4) 177 (72.0) 24 (9.8) 44 (17.9) 1,448 (1.5) 68,040 (69.1) 6,321 (6.4) 22,628 (23.0) P< .0001 10,070 (12.6) 47,282 (59.0) 16,457 (20.5) 6,308 (7.9) 983 (9.9) 5,610 (56.3) 2,325 (23.4) 1,040 (10.4) 537 (13.3) 2,249 (55.5) 939 (23.2) 326 (8.1) 522 (12.8) 2,028 (49.9) 1,222 (30.1) 293 (7.2) 29 (11.8) 139 (56.5) 41 (16.7) 37 (15.0) 12,141 (12.3 57,308 (58.2) 20,984 (21.3) 8,004 (8.1) P< .0001 24,407 (30.5) 29,848 (37.3) 22,776 (28.4) 14,968 (18.7) 17,793 (22.2) 2,481 (24.9) 3,467 (34.8) 2,627 (26.4) 1,633 (16.4) 3,012 (30.3) 1,435 (35.4) 1,166 (28.8) 1,265 (31.2) 1,011 (25.0) 977 (24.1) 1,096 (27.0) 1,504 (37.0) 1,533 (37.7) 1,158 (28.5) 879 (21.6) 74 (30.1) 70 (28.5) 58 (23.6) 45 (18.3) 86 (35.0) 29,493 (30.0) 36,055 (36.6) 28,259 (28.7) 18,815 (19.1) 22,747 (23.1) P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 17,091 (21.3) 1,415 (14.2) 991 (24.5) 756 (18.6) 49 (19.9) 20,302 (20.6) 7,316 (9.1) 55,710 (69.5) 1,066 (10.7) 7,477 (75.1) 444 (11.0) 2,616 (64.6) 340 (8.4) 2,969 (73.0) 25 (10.2) 172 (69.9) 9,191 (9.3) 68,944 (70.0) P<.0001 2,241 (4.0) 18,198 (32.7) 260 (4.0) 2,629 (40.4) 122 (4.3) 1,029 (36.1) 122 (4.2) 1,035 (35.7) 9 (5.1) 70 (39.6) 2,754 (4.1) 22,961 (33.8) NS P<.0001 60,037 (74.9) 6,549 (65.8) 2,793 (69.0) 2,845 (70.0) 169 (68.7) 72,393 (73.5) Cancer Stage 125 In Situ Localized/Regional Distant Unknown/unstaged Gleason Score Score 2-4 Score 5-7 Score 8-10 Unknown Treatment(s) Received (Yes) Any Surgery Type Any Radiation Type Hormone Therapy Chemotherapy Watchful Waiting Only Surgery Type Radical Prostatectomy Other Surgery/Cryo/Thermo None Local/Regional Stage Both Surgery & rad Neither Surgery/Rad Charlson Comorbidity Index 0 125 Variable White (n=80,117) No. (%) Latino (n=4,051) No. (%) Asian (n=4,065) No. (%) AIAN/ Other (n=246) All (n=98,437) No. (%) P value 14,088 (17.6) 5,992 (7.5) African American (n=9,958) No. (%) 2,094 (21.0) 1,315 (13.2) 1 2+ Specific Comorbidities (Yes) Hypertension Anemia Hepatitis Arthritis Anxiety Depression Bipolar Schizophrenia 813 (20.1) 445 (11.0) 798 (19.6) 422 (10.4) 44 (17.9) 33 (13.4) 17,837 (18.1) 8,207 (8.3) P< .0001 34,475 (43.0) 10,153 (12.7) 773 (1.0) 4,924 (6.2) 1,022 (1.3) 2,056 (2.6) 227 (0.3) 144 (0.2) 6,096 (61.2) 2,094 (21.0) 161 (1.6) 1,153 (11.6) 86 (0.9) 162 (1.6) 25 (0.3) 64 (0.6) 1,762 (43.5) 505 (12.5) 62 (1.5) 315 (7.8) 76 (1.9) 113 (2.8) 9 (0.2) 15 (0.4) 2,304 (56.7) 562 (13.8) 81 (2.0) 309 (7.6) 40 (1.0) 65 (1.6) 8 (0.2) 9 (0.2) 92 (37.4) 32 (13.0) 4 (1.6) 18 (7.3) 2 (0.8) 4 (1.6) 0 (0) 1 (0.4) 44,729 (45.4) 13,346 (13.6) 1,081 (1.1) 6,719 (6.8) 1,226 (1.3) 2,400 (2.4) 269 (0.3) 233 (0.2) P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 P<.0001 NS P<.0001 126 126 Table 35: Odds of Stages II/III/IV and Race/ethnicity and Poverty Level among Prostate Cancer Cases N (%) Distant Stage Race/Ethnicity White African American Latino Asian 4,631 (7.9) 856 (11.8) 308 (9.9) 343 (10.7) Odds Ratio (95% confidence interval) (n=72,345) Main Effects Full Model Main Effects Full Model With Poverty 1.0 (referent) 1.78 (1.63-1.95) 1.18 (1.03-1.36) 1.17 (0.99-1.37) 1.0 (referent) 1.53 (1.38-1.69) 1.10 (0.96-1.27) 1.12 (0.96-1.32) Main Effects Full Model Main Effects Full Model With Race/Ethnicity 3.0 (referent) 1.13 (1.05-1.22) 1.29 (1.19-1.40) 1.67 (1.53-1.83) Interaction Full Model 1.0 (referent) 1.10 (1.02-1.19) 1.21 (1.11-1.31) 1.40 (1.26-1.55) Poverty Very Low 1,784 (6.9) Low 1,769 (8.1) Middle 1,519 (9.6) High 1,066 (11.8) Very Low Poverty White African American Latino Asian Low Poverty White African American Latino Asian Middle Poverty White African American Latino Asian High Poverty White African American Latino Asian White Very Low Poverty Low Poverty 1,632 (6.8) 33 (6.7) 41 (9.2) 78 (8.8) 1.0 (referent) 1.01 (0.70-1.46) 1.27 (0.90-1.78) 0.98 (0.75-1.29) 1,531 (8.0) 88 (9.3) 55 (8.7) 95 (9.8) 1.0 (referent) 1.43 (1.13-1.82) 1.02 (0.76-1.38) 0.99 (0.77-1.27) 1,122 (9.2) 208 (11.4) 88 (9.3) 101 (11.8) 1.0 (referent) 1.69 (1.42-2.01) 1.04 (0.81-1.34) 1.25 (0.97-1.61) 346 (10.1) 527 (13.2) 124 (11.3) 69 (13.7) 1.0 (referent) 1.66 (1.41-1.94) 1.21 (0.96-1.54) 1.42 (1.04-1.92) 1,632 (6.8) 1,531 (8.0) 1.0 (referent) 1.10 (1.02-1.19) 127 Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latino Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty N (%) Distant Stage 1,122 (9.2) 346 (10.1) Odds Ratio (95% confidence interval) (n=72,345) 33 (6.7) 88 (9.3) 208 (11.4) 527 (13.2) 1.0 (referent) 1.56 (1.01-2.40) 1.95 (1.31-2.90) 2.11 (1.45-3.09) 41 (9.2) 55 (8.7) 88 (9.3) 124 (11.3) 1.0 (referent) 0.89 (0.57-1.38) 0.96 (0.64-1.45) 1.24 (0.84-1.84) 78 (8.8) 95 (9.8) 101 (11.8) 69 (13.7) 1.0 (referent) 1.11 (0.80-1.54) 1.49 (1.08-2.07) 1.87 (1.30-2.69) 1.17 (1.07-1.28) 1.29 (1.13-1.48) 128 Five-Year Overall Survival Overall five year survival is shown in figure 12. Prostate cases in the very low poverty group had the highest survival (76.0%) followed by Asians (74.5%), Whites (73.0%), low poverty (72.9%), Latinos (71.6%), middle poverty (69.5%), African Americans (66.7%) and high poverty level (65.4%). Survival within racial/ethnic group by poverty level (figure 13) exhibited a 15% difference between the highest survival (78.7%) among Asians in very low poverty and the lowest survival in African Americans in high poverty groups (63.5%). 128 Figure 11: Prostate Cancer-Specific 5-Year Survival Five Year Survival % Prostate-Specific Survival by Poverty and Race/Ethnic Groups 94.0 93.0 92.0 91.0 90.0 89.0 88.0 87.0 86.0 Series1 Very Low Low Middle High 93.1 92.1 91.0 88.9 African American Latino 89.0 Poverty Group 90.6 White Asian 92.2 92.8 Racial/Ethnic Group Figure 12: Prostate Cancer-Specific 5-Year Survival by Racial/Ethnic Group and Poverty Level Prostate-Specific Survival by Racial/Ethnic Group and Socioeconomic Status 94.3 95.0 94.0 92.7 Five Year Survival % 93.0 93.1 93.1 92.7 African American Latino 92.4 92.0 92.4 91.5 90.9 91.1 91.0 92.0 White 90.2 91.0 90.0 89.2 89.0 88.0 88.5 87.7 87.0 Asian 86.0 White 85.0 Latino 84.0 Very Low Low Poverty Group African American Middle High 129 Racial/Ethnic Group Figure 13: Overall 5-Year Survival for Prostate Cancer Five Year Survival % Prostate Overall Survival by Poverty and Race/Ethnic Groups 78.0 76.0 74.0 72.0 70.0 68.0 66.0 64.0 62.0 60.0 Series1 Very Low Low Middle High 76.0 72.9 69.5 65.4 African American Latino 66.7 Poverty Group 71.6 White Asian 73.0 74.5 Racial/Ethnic Group Figure 14: Overall 5-Year Survival by Racial/Ethnic Group and Poverty Level in Prostate Cancer 130 Prostate Overall Survival By Racial/Ethnic Group and Socioeconomic Status 78.7 74.3 78.4 75.8 80.0 74.6 76.1 71.3 70.0 72.9 73.6 68.9 66.6 67.2 69.2 71.3 69.1 African American Latino 63.5 60.0 50.0 White 40.0 30.0 20.0 Asian 10.0 White Racial/Ethnic Latino Group 0.0 Very Low Low Poverty Group African… Middle 130 High Prostate Cancer-Specific Mortality Prostate cancer-specific mortality main effects analysis (models A-C with poverty/race) The main effects of race/ethnicity and poverty on mortality were estimated. All covariates met the proportional hazards assumptions. African American men with prostate cancer were at significant increased hazard for prostate-specific mortality when compared to Whites in both unadjusted (HR 1.44, 95% CI 1.34-1.55) and fully adjusted models with poverty level added (HR 1.19, 95% CI 1.09-1.30) (table 36). Asian men, conversely were at significant decreased risk than white men after full adjustment (HR 0.76, 95% CI 0.65-0.89). Latino men initially had an increased a 20% increased risk in the unadjusted analysis but with the addition of treatment and cancer factors, the risk became insignificant with an attenuated final risk estimate of HR 1.07 (95% CI 0.951.21) compared to White men. All poverty levels experienced excess crude mortality hazards compared to those in the very low poverty group. Those in the middle and high poverty groups continued to experience excess mortality through adjustment but only the high poverty level showed significant excess mortality after the inclusion of race/ethnicity (HR 1.17, 95% CI 1.071.28). Treatment and tumor factors significantly impacted all risk estimates, more than any other factors included in the model. Prostate cancer-specific mortality interaction analysis (models A-C) The test for interaction was initially non-significant (p=.40) and trended toward significance in adjusted models (p=.12). African Americans in the very low poverty trended toward decreased risk (HR 0.85, 95% CI 0.68-1.07). African Americans in the 131 middle and high poverty groups maintained excess mortality hazard of 28% and 33% compared to Whites respectively. In addition, African Americans within the low poverty level were significantly different from African Americans in the middle and high poverty groups as demonstrated by non-overlapping confidence intervals. Within African Americans, those in the middle and high poverty levels displayed unadjusted excess risk of 65% and 95%. After adjustment, African Americans in high poverty were at significant increase mortality hazard (HR 1.43, 1.03-1.98) and those in the middle poverty level trended toward increased risk (HR 1.33, 95% CI 0.94-1.87). Latinos showed no significant different mortality hazard when compared to Whites at all poverty levels. White men displayed unadjusted increased risk at each poverty level compared to Whites in the very low poverty level. White at each of the 132 poverty level was influenced equally by demographic factors as by treatment and tumor characteristics. Within both Latinos and Asians, those in the high poverty level compared to the very low poverty groups showed unadjusted excess mortality hazard which was attenuated after adjustment. Prostate cancer-specific mortality supplemental analysis (model D) The inclusion of the exploratory variable had significant influence on poverty but minimal impact on risk estimate by race/ethnicity. The risk estimate for the high poverty group reduced from HR 1.17 to 1.11 (1.02-1.22). 