Late-stage Breast Cancer Diagnosis Research Articles

Effects of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States.

Interventions aimed at upstream factors contributing to late-stage diagnoses could reduce disparities and improve breast cancer outcomes. This study examines the association between measures of housing stability and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States. We studied 67,588 women aged 66-90 from the SEER-Medicare linked database (2010-2015). The primary outcome was breast cancer stage at diagnosis. Multinomial regression models adjusted for individual and neighborhood socio-economic factors were performed using a three-category outcome (stage 0, early stage, and late stage). Key census tract-level independent variables were residence in the same house as the previous year, owner-occupied homes, and an index of contemporary mortgage lending bias. In models adjusted for individual factors, higher levels of mortgage lending bias were associated with later stage diagnosis (RR = 1.10, 95% CI 1.02-1.20; RR = 1.31, 95% CI 1.16-1.49; RR = 1.41, 95% CI 1.24-1.60 for least to high, respectively). In models adjusted for individual and neighborhood socio-economic factors, moderate and high levels of mortgage lending bias were associated with later stage diagnosis (RR = 1.16, 95% CI 1.02-1.33 for moderate and RR = 1.18, 95% CI 1.02-1.37 for high). Owner occupancy and tenure were not associated with later stage diagnosis in adjusted models. Contemporary mortgage lending bias demonstrated a significant gradient relationship with later stage at diagnosis of breast cancer. Policy interventions aimed at reducing place-based mortgage disinvestment and its impacts on local resources and opportunities should be considered as part of an overall strategy to decrease late-stage breast cancer diagnosis and improve prognosis.

Patterns of screening and access to care in women diagnosed with breast cancer in a safety-net system.

1531 Background: Screening mammography reduces breast cancer specific mortality by allowing diagnosis at an earlier stage. Inconsistent guidelines and limitations in access to primary care are among factors that affect screening uptake. In this study, we reviewed patient characteristics, access to care, and uptake of screening mammogram (SMMG) prior to a breast cancer diagnosis at a safety-net system. Methods: Parkland Health (PH) is the safety-net system for Dallas County and is affiliated with the University of Texas Southwestern Simmons Comprehensive Cancer Center. Electronic medical records of patients with a new invasive breast cancer diagnosis between 2018 and 2019 at PH were reviewed and data on demographics and clinical presentation were collected. Patients were categorized based on SMMG uptake prior to their cancer diagnosis: never-screened, ever-screened (SMMG > 2 years prior to diagnosis) and recent-screened (SMMG within 2 years of diagnosis). Results: A total of 468 new breast cancer cases were identified (48.7% Hispanic; 32.9% Black). Of these, 12.2% were younger than 40 and 27.8% were 40-49. Payer mix included 64.7% uninsured and 34.8% Medicare/Medicaid. Half of the patients (50.6%) never had SMMG. Never-screened rate was higher among younger patients (98.2% in < 40; 53.8% in 40-49; and 39.4% in >50; p < 0.0001). Only 25.2% (118/468) of the patients had a SMMG within 2 years prior to their diagnosis. Among patients aged >40, 44.0% (181/411) were never-screened and 28.7% (118/411) were recent-screened. Screen-detection rate among patients >40 was 40.1% (165/411), of whom 22 (13.3% of screen-detects) were diagnosed with cancer on their first ever SMMG. Race/ethnicity, insurance status, and positive family history of cancer were not significantly different between never, ever, and recent-screened patients. Screening uptake correlated with nodal status, tumor size and stage at diagnosis. Patients who had a prior encounter at Parkland were more likely to have a SMMG. Patients established with Parkland primary care at diagnosis were more likely to have early-stage disease. In patients < 40, 24.6% presented with stage IV disease at diagnosis and 57.9% did not have a primary care provider. A positive family history of breast/ovarian cancer or any cancer was noted in 38.6% and 57.9% of patients < 40, respectively. Conclusions: In the cohort presented here, 27.8% of patients were between age 40-49, reflecting the impact of a gap in the guideline, which is now addressed in the most recent iteration of USPSTF guidelines. Connecting ED users to primary care, identifying higher risk individuals through family history, and increasing SMMG rate in the primary care setting are among strategies to reduce the rate of late-stage diagnosis of breast cancer, particularly among vulnerable patient populations.

