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The Societal and
Economic Impact of
Cancer Health Disparities
1776 I STREET NW 9TH FLOOR, WASHINGTON, DC 20006
www.c-changetogether.org
ACKNOWLEDGEMENT
C-Change would like to acknowledge the support and guidance its members and partners who serve on the
Implementing a National Health Disparities Strategy Advisory Committee and the efforts by staff who all
contributed to the development and preparation of this document.
ADVISORY COMMITTEE MEMBERS
Armin D. Weinberg, PhD, Co-Chair …………………………………..…………………..………..…Life Beyond Cancer Foundation
Harold P. Freeman, MD, Co-Chair……………………………………..Ralph Lauren Center for Cancer Care and Prevention
Ms. Carolyn Angeleri RiccI…………………………………………………………………………………..……………………Avon Foundation
John E. Arradondo, MD, MPH ………………………………………………………..………….Volunteer State Medical Association
Joel Beetsch, PhD …………………………………………………………………………………………………………………………Sanofi-Aventis
Clem Bezold, PhD ……………………………………………………………..………………………………Institute for Alternative Futures
Linda Blount, MPh …………………………………………………………………………………………..……………………………….WFG Equity
Sharon Brigner, MS, RN …………………………….…………………………………………………….……………………………………..PhRMA
Jimmy Boyd ………………………..……………………………………………………………………….............………Men's Health Network
Ahmed Calvo, MD, MPH……………………….........................................Health Resources and Services Administration
Erica Childs-Warner ………………………………………………………………..……………………………….Prevent Cancer Foundation
Franz Fanuka ……………………………………………………..………National Forum for Heart Disease and Stroke Prevention
S. Orlene Grant, RN, BSN, MSN …………………………………………………………………………………………The Grant Group, LLC
Deborah Guadalupe Duran, PhD ………………………………………………..……………………………….National Cancer Institute
Nikki S. Hayes, MPH ……………………………………………………………..………..Centers for Disease Control and Prevention
Cheryl G. Healton, DrPh, MPA ………………………………………………………………….…LEGACY | for Longer Healthier Lives
Nina Hill, PhD ………………………………………………………………………………………………….………………Pfizer Pharmaceuticals
Marc Hurlbert, PhD ……………………………………………………………………………………..………………………….Avon Foundation
Rebecca Johnson ……………………………….…………………….….National Association of City and County Health Officials
Lovell Jones, PhD …………………………………………………….……...……..University of Texas MD Anderson Cancer Center
Judith Salmon Kaur, MD ……………………………………………………………….…Mayo Clinic Comprehensive Cancer Center
Gerard T. Kennealey, MD …………………………………………………………….……………………………………………….Cephalon Inc.
Maureen Y. Lichtveld, MD, MPH …………………….Tulane University School of Public Health and Tropical Medicine
Kim Linthicum ……………………………………………………………………………………………........................Myriad Genetics, Inc.
Mr. Andy Miller, MHSE, CHES …………………………………………………………………………………………………………LIVESTRONG
Neal A. Palafox, MD, MPH ……………………………………………..University of Hawaii-John A. Burns School of Medicine
Janet A. Phoenix, MD, MPH ………………………………………………………………………………….…………..The Grant Group, LLC
Marcus G. Plescia, MD, MPH …………………………………………………………. Centers for Disease Control and Prevention
Amelie G Ramirez, DrPH ……………………………………………University of Texas Health Science Center at San Antonio
Wayne Rawlins, MD, MBA …………………………………………………………………………………………………………………………Aetna
Rochelle Rollins, PhD ……………………………………………………………….U.S. Department of Health and Human Services
Anthony Signorelli ………………………………………………………………………………………………………..Advertising Council, Inc.
Elizabeth Thompson ……………………………………………………………………………………………..Susan G. Komen for the Cure
Shaan K. Trotter, MS ………………………………………Lurie Comprehensive Cancer Center of Northwestern University
Donald Warne, MD, MPH ………………………………………………………….Aberdeen Area Tribal Chairmen's Health Board
C-CHANGE STAFF
Brian Alexander, Gary Gurian, MPA, and Tasha Tilghman-Bryant, MPA
C-CH
HANGE
The mission of C-Change iss to eliminaate cancer as
a a major public healtth problem at the earliest possiblee
timee by leveragging the exp
pertise and resources of our mem
mbers. A 50
01(c)(3), C-C
Change is comprised o
of
apprroximately 150
1 of the Nation’s can
ncer leaderss from the private, public, and no
on-profit sectors. Thesee
leaders collaboraate on issues spanning the
t continuu
um of researrch, prevention, and caree - that cann
not be solved
d
a
by one organizattion or even one sector alone.
PREFFACE
C-Ch
hange comm
missioned ressearchers at the Center for
f Health Disparities So
olutions (CHD
DS) at the Jo
ohns Hopkinss
Bloo
omberg Scho
ool of Publicc Health to develop thiss Case Stateement on th
he Societal and
a Econom
mic Impact of
Canccer Health Disparities.
D
We
W gratefullyy acknowledgge the effortts of the following CHDSS staff in devvelopment of
this document:
d
Darrrell J. Gaskin,, PhD
Thom
mas A. LaVeiist, PhD
Patriick Richard, PhD
Jean G Ford, MD
D, MPH
SPEC
CIAL ACKNOWLEDGEMEENT
C-C
Changge wo
ould liike to
o than
nk
Sussan G.
G Kom
men for the
e Curre
f th
for
heir ge
enero
ous co
ontrib
bution
n to
deve
elopm
ment of
o thiis doccument.
TABLE OF CONTENTS
Executive Summary ...................................................................................................................... 1
A. Introduction: Purpose & Overview ............................................................................. 1
B. Key Findings ................................................................................................................1
C. Methodology ................................................................................................................3
D. Recommendations ........................................................................................ 3
Section I
Data & Methodology ...................................................................................................... 5
A. Factors Associated with Cancer Prevalence ....................................................... 5
B. Impact of Cancer Health Disparities ................................................................. 5
C. Computing the Costs of Premature Cancer Death .............................................. 5
D. Direct Medical Costs of Cancer Disparities ....................................................... 7
E. Estimating the Indirect Costs of Cancer ............................................................ 9
F. Limitations ................................................................................................... 9
Section II
Literature Review ........................................................................................................ 10
A. Disparities in Cancer Incidence, Mortality, and Survival ..................................... 10
B. Socioeconomic Disparities in Cancer Burden .................................................... 14
C. Disparities in Lung Cancer .............................................................................. 15
D. Disparities in Colorectal Cancer ....................................................................... 16
E. Disparities in Prostate Cancer .......................................................................... 17
F. Disparities in Breast Cancer ............................................................................ 17
G. Cost of Cancer ............................................................................................... 18
Section III
Current Trends in Disparities in Cancer Incidence and Mortality Rates (Cancer Incidence
and Mortality by Race/Ethnicity and Gender) .................................................................... 20
A. Factors Associated with Cancer Prevalence ....................................................... 26
B. Results for Any Cancer ................................................................................... 26
C. Colon, Breast, Lung, and Prostate Cancer .......................................................... 30
D. Discussion ..................................................................................................... 39
Section IV
Societal and Economic Impact of Cancer Health Disparities .......................................... 40
A. Cost of Premature Death ................................................................................. 40
B. The Direct Cost of Cancer Disparities ............................................................... 43
C. The Indirect Cost of Cancer Disparities ............................................................. 45
Recommendations ........................................................................................................ 48
References ................................................................................................................... 50
Cancer Disparities Publications 2000-2010 .................................................................... 55
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EXECUTIVE SUMMARY
Purpose and Overview
This case statement describes the societal & economic impact [e.g., Years of Potential
Life Lost, etc.] of current cancer morbidity and mortality rates among minority and other
disparate populations in the hope that the detailed information and data will help spur a national
effort to address cancer health disparities.
Racial and economic disparities in cancer are prevalent in the United States. While
morally unacceptable, cancer disparities exact a substantial toll on society in terms of premature
death, lower productivity and the costs of medical care. This case statement outlines the
significant societal and economic costs cancer health disparities impose on the national
community. A review and summary of the cancer health disparities literature from 2000-2010
highlights documented disparities and identifies areas where more research and data are needed.
To better understand the burden of cancer in the population, the case statement also explores the
association between cancer prevalence and demographic, socioeconomic, obesity and other
health factors. Most importantly, the report estimates the economic costs of cancer disparities to
society.
There are other types of costs that we don’t consider in this report. There are the
emotional and psychic costs for cancer victims and their families and friends. There are the costs
borne by caregivers of cancer victims. This would include the value of hours of service family
members and friends provide cancer victims.
What’s indisputable is that cancer exacts a heavy toll on the United States, and the
disparities in cancer incidence and deaths not only add to that toll but also underscore the need to
examine why such disparities exist.
Key Findings
The literature on cancer health disparities shows:
•
For all cancers and the most common cancer sites, African-American men have the
highest incidence and mortality rates compared to white, Asian and Hispanic men. (See
section 3, pages 12-16.)
•
Asian and Hispanic men have lower incidence and mortality rates than white men for all
cancers and for most individual cancer sites, with the exception of stomach, and liver and
bile duct cancers. (See section 3, pages 12-13.)
•
White women have the highest incidence rates for all cancers, but African-American
women have the highest mortality rates. (See section 3, pages 12-16.)
•
Persons with low socioeconomic status have higher rates of cancer incidence and
mortality and lower rates of cancer survival compared to more affluent persons. (See
section 3, pages 16-18.)
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•
The lack of individual income, insurance and education data limits researchers’ ability to
study the association between socioeconomic status and cancer. (See section 3, page 16.)
•
Despite higher rates of cancer, African Americans and the poor were more likely to
receive suboptimal cancer care compared to whites and affluent persons. (See section 3,
pages 18-21.)
An analysis of recent trends in cancer shows:
•
Recent federal data show that racial and ethnic disparities in cancer persist. (See section
4, pages 21-25.)
•
Socioeconomic disparities in cancer incidence rates do not show a consistent pattern,
such that persons living in poorer communities or less educated communities are
consistently disadvantaged. (See section 4, pages 25-28.)
•
However, cancer mortality rates do show a consistent pattern — poorer and less educated
communities have higher rates of death compared to affluent and better educated
communities. (See section 4, pages 25-28.)
•
Traditionally disadvantaged groups tended to have lower prevalence rates of cancer. This
probably reflects lower screening rates and survival rates for vulnerable groups. (See
section 4, pages 29-31.)
The estimates of the costs of cancer health disparities show:
•
Disparities in cancer health are costly. (See section 5, page 43.)
•
The annual costs of racial/ethnic disparities are almost $197 billion:
o $193 billion for premature death (see section 5, page 44);
o $2.3 billion for direct medical costs (see section 5, pages 45-46); and
o $471.5 million for lost productivity (see section 5, page 47-49).
•
Because whites have higher cancer incidence and mortality rates than Asians and
Hispanics, they bear 80% of the costs of cancer disparities. African Americans bear 17%
of the costs of cancer disparities. (See section 5, page 44.)
•
The costs of socioeconomic cancer disparities are almost $37 billion. These estimates are
conservative because they represent disparities between counties by socioeconomic status
versus disparities between individual by socioeconomic status. (See section 5, page 43
and section 2, pages 6-7.)
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Methodology
The case statement consists of four analyses:
• A systematic review of the recent literature published from 2000-2010 on racial, ethnic
and socioeconomic disparities in cancer.
•
An analysis of recent trends in cancer incidence and mortality using data from the
Centers for Disease Control and Prevention (CDC), National Vital Statistics System,
National Cancer Institute (NCI) Surveillance, Epidemiology and End Results (SEER)
Program.
•
An analysis of cancer prevalence using the Medical Expenditure Panel Survey (MEPS)
2002-2007.
•
An analysis of these data sources to compute the economic burden of cancer incidence
and mortality and its variance with race/ethnicity and socioeconomic status.
◦ Economic costs of racial/ethnic disparities were calculated by simulating the
decline in the number of cancer deaths and new cancer cases if racial/ethnic
differences in cancer burden were eliminated.
◦ To estimate the potential cost savings associated with eliminating socioeconomic
disparities in cancer burden, cancer rates and their associated costs were
compared between counties with high poverty rates against counties with low
poverty rates. A similar analysis was also conducted for counties with high and
low levels of educational attainment.
Notable limitations include:
• Lack of Good Data:
◦ Cancer registries do not collect/report socioeconomic information.
◦ Population-based surveys do not collect survival or stage information.
◦ Administrative data lacks clinical, demographic and socioeconomic information.
•
The inability to look at vulnerable subgroups of interest:
◦ Socioeconomics: poor and near poor, uninsured and underinsured, & poorly
educated;
◦ Race/ethnicity: Hispanic subgroups, Asian subgroups, & Native Americans and
Alaska Natives.
Recommendations
•
Society needs to focus on cancer prevention and cancer treatment that result in long-term
survival.
◦ Programs designed to lower individual risk of cancer, if they are successful in
reducing incidence and mortality among whites and African Americans to levels
experienced by Asians and Hispanics, would yield substantial economic value to
the nation.
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•
The nation should invest in policies and programs designed to reduce cancer risks
associated with environmental factors and unhealthy behaviors.
◦ Regardless of socioeconomic status, individuals from racial and ethnic minority
populations experience a myriad of stressors in the context of their social location.
The effects of social location are mediated by lifetime exposure to social and
environment factors, as well as individually modifiable behavioral risks. Such
social and individual-level exposures translate into differential health care quality
and access.
•
Strategies to reduce race disparities in the quality of cancer-related care must also address
the need for care coordination to promote rational utilization of preventive and
therapeutic care, including management of comorbid conditions.
◦ The available evidence points to opportunities to intervene to reduce disparities in
cancer-related care, through health risk management, access to a usual source of
care, access to primary and secondary treatment, and adjuvant therapies.
However, the evidence also suggests that despite health care coverage, many
members of racial and ethnic minority populations underutilize services for cancer
detection, diagnosis and treatment.
