1. Breast Cancer among Socioeconomically Vulnerable Women in Vulnerable Places Historical Evidence of Better Care in Canada than in the United States

2. Presenter Disclosures No relationships to disclose Funding source: Canadian Institutes of Health Research Grant no. 67161-2 Manuscript status: In review for publication consideration in a peer-reviewed scientific journal

3. Abstract We studied the effects of poverty, health insurance and primary care (PC) on optimum breast cancer care † among women in pre- Affordable Care Act (ACA) California and Ontario. Canadian advantages in the most disadvantaged places: high poverty neighborhoods (RR = 1.65) and communities that lacked specialist physicians (RR = 1.33) were explained by better health insurance coverage and greater access to PC physicians (PCP). These protective Canadian effects suggested ways to maximize ACA protections. Ensure the newly insured, public and private, are adequately insured, without having to bare exorbitant out-of- pocket costs. Expand Medicaid across all 50 states. Bolster the supply of PCPs and allied professionals. † Optimum care: diagnosed early (small, node negative tumor) and received breast conserving surgery (lumpectomy) followed by radiation therapy

4. Background

5. Human, Clinical & Scientific Contexts Why study breast cancer? Relatively common over the life course Effective screens exist Effective treatment regimes exist Timely diagnoses & best treatments matter Excellent prognoses can be expected: Long survival & high quality of life It is a sentinel health care quality indicator.

6. Income and Breast Cancer Care † † Systematic review and meta-analysis of 100+ study outcomes US-Canada studies that did not account for income found nil to null differences on breast cancer care“ Comparisons of national ‘haystacks’ tend to lose important ‘needles’ of knowledge” Studies of breast cancer care in impoverished places in the US and Canada found large Canadian advantages in diagnosis, treatment and survival The more impoverished the people and places the larger have been the Canadian advantages Focusing on the experiences of the most vulnerable magnifies clinical, policy and human significance Knowledge gap: Better health insurance coverage accounted for most, but not all, of observed Canadian advantages

7. Primary Care and Mortality † † Review of 35 national/US analyses (cancer, heart disease & all-cause mortality) Barbara Starfield, the late, preeminent PC researcher and advocate commented that “insurance is necessary, but not sufficient” to explain these advantages—“Canada’s more PC-orientation probably also plays a significant protective role.” PCPs are much more prevalent among the Canadian vs. US physician workforces (47% vs. 27%) PCP supply-mortality associations were consistently and strongly protective (28 of 35 study outcomes) Knowledge gap: We are not aware of any US-Canada study of breast cancer care that observed the effects of poverty, health insurance and physician supplies, PCPs and specialists.

8. Study Hypotheses 1.Poverty better predicts suboptimum breast cancer care in the United States. 2.Primary care physician supply better predicts optimum care in Canada. 3.The hypothesized Canadian advantage among women who lived in poverty would be completely explained by their better health insurance coverage and greater access to primary care.

9. Methods Comparison of Pre-ACA Historical Cohorts: High Poverty Neighborhoods Oversampled in California and Ontario, Women with Breast Cancer Diagnosed Between 1996 & 2000 Followed to 2011

10. Sampling High Poverty Neighborhoods Enhanced California and Ontario cancer registries Comprehensive, reliable and valid Diverse places well represented Random samples stratified by poverty: > 30% & < 30% poor Respectively, 6,300 & 950 women (multi-“controls”) Comparably poor places defined by Census Bureaus CT poverty prevalence of 30+% (US, 2000) Poorest CTs, Stats Can’s low-income criterion (2001) Mdn incomes, purchasing power-adjusted in USD: $23,175 (California) & $23,800 (Ontario) Note. CT = census tract, Mdn = median, Stats Can = Statistics Canada, USD = US dollar

11. Measuring Community PCP Supply Participants joined to county-level active physician data AMA and CIHI databases (2000/2001) The threshold effect, above which participants were more likely to receive optimum care, was identified by exploring increments (0.25 physicians / 10,000): > 7 PCPs per 10,000 community inhabitants PCPs reported specialty as general or family practice General internists in the US and emergency family medicine physicians in Canada were also included Note. AMA = American Medical Association, CIHI = Canadian Institute for Health Information

12. Practical Statistical Analyses Optimum care: diagnosed early, had lumpectomy & RT (NCCN guideline-based). Clinically valid: those not 3-times more likely to have died over 10 years Rates were directly and internally adjusted for age and place: large or small urban or rural Rates reported per 100 participants (percentages) Standardized rate ratio (RR) comparisons with (95% CIs) Logistic (care) or Cox (survival) regression models adjusted for multiple predictive and potentially confounding factors Notes Notes. CI = confidence interval, NCCN = National Comprehensive Cancer Network, RT = radiation therapy. Key study variables had < 3% missing data which was not confounding. Covariates: age, place, tumor grade and hormone receptor status.