Abstract 4797: Impact of COVID-19 pandemic on breast cancer screening and diagnosis stage in a large midwestern United States academic medical center

Abstract Background: Access to breast cancer screening mammogram services decreased in association with the COVID-19 pandemic. There is also evidence for increases in late-stage breast cancer diagnoses. Our objectives were to determine: 1) the COVID-19-affected period on mammogram screening, 2) the proportion of pandemic-associated missed or delayed mammogram screening visits overall and by race/ethnicity and age group, and 3) evidence for pandemic-associated shifts in diagnosis stage. Methods: Screening mammogram encounter data between 1-1-2019 and 12-31-2022 were extracted from EPIC for females ≥ 40 years old for the screening analysis. We used Bayesian state space models to describe weekly screening mammogram counts, modeling an interruption that phased in and out from 3-1-2020 to 9-1-2020. We used the posterior predictive distribution to simulate differences between a predicted, uninterrupted process and the observed screening mammogram counts. Breast cancer diagnoses at ≥ 21 years from the tumor registry between 12-1-2018 and 11-30-2021 were included in the stage analysis. We used logistic regression models to estimate late-stage diagnosis odds comparing matched three-month periods during the pandemic to before the pandemic. Results: A total of 319,492 encounters among 146,644 women were included. Model-estimated screening mammograms dropped by 98.8% (95% CI 95.1 to 100) between 3-15-2020 and 5-24-2020, returning to pre-pandemic levels or higher after this period. Drops in screening mammogram encounters did not vary significantly by race/ethnicity or age group (p > .75). Among 4,669 breast cancer diagnoses, we found no significant differences in the odds of late-stage diagnoses for any period vs. the same period before the pandemic (Table). Conclusions: These data suggest a short-term pandemic effect on screening mammograms. Evidence for increases in late-stage diagnoses is limited. These results may inform future pandemic planning. OR (95% CI) Period comparison 1.19 (0.74 to 1.91) 3/2020 to 5/2020 vs. 3/2019 to 5/2019 1.05 (0.69 to 1.60) 6/2020 to 8/2020 vs. 6/2019 to 8/2019 1.14 (0.73 to 1.78) 9/2020 to 11/2020 vs. 9/2019 to 11/2019 0.92 (0.60 to 1.41) 12/2020 to 2/2021 vs. 12/2019 to 2/2020 1.08 (0.71 to 1.65) 3/2021 to 5/2021 vs. 3/2019 to 5/2019 0.89 (0.59 to 1.33) 6/2021 to 8/2021 vs. 6/2019 to 8/2019 1.31 (0.84 to 2.04) 9/2021 to 11/2021 vs. 9/2019 to 11/2019 Citation Format: Kimberly J. Johnson, RJ Waken, Caitlin P. O'Connell, Derek Brown. Impact of COVID-19 pandemic on breast cancer screening and diagnosis stage in a large midwestern United States academic medical center [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4797.

Classification of Early Breast Cancer using Convolutional Neural Networks

Breast cancer is a life-threatening disease that impacts millions of people worldwide, necessitating heightened awareness and effective early detection strategies. Current methods for determining the presence and type of breast cancer in a clinical breast exam conducted by healthcare providers may be inaccurate, imprecise, and prone to error. The goal of this research project is to offer a second opinion for physicians to predict whether or not a patient has early onset breast cancer through a machine learning model developed from an image dataset, to significantly reduce the error in breast cancer staging, making the process more efficient in healthcare. Early detection is crucial for successful treatments of breast cancer, as treatment options in late-stage diagnosis of breast cancer can have a much worse prognosis. In the US, 240,000 cases of breast cancer are diagnosed per year in both men and women with a mortality rate of approximately 42,000 per year. [1] To approach this problem, I have tested an MLP Classifier, Logistic Regression, Ridge Classifier, Random Forest Classifier, Decision Tree Classifier, Support Vector Classifier, and trained a Convolutional Neural Network to compare various results and determine the best accuracy. The Convolutional Neural Network yielded impressive outcomes, reaching up to 79% testing accuracy and 98% training accuracy. The final model exhibits the capability to identify early signs of breast cancer and differentiate between malignant and benign tumours with relatively high accuracy showing the potential of AI in combination with medical imaging that can assist medical staff in diagnosis.