132 Table 36: Prostate Cancer-Specific 5-Year Mortality Cox Proportional Hazard HR (95% CI) Prostate Specific Mortality Crude model SES/ Race/ethnicity Model A Model A + B Variables A: Age, marital status, era of diagnosis, SEER registry Variables B: Comorbidities, Chronic condition, Mental Health condition Main Effects Model Model A + B + C Secondary Analysis Model A+B+C+D Variables D: Medicaid, immigrant, delay Variables C: Stage, Gleason Score, surgery, radiation, WW, chemo, hormone therapy Model ABC & Poverty Model ABCD & Poverty 133 Race/Ethnicity White Latino African American Asian 1.0 1.20 (1.08-1.35) 1.44 (1.34-1.55) 0.92 (0.81-1.04) 1.0 1.28 (1.14-1.44) 1.65 (1.53-1.79) 0.95 (0.81-1.11) 1.0 1.28 (1.14-1.44) 1.65 (1.53-1.78) 0.95 (0.81-1.11) 1.0 1.11 (0.99-1.25) 1.28 (1.19-1.38) 0.78 (0.67-0.91) 1.0 1.07 (0.95-1.21) 1.19 (1.09-1.30) 0.76 (0.65-0.89) Model ABC & Race 1.0 1.05 (0.93-1.19) 1.17 (1.07-1.28) 0.74 (0.63-0.88) Model ABCD & Race Poverty Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.17 (1.10-1.25) 1.34 (1.26-1.43) 1.69 (1.57-1.81) 1.0 1.14 (1.06-1.21) 1.24 (1.16-1.33) 1.62 (1.50-1.75) 1.0 1.0 1.06 (1.00-1.14) 1.14 (1.06-1.21) 1.24 (1.16-1.33) 1.09 (1.01-1.17) 1.61 (1.50-1.74) 1.26 (1.17-1.36) Interaction Model 1.0 1.05 (0.99-1.12) 1.06 (0.99-1.14) 1.17 (1.07-1.28) 1.0 1.03 (0.97-1.10) 1.02 (0.95-1.09) 1.11 (1.02-1.22) 1.0 1.06 (0.77-1.46) 0.97 (0.71-1.33) 0.81 (0.62-1.05) 1.0 1.11 (0.81-1.53) 1.06 (0.77-1.45) 0.83 (0.62-1.11) 1.0 1.11 (0.81-1.54) 1.06 (0.77-1.46) 0.83 (0.62-1.10) 1.0 1.13 (0.82-1.56) 1.01 (0.74-1.39) 0.72 (0.54-0.96) 1.0 1.13 (0.82-1.56) 1.02 (0.74-1.40) 0.68 (0.51-0.91) 1.0 1.04 (0.80-1.36) 0.92 (0.74-1.16) 0.91 (0.72-1.14) 1.0 1.22 (0.93-1.58) 1.10 (0.87-1.38) 0.89 (0.70-1.14) 1.0 1.21 (0.93-1.58) 1.10 (0.87-1.38) 0.89 (0.69-1.14) 1.0 1.10 (0.85-1.43) 0.85 (0.68-1.07) 0.80 (0.63-1.02) 1.0 1.07 (0.82-1.39) 0.85 (0.67-1.06) 0.76 (0.60-0.98) Very Low Poverty White Latino African American Asian Low Poverty White Latino African American Asian 133 Prostate Specific Mortality 134 Middle Poverty White Latino African American Asian High Poverty White Latino African American Asian White Very Low Poverty Low Poverty Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latino Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty Type III χ2 p-value Crude model Model A Model A + B Model A + B + C Secondary Analysis Model A+B+C+D 1.0 1.00 (0.81-1.23) 1.21 (1.05-1.40) 0.84 (0.66-1.07) 1.0 1.14 (0.92-1.41) 1.55 (1.34-1.80) 0.90 (0.70-1.17) 1.0 1.13 (0.91-1.41) 1.55 (1.33-1.80) 0.90 (0.70-1.17) 1.0 1.01 (0.81-1.25) 1.28 (1.10-1.49) 0.73 (0.56-0.94) 1.0 0.97 (0.78-1.20) 1.30 (1.12-1.51) 0.75 (0.57-0.97) 1.0 1.14 (0.94-1.39) 1.30 (1.15-1.48) 0.95 (0.71-1.28) 1.0 1.32 (1.08-1.61) 1.59 (1.39-1.82) 1.07 (0.79-1.46) 1.0 1.18 (0.96-1.44) 1.59 (1.39-1.82) 1.08 (0.80-1.46) 1.0 1.14 (0.94-1.40) 1.33 (1.16-1.52) 0.83 (0.61-1.13) 1.0 1.13 (0.92-1.39) 1.28 (1.11-1.46) 0.81 (0.59-1.11) 1.0 1.17 (1.10-1.25) 1.31 (1.22-1.41) 1.46 (1.31-1.62) 1.0 1.11 (1.03-1.19) 1.14 (1.06-1.24) 1.26 (1.13-1.42) 1.0 1.11 (1.03-1.19) 1.14 (1.06-1.24) 1.26 (1.12-1.41) 1.0 1.06 (0.99-1.14) 1.05 (0.97-1.13) 1.09 (0.97-1.22) 1.0 1.04 (0.97-1.11) 1.00 (0.93-1.08) 1.05 (0.94-1.18) 1.0 1.12 (0.76-1.64) 1.65 (1.17-2.31) 1.96 (1.41-2.71) 1.0 1.15 (0.78-1.69) 1.68 (1.19-2.36) 1.90 (1.37-2.63) 1.0 1.14 (0.78-1.68) 1.67 (1.18-2.34) 1.89 (1.36-2.61) 1.0 0.89 (0.61-1.31) 1.33 (0.94-1.87) 1.43 (1.03-1.98) 1.0 0.86 (0.59-1.27) 1.28 (0.91-1.80) 1.32 (0.95-1.82) 1.0 1.16 (0.77-1.75) 1.24 (0.85-1.81) 1.60 (1.11-2.29) 1.0 1.10 (0.73-1.66) 1.17 (0.80-1.71) 1.50 (1.04-2.16) 1.0 1.21 (0.80-1.82) 1.16 (0.80-1.70) 1.50 (1.04-2.15) 1.0 1.03 (0.69-1.55) 0.93 (0.64-1.37) 1.10 (0.76-1.58) 1.0 0.98 (0.65-1.47) 0.85 (0.58-1.25) 1.05 (0.73-1.51) 1.0 1.32 (0.93-1.86) 1.37 (0.96-1.96) 1.72 (1.17-2.52) 1.0 1.0 1.0 1.21 (0.80-1.82) 1.19 (0.84-1.68) 1.18 (0.84-1.67) 1.24 (0.87-1.77) 1.25 (0.88-1.78) 1.06 (0.74-1.51) 1.25 (0.85-1.85) 1.63 (1.11-2.40) 1.64 (1.11-2.42) Wald Test for Interaction (4 df) 1.0 1.17 (0.83-1.65) 1.10 (0.77-1.57) 1.25 (0.85-1.85) 9.38 P=0.40 13.87 P=.13 13.84 P=.13 14.21 P=.12 13.89 P=.13 All-Cause Specific Mortality Prostate cancer all-cause specific mortality main effects analysis (models A-C with poverty/race) A series of models with the same sets of covariates in the cancer-specific analysis were employed. All covariates met the proportional hazards assumptions. African Americans displayed unadjusted excess mortality risk (HR 1.30, 95% CI 1.25-1.35) which was primarily explained by treatment and tumor character and poverty (HR 1.03, 95% CI 0.98-1.08) (table 37). Both Asian and Latinos experienced decreased risk compared to Whites in the fully adjusted model (HRs 1.15-1.56). A poverty risk gradient from low to high existed in both the crude and adjusted model (HRs 1.07-1.21) albeit attenuated. Prostate cancer all-cause specific mortality interaction analysis (models A-C) There is no evidence of an interaction effect for poverty and race/ethnicity. African American men in each poverty level showed no differences with White men in the same poverty levels. Latinos in each poverty level except the low poverty group trended toward decreased mortality hazard than white men in the same poverty group. Asian men exhibited decreased mortality hazard for each poverty group when compared to White men with mortality hazards ranging from HR 0.75-0.84. A risk gradient by poverty level was identified in each racial/ethnic group in the crude model and continued to the final model but the hazard ratios reached statistical significance only for Whites and African Americans. Similar to the main analysis, Asians and African Americans with both the middle and high poverty groups were considerably 135 different. Additionally, within the White group, the low poverty level was significantly different from middle and high poverty levels. Prostate cancer all-cause specific mortality supplemental analysis (model D) The additional variables enhanced protection among Latinos and Asians and decreased the risk of death among the higher poverty groups. The supplemental variables decreased the excess risk among poverty levels and further heightened the reduced risk of Latinos and Asians. 136 Overall Results African Americans experience continued increased risk of death compared to Whites for all cancer-specific mortality after adjustment for key factors (table 38). In addition, African Americans displayed crude high mortality hazard for all-cause mortality across the three cancers but after controlling for tumor and treatment characteristics and poverty, only colorectal cancer cases exhibited elevated risk. Asians showed significant decreased risk compared to Whites for all but breast and colorectal cancer specific mortality. In the cancer-specific models, Latinos displayed excess mortality risk which disappeared with adjustment. Latinos also displayed decreased breast mortality risk when compared to White women for all-cause mortality among prostate and breast cases. In the main effects analysis, those in high poverty areas demonstrated increased risk of mortality for both cancer-specific and all-cause mortality when compared to those in the very low poverty areas regardless of cancer type regardless of race/ethnicity. Cancer cases living in middle poverty areas experienced increased unadjusted mortality risk and cancer-specific mortality. Of those in middle poverty areas, only the all-cause mortality and colorectal cancer-specific mortality compared to cases in the very low 136 Table 37: All-Cause 5-Year Mortality Prostate Cancer Cox Proportional Hazard HR (95% CI) All-Cause Mortality Prostate Crude model SES/ Race/ethnicity Model A Model A + B Model A + B + C Variables B: Comorbidities, Chronic condition, Mental Health condition Main Effects Model Variables C: Stage, Gleason Score, surgery, radiation, WW, chemo, hormone therapy Variables A: Age, marital status, era of diagnosis, SEER registry Secondary Analysis Model A-D Variables D: Medicaid, immigrant, delay Model ABC & Poverty Model ABCD & Poverty 137 Race/Ethnicity White Latino African American Asian 1.0 1.05 (0.98-1.11) 1.30 (1.25-1.35) 0.93 (0.87-0.99) 1.0 1.09 (1.02-1.16) 1.33 (1.28-1.39) 0.93 (0.86-1.00) 1.0 1.04 (0.97-1.10) 1.27 (1.22-1.32) 0.89 (0.82-0.96) 1.0 0.98 (0.92-1.04) 1.13 (1.08-1.18) 0.82 (0.76-0.89) 1.0 0.93 (0.87-0.99) 1.03 (0.98-1.08) 0.79 (0.73-0.86) Model ABC & Race 1.0 0.91 (0.85-0.98) 1.02 (0.97-1.07) 0.76 (0.70-0.82) Model ABCD & Race Poverty Very Low Poverty Low Poverty Middle Poverty High Poverty 1.0 1.15 (1.12-1.19) 1.34 (1.29-1.38) 1.56 (1.50-1.62) 1.0 1.12 (1.08-1.15) 1.25 (1.21-1.30) 1.44 (1.38-1.50) 1.0 1.0 1.10 (1.06-1.13) 1.06 (1.03-1.10) 1.22 (1.17-1.26) 1.15 (1.11-1.19) 1.36 (1.30-1.41) 1.21 (1.16-1.26) Interaction Model 1.0 1.07 (1.03-1.10) 1.16 (1.11-1.20) 1.21 (1.15-1.26) 1.0 1.04 (1.01-1.08) 1.11 (1.07-1.15) 1.13 (1.08-1.18) 1.0 0.89 (0.75-1.07) 0.96 (0.81-1.13) 0.83 (0.73-0.95) 1.0 0.94 (0.78-1.12) 1.02 (0.87-1.20) 0.86 (0.75-1.00) 1.0 0.94 (0.78-1.12) 0.99 (0.84-1.16) 0.83 (0.72-0.96) 1.0 0.91 (0.76-1.09) 0.97 (0.82-1.14) 0.79 (0.68-0.92) 1.0 0.93 (0.77-1.11) 0.95 (0.81-1.12) 0.73 (0.63-0.85) 1.0 0.92 (0.80-1.06) 1.06 (0.95-1.18) 0.94 (0.84-1.05) 1.0 1.03 (0.89-1.19) 1.16 (1.04-1.30) 0.90 (0.79-1.02) 1.0 1.01 (0.87-1.17) 1.11 (1.00-1.25) 0.87 (0.77-0.99) 1.0 0.97 (0.84-1.12) 0.99 (0.88-1.11) 0.84 (0.74-0.95) 1.0 0.93 (0.80-1.08) 0.98 (0.88-1.10) 0.79 (0.70-0.90) Very Low Poverty White Latino African American Asian Low Poverty White Latino African American Asian 137 All-Cause Mortality Prostate 138 Middle Poverty White Latino African American Asian High Poverty White Latino African American Asian White Very Low Poverty Low Poverty Middle Poverty High Poverty African American Very Low Poverty Low Poverty Middle Poverty High Poverty Latino Very Low Poverty Low Poverty Middle Poverty High Poverty Asian Very Low Poverty Low Poverty Middle Poverty High Poverty Type III χ2 p-value Crude model Model A Model A + B Model A + B + C 1.0 0.92 (0.93-1.03) 1.02 (0.94-1.10) 0.85 (0.75-0.96) 1.0 1.00 (0.89-1.12) 1.12 (1.03-1.22) 0.83 (0.73-0.95) 1.0 0.94 (0.84-1.05) 1.10 (1.01-1.19) 0.81 (0.71-0.93) 1.0 0.90 (0.81-1.01) 1.02 (0.94-1.10) 0.75 (0.66-0.85) 1.0 0.88 (0.78-0.99) 1.03 (0.95-1.11) 0.74 (0.65-0.84) 1.0 0.94 (0.84-1.05) 1.13 (1.06-1.21) 0.94 (0.84-1.05) 1.0 1.05 (0.94-1.18) 1.19 (1.11-1.28) 0.94 (0.81-1.10) 1.0 1.00 (0.90-1.12) 1.15 (1.07-1.24) 0.89 (0.76-1.04) 1.0 0.87 (0.77-1.00) 1.03 (0.95-1.12) 0.80 (0.69-0.94) 1.0 0.93 (0.83-1.05) 1.04 (0.97-1.12) 0.77 (0.66-0.91) 1.0 1.15 (1.11-1.19) 1.34 (1.29-1.39) 1.47 (1.39-1.56) 1.0 1.10 (1.06-1.14) 1.23 (1.18-1.28) 1.32 (1.24-1.40) 1.0 1.08 (1.05-1.12) 1.20 (1.16-1.25) 1.27 (1.20-1.35) 1.0 1.06 (1.03-1.10) 1.16 (1.11-1.21) 1.18 (1.11-1.25) 1.0 1.04 (1.00-1.07) 1.11 (1.06-1.15) 1.11 (1.05-1.18) 1.0 1.27 (1.05-1.55) 1.43 (1.20-1.71) 1.75 (1.48-2.07) 1.0 1.25 (1.03-1.52) 1.35 (1.13-1.61) 1.53 (1.30-1.82) 1.0 1.22 (1.01-1.49) 1.34 (1.12-1.60) 1.48 (1.25-1.75) 1.0 1.08 (0.89-1.31) 1.21 (1.02-1.45) 1.30 (1.10-1.53) 1.0 1.08 (0.89-1.31) 1.20 (1.00-1.43) 1.22 (1.03-1.44) 1.0 1.19 (0.94-1.49) 1.39 (1.13-1.71) 1.55 (1.27-1.90) 1.0 1.21 (0.96-1.52) 1.31 (1.06-1.61) 1.48 (1.21-1.82) 1.0 1.17 (0.93-1.47) 1.20 (0.98-1.48) 1.36 (1.11-1.67) 1.0 1.13 (0.90-1.42) 1.15 (0.93-1.41) 1.24 (0.98-1.56) 1.0 1.08 (0.82-1.42) 1.12 (0.88-1.44) 1.12 (0.91-1.37) 1.0 1.29 (1.09-1.54) 1.36 (1.14-1.63) 1.64 (1.35-2.00) 1.0 1.0 1.0 1.15 (0.96-1.36) 1.14 (0.95-1.35) 1.12 (0.94-1.34) 1.19 (0.99-1.42) 1.18 (0.98-1.41) 1.10 (0.92-1.32) 1.20 (0.98-1.46) 1.44 (1.18-1.76) 1.36 (1.11-1.66) Wald Test for Interaction (4 df) 5.39 4.75 