Receipt of Guideline-Concordant Care Does Not Explain Breast Cancer Mortality Disparities by Race in Metropolitan Atlanta

View More View Less
  • 1 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia;
  • | 2 Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah;
  • | 3 Winship Cancer Institute, Emory University, and
  • | 4 Emory University School of Medicine, Atlanta Georgia; and
  • | 5 Department of Biostatistics and Bioinformatics, and
  • | 6 Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, Georgia.

Background: Racial disparities in breast cancer mortality in the United States are well documented. Non-Hispanic Black (NHB) women are more likely to die of their disease than their non-Hispanic White (NHW) counterparts. The disparity is most pronounced among women diagnosed with prognostically favorable tumors, which may result in part from variations in their receipt of guideline care. In this study, we sought to estimate the effect of guideline-concordant care (GCC) on prognosis, and to evaluate whether receipt of GCC modified racial disparities in breast cancer mortality. Patients and Methods: Using the Georgia Cancer Registry, we identified 2,784 NHB and 4,262 NHW women diagnosed with a stage I–III first primary breast cancer in the metropolitan Atlanta area, Georgia, between 2010 and 2014. Women were included if they received surgery and information on their breast tumor characteristics was available; all others were excluded. Receipt of recommended therapies (chemotherapy, radiotherapy, endocrine therapy, and anti-HER2 therapy) as indicated was considered GCC. We used Cox proportional hazards models to estimate the impact of receiving GCC on breast cancer mortality overall and by race, with multivariable adjusted hazard ratios (HRs). Results: We found that NHB and NHW women were almost equally likely to receive GCC (65% vs 63%, respectively). Failure to receive GCC was associated with an increase in the hazard of breast cancer mortality (HR, 1.74; 95% CI, 1.37–2.20). However, racial disparities in breast cancer mortality persisted despite whether GCC was received (HRGCC: 2.17 [95% CI, 1.61–2.92]; HRnon-GCC: 1.81 [95% CI, 1.28–2.91] ). Conclusions: Although receipt of GCC is important for breast cancer outcomes, racial disparities in breast cancer mortality did not diminish with receipt of GCC; differences in mortality between Black and White patients persisted across the strata of GCC.

Background

In the United States, racial disparities in breast cancer outcomes are well-documented, with non-Hispanic Black (NHB) women more likely to die of their disease than their non-Hispanic White (NHW) counterparts.13 The disparity is in part attributed to stage and subtype—NHB women are more likely to be diagnosed with a metastatic cancer and/or a triple-negative subtype, both of which have a poor prognosis due to limited treatment options.4,5 However, our group recently reported that the most pronounced racial disparities in breast cancer mortality were observed among women with nonmetastatic, estrogen receptor–positive (ER+) tumors.6,7 Such tumors are known to have a favorable prognosis, given multiple highly effective biomarker-driven treatment regimens.8 One explanation for such paradoxical findings is that disparities in survival outcomes among women with early-stage or ER+ disease may result from factors downstream of their cancer diagnosis, such as the failure to receive guideline-concordant care (GCC).9

Clinical guidelines for women diagnosed with stage I–III breast cancer have been developed based on results from multiple clinical trials, and failure to receive GCC has adverse effects on breast cancer outcomes.1013 Nonadherence to guidelines could arise from multiple factors, including structural racism, barriers to access, tumor and patient characteristics, or clinician and patient preferences.11 Therefore, nonadherence to clinical guidelines may be a contributing factor to the observed race disparity in breast cancer mortality.9,14 Observational studies assessing adherence to clinical guidelines are important in understanding patient outcomes; however, few studies have examined the receipt of GCC as a possible driver of disparate outcomes in a population-based setting.

To address this knowledge gap, we evaluated how failure to receive GCC contributes to breast cancer mortality overall and to disparities in breast cancer mortality among NHB and NHW women diagnosed with a first primary stage I–III breast cancer in metropolitan Atlanta, Georgia.

Patients and Methods

Study Population

Using the Georgia Cancer Registry (GCR), we identified women diagnosed with breast cancer between 2010 and 2014 while residing in metropolitan Atlanta (ie, Fulton, DeKalb, Gwinnett, Cobb, or Clayton counties). Patients with breast cancer were included if they were diagnosed with an invasive stage I–III first primary breast tumor and were classified as being NHW or NHB. Race was based on US Census Bureau definitions, and Hispanic ethnicity was determined by the North American Association of Central Cancer Registries Hispanic Identification Algorithm.15,16 Additional criteria required that women had information available for assigning tumor subtype (ER, the progesterone receptor used to define women as hormone receptor–positive [HR+] or hormone receptor–negative [HR–], and HER2 expression) and had received surgery as part of their local therapy. Hormone receptor and HER2 status were used to assign women as HR+/HER2−, HR+/HER2+, HR−/HER2+, or HR−/HER2−. Detailed information on the differences between women who received surgery (94.6%) and those who did not (5.4%) are reported in supplemental eTables 1–3 (available with this article at JNCCN.org). Follow-up information was available on patients through December 31, 2016. The outcome of interest was breast cancer mortality (ICD-10-CM code C50), which was determined from death certificate data.