Disparities Contributing to Late-Stage Diagnosis of Lung, Colorectal, Breast, and Cervical Cancers: Rural and Urban Poverty in Florida.

Despite advances in cancer screening, late-stage cancer diagnosis is still a major cause of morbidity and mortality in the United States. In this study, we aim to understand demographic and geographic factors associated with receiving a late-stage diagnosis (LSD) of lung, colorectal, breast, or cervical cancer. (1) Methods: We analyzed data of patients with a cancer diagnosis between 2016 and 2020 from the Florida Cancer Data System (FCDS), a statewide population-based registry. To investigate correlates of LSD, we estimated multi-variable logistic regression models for each cancer while controlling for age, sex, race, insurance, and census tract rurality and poverty. (2) Results: Patients from high-poverty rural areas had higher odds for LSD of lung (OR = 1.23, 95% CI (1.10, 1.37)) and breast cancer (OR = 1.31, 95% CI (1.17,1.47)) than patients from low-poverty urban areas. Patients in high-poverty urban areas saw higher odds of LSD for lung (OR = 1.05 95% CI (1.00, 1.09)), breast (OR = 1.10, 95% CI (1.06, 1.14)), and cervical cancer (OR = 1.19, 95% CI (1.03, 1.37)). (3) Conclusions: Financial barriers contributing to decreased access to care likely drive LSD for cancer in rural and urban communities of Florida.

A Geospatial Model to Identify Areas Associated with Late- Stage Breast Cancer: A Spatial Epidemiology Approach.

The aim of this study was to show how a geospatial model can be used to identify areas with a higher probability for late-stage breast cancer (BC) diagnoses. Our study considered an ecological design. Clinical records at a tertiary care hospital were reviewed in order to obtain the place of residence and stage of the disease, which was classified as early (0-IIA) and late (IIB-IV) and whose diagnoses were made during the 2013-2017 period. Then, they were geolocated to identify the distribution and spatial trend. Subsequently, the pattern of location, i.e. scattered, random and concentrated, was statistically assessed and a geospatial model was elaborated to determine the probability of late diagnoses in the state of Jalisco, Mexico. There were 1 954 (N) geolocated BC diagnoses: 58.3% were late. During the five-year period, a southwest-northeast trend was identified, nearly 9.5% of the surface of Jalisco, where 6 out of 10 (n= 751) late- stage diagnoses were concentrated. A concentrated and statistically significant pattern was identified in the southern, central and northern Pacific area of Jalisco, where the geospatial model delimited the places with the highest probability of late clinical stages (p <0.05). The geographical differences associated with the late diagnoses of BC suggest it is necessary to adapt and focus the strategies for early detection as an alternative to create a major impact on the population. Reproducible analysis tools were used in other contexts where geolocation data are available to complement public policies and strategies aimed to control BC.

Abstract P1-06-01: Associations of historic and contemporary “redlining” with breast cancer stage at diagnosis