4.34 P=.799 P=.86 P=.89 1.0 1.04 (0.83-1.31) 1.05 (0.85-1.29) 1.18 (0.96-1.44) 8.53 P=.48 138 Secondary Analysis Model A-D 3.09 P=.96 poverty areas remained significant after adjustment for covariates. Low poverty area cancer cases when compared to cases in the very low poverty areas showed increased risk of death for all-cause mortality among prostate cancer cases yet crude models for both cancer-specific and all-cause mortality across the three cancers displayed increased risk. The influence of race/ethnicity on poverty estimates is shown in table 39. Race/ethnicity had the strongest influence on the high poverty areas in the cancer-specific mortality analysis. Poverty helps to explain a significant amount of the African American excess risk after tumor and treatment but did does not eliminate the increased risk. Regardless of poverty level, Asians consistently experience decreased mortality hazard with one exception. Within the high poverty group, Asians appear to have a slight increased risk of colorectal-specific mortality compared to Whites. African Americans generally showed either no difference or increased risk at all poverty levels when compared to Whites in the same poverty level for the cancer-specific mortality analysis. The increased risk among African Americans compared to Whites became more prominent within the middle and high poverty levels. However, African American prostate cases in low poverty areas appear to experience decreased prostate cancer-specific mortality than Whites even when in the main effects model, African Americans displayed increased risk of death compared to Whites. Yet African Americans in the very low poverty level had nearly double the breast cancer-specific mortality hazard than those in the middle poverty level. Latinos showed varying levels of risk from decreased risk when compared to whites to increased risk which were found at each poverty level as well. One exception was Latinos in the middle poverty group when compared to Whites at the same poverty level were at increased risk for breast cancer- 139 specific mortality when at all other poverty levels, decreased risk was found. After full adjustment, no positive association between poverty and mortality was found in breast and prostate specific cancer mortality. The positive gradient was present for particular racial/ethnic groups primarily in the all-cause analysis. Racial/ethnic risk differences within poverty levels were present at each poverty level except for the low poverty group. Table 38: Summary of Significant Mortality Hazard Ratios Cancer-Specific Mortality Breast Colorectal Prostate All-Cause Mortality Breast Colorectal Prostate Significant Mortality Hazard Ratios Increased HR Decreased HR AA, high poverty, Latina middle poverty AA, middle poverty, high poverty, Latino VL poverty AA, High poverty Latina high poverty None Middle poverty, high poverty AA, middle poverty, high poverty All poverty levels Latina, Asian Asian Asian, Latino Asian AA=African American, WH=White, AS=Asian, LA= Latino; Poverty Levels: VL= Very low, Lo=Low, Mid=Middle, Hi=High Exploratory Variables Delays The breast cancer cases that experienced delays were older, African American, living in high poverty areas and with a greater comorbidity score and chronic conditions. They also were disproportionately in stage IV or distant stages and with unknown grade and ER status tumors. Additionally, those with delays largely did not receive adjuvant chemotherapy regardless of indication and a significant fewer proportion received mastectomies or BCS. Colorectal cancer treatment delays were more prevalent among the older, male, African American and Asian cases and those who had Medicaid. The CRC patients with delays had higher Charlson comorbidities index scores, stage I or unknown 140 stage, well and unknown differentiated tumors. Cases with CRC delays displayed less guideline treatment receipt, surgery, or chemo. Interestingly, there were no differences in delays by radiation receipt and less delays among those with chronic conditions. Table 39 Summary of Findings for Breast, Colorectal and Prostate Cancer Cases Main EffectsRace/Ethnicity Influence on Poverty Level (Very Low Poverty=referent group) High Middle Main Effect- Poverty Influence on Race/Ethnicity (White = referent group) Interaction Analysis Low African American Latino Asian Adjusted SES Gradient within R/E Adjusted Differences by R/E within SES Cancer-Specific Mortality Breast Yes Some Some Yes Yes Some None Colorectal Yes Some No Yes Some Some AA Prostate Yes Some Some Yes Some Some AA All-Cause Mortality Breast No No No Yes No Some WH, AS Colorectal Some Some No Yes Some Some AA, WH Prostate No Some Some Yes Yes Some AA,WH, LA VL, Mid, Hi All VL, Mid, Hi VL, Lo, Hi VL, Mid, Hi All Yes = change of ≥ 5% in mortality hazard. Some = change of < 5% in mortality hazard. No= no change in mortality hazard Immigrant Among the breast, colorectal and prostate cancer cases, immigrants were slightly older, overrepresented in higher poverty areas, had Medicaid, were Hispanic and Asian, and had more chronic disease. Immigrants with breast cancer received more BCS than non-immigrants. Immigrant CRC cases also experienced slightly less guideline treatment than non-immigrants or unknown status. Immigrants with prostate cancer had a slight 141 increase in Gleason scores, and had greater proportions of hormone therapy and chemotherapy use than others, The immigrant unknown status is a perplexing category given the favorable characteristics found of this group. Across cancer sites, those in the unknown immigrant category are younger, had better prognostic tumor characteristics, less delay, less poverty, and slightly more Latinos than non-immigrants. They also had lower comorbidity scores and chronic conditions and higher receipt of guideline treatment. Medicaid Supplemental Insurance Cancer cases with Medicaid were older, more racially/ethnically diverse, living in high and middle poverty areas, experienced more delays and had higher comorbidity index scores, chronic conditions and mental health conditions than those without Medicaid. They also had poorer prognostic tumor characteristics and less treatment received than non-Medicaid recipients. The CRC cases with Medicaid had greater proportions of unstaged tumors and less guideline therapy received. Among the prostate cancer cases, those with Medicaid used significantly more hormone therapy and greater proportions received no treatment. In summary, the exploratory variables had the greatest impact on poverty estimates and relatively little influence on racial/ethnic group estimates. The high poverty group estimates were reduced 5-7% in each of the analysis. For example, in the prostate all-cause mortality analysis, the addition of race/ethnicity caused no change in the high poverty excess risk mortality hazard. However, the addition of the exploratory variables further decreased the risk by 7% but remained significant (HR 1.13, 95% CI 1.08-1.18). 142 CHAPTER V DISCUSSION Discussion The purpose of this study is to describe the relationship between SES and race/ethnicity while accounting for contributing factors on stage of disease and survival. Our results from a large, representative multiethnic cohort of breast, colorectal and prostate cancer cases underscore the complex interplay between race/ethnicity and poverty. This large dataset enabled us to evaluate the impact of demographic, comorbidity, tumor and treatment information on racial/ethnic and poverty groups. In addition, we were able to test for the interaction of race/ethnicity and poverty and identify a race/ethnicity-poverty interaction for breast all-cause mortality, one of six mortality outcomes investigated. We found that both race/ethnicity and poverty are independent risk factors for poorer cancer outcomes. Furthermore, a SES gradient is not consistent within racial/ethnic groups experiencing breast, prostate or colorectal cancers. In addition, we identified that poverty has a greater impact on racial/ethnic risk than the influence of race/ethnicity within poverty. Significant differences between racial/ethnic groups were identified after full adjustment. Some significant differences between poverty levels remained after accounting for covariates. In this study, we confirmed increased risk of cancer-specific death for African Americans and decreased risk for prostate cancer-specific mortality and all-cause mortality among Asians when compared to Whites for prostate, colorectal and breast cancers. Latinos consistently showed unadjusted increased risk which diminished after adjustment for tumor and treatment factors and poverty except for all-cause mortality for 143 CRC and breast which lacked initial heightened mortality risk. Individuals with cancer living in areas with the highest poverty levels experienced increased risk of all-cause and cancer-specific mortality when compared to those living in areas with the lowest poverty for the three cancers within Whites, Asians and in the main effects analysis. Few racial/ethnic differences within SES levels were unexpected and differed from the main effects model of racial/ethnic groups. The adjusted mortality hazard for African Americans in low poverty was suggestive of protection against risk of prostatespecific death while those in the middle and high poverty areas were at increased risk when compared to Whites of the same poverty level. The African American-White difference in breast cancer-specific mortality was double in the very low poverty than the middle poverty level estimate. Although the confidence intervals overlapped, this is suggestive of greater differences between more affluent African American and White women than their less affluent counter parts. Latinas in middle poverty were at increased risk of breast cancer specific death when compared to White women at the same poverty level yet at decreased risk at all other poverty levels. Asians in high poverty areas may be at increased risk for colorectal-specific mortality when compared to Whites, the only display of increased mortality risk of Asians across cancers and outcomes. In the main effects models, poverty was not influenced by race/ethnicity for allcause mortality. In the cancer-specific mortality models, the risk difference by poverty was attenuated by race/ethnicity primarily in the high poverty levels. Among African Americans, poverty was the second most influential factor after tumor and treatment characteristics. Poverty had much less impact on risk of death among Latinos and Asians than others. Tumor and treatment characteristics had the most significant influence on 144 mortality risk while demographic variables, primarily age, also had limited impact. Comorbidities explained very little if