•
There are lessons to be learned from the advantage Asians and Hispanics have over
whites and African Americans that can help society address modifiable risk factors.
◦ Society should address modifiable risk factors such as physical inactivity, obesity,
heavy alcohol consumption, diets high in red or processed meats, diets that lack
fruits and vegetables, diets high in animal fat, smoking, and environmental
hazards such as radon, asbestos, air pollution, and certain metals (chromium,
cadmium, arsenic) (American Cancer Society, 2009).
•
Community-level interventions are warranted.
◦ At the community level, such interventions might address community norms and
lifestyles that promote adverse health outcomes in racial/ethnic minority
populations.
◦ Community-level interventions should also address potential adverse effects of
the physical and social environment, as well community differences in health care
availability.
•
We need better data.
◦ Cancer registries should collect socioeconomic data (income and education).
◦ Cancer registries should link existing data to social security records.
◦ Population surveys should develop cancer supplements to collect stage and
survival information.
◦ Possible national survey of new cancer patients based on a sample of new cancer
registrants.
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I. Data and Methodology
Factors Associated with Cancer Prevalence
Medical Expenditure Panel Survey (MEPS) data from 1996-2007 were used to explore
the association between cancer prevalence and socioeconomic status and access to care. Five sets
of models were estimated to identify the association between the likelihood of having a cancer
diagnosis and several factors, particularly race/ethnicity, income, education, health insurance and
obesity. The dependent variables of interest were whether the survey respondent reported having
any cancer, lung, colorectal, breast or prostate cancer. Models were estimated for any type of
cancer combined, and for breast, colon and rectal, lung and prostate cancer separately. Other
factors were used as control variables in the analysis: rural-urban location and year indicators
variables.
The models were estimated using the logistic regression techniques. Because MEPS has
a complex sampling design and data was pooled across 11 years, the longitudinal weights and
designed factors were used to conduct the models. The models were estimated using STATA
version 10.
Impact of Cancer Health Disparities
There are several sources of societal and economic impact of cancer health disparities.
This case statement attempts to quantify three types of costs: the cost of premature death, the
cost of medical care to cancer patients, and the indirect cost of cancer on economic productivity
through lost wages and hours worked. The case statement’s computation is based on three sets of
analyses: the cost of premature death due to cancer disparities, the direct medical costs of cancer
disparities, and the indirect costs of cancer disparities. The two types of disparities considered
are racial/ethnic disparities and socioeconomic disparities. For racial/ethnic disparities, the
report examines differences in cancer incidence and mortality across whites, African Americans,
Asians, American Indians and Alaskan Natives, and Hispanics. For socioeconomic disparities,
the report used two measures — poverty status and educational status. Counties were ranked and
then compared by their level of poverty and educational attainment.
Computing the Costs of Premature Cancer Death
To estimate the costs of premature death due to cancer disparities, the years of life lost as
a result of disparities were estimated and valued at society’s willingness to pay for a quality
adjusted life year. An elimination of disparities would likely result in a reduction of cancer
mortality rates within each age cohort to the rate for the race/ethnic group with the lowest rate.
We selected the lowest rate for age cohorts. This rate may be attainable across the entire age
cohort because it is an actual rate for one of the race/ethnic groups within the age cohort.
Presumably, if society can determine why one racial/ethnic group within the age cohort has low
cancer mortality, then it may be replicated for other racial/ethnic groups in that age cohort. For
the most part, Asians had the lowest cancer mortality rates.
First, the number of excess cancer deaths was computed by age cohort using cancer
mortality data published by the Centers for Disease Control and Prevention National Vital
Statistics System (www.cdc.gov/nchs/nvss/mortality_tables.htm). The number of deaths and
crude death rates were obtained by age and race for 2002 to 2006 (there were ten age groups: 14, 5-14, 14-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84 and 85 and over). Then the number of
deaths that would have occurred for each racial/ethnic group was estimated if every group’s
death rate were equal to the racial/ethnic group with the lowest death rate within the age
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category. The difference between the actual number of deaths and the estimated deaths
represents “excess deaths.” If an age-race cohort did not have sufficient numbers of persons in
the population to compute a valid death rate, this age-race cohort was excluded from the
analysis.
Second, number of years of life lost in each racial/ethnic group was computed assuming
that all persons would live the life expectancy of that particular age category. For example, for
persons 1-4, the life expectancy is 76.8 additional years; for persons 35-44, the life expectancy is
41.3 additional years; and for 75-84, the life expectancy is 10 additional years. The life
expectancy data is published by the CDC in the 2008 National Vital Statistics report (Arias,
2008). Third, low, moderate and high estimates were computed for the cost of premature cancer
deaths by valuing the years of a life lost at society’s willingness to pay for a quality adjusted life
year. For the conservative estimate, each year of life loss was quantified at $50,000, the standard
value used to evaluate medical intervention using cost effectiveness analysis (Hirth et al., 2000).
The lower and upper bounds recommended by Braithwaite and colleagues (2008) were used to
compute the moderate and high estimates. In a relatively recent study, they estimated the value
of a quality adjusted life year to range from $95,000 to $264,000 (Braithwaite et al., 2008).
Data from the Surveillance, Epidemiology and End Results (SEER) Program of the
National Cancer Institute were used to compute the costs of socioeconomic disparities in cancer
mortality (specifically, SEER/STAT to compute the number of deaths and crude death rates for
counties by the degree of poverty status and the level of educational attainment. These are two
separate analyses). Analysis was based on poverty status by dividing the counties evenly into
five poverty status groups: counties with 0.0-6.23% poverty rate; 6.24%-8.31%; 8.32%-10.72%;
10.73%-14.60%; and 14.61%-55.74%. A second analysis used educational status by dividing the
counties evenly into five educational attainment groups based on the percentage of adults in the
county with less than a high school education: 3.04-15.04%; 15.05%-18.90%; 18.91%-23.48%;
23.49%-30.48%; and 30.49%-65.30%. Similar to the race/ethnicity analysis, value of the number
of years of life loss due to excess cancer deaths was computed.
After obtaining the number of deaths and crude death rates by socioeconomic status
(SES) group for 2002 to 2006, the number of deaths that would have occurred for each SES
group, if every county’s death rate were equal to the SES group with the lowest death rate within
the age category, was estimated. The difference between the actual number of deaths and the
estimated deaths represents “excess deaths.” The number of years of life loss in each SES group
is assuming that all persons would live the life expectancy of that particular age category. Based
on the number of years of life loss, we estimated low, moderate and high costs of premature
death due to SES disparities in cancer mortality.
The SES analysis differs from the racial/ethnicity analysis because it is based on area
level differences in cancer rates for counties divided by SES. The race/ethnic analysis is based
on actual racial/ethnic mortality differences. The SES analysis compares cancer mortality rates
of persons living in poor versus affluent counties rather than comparing the rates of poor versus
affluent individuals. It is suspected that the variation across counties is smaller than the variation
across individuals. Hence, the estimated costs of premature cancer deaths due to SES disparities
will be lower because we are equalizing rates across counties by SES, not across individuals by
SES.
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Direct Medical Costs of Cancer Disparities
The direct medical costs of cancer disparities are equal to the excess number of cancer
cases associated with disparities multiplied by the incremental costs of cancer. This calculation
involves two steps: 1) computing the number of excess cancer cases attributable to disparities;
and 2) the incremental costs of cancer. The number of excess cancer cases was calculated using
cancer incidence data from the CDC and SEER 17 for the years 2002-2006. The SEER 17
represents 17 geographic areas and 26.2% of the U. S. population. The costs estimate for the
SEER 17 areas were then multiplied by 3.816 to estimate the national costs. This analysis was
conducted by race/ethnic group and by classifying counties into SES groups.
For each race/ethnic/SES group, the incidence rate, incidence counts, and population
were obtained for ten age cohorts: 1-4, 5-14, 14-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84
and 85 and over. After estimating the number of cancer cases that would have occurred for each
race/ethnic/SES group if every group’s incidence rate were equal to lowest incident rate within
the age category, the report’s estimate of “excess cancer cases” equals the actual number of
cancer cases minus the estimated number of cancer cases. This analysis was conducted for all
cancer cases, and lung, colorectal, breast and prostate cancer separately.
Data from the Medical Expenditure Panel Survey (MEPS) were used to compute the
incremental costs of cancer. The MEPS is a longitudinal survey that covers the United States
civilian non-institutionalized population. It is co-sponsored by the Agency Health Care Research
and Quality (AHRQ) and the National Center for Health Statistics and fielded by AHRQ based
on a sampling frame of the National Health Interview Survey. The MEPS is a nationally
representative survey of health care use, insurance coverage, medical expenditures, and sources
of payment for the U.S. civilian non-institutionalized population. The MEPS is widely used as an
authoritative source of information on the nation’s healthcare use. AHRQ uses it to monitor the
nation’s progress on healthcare disparities (AHRQ, 2006). More information about the MEPS is
available on their website, www.meps.ahrq.gov/mepsweb.
Six waves of data were pooled from the 2002-2007 MEPS to increase the sample size to
sufficient numbers of respondents with cancer. For this analysis, we used the Household
Component (HC) files, which are the core components of the survey; the condition files; and the
pooled estimations. HC collects demographic characteristics, health conditions, health status,
medical services utilization, access to care, satisfaction with care, health insurance coverage, and
income data for each person surveyed. Combining the HC component files with the
corresponding Health Condition files measured the different types of cancer conditions.
Subsequently, the resulting files were merged with the pooled estimation linkage file from the
MEPS to restrict the analytic sample to unique individuals only. The MEPS overlapping design
allows repeated observations on the same individuals for several rounds. Hence, it is important to
restrict the sample to unique individuals to compute appropriate standard errors in pooled
estimations. The analytic sample for direct savings was restricted to a total of 71,549 individuals
older than 18 years old with non-missing observations.
A model to predict healthcare expenditures data from the 2000-2007 MEPS was
developed. Total expenditures in the MEPS include both out-of-pocket and third party payments
to health care providers but do not include health insurance premiums. Expenditures for hospitalbased services include those for both facility and separately billed physician services. Total
expenditures include inpatient, emergency room, outpatient (hospital, clinic and office-based
visits), prescription drugs, and other (e.g., home health services, vision care services, dental care,
ambulance services, and medical equipment). The prescription drug expenditures do not include
over-the-counter purchases.
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A modified version of Aday and Andersen’s (1974) Behavioral Model of Health Services
Use was used to estimate direct medical costs for patients with cancer compared to those without
any cancer. Healthcare expenditures were predicted using demographic, socioeconomic status,
location and health status measures. The demographic factors were age, race/ethnicity and
gender. The socioeconomic factors were education, income and health insurance status. The
location factors were Census region and urban-rural residence. The primary variable of interest
is whether the respondent had cancer, and cancer was measured in two ways. The first measure
was a simple binary variable that indicated whether the respondent had cancer. The second
measures were five binary indicators of different cancer types such as colon, breast, lung,
prostate, and other types of cancer. The health condition files were used to measure different
types of cancer condition. In the MEPS, health conditions such as cancer were collected from
respondents who reported using health care services such as hospital visits or drug purchases.
These conditions were subsequently recorded by the interviewer as verbatim text, which
was then coded by professionals using the International Classification of Diseases, Ninth
Revision (ICD-9). The ICD-9 codes were subsequently collapsed into meaningful category codes
using the Clinical Classification System (CCS) developed by the U.S. Department of Health and
Human Services (HHS) (Cohen and Krauss, 1997). CCS codes from 11 to 45 were used to
measure the different types of cancer conditions. The other health status measures were selfreported general health status, self-reported mental health status, measures of functional status
and limitations, the number of instrumental activities of daily living (IIADL), body mass index,
and the presence of other chronic conditions (diabetes, asthma, asthma attack, high blood
pressure, heart attack, angina, other heart disease, stroke, emphysema, joint pain and arthritis).
To estimate the incremental cost of cancer in general, we estimated the model using the
simple any cancers binary variable. To estimate the incremental costs of lung, colorectal, breast
and prostate cancers, we estimated the model using the five binary variables for the different
sites of cancer.
A two-part model approach was used to predict healthcare expenditures. The two-part
model accounts for systematic differences in the characteristics of individuals with cancer
compared to those without any cancer. The first part of the model consisted of estimating logistic
regression models to estimate the probability of having any type of direct expenditures. The
second part consisted of using Generalized Linear Models with Log link and Gamma distribution
to predict levels of direct expenditures conditional on individuals with positive expenditures. We
conducted the different diagnostic and specifications tests as recommended by Manning and
Mullahy (1998, 2001). We computed unconditional levels of healthcare expenditures by
multiplying the probabilities obtained from the first part of the model by predicted levels of
expenditures from the second part of the model. Subsequently, we computed the incremental
values for each type of cancer condition.
The incremental cost of cancer was computed by using our model to predict medical
costs if a person has any type of cancer and subtracting the predicted medical costs if a person
does not have cancer (Deb, Manning and Norton, 2006). To do this calculation, the probabilities
of having medical costs for persons with cancer and persons without cancer must be taken into
account. Similar calculations were computed for each cancer site including colon, breast, lung,
prostate, and other sites of cancer, each time comparing them to predicted medical costs if
persons did not have cancer.
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Estimating the Indirect Costs of Cancer
Similar to the direct medical costs of cancer, the indirect costs equals the number of
excess cancer cases due to disparities times the estimated productivity loss associated with
having cancer. Using MEPS data for the years 2002-2007, the value of productivity loss was
computed by estimating the incremental effect of cancer on the number of disability days, hours
worked and wages among working age adults. The analytic sample of 50,952 was restricted to
individuals between 24 and 65 years old with non-missing observations to capture information
on individuals who are active in the labor force.
We used a two-part model approach to predict indirect costs due to cancer. The
dependent variables were number of disability days, annual works hours, and hourly wages. The
first part of the model estimated the probability of having any disability days, hours of work, or
any wages. The second part predicted levels of disability days and hourly wages conditional on
individuals with positive days or hourly wages. To compute the value of lost wages, the
reduction in wages due to cancer was multiplied by the mean number of hours worked by
persons with cancer.
