Download Selected SWOG Research

Cancer Disparities
Research in SWOG
Joseph Unger, PhD
Coordinating Statistician for SWOG NCORP
SWOG Statistical Center
Fred Hutchinson Cancer Research Center
SWOG NCORP Research Base Clinical Trials Workshop
September 14, 2016
SWOG’s Mission
 To design, direct, and participate in research that
leads to more effective screening, prevention and
treatment of cancers in adolescents, young adults,
and adults
 To improve survival, quality of life, and the
survivorship experience for those with cancer
 To improve quality of cancer care and reduce health
care disparities through clinical, epidemiologic, and
translational research, with the ultimate goal of
improving cancer outcomes
Cancer Care Delivery Research Committee
Cancer Care Delivery Research
 Multidisciplinary field of scientific investigation
 Examines how social factors, financing systems,
organizational structures/processes, health technologies,
and healthcare provider and individual behaviors affect:
– cancer outcomes
– access to and quality of care
– cancer care costs
– health and well-being of cancer patients and survivors
Health Disparities
 Health disparities are differences in the incidence,
prevalence, mortality, and burden of diseases that exist
among specific population groups
 These differences in outcome can result from:
– Disparities in access to care across the cancer
diagnosis/care continuum (Prevention, Screening,
Treatment, Survivorship and End of Life Care)
– Differences in tumor biology
– Differences in host factors (e.g., comorbidities,
obesity, pharmacogenomics)
Prior SWOG Health Disparities Research
 Age and income disparities in trial
enrollment
– Generalizability
– Access
– Policy
 Race / Ethnic / SES / Insurance disparities
– Treatment quality
– Outcome
 Uptake of new/standard-of-care therapies
 Differences in tumor biology
Selected SWOG Research: Accrual Representativeness
Percent of patients in clinical treatment
trials by subgroup
“Underrepresentation of
patients 65 years of age or
older in cancer-treatment
trials”*
 Compared enrollment patterns
Percent
in SWOG to U.S. cancer
population
 Good representation of
females and blacks, but
dramatic underrepresentation
of older patients
 Included in IOM report
 Subsequent policy change by
U.S. Cancer Population
SWOG
Medicare (in 2000) to cover
routine care costs of clinical
trials
* Hutchins, et al, NEJM, 1999
Selected SWOG Research: Older patient enrollment by method of payment
Medicare and enrollment of older patients
“Impact of the Year 2000
Medicare Policy Change on
Older Patient Enrollment to
Cancer Clinical Trials”*
 Examined enrollment patterns
by age in SWOG before vs.
after the Medicare policy
change
 Observed an increase in older
patient enrollment overall
 Only among those with
Medicare + private insurance
 Implications: Marginal
‘93-’99 ‘00-’03
P-value
26.5%
38.1%
<.0001
% Med+Private 8.3%
15.2%
<.0001
% Med Only
9.7%
.50
% > 65 yrs
9.9%
additional costs of trial
participation (i.e. co-pays, coinsurance) likely still barriers
for patients
* Unger et al, JCO, 2006
SES and Clinical Trial Participation
SES and Clinical Trial
Participation
 Clinical trial participation by SES not well studied
 Absence of patient-level SES data in NCI-sponsored
trials
 Despite evidence suggesting that SES may be
related to both access and outcomes for a range of
diseases
− Whitehall studies (Marmot, Lancet, 1991)
− Link & Phelan, Social Conditions as Fundamental Causes of
Disease, 1995
SES and Clinical Trial Participation
SES and Clinical Trial
Participation (cont’d)
 One approach: Use area-level SES estimates from
zip code (matched to Census data) as partial
surrogate for patient-specific SES
 Useful for statistical adjustment but represents
different construct
 Factors pertaining to neighborhood or regional
environment
 Inadequate for examining relationship between SES
and trial participation
SES and Clinical Trial Participation
Web Survey Study Design
 Need to reach beyond the usual consortium-
sponsored cooperative group data
 Web-based survey study
– Collaboration with NexCura®, provider of online
treatment decision tools for cancer patients
– Linked to major cancer oriented websites (i.e.
American Cancer Society)
Selected SWOG Research: Accrual Representativeness
Forest plot of the association
of income and clinical trial participation
Line of Equal Odds
(OR = 1.0)
Factor
Age
Category
>65 years
<65 years
Sex
Female
Male
Race
Black
White
Education
<College
>College
Comorbidities 0 or 1
>2
Distance
<13 miles
>13 miles
to clinic
OR
0.79
0.42
0.69
0.69
1.51
0.67
0.66
0.74
0.76
0.65
0.64
0.73
 Enrollment patterns by SES
pvalue
.06
.005
.005
.13
.49
<.001
.02
.06
.07
.03
.005
.08
 Web-based survey, n=5500
 Lower income patients less
likely to participate across
nearly all subgroups, even in
Medicare covered population
0.4
Odds of clinical trial participation
for lower income patients:
“Patient income level and
cancer clinical trial
participation”*
0.6
0.8
Lower Odds
1.0
1.2
Higher Odds
* Unger et al, JCO, 2013
Selected SWOG Research: Participation Patterns by Socioeconomic Status
Forest plot of the association
of income and clinical trial participation
“Patient Income Level and Cancer
Clinical Trial Participation: A
Prospective Survey Study”*
 Analysis repeated using
prospective survey data from
S0316 (the “Barriers Study”)
 Same pattern found, validating
prior finding
 Implications: Increased lower
income patient participation
would:
 speed trial conduct
 promote fairness
 improve generalizability
Unger et al., JAMA Oncol, 2015
Selected SWOG Research: Treatment Quality
Outcomes from matched case control
study in SWOG adjuvant breast cancer
100%
P = .51
P = .04
P = .0002
P = .005
80%
“Treatment quality and
outcomes of African
American vs. white breast
cancer patients”*
 SWOG studies w/uniform
stage, treatment, and
follow-up
 Despite having similar
60%
relative dose intensity
(RDI) as white patients,
African American women
had:
40%
20%
 More treatment delays
0%
RDI
Tx DelayDiscont.
African American
10-Year
DFS
White
10-Year
OS
and early discontinuation
of treatment
 Worse survival
outcomes
* Hershman et al, JCO, 2009
Selected SWOG Research: Race Disparities in Outcome
“Racial Disparities in Cancer
Survival Among Randomized
Clinical Trial Patients of SWOG”*
Forest plot of the association of
race and overall survival
 Examined overall survival
Non-Sex
Specific
Cancers
Sex
Specific
Cancers
by self-reported race in
multiple cancers
Hazard
pCancer
Type Ratio value
Lung
Adv NSCLC 0.91
.20
Myeloma
Multiple 0.95
.34
Colon
Early Stage 1.03
.87
Leukemia Acute Myeloid 1.12
.12
Lung
Limited SC 1.13
.29
Lymphoma
Adv NHL 1.20
.10
 Uniform stage, treatment,
Prostate
Breast
Breast
Ovarian
 Implication: Unrecognized
Advanced
Pre-Meno
Post-Meno
Advanced
Risk of Death for
African American Patients:
and follow-up
 Risk of death was higher for
blacks in sex-specific
cancers only
1.21 .001
1.41 .007
1.49 <.001
1.61 .002
0.8
Lower Risk
1
1.2
1.4
1.6
1.8
Higher Risk
2
interactions of tumor
biological, hormonal, and/or
inherited host factors may
be contributing to
differential survival
outcomes by race in sexspecific malignancies.
* Albain et al, JNCI, 2009
Selected SWOG Research: Diffusion of New Treatment
Cumulative Incidence of Docetaxel Use
By SES and Demographic Factors
“Diffusion of Docetaxel Use
in Patients Presenting with
Metastatic Prostate Cancer”*
 Examined docetaxel use, the
standard of care in castrationresistant prostate cancer
 5-year cumulative incidence
 Examined 5-year cumulative
incidence by SES and
demographic factors
 Finding: Diffusion of docetaxel
use slower for older patients,
blacks, and patients with lower
SES
 Implication: Opportunities to
improve uptake of new
therapies in disadvantaged
populations
* Unger et al, JNCI, 2015
Selected SWOG Research: Survival Outcomes by Body Mass Index (BMI)
Forest plot of the association of
BMI and overall survival
Cancer
“Association between body mass
index (BMI) and cancer survival
in a pooled analysis of 22 clinical
trials”*
Hazard
pTreatment Ratio Value
Bladder
BCG
Sarcoma-GIST
Gleevec
NSCLC
Carboplatin/Taxol
Prostate
ADT
Prostate
Docetaxel
Colorectal
5-FU
NHL
CHOP
Renal
a-IFN
AML
ara-C-DNR
NSCLC
CDDP/Vinorelbine
Breast
CAF x 6
Ovarian
Paclitaxel
Breast
AC + Paclitaxel
Breast
CMF x 6
 Examined association of BMI
(overweight vs. <overweight)
across panel of different
cancer types and treatments
0.69 .02
0.73 .006
0.76 .01
0.79 .01
0.81 .13
0.88 .12
0.89 .47
0.98 .90
1.04 .52
1.05 .65
1.18 .28
1.18 .38
1.23 .16
1.27 .42
 11,724 patients from 22 trials
 In no cases was elevated BMI
associated with worse survival
 For some cancers, patients
with higher BMI did better
0.6
0.8
BMI> 25 kg/m2 better OS
1
1.2
1.4
1.6
BMI> 25 kg/m2 worse OS
Overall mean HR = 0.96 (p=.06)
* Greenlee & Unger, et al, CEBP, 2016
Selected SWOG Research: Survival Outcomes by Body Mass Index (BMI)
Forest plot of the association of
BMI and overall survival, by sex
MALE
Cancer
Bladder
NHL
Sarcoma-GIST
Prostate
Prostate
NSCLC
Renal
NSCLC
Colorectal
AML
Treatment
BCG
CHOP
Gleevec
ADT
Docetaxel
Carboplatin/Taxol
a-IFN
Cisplatin/Vinorelbine
5-FU
ara-C-DNR
Overall
Hazard
Ratio
pValue
0.71
0.72
0.76
0.79
0.81
0.82
0.86
0.86
0.88
1.03
.05
.12
.07
.01
.13
.17
.37
.27
.27
.76
0.82
.003
0.52
0.69
0.70
0.89
1.08
1.14
1.18
1.18
1.19
1.25
1.27
1.40
.14
.08
.05
.35
.45
.60
.38
.28
.49
.12
.42
.11
1.04
.86
 In sex-stratified analyses,
elevated BMI associated with
better OS among men
(p=.003) but not among
women (p=.86)
 Implication: Association
between BMI and survival not
consistent
 Disease, stage, and gender
specific body size
recommendations for cancer
survivors may be warranted
FEMALE
Bladder
NSCLC
Sarcoma-GIST
Colorectal
AML
Renal
Ovarian
Breast
NHL
Breast
Breast
NSCLC
Overall
BCG
Carboplatin/Taxol
Gleevec
5-FU
ara-C-DNR
a-IFN
Paclitaxel
CAF x 6
CHOP
AC + Paclitaxel
CMF x 6
Cisplatin/Vinorelbine
“Association between body mass
index (BMI) and cancer survival
in a pooled analysis of 22 clinical
trials”*
0.4
0.6 0.8
BMI>25kg/m2
Better OS
1
1.2 1.4
1.6
BMI>25kg/m2
Worse OS
* Greenlee & Unger, et al, CEBP, 2016
New Initiatives to Study/Decrease Disparities
 Financial impact of cancer treatments,
especially out of pocket costs
– Impact on disparities, outcomes
– Risk assessment
– Strategies for mitigation of financial “toxicity”
 Value of Information research
– Assessing the economic return on SWOG’s clinical
trial portfolio
 Novel support tools for patients
– Text messaging
Creating Tools to Assess Financial Health in Real Time
S1417CD: Primary Objective
 To estimate the incidence of self-reported treatment-related financial
hardship in patients with newly diagnosed metastatic colorectal cancer
(mCRC)
Key Secondary Objectives
 Assess feasibility of longitudinal financial assessments in NCORP
 Determine groups at higher risk for financial hardships
 Association between financial hardship and HRQoL
 Assess caregivers’ perceptions about costs and caregiver burden
 Link self-reports with credit report histories (TransUnion)
19
Summary
Cancer disparities research in SWOG has had real
world policy impact at the highest level
The Road Ahead…
Translating observational findings to
interventions, in order to improve the quality of
care and reduce healthcare disparities