any of the mortality differences among the racial/ethnic groups or poverty levels. The second objective of the study was to assess and describe delays of care within SES and Racial/ethnic groups. Delays to the initiation of treatment were assessed for the breast and colorectal cancer cases. We identified significant differences of delays in care by poverty level and racial/ethnic group across for both CRC and breast cancer (p<.0001). Delays, Medicaid Supplemental Insurance and immigrant status are important potential factors but still do not explain disparities in their current form. Delay was combined with immigrant and Medicaid variables for exploratory mortality analysis. Identifying Medicare beneficiaries with Medicaid can potentially be used to indicate low individual income and limited assets in a database devoid of individual SES measures. Further improvement of this indicator variable may result in the ability to conduct multilevel analysis of SES. Until then, this variable must be refined to decrease misclassification. Similarly, the immigrant variable has the potential for use to further clarify excess risk and protection experienced by particular subgroups. Given the large proportion of unknown immigrant status cases which show unique characteristics, it is difficult to interpret much at this time. These variables explained more of the poverty increased risk than that of African Americans. This may be due to the correlation of Medicaid and high poverty and the poorer prognosis among those with Medicaid. Similarly, the immigrant unknown group showed some protective factors and could explain the strengthening of decreased risk among Asian and Latinos. The delays variable also captures association with poorer prognostic factors. These variables have 145 the potential to further characterize sub-groups of Medicare beneficiaries at higher and decreased risk for poorer outcomes. However, at this time, they require additional refinement for current use. The results of the race/ethnicity and SES analysis, without the exploratory variables will be compared to the existing body of literature. Given the strong influence of age on the role of SES on cancer outcomes5, 74, studies with a similar elderly population or age stratification will be utilized for comparison. Our conceptual framework of Freeman and Ward guides this research, and we will review the study results in the context of the social determinants of health disparities including the economic, cultural and social influences on the cancer continuum. Summary of Principal Implications We found persistent excess adjusted mortality among African Americans, high poverty cases and decreased poverty among Asians and some Latinos. The possible reasons for outcome differences are complex and multi-factorial. This study accounted for differences in stage at diagnosis, demographic characteristics (region, age, marital status, urban status), comorbidities (Charlson Comorbidity Index, chronic and mental health conditions), tumor characteristics (grade, hormone receptor status), and treatment received (surgery, radiation, chemotherapy and guideline therapy). There are certainly limitations to the Medicare-SEER dataset as described below. The remaining excess risk may also be due to unmeasured factors in this study including health behaviors, screening behaviors and treatment complications as well as other unmeasured biologic and social elements. We also found that poverty does not influence mortality similarly across racial/ethnic groups. The causes are likely to be found in the in the social determinants of 146 health disparities framework including gene-environment interaction, socio-economic factors and cultural and social behaviors. The social determinants of cancer disparities framework posits that disease and disease outcomes are both influenced by and occur within social, cultural and economic environments204. Economic influences such as are poverty impacts access and use of cancer screening and treatment, delays and access to resources that support protective factors (proper diet, physical activity)19, 204. Culture influences beliefs and acceptance of protective health behaviors and treatment utilization19. Social factors reflect the interpersonal and institutional contexts in which racial discrimination inside and outside of the health care system persist and impact treatment recommendations19, 204, and potentially creating chronic stressors altering immune functioning and potentially endogenous hormone levels204. A positive relationship between poverty and mortality outcomes, or a poverty gradient, suggests that with increasing income, individuals have better access to health care, increased understanding of health promoting behaviors and less acceptance of high risk behaviors202. One suggested cause of a SES gradient experienced by some racial/ethnic groups is an ‘amenability index’186. This ‘amenability index’ refers to the process as the cancer treatment improves, those with more resources have more access to that treatment. A recent study of breast cancer mortality rates of 50 cities in the US revealed emerging Black-White disparities emerging in the 1990’s and worsening over the 20 year period since186. Sixteen of the 41 cities showed a decrease in mortality rate of less than 10% for Blacks and greater than 20% for Whites which resulted in an increase in the Black-White rate ratio for the time period. The authors conclude that genetic 147 differences can’t explain the contrasting experiences by cities over the four sets of 5 year periods. Rather, the authors suggest that different access and uptake of screening and treatment advances has resulted in the widening gap. The authors further propose that the disparity in screening, quality of the screening process, access to treatment and quality of the treatment contribute to create these noted disparities. The ‘amenability’ index supports an SES gradient with improving outcomes as income improves. The lack of such a gradient suggests that there remain barriers and challenges even to those in the higher SES levels to access these new technologies and treatments to mitigate potential biological and tumor-related differences. Racial differences may still be evident within the same level of SES, because individuals may vary in their power, prestige, opportunities, health behaviors and gene-environment interaction202, 205. The socioeconomic status of an individual influences cancer risk factors such as tobacco use, nutrition, physical activity level and obesity8. Latinos experience disproportionately higher levels of poverty, lower educational attainment and lower health literacy in the US195. Latinos and Whites have the largest absolute difference (30.4%) in poverty while African Americans show an 8.8% difference. Poverty and education are correlated as non-completion of high school increases with increasing poverty 194. Poverty differs by country of origin with the greatest differences found between Hispanics born in the US and those born outside of the US (27.7% absolute difference)194. Societal barriers impact access to and utilization of health care services. In addition, the lingering effects of historical and current racial discrimination in the US impacts provider-patient communication19, screening and treatment recommendations19 and results in the accumulation of lifetime stress as a member of a minority group204. 148 Lastly, culture influences health behaviors such as attitudes toward illness, acceptance of medical treatments or use of alternative treatments, diet, reproductive behaviors and breastfeeding204. This complex relationship between poverty and African Americans and Latinos is not well understood. Risk factors for breast, CRC and prostate cancer vary among African Americans and Latinos and poverty levels. Although African American women have considerably lower breast cancer incidence, the higher mortality rate is attributed to less frequent screening20, 183, 199, 200, later stage disease22, 200, more aggressive tumors20, 22, 183, access to quality treatment183, 198200 , the impact of lower SES20, 83 and influence of obesity20, 183 on treatment and health. Furthermore, differential reproductive183 and breastfeeding patterns183 and lower use of hormone replacement therapy have been identified as contributors to a disproportionate number of poorer prognosis tumors among African Americans. In summary, the contributors fall into hormonal, genetic, environmental and socioeconomic domains22 in line with our framework for social determinants of health disparities. Breastfeeding is linked to post-menopausal breast cancer risk and displays racial/ethnic and SES differences in behaviors. Given the lower breast-feeding rates among African Americans than other groups183, 187 regardless of income187, the reproductive and breast-feeding patterns may increase their risk of triple negative tumors (ER- PR- HER2-), poorer prognosis breast cancer tumors183. Lifestyle factors such as obesity and environmental exposures are thought to influence breast cancer tumors among African American women203. Obesity is associated with increased risk of postmenopausal breast cancer and increased risk of advanced stage breast cancer at diagnosis183.Varying obesity rates are associated with different breastfeeding patterns, 149 living in environments that promote obesity and low physical activity levels 196. Obesity has been estimated to account for 3.1-8.4% of breast cancer deaths among African American women. A national survey identified African American obesity rates that were 10% higher than Mexican American rates and 20% higher than Whites196. A recent review stated that approximately 50% of African American women are obese compared to 35% of White women183 but after age 74, White women have higher levels of obesity than African American women205. African American women and Latinos have lower levels of physical activity than White women183, 204 in national surveys where occupational activity is often not measured8. Physical activity has an inverse relationship with educational level8. Evidence is growing of the benefit of moderate physical activity among breast and colorectal cancer patients to reduce risk of recurrence199. African American women and Latinas have lower utilization rates of postmenopausal hormone replacement therapy183, 204. In addition, due to African American women’s higher hysterectomy rates, it is thought that estrogen-only therapy may be more frequently prescribed than the estrogen plus progestin therapy associated with lower breast cancer risk183. Although the current screening rates of African American women are similar to those of Whites200, African American women have less frequent mammograms, longer intervals between mammograms and less timely follow-up of abnormal mammograms which could contribute to the later stage at diagnosis200. Africans Americans are consistently diagnosed at later stages than Whites for breast40, 42, 178, 191, 203, 207. The possible reasons for this are lower access to screening189, 190, 198 and biological differences198. Diagnosis at later stage of disease results in less treatment choices and less 150 effective treatment compared to early stage diagnosis200. Insurance and SES explained approximately 39% 204 to 50%-60% 203 of the risk difference of distant stage breast cancer between African American and White women related to lack of access to screening198 and delayed follow-up39,40,43,178,203,204. African American women are disproportionately diagnosed with hormone receptor negative and high-grade breast cancer tumors than other women203. These tumors result in fewer treatment choices and poorer prognosis than lower grade and estrogen-positive tumors. The large magnitude of difference between prognostic phenotype of breast cancer tumors among African American women and White women is partially explained by fewer better prognostic ER+ cancer among African American women in one state-specific study179 and the significantly more ER+ tumors among White women183. There are investigations into understanding what potential genetic differences may exist between African American and White women that influence breast cancer disparities183. It is suggested that genes alone are not the cause but a geneenvironmental interaction could be at play and result in differential outcomes183, 203 and that obesity also contributes203 to the disproportionate frequency of poorer prognostic tumors. The poorer prognostic triple negative tumors more frequent in African American women are associated with behaviors found among African American183 women and Latinas204 including younger age at first pregnancy and increased parity. These factors confer a 7% reduction in breast cancer incidence of Latinas compared to Whites204. However, the reproductive factors associated with risk reduction of triple negative tumors are breast-feeding and child-bearing behaviors less frequent among African Americans but more prevalent in Latinas183. 151 Three factors influence the availability and quality of cancer care: structural barriers, influence on physician recommendations and patient choice and decisionmaking8. The higher risk of later stage diagnosis among African Americans results in tumors that require more aggressive treatment with less assurance of tumor response than early stage cancers199 yet African Americans consistently receive less aggressive treatment183. However, African Americans who receive the same treatment as Whites show similar outcomes8, 199, 200, but some question of differential responses to chemotherapy treatment remain8, 200. Africans Americans are less likely to receive surgical treatment40, 182, 200, mastectomy178, radiation40, and adjuvant chemotherapy200 than other racial/ethnic groups for the three cancers. African American women were found to have higher levels of comorbidities than White women with breast cancer178. The presence of particular comorbidities and obesity can influence and limit physician treatment recommendations and patient choices as well as increase risk of post-hospitalization complications178. It is possible that particular health behaviors and gene-environmental factors that contribute to the mortality disparity do not change by income or poverty level among African Americans resulting in a lack of a positive association between mortality and poverty. In a national survey of cardiovascular risk factors of women 25-64 years of age from 1988-1994, SES could not explain the differences in BMI between African American and White women and Latinas and White women214. BMI levels increased as education increased but African American and Latino women had considerably higher levels of BMI at each educational level compared to White women. 152 Even with higher obesity rates, Latinas experience better outcomes than African American women. The unique Latino experience has been described as the ‘Hispanic paradox’ where this seemingly group of lower income individuals experience lower incidence of cancer and better outcomes than Whites and African Americans201, 202, 204. The Hispanic Paradox appears in effect when an SES gradient is absent for Latinos. The mechanisms for the Hispanic Paradox are still unclear. One theory is the better health of immigrants to this country. Another idea is the ‘Fewer Smoker Hypotheses’ where Latinos have lower rates of smoking than Whites and African Americans201. Related to the breast cancer, Latinos display higher rates of breastfeeding187 and longer breastfeeding behaviors187, lower use of post-menopausal hormone therapy 204, lower smoking rates195 and higher parity204. Latinos born outside of the US tend to have more favorable outcomes than US Whites while Latinos born within the US have outcomes similar to Whites204. The process of acculturation, or adaptation to and adoption of US behaviors, practices and attitudes impacts health behaviors such as smoking, diet, physical activity and alcohol use such that increased acculturation results in poorer health behaviors204. National survey data show that Latinos in general have lower rates of screening for breast189, 195, 197. Hispanic women report less uptake (46.5) of mammography screening in the past year compared to White women (51.5%) 197. A trend analysis of national survey results from 2000-2008 displayed that Asian and Latinas had lower prevalence of mammography within 2 years than White women 190. New immigrants and those without a regular source of medical care also showed lower mammography uptake190. Treatment receipt differences exist among Mexican and Puerto Ricans in the 153 US compared to Whites40. Latinas are less likely to receive mastectomy compared to White women and experienced greater post-operative complications178. Our excess mortality risk for Latinas was fully explained by treatment and tumor characteristics while Asians experienced persistent but non-significant decreased breast cancer specific mortality. Similar to breast cancer, colorectal cancer risk factors that were not examined in this study but have been identified as contributors to excess mortality risk among African Americans include health behaviors and beliefs, treatment preferences, lack of treatment compliance, post-treatment surveillance, and access to high quality cancer care50, 73. Higher incidence50, 72, less CRC screening50, 72, 200, late stage disease50, 72, more proximal tumors50, 72, tumor aggressiveness50, 73, inherited or acquired genetic abnormalities 50, 72, 73 and higher prevalence of comorbidities, particularly obesity50, 73, 200 and lower SES levels50, 72, 73 may contribute to the racial disparity in CRC mortality. It is suggested that changes in diet over the past 50 years among African Americans have resulted in increased risk for CRC, higher incidence when compared to Whites50. Obesity both increases risk for CRC and risk for death50, 72, 73. High levels of physical activity are protective of CRC and help lower risk of recurrence. African Americans and Latinos have higher rates of obesity and lower levels of physical activity compared to Whites50, 73, 200 . Health beliefs toward cancer and cancer treatment efficacy can impact a person’s utilization of screening and treatment services. Fatalism, or the belief that cancer cannot be treated or cured has been found at higher levels among African Americans, Latinos and particular Asian sub-groups72, 73. The differential receipt of treatment is an important contributor to mortality risk. Treatment differences have been attributed to higher 154 treatment refusal rate 72, 73 or less recommended treatment200, poorer quality of surgeon50, 72 and lack of access to high quality subspecialists73. In addition, post treatment surveillance is less frequent among racial/ethnic minorities than White CRC survivors50, 72, 73, 200 . Use of colorectal cancer screening to identify and remove precancerous polyps is lower among African Americans than Whites200. The Robbins study estimated that distant stage of disease explained up to 60% of the African American-White CRC mortality disparity206. CRC mortality by SES displayed an inverse relationship between CRC mortality and poverty among Whites and African Americans largely due to the SES gradient for screening and beliefs around screening202. Africans Americans are consistently diagnosed at later stages than Whites for colorectal cancers191, 198, 199. African Americans were identified as showing an increased risk of emergent admission, in-hospital mortality and 30-day readmission after colorectal surgery even after controlling for comorbidity status209. Latino and Asian immigrants have been shown to have lower mortality risk compared to US born Whites50,73. Acculturation into US society has a deleterious effect on mortality risk73. Asians have been found to have a high rate of better prognostic distal tumors which may contribute to their lower mortality risk73. We found considerable differences in cancer site by racial/ethnic group. Distal cancers were found in 34% of Asians compared to 27% of African Americans, 26% of Whites and 25% of Latinos. Genetics may also contribute CRC mortality differences73. Mortality differences by SES level are attributed to screening rates impacting late stage diagnosis among those in the lower SES levels50, 72. 