Limitations
This study has several limitations primarily associated with data problems. There are
many studies that focus on African-American/White cancer disparities; however, few studies
examine Hispanics, Asians, Native American, Alaskan Natives, Hawaiians and Pacific Islanders.
Studies on these minority groups focus exclusively on their disadvantages relative to Whites and
ignore data that show their advantages relative to Whites. This bias in the literature is due in part
to small sample sizes and the lack of data on Hispanic and Asian subgroups. Relatively few
studies focus on socioeconomic disparities, primarily because cancer registries do not routinely
collect economic, education or health insurance status information. Consequently, it is
expensive, time-consuming and legally difficult to conduct studies of socioeconomic disparities
because such studies involve linking confidential socioeconomic information to cancer registry
data. Using administrative data from health plans is problematic because these data lack the
clinical information required to categorize cancer by severity and survival rate. The national
population-based surveys do not offer a satisfactory solution because, while these surveys have
detailed socioeconomic data, they lack data on cancer stage, severity and length of survival.
These data limitations hinder this analysis as well. We were unable to include Native Americans,
Alaska Natives, Hawaiians and Pacific Islanders in our analyses. Similarly, we cannot
incorporate the diversity within Hispanics and Asians, specifically looking at subgroups that
prior literature has shown to be disadvantaged relative to Whites. The lack of individual
socioeconomic information forced us to use area level measures of income and education. This
biases estimates of socioeconomic cancer disparities downward.
This report’s estimates of cost due to premature death are conservative due to the fact that
$50,000 was used to value years of life, as opposed to the higher amounts suggested by
Braithwaite (2008). The costs associated with socioeconomic disparities are also conservative.
The poverty and education data used in this analysis were measured at the county level as
opposed to the individual level because the report examines differences in cancer incidence and
mortality rates between low socioeconomic counties and high socioeconomic counties. Because
low socioeconomic counties have residents who span the level of socioeconomic status, cancer
disparities between counties are smaller than cancer disparities between individuals. This
limitation of the CDC and NCI data tends to reduce this report’s estimated costs of
socioeconomic cancer disparities.
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II. Literature Review
A systematic review of recent literature on racial, ethnic and socioeconomic disparities in
cancer specifically focused on cancer incidence, mortality, survival, treatment and costs for the
four most prevalent cancers: lung, colorectal, breast and prostate. A search using
www.pubmed.gov of the National Library of Medicine, National Institute of Health led to a
literature review of 122 peer-reviewed articles published from 1999 through 2010. These articles
were identified using the following search terms: cancer prevalence, cancer incidence, cancer
mortality, cancer treatment, cancer diagnoses, cancer survival, socioeconomic status,
race/ethnicity, minority, racial differences/disparities, lung cancer, prostate cancer, colorectal
cancer, breast cancer, cancer treatment, and cancer costs. Also reviewed: reports published by
the American Cancer Society, the National Cancer Institute, the Agency for Healthcare Quality
and Research, and information published on the website of the National Cancer Institute and the
Office of Minority Health. Based on the review of the research literatures, this report concluded
the following:
1. African Americans and persons with low socioeconomic status bear a disproportionate
cancer burden. They experience higher incidence and mortality rates.
2. Despite a disproportionate cancer burden, African Americans and persons of low
socioeconomic status are more likely to receive suboptimal cancer care compared to
Whites and more affluent cancer patients.
3. Cancer imposes a significant burden on the United States in terms of loss of life, quality
of life and economic activity.
4. The cost of cancer care varies by site, with lung and colorectal cancers being the most
expensive, followed by prostate and breast cancers.
Disparities in Cancer Incidence, Mortality and Survival
While cancer is a leading cause of death among all racial and ethnic groups, the mortality
burden is not distributed evenly among the various groups (ACS, 2009). African Americans and
persons of low socioeconomic status bear the greatest cancer burdens. Despite improvements in
cancer survival rates for all racial and ethnic groups since 1988, race disparities persist (Clegg et
al., 2002). For all cancers and the most common cancer sites, African-American men have the
highest incidence and mortality rates, higher than those of Whites, Hispanics and Asians.
Hispanic and Asian/Pacific Islander men tend to have lower incidence and mortality rates than
white men for all cancers combined, and for most individual cancers. (See table 2.1) Hispanic
and Asian/Pacific Islander men’s rates only exceed white men’s for stomach, and liver and bile
duct cancers (Horner, Ries, Krapcho et al., 2009).
The racial differences among women are not so straightforward. While white women
have the highest incidence rates for all cancers, African-American women have the highest
mortality rates (ACS, 2009). Compared to white women, African-American women have a lower
incidence of breast cancer, a similar incidence of lung cancer and a higher incidence of colorectal
cancer. Despite lower incidence rates for breast cancer, African-American women have higher
breast cancer mortality rates than white women. African-American women have higher rates of
mortality for colorectal cancer but lower mortality rates for lung cancer compared to white
women. Hispanic and Asian/Pacific Islander women uniformly have lower incidence and
mortality rates for all cancers and for the three most common cancers compared to both AfricanAmerican and white women. Because the population of Pacific Islanders is relatively small, they
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are combined with Asians. Combining Pacific Islanders with Asians may obscure their higher
cancer burden relative to Whites.
Table 2.1 Age Adjusted SEER Cancer Incidence and Mortality Rates by Site, Race
and Ethnicity, US 2002-2006
Incidence Rates
White
Asian &
Pacific Islander
Hispanic
Men
All sites
Lung
Colorectal
Prostate
African
American
544.3
77.6
56.9
153.0
633.7
104.3
69.3
239.8
349.1
53.4
46.9
91.1
409.7
43.2
46.3
133.4
Women
All Sites
Lung
Colorectal
Breast
420.5
54.8
42.1
127.8
398.9
54.7
53.5
117.7
287.5
28.1
34.6
89.5
312.5
24.7
32.2
88.3
Mortality Rates
Men
All sites
Lung
Colorectal
Prostate
226.4
69.9
21.4
23.6
304.2
90.1
31.4
56.3
135.4
36.9
13.8
10.6
154.7
33.9
16.1
19.6
Women
All Sites
Lung
Colorectal
Breast
157.3
41.9
14.9
23.9
183.7
40.0
21.6
33.0
95.1
18.2
10.0
12.5
103.9
14.4
10.7
15.5
Per 100,000, age adjusted to the 2000 U.S. standard population. Hispanic category is
not mutually exclusive of whites, African Americans, Asians/Pacific Islanders and
American Indians/Alaska Native
Source: Horner, Ries L, Krapcho et al. (eds). SEER Cancer Statistics Review, 19752006, National Cancer Institute. Bethesda, MD, http://seer.cancer.gov/csr/1975_2006/,
based on November 2008 SEER data submission, posted to the SEER web site, 2009.
Whites have consistently higher rates of survival than African Americans. (See table 2.2)
Five-year survival rates for white males range from 4-27% higher than those for African11 | P a g e
14 | P a g e
American males. Five-year survival rates for white females range from 16-26% higher than
those for African-American females. Disparities in survival rates are due in part to racial
differences in stage of diagnosis and treatment (ACS, 2009). However, the survival advantage
for whites may occur even in the context of randomized controlled therapeutic trials featuring
uniform stage, treatment and follow up. Albain et al. (2009) report that whites’ advantage in
breast, cervical and prostate cancer persists after adjusting for prognostic factors.
Table 2.2 5-Year Relative Survival Rates for All Cancers and Three Most Common
Cancer Sites by Race and Gender, 1999-2005
White
African American
Rate Ratios
Men
67.0
60.6
1.11
All
13.7
10.8
1.27
Lung
66.3
55.5
1.19
Colorectal
99.9
96.5
1.04
Prostate
Women
66.9
55.2
1.21
All
18.3
14.5
1.26
Lung
65.9
56.7
1.16
Colorectal
90.3
77.9
1.16
Breast
Source: Horner, Ries, Krapcho et al. (eds). SEER Cancer Statistics Review, 1975-2006,
National Cancer Institute. Bethesda, MD, http://seer.cancer.gov/csr/1975_2006/, based
on November 2008 SEER data submission, posted to the SEER web site, 2009.
Race disparities in cancer incidence and mortality are due in part to differences in: 1)
exposures to cancer causing agents; 2) access to screening and cancer care after the onset of
symptoms; 3) access to appropriate cancer treatment; 4) contextual and environmental factors
that affect cancer survival; and 5) genetic factors (Hackbarth, 2001; Wong et al., 2009). These
disparities in cancer risk factors stem from cultural and language differences and societal
inequities related to discrimination, provider bias, patient mistrust, poor patient-provider
communication, poor adherence and low quality care (ACS, 2008). While a few studies have
reported genetic differences by race, these differences typically make only a modest impact on
cancer mortality.
There is little evidence to indicate that cancer disparities are fundamentally due to genetic
differences among race groups. The most often cited example of the influence of genetics in
cancer disparities is documented for prostate and breast cancers (ACS, 2008). There are several
reports of racial differences in the frequency of functional polymorphisms that have been
implicated in carcinogenesis and/or cancer survival. However, the magnitude of the contribution
of such genetic differences to the observed racial disparities has been consistently minimal or
absent (Collins, 2004). While this remains a legitimate area of scientific investigation, the
available evidence suggests differences in socioeconomic status, differences in exposure to
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carcinogens, clinical factors, and disparities in cancer treatment as primary drivers of racial
disparities (ACS, 2009).
Since race and ethnicity are related to socioeconomic status, some of the disparities
between African Americans and whites are associated with race differences in income, education
and health insurance coverage. (See table 2.3) African Americans have higher poverty rates,
lower educational attainment and are more likely to be uninsured or covered by Medicaid
compared to whites (DeNavas-Walt, Proctor, and Smith, 2008). However, it should be noted that
despite having lower socioeconomic status, Hispanics do not have a higher cancer burden than
whites. Asians have the lowest cancer burden. In comparison to whites, Asians have higher
median income and education attainment but higher poverty rates, percentage uninsured and
covered by Medicaid. This paradoxical disparity in cancer burden among whites, Hispanics and
Asians, despite higher rates of poverty for the latter two groups, suggests that other factors in
addition to poverty status account for African-American/white disparities in cancer mortality and
morbidity. The apparent disadvantage of whites relative to Hispanics and Asians is understudied
and not well understood.
Table 2.3 Poverty Rate, Educational Attainment* and Health Insurance Coverage by
Race and Hispanic Origin, 2008.
White,
Non African
Asian &
Hispanic
Hispanic
American
Pacific Islander
24.7
11.8
23.2
Poverty Rate 8.6
9.4
Less the HS
High School 31.2
Graduate
18.1
34.9
11.2
19.1
37.0
30.1
10.8
19.1
17.6
30.7
Uninsured
9.5
25.4
11.6
24.3
Medicaid
17.0
11.9
9.5
6.8
Medicare
75.0
52.2
68.2
43.8
Privately
Insured
4.3
4.1
2.2
1.9
Military
Health Care
Source: DeNavas-Walt, Carmen, Proctor and Smith, US Census Bureau, Current
Population Reports, P60-236, Income, Poverty and Health Insurance Coverage in the
United States: 2008, US Government Printing Office, Washington, DC 2009.
US Census Bureau, Current Population Survey, 2008 Annual Social and Economic
Supplement Tables 3-6. Internet Release Date: April 2009.
* Computed for Persons 18 years of age and older.
* Health insurance totals sum to more than 100 because individuals can be covered by
more than one type of health insurance.
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Socioeconomic Disparities in Cancer Burden
Prior studies consistently show that cancer burden increases as socioeconomic status
(SES) declines (ACS, 2008). However, studying the association between SES and cancer burden
is complicated because cancer registries do not routinely collect data on SES. Consequently,
researchers must link area-level SES data from the Census or individual-level data from other
sources. From the Census, researchers have relied on median household income, poverty rates
and the percentage of adults with high school or college education within a zip code or census
tract.
Researchers have also used Medicaid enrollment as a measure of SES. Area level studies
usually show some association between cancer burden and SES (Du et al., 2005). Xiao et al.
(2007) found a negative association between median income and the percentage of college
graduates in a Census tract and the likelihood of being diagnosed with late-stage prostate cancer.
Du et al. (2006) found that racial disparities in prostate cancer mortality were related to arealevel SES. The relationship between cancer health and area-level SES appears to be independent
of racial disparities in cancer (Albain et al., 2009); however, it is important to stress that data
limitations hamper efforts to fully characterize the SES-race-cancer interaction. In studies that
have been able to measure SES with individual-level data (i.e., the SES of the individual rather
the community where they live), the association between SES and cancer burden is stronger.
This seems to explain a substantial portion of the racial disparity in cancer burden (Albano et al.,
2007). Persons with higher educational attainment have lower mortality rates for cancer. This
association was consistent across sites, race and gender groups (Albano et al., 2007). Persons
with low educational attainment have greater rates of cancer incidence, lower survival rates and
higher mortality rates. Among Medicare beneficiaries, cancer mortality rates decline with
income and educational attainment. Persons with private insurance have lower mortality rates
compared to persons who are uninsured or covered by Medicaid.
Socioeconomic status influences cancer burden in four ways (ACS, 2008):
1) Lower socioeconomic status increases exposure to cancer risk factors such as higher rates
of smoking, heavy drinking, obesity, physical inactivity, and exposure to environmental
carcinogens.
2) Lower socioeconomic status reduces the likelihood of cancer screening and early
detection.
3) Lower socioeconomic status reduces the likelihood of timely treatment.
4) Lower socioeconomic status is associated with less effective contextual support for
cancer patients and greater contextual stress for cancer survivors. The data on the
relationship between cancer burden and socioeconomic status are not as rich as the data
for racial and ethnic differences, primarily because cancer registries do not include
socioeconomic measures.
This presents a significant impediment to effective
surveillance of cancer health disparities.
