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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