Abstract Breast cancer is the most commonly diagnosed cancer among US women across all racial/ethnic groups. Stage at diagnosis is one of the major factors determining breast cancer prognosis. The 5-year relative survival for breast cancer ranges from 99% for localized stage at diagnosis, to 84% for regional stage, and to 27% for distant (metastatic) stage breast cancer. The proportion of women diagnosed with breast cancer at later stages (regional and distant) in the US are higher among women with lower socio-economic status and among non-Hispanic Black women. Historic mortgage security redlining, implemented by the Home Owners’ Loan Corporation (HOLC) in the 1930s across numerous US cities, continues to have a negative influence on breast cancer stage at diagnosis, largely due to continued social and economic isolation and poor living environments resulting in many adverse consequences, including lower education, limited job opportunities, no/limited health insurance coverage, and suboptimal access to care, including cancer screening services. Additionally, studies have reported that living in areas with greater contemporary mortgage lending bias towards the non-Hispanic Black population (measured as higher odds of mortgage denial) is associated with late-stage breast cancer diagnosis in several US metropolitan areas. In this study, we aim to examine the association between the historic HOLC-based “redlining” and contemporary mortgage lending bias and stage of breast cancer at diagnosis among women aged 18 years and older in New Jersey diagnosed with first primary invasive breast cancer in 2010-2015 (N= 32,939). The study population was derived from the New Jersey State Cancer Registry. Historic “redlining” data based on 1930s neighborhood boundaries and transformed to corresponding 2010 census tracts borders were obtained from Inter-university Consortium for Political and Social Research. Mortgage lending bias score for the study period was calculated at the census tract level following methodology developed by Beyers and colleagues. Associations between census tract-level historic “redlining”, contemporary mortgage lending bias and breast cancer stage at diagnosis were evaluated using multinomial logistic regression models after adjusting for age alone, and then for age, race/ethnicity, marital status, and health insurance status. The study included 21,038 local, 9,765 regional, and 2,136 distant stage breast cancer cases. After adjusting for age, race/ethnicity, marital status, and health insurance, women living in historically redlined census tracts were more likely to be diagnosed with regional (OR=1.23; 95% CI 1.03-1.48) and distant (OR=1.55; 95% CI 1.09-2.22) stage breast cancer compared to women living in other census tracts. Odds for regional (OR=1.14; 95% CI 1.06-1.23) and distant (OR=1.33; 95% CI 1.16-1.53) stage breast cancer were also significantly higher for women living in areas with highest mortgage lending bias score. Stratifying by age (&amp;lt; 65 and &amp;gt;=65 years) showed similar patterns (data not shown). Both historic “redlining” and contemporary mortgage lending bias were associated with being diagnosed with breast cancer at later stages, notably distant stage. Targeting the legacy of systematic racism and addressing any contemporary discriminatory policies may help reduce breast cancer disparities in the diagnosis stage and thus mortality. Association between the historic redlining and contemporary mortgage lending bias and stage of breast cancer at diagnosis among women aged ≥18 years in New Jersey, 2010-2015 Note: For contemporary mortgage lending bias, highest score is indicative of highest bias (highest proportion of mortgage denial) towards Black people. *Fully adjusted model includes covariates: age, race/ethnicity, marital status, health insurance status ^Areas designated ”Best” were individual housing markets with sufficient levels of financing and were preserved exclusively for White and wealthy population. Areas defined as “Hazardous” were those lacking financial resources and were designated for Black and poor population. Citation Format: Daniel Wiese, Antoinette M. Stroup, Ahmedin Jemal, Kevin A. Henry, Farhad Islami. Associations of historic and contemporary “redlining” with breast cancer stage at diagnosis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-06-01.

Drivers of disparities in stage at diagnosis among women with breast cancer: South African breast cancers and HIV outcomes cohort.

In low- and middle-income countries (LMICs), advanced-stage diagnosis of breast cancer (BC) is common, and this contributes to poor survival. Understanding the determinants of the stage at diagnosis will aid in designing interventions to downstage disease and improve survival from BC in LMICs. Within the South African Breast Cancers and HIV Outcomes (SABCHO) cohort, we examined factors affecting the stage at diagnosis of histologically confirmed invasive breast cancer at five tertiary hospitals in South Africa (SA). The stage was assessed clinically. To examine the associations of the modifiable health system, socio-economic/household and non-modifiable individual factors, hierarchical multivariable logistic regression with odds of late-stage at diagnosis (stage III-IV), was used. The majority (59%) of the included 3497 women were diagnosed with late-stage BC disease. The effect of health system-level factors on late-stage BC diagnosis was consistent and significant even when adjusted for both socio-economic- and individual-level factors. Women diagnosed in a tertiary hospital that predominantly serves a rural population were 3 times (OR = 2.89 (95% CI: 1.40-5.97) as likely to be associated with late-stage BC diagnosis when compared to those diagnosed at a hospital that predominantly serves an urban population. Taking more than 3 months from identifying the BC problem to the first health system entry (OR = 1.66 (95% CI: 1.38-2.00)), and having luminal B (OR = 1.49 (95% CI: 1.19-1.87)) or HER2-enriched (OR = 1.64 (95% CI: 1.16-2.32)) molecular subtype as compared to luminal A, were associated with a late-stage diagnosis. Whilst having a higher socio-economic level (a wealth index of 5) reduced the probability of late-stage BC at diagnosis, (OR = 0.64 (95% CI: 0.47-0.85)). Advanced-stage diagnosis of BC among women in SA who access health services through the public health system was associated with both modifiable health system-level factors and non-modifiable individual-level factors. These may be considered as elements in interventions to reduce the time to diagnosis of breast cancer in women.