Cancer mortality is inversely related to health insurance coverage. A Michigan-based
study combined cancer registry data, Medicaid enrollment files, and death certificate data and
reported that Medicaid enrollees were a disproportionate share of the state cancer patients
(Bradley et al., 2001). Among non-elderly women, Medicaid enrollees had a higher incidence of
breast, lung, and colorectal cancer. Among older adults, both male and female Medicaid
enrollees had a higher incidence of lung and colorectal cancer. Similarly, Medicaid enrollees
younger than 65 were more likely to be diagnosed at advanced disease stages, i.e., stage III or
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IV. The adjusted odds ratios, comparing Medicaid to non-Medicaid enrollees for lung, colorectal
and breast cancers, were 1.57, 1.59 and 1.77, respectively.
Table 2.4 Relative Risk of Cancer by Site Between Medicaid and Non Medicaid Enrollees
in Michigan.
Men (Ages 25-64)
Men (Ages 65 and older)
5.25
1.94
Lung
2.48
1.31
Colorectal
1.02
0.95
Prostate
Women (Ages 25-64)
Women (Ages 65 and older)
4.48
1.91
Lung
1.86
1.71
Colorectal
1.37
0.99
Breast
Source: Based on calculations from data published in Bradley, Givens and Roberts (2001).
Disparities in Lung Cancer
Lung cancer is the leading cause of cancer death. African-American men and low-income
persons bear the greatest lung cancer burden. There are major gender differences in the pattern
of lung cancer disparities. Cancer incidence and mortality for African-American males is
substantially greater than for white males, while the lung cancer mortality experience for
African-American females is closer to that of white females. Cigarette smoking is the leading
risk factor for lung cancer. Disparities exist in the prevalence of smoking, comparing African
Americans, Asian and Pacific Islanders, Hispanics/Latinos, and whites. Compared to whites,
African Americans and Hispanics are less likely to smoke in adolescence (Hernandez et al.,
2010). However, both Hispanics and African Americans tend to take up smoking at a later age
so that by mid-life both groups have higher smoking prevalence compared to non-Hispanic
whites (LaVeist, 2005). Hispanics and African Americans are less likely to quit compared with
whites, and both minority groups are more likely to smoke cigarettes that are high in tar and
nicotine compared to whites. This, at least in part, explains higher lung cancer mortality among
African Americans compared to whites.
While there is no recommended evidence-based screening method for lung cancer, racial
disparities in lung cancer survival are well documented. Some of the disparity in survival for
non-small cell lung cancer can be attributed to disparities in treatment (Bach, 1999). Among
patients who undergo similar treatment, survival rates were similar. However, African
Americans were 16% less likely to undergo surgery. Shugarman et al. (2009) found significant
racial differences in treatment among Medicare lung cancer patients. Compared to whites,
African Americans were 66% less likely to receive timely and appropriate treatment and were
50% less likely to receive resection. The reasons for the relatively lower rate of surgical resection
are not completely understood. Among stage III cancer patients, African Americans were 34%
less likely to receive timely surgery, chemotherapy, or radiation compared to whites. African
Americans were 51% less likely to receive chemotherapy in a timely fashion for stage IV cancer
compared to whites (Shugarman et al., 2009).
Bryant and Cerfolio (2008) found that African Americans’ survival disadvantage was
attributable to higher rates of smoking, lower income, greater treatment delays and lower use of
15 | P a g e
neo-adjuvant therapy in a sample of 930 non-small cell lung cancer patients. African Americans
had lower five-year survival rates than whites for stage I (RR=0.93), stage II (RR=0.85), and
stage III (RR=0.63) cancers. However, among persons diagnosed with stages I and II cancer, this
difference disappears after controlling for socioeconomic status and smoking. The difference
also disappears for patients diagnosed with stage III lung cancer after controlling for
socioeconomic status, smoking and receipt of neo-adjuvant chemoradiotherapy. Some studies
reported no racial disparities in survival when patients had comparable functional status and
access to treatment (Blackstock et al., 2002; Mulligan et al., 2006). Blackstock and colleagues
report that race differences in lung cancer patients can be explained by race differences in predisease performance status (i.e., the ability to do strenuous physical activity). There were no
race differences in lung cancer survival and stage of disease among patients using the military
health care system (Mulligan et al., 2006). (On a population basis, it is unclear to what extent
racial differences in the prevalence of comorbid conditions contribute to the observed patterns of
lung cancer disparities.)
Disparities in Colorectal Cancer
Colorectal cancer represents about 10% of new cancer cases and 9% of cancer deaths.
Because risk factors for colorectal cancer are not well defined, there currently is no
recommended strategy for primary prevention of colorectal cancer for the population at average
risk. However, available screening modalities detect colorectal cancer at early stages and are
effective to prevent mortality. African Americans are at greater risk of being diagnosed with
colorectal cancer and have a higher rate of mortality than any other racial or ethnic group.
Mortality differences ranging from 45-90% persisted even when patients were stratified by
educational attainment (Albano, 2007).
Despite the disparities in colorectal cancer burden, African Americans are less likely to
receive screening. Using data from the Medicare Current Beneficiary Survey for 2000, 2003,
and 2005, Doubeni and colleagues found that a higher proportion of non-Hispanic whites,
compared to African Americans and Hispanics, had undergone lower-gastrointestinal endoscopy
within five years and/or home fecal occult blood test within the past year. During the study
period, Hispanic/white differences persisted; however, African-American/white differences
narrowed in 2003 and increased in 2005 (Doubeni et al., 2010). In addition, following flexible
sigmoidoscopy, African Americans have a lower follow-up rate for screen-detected
abnormalities than whites, with little difference in the yield of colorectal neoplasia (Laiyemo et
al., 2010). Moreover, once diagnosed with colorectal cancer, African Americans are less likely
than whites to receive standard therapy. White et al. (2008) estimated that African Americans
were 16% less likely to receive standard therapy.
Among Medicare patients, African Americans had lower rates of resection, sphincter
preservation and adjuvant radiotherapy for patients with resectable rectal cancer (Morris et al.,
2004). Esnola et al. (2008) reported that among more representative population of colorectal
cancer patients, African Americans were less like to receive surgical resection and more likely to
receive local excision versus segmental resection. There are also treatment disparities by
socioeconomic status. Persons living in zip codes with low educational attainment and persons
covered by Medicaid or who are uninsured were also less likely to receive surgical resection
(Esnola et al., 2008).
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Disparities in Prostate Cancer
Prior studies show that African Americans were at greater risk of having prostate cancer
compared to whites. This race disparity persisted regardless of socioeconomic status and age
group. Using data from the California SEER, Cheng and colleagues estimated odds ratios for
age and SES groups (Cheng et al., 2009). These estimates ranged from 1.30 to 2.68, depending
on the age group and SES (Cheng et al., 2009). Race disparities were greater for younger and
more affluent groups. African Americans were more likely to be diagnosed with later-stage and
higher-grade prostate cancer (Klassen et al., 2004; Xiao et al., 2007; Hoffman et al., 2001). The
odds of being diagnosed with a late stage were 1.36 to 2.26 times higher for African-American
men compared to white men. Compared to whites, the odds of being diagnosed with a higher
grade of prostate cancer for African Americans were 1.32-1.44 to 1.
Between ages 50 and 64, African Americans are more likely to report prostate cancer
screening within the last year than whites. However, between ages 65 and 79, the screening
prevalence is greater for whites, and it is comparable for both groups beginning around the age
of 80. Similarly, the screening rates are higher for Hispanics than for whites between ages 50 and
59, but the rates for whites are higher between ages 60 and 79 and beginning at age 80, screening
rates for Hispanics become appreciably greater than those for whites. While screening for
prostate cancer is widely available through digital rectal exam and the test for prostate-specific
antigen (PSA), PSA testing is controversial. Prostate cancer screening has not been shown to
reduce mortality, and there is growing concern about a possible unfavorable balance of risks vs.
benefits of PSA testing (Jerant et al., 2004). Under these circumstances, the effect of differences
in screening patterns on race differences in prostate cancer mortality remains an open question.
Once diagnosed, African Americans were at a greater risk of mortality from prostate
cancer (Albano et al., 2007; Albain et al., 2009; Thompson et al., 2001; Du et al., 2006). The
disparity in mortality risk was inversely related to educational attainment. The relative risk of
death was 3.32 for persons with a high school diploma or less and 2.19 for persons with more
than a high school education (Albano et al., 2007). Hazard ratios ranged from 1.17 to 1.39 for
cancer survival (Albain et al., 2009; Thompson et al., 2001; Du et al., 2006). Differences in
survival can be explained by race variations in receipt of primary therapies (Holmes et al., 2009).
Hazard ratios declined from 1.26 to 0.98 when use of primary therapies was considered. African
Americans were more likely to receive watchful waiting alone and less likely to receive radiation
plus androgen deprivation therapy (Holmes et al., 2009; Shavers et al., 2004). Race disparity in
survival disappears in small geographic units, suggesting modifiable societal factors were
responsible for African-American/white differences. Shavers and colleagues reported that
African Americans and Hispanics were less likely to have a urology visit or PSA test but had a
higher probability of receiving a bone scan. Compared to whites, African Americans had fewer
primary care visits and were more likely to receive watchful waiting (OR = 1.4).
Disparities in Breast Cancer
Despite lower incidence rates of breast cancer, African American women have lower
survival rates and higher mortality rates than white women. Depending on the study, AfricanAmerican women have hazard rates that are 10-80% greater than those for non-Hispanic white
women. Disparities vary by age, socioeconomic status and region of the country. Race
disparities are greatest among younger women (Swanson, Haslam and Azzouz, 2003).
Compared to their white peers, younger African-American women are diagnosed at a more
advanced stage of breast cancer, tend to have larger tumors and are more likely to have lymph
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node positive tumors. They are also more likely to have triple negative tumors, an especially
aggressive form of breast cancer.
After controlling for socioeconomic status, race disparities diminish but do not
disappear. Albano reports that race disparity in the relative risk of dying was 1.43 for women
with less than or equal to a high school education compared to 1.68 for women with more than a
high school education. After controlling for treatment and socioeconomic status, Du, Fang and
Meyer (2008) report that the African American to non-Hispanic white hazard ratios decline from
1.83 to 1.21. In addition to race disparities, there are significant disparities by socioeconomic
status. Bradley, Given and Roberts (2001) report that women enrolled in Medicaid not only had
a higher incidence rate, but they were diagnosed at a later stage and had a higher risk of death
than women with other insurance or who lacked insurance.
Cost of Cancer
Numerous studies have assessed the cost of cancer. The cost of cancer can be divided
into three main parts: the cost of cancer treatment, the indirect cost of cancer due to lower
productivity for cancer patients, and the cost of premature death due to cancers (Taylor, 2005;
Yabroff et al., 2007; NHLBI, 2009). The cost of cancer treatment is the direct medical care
expenditures attributed to cancer care and additional care provided patients for other conditions
due to complications associated with cancer. The indirect cost of cancer is the value of the lost
days of work and the lower wages incurred by cancer patients due to their illness. The cost of
premature death is the value of the years of life lost due to cancer mortality. Presumably, persons
whose lives are cut short by cancer would have contributed to their families, communities and
society.
Yabroff and colleagues (2007) conducted a comprehensive review of 60 studies
published from 1995 through 2006 on the cost of cancer treatment. Researchers have computed
the costs of cancer prevalence and the costs of the initial and last year of life phases of care for
the most common cancer sites. They found some variation in the cost estimates based on cancer
site and stage, the demographic profile of the population, and the type of care rendered. Despite
the variation, some patterns emerge. The annual and lifetime costs of cancer are expensive,
ranging from several thousand dollars to tens of thousands of dollars. Lung and colorectal
cancers were more expensive than breast and prostate cancers. The lifetime costs, regardless of
cancer site, were u-shaped, i.e., higher at both the initial and last-year-of- life phases of care.
Using Medicare administrative claims, Penberthy and colleagues (1999) found that the average
costs during the initial treatment period were $24,910 for colorectal cancer, $21,351 for lung
cancer, $14,361 for prostate cancer and $12,141 for breast cancer.
Lung cancer appears to be the most expensive cancer site. Kutikova and colleagues
(2005) found that incremental cost attributable to lung cancer was $42,990 over a two-year
period. Studies identified by Yabroff and colleagues (2007) report a range of $29,178 in 1990
dollars over 1.7 years to $47,941 in 1992 dollars over 2 years. During the initial treatment phase
(about 6 months), total costs per month were $11,496, and during the terminal treatment phase
(the last 6 months), costs were $9,399 per month (Kutikova, 2005).
Colorectal cancer appears to be the second most frequently studied. Yabroff and
colleagues (2007) reviewed nine studies. Cost estimates for initial phase of treatment ranged
from $16,197 for patients aged 35 and older to $29,384 for patients over 50. For the last 6
months of life, cost estimates ranged from $16,062 for elderly patients to $23,000 for patients of
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all ages.1 Lifetime medical costs for long-term colorectal cancer survivors were almost $20,000
compared to Medicare patients without the disease. Using NCI SEER data linked to Medicare
claims, Wright and colleagues estimated the 16 month net estimated cost of colorectal cancer
care was $33,451. While there were racial differences in unadjusted colorectal cancer care costs,
these differences disappear after adjusting for covariates (Wright et al., 2007). The variation in
the cost of care estimates is due to the variation in data sources, the age of the population, the
methodology for identifying colorectal cancer patients, and the methods used to estimate
longitudinal costs (Yabroff et al., 2009).
Most of the cost-of-treatment studies focus on breast cancer. A review of 29 U.S. costof-illness studies for breast cancer found that the lifetime per-patient costs of breast cancer
ranged from $20,000 to $100,000 (Campbell et al., 2008). A study of women in five managed
care plans showed adjusted per member per month costs of 2.28 times higher for women with
breast cancer compared to those who do not have breast cancer (Barron et al., 2008). The
estimated annual costs for a breast cancer patient were at least $12,828 higher than those for
women without breast cancer (Barron et al., 2008). Like the other cancer sites, the cost of
treating prostate cancer is u-shaped. The cost of treating the initial phase of prostate cancer
ranged from $6,375 in 1996 dollars in men aged 65 years and older to $17,226. The cost of
treating prostate cancer in the last year of life ranged from $12,061 to $24,260 (Yabroff et al.,
2007).