Exposure Assessment

Guideline Care

Receipt of guideline care was determined based on the 2011 NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Breast Cancer (Table 1).17 The NCCN Guidelines were used to inform the indication for each type of breast cancer therapy for every patient in our dataset. Information regarding the type of surgery received (mastectomy vs breast-conserving surgery), tumor receptor status (ER, progesterone receptor, and HER2 expression), tumor size, lymph node involvement, and 21-gene recurrence score data (obtained via direct linkage with Genomic Health Inc.) were ascertained from the GCR (supplemental eTables 4–7).

Table 1.

2011 NCCN Guidelines17 Recommendations

Table 1.

We first grouped women based on their breast cancer subtype as HR+/HER2−, HR+/HER2+, HR−/HER2+, or HR−/HER2−, as described in the previous section. These derived tumor subtypes informed the type of biomarker-driven therapies (endocrine therapy and anti-HER2 therapy) that a patient should receive. We then examined the possible combinations of surgery type, tumor size, lymph node status, and 21-gene recurrence score to determine treatment indications. Based on these patient-level data in the GCR, each woman was classified as indicated, discretionary, not indicated, or indeterminate (missing) for each type of therapy. The latter occurred among 6.6% of NHB women and 5.2% of NHW women.

Once the indication for each therapy was determined, we assessed whether the patient received therapy consistent with the NCCN Guidelines17 using GCR treatment data. The GCR regularly collects information on surgery type and initiation of chemotherapy, radiation, and endocrine therapies. Anti-HER2 therapies were captured as chemotherapy in the GCR analytic database until 2013 and were coded as immunotherapy in subsequent years. Using natural language processing (NLP), we searched the treatment-related text fields of the GCR database for specific character strings related to an indication of trastuzumab receipt before 2013.18 In addition, there were 271 women (3.6%) who did not have information available on the initiation of endocrine therapy. For these women, we used NLP to identify the receipt of endocrine therapy in treatment-related text fields.

Patients who were indicated for a treatment regimen and did not receive it, or those who received therapy without an indication, were classified as having received non-GCC for that treatment modality. Conversely, if a patient’s indication was consistent with the receipt or nonreceipt of a specific therapy, then the patient was classified as guideline-concordant. Among women for whom a therapy was discretionary (based on the NCCN Guidelines17), any value (eg, receipt, nonreceipt, and missing/unknown value) resulted in a classification of guideline-concordant for that therapy.

Our primary exposure of interest was receipt of GCC across all treatment modalities and for each individual treatment modality. In a sensitivity analysis, we allowed for each patient to have any one therapy not meeting guideline care.

Covariates

We collected information on age at diagnosis (continuous), stage of disease (I–III), derived breast cancer subtype (HR+/HER2−, HR+/HER2+, HR−/HER2+, or HR−/HER2−), a US Census–derived area-based measure of socioeconomic status (SES; 0% to <5%, 5% to <10%, 10% to <20%, and 20%–100% below poverty level), marital status (single, married, divorced/separated/other), and insurance type (private, Medicare, Medicaid, military/other, and uninsured). SES status is based on census tract–level poverty data that are published annually from the American Community Survey19 and have been used widely in population-based studies.20,21

Statistical Methods

Descriptive statistics were calculated as median values with interquartile ranges or as n (%) for covariates of interest across the NHB and NHW population subgroups. We also report the frequency (%) of women who failed to receive GCC by treatment modality.

Follow-up was defined as time in months, from the date of surgery until whichever was first: (1) a mortality event, (2) the last date of contact in registry, or (3) December 31, 2016. We used multivariable-adjusted Cox proportional hazard models to calculate the hazard ratios (HRs) and 95% confidence intervals for the association between the receipt of GCC for joint concordance of all treatment modalities and each treatment modality independently with breast cancer mortality.

To address our second aim, we estimated the association between race and breast cancer mortality and whether this association was modified by the receipt of GCC (yes/no). Interaction describes the differences in the effect of one exposure across the strata of another exposure, which depends on the scale.22,23 In this analysis, we assessed additive and multiplicative interaction for the effect of race on breast cancer mortality by receipt of GCC.24 The presence of interaction between race and GCC was estimated with the common referent approach to calculate the relative excess risk due to interaction (RERI), evaluating the departure of the effect on the additive scale.24,25 We calculated the 95% confidence interval for the RERI using the delta method.23,26,27 The presence of multiplicative interactions, indicating whether the combined effect of race and GCC was greater than the product of the individual effects, was assessed by comparing stratum-specific effect estimates.25

