Download understanding the effect of socioeconomic gradient

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).
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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.
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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.
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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
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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
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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
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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
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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).
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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
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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. These prognostic differences are likely due to different
underlying risk factor distributions among the sub-groups. The prostate cancer mortality
differences among Black and White men are largely explained by tumor and treatment
differences prevalent among those in the higher poverty levels. Colorectal cancer
differences are potentially related to access to diagnostic and treatment services to
improve survival.
171
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APPENDIX A
LITERATURE SEARCH STRATEGY
191
A. 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