The National Institutes of Health estimate the economic cost of cancer was $259.6 billion
in 2009: $99 billion in direct medical costs, $19.6 billion in lost productivity and $124.8 billion
in premature death (NHLBI, 2009). Using a willing-to-pay approach and life tables from the
Berkeley Mortality Database, Yabroff and colleagues (2008) estimated the cost of premature
death due to cancer in the year 2000 was $960.6 billion. Lung cancer deaths were the most costly
at $270.8 billion, or 28% of the total cost, followed by colorectal at $90.9 billion (9.5%), female
breast cancer at $85.3 billion (8.9%) and prostate cancer at $34.8 billion. These costs are
expected to increase dramatically, even if mortality rates remain constant, because the population
is aging. Using a human capital approach to value premature death suggests that the cost of
cancer mortality was approximately $115.8 billion in 2000 (Bradley et al., 2008).
1
For comparison purposes, costs cited from the Yabroff review were converted into 2002
dollars.
19 | P a g e
22 | P a g e
III. Current Trends in Disparities in Cancer Incidence and Mortality Rates
Recent data on cancer disparities show that racial and ethnic disparities in cancer persist.
(See table 3.1) African-American males are at greatest risk of cancer, followed by white males
and Hispanic males. Asian/Pacific Islander (API) and American Indian/Alaska Native (AI/AN)
males have the lowest risk of cancer. Compared to white males, relative risks of cancer are 0.57
for APIs and 0.61 for AI/ANs. The cancer risks for Asian/Pacific Islander and American
Indian/Alaska Native males were below the cancer risks for white and African-American
females. White women had the highest risk of cancer, followed by African-American women,
then Hispanic women. API and AI/AN women have the lowest cancer risks among women.
Based on this data, it is clear that eliminating disparities in cancer incidence between African
Americans and whites would be a modest goal because these two groups tend to have the highest
levels of cancer incidence. A more ambitious goal would be to reduce the cancer incidence for
all race/ethnic groups to levels experienced by APIs and AI/ANs. Such a goal would have a
substantial impact on number of persons diagnosed with cancer. Not only would such a
campaign impact the cancer risks for African Americans, but also for whites and Hispanics.
Table 3.1 Age-Adjusted Cancer Incidence Rates by Race/Ethnicity and Gender, 2002 2006
Males
White
African
Asian/Pacific American
Hispanic
American Islander
Indian/Alaskan
Native
548.9
621.8
332.6
313.3
429.9
All-cause
86.2
104.8
50.2
57.1
49.6
Lung
58.2
67.9
43.8
37.4
50.0
Colorectal
145.3
229.3
81.7
81.3
130.4
Prostate
Females
White
African
American
Asian/Pacific
Islander
American
Hispanic
Indian/Alaskan
Native
419.9
390.3
274.8
262.2
327.2
All-cause
57.3
51.5
27.5
40.6
26.6
Lung
42.6
51.6
33.0
30.3
35.1
Colorectal
123.2
113.0
81.0
66.7
90.2
Breast
Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2006
Incidence and Mortality Web-based Report. Atlanta: U.S. Department of Health and Human
Services, Centers for Disease Control and Prevention and National Cancer Institute; 2010.
Available at: www.cdc.gov/uscs.
Racial/ethnic disparities in cancer mortality follow a pattern somewhat similar to cancer
incidence rates. (See table 3.2.) African-American males were at greatest risk, followed by white
20 | P a g e
males, then African-American females and then white females. API and AI/AN males had lower
cancer mortality than African Americans and whites, regardless of gender. Unlike cancer
incidence rates, African-American women have higher mortality rates than white women with
the exception of lung cancer, where the mortality rate is relatively close. Similar to cancer
incidence rates, equalizing cancer mortality between African Americans and whites would have
a modest impact; however, reducing cancer mortality to the level experienced by APIs and
AI/ANs would reduce the cancer burden substantially.
Table 3.2 Age-Adjusted Cancer Mortality Rates by Race/Ethnicity and Gender, 2002 - 2006
Males
White
African
Asian/Pacific American
Hispanic
American Islander
Indian/Alaskan
Native
226.7
304.2
135.4
146.4
154.5
All-cause
69.9
90.1
36.9
41.2
33.8
Lung
21.4
31.4
13.8
15.3
16.1
Colorectal
23.6
56.3
10.6
16.8
19.6
Prostate
Females
White
African
American
Asian/Pacific
Islander
American
Hispanic
Indian/Alaskan
Native
157.3
183.7
95.1
110.1
103.8
All-cause
41.9
40.0
18.2
28.3
14.4
Lung
14.9
21.6
10.0
10.2
10.7
Colorectal
23.9
33.0
12.5
14.3
15.5
Breast
Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2006
Incidence and Mortality Web-based Report. Atlanta: U.S. Department of Health and Human
Services, Centers for Disease Control and Prevention and National Cancer Institute; 2010.
Available at: www.cdc.gov/uscs.
Socioeconomic disparities in cancer incidence rates do not pattern uniformly, so
that persons living in poor communities are consistently disadvantaged. For all cancers,
persons in more affluent counties and counties with the lowest percentage of high
school drop-outs had the highest cancer incidence rates. (See table 3.3.) This was also
true for breast and prostate cancer, but was not true for lung and colorectal cancer.
Cancer incidence rates for lung and colorectal cancer followed the traditional
health-wealth gradient whereas poverty increases cancer incidence rates increase.
21 | P a g e
24 | P a g e
Table 3.3 Age-Adjusted Cancer Incidence Rates by County Level Socioeconomic Status,
2002 - 2006
% below the poverty line
Poverty Status
0%-6.23%
6.24%8.32%10.73%14.61%8.31%
10.72%
14.60%
55.74%
All-cause
Lung
Colorectal
Breast
Prostate
479.6
61.8
49.5
131.9
165.6
463.4
61.6
49.6
125.8
157.7
Education Status % less than HS grad
3.04%15.05%15.04%
18.90%
458.1
63.6
46.4
124.2
152.5
448.5
61.7
49.0
116.9
159.9
455.8
74.4
51.7
111.5
151.0
18.91%23.48%
23.49%30.48%
30.49%65.30%
475.0
469.8
474.4
435.3
456.5
All-cause
60.5
63.9
69.2
56.4
76.8
Lung
48.3
49.5
51.0
47.8
50.7
Colorectal
132.0
127.3
122.7
115.7
109.6
Breast
167.6
158.1
162.2
154.4
144.3
Prostate
Source: Cancer incidence data for 2002-2006 from the National Cancer Institute
Surveillance, Epidemiology and End Results (SEER) Program.
Cancer mortality rates do follow the traditional health-wealth gradient. For all cancers,
and the four most common cancers, persons living in high poverty counties and counties with
high percentages of high school drop-outs had higher cancer mortality rates. (See table 3.4.)
This is surprising given the higher incidence rates for more affluent and better educated counties.
However, this is probably an indication that persons living in poorer and less educated counties
have less access to cancer treatments that may prolong survival. Also, the lower incidence rates
may be due to a higher percentage of undiagnosed cancers in the poorer and less educated
counties.
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25
Table 3.4. Age-Adjusted Cancer Mortality Rates by County Level Socioeconomic
Status, 2002 - 2006
% below the poverty line
0%-6.23%
6.24%8.32%10.73%14.61%8.31%
10.72%
14.60%
55.74%
747.7
783.2
840.4
851.3
911.2
All-cause
49.5
53.2
57.1
53.6
56.7
Lung
17.6
17.7
18.6
18.5
19.8
Colorectal
24.4
23.6
25.0
24.7
25.9
Breast
24.9
23.9
26.1
26.3
30.1
Prostate
% less than HS grad
3.04%15.05%18.91%23.49%30.49%15.04%
18.90%
23.48%
30.48%
65.30%
752.0
790.5
847.6
849.8
891.9
All-cause
49.9
53.3
56.2
54.7
55.8
Lung
17.2
17.8
19.3
18.6
19.1
Colorectal
24.3
24.3
25.0
24.7
24.5
Breast
25.3
24.6
26.3
26.1
28.0
Prostate
Source: Cancer mortality data for 2002-2006 from the National Cancer Institute
Surveillance, Epidemiology and End Results (SEER) Program.
The association between race/ethnicity and cancer burden persists even after controlling
for the level of poverty and education in the county. (See tables 3.5 and 3.6.) Among counties
with similar socioeconomic status, African-American males had the highest rates of cancer
incidence and mortality, followed by white males, white females and African-American females.
APIs and AI/ANs have the lowest rates of cancer incidence and mortality within each
socioeconomic group.
23 | P a g e
26 | P a g e
Table 3.5 Race and Gender Specific, Age-Adjusted Cancer Incidence and Mortality
Rates, By Poverty Status of Counties, 2002-2006
0%6.24%8.32%10.73%14.61%6.23%
8.31%
10.72%
14.60%
55.74%
Incidence Rates
Male
579.4
569.8
566.2
568.8
576.2
White
627.1
636.8
642.0
680.6
672.5
African American
304.7
423.3
280.9
243.4
261.5
AI/AN
334.7
382.4
334.9
359.6
304.2
API
454.3
417.4
415.9
398.3
383.7
Hispanic
Female
450.3
440.0
436.8
439.3
418.1
White
400.8
403.8
406.5
409.2
396.0
African American
312.9
397.1
249.3
192.1
219.1
AI/AN
271.9
306.7
282.4
295.2
246.1
API
354.7
321.5
313.3
299.2
295.9
Hispanic
Mortality Rates
Male
60.0
60.1
57.2
60.8
64.4
White
67.8
67.8
69.0
74.3
76.4
African American
33.8
66.4
32.1
23.0
30.9
AI/AN
42.2
52.1
43.1
52.2
42.8
API
52.5
50.8
47.2
43.6
46.0
Hispanic
Female
45.0
44.4
42.1
44.6
45.5
White
51.2
53.9
55.2
56.7
56.4
African American
34.9
65.8
32.5
18.5
20.6
AI/AN
30.9
37.5
32.5
37.4
29.5
API
36.7
35.4
31.6
30.8
30.9
Hispanic
Source: Cancer incidence and mortality data for 2002-2006 from the National Cancer
Institute Surveillance, Epidemiology and End Results (SEER) Program.
24| P
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27
Table 3.6 Race and Gender Specific, Age-Adjusted Cancer Incidence and Mortality
Rates, By Education Status of Counties, 2002-2006
3.04%15.05%18.91%23.49%30.49%15.04%
18.90%
23.48%
30.48%
65.30%
Incidence Rate
Male
566.6
581.8
578.6
562.4
576.0
White
628.5
645.3
705.7
651.7
661.8
African American
440.3
310.7
241.0
188.5
265.9
AI/AN
340.2
376.0
317.9
356.5
319.2
API
436.5
441.6
399.6
398.8
381.5
Hispanic
Female
444.3
449.5
439.4
438.4
421.3
White
401.4
398.9
424.1
400.8
387.7
African American
429.0
265.1
190.2
145.4
233.9
AI/AN
277.7
303.0
260.9
294.3
261.2
API
349.4
336.0
308.2
297.2
301.5
Hispanic
Mortality Rate
Male
57.2
60.9
61.5
60.6
64.6
White
68.6
69.5
76.9
71.8
71.3
African American
65.5
33.2
21.0
20.8
35.2
AI/AN
42.7
51.1
39.4
51.7
46.0
API
50.1
51.7
49.4
44.0
42.5
Hispanic
Female
43.3
45.2
44.6
44.3
46.0
White
52.5
52.4
59.0
56.5
52.7
African American
67.6
30.4
22.1
12.5
19.5
AI/AN
32.1
36.3
30.7
37.7
30.3
API
36.2
34.9
33.9
30.7
29.4
Hispanic
Source: Cancer incidence and mortality data for 2002-2006 from the National Cancer
Institute Surveillance, Epidemiology and End Results (SEER) Program.
25 | P a g e
28 | P a g e
Factors Associated with Cancer Prevalence
The association between cancer prevalence and race, income, insurance, and education
was estimated using the Medical Expenditure Panel Survey (MEPS) data from 1996-2007. The
goal of this analysis was to provide some insight into how variations in race and socioeconomic
factors influence the prevalence of cancer. This analysis was conducted for any cancer and for
the four most common cancer sites (lung, colorectal, breast and prostate). Initially, the model
was estimated using MEPS data from 2002-2007 to include body mass index, exercise and
smoking behavior as predictors in the model. This data is not available in the MEPS for the prior
years. We compared the results of our model with and without these variables and concluded
that they did not confound the associations between cancer prevalence and race, income,
insurance, and education. Therefore, we dropped these variables from the analysis and were able
to expand our data to include the years 1996-2001. This increased the number of persons with
cancer in our analysis and was important for our analysis of the four most common cancer sites.
Results for Any Cancer
Four different model specifications were used to compare the results for robustness and
to address potential co-linearity between education, income, and insurance status. (See tables 3.7
and 3.8.) Minority patients, including African-Americans and Hispanics, are less likely to have
been diagnosed with any types of cancer compared to white patients in all four models after
controlling for demographic, social-economic, access, and health behaviors variables (P<0.001).
To compute the odds ratio relative to whites, the coefficients for African-Americans and
Hispanics were exponentiated (i.e., e-0.45=0.64 and e -.50= 0.61). African-Americans had 36% and
Hispanics had 39% lower odds of having cancer compared to whites. In models that controlled
for different income levels but not for education, any cancer prevalence was negatively
associated with low-income patients in families with income less than 200% of the Federal
Poverty Level (FPL) compared to those with income over 400% of FPL (P<0.10). However,
there was no significant association between any cancer prevalence and different income levels
in models that controlled for different levels of education, including individuals with less than
high school degrees, high school degrees, and college graduates. In fact, there were significant
negative associations between any cancer prevalence and lower levels of education such as no
high school education (up to 8th grade) and some high school (9th-11th grades) compared to
those with a high school diploma or GED (P<0.001). There was no statistical difference between
high school graduates and persons with college experience. The odds of having cancer were
35% lower for persons with no high school training and 27% lower with some high school. Yet,
the sizes of the coefficients for lower levels of education were reduced but remained significant
in full models that controlled for health insurance status and different income levels. Finally, any
cancer prevalence was negatively associated with ‘un-insurance’ status compared to patients
with private insurance (P<0.001) even after controlling for education and income. The odds of an
uninsured person having cancer were 25% lower compared to a privately insured person.