We verified the proportional hazards assumption for all variables by checking the ln-ln survival curves for any gross violation.28 Potential confounders included in the models were based on a priori knowledge and graphical-based methods (using a directed acyclic graph).29 For the association between receipt of GCC and breast cancer mortality, confounders included race, disease stage, age at diagnosis, SES, derived breast cancer subtype, and insurance type (supplemental eFigure 1). For the interaction model including race and GCC, age at diagnosis was the only confounder identified, based on our graphical assessment (supplemental eFigure 2). The other covariates (eg, disease stage, SES, derived breast cancer subtype, and insurance type) are on the causal path between race and breast cancer mortality, and including them in the model could have potentially induced bias.30 However, to be consistent with prior studies of race disparities in breast cancer outcomes, we present additional results from analyses adjusting for disease stage, SES, derived breast cancer subtype, and insurance type.

The association between GCC and breast cancer mortality may be susceptible to immortal person-time bias31,32 because of exposure assignment after the initiation of follow-up, which could lead to a mortality event occurring before the start of an indicated treatment. To evaluate the potential bias, we performed landmark analyses. We extended the initiation of follow-up in 3-month intervals, up until 12 months after the recorded date of surgery for each treatment modality and combination. All analyses were conducted using R version 3.5 (R Foundation for Statistical Computing) and SAS 9.4 (SAS Institute Inc).

Results

We identified 7,046 (2,784 NHB and 4,262 NHW) study-eligible women treated with surgery in the metropolitan Atlanta area. On average, NHB women were younger (median age, 56 vs 60 years), less likely to have private health insurance (57% vs 64%), and less likely to live in a high-SES neighborhood (6.2% vs 32%) compared with NHW women (Table 2). Receipt of overall GCC was comparable between NHB and NHW women (65% vs 63%, respectively; Table 3. Receipt of overall GCC was similar for NHB and NHW women with HR+/HER2−, HR+/HER2+, or HR−/HER2− breast cancer (68% vs 65%; 60% vs 60%; 82% vs 80%, respectively), whereas NHB women diagnosed with HR−/HER2+ breast cancer were less likely to receive GCC compared with NHW women (66% vs 72%, respectively). Across individual treatment modalities, NHB and NHW women were nearly equally likely to receive guideline-concordant radiation (88% vs 91%), chemotherapy (87% vs 86%), endocrine (83% vs 80%), and HER2-targeted (95% vs 96%) therapies (Table 3).

Table 2.

Patient Demographic, Clinicopathologic, and Treatment Characteristics

Table 2.
Table 3.

Receipt of Overall GCC and Individual Treatment Modalities

Table 3.

Main Effects

We observed an increase in the hazard of breast cancer mortality among women who did not receive GCC (HR, 1.74; 95% CI, 1.37–2.20) compared with those who did. We similarly found an increase in the hazard of breast cancer mortality among women who were discordant for chemotherapy (HR, 1.69; 95% CI, 1.24–2.31), radiation therapy (HR, 1.92; 95% CI, 1.36–2.71), endocrine therapy (HR, 1.70; 95% CI, 1.29–2.24), and anti-HER2 therapy (HR, 1.81; 95% CI, 1.21–2.71) (Table 4). Mutual adjustment of the individual treatment modalities yielded similar results (data not shown).

Table 4.

Multivariable-Adjusted Association Between Receipt of GCC and Breast Cancer Mortality

Table 4.

In our sensitivity analysis, defining a patient with GCC as having received at least 3 treatment modalities consistent with guidelines, we observed a slightly more pronounced estimate of association (HR, 2.43; 95% CI, 1.72–3.42). These results suggest that the potential misclassification of GCC is not an explanation for our observed results. In our landmark analyses, we saw similar HR estimates as we increased the time since surgery for receipt of GCC. Our findings are thus robust to any potential immortal person-time bias (supplemental eTable 8).

Racial Disparities

The overall racial disparity in breast cancer mortality in our cohort was 1.98 (95% CI, 1.59–2.46),7 which persisted even within the strata of the receipt of overall GCC (Table 5). Among women who received GCC across all treatment modalities, we observed a 2-fold increase in the NHB–NHW hazard of breast cancer mortality (HR, 2.17; 95% CI, 1.61–2.91). Among those who did not receive GCC, the NHB–NHW HR was similar, although less pronounced (HR, 1.81; 95% CI, 1.28–2.91; Table 5). In the common referent approach to assess departure on the additive scale, NHW women receiving discordant care had no greater risk of mortality compared with NHW women receiving concordant care. Conversely, NHB women had a 2-fold increased mortality rate regardless of whether they received overall concordant or discordant care.

Table 5.

Association Between Receipt of GCC and Breast Cancer Mortality

Table 5.

Although NHB women were consistently more likely to die of their disease compared with NHW women, most pronounced race disparities were among those who received GCC for most independent therapeutic regimens (Table 5). Among women who were discordant for chemotherapy, radiation, or anti-HER2 therapy, the racial disparity in breast cancer mortality was attenuated and near-null. One exception was for endocrine therapy, with NHB women classified as receiving discordant care having a 2.35-fold increased hazard of breast cancer mortality compared with NHW women classified as received discordant care (95% CI, 1.48–3.73). The disparity among women concordant for endocrine therapy was less pronounced (NHB vs NHW HR, 1.92; 95% CI, 1.50–2.45). There was no evidence of additive or multiplicative interaction between race and receipt of GCC in breast cancer mortality. In models additionally adjusting for stage, insurance type, derived breast cancer subtype, and SES, we observed similar although attenuated associations in the disparities across treatment modalities (Table 5).

Discussion

We observed that as clinical guidelines would suggest, failure to receive GCC was associated with an increased hazard of breast cancer mortality. This was noted for the receipt of GCC overall and across each treatment modality. Despite the importance of GCC for health outcomes, the receipt of GCC did not seem to influence racial disparities in breast cancer mortality. Although NHB and NHW women were equally likely to receive care consistent with guidelines for all treatment modalities combined, NHB women had a nearly 2-fold increase in breast cancer mortality compared with their NHW counterparts within the strata of GCC receipt.

There are few previous population-based studies on receipt of GCC in relation to racial disparities in breast cancer mortality. Early investigations (circa 1990–2005) have reported that minority women are less likely to receive appropriate adjuvant therapy, although findings appear to be mixed and few studies report survival disparities.33,34 The most recent, a study among women residing in rural Georgia, reported that NHB women were more likely to receive GCC compared with NHW women.33 However, the authors of that study did not evaluate treatment modalities in combination with racial disparities in breast cancer mortality. Similarly, in the CDC's Patterns of Care Study, investigators likewise reported a greater proportion of NHB women received guideline care for chemotherapy.36 Both of these previous studies included women diagnosed with invasive breast cancer prior to the introduction of the 21-gene recurrence score and consideration of tumor subtype for chemotherapy, which may partially explain the differences.37 Chemotherapy indication for women aged >70 years is subject to underlying comorbidities. The study in rural Georgia considered women aged >70 years to have a discretionary indication for chemotherapy, whereas the CDC study included chemotherapy indication for women aged >70 years because they were able to adjust for underlying comorbidities. In our study, we did not use age as an indicator for chemotherapy, but our results were similar in a sensitivity analysis excluding these women (data not shown). Another potential difference between our study and previous studies is that the metropolitan Atlanta area is a diverse population with approximately 50% NHB residents. This may mitigate some of the initial barriers in treatment initiation compared with rural regions in the southeast or other areas in the United States.

Although our study captured guideline care based on the indication and receipt of breast cancer therapies, we did not capture information on other aspects regarding quality of care, such as treatment facility characteristics or healthcare access, that may have influenced the observed results.38 Higher-quality care may lead to additional workup by tumor boards or care coordination, leading to better treatment timelines, which may not be equitable across racial/ethnic groups.39

We acknowledge several limitations of this study. Our intent was to understand the association between receipt of guideline-concordant first-line therapy and breast cancer mortality and potential racial disparities in this mortality; however, we did not account for the timing of treatment initiation, duration, or completion of adjuvant therapies, which have been associated with patient outcomes and may vary by race.4042 We excluded women who did not receive surgery, which likely represents a population with poor outcomes compared with women who did receive surgery. Further exploration of the decision to forgo surgical treatment is important for future research. We similarly did not have information on adherence to endocrine therapy. In the United States, poor adherence and early discontinuation of endocrine therapy have been previously reported, with some studies suggesting racial differences.4345 These may be important considerations for future investigations as we work to identify multilevel targets for intervention. We assumed that women who were discretionary for a treatment modality received GCC, but further exploration of the decision to forgo adjuvant therapy (and potential racial disparities within that decision) may be important for future research. Receipt of anti-HER2 therapy was determined using NLP from GCR treatment text fields among women diagnosed before 2013, which could result in misclassification of anti-HER2 therapy. To evaluate the ability of our NLP to correctly classify women as having received/not received anti-HER2 therapy, we compared results with the GCR treatment variable for women diagnosed after 2013. Results were largely consistent; we observed a 99% specificity and 97% sensitivity using the GCR treatment variable as the gold standard. Although treatment-related data in cancer registries are often underreported and may be subject to misclassification,46 our findings are similar to those of Guy et al,35 who used chart abstraction to identify first-line therapy. Finally, we were unable to collect information on comorbid conditions, which may impact both treatment adherence and efficacy.32 Women with coronary artery disease or diabetes at diagnosis often have poor completion of taxane-based chemotherapy and are more likely to experience adverse effects from breast cancer treatments, which may affect prognosis.48 Such comorbidities are more likely to present among NHB women, which could in part contribute to the observed disparity.49

Conclusions

Racial disparities in breast cancer survival outcomes are complex.50 In this study, we observed that although GCC was important for patient prognosis overall, we did not find evidence that differences in receipt of GCC contributed to disparate cancer outcomes between NHB and NHW women with breast cancer. Rather, NHB women consistently had worse breast cancer survival outcomes than NHW women, regardless of GCC status. Future studies may be strengthened from a multilevel approach to incorporate information on healthcare access, neighborhood characteristics, characteristics of the treating facilities, treatment duration and completion, and the presence and management of comorbid conditions.

References

  • 1.

    Ademuyiwa FO, Edge SB, Erwin DO, et al. Breast cancer racial disparities: unanswered questions. Cancer Res 2011;71:640644.

  • 2.

    DeSantis CE, Ma J, Goding Sauer A, et al. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017;67:439448.

  • 3.

    Hershman D, McBride R, Jacobson JS, et al. Racial disparities in treatment and survival among women with early-stage breast cancer. J Clin Oncol 2005;23:66396646.

  • 4.

    Rosenberg J, Chia YL, Plevritis S. The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breast cancer survival in the U.S. SEER database. Breast Cancer Res Treat 2005;89:4754.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Curtis E, Quale C, Haggstrom D, et al. Racial and ethnic differences in breast cancer survival: how much is explained by screening, tumor severity, biology, treatment, comorbidities, and demographics? Cancer 2008;112:171180.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Collin LJ, Yan M, Jiang R, et al. Oncotype DX recurrence score implications for disparities in chemotherapy and breast cancer mortality in Georgia. NPJ Breast Cancer 2019;5:32.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Collin LJ, Jiang R, Ward KC, et al. Racial disparities in breast cancer outcomes in the metropolitan Atlanta area: new insights and approaches for health equity. JNCI Cancer Spectr 2019;3:pkz053.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Olopade OI, Grushko TA, Nanda R, et al. Advances in breast cancer: pathways to personalized medicine. Clin Cancer Res 2008;14: 79887999.

  • 9.

    Daly B, Olopade OI. A perfect storm: how tumor biology, genomics, and health care delivery patterns collide to create a racial survival disparity in breast cancer and proposed interventions for change. CA Cancer J Clin 2015;65:221238.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Gradishar WJ, Anderson BO, Balassanian R, et al. NCCN Guidelines Insights: Breast Cancer, Version 1.2017. J Natl Compr Canc Netw 2017;15:433451.

  • 11.

    Wu X, Richardson LC, Kahn AR, et al. Survival difference between non-Hispanic black and non-Hispanic white women with localized breast cancer: the impact of guideline-concordant therapy. J Natl Med Assoc 2008;100:490498.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Inwald EC, Ortmann O, Zeman F, et al. Guideline concordant therapy prolongs survival in HER2-positive breast cancer patients: results from a large population-based cohort of a cancer registry. BioMed Res Int 2014;2014:137304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    LeMasters T, Madhavan SS, Sambamoorthi U, et al. Receipt of guideline-concordant care among older women with stage I-III breast cancer: a population-based study. J Natl Compr Canc Netw 2018;16:703710.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Griggs JJ, Culakova E, Sorbero MES, et al. Social and racial differences in selection of breast cancer adjuvant chemotherapy regimens. J Clin Oncol 2007;25:25222527.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    NAACCR Race and Ethnicity Work Group. NAACCR guideline for enhancing Hispanic/Latino identification: revised NAACCR Hispanic/Latino identification algorithm [NHIA v2.2.1]. Accessed December 7, 2020. Available at: https://www.naaccr.org/wp-content/uploads/2016/11/NHIA-v2.2.1.pdf

  • 16.

    Humes KR, Jones NA, Ramirez RR. Overview of race and Hispanic origin: 2010. Accessed December 7, 2020. Available at: https://www.census.gov/library/publications/2011/dec/c2010br-02.html

    • Search Google Scholar
    • Export Citation
  • 17.

    Carlson RW, Allred DC, Anderson BO, et al. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer. Version 2.2011. Obtained with permission from NCCN on December 7, 2020. To view the most recent version, visit NCCN.org

    • Search Google Scholar
    • Export Citation
  • 18.

    Morrison FP, Li L, Lai AM, et al. Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes? J Am Med Inform Assoc 2009;16:3739.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    U.S. Census Bureau. American Community Survey. Accessed December 7, 2020. Available at: https://www.census.gov/programs-surveys/acs

  • 20.

    Knoble NB, Alderfer MA, Hossain MJ. Socioeconomic status (SES) and childhood acute myeloid leukemia (AML) mortality risk: analysis of SEER data. Cancer Epidemiol 2016;44:101108.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Kish JK, Yu M, Percy-Laurry A, et al. Racial and ethnic disparities in cancer survival by neighborhood socioeconomic status in Surveillance, Epidemiology, and End Results (SEER) registries. J Natl Cancer Inst Monogr 2014;2014:236243.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    VanderWeele TJ, Knol MJ. A tutorial on interaction. Epidemiol Methods 2014;3:3372.

  • 23.

    VanderWeele TJ, Vansteelandt S. Invited commentary: some advantages of the relative excess risk due to interaction (RERI)—towards better estimators of additive interaction. Am J Epidemiol 2014;179:670671.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol 2012;41:514520.

  • 25.

    VanderWeele TJ. On the distinction between interaction and effect modification. Epidemiology 2009;20:863871.

  • 26.

    Richardson DB, Kaufman JS. Estimation of the relative excess risk due to interaction and associated confidence bounds. Am J Epidemiol 2009;169:756760.

  • 27.

    Hosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology 1992;3:452456.

  • 28.

    Hess KR. Graphical methods for assessing violations of the proportional hazards assumption in Cox regression. Stat Med 1995;14:17071723.

  • 29.

    Howards PP, Schisterman EF, Poole C, et al. “Toward a clearer definition of confounding” revisited with directed acyclic graphs. Am J Epidemiol 2012;176:506511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology 2009;20:488495.

  • 31.

    Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008;167:492499.

  • 32.

    Lévesque LE, Hanley JA, Kezouh A, et al. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ 2010;340:b5087.

  • 33.

    Shavers VL, Brown ML. Racial and ethnic disparities in the receipt of cancer treatment. J Natl Cancer Inst 2002;94:334357.

  • 34.

    Bickell NA, Wang JJ, Oluwole S, et al. Missed opportunities: racial disparities in adjuvant breast cancer treatment. J Clin Oncol 2006;24:13571362.

  • 35.

    Guy GP Jr, Lipscomb J, Gillespie TW, et al. Variations in guideline-concordant breast cancer adjuvant therapy in rural Georgia. Health Serv Res 2015;50:10881108.

  • 36.

    Wu XC, Lund MJ, Kimmick GG, et al. Influence of race, insurance, socioeconomic status, and hospital type on receipt of guideline-concordant adjuvant systemic therapy for locoregional breast cancers. J Clin Oncol 2012;30:142150.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:28172826.

  • 38.

    Wheeler SB, Reeder-Hayes KE, Carey LA. Disparities in breast cancer treatment and outcomes: biological, social, and health system determinants and opportunities for research. Oncologist 2013;18:986993.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Bach PB, Pham HH, Schrag D, et al. Primary care physicians who treat blacks and whites. N Engl J Med 2004;351:575584.

  • 40.

    Bleicher RJ, Ruth K, Sigurdson ER, et al. Time to surgery and breast cancer survival in the United States. JAMA Oncol 2016;2:330339.

  • 41.

    Fedewa SA, Edge SB, Stewart AK, et al. Race and ethnicity are associated with delays in breast cancer treatment (2003-2006). J Health Care Poor Underserved 2011;22:128141.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Fedewa SA, Ward EM, Stewart AK, et al. Delays in adjuvant chemotherapy treatment among patients with breast cancer are more likely in African American and Hispanic populations: a national cohort study 2004-2006. J Clin Oncol 2010;28:41354141.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Roberts MC, Wheeler SB, Reeder-Hayes K. Racial/ethnic and socioeconomic disparities in endocrine therapy adherence in breast cancer: a systematic review. Am J Public Health 2015;105(Suppl 3):e415.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Wigertz A, Ahlgren J, Holmqvist M, et al. Adherence and discontinuation of adjuvant hormonal therapy in breast cancer patients: a population-based study. Breast Cancer Res Treat 2012;133:367373.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Hershman DL, Kushi LH, Shao T, et al. Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol 2010;28:41204128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46.

    Noone AM, Lund JL, Mariotto A, et al. Comparison of SEER treatment data with Medicare claims. Med Care 2016;54:e5564.

  • 47.

    Tammemagi CM, Nerenz D, Neslund-Dudas C, et al. Comorbidity and survival disparities among black and white patients with breast cancer. JAMA 2005;294:17651772.

  • 48.

    Hershman DL, Till C, Wright JD, et al. Comorbidities and risk of chemotherapy-induced peripheral neuropathy among participants 65 years or older in Southwest Oncology Group clinical trials. J Clin Oncol 2016;34:30143022.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Cossrow N, Falkner B. Race/ethnic issues in obesity and obesity-related comorbidities. J Clin Endocrinol Metab 2004;89:25902594.

Submitted May 18, 2020; final revision received November 18, 2020; accepted for publication December 2, 2020.

Published online August 16, 2021.

Author contributions: Study concept and design: Collin, McCullough. Methodology: Collin, Gogineni, Subhedar, Lipscomb, Torres, Lin, McCullough. Data analysis, curation, and interpretation: Collin, Yan, Jiang, Ward, Switchenko, Miller-Kleinhenz, McCullough. Funding acquisition: Ward, McCullough. Writing – first draft: Collin, McCullough. Writing - review and editing: All authors.

Disclosures: Dr. Gogineni has disclosed serving on an advisory board for Pfizer and Lilly, and receiving institutional research funding from Pfizer, Calithera, and Merck. The remaining authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: This work was supported in part by the Cancer Prevention and Control Research program and the Winship Research Informatics shared resources, a core supported by the Winship Cancer Institute of Emory University. Dr. Collin was supported by the NCI of the NIH under award number F31CA239566. The collection of cancer incidence data used in this study was supported by contract number HHSN261201800003I, task order number HHSN26100001 from the NCI, and cooperative agreement number 5NU58DP003875-04 from the CDC.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The findings and conclusions are those of the authors and do not necessarily represent the official position of their affiliations or the CDC.

Correspondence: Lauren E. McCullough, PhD, Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322. Email: lauren.mccullough@emory.edu

Supplementary Materials

  • 1.

    Ademuyiwa FO, Edge SB, Erwin DO, et al. Breast cancer racial disparities: unanswered questions. Cancer Res 2011;71:640644.

  • 2.

    DeSantis CE, Ma J, Goding Sauer A, et al. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017;67:439448.

  • 3.

    Hershman D, McBride R, Jacobson JS, et al. Racial disparities in treatment and survival among women with early-stage breast cancer. J Clin Oncol 2005;23:66396646.

  • 4.

    Rosenberg J, Chia YL, Plevritis S. The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breast cancer survival in the U.S. SEER database. Breast Cancer Res Treat 2005;89:4754.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Curtis E, Quale C, Haggstrom D, et al. Racial and ethnic differences in breast cancer survival: how much is explained by screening, tumor severity, biology, treatment, comorbidities, and demographics? Cancer 2008;112:171180.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Collin LJ, Yan M, Jiang R, et al. Oncotype DX recurrence score implications for disparities in chemotherapy and breast cancer mortality in Georgia. NPJ Breast Cancer 2019;5:32.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Collin LJ, Jiang R, Ward KC, et al. Racial disparities in breast cancer outcomes in the metropolitan Atlanta area: new insights and approaches for health equity. JNCI Cancer Spectr 2019;3:pkz053.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Olopade OI, Grushko TA, Nanda R, et al. Advances in breast cancer: pathways to personalized medicine. Clin Cancer Res 2008;14: 79887999.

  • 9.

    Daly B, Olopade OI. A perfect storm: how tumor biology, genomics, and health care delivery patterns collide to create a racial survival disparity in breast cancer and proposed interventions for change. CA Cancer J Clin 2015;65:221238.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Gradishar WJ, Anderson BO, Balassanian R, et al. NCCN Guidelines Insights: Breast Cancer, Version 1.2017. J Natl Compr Canc Netw 2017;15:433451.

  • 11.

    Wu X, Richardson LC, Kahn AR, et al. Survival difference between non-Hispanic black and non-Hispanic white women with localized breast cancer: the impact of guideline-concordant therapy. J Natl Med Assoc 2008;100:490498.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Inwald EC, Ortmann O, Zeman F, et al. Guideline concordant therapy prolongs survival in HER2-positive breast cancer patients: results from a large population-based cohort of a cancer registry. BioMed Res Int 2014;2014:137304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    LeMasters T, Madhavan SS, Sambamoorthi U, et al. Receipt of guideline-concordant care among older women with stage I-III breast cancer: a population-based study. J Natl Compr Canc Netw 2018;16:703710.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Griggs JJ, Culakova E, Sorbero MES, et al. Social and racial differences in selection of breast cancer adjuvant chemotherapy regimens. J Clin Oncol 2007;25:25222527.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    NAACCR Race and Ethnicity Work Group. NAACCR guideline for enhancing Hispanic/Latino identification: revised NAACCR Hispanic/Latino identification algorithm [NHIA v2.2.1]. Accessed December 7, 2020. Available at: https://www.naaccr.org/wp-content/uploads/2016/11/NHIA-v2.2.1.pdf

  • 16.

    Humes KR, Jones NA, Ramirez RR. Overview of race and Hispanic origin: 2010. Accessed December 7, 2020. Available at: https://www.census.gov/library/publications/2011/dec/c2010br-02.html

    • Search Google Scholar
    • Export Citation
  • 17.

    Carlson RW, Allred DC, Anderson BO, et al. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer. Version 2.2011. Obtained with permission from NCCN on December 7, 2020. To view the most recent version, visit NCCN.org

    • Search Google Scholar
    • Export Citation
  • 18.

    Morrison FP, Li L, Lai AM, et al. Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes? J Am Med Inform Assoc 2009;16:3739.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    U.S. Census Bureau. American Community Survey. Accessed December 7, 2020. Available at: https://www.census.gov/programs-surveys/acs

  • 20.

    Knoble NB, Alderfer MA, Hossain MJ. Socioeconomic status (SES) and childhood acute myeloid leukemia (AML) mortality risk: analysis of SEER data. Cancer Epidemiol 2016;44:101108.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Kish JK, Yu M, Percy-Laurry A, et al. Racial and ethnic disparities in cancer survival by neighborhood socioeconomic status in Surveillance, Epidemiology, and End Results (SEER) registries. J Natl Cancer Inst Monogr 2014;2014:236243.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    VanderWeele TJ, Knol MJ. A tutorial on interaction. Epidemiol Methods 2014;3:3372.

  • 23.

    VanderWeele TJ, Vansteelandt S. Invited commentary: some advantages of the relative excess risk due to interaction (RERI)—towards better estimators of additive interaction. Am J Epidemiol 2014;179:670671.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. Int J Epidemiol 2012;41:514520.

  • 25.

    VanderWeele TJ. On the distinction between interaction and effect modification. Epidemiology 2009;20:863871.

  • 26.

    Richardson DB, Kaufman JS. Estimation of the relative excess risk due to interaction and associated confidence bounds. Am J Epidemiol 2009;169:756760.

  • 27.

    Hosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology 1992;3:452456.

  • 28.

    Hess KR. Graphical methods for assessing violations of the proportional hazards assumption in Cox regression. Stat Med 1995;14:17071723.

  • 29.

    Howards PP, Schisterman EF, Poole C, et al. “Toward a clearer definition of confounding” revisited with directed acyclic graphs. Am J Epidemiol 2012;176:506511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology 2009;20:488495.

  • 31.

    Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008;167:492499.

  • 32.

    Lévesque LE, Hanley JA, Kezouh A, et al. Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes. BMJ 2010;340:b5087.

  • 33.

    Shavers VL, Brown ML. Racial and ethnic disparities in the receipt of cancer treatment. J Natl Cancer Inst 2002;94:334357.

  • 34.

    Bickell NA, Wang JJ, Oluwole S, et al. Missed opportunities: racial disparities in adjuvant breast cancer treatment. J Clin Oncol 2006;24:13571362.

  • 35.

    Guy GP Jr, Lipscomb J, Gillespie TW, et al. Variations in guideline-concordant breast cancer adjuvant therapy in rural Georgia. Health Serv Res 2015;50:10881108.

  • 36.

    Wu XC, Lund MJ, Kimmick GG, et al. Influence of race, insurance, socioeconomic status, and hospital type on receipt of guideline-concordant adjuvant systemic therapy for locoregional breast cancers. J Clin Oncol 2012;30:142150.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:28172826.

  • 38.

    Wheeler SB, Reeder-Hayes KE, Carey LA. Disparities in breast cancer treatment and outcomes: biological, social, and health system determinants and opportunities for research. Oncologist 2013;18:986993.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39.

    Bach PB, Pham HH, Schrag D, et al. Primary care physicians who treat blacks and whites. N Engl J Med 2004;351:575584.

  • 40.

    Bleicher RJ, Ruth K, Sigurdson ER, et al. Time to surgery and breast cancer survival in the United States. JAMA Oncol 2016;2:330339.

  • 41.

    Fedewa SA, Edge SB, Stewart AK, et al. Race and ethnicity are associated with delays in breast cancer treatment (2003-2006). J Health Care Poor Underserved 2011;22:128141.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Fedewa SA, Ward EM, Stewart AK, et al. Delays in adjuvant chemotherapy treatment among patients with breast cancer are more likely in African American and Hispanic populations: a national cohort study 2004-2006. J Clin Oncol 2010;28:41354141.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Roberts MC, Wheeler SB, Reeder-Hayes K. Racial/ethnic and socioeconomic disparities in endocrine therapy adherence in breast cancer: a systematic review. Am J Public Health 2015;105(Suppl 3):e415.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Wigertz A, Ahlgren J, Holmqvist M, et al. Adherence and discontinuation of adjuvant hormonal therapy in breast cancer patients: a population-based study. Breast Cancer Res Treat 2012;133:367373.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 45.

    Hershman DL, Kushi LH, Shao T, et al. Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol 2010;28:41204128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 46.

    Noone AM, Lund JL, Mariotto A, et al. Comparison of SEER treatment data with Medicare claims. Med Care 2016;54:e5564.

  • 47.

    Tammemagi CM, Nerenz D, Neslund-Dudas C, et al. Comorbidity and survival disparities among black and white patients with breast cancer. JAMA 2005;294:17651772.

  • 48.

    Hershman DL, Till C, Wright JD, et al. Comorbidities and risk of chemotherapy-induced peripheral neuropathy among participants 65 years or older in Southwest Oncology Group clinical trials. J Clin Oncol 2016;34:30143022.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 49.

    Cossrow N, Falkner B. Race/ethnic issues in obesity and obesity-related comorbidities. J Clin Endocrinol Metab 2004;89:25902594.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 478 478 72
PDF Downloads 264 264 59
EPUB Downloads 0 0 0