A Multi-faceted Intervention Aimed at Black-White Disparities in the Treatment of Early Stage Cancers: The ACCURE Pragmatic Quality Improvement trial

Cykert, Samuel; Eng, Eugenia; Manning, Matthew; Robertson, Linda; Heron, Dwight E.; Jones, Nora; Schaal, Jennifer; Lightfoot, Alexandra F.; Zhou, Haibo; Yongue, Christina; Gizlice, Ziya

doi:10.1016/j.jnma.2019.03.001

Cited by 46 publications

(86 citation statements)

References 26 publications

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“…These could include educational strategies to improve knowledge of signs, symptoms, and risk factors for HNSCC and access to specialty care to rapidly diagnose symptomatic patients and initiate treatment. Patient navigator programs that result in increased patient education and overcome cultural barriers have shown promise in narrowing the treatment gap in certain cancers 36,37 . A recent interventional program to address disparities in breast cancer found that patient navigators and community‐based outreach increased screening rates substantially over 2 years, with 95% of screened patients being minorities 38 .…”

Section: Discussionmentioning

confidence: 99%

Explaining Racial Disparities in Surgically Treated Head and Neck Cancer

Jassal

Cramer

2020

The Laryngoscope

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Objectives/Hypothesis To assess the causative factors that contribute to racial disparities in head and neck squamous cell carcinoma (HNSCC) and establish the role of hospital factors in racial disparities. Study Design Retrospective database analysis. Methods Patients with surgically treated HNSCC were identified using the National Cancer Database (2004–2014). Logistic and proportional‐hazard regression models were used to characterize the factors that contribute to racial disparities. Differences in quality of care received were compared among black and white patients using previously validated metrics. Results We identified 69,186 eligible patients. Black patients had a 48% higher mortality than white patients (HR 1.48; 95% confidence interval [CI], 1.41–1.54). Black patients had a lower mean quality score (67.6%; 95% CI, 66.8%–69.4%) compared with white patients (71.2%: 95% CI, 71.0%–71.4%) for five quality metrics. After adjusting for differences in patient, oncologic, and hospital factors we were able to explain 60% of the excess mortality for black patients. Oncologic factors at presentation accounted for 57.7% of observed mortality differences, whereas hospital characteristics and quality of care accounted for 11.5%. After adjusting for these factors, black patients still had a 19% higher mortality (HR 1.19; 95% CI, 1.14–1.24). Conclusions Oncologic factors at presentation are a major contributor to racial disparities in outcomes for HNSCC. Hospital factors, such as quality, volume, and safety‐net status, constitute a minor factor in the mortality difference. Resolving existing disparities will require detecting head and neck cancer at an earlier stage and improving the quality of care for black patients. Level of Evidence 3. Laryngoscope, 131:1053–1059, 2021

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Section: Discussionmentioning

confidence: 99%

Explaining Racial Disparities in Surgically Treated Head and Neck Cancer

Jassal

Cramer

2020

The Laryngoscope

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“…The intervention improved treatment completion and helped reduce racial disparities in treatment of these patients. 38 ACCURE exemplifies the power of harnessing big data to address health disparities in breast cancer care. More of these types of projects are needed.…”

Section: Health Equitymentioning

confidence: 99%

Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes

Jourquin

Reffey

Jernigan

et al. 2019

JCO Precision Oncology

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Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

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“…Studies have demonstrated that physicians using a shared decision‐making model and using supportive communication techniques and hospitals providing cultural competency training can start to bridge the gap in racial disparities in treatment 19 . Additional solutions such as system‐based intervention and using a real‐time registry with feedback and navigation have shown a decrease in racial disparities and have increased the completion of treatment for minority patients with lung cancer 20,21 …”

Section: Discussionmentioning

confidence: 99%

Disparate outcomes in nonsmall cell lung cancer by immigration status

et al. 2021

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Objective The purpose of this study was to evaluate overall survival (OS) outcomes by race, stratified by country of origin in patients diagnosed with NSCLC in California. Methods We performed a retrospective analysis of nonsmall cell lung cancer (NSCLC) patients diagnosed between 2000 and 2012. Race/ethnicity was defined as White (W), Black (B), Hispanic (H), and Asian (A) and stratified by country of origin (US vs. non‐US [NUS]) creating the following patient cohorts: W‐US, W‐NUS, B‐US, B‐NUS, H‐US, H‐NUS, A‐US, and A‐NUS. Three multivariate models were created: model 1 adjusted for age, gender, stage, year of diagnosis and histology; model 2 included model 1 plus treatment modalities; and model 3 included model 2 with the addition of socioeconomic status, marital status, and insurance. Results A total of 68,232 patients were included. Median OS from highest to lowest were: A‐NUS (15 months), W‐NUS (14 months), A‐US (13 months), B‐NUS (13 months), H‐US (11 months), W‐US (11 months), H‐NUS (10 months), and B‐US (10 months) (p < 0.001). In model 1, B‐US had worse OS, whereas A‐US, W‐NUS, B‐NUS, H‐NUS, and A‐NUS had better OS when compared to W‐US. In model 2 after adjusting for receipt of treatment, there was no difference in OS for B‐US when compared to W‐US. After adjusting for all variables (model 3), all race/ethnicity profiles had better OS when compared to W‐US; B‐NUS patients had similar OS to W‐US. Conclusion Foreign‐born patients with NSCLC have decreased risk of mortality when compared to native‐born patients in California after accounting for treatments received and socioeconomic differences.

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A Multi-faceted Intervention Aimed at Black-White Disparities in the Treatment of Early Stage Cancers: The ACCURE Pragmatic Quality Improvement trial

Cited by 46 publications

References 26 publications

Explaining Racial Disparities in Surgically Treated Head and Neck Cancer

Explaining Racial Disparities in Surgically Treated Head and Neck Cancer

Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes

Disparate outcomes in nonsmall cell lung cancer by immigration status

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