13. Results

14. Effect of Neighborhood Poverty on Rate of Optimum Breast Cancer Care Within-Country Adjusted Rates (%) California Lower poverty33.6 High poverty (30+% poor)23.1 RR = 0.69 (0.63, 0.75) Ontario Lower poverty34.8 High poverty38.1 RR = 1.09 (0.92, 1.30) Between-Canada/US within High Poverty Neighborhoods Between-Canada/US within High Poverty Neighborhoods RR = 1.65 (1.39, 1.96) US Subsample (Un- or Publicly-Insured [Rate = 18.0%]) RR = 2.12 (1.76, 2.56)

15. Effect of Community PCP Density on Rate of Optimum Breast Cancer Care Within-Country Adjusted Rates (%) California Lower PCP density29.2 High PCP density (7+ PCPs/10,000)31.2 RR = 1.07 (1.00, 1.14) Ontario Lower PCP density29.9 High PCP density42.9 RR = 1.43 (1.20, 1.70) Between-Can/US within High PCP Density Communities Between-Can/US within High PCP Density Communities RR = 1.38 (1.20, 1.58)

16. Health Insurance & PC Explained Breast Cancer Care & Ultimately Survival Differences Between-Countries When the main and interacting effects of poverty, PCP supply and country were accounted for with a logistic regression there was no main effect of country on optimum care. When the main and interacting effects of poverty, health insurance, PCP supply, optimum care and country were accounted for with a Cox regression there was no main effect of country on survival.

17. Specialist Physician (SP) Density & Optimum Breast Cancer Care: Addendum Within-Country Adjusted Rates (%) California (CA) Lower SP density (< 13 SPs/10,000)25.8 High SP density (72.5%)32.4 RR = 1.26 (1.15, 1.38) Ontario (ON) Lower SP density34.2 High SP density (18.8%)36.0 RR = 1.05 (0.88, 1.25) Between-Canada/US within Lower SP Communities † RR = 1.33 (1.17, 1.51) † † SP-underserved communities in ON (M = 6.7, SD = 1.3) had, on average, nearly 2 more PCPs per 10,000 inhabitants than similarly underserved communities in CA (M = 4.9, SD = 0.9); F = 1,447.73, p < 001.

18. Discussion

19. Summary All three study hypotheses were supported. In addition to more prevalent optimum care in communities that were well supplied and served by PCPs, women with breast cancer in Ontario were particularly advantaged in the most disadvantaged places: high poverty neighborhoods and underserved communities that lacked specialist physicians. Canadian advantages in care and survival among those who lived in poverty were fully explained by their better health insurance coverage and greater access to primary care.

20. Interpretation: Human Significance Applying this study’s effects to US population parameters on breast cancer among the inadequately insured and impoverished we estimate that over the course of a generation more than 200,000 American women who lived in poverty with breast cancer were cared for less optimally than had they had access to a universally accessible, primary care- oriented health care system.

21. Conclusions This study’s historical observations of Canadian health care protections suggested ways to maximize ACA protections. Policy makers ought to ensure that the newly insured, whether through private insurance exchanges or public insurance expansions, are indeed, adequately insured. No one should have to bare exorbitant out-of-pocket costs for medically necessary cancer care or any other, and the Medicaid program should be equitably expanded across all 50 states. In concert with ultimately insuring all Americans, policies that expand the supply of PCPs hold the promise of eradicating remaining barriers to the provision of high quality health care for all.

22. Policy Recommendations The United States ought to institute single payer reform and strengthen its primary care system. To the extent that single payer reform is not politically feasible, strengthening primary care is probably the best way to maximize the ACA’s benefits.

23. Potential Limitations 1. Race/Ethnicity Alternative Explanation Findings replicated among the subsample of non-Hispanic white women in California vs. the entire ethnically diverse Ontario sample 2.Income Differences (US poor are poorer on average than Canadian poor) Findings replicated among California-Ontario subsamples with nearly identically low incomes Even granting this: It is instructive to know that women who live in Canada’s poorest neighborhoods are so much better insured and cared for than women who live in America’s poorest neighborhoods.

24. Future Research To optimize breast cancer care with an adequate PC workforce of at least 7 PCPs per 10,000 community residents we estimated that another 1,700 PCPs are needed in California. Though PC was more effective in Ontario, a PCP supply gap of 325 PCPs was estimated there as well. Systematic replications are needed to identify current evidence- based gaps in PC across other states, provinces and health outcomes. Social workers work with PCPs and others, often leading primary care efforts in diverse health and mental health field’s of practice. This study allowed for observation of the role of PCPs (availability of administrative data), but not of social workers. Future studies ought to incorporate the value of social work roles and their protective effects.

25. Co-Investigators InvestigatorAffiliation__________ Kevin GoreySchool of Social Work, University of Windsor, Ontario, Canada Caroline Hamm † Department of Oncology Isaac LuginaahDepartment of Geography Guangyong Zou ‡ Dept. Epidemiology & Biostatistics Western University, London, ON Eric HolowatyDalla Lana School of Public Health University of Toronto, Ontario † & Oncology Department, Windsor Regional Cancer Center, Ontario ‡ & Robarts Research Institute, London, Ontario

26. Acknowledged Administrative, Logistical or Research Support SupporterAffiliation_________________ Kurt SnipesCancer Surveillance and Research Janet BatesBranch, California Department of Gretchen AghaPublic Health Dee WestCancer Registry of Greater California Marta Induni Glen Halvorson Donald Fung Arti Parikh-Patel Madhan BalagurusamySchool of Social Work Nancy RichterUniversity of Windsor Charles SagoeCancer Care Ontario John David StanwayCanadian Institute for Health Information

27. Disclaimer Other Agencies Involved in Data Management: National Cancer Institute (United States), Cancer Prevention and Public Health Institutes of California, Centers for Disease Control and Prevention, University of Southern California The ideas and opinions expressed herein are those of the presenters and endorsement by any affiliated or data-supportive agencies or their contractors and subcontractors are not intended nor should they be inferred.

28. Principal Investigator Kevin Gorey For more information about our research see my academic website at: www.uwindsor.ca/gorey For any additional information, including reprint requests, feel free to contact me at: gorey@uwindsor.ca