Disparities in Late-Stage Breast and Colorectal Cancer Diagnosis Among Hispanic, Non-Hispanic White, and Non-Hispanic Black Patients: a Retrospective Cohort Study of Texas Medicare Beneficiaries.

Disparities in late-stage breast or colorectal cancer diagnosis in younger populations are associated with social determinants of health (SDOH; education, poverty, housing, employment). We hypothesized that, in older Medicare beneficiaries, disparities in late-stage cancer diagnosis between Hispanic, non-Hispanic Black (NHB), and non-Hispanic White (NHW) patients would be associated with SDOH, comorbidities, and primary care physician (PCP) access. We analyzed 2005-2017 Texas Cancer Registry data linked with Medicare data for patients aged ≥ 66 (n = 86,501). Variables included age at diagnosis, sex, comorbidities, poverty level, education, PCP, and relevant cancer screening within 1year. For breast cancer in women (Hispanic, n = 6380; NHW, n = 39,225; NHB, n = 4055), a fully adjusted model showed significantly higher odds of late-stage cancer diagnosis only in NHB patients (odds ratio [OR] 1.11, 95% confidence interval [CI] 1.01-1.22) compared with NHW; adjustment for comorbidities and SDOH partially decreased the odds of late-stage diagnosis relative to NHWs. Interaction terms between race-ethnicity and poverty were not significant. For colorectal cancer, a fully adjusted multivariate model showed significantly higher odds of late-stage diagnosis only among NHBs (n = 3318, OR 1.29, 95% CI 1.19-1.40) relative to NHWs (n = 27,470); adjustment for SDOH partially decreased the odds of late-stage diagnosis in NHB patients. Interaction terms between race-ethnicity and poverty were not significant. Racial disparities in late-stage breast and colorectal cancer diagnoses remain after adjustment for SDOH and clinically relevant factors, underscoring the need to optimize access to screening and timely cancer treatment in racial/ethnic minorities.

DISPARITIES IN LATE-STAGE BREAST/COLORECTAL CANCER DIAGNOSIS AMONG HISPANIC, WHITE, &amp; BLACK TX MEDICARE PATIENTS

Abstract Disparities in late-stage breast or colorectal cancer diagnosis in younger populations are associated with Social Determinants of Health-SDOH (education, poverty, housing, employment). We hypothesized that—in older Medicare beneficiaries—disparities in late-stage cancer diagnosis between Hispanics, non-Hispanic Blacks (NHB) and non-Hispanic white (NHW) would be associated with SDOH, comorbidities and primary care-PCP access. We thus used 2005–2017 Texas Cancer Registry data linked with Medicare data for patients aged ≥66 (Nf86,501). Variables included were age at diagnosis, sex, comorbidities, poverty level, education, primary care provider, and breast/colorectal cancer screening within 1 year. For breast cancer in women (Hispanic n=6380, NHW n=39, 225, non-Hispanic Black n=4055), fully adjusted model showed significantly higher odds of late-stage cancer diagnosis only in NH Black patients (OR= 1.11 95% CI= 1.01–1.22) compared with NHW; adjustment for comorbidities and SDOH partially decreased the odds of late-stage diagnosis relative to NWH. Interaction terms between race-ethnicity and poverty were not significant. For colorectal cancer, fully adjusted multivariