Gaps in breast cancer interception efforts at a tertiary cancer center: who are we serving?

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Rina Yadav, Bailey Johnson, Savannah Phipps, Kali Konstantinopoulos, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5434423/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose High-risk breast cancer screening programs (HRSPs) provide personalized interventions that can decrease one’s risk of breast cancer (BC). However, HRSPs introduce inequitable access and utilization by disadvantaged groups. Here, we retrospectively reviewed patients referred to HRSPs to discern race and socioeconomic status differences between those who attended the program versus those who did not. Methods A retrospective analysis was performed on two cohorts presenting for mammography at Indiana academic health centers: 1) patients identified as at increased risk of BC and referred to a HRSP via lay navigators and 2) newly diagnosed BC patients. Individuals attending the HRSP were compared to high-risk patients who declined/did not respond to HRSP invitation, and to newly diagnosed BC individuals. Age, race, insurance status, and neighborhood socioeconomic status were compared between cohorts using Chi-square or Fisher’s exact tests. Results Between 6/2020 and 6/2022, 601 women met criteria for contact by HRSP navigators. Patients accepting appointments were predominately White and privately insured. Black patients more frequently decline appointments compared to White (40.1% v. 29.1%, p = 0.008), and represented a higher proportion of those not attending HRSPs compared to those who did (19.8% v. 10.8%, p = 0.004). Patients diagnosed with BC were more commonly Black (19.4% v. 10.8%, p = 0.003), not privately insured (75.6% v. 84.2%, p = 0.012), and had lower median household incomes (69,611 v.72,656, p = 0.04). Conclusions Significant racial disparities exist amongst patients seen at a tertiary care center’s HRSP. As BC screening/prevention becomes more personalized, efforts should be focused on equitable delivery to minimize inequitable BC outcomes. Breast Cancer Racial Disparities High-Risk Clinic African American Women Patient Navigator Introduction While average risk for breast cancer in women living in the United States is 12–13%, there is wide variation in risk based on genetic predisposition, family history, breast density, and hormonal factors, among others [ 1 – 4 ]. Given variable levels of risk per individual, the approach to screening and risk reduction should also be personalized. Screening with both mammogram and magnetic resonance imaging (MRI) is recommended for patients at high lifetime risk of breast cancer based on family history or genetic predisposition [ 5 ]. In addition, automated breast ultrasound (ABUS) and abbreviated MRI are increasingly available as adjunctive screening options for patients at increased risk due to breast density [ 6 ]. Beyond early detection, risk reducing strategies such as chemoprevention options are available and can reduce one’s lifetime risk by up to 50% [ 7 – 8 ]. Additionally, many high-risk women benefit from modifiable lifestyle changes associated with reduced risk including healthy diets, physical activity, and alcohol moderation [ 9 ]. Despite evidence of benefits and guidelines supporting their use, enhanced screening and risk reducing options are vastly underutilized [ 10 ]. Many patients are unaware of their personal breast cancer risk or options, and many healthcare providers lack the expertise or time to discuss these approaches [ 10 ]. Therefore, high-risk programs have been implemented in many health care systems to provide personalized breast cancer risk assessment, implement individualized screening plans, and educate about risk reducing treatment options. As with advances in cancer treatment, there is concern that more ‘precision’ or ‘personalized’ methods to cancer prevention will widen disparities that already exist. High-risk screening programs introduce additional system and individual level barriers in access that may result in inequitable utilization by already disadvantaged groups experiencing breast cancer disparities, including Black women or those from areas with high social vulnerability [ 11 ]. Here, we retrospectively reviewed patients referred to a patient navigator led high-risk program at a comprehensive cancer center with the goal of discerning race and socioeconomic status (SES) differences in who utilized the program, compared to those who did not. In addition, in order to determine disparities between those utilizing the program and those developing breast cancer, we compared demographics of patients served by the program to patients newly diagnosed with breast cancer at the same mammography screening sites. Methods A retrospective chart review was performed upon institutional review board approval for the below patient populations. High-risk clinic cohort Women contacted by the lay patient navigator for the Indiana University Simon Comprehensive Cancer Center (IUSCCC) high-risk clinic between June 2020 and June 2022 were identified through the program’s institutional database. Women contacted by the navigator were those who presented for screening mammography at academic health center sites in central Indiana and were at highest risk for breast cancer. All women presenting for screening mammogram at these sites undergo risk assessment using the Gail and Tyrer-Cuzick version 8 models [ 12 ]. Those age < 65 who have a lifetime risk estimate over 20% are sent letters informing them of their risk and providing information for the high-risk program. Given low response rates to the letters, the program also utilizes patient navigation to contact patients via phone. Navigation services are focused on those patients with the highest, most immediate risk, defined as Gail 5-year risk 3% or a TC 10-year risk 8% [ 12 ]. Navigators attempt to contact patients three times and leave voicemail messages when able. Upon reaching a patient, the navigator provides education around that patient’s individual risk factors, what to expect from a high-risk clinic visit, and the potential benefits, while also addressing any barriers to care. The high-risk clinic sees patients referred from other avenues; however, the analysis presented here focuses only on those patients contacted through the mammography-based patient navigation program. Breast cancer cohort Women diagnosed with a new breast cancer after screening mammogram at the same academic health center sites where high-risk program screening is performed and over the same time frame of June 2020 to June 2022 were identified through retrospective review. Those with a prior history of breast cancer or those previously seen in the high-risk clinic were excluded. Those older than 65 were also excluded to match the high-risk clinic cohort. Statistical analysis Age, self- reported race, insurance status, and zip code were collected for all groups. Zip code was used to determine social vulnerability index (SVI) [ 13 ], poverty rate defined as proportion of population whose poverty status has been determined in that zip code who are below the federal poverty level, and median household income. SVI was calculated using the Center for Disease Control (CDC) SVI 2020 formula, which includes American Community Survey data estimates for SES, household characteristics, racial and ethnic minority status, and housing type and transportation [ 14 ]. Community health informants were performed by the POLIS center at Indiana University Indianapolis. Patients seen in the high-risk clinic were compared to high-risk patients who were not reached or did not attend using Chi-square test or Fisher’s exact test for categorical variables and Wilcoxon test for continuous variables. Similar comparisons were performed between patients seen in the high-risk clinic and patients diagnosed with breast cancer in the same time frame at the same mammography screening sites. Type 1 error rates on all comparisons were set at alpha < 0.05. Results High- risk clinic population Between 6/2020 and 6/2022, 601 women completing Tyrer Cuzick and Gail risk assessments in mammography met criteria to be contacted by the high-risk program’s lay navigator. Of these, 46.1% (n=277) were reached and accepted an appointment in the program. An appointment in the program was declined by 30.1% (n=184) and 23.1% (n=139) were never reached. Of participants who accepted an appointment, 80.1% attended that appointment (n=222). Table 1 shows the characteristics of high-risk participants who attended a program clinic visit due to lay navigator phone calls. Most participants were White (84.7%, n=188) and privately insured (84.2%, n=186). The median age was 49 (range 23-65). At a clinic visit, the majority of participants were recommended to undergo breast MRI screening (67.1%, n=149), and most followed up with at least one MRI subsequently performed or scheduled (76.5% of those recommended, n=114). Based on family history, the majority were also recommended genetic testing (77%, n=171) and had this performed (70.2% of those recommended, n=122). Approximately half of patients were recommended chemoprevention with tamoxifen or other agents (52.3%, n=116); however, only one-third subsequently filled a prescription for the medication after their first consultation (30.2% of those recommended, n = 35). Table 1. Comparison of patients at high-risk for breast cancer who attended a high- clinic, versus those who did not. Variable High-risk clinic attendees N = 222 High-risk clinic non-responders N=379 p-value Age, yrs (median, range) Age groups (n, %) < 40 40-49 50-59 ³60 49 (23-65) 45 (20.3) 69 (31.1) 66 (29.7) 42 (18.9) 59 (27-65) 8 (2.1) 42 (11.1) 160 (42.2) 169 (44.6) <0.001 <0.001 Race (n, %) White Black Asian Native American/Alaskan Pacific Islander Other Unknown 188 (84.7) 24 (10.8) 3 (1.4) 2 (0.9) 2 (0.9) 0 3 (1.4) 296 (78.1) 75 (19.8) 5 (1.3) 0 0 1 (0.3) 2 (0.5) 0.004 Insurance (n, %) Private State-sponsored, Medicare, Medicaid Uninsured 186 (84.2) 32 (14.5) 3 (1.4) 300 (80.4) 70 (18.8) 3 (0.8) 0.33 Area socioeconomic status by zip code Social vulnerability index Median household income, $ Area poverty index, % 0.66 72, 656 11.46 0.68 70,253 12.58 0.19 0.15 0.41 Comparison to high-risk women not attending a clinic visit Comparisons between high-risk patients attending versus not attending a high-risk clinic appointment after lay navigator contact are detailed in Table 1. Compared to those attending a high-risk program clinic visit, those who were not reached or did not attend were more likely to be older (median age 49 vs. 59, p <0.001). Rates of uptake of an appointment within the program decreased with increasing age (acceptance rates by decade < 40: 86.7%, 40-49: 62.2%, 50-59: 29.2%, 60-65: 19.9%). Patients who made and attended appointments in the program were more likely to be White; Black patients represented a higher proportion of those who declined or did not attend an appointment, compared to those who accepted and attended (19.8% vs 10.8%, p = 0.004). Rates of participants not being reachable was similar between White and Black high-risk women (23.6% vs. 25.5%, p=0.16). The racial disparity in who was seen in the clinic was driven primarily by higher rates of decline in Black versus White women (40.1% versus 29.1%, p = 0.008). Rates of no-shows to scheduled appointments were also higher among Black versus White women, although numbers were small and this was not statistically significant (30.3% versus 21.2%, p = 0.23). There was no statistically significant difference in the insurance status of patients who attended a clinic appointment, versus those who did not (proportion privately insured 84.2% vs. 80.4%, p=0.33). In addition, there was no difference in social vulnerability index (0.66 vs. 0.68, p = 0.19), mean household income (72,656 vs. 70,235, p =0.15), or area poverty rate (11.46 vs. 12.58, p=0.41) based on zip codes for patients attending appointments versus those who did not, respectively. Comparison to women diagnosed with breast cancer Between 6/2020 and 6/2022, 685 women age < 65 were diagnosed with a new breast cancer when presenting at the same mammography sites where risk assessments were performed. Compared to those attending and served by the high-risk program, patients diagnosed with breast cancer at the same mammography sites were more likely to be older (median age 54 vs. 49 p < 0.001) and Black (19.4% vs. 10.8% p =0.003), and less likely to be privately insured (75.6% vs. 84.2%, p=0.012). Cancer cases came from areas with a lower median household income than those seen in the high-risk clinic (69,611 vs. 72, 656 p = 0.04); there was no difference in social vulnerability index (0.64 vs. 0.66, p=0.11) or area poverty rate (11.5 vs. 12.0, p =0.26) between the two groups. Table 2. Comparison of patients at high-risk for breast cancer who attended a high-risk clinic, versus those diagnosed with breast cancer at the same mammography sites over the same timeframe. Variable High-risk clinic attendees N = 222 Breast cancer cases N=685 p-value Age, yrs (median, range) Age groups (n, %) < 40 40-49 50-59 ³60 49 (23-65) 45 (20.3) 69 (31.1) 66 (29.7) 42 (18.9) 54 (24-65) 68 (9.9) 176 (25.7) 245 (35.8) 196 (28.6) <0.001 <0.001 Race (n, %) White Black Asian Native American/Alaskan Pacific Islander Other Unknown 188 (84.7) 24 (10.8) 3 (1.4) 2 (0.9) 2 (0.9) 0 3 (1.4) 513 (74.9) 133 (19.4) 26 (3.8) 0 3 (0.4) 2 (0.3) 8 (1.2) 0.002 Insurance (n, %) Private State-sponsored, Medicare, Medicaid Uninsured 186 (84.2) 32 (14.5) 3 (1.4) 518 (75.6) 160 (23.4) 7 (1.0) 0.012 Area socioeconomic status by zip code Social vulnerability index Median household income, $ Area poverty index, % 0.66 72, 656 11.46 0.64 69,611 12.0 0.11 0.04 0.26 Conclusion Optimal breast cancer screening and prevention needs to be delivered equitably, with specific attention to disadvantaged populations. If not, advancements can further disparities in breast cancer outcomes. Our results indicate a disparity by race in which patients are seen in a high-risk clinic at a tertiary care center, both in comparison to those at high-risk who are not seen in the clinic, and in comparison to those diagnosed with breast cancer. This difference is impactful as less opportunity to discuss or access high-risk surveillance and risk reduction strategies may worsen existing racial disparities in breast cancer outcomes [ 15 ]. Prior studies have documented racial disparities in rates of screening MRIs, chemoprevention uptake, and genetic testing [ 16 – 17 ]. Black women are equally likely, if not more likely, to present for screening mammogram than White women, but are less likely to access more advanced imaging technologies [ 18 ]. To our knowledge, this is the first description of racial disparities in uptake of appointments in a high-risk clinic, a service offered at many academic and community oncology practices. While our study documents a racial disparity in uptake, the motivations and barriers are not known and will be the subject of future work. Decision-making around breast cancer risk management options is complicated, and likely different between racial and cultural groups [ 19 ]. At the individual level, members of racial minority groups have less awareness of individual risk and more negative perceptions around preventative care [ 20 ]. Some studies also suggest higher rates of fatalism or religiosity may also influence Black women’s thoughts toward breast cancer screening and risk reduction [ 21 ]. At the system level, implicit biases may limit provider discussions around need for specialized services or risk assessment, and limited availability of high-risk services such as breast MRI may further disadvantage minority populations [ 22 ]. While the goal of a high-risk clinic is to improve education and reduce communication barriers around breast cancer risk and screening, these same barriers likely limit access to such clinics in the first place. It is well documented that patient navigators improve cancer screening rates [ 23 – 24 ]. The population of patients studied here were all contacted by a lay patient navigator to provide education around high-risk services and address barriers to access. While rates of patients who were reached by the lay navigator were similar across race, Black patients were significantly more likely to decline an appointment compared to White patients. Prior work suggested patients report better communication and understanding with the healthcare team when the healthcare team member is of a similar racial or ethnic background. As a next step, in a currently ongoing study, we are evaluating the impact of Black lay navigators to specifically improve uptake and attendance of Black patients to appointments in the high-risk program, and subsequent access to high-risk services such as breast MRI. The navigators utilized are recruited and trained by a Black women’s advocacy organization that is embedded in the local community the high-risk clinic is meant to serve. In addition to racial differences, we also found a disparity in insurance coverage and neighborhood economic status between patients seen in the high-risk clinic and those diagnosed with breast cancer. Lower neighborhood SES is associated with worse breast cancer outcomes, regardless of race [ 25 – 26 ]. Stage at diagnosis is associated with SES and insurance status, suggesting that late presentation or lack of adherence to screening is a driver of worse prognosis in those with lower SES. Beyond this, patients with lower SES may have worse tumor biology, including higher rates of triple negative breast cancer and higher 21-gene recurrence scores, potentially related to dysregulation of stress pathways [ 27 – 28 ]. Lack of insurance coverage for specialized preventative care and high cost of surveillance technologies like breast MRI may further disparities in a group already experiencing worse outcomes. There are several limitations of this analysis, including those due to its retrospective nature. Our dataset is not able to account for discussions patients may be having with other healthcare providers, or whether patients may have followed up outside of our healthcare system. Whether patients engage with preventative care is related to many factors, many of which are not included here, such as employment and education status. Prior analysis of the Breast Cancer Surveillance Consortium registry did not find racial differences in use of breast MRI in high-risk patients, but did find disparity by education level, with those with a high school or lower education level being less likely to undergo MRI screening [ 29 ]. We collected and analyzed SES only at the neighborhood level via zip codes. This may have missed differences in program utilization by SES at the individual level that will be important to inform future interventions. Future work will focus on discerning which factors drive racial disparities in use of high-risk services, including social risks and social determinants of health. As cancer interception efforts become more “personalized,” disparities in which at-risk people receive enhanced surveillance and risk reduction interventions will continue to widen without dedicated attention to inequities. There is a critical need for novel strategies to increase the penetrance of risk assessment information and access to high-risk interventions in Black women at higher risk for breast cancer. Declarations This study is funded by the Catherine Peachey Foundation. We also acknowledge assistance from the IU Polis Center to collect and analyze socioeconomic variables. Ethics Approval This is a retrospective study and patient informed consent was not obtained. The study was approved by the Indiana University Institutional Review Board. Consent to Participate This is a retrospective study and patient informed consent was not obtained. Consent to Publish This is a retrospective study and patient informed consent and consent to publish was not obtained. No individual data is included. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by R.Y. and T.B. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at Indiana University. References (2024) 1/17/2024). Key Statistics for Breast Cancer. Retrieved 8/19/2024. Louise A, Brinton CS, Robert N, Hoover, Joseph F, Fraumeni (2009) Menstrual Factors Risk Breast Cancer Cancer Invest 6(3):245–254 Lubin JH, Burns PE, Blot WJ et al (1982) Risk factors for breast cancer in women in Northern Alberta, Canada, as related to age at diagnosis. J Natl Cancer Inst 68:211–217 Titus-Ernstoff L, Longecker MP, Newcomb PA et al (1998) Menstrual factors in relation to breast cancer risk. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5434423","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382776159,"identity":"d07e5a1d-24c6-44d5-a48b-e76f916ed539","order_by":0,"name":"Rina Yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYDACHiD+UGEjB2YwsBGphXHGmTRj0rQw87YdTmwgWot5zxnDDzxszOkbzpwxYPhQdpiwFpmzPcYSEjxsuRvO9hgwzjhHhBYJfh4DCQMJntwN53kMQC4kSovxjwQDiXQDkJa/RGnh7TGTOJBgkGAAdBgzI1FaeI6VWTYcSDCceeZYwcGec+nEaEnefPvvv//yfGeSNz74UWZNWAsDA4cBnHGAGPVAwP4AnTEKRsEoGAWjABUAAB5AOfzuil7UAAAAAElFTkSuQmCC","orcid":"","institution":"University of Kentucky School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Rina","middleName":"","lastName":"Yadav","suffix":""},{"id":382776162,"identity":"f13d0484-3067-4e99-b637-536bef2081ae","order_by":1,"name":"Bailey Johnson","email":"","orcid":"","institution":"Indiana University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bailey","middleName":"","lastName":"Johnson","suffix":""},{"id":382776164,"identity":"1677dd6a-52d6-42ab-b5df-15d3791060c9","order_by":2,"name":"Savannah Phipps","email":"","orcid":"","institution":"Indiana University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Savannah","middleName":"","lastName":"Phipps","suffix":""},{"id":382776165,"identity":"4358734b-297e-4f94-9f8a-ca963c41dd25","order_by":3,"name":"Kali Konstantinopoulos","email":"","orcid":"","institution":"Indiana University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kali","middleName":"","lastName":"Konstantinopoulos","suffix":""},{"id":382776167,"identity":"3862a7ef-02d2-406f-82c2-9ab2a0f27218","order_by":4,"name":"Manjari Lokender","email":"","orcid":"","institution":"Indiana University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Manjari","middleName":"","lastName":"Lokender","suffix":""},{"id":382776173,"identity":"cb78a501-d7a0-49dd-a8a9-3f87e685711c","order_by":5,"name":"Jane Ambro","email":"","orcid":"","institution":"Indiana University Health","correspondingAuthor":false,"prefix":"","firstName":"Jane","middleName":"","lastName":"Ambro","suffix":""},{"id":382776177,"identity":"d59a6289-cc2c-4ab0-94e2-fe5349de7b8f","order_by":6,"name":"Sandra Althouse","email":"","orcid":"","institution":"Indiana University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Althouse","suffix":""},{"id":382776181,"identity":"79f3d806-6de1-45c0-b17e-f2ca34c5386d","order_by":7,"name":"Lisa Hayes","email":"","orcid":"","institution":"Pine-4-Ever Ending Disparatities","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Hayes","suffix":""},{"id":382776183,"identity":"dbbde740-17dd-4d0b-a772-4e3797e6007a","order_by":8,"name":"Tarah Balling","email":"","orcid":"","institution":"Indiana University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tarah","middleName":"","lastName":"Balling","suffix":""}],"badges":[],"createdAt":"2024-11-11 20:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5434423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5434423/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82551452,"identity":"147804ea-3999-4f2a-aa32-658112322617","added_by":"auto","created_at":"2025-05-12 20:23:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":529799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5434423/v1/3b60aa8a-eda0-47a2-bc9a-018d9a4844d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gaps in breast cancer interception efforts at a tertiary cancer center: who are we serving?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhile average risk for breast cancer in women living in the United States is 12\u0026ndash;13%, there is wide variation in risk based on genetic predisposition, family history, breast density, and hormonal factors, among others [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given variable levels of risk per individual, the approach to screening and risk reduction should also be personalized. Screening with both mammogram and magnetic resonance imaging (MRI) is recommended for patients at high lifetime risk of breast cancer based on family history or genetic predisposition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, automated breast ultrasound (ABUS) and abbreviated MRI are increasingly available as adjunctive screening options for patients at increased risk due to breast density [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Beyond early detection, risk reducing strategies such as chemoprevention options are available and can reduce one\u0026rsquo;s lifetime risk by up to 50% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, many high-risk women benefit from modifiable lifestyle changes associated with reduced risk including healthy diets, physical activity, and alcohol moderation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite evidence of benefits and guidelines supporting their use, enhanced screening and risk reducing options are vastly underutilized [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Many patients are unaware of their personal breast cancer risk or options, and many healthcare providers lack the expertise or time to discuss these approaches [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, high-risk programs have been implemented in many health care systems to provide personalized breast cancer risk assessment, implement individualized screening plans, and educate about risk reducing treatment options.\u003c/p\u003e \u003cp\u003eAs with advances in cancer treatment, there is concern that more \u0026lsquo;precision\u0026rsquo; or \u0026lsquo;personalized\u0026rsquo; methods to cancer prevention will widen disparities that already exist. High-risk screening programs introduce additional system and individual level barriers in access that may result in inequitable utilization by already disadvantaged groups experiencing breast cancer disparities, including Black women or those from areas with high social vulnerability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHere, we retrospectively reviewed patients referred to a patient navigator led high-risk program at a comprehensive cancer center with the goal of discerning race and socioeconomic status (SES) differences in who utilized the program, compared to those who did not. In addition, in order to determine disparities between those utilizing the program and those developing breast cancer, we compared demographics of patients served by the program to patients newly diagnosed with breast cancer at the same mammography screening sites.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e A retrospective chart review was performed upon institutional review board approval for the below patient populations.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHigh-risk clinic cohort\u003c/h2\u003e \u003cp\u003eWomen contacted by the lay patient navigator for the Indiana University Simon Comprehensive Cancer Center (IUSCCC) high-risk clinic between June 2020 and June 2022 were identified through the program\u0026rsquo;s institutional database. Women contacted by the navigator were those who presented for screening mammography at academic health center sites in central Indiana and were at highest risk for breast cancer. All women presenting for screening mammogram at these sites undergo risk assessment using the Gail and Tyrer-Cuzick version 8 models [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Those age\u0026thinsp;\u0026lt;\u0026thinsp;65 who have a lifetime risk estimate over 20% are sent letters informing them of their risk and providing information for the high-risk program. Given low response rates to the letters, the program also utilizes patient navigation to contact patients via phone. Navigation services are focused on those patients with the highest, most immediate risk, defined as Gail 5-year risk 3% or a TC 10-year risk 8% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Navigators attempt to contact patients three times and leave voicemail messages when able. Upon reaching a patient, the navigator provides education around that patient\u0026rsquo;s individual risk factors, what to expect from a high-risk clinic visit, and the potential benefits, while also addressing any barriers to care. The high-risk clinic sees patients referred from other avenues; however, the analysis presented here focuses only on those patients contacted through the mammography-based patient navigation program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBreast cancer cohort\u003c/h2\u003e \u003cp\u003eWomen diagnosed with a new breast cancer after screening mammogram at the same academic health center sites where high-risk program screening is performed and over the same time frame of June 2020 to June 2022 were identified through retrospective review. Those with a prior history of breast cancer or those previously seen in the high-risk clinic were excluded. Those older than 65 were also excluded to match the high-risk clinic cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAge, self- reported race, insurance status, and zip code were collected for all groups. Zip code was used to determine social vulnerability index (SVI) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], poverty rate defined as proportion of population whose poverty status has been determined in that zip code who are below the federal poverty level, and median household income. SVI was calculated using the Center for Disease Control (CDC) SVI 2020 formula, which includes American Community Survey data estimates for SES, household characteristics, racial and ethnic minority status, and housing type and transportation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Community health informants were performed by the POLIS center at Indiana University Indianapolis.\u003c/p\u003e \u003cp\u003ePatients seen in the high-risk clinic were compared to high-risk patients who were not reached or did not attend using Chi-square test or Fisher\u0026rsquo;s exact test for categorical variables and Wilcoxon test for continuous variables. Similar comparisons were performed between patients seen in the high-risk clinic and patients diagnosed with breast cancer in the same time frame at the same mammography screening sites. Type 1 error rates on all comparisons were set at alpha\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eHigh- risk clinic population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBetween 6/2020 and 6/2022, 601 women completing Tyrer Cuzick and Gail risk assessments in mammography met criteria to be contacted by the high-risk program\u0026rsquo;s lay navigator. Of these, 46.1% (n=277) were reached and accepted an appointment in the program. An appointment in the program was declined by 30.1% (n=184) and 23.1% (n=139) were never reached. Of participants who accepted an appointment, 80.1% attended that appointment (n=222).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 shows the characteristics of high-risk participants who attended a program clinic visit due to lay navigator phone calls. \u0026nbsp; Most participants were White (84.7%, n=188) and privately insured (84.2%, n=186). The median age was 49 (range 23-65). At a clinic visit, the majority of participants were recommended to undergo breast MRI screening (67.1%, n=149), and most followed up with at least one MRI subsequently performed or scheduled (76.5% of those recommended, n=114). \u0026nbsp; Based on family history, the majority were also recommended genetic testing (77%, n=171) and had this performed (70.2% of those recommended, n=122). Approximately half of patients were recommended chemoprevention with tamoxifen or other agents (52.3%, n=116); however, only one-third subsequently filled a prescription for the medication after their first consultation (30.2% of those recommended, n = 35). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eComparison of patients at high-risk for breast cancer who attended a high- clinic, versus those who did not.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-risk clinic attendees\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 222\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-risk clinic\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003enon-responders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=379\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eAge, yrs (median, range)\u003c/p\u003e\n \u003cp\u003eAge groups (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt; 40\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;40-49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;50-59\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026sup3;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e49 (23-65)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (20.3)\u003c/p\u003e\n \u003cp\u003e69 (31.1)\u003c/p\u003e\n \u003cp\u003e66 (29.7)\u003c/p\u003e\n \u003cp\u003e42 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e59 (27-65)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (2.1)\u003c/p\u003e\n \u003cp\u003e42 (11.1)\u003c/p\u003e\n \u003cp\u003e160 (42.2)\u003c/p\u003e\n \u003cp\u003e169 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eRace (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Native American/Alaskan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Pacific Islander\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e188 (84.7)\u003c/p\u003e\n \u003cp\u003e24 (10.8)\u003c/p\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e296 (78.1)\u003c/p\u003e\n \u003cp\u003e75 (19.8)\u003c/p\u003e\n \u003cp\u003e5 (1.3)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eInsurance (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Private\u003c/p\u003e\n \u003cp\u003eState-sponsored, Medicare, \u0026nbsp;Medicaid\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uninsured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e186 (84.2)\u003c/p\u003e\n \u003cp\u003e32 (14.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e300 (80.4)\u003c/p\u003e\n \u003cp\u003e70 (18.8)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e\u003cdel cite=\"mailto:[email protected]\" datetime=\"2024-09-16T18:22\"\u003e\u0026nbsp;\u003c/del\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eArea socioeconomic status by zip code\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Social vulnerability index\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median household income, $\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Area poverty index, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e72, 656\u003c/p\u003e\n \u003cp\u003e11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e70,253\u003c/p\u003e\n \u003cp\u003e12.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison to high-risk women not attending a clinic visit\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eComparisons between high-risk patients attending versus not attending a high-risk clinic appointment after lay navigator contact are detailed in Table 1. Compared to those attending a high-risk program clinic visit, those who were not reached or did not attend were more likely to be older (median age 49 vs. 59, p \u0026lt;0.001). Rates of uptake of an appointment within the program decreased with increasing age (acceptance rates by decade \u0026lt; 40: 86.7%, 40-49: 62.2%, 50-59: 29.2%, 60-65: 19.9%). Patients who made and attended appointments in the program were more likely to be White; Black patients represented a higher proportion of those who declined or did not attend an appointment, compared to those who accepted and attended (19.8% vs 10.8%, p = 0.004). Rates of participants not being reachable was similar between White and Black high-risk women (23.6% vs. 25.5%, p=0.16). \u0026nbsp;The racial disparity in who was seen in the clinic was driven primarily by higher rates of decline in Black versus White women (40.1% versus 29.1%, p = 0.008). \u0026nbsp;Rates of no-shows to scheduled appointments were also higher among Black versus White women, although numbers were small and this was not statistically significant (30.3% versus 21.2%, p = 0.23). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was no statistically significant difference in the insurance status of patients who attended a clinic appointment, versus those who did not (proportion privately insured 84.2% vs. 80.4%, p=0.33). In addition, there was no difference in social vulnerability index (0.66 vs. 0.68, p = 0.19), mean household income (72,656 vs. 70,235, p =0.15), or area poverty rate (11.46 vs. 12.58, p=0.41) based on zip codes for patients attending appointments versus those who did not, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison to women diagnosed with breast cancer\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBetween 6/2020 and 6/2022, 685 women age \u0026lt; 65 were diagnosed with a new breast cancer when presenting at the same mammography sites where risk assessments were performed. Compared to those attending and served by the high-risk program, patients diagnosed with breast cancer at the same mammography sites were more likely to be older (median age 54 vs. 49 p \u0026lt; 0.001) and Black (19.4% vs. 10.8% p =0.003), and less likely to be privately insured (75.6% vs. 84.2%, p=0.012). Cancer cases came from areas with a lower median household income than those seen in the high-risk clinic (69,611 vs. 72, 656 p = 0.04); there was no difference in social vulnerability index (0.64 vs. 0.66, p=0.11) or area poverty rate (11.5 vs. 12.0, p =0.26) between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eComparison of patients at high-risk for breast cancer who attended a high-risk clinic, versus those diagnosed with breast cancer at the same mammography sites over the same timeframe.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-risk clinic attendees\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 222\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreast cancer cases\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=685\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eAge, yrs (median, range)\u003c/p\u003e\n \u003cp\u003eAge groups (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt; 40\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;40-49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;50-59\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026sup3;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e49 (23-65)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (20.3)\u003c/p\u003e\n \u003cp\u003e69 (31.1)\u003c/p\u003e\n \u003cp\u003e66 (29.7)\u003c/p\u003e\n \u003cp\u003e42 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e54 (24-65)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68 (9.9)\u003c/p\u003e\n \u003cp\u003e176 (25.7)\u003c/p\u003e\n \u003cp\u003e245 (35.8)\u003c/p\u003e\n \u003cp\u003e196 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eRace (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Asian\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Native American/Alaskan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Pacific Islander\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e188 (84.7)\u003c/p\u003e\n \u003cp\u003e24 (10.8)\u003c/p\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e513 (74.9)\u003c/p\u003e\n \u003cp\u003e133 (19.4)\u003c/p\u003e\n \u003cp\u003e26 (3.8)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e3 (0.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2 (0.3)\u003c/p\u003e\n \u003cp\u003e8 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eInsurance (n, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Private\u003c/p\u003e\n \u003cp\u003eState-sponsored, Medicare, Medicaid\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Uninsured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e186 (84.2)\u003c/p\u003e\n \u003cp\u003e32 (14.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e518 (75.6)\u003c/p\u003e\n \u003cp\u003e160 (23.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eArea socioeconomic status by zip code\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Social vulnerability index\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Median household income, $\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Area poverty index, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e72, 656\u003c/p\u003e\n \u003cp\u003e11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003e69,611\u003c/p\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOptimal breast cancer screening and prevention needs to be delivered equitably, with specific attention to disadvantaged populations. If not, advancements can further disparities in breast cancer outcomes. Our results indicate a disparity by race in which patients are seen in a high-risk clinic at a tertiary care center, both in comparison to those at high-risk who are not seen in the clinic, and in comparison to those diagnosed with breast cancer. This difference is impactful as less opportunity to discuss or access high-risk surveillance and risk reduction strategies may worsen existing racial disparities in breast cancer outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrior studies have documented racial disparities in rates of screening MRIs, chemoprevention uptake, and genetic testing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Black women are equally likely, if not more likely, to present for screening mammogram than White women, but are less likely to access more advanced imaging technologies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To our knowledge, this is the first description of racial disparities in uptake of appointments in a high-risk clinic, a service offered at many academic and community oncology practices. While our study documents a racial disparity in uptake, the motivations and barriers are not known and will be the subject of future work. Decision-making around breast cancer risk management options is complicated, and likely different between racial and cultural groups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. At the individual level, members of racial minority groups have less awareness of individual risk and more negative perceptions around preventative care [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Some studies also suggest higher rates of fatalism or religiosity may also influence Black women\u0026rsquo;s thoughts toward breast cancer screening and risk reduction [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. At the system level, implicit biases may limit provider discussions around need for specialized services or risk assessment, and limited availability of high-risk services such as breast MRI may further disadvantage minority populations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While the goal of a high-risk clinic is to improve education and reduce communication barriers around breast cancer risk and screening, these same barriers likely limit access to such clinics in the first place.\u003c/p\u003e \u003cp\u003eIt is well documented that patient navigators improve cancer screening rates [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. \u003csup\u003eThe\u003c/sup\u003e population of patients studied here were all contacted by a lay patient navigator to provide education around high-risk services and address barriers to access. While rates of patients who were reached by the lay navigator were similar across race, Black patients were significantly more likely to decline an appointment compared to White patients. Prior work suggested patients report better communication and understanding with the healthcare team when the healthcare team member is of a similar racial or ethnic background. As a next step, in a currently ongoing study, we are evaluating the impact of Black lay navigators to specifically improve uptake and attendance of Black patients to appointments in the high-risk program, and subsequent access to high-risk services such as breast MRI. The navigators utilized are recruited and trained by a Black women\u0026rsquo;s advocacy organization that is embedded in the local community the high-risk clinic is meant to serve.\u003c/p\u003e \u003cp\u003eIn addition to racial differences, we also found a disparity in insurance coverage and neighborhood economic status between patients seen in the high-risk clinic and those diagnosed with breast cancer. Lower neighborhood SES is associated with worse breast cancer outcomes, regardless of race [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Stage at diagnosis is associated with SES and insurance status, suggesting that late presentation or lack of adherence to screening is a driver of worse prognosis in those with lower SES. Beyond this, patients with lower SES may have worse tumor biology, including higher rates of triple negative breast cancer and higher 21-gene recurrence scores, potentially related to dysregulation of stress pathways [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Lack of insurance coverage for specialized preventative care and high cost of surveillance technologies like breast MRI may further disparities in a group already experiencing worse outcomes.\u003c/p\u003e \u003cp\u003eThere are several limitations of this analysis, including those due to its retrospective nature. Our dataset is not able to account for discussions patients may be having with other healthcare providers, or whether patients may have followed up outside of our healthcare system. Whether patients engage with preventative care is related to many factors, many of which are not included here, such as employment and education status. Prior analysis of the Breast Cancer Surveillance Consortium registry did not find racial differences in use of breast MRI in high-risk patients, but did find disparity by education level, with those with a high school or lower education level being less likely to undergo MRI screening [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We collected and analyzed SES only at the neighborhood level via zip codes. This may have missed differences in program utilization by SES at the individual level that will be important to inform future interventions. Future work will focus on discerning which factors drive racial disparities in use of high-risk services, including social risks and social determinants of health.\u003c/p\u003e \u003cp\u003eAs cancer interception efforts become more \u0026ldquo;personalized,\u0026rdquo; disparities in which at-risk people receive enhanced surveillance and risk reduction interventions will continue to widen without dedicated attention to inequities. There is a critical need for novel strategies to increase the penetrance of risk assessment information and access to high-risk interventions in Black women at higher risk for breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003eThis study is funded by the Catherine Peachey Foundation. We also acknowledge assistance from the IU Polis Center to collect and analyze socioeconomic variables.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003e This is a retrospective study and patient informed consent was not obtained. The study was approved by the Indiana University Institutional Review Board.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eThis is a retrospective study and patient informed consent was not obtained.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003e This is a retrospective study and patient informed consent and consent to publish was not obtained. No individual data is included.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by all authors. The first draft of the manuscript was written by R.Y. and T.B. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at Indiana University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e(2024) 1/17/2024). Key Statistics for Breast Cancer. 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Am Soc Clin Oncol Educ Book 41:e29\u0026ndash;e46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatapodi MC et al (2004) Predictors of perceived breast cancer risk and the relation between perceived risk and breast cancer screening: a meta-analytic review. Prev Med 38(4):388\u0026ndash;402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCragun D et al (2017) Racial disparities in BRCA testing and cancer risk management across a population-based sample of young breast cancer survivors. Cancer 123(13):2497\u0026ndash;2505\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElewonibi BR et al (2018) Examining Mammography Use by Breast Cancer Risk, Race, Nativity, and Socioeconomic Status. J Immigr Minor Health 20(1):59\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadamsee TJ et al (2017) Decision making for breast cancer prevention among women at elevated risk. 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Cancer 122(4):611\u0026ndash;617\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Racial Disparities, High-Risk Clinic, African American Women, Patient Navigator","lastPublishedDoi":"10.21203/rs.3.rs-5434423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5434423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003ePurpose\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHigh-risk breast cancer screening programs (HRSPs) provide personalized interventions that can decrease one\u0026rsquo;s risk of breast cancer (BC). However, HRSPs introduce inequitable access and utilization by disadvantaged groups. Here, we retrospectively reviewed patients referred to HRSPs to discern race and socioeconomic status differences between those who attended the program versus those who did not.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA retrospective analysis was performed on two cohorts presenting for mammography at Indiana academic health centers: 1) patients identified as at increased risk of BC and referred to a HRSP via lay navigators and 2) newly diagnosed BC patients. Individuals attending the HRSP were compared to high-risk patients who declined/did not respond to HRSP invitation, and to newly diagnosed BC individuals. Age, race, insurance status, and neighborhood socioeconomic status were compared between cohorts using Chi-square or Fisher\u0026rsquo;s exact tests.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBetween 6/2020 and 6/2022, 601 women met criteria for contact by HRSP navigators. Patients accepting appointments were predominately White and privately insured. Black patients more frequently decline appointments compared to White (40.1% v. 29.1%, p\u0026thinsp;=\u0026thinsp;0.008), and represented a higher proportion of those not attending HRSPs compared to those who did (19.8% v. 10.8%, p\u0026thinsp;=\u0026thinsp;0.004). Patients diagnosed with BC were more commonly Black (19.4% v. 10.8%, p\u0026thinsp;=\u0026thinsp;0.003), not privately insured (75.6% v. 84.2%, p\u0026thinsp;=\u0026thinsp;0.012), and had lower median household incomes (69,611 v.72,656, p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSignificant racial disparities exist amongst patients seen at a tertiary care center\u0026rsquo;s HRSP. As BC screening/prevention becomes more personalized, efforts should be focused on equitable delivery to minimize inequitable BC outcomes.\u003c/p\u003e","manuscriptTitle":"Gaps in breast cancer interception efforts at a tertiary cancer center: who are we serving?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-04 08:32:36","doi":"10.21203/rs.3.rs-5434423/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5f401fc-0e03-4bb2-8fd7-50fece5bbb73","owner":[],"postedDate":"December 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T06:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-04 08:32:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5434423","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5434423","identity":"rs-5434423","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}