26| P
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29
Table 3.7 Logistic Regression Analysis – Cancer Prevalence, Demographic, Health
Behavior and Socioeconomic Factors, 2002-2007
Base
Base
Base Model Base Model Full Model
Model
Model
Plus
Plus
Plus
Poverty
Insurance
Education
-0.15**
-0.13*
-0.14**
-0.15**
-0.13**
Female
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
0.77***
0.73***
0.76***
0.75***
0.72***
Age 35-44
(0.15)
(0.15)
(0.15)
(0.15)
(0.15)
1.06***
1.03***
1.05***
1.05***
1.01***
Age 45-54
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
1.47***
1.43***
1.45***
1.45***
1.41***
Age 55-64
(0.13)
(0.13)
(0.13)
(0.13)
(0.13)
1.81***
1.80***
1.82***
1.77***
1.75***
Age 65-74
(0.13)
(0.13)
(0.13)
(0.13)
(0.14)
1.67***
1.69***
1.69***
1.62***
1.63***
Age 75+
(0.14)
(0.14)
(0.14)
(0.15)
(0.15)
-0.49*** -0.45***
-0.49***
-0.49***
-0.45***
African American
(0.11)
(0.11)
(0.11)
(0.11)
(0.11)
-0.60*** -0.52***
-0.60***
-0.57***
-0.50***
Hispanic
(0.12)
(0.12)
(0.12)
(0.12)
(0.12)
-0.19
-0.21
-0.2
-0.2
-0.22
Asian
(0.18)
(0.17)
(0.18)
(0.18)
(0.18)
-0.81*** -0.77***
-0.79***
-0.80***
-0.77***
Other Race
(0.25)
(0.25)
(0.25)
(0.26)
(0.26)
-0.18*
-0.12
-0.16*
-0.15
-0.11
Smoking status
(0.1)
(0.09)
(0.1)
(0.09)
(0.09)
-0.06
-0.08
-0.06
-0.06
-0.08
Exercise
(0.07)
(0.07)
(0.07)
(0.07)
(0.07)
0.85***
0.87***
0.85***
0.85***
0.87***
Underweight
(0.11)
(0.11)
(0.11)
(0.11)
(0.11)
-0.18**
-0.17**
-0.18**
-0.18**
-0.17**
Overweight
(0.08)
(0.08)
(0.08)
(0.08)
(0.08)
-0.36*** -0.33***
-0.35***
-0.36***
-0.33***
Obesity
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
-0.46***
-0.43***
No High School
(0.13)
(0.13)
-0.36***
-0.32***
Some High School
(0.12)
(0.12)
-0.21
-0.2
College
27 | P a g e
30 | P a g e
Table 3.7 Logistic Regression Analysis – Cancer Prevalence, Demographic, Health
Behavior and Socioeconomic Factors, 2002-2007
Base
Base
Base Model Base Model Full Model
Model
Model
Plus
Plus
Plus
Poverty
Insurance
Education
(0.13)
(0.13)
-0.12
-0.05
Low Income
(0.09)
(0.1)
-0.12
-0.07
Middle Income
(0.07)
(0.07)
0.11
0.12
0.08
MSA
(0.08)
(0.08)
(0.08)
0.01
0.06
Public Insurance
(0.09)
(0.09)
-0.35***
-0.29**
Uninsured
(0.13)
(0.13)
-4.69*** -4.39***
-4.74***
-4.75***
-4.42***
Constant
(0.12)
(0.17)
(0.14)
(0.15)
(0.19)
72806
72806
72806
72806
72806
Observations
Source: Based on authors’ analysis of the Medical Expenditure Panel Survey 2002-2007
Coefficients with standard errors in parentheses. Exponentiate the coefficient to compute
the odds ratio relative to the reference group, e.g., e -0.13 = 0.88.
Stars denote level of statistical significance: * - p < 0.05, ** - p < 0.01, and *** - p < 0.001.
28| P
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31
Table 3.8 Logistic Regression Analysis – Cancer Prevalence, Demographic, Health
Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model Base Model Full Model
Plus
Plus Income
Education
and
Insurance
-0.04
-0.02
-0.04
-0.03
Female
(0.06)
(0.06)
(0.07)
(0.07)
0.50***
0.47***
0.48***
0.46***
Age 35-44
(0.15)
(0.15)
(0.15)
(0.15)
0.89***
0.86***
0.85***
0.84***
Age 45-54
(0.12)
(0.12)
(0.12)
(0.12)
1.37***
1.34***
1.33***
1.31***
Age 55-64
(0.12)
(0.12)
(0.12)
(0.12)
1.82***
1.81***
1.78***
1.77***
Age 65-74
(0.13)
(0.13)
(0.13)
(0.13)
1.80***
1.82***
1.79***
1.79***
Age 75+
(0.13)
(0.13)
(0.14)
(0.14)
-0.43***
-0.40***
-0.40***
-0.38***
African American
(0.11)
(0.11)
(0.11)
(0.11)
-0.72***
-0.67***
-0.64***
-0.61***
Hispanic
(0.12)
(0.12)
(0.12)
(0.12)
-0.09
-0.09
-0.09
-0.09
Asian
(0.1)
(0.1)
(0.1)
(0.1)
-0.92***
-0.89***
-0.88***
-0.87***
Other Race
(0.29)
(0.29)
(0.29)
(0.29)
-0.38***
-0.32***
No High School
(0.12)
(0.12)
-0.37***
-0.33***
Some High School
(0.11)
(0.11)
-0.2
-0.19
College
(0.13)
(0.13)
-0.18*
-0.13
Low Income
(0.1)
(0.1)
-0.05
-0.01
Middle Income
(0.07)
(0.07)
-0.05
-0.02
Public Insurance
(0.09)
(0.09)
-0.39***
-0.36***
Uninsured
(0.13)
(0.13)
0.09
0.07
MSA
29 | P a g e
32 | P a g e
Table 3.8 Logistic Regression Analysis – Cancer Prevalence, Demographic, Health
Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model Base Model Full Model
Plus
Plus Income
Education
and
Insurance
(0.08)
(0.08)
-4.86***
-4.55***
-4.83***
-4.57***
Constant
(0.11)
(0.15)
(0.13)
(0.17)
82889
82889
82889
82889
Observations
Source: Based on authors’ analysis of the Medical Expenditure Panel Survey, 1996-2007.
Coefficients with standard errors in parentheses. Exponentiate the coefficient to compute
the odds ratio relative to the reference group, e.g., e -0.13 = 0.88.
Stars denote level of statistical significance: * - p < 0.1, ** - p < 0.05, and *** - p < 0.01.
Running additional models that included behavioral measures such as BMI, smoking, and
exercise using data from the 2002-2007 MEPS further tested the robustness of our results. These
variables were not used in previous models because they were not consistently reported in the
MEPS before 2002. Similar results to those reported above using MEPS data from 1996-2007
were found in all five models, except for individuals with family income that is below 200% of
the FPL, whereas the marginal significance found above disappeared. Additionally, any cancer
prevalence was positively associated with underweight (BMI<18.5, OR= 2.33, P<0.001) and
negatively associated with overweight (BMI between 25 and 29.9 kg/m2, OR= 0.83, P<0.05),
and obesity (BMI>29.9, OR=0.70, P<0.001) compared to patients with normal weight (BMI
between 18.5 and 24.9). Compared to normal weight persons, the odds of underweight persons
having cancer were 2.3 times greater, while the odds for overweight and obese persons were
16% and 30% lower respectively. This may be an indication that persons with cancer tend to
lose weight rather than underweight persons being at greater cancer risk.
Colon, Breast, Lung, and Prostate Cancer
The characteristics associated with cancer prevalence were further explored by stratifying
the sample by common types of cancer such as colon, breast, lung, and prostate cancer. (See
tables 3.9 through 3.12.) MEPS data from 1996-2007 were used for the stratified analysis
because of sample size. Colon cancer was positively associated with African Americans
compared to white patients in all four models (P<0.05). Breast cancer was negatively associated
with African-American females (significance levels range from P<0.10 to p<0.05) compared to
white patients, but the significance disappeared in full models that controlled for education,
income, and insurance status at the same time. Breast cancer was also negatively associated with
public insurance and the uninsured (p<0.10) compared to private/employer insurance, but the
significance level disappeared when controlling for different levels of education as well.
Lung cancer was positively associated with African-Americans compared to white
patients, but the significance disappeared when controlling for education, income, and insurance
status. Furthermore, lung cancer was positively associated with public insurance (p<0.001)
compared to patients with private insurance, even after controlling for income and education.
Lower income individuals with annual family income less than 200% of the FPL were less likely
to have prostate cancer compared to those making above 400% of the FPL (P<0.10). However,
30 | P a g e
the associations indicated above disappeared in all the models that combined colon, lung, and
breast cancer for women as well as colon, lung, and prostate cancer for men.
31 | P a g e
34 | P a g e
Table 3.9 Logistic Regression Analysis – Lung Cancer Prevalence, Demographic, Health
Behavior and Socioeconomic Factors, 1996-2007
Base Model
Female
Age 35-44
Age 45-54
Age 55-64
Age 65-74
Age 75+
African American
Hispanic
Asian
-0.18
(0.31)
0.99
(1.14)
1.47
(0.91)
3.21***
(0.76)
3.89***
(0.74)
3.50***
(0.77)
0.83**
(0.39)
-0.78
(0.76)
0.3
(0.51)
No High School
High School
College
Base Model
Plus
Education
-0.22
(0.31)
1.04
(1.14)
1.51*
(0.92)
3.26***
(0.76)
3.90***
(0.75)
3.47***
(0.77)
0.75*
(0.39)
-0.9
(0.8)
0.28
(0.51)
1.56
(1.06)
1.44
(1.03)
1.25
(1.1)
Low Income
Middle Income
Public Insurance
Uninsured
MSA
Constant
Observations
-9.90***
(0.74)
80054
-11.26***
(1.25)
80054
Base Model
Plus Income
and Insurance
-0.25
(0.32)
0.98
(1.14)
1.45
(0.93)
3.12***
(0.77)
3.36***
(0.76)
2.85***
(0.8)
0.59
(0.41)
-0.98
(0.78)
0.31
(0.52)
0.27
(0.43)
-0.36
(0.45)
1.07***
(0.41)
-1.04
(1.06)
(0.02
(0.39)
-9.83***
(0.83)
80054
Full Model
-0.29
(0.32)
1.01
(1.13)
1.47
(0.93)
3.16***
(0.77)
3.40***
(0.76)
2.89***
(0.8)
0.55
(0.41)
-1.01
(0.82)
0.31
(0.52)
1.33
(1.03)
1.37
(1.02)
1.22
(1.1)
0.2
(0.41)
-0.43
(0.44)
1.02***
(0.39)
-1.09
(1.05)
0.05
(0.39)
-11.08***
(1.3)
80054
32 | P a g e
Table 3.9 Logistic Regression Analysis – Lung Cancer Prevalence, Demographic, Health
Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model
Plus
Education
Base Model
Plus Income
and Insurance
Full Model
Source: Based on authors’ analysis of the Medical Expenditure Panel Survey, 1996-2007.
Coefficients with standard errors in parentheses. Exponentiate the coefficient to compute
the odds ratio relative to the reference group, e.g., e -0.13 = 0.88.
Stars denote level of statistical significance: * - p < 0.1, ** - p < 0.05, and *** - p < 0.01.
33 | P a g e
36 | P a g e
Table 3.10 Logistic Regression Analysis – Colon Cancer Prevalence, Demographic,
Health Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model
Base Model
Full Model
Plus Education Plus Income
and
Insurance
-0.73**
-0.74**
-0.71**
-0.70**
Female
(0.31)
(0.32)
(0.31)
(0.31)
0.68
0.68
0.76
0.75
Age 35-44
(1.24)
(1.24)
(1.24)
(1.24)
3.20***
3.21***
3.31***
3.32***
Age 45-54
(1.08)
(1.08)
(1.1)
(1.1)
3.48***
3.49***
3.62***
3.63***
Age 55-64
(1.06)
(1.06)
(1.07)
(1.07)
4.66***
4.68***
5.01***
5.02***
Age 65-74
(1.04)
(1.04)
(1.09)
(1.09)
4.99***
5.02***
5.36***
5.38***
Age 75+
(1.04)
(1.04)
(1.07)
(1.07)
0.91**
0.93**
0.94**
0.96**
African American
(0.37)
(0.37)
(0.4)
(0.39)
0.72
0.75
0.67
0.69
Hispanic
(0.44)
(0.47)
(0.47)
(0.49)
0.61
0.62
0.59
0.59
Asian
(0.46)
(0.45)
(0.46)
(0.45)
0.79
0.81
0.81
0.81
Other Race
(1.03)
(1.01)
(1.03)
(1.02)
0.22
0.19
No High School
(0.65)
(0.67)
0.26
0.21
Some High School
(0.68)
(0.69)
0.49
0.48
College
(0.75)
(0.75)
-0.16
-0.15
Low Income
(0.41)
(0.4)
0.26
0.26
Middle Income
(0.34)
(0.34)
-0.24
-0.23
Public insurance
(0.38)
(0.39)
0.81
0.81
Uninsured
(0.52)
(0.52)
-0.01
-0.02
MSA
(0.4)
(0.4)
-10.71***
-11.00***
-11.04***
-11.28***
Constant
(1.0)
(1.13)
(1.09)
(1.19)
34| P
| Paaggee
37
Table 3.10 Logistic Regression Analysis – Colon Cancer Prevalence, Demographic,
Health Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model
Base Model
Full Model
Plus Education Plus Income
and
Insurance
81653
81653
81653
81653
Observations
Source: Based on authors’ analysis of the Medical Expenditure Panel Survey, 19962007.
Coefficients with standard errors in parentheses. Exponentiate the coefficient to
compute the odds ratio relative to the reference group, e.g., e -0.13 = 0.88.
Stars denote level of statistical significance: * - p < 0.1, ** - p < 0.05, and *** - p < 0.01.
35 | P a g e
38 | P a g e
Table 3.11 Logistic Regression Analysis – Breast Cancer Prevalence, Demographic,
Health Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model
Base Model Full Model
Plus
Plus Income
Education
and
Insurance
2.56***
2.53***
2.51***
2.49***
Age 35-44
(0.59)
(0.59)
(0.59)
(0.59)
3.35***
3.34***
3.29***
3.29***
Age 45-54
(0.62)
(0.62)
(0.61)
(0.62)
3.85***
3.85***
3.81***
3.82***
Age 55-64
(0.61)
(0.61)
(0.61)
(0.62)
4.04***
4.08***
4.08***
4.11***
Age 65-74
(0.64)
(0.64)
(0.65)
(0.65)
3.32***
3.40***
3.44***
3.49***
Age 75+
(0.66)
(0.65)
(0.66)
(0.66)
-0.68**
-0.62*
-0.55*
-0.52
African American
(0.32)
(0.32)
(0.32)
(0.32)
-0.37
-0.25
-0.15
-0.09
Hispanic
(0.3)
(0.3)
(0.3)
(0.3)
-0.07
-0.05
-0.06
-0.05
Asian
(0.26)
(0.26)
(0.26)
(0.26)
-1.54
-1.51
-1.47
-1.46
Other Race
(0.96)
(0.96)
(0.96)
(0.96)
-0.54
-0.39
No High School
(0.33)
(0.35)
-0.32
-0.26
Some High School
-0.32
(0.32)
-0.03
-0.02
College
-0.34
(0.34)
-0.4
-0.34
Low Income
(0.27)
(0.28)
0.1
0.15
Middle Income
(0.2)
(0.21)
-0.44*
-0.39
Public insurance
(0.25)
(0.26)
-0.89*
-0.84*
Uninsured
(0.47)
(0.47)
0.0
-0.03
MSA
(0.23)
(0.23)
-8.53***
-8.27***
-8.40***
-8.21***
Constant
(0.59)
(0.64)
(0.63)
(0.67)
36 | P a g e
Table 3.11 Logistic Regression Analysis – Breast Cancer Prevalence, Demographic,
Health Behavior and Socioeconomic Factors, 1996-2007
Base Model
Base Model
Base Model Full Model
Plus
Plus Income
Education
and
Insurance
44202
44202
44202
44202
Observations
Source: Based on authors’ analysis of the Medical Expenditure Panel Survey, 1996-2007.
Coefficients with standard errors in parentheses. Exponentiate the coefficient to compute
the odds ratio relative to the reference group, e.g., e -0.13 = 0.88.
Stars denote level of statistical significance: * - p < 0.1, ** - p < 0.05, and *** - p < 0.01.
37 | P a g e
40 | P a g e
Table 3.12 Logistic Regression Analysis – Prostate Cancer Prevalence, Demographic,
Health Behavior and Socioeconomic Factors, 1996-2007
Age 45-54
Age 55-64
Age 65-74
Age 75+
African American
Hispanic
Asian
Other Race
Base
Model
Base Model
Plus
Education
11.81***
(0.51
13.76
(.)
14.38***
(0.3)
15.14***
(0.27)
-0.01
(0.33)
-0.59
(0.38)
0.24
(0.31)
-0.61
(0.69)
11.77
(.)
13.70***
(0.51)
14.32***
(0.5)
15.09***
(0.5)
0
(0.33)
-0.61
(0.41)
0.23
(0.31)
-0.62
(0.69)
-0.18
(0.34)
-0.47
(0.36)
-0.03
(0.4)
No high School
Some High School
College
Low Income
Middle Income
Public Insurance
Uninsured
MSA
Constant
Observations
-18.94***
(0.22)
29953
-18.64***
(0.57)
29953
Base Model
Plus
Income,
Insurance
11.79
(.)
13.77***
(0.52)
14.48***
(0.54)
15.32***
(0.55)
0.07
(0.33)
-0.54
(0.4)
0.25
(0.31)
-0.47
(0.71)
-0.58*
(0.34)
0.03
(0.27)
-0.25
(0.26)
0.08
(0.6)
0.66**
(0.28)
-19.47***
(0.52)
29953
Full Model
11.77***
(0.52)
13.72
(.)
14.44***
(0.31)
15.27***
(0.27)
0.04
(0.33)
-0.63
(0.42)
0.23
(0.31)
-0.53
(0.71)
0.05
(0.36
-0.37
(0.36)
0.01
(0.4)
-0.59*
(0.35)
0.06
(0.28)
-0.25
(0.27)
0.08
(0.61)
0.67**
(0.28)
-19.30***
(0.39)
29953
Source: Based on authors’ analysis of the Medical Expenditure Panel Survey, 1996-2007.
38 | P a g e
Table 3.12 Logistic Regression Analysis – Prostate Cancer Prevalence, Demographic,
Health Behavior and Socioeconomic Factors, 1996-2007
Base
Model
Base Model
Plus
Education
Base Model
Plus
Income,
Insurance
Full Model
Coefficients with standard errors in parentheses. Exponentiate the coefficient to compute
the odds ratio relative to the reference group, e.g., e -0.13 = 0.88.
Stars denote level of statistical significance: * - p < 0.1, ** - p < 0.05, and *** - p < 0.01.
Discussion
An analysis of the Medical Expenditure Panel Survey (MEPS) explored patterns of
cancer prevalence comparing various racial/ethnic groups, socioeconomic status and health
insurance status using multivariate regression analytic techniques with appropriate controls,
including age, gender, and obesity. The results revealed seemingly counterintuitive findings.
Traditionally disadvantaged groups tended to report lower prevalence rates of cancer (cancer for
any site) diagnosis. There was a lower prevalence of all-site cancer diagnoses among less welleducated persons compared with more highly educated persons. The analysis also found lower
prevalence of all-site cancer diagnosis among persons without health insurance compared to
privately insured persons. There was also a lower prevalence of all-site cancer diagnosis among
African-Americans and Hispanics compared to whites.
Analyses of specific cancers reveal a somewhat more complex set of patterns. The
analysis found: higher colorectal cancer prevalence among African-Americans compared to
whites; higher lung cancer prevalence among those with public health insurance compared with
privately insured; and lower prevalence of prostate cancer among lower income men.
We caution against interpreting the findings as evidence of lower cancer risk among
traditionally socially disadvantaged groups. The results may be reflecting lower cancer survival
rates among disadvantaged populations, which have been well documented (Horner et al., 2009).
That is, if socially disadvantaged groups have lower cancer survival rates, then members of
socially disadvantaged groups who remain alive to respond to surveys such as MEPS are selected
disproportionately from those without cancer. Moreover, cancer survival varies by the type of
cancer. For example, for lung cancer, which is usually associated with a relatively short
survival, incidence has a proportionally greater effect (relative to survival) on prevalence,
compared to breast and prostate cancer which are associated with more prolonged survival.
Additionally, it has been well documented that persons from socially disadvantaged
groups tend to be diagnosed at later stages of disease (Marlow et al., 2010). MEPS data do not
include data on stage at diagnosis, a major determinant of survival. Differences in healthcare
access and utilization among socially disadvantaged groups are well known (Laiyemo et al.,
2010; Carpenter et al., 2010). Differences in healthcare access and utilization can also influence
self-reported survey data if persons from socially disadvantaged groups are more likely to be
unaware of their cancer status. Survey respondents cannot report a cancer diagnosis if they have
not received a diagnosis as a result of underutilization of health services and screening, or lower
quality care (Onega et al., 2010).
39 | P a g e
42 | P a g e
IV. The Societal and Economic Impact of Cancer Health Disparities
The economic impact of cancer disparities was costly to the United States. We estimated
the costs of cancer disparities across racial/ethnic groups and across socioeconomic groups. The
annual costs of racial/ethnic disparities were $193 billion for premature death, $2.3 billion for
direct medical costs and $471.5 million for lost productivity. The annual costs for disparities
between counties at the poverty level were $36.7 billion for premature death, $236.9 million for
direct medical costs and $42.5 million for lost productivity. The annual costs for disparities
between counties at the poverty level were $35.9 billion for premature death, $344.5 million for
direct medical costs and $77 million for lost productivity.
This report’s estimates compared to NIH’s 2006 estimate of the economic costs of
cancer: $124.8 billion for premature death, $99 billion for direct costs, and $19.6 billion for lost
productivity (National Heart, Lung, and Blood Institute, 2009). In 2006, there were 559,233
deaths attributable to cancers, which equates to an estimated 9.25 million years of life lost. The
NIH used a value of lifetime earnings methodology to estimate the economic costs of cancer
mortality at $124.8 billion. However, this methodology would tend to under-value the cost of
cancer mortality because 72% of cancer deaths occur in persons ages 55-74. These are persons
at the ends of their careers or who are retired; therefore, the value of their remaining lifetime
earnings will be relatively low compared to younger persons.
Consequently, we believe a willingness-to-pay measure is a better indicator of the value
of their lives than their potential wages. Such an estimate is based on society’s willingness to
pay for medical interventions to prevent death, which considers more than just a person’s value
in the labor market but also their intrinsic value to family, friends and the community at large.
Using the cost effectiveness estimates of $50,000 to $264,000 per quality life year (Braithwaite,
2008), we estimate the cost of cancer mortality to be $462 billion to $2.44 trillion.
The Cost of Premature Death
If society were able to eliminate racial disparities in cancer mortality, this analysis
estimates there would have been 243,160 fewer cancer deaths (or 3.87 million fewer years of life
lost) in 2006. The elimination of cancer disparities would have reduced the economic costs of
cancer deaths by $193 billion to over $1 trillion in 2006; and over a five-year period (2002-2006)
by $955 billion to $5.5 trillion in constant dollars. (See Table 4.1) Most of the costs saving are
attributable to a reduction in cancer mortality for whites (80%) and African Americans (17%),
who tend to have the higher death rates relative to Asians and Hispanics.
40| P
| Paaggee
43
Table 4.1. Cost of Premature Cancer Deaths Due to Race Disparities in Cancer Mortality
in Billions of Dollars
Year
Number
$50,000
$183,000
$264,000
of Life
per lifeper lifeper lifeYears
year
year
year
Lost
(000s)
3876
193
709
1023
2006
3774
188
690
996
2005
3835
192
702
1012
2004
3775
189
691
997
2003
3859
193
706
1019
2002
19120
955
3498
5047
Total
Source: Based on authors’ calculation using cancer mortality data from Centers for
Disease Control and Prevention National Vital Statistics System and life expectancy data
from Arias. United States life tables, 2004. National vital statistics reports; vol. 56 no 9.
Hyattsville, MD: National Center for Health Statistics. 2007. Life-year values based on
recommendation in Braithwaite et al. “What Does the Value of Modern Medicine Say
About the $50,000 per Quality-Adjusted Life-Year Decision Rule?” Medical Care 2008; 46:
349–356.
Cancer burden also varies by socioeconomic status. The costs of socioeconomic cancer
disparities were estimated two ways: 1) by reducing disparities between counties with different
poverty rates; and 2) by reducing disparities between counties with different levels of
educational attainment. Cancer disparities associated with poverty results in over 734.7 thousand
life years lost, annually. (See table 4.2) Eliminating cancer disparities between counties with
different levels of poverty would result in a cost savings ranging from $36.7 billion to $194
billion annually and from $183.7 billion to almost a trillion dollars over a five-year period. Lung
cancer was the most expensive, ranging from $13.8 billion to $73.6 billion annually. Prostate
cancer was least expensive, ranging from low estimate of $1.6 billion to $8.3 billion.
41 | P a g e
44 | P a g e
Table 4.2 Cost of Premature Cancer Deaths Due to Disparities in Cancer Mortality Across
Counties with Different Poverty Rates in Billions of Dollars for All Cancers, Lung,
Colorectal, Breast and Prostate
Number of Life
$50,000 per
$183,000
$264,000
Years Lost
life-year
per lifeper life(000s)
year
year
All Cancer
734.7
36.7
134.4
194.0
Annual
3,673.3
183.7
672.2
969.7
5 Year Total (2002-2006)
Lung Cancer
276.9
13.8
50.7
73.1
Annual
1,384.6
69.2
253.3
365.5
5 Year Total (2002-2006)
Colorectal Cancer
74.3
3.7
13.5
16.6
Annual
371.4
18.6
68.0
98.1
5 Year Total (2002-2006)
Breast Cancer
45.9
2.3
8.4
12.1
Annual
229.3
11.5
42.0
60.5
5 Year Total (2002-2006)
Prostate Cancer
31.1
1.6
5.7
8.3
Annual
155.8
7.8
28.5
41.1
5 Year Total (2002-2006)
Source: Based on authors’ calculation using cancer mortality data from Centers for
Disease Control and Prevention National Vital Statistics System and life expectancy data
from Arias United States life tables, 2004. National vital statistics reports; vol. 56 no 9.
Hyattsville, MD: National Center for Health Statistics. 2007. Life-year values based on
recommendation in Braithwaite et al. “What Does the Value of Modern Medicine Say
About the $50,000 per Quality-Adjusted Life-Year Decision Rule?” Medical Care 2008; 46:
349–356.
The costs of premature death associated with disparities in cancer mortality across
counties with different levels of educational attainment ranged from $35.9 billion to $189.5
billion. These disparities were associated with almost 718 thousand years of life lost. The fiveyear costs (2002-2006) ranged from $179.5 billion to $947.7 billion. Lung cancer was most
expensive, followed by colorectal, breast and prostate cancer. (See table 4.3.)
42| P
| Paaggee
45
Table 4.3 Cost of Premature Cancer Deaths Due to Educational Attainment Disparities in
Cancer Mortality in Billions of Dollars for All Cancers, and Lung, Colorectal, Breast and
Prostate Cancers
Reduce Mortality to Lowest Number of Life
$50,000
$183,000
$264,000
Education Counties
Years Lost (000s)
per lifeper lifeper lifeyear
year
year
All Cancer
718.0
35.9
131.4
189.5
Annual
3,589.8
179.5
656.9
947.7
5 Year Total (2002-2006)
Lung Cancer
301.8
15.1
55.2
79.7
Annual
1509.1
75.4
276.2
398.4
5 Year Total (2002-2006)
Colorectal Cancer
80.0
4.0
1460
21.1
Annual
400.0
20.0
73.2
105.6
5 Year Total (2002-2006)
Breast Cancer
49.1
2.5
9.0
13.0
Annual
245.6
12.3
44.9
64.8
5 Year Total (2002-2006)
Prostate Cancer
23.1
1.2
4.2
6.1
Annual
115.5
5.8
21.1
30.5
5 Year Total (2002-2006)
Source: Based on authors’ calculation using cancer mortality data from Centers for
Disease Control and Prevention National Vital Statistics System and life expectancy data
from Arias United States life tables, 2004. National vital statistics reports; vol. 56 no 9.
Hyattsville, MD: National Center for Health Statistics. 2007. Life-year values based on
recommendation in Braithwaite et al. “What Does the Value of Modern Medicine Say
About the $50,000 per Quality-Adjusted Life-Year Decision Rule?” Medical Care 2008; 46:
349–356.
The Direct Cost of Cancer Disparities
The actual and incremental costs of medical care were computed for cancer patients using
data from the 2002-2007 Medical Expenditure Panel Survey. (See table 4.4.) The actual average
medical costs for persons with cancer ranged from $9,830 for persons with any type of cancer to
$21,147 for persons with lung cancer. This is substantially higher than the average medical costs
of $4,157 for persons without cancer. After adjusting for demographic characteristics,
socioeconomic factors, and other health conditions, the incremental cost of cancer was $4,347.
Cost estimates for the four most common cancers ranged from $1,784 for prostate cancer to
$9,027 for lung cancer.
43 | P a g e
46 | P a g e
Table 4.4 The actual and incremental costs of cancer, for all cancers and selected cancer
sites, 2002-2007
Average
Incremental
95% Confidence Interval
Costs
Costs
$ 9,830
$4,347
$4,270 $4,424
Any Cancer
$21,147
$9,027
$8,859 $9,194
Lung
$15,243
$8,236
$8,082 $8,389
Colorectal
$13,657
$7,946
$7,801 $8,091
Breast
$12,297
$1,784
$1,753 $1,815
Prostate
Source: Based on authors’ calculations using the MEPS 2002-2007.
To compute the direct costs of cancer disparities, the number of cases due to disparities in
cancer incidence was estimated. (See table 4.5.) For 2002-2006, there were on average
1,334,544 new cancer cases a year. If racial disparities in cancer incidence were eliminated, the
annual number of new cancer cases would decline by 531,983. This reduction would result in
$2.3 billion savings on annual medical expenditures for persons with cancer. The annual cost
savings associated with the elimination of racial disparities in cancer incidence for the four most
common cancers ranged from $166.8 million for prostate cancer to $814 million for lung cancer.
Table 4.5 The Reduction in Medical Care Associated with an Elimination of Racial/Ethnic
Disparities in Cancer Incidence.
Incremental
Average
Reduction in Reduction in
Cost
Number of
Cancer Cases Medical Costs
Cancer Cases
(millions)
$ 4,347
1,334,544
531,983
$2,312.7
All Cancer
$9,027
190,687
90,253
$814.7
Lung
$8,236
142,839
47,666
$392.5
Colorectal
$7,946
184201
84,838
$674.1
Breast
$1,784
189838
93,487
$166.7
Prostate
Source: Based on Calculations by the Authors Using Data from the Medical Expenditure
Panel Survey 2002-2007 and Cancer incidence data for 2002-2006 from the U.S. Cancer
Statistics Working Group. United States Cancer Statistics: 1999–2006 Incidence and
Mortality Web-based Report. Atlanta: U.S. Department of Health and Human Services,
Centers for Disease Control and Prevention and National Cancer Institute; 2010.
Available at: www.cdc.gov/uscs
Incidence data from SEER/STAT 2002-2006 were used to calculate the reduction in new
cancer cases to compute the cost savings associated with the elimination of socioeconomic
cancer disparities. This incidence data is only for the 17 geographic areas covered by SEER. It
represents 26.2% of the U.S. population. The cost savings for this population were computed
and then extrapolated to the nation by multiplying by 3.817 (the reciprocal of 0.262). This
analysis looked at: 1) the elimination of disparities between counties categorized by poverty
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status; and 2) the elimination of disparities between counties categorized by educational
attainment. (See table 4.6.) The elimination of poverty disparities in cancer incidence would
reduce the number of new cancer cases by 14,278 in the SEER 17 geographic areas. This change
in the annual number of cancer cases would lower health expenditures by $62.1 in the SEER 17
geographic areas and by $236 million nationally. Among the four most common cancers, the
cost savings associated with the reductions in colorectal cancer were $228.7 million nationally.
Eliminating educational disparities in cancer incidence would result in a reduction of 20,763
cancer cases in SEER 17. This decline in new cancer cases corresponds to a $90.3 million
reduction in healthcare costs in SEER 17 and an estimated $344.5 million nationally.
Table 4.6 The Reduction in Medical Care Costs Associated with an Elimination of
Socioeconomic Disparities in Cancer Incidence.
Incremental Average
Reduction
Reduction in
Reduction in
Cost
Number of in Cancer
Medical Cost
Medical Costs
Cancer
Cases
for SEER 17
of US
Cases
(millions)
Poverty Disparities
$4,347
338,210
14,278
$62.1
$236.9
All Cancer
$9,027
45,277
1,980
$17.9
$68.2
Lung
$8,236
35,673
7,278
$59.9
$228.7
Colorectal
$7,946
49,551
5,023
$39.9
$152.3
Breast
$1,784
51,057
4,596
$8.2
$31.3
Prostate
Education Disparities
$4,347
338,210
20,763
$90.3
$344.5
All Cancer
$9,027
45,277
4,802
$43.3
$165.4
Lung
$8,236
35,673
1,927
$15.9
$60.6
Colorectal
$7,946
49,551
3,971
$31.6
$120.4
Breast
$1,784
51,057
6,066
$10.8
$41.3
Prostate
Source: Based on Calculations by the Authors Using Data from the Medical Expenditure
Panel Survey 2002-2007 and cancer incidence data for 2002-2006 from the National Cancer
Institute Surveillance, Epidemiology and End Results (SEER) Program.
The Indirect Cost of Cancer Disparities
Elimination of disparities in cancer incidence would also result in cost savings
attributable to improved labor market outcomes, i.e., fewer disability days, more hours worked
and higher wages for working age adults. The 2002-2007 MEPS was used to compute the
incremental indirect costs of cancer. These estimates are based on three regression models to
predict disability days, hours worked and hourly wages. These models were then used to estimate
the change in these labor market outcomes associated with having any cancer, and separately for
lung, colorectal, breast and prostate cancers. The incremental indirect cost for any cancer was
$1,972. The incremental indirect costs for the four most common cancers ranged from $2,291 for
persons with prostate cancer to $3,947 for persons with colorectal cancer. The reduction in
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productivity costs for any cancer was $471.5 million. Breast and lung cancer had the greatest
reductions in productivity costs at about $116 million each. (See table 4.7.)
Table 4.7 The Reduction in Indirect Care Associated with an Elimination of
Racial/Ethnic Disparities in Cancer Incidence.
Incremental
Average
Reduction in Reduction in
Cost
Number of
Cancer Cases Productivity
Cancer Cases
Costs
(millions)
$1,972
574,865
239,089
$471.5
All Cancer
$3,404
62,660
34,104
$116.1
Lung
$3,947
50,334
14,668
$57.9
Colorectal
$2,390
108,233
48,869
$116.8
Breast
$2,291
72,154
44,792
$102.6
Prostate
Source: Based on Calculations by the Authors Using Data from the Medical Expenditure
Panel Survey 2002-2007 and Cancer incidence data for 2002-2006 from the U.S. Cancer
Statistics Working Group. United States Cancer Statistics: 1999–2006 Incidence and
Mortality Web-based Report. Atlanta: U.S. Department of Health and Human Services,
Centers for Disease Control and Prevention and National Cancer Institute; 2010.
Available at: www.cdc.gov/uscs
To estimate the potential cost saving associated with eliminating SES disparities in
cancer, we compared cancer rates and their associated costs between counties with high poverty
rates against counties with low poverty rates. (See table 4.8.) Similar analysis was conducted for
counties with high and low levels of educational attainment. An elimination of cancer disparities
among counties with various level of poverty would reduce costs related to lost productivity by
$42.5 million for any cancer and $24.4 million and $23.7 million for breast and prostate cancer
respectively. The elimination of cancer disparities among counties with different percentages of
adults with low levels of educational attainment would reduce productivity losses by $77 million
for any cancer, and $35.1 million for prostate cancer, followed by $22.7 million for lung cancer.
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Table 4.8 The Reduction in Indirect Cancer Costs Associated with an Elimination of
Socioeconomic Disparities in Cancer Incidence
Incremental Average
Reduction in Reduction in Reduction in
Cost
Number of
New Cancer Medical Cost Medical Costs
New Cancer Cases
for SEER 17 of US
Cases
(millions)
Poverty Disparities
$1972
147,970
5,652
$11.1
$42.5
All Cancer
$3404
14,380
1,078
$3.7
$14.0
Lung
$3947
12,606
706
$2.8
$10.6
Colorectal
$2390
29,058
2,678
$6.4
$24.4
Breast
$2291
19,553
2,705
$6.2
$23.7
Prostate
Education Disparities
$1972
147,970
10,228
$20.2
$77.0
All Cancer
$3404
14,380
1745
$5.9
$22.7
Lung
$3947
12,606
840
$3.3
$12.7
Colorectal
$2390
29,058
2314
$5.5
$21.1
Breast
$2291
19553
4017
$9.2
$35.1
Prostate
Source: Based on Calculations by the Authors Using Data from the Medical Expenditure
Panel Survey 2002-2007 and cancer incidence data for 2002-2006 from the National Cancer
Institute Surveillance, Epidemiology and End Results (SEER) Program.
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Recommendations
Racial and ethnic disparities and socioeconomic disparities in cancer persist and are costly, not
only in the monetary sense but also in losses that cannot be measured economically. What can be
done?
1. Society needs to focus on cancer prevention and cancer treatment that result in longterm survival. Programs designed to lower individual risk of cancer, if they are successful in
reducing incidence and mortality among whites and African Americans to levels experienced
by Asians and Hispanics, would yield substantial economic value to the nation. Similarly, if
cancer mortality for persons with low socioeconomic status could be lowered to mortality
rates experienced by more affluent and better educated persons, substantial costs due to
premature death could be avoided.
2. The nation should invest in policies and programs designed to reduce cancer risks
associated with environmental factors and unhealthy behaviors. Regardless of
socioeconomic status, individuals from racial and ethnic minority populations experience a
myriad of stressors in the context of their social location. The effects of social location are
mediated by lifetime exposure to social and environment factors, as well as individually
modifiable behavioral risks. Such social and individual-level exposures translate into
differential health care quality and access.
3. Policymakers, providers, and communities need to develop strategies to reduce racial
disparities in the quality of cancer-related care that also address the need for care
coordination to promote rational utilization of preventive and therapeutic care,
including management of comorbid conditions. The available evidence points to
opportunities to intervene to reduce disparities in cancer-related care through health risk
management, access to a usual source of care, access to primary and secondary treatment, and
adjuvant therapies. However, the evidence also suggests that despite health care coverage,
many members of racial and ethnic minority populations underutilize services for cancer
detection, diagnosis and treatment.
4. Society should address modifiable risk factors such as physical inactivity, obesity, heavy
alcohol consumption, a diet high in red or processed meats, a diet that lacks fruits and
vegetables, a diet high in animal fat, smoking, and environmental hazards such as radon,
asbestos, air pollution, and certain metals (chromium, cadmium, arsenic) (ACS, 2009).
Racial disparities in health care outcomes have many causes. They occur within a broader
context and a social/political environment that produces systemic racial/ethnic inequalities.
The fundamental social causes of such disparities (e.g., poverty, residential segregation)
cannot be addressed absent the political will to implement appropriately targeted social
policies. This case statement underscores the urgent need for intervention to reduce cancer
disparities.
5. Community-level interventions are warranted. At the community level, such interventions
might address community norms and lifestyles that promote adverse health outcomes in
racial/ethnic minority populations. Community-level interventions should also address
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51
potential adverse effects of the physical and social environment, as well community
differences in health care availability.
6. Strategies to reduce race disparities in the quality of cancer-related care must also
address the need for care coordination to promote rational utilization of preventive and
therapeutic care, including management of comorbid conditions. The available evidence
points to opportunities to intervene to reduce disparities in cancer-related care, through health
risk management, access to a usual source of care, and access to primary and secondary
treatment, and adjuvant therapies. However, the evidence also suggests that despite health care
coverage many members of racial and ethnic minority populations underutilize services for
cancer detection, diagnosis and treatment.
7. Finally, we need better data. Cancer registries should collect socioeconomic data (income,
education and health insurance status). To make use of existing cancer registry data to
compute survival and mortality rates, cancer registries should be linked to social security
records. This will permit the computation of trends in cancer incidence, survival and
mortality. Population surveys (e.g. the National Health Interview Survey) should develop
cancer supplements to collect stage and survival information. Also, information on health
behaviors could be collected using a national survey of new cancer patients based on a sample
of new cancer registrants.
Any efforts to address the disparities in cancer health must address not only the monetary costs
of such disparities, but also the costs that cannot be translated into dollars and cents.
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