155 Poor diet is linked to CRC risk and a cause of obesity. In the US, the CRC incidence rates of Latinos are similar to those of Whites yet higher than residents of Puerto Rico, Central American and South America204. Latinos are the only ethnic group that did not display a positive association between SES and CRC mortality in California, meaning that at higher SES levels, increased mortality risk ocurred202. The positive association identified for African Americans and other ethnic groups was thought to be related to increased risk factors and lower access to screening as SES decreases. The lack of an association suggests that risk factors (obesity, diet, physical inactivity) actually remain or worsen as SES improves for Latinos202. This may be due to the acculturation of Latinos in the US to a diet higher in fat, refined carbohydrates, animal protein and lower levels of physical activity 204. Obesity prevalence among Latinos varies by immigrant status. Mexican American men born in the US had considerably higher prevalence of obesity than Mexican American men born in Mexico (39% vs 26%) yet the difference between US born and Mexican-born women was smaller (3%) 196. Men who spoke English at home had 12% higher obesity prevalence than those who spoke Spanish. An analysis of NHANES data revealed that obesity and income and education had an inverse relationship among women but the relationship varied by race/ethnicity among men188. From 1976 through 2010, the obesity prevalence has nearly doubled for Mexican women and more than doubled among Mexican men197. Hispanic women reported receiving colorectal cancer screening (51%) at lower rates than non-Hispanic White women (60%) and a greater difference was observed between Hispanic men (42%) and White men (63%). Low rates of colorectal cancer screening were found among Hispanic men regardless of country of origin197. A positive 156 relationship between SES and CRC mortality has been identified among Latinos likely due to the resource intensive use of colonoscopy as a screening method201 requiring health insurance or increased income to access. This association was not found in our study, likely due to later introduction of colonoscopy for CRC screening. We found a positive association between poverty and CRC-specific mortality among Blacks but not Latinos. Lower SES is associated with less odds of curative surgery, neoadjuvant therapy, radiation and chemotherapy and increased odds of a permanent stoma after surgery among CRC cases180. Although Latinos did experience suggested differences in mortality risk compared to Whites at the lowest poverty levels, the absence of poverty differences within Latinos could be due to the distribution of health behaviors and access and use of medical services. Similar to colorectal cancer incidence and death rates, prostate cancer incidence and death rates are higher among African Americans than Whites191. Race has been described as both a risk factor and prognostic factor for prostate cancer177. Screening use213, tumor grade177, stage177, 210, 213 and overall health177, less aggressive treatment182, 200, 213 , and lower SES status177, 182, 213 have been identified as contributors to African American-White mortality differences. Yet, PSA testing has resulted in an increase in localized disease and a potential over diagnosis of cancer requiring little if any treatment177. Thus, the gap is in the use of testing for the early detection of more aggressive and higher metastatic disease present among African American men than others in order to truly impact mortality rates213. For African American men, increased SES offered no improvement of tumor grade as displayed by White men116. In fact, one analysis suggests that the prostate tumor grade of African Americans may be especially 157 susceptible to environmental influences184. Unexplained excess mortality risk among African Americans could be reflective of gene-environmental interactions and exposures, lifestyle differences, or biology177, 200, 213. Siegal et al. investigated death rates using death certificates for African Americans, Whites and Latinos aged 25-64 years old in 2007199. They conclude that eliminating SES disparities as measured by educational attainment would avert twice as many premature deaths as eliminating racial disparities. The inverse association between increased deaths with decreasing educational attainment was considerably weaker among Latinos than Whites and African Americans. In our analysis, the importance of poverty as a mediator for cancer outcomes holds true for all but breast cancer. Our breast cancer analysis suggests that Black-White differences are much greater particularly among the more affluent and poverty level risk differences within Blacks are marginal for both breast cancer-specific and all-cause mortality in this elderly population. Further investigation into the distribution of risk factors, screening behaviors, tumor characteristics and treatment access, preferences and utilization within the racial/ethnic groups by SES may help us identify modifiable factors that contribute to Black-White and Latino-White risk differences and develop interventions to achieve equal breast, colorectal and prostate cancer outcomes. Comparison of Findings to Literature Breast Cancer Most current studies show results similar to our excess risk of death for breast cancer-specific mortality in both adjusted and unadjusted models. However, a study of Medicare-SEER stage 0-IV breast cancer cases aged 68 and older from 1994-1999 found 158 an unadjusted breast cancer-specific mortality hazard of 1.63 (95% CI 1.48-1.80) which was nearly eliminated after controlling for all similar covariates and one additional key variable unmeasured in the current study, screening history29. The Curtis study focused on racial/ethnic difference by stage of disease and identified the greatest African American-White difference among those with Stage II/III cancer (HR 1.30, 95% CI 1.101.54). Curtis et al also showed excess risk of death for Latinas in the crude model but decreased risk after full adjustment, similar to our results. Asian women displayed decreased mortality risk with little changes upon the addition of predictive variables for most stages except stage IV cancers. In our study, after demographic variables were introduced, Asian women risk was not influenced by other covariates. Wu and colleagues estimated age-attained hazard ratios for the California Breast Cancer Survivorship Consortium cases, a dataset of six breast cancer epidemiological studies enrolling women of any age from the early 1990s to early 2000s, resulting in 12,787 female breast cancer cases. Their full models included health behavior indicators such as smoking, alcohol use, obesity, all before the cancer diagnosis, but lacked comorbidities172. In their study, African Americans showed a fully adjusted predicted mortality hazard of 1.13 (0.97-1.33), while decreased mortality was found for Asian women (HR 0.60, 0.37-0.97) and Latinas (HR 0.84, 0.70-1.00). Even with the additional information of behaviors that increase risk for breast cancer, the excess breast cancerspecific mortality among African Americans and the decreased mortality for others were not eliminated. In an analysis of Medicare-SEER breast cancer cases by Sait K et al, treatment differences were identified between elderly African American and White women 159 diagnosed at stages I-IIIA211. Unadjusted all-cause mortality was increased among African Americans but reduced after adjustment for patient, tumor characteristics and SES. The persistent mortality difference after adjustment was found among African American women with node-negative tumors (HR 1.11, 95% CI 1.01-1.22). Our study of women with stages I-IV found a crude 42% increased risk of all-cause mortality which was partially explained by demographics, comorbidities, tumor and treatment factors and SES. The analysis of mortality risk by SES is not consistent with our current results. Unlike our study, Du et al, using 35,029 Medicare-SEER breast cancer cases aged 65 and older from 1992-1999, identified no increased breast cancer-specific mortality hazard among low SES breast cancer cases using a composite SES measure (HR 1.01, 95% CI 0.87-1.17) or percent at or below poverty (HR 1.04, 95% CI 0.89-1.21)47. All covariates were similar to the current study except the addition of tumor size and the measurement of SES. Du and colleagues used quartiles of the poverty data (≤3.62%, 3.63%-6.62%, 6.63%-11.99%, ≥12%) whereas we used poverty cut-off points recommended in the literature and generally accepted as significantly associated with health outcomes (20% or more in census tract in poverty is considered an impoverished area163). Our results are suggestive of differences in the subgroup analysis. The greatest magnitude of Black-White difference was in the very low poverty level. Latinas showed decreased mortality compared to White women at all poverty level except in the middle level where significant excess risk of death was found. Asian women showed little difference when compared to White women at each poverty level. Parise et al conducted the only comparable study using an interaction analysis and stratification by stage215. 160 This California registry investigation included 179,143 breast cancer cases of all ages with stages I-III identified in the 2000-2010 period. A race-SES interaction was significant among Stages II and III cases (P<0.05). The hazard ratios of mortality risk for Black women compared to White women within the highest SES levels in each stage ranged from HR 1.47 (95% CI 1.0.96-2.27) in stage I, HR 1.41 (1.15-1.73) for Stage II cases and HR 1.53 (95% CI 1.22-1.92) among those in stage III. Similar to our study, the investigators found higher black/white differences in the highest SES groups. In the Albano analysis of breast cancer death rates using death certificates and relative risk ratios for White and Black women under 65 years old and identified significant differences by educational attainment174. The relative risk of breast cancer mortality (RR) of Black women compared with White women is 1.43 (95% CI 1.37-1.48) for women with 12 years or less of education. The Black-White relative risk of breast cancer mortality is 1.68 (95% CI 1.60-1.76) for women with more than 12 years of education. Although Albano and colleagues investigated a younger age group than the current study (< 65), it is one of a few analyses of mortality within and across SES and race/ethnic groups. The initiation of Medicare and Social Security benefits at age 65 may mitigate some of the effect of education and SES among the elderly as well as attenuate the racial differences of the older groups. However, it establishes Black-White differences by educational level and a differential impact of educational status within Whites and Blacks. Our study also revealed a greater magnitude of mortality hazard of African Americans compared to Whites in the very low poverty level rather than within each group in the high poverty areas. The Albano study additionally found that the breast 161 cancer mortality relative risk of lower educational level to higher educational level was greater within Whites than African Americans174. Our analysis also showed that within the African American group, there is no difference in risk of death for high vs. very low poverty while among White women, high poverty cases experienced increased risk of death compared to White women in very low poverty areas. In fact, African American women in the low poverty areas appear to potentially experience decreased risk of death when compared to very low poverty. Latinas showed a trend toward decreased risk in the low and high poverty areas compared to very low poverty. White and Asian women displayed a positive association between poverty and mortality risk. Our results are consistent with the literature on race/ethnicity mortality differences. Stratified and interaction analysis to which we can readily compare our results are lacking. However, the risk differences identified in the current study are in the similar direction as those found in other studies using various SES measures. Colorectal Cancer Studies to date have exhibited racial/ethnic and SES differences in risk of death for colorectal cancer-specific mortality. The risk difference for Latinos in particular depends on the age group and stages included in the analysis. Two studies from the California cancer registry which included a diverse cohort of CRC cases of all ages found decreased mortality risk for both Asians and Latinos71, 181. Medicare-SEER analysis show both increased hazard for African Americans and either increased109, decreased73 or showed no difference171 among Latinos compared to Whites. Gomez et al. analyzed Medicare-SEER CRC cases from 1992-1996. Increased mortality was identified for African Americans and Latino males in both crude and 162 adjusted cancer-specific and all-cause mortality models109. Stage explained the higher mortality risk among Latino males while both stage and SES were responsible for a portion but not all of the African American-White difference. Similar to our study, comorbidities had no effect on the risk estimates. The greatest contributors to the excess risk among both Latinos and African Americans in the current study were tumor and treatment characteristics and poverty but the excess remained. An analysis of Medicare-SEER stage I-III CRC cases 66 years and older in 16 SEER registries from 1999-2002 revealed persistent increased hazard ratios for African Americans compared to Whites (HR 1.24, 95% CI 1.14-1.35) and compared to Asians (HR 1.56, 95% CI 1.33-1.82)73. Our study included stage IV cases and additional treatment information in the model which reduced the hazard ratio to 1.19 for African Americans compared to Whites. White et al found a decreased risk of mortality among Latinos compared to Whites in both the crude and adjusted models (HR 0.85, 95% CI 0.70-1.02). In our study, Latinos had a considerably greater proportion of stage IV cases than whites (9.8% vs 7.3%). It is possible that a portion of risk difference is attributed to the poorer outcomes of the stage IV cases in our cohort. Haas et al investigated the underuse of treatment modalities by racial/ethnic groups among the Medicare-SEER CRC cases from 1992-2005 and followed through 2007171. This study showed that African Americans were less likely than Whites to receive surgery (OR 0.82, 95% CI 0.70-0.95), chemotherapy (OR 0.61, 95% CI 0.500.73) and radiation therapy (OR 0.68, 95% CI 0.53-0.88) when indicated. African Americans displayed increased cancer specific and all-cause mortality risk in all groups except all-cause mortality for those eligible for surgical resection and radiation. After full 163 adjustment, any increased mortality risk among Latinos compared to whites disappeared. The authors conclude that socio-demographic factors and availability of cancer specialists marginally mediate the black-white mortality risk differences. A meta-analysis analysis of 10 studies from 1966-2006 on colon cancer mortality for African Americans compared to Whites with SES measures revealed a marginally increased colon cancer-specific mortality of HR 1.13 (1.01-1.28) and all-cause mortality hazard of HR 1.14 (95% CI 1.00-1.29)185. The study confronted difficulties in the inconsistent study designs, variables used, outcomes analyzed and age groups represented. Three of the ten studies were of the Medicare population (65 years and older). Yet, the meta-analysis result is very consistent with the direction of our own excess colorectal cancer-specific mortality risk among African Americans even when accounting for SES in the model (HR 1.19, 95% CI 1.10-1.29) and all-cause mortality (HR 1.10, 95% CI 1.04-116). Similar SES analyses have found increased mortality risk for those at the lowest SES levels. The Le study with California Cancer Registry data of CRC cases of all ages displayed higher mortality hazards among the lowest SES level for colon cancer–specific mortality (HR 1.26, 95% CI 1.20-1.32) and rectal cancer-specific mortality (HR 1.33, 95% CI 1.24-1.42) when compared to those in the highest SES areas71. These hazard ratios are considerably higher than in our current study even when all but comorbidities and more extensive treatment information was included in their model. Although many studies have associated lower SES with increased mortality risk180, Marcella et al suggested that among those 60 years and older, race-specific effects are stronger than SES whereas SES plays a stronger role among those younger than 60 years old74. The 164 study authors conclude that SES mediates racial/ethnic stage-specific mortality outcomes. Half of the black-white mortality difference is described as a result of later stage at diagnosis among African Americans. The Albano study of mortality data found parallel differences using relative risk estimated between and within racial/ethnic groups174. A greater relative risk for lower education compared to higher education within African Americans than Whites was exhibited. The African American-White difference displayed a greater magnitude of relative risk within the lower educational attainment group than higher educational group. In our study, the high-very low poverty differences were similar for Whites and African Americans for All-cause mortality And higher within African Americans for CRCspecific mortality. The African American-White difference was significantly greater within the high poverty group than the very low poverty group for CRC-specific mortality. Although colorectal cancer studies have shown varied results, our results are comparable to literature analyzing similar cancer populations and outcomes. Marginal differences found between studies may be due to the inclusion of younger cases, combining colon and rectal cases and more extensive treatment and comorbidity information in the Medicare-SEER dataset than the cancer registry investigations. Increased mortality risk was found for African Americans and high and middle poverty groups for both colorectal cancer-specifically and all-cause mortality. Prostate Cancer Additional studies support our current findings of excess prostate cancer-specific mortality among African Americans compared to Whites and null differences in all-cause 165 mortality. Du XL et al. conducted an analysis of 61,228 men aged 65 and older with local/regional stage prostate cancer in the Medicare-SEER dataset from 1992-1999. The Du study found no African American-White difference in all-cause mortality and an increased mortality risk for African Americans compared to Whites for prostate cancerspecific mortality (HR 1.17, 95% CI 0.99-1.37) as was identified in our study. Latinos experienced decreased mortality risk for all-cause and prostate specific cancer outcomes in Du’s investigation, unlike our study. The Du study also found considerably higher increased risk among low SES groups than in our study for both all-cause mortality (HR 1.31, 95% CI 1.25-1.37) and prostate-specific cancer mortality (HR 1.40, 95% CI 1.201.64). Du’s five-year survival estimates are also approximately 5-7% higher than in our current study. Du divided the poverty variable into quartiles. Du also had had substantially less Latinos in the dataset than on our study and omitted Asian ethnicity from the analysis. Our study which includes stage IV cases displayed considerably higher proportions of stage IV disease among African Americans (8.7%) and Latinos (7.7%) than Whites (6%) and poorer prognostic features (Gleason scores 8-10). The poorer prognostic tumors may drive the higher risk among Latinos in our study. A Detroit study of 98,484 mostly elderly prostate cancer cases diagnosed 19881992 utilized a SES variable including professional category and poverty status to find mortality rate difference by ethnic and SES group when controlling for race, age, tumor grade, SES and lastly treatment (radical prostatectomy, radiation or none)182. The adjusted all-cause mortality analysis found no difference between African Americans and Whites (HR 1.03, 95% CI 0.96-1.11) among the localized and regional stage prostate cancer cases. Those in the poverty category exhibited increased risk (HR 1.33, 95% CI 166 1.23-1.46) for both the localized and regional stage analysis. Similar to our study, this study by Schwartz et al highlights the effect of treatment and SES on racial disparities in all-cause and prostate-specific mortality. Two additional Medicare-SEER analyses provide further investigation into contributors of the Black-White mortality gap212, 213. A study by Putt and colleagues identified the greater impact of increasing comorbidities in the Medicare-SEER prostate cancer sample on survival among White males than African Americans212. As a result, the Black-White survival gap is greatest among those without comorbidities (RR1.53, 95% CI 1.14-2.04) and narrows as comorbidity levels increase (RR1.09, 95% CI 0.811.47). In addition, the mortality risk difference between those without comorbidities to those with greater comorbidities was greater in magnitude within Whites (RR 4.37, 95% CI 3.91-4.89) than within African Americans (RR 3.12, 95% CI 2.48-3.93). Our study showed that for prostate cancer specific mortality, comorbidities had no impact on hazard ratios and a marginal impact on African American and White estimates for all-cause mortality. Comorbidities accounted for a 25% reduction in the excess risk of Latinos in high poverty, a 36% decline for Latinos in middle poverty and 19% decline for Latinos in low poverty compared to Latinos in very low poverty. In the main effects analysis, the excess mortality risk for Latinos was reduced 56% after accounting for comorbidities (absolute decrease of 5%) and African Americans experienced a reduction of 18% (absolute decrease of 8%). Comorbidities were a greater contributes to Latino-White differences than Black-White mortality risk difference in our sample. Taksler et al identified contributors to African-American-White mortality differences in the Medicare-SEER prostate cancer cases among elderly men213. First they 167 estimated an excess of 1,320 Black male deaths compared to White male deaths per 100,000. They found that 76% of the excess deaths were due to a higher incidence among African Americans than White males. Of the 76%, 45% is related to the increased local/regional prostate cancer incidence, 26% metastatic cancer incidence, and 5% unstaged prostate cancer incidence. If African American males had the same level of prostate screening, comorbidities and income as White males, 8% of the metastatic incidence contributor (26%) would be eliminated. Additionally, Blacks had 24-40% higher metastatic incidence rate than affluent blacks. Through their Cox Proportional Hazard Rate Model, the authors concluded that tumor characteristics explained 50% of the African American-White mortality difference, comorbidities explained 4%, treatment and physician choice 17% and SES explained 15% for a total of 86% of the risk difference. In our analysis, comorbidities accounted for 0% of the difference; tumor and treatment factors explained 57% of the difference and poverty 14%. These studies show the importance of SES on mortality risk but not differing levels of risk by level of poverty. The Albano study of relative risk of African Americans compared to Whites using National Health Statistics data identified a marginally higher relative risk of high education to low within African Americans (RR 1.51, 95% CI 1.03-2.22) than our mortality hazard when comparing poverty levels within African Americans174. Albano’s results show a stronger education gradient within African Americans than Whites for prostate cancer. Additionally, the greater relative risk for African Americans compared to Whites is among those with less than a high school diploma. Like the educational comparison, we did find a difference by poverty within Whites and African Americans 168 for all-cause and cancer-specific mortality, and a persistent African American-White difference among those in the highest poverty areas for prostate cancer-specific mortality but not all-cause mortality. Although survival differences are narrowing between ethnic/racial groups183, persistent differences by poverty and racial/ethnic groups continue. Few studies have investigated the interaction of race/ethnicity and SES in prostate cancer outcomes. Allcause mortality in our study and others show no African American-White differences, but decreased mortality for Latinos, Asians and increased mortality risk as poverty levels increase. The African American-White prostate cancer-specific mortality risk difference is concentrated in the highest poverty levels with no differences found in the lower levels of area poverty. Limitations There are several limitations that must be noted regarding this analysis. Although the Medicare-SEER database provides a large number of cancer cases for each of the cancers, there are still limited cases for each racial/ethnic minority group. Thus, interaction analysis is constrained by the smaller number of racial/ethnic cases in each of the sub-groups. This is reflected in the wide confidence intervals in this current analysis. As the racial/ethnic categorical reporting improves in this dataset and with additional years more successful interaction or stratified analysis will be possible. Other limitations include the lack of health behavior measurements in the dataset that impact risk of cancer outcomes. For example, we were unable to measure characteristics such as obesity and smoking which influence treatment decisions as well as health outcomes independently. We added to the Charlson Comorbidity Index 169 indicators of chronic physical and mental health conditions. However, neither displayed much influence on mortality. We did not measure screening frequency or diagnosis delay in our analysis. Similarly, unmeasured treatment complications may have accounted for some of the excess and decreased risk within the sub-groups. There may be biologic or genetic factors unmeasured in this study. In addition, our measures used for race/ethnicity and socioeconomic status may be flawed. Research studies to date utilize numerous different methods for measuring SES. This study utilized the simplest measurement supported by research in its connection to outcomes, percent of people at or below poverty. We were unable to include measures of assets, an important component of social class. Our study does not include a measure of individual socioeconomic status. There may be misclassification of racial/ethnic group measurement. Medicare-SEER provides six variables with racial/ethnic information. We utilized all of the variables to identify members of racial/ethnic minority groups. However, reducing six variables into one race/ethnicity variables offers considerable opportunity for misclassification. Future Research The interaction of race/ethnicity and SES requires further investigation with datasets with larger numbers of non-White cases and individual-level socioeconomic status information. Including information on risk factors such as health behaviors, screening frequency and quality are also essential to further elucidate the causes of increased mortality among African Americans and cancer patients in high poverty areas. Further investigation into the potentially unique disadvantageous experience of African Americans in affluent neighborhoods related to breast and prostate cancer mortality. 170 Conclusion A positive relationship between poverty and cancer-specific and all-cause mortality for breast, prostate and CRC was found only for some ethnic groups. An interaction effect between poverty and race/ethnicity is found for breast cancer specific mortality. Differences in the levels of poverty within racial/ethnic groups were also found in breast all-cause and prostate cancer specific mortality. For some, as poverty increases mortality risk also increases. For particular racial/ethnic groups such as African Americans and Latinos, decreased poverty does not lessen mortality risk. Poverty provides significant influence on racial/ethnic outcomes but is not consistent across groups. If the lack of difference within African Americans by poverty level holds, then public health interventions must work to decrease the risk factors for both incidence and mortality among African Americans at all income levels particularly for breast and prostate cancer. The persistent excess mortality risk among African Americans with breast cancer is due to the overwhelming differences among poorer prognostic tumors in our study including all stages of invasive cancers at all poverty levels which accounts for the lack of poverty gradient. 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Literature Search Strategy Category Key words or MESH terms used Deprivation index and health outcomes ("deprivation index") AND (survival[tiab] OR mortality[mh]) Total number of citations Articles read 53 53 Socioeconomic factors and cancer (("neoplasms"[MeSH Terms] OR "neoplasms"[All Fields] OR "cancer"[All Fields]) AND "socioeconomic factors"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 1,159 40 Prostate cancer and SES ("prostatic neoplasms"[MeSH Terms] OR ("prostatic"[All Fields] AND "neoplasms"[All Fields]) OR "prostatic neoplasms"[All Fields] OR ("prostate"[All Fields] AND "cancer"[All Fields]) OR "prostate cancer"[All Fields]) AND "socioeconomic factors"[All Fields] 481 51 Breast cancer and SES (("breast neoplasms"[MeSH Terms] OR ("breast"[All Fields] AND "neoplasms"[All Fields]) OR "breast neoplasms"[All Fields] OR ("breast"[All Fields] AND "cancer"[All Fields]) OR "breast cancer"[All Fields]) AND "socioeconomic factors"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 303 43 Colon cancer and SES (("colonic neoplasms"[MeSH Terms] OR ("colonic"[All Fields] AND "neoplasms"[All Fields]) OR "colonic neoplasms"[All Fields] OR ("colon"[All Fields] AND "cancer"[All Fields]) OR "colon cancer"[All Fields]) AND "socioeconomic factors"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 76 32 192 Cancer and SES Total number of citations Articles read (("rectal neoplasms"[MeSH Terms] OR ("rectal"[All Fields] AND "neoplasms"[All Fields]) OR "rectal neoplasms"[All Fields] OR ("rectal"[All Fields] AND "cancer"[All Fields]) OR "rectal cancer"[All Fields]) AND "socioeconomic factors"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 33 25 Colorectal cancer and SES (colorectal[All Fields] AND "socioeconomic factors"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 81 57 Rectal cancer Treatment outcomes and SES (("rectal neoplasms"[MeSH Terms] OR ("rectal"[All Fields] AND "neoplasms"[All Fields]) OR "rectal neoplasms"[All Fields] OR ("rectal"[All Fields] AND "cancer"[All Fields]) OR "rectal cancer"[All Fields]) AND "socioeconomic factors"[All Fields]) AND "Treatment Outcome"[MeSH Terms] 10 10 Colon cancer Treatment outcomes and SES (("Treatment Outcome"[MeSH Terms])) AND ((colon cancer and AND "socioeconomic factors") AND (survival[tiab] OR mortality[mh])) 8 8 Breast cancer Treatment outcomes and SES (((breast cancer and AND "socioeconomic factors") AND (survival[tiab] OR mortality[mh]))) AND ("Treatment Outcome"[MeSH Terms]) 21 21 Prostate cancer Treatment outcomes and SES ((prostate cancer and AND "socioeconomic factors")) AND ("Treatment Outcome"[MeSH Terms]) 18 19 ("treatment delay"[All Fields] OR "Diagnosis delay"[All Fields]) AND 196 59 Category Key words or MESH terms used Rectal cancer and SES 193 Delays and cancer Diagnosis and treatment 194 Total number of citations Articles read (("prostatic neoplasms"[MeSH Terms] OR ("prostatic"[All Fields] AND "neoplasms"[All Fields]) OR "prostatic neoplasms"[All Fields] OR ("prostate"[All Fields] AND "cancer"[All Fields]) OR "prostate cancer"[All Fields]) AND "delay"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 195 37 Diagnosis and treatment Delays and breast cancer outcomes (("breast neoplasms"[MeSH Terms] OR ("breast"[All Fields] AND "neoplasms"[All Fields]) OR "breast neoplasms"[All Fields] OR ("breast"[All Fields] AND "cancer"[All Fields]) OR "breast cancer"[All Fields]) AND "delay"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 483 65 Diagnosis and treatment Delays and colon cancer outcomes (("colonic neoplasms"[MeSH Terms] OR ("colonic"[All Fields] AND "neoplasms"[All Fields]) OR "colonic neoplasms"[All Fields] OR ("colon"[All Fields] AND "cancer"[All Fields]) OR "colon cancer"[All Fields]) AND "delay"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 138 71 Diagnosis and treatment Delays and rectal cancer outcomes (("rectal neoplasms"[MeSH Terms] OR ("rectal"[All Fields] AND "neoplasms"[All Fields]) OR "rectal neoplasms"[All Fields] OR ("rectal"[All Fields] AND "cancer"[All Fields]) OR "rectal cancer"[All Fields]) AND "delay"[All Fields]) AND (survival[tiab] OR "mortality"[MeSH Terms]) 104 43 (("neoplasms"[MeSH Terms] OR "neoplasms"[All Fields] OR "cancer"[All Fields]) AND ("immigrant"[All Fields] OR "National origin"[All Fields])) 42 30 Category Key words or MESH terms used Delays and outcomes (survival[tiab] OR "mortality"[MeSH Terms]) Diagnosis and treatment Delays and prostate cancer outcomes Immigrants Immigrant and cancer outcomes Category Key words or MESH terms used Total number of citations Articles read 463 42 AND (survival[tiab] OR "mortality"[MeSH Terms]) Immigrant and treatment outcomes ("Treatment Outcome") AND "national origin" OR "immigrant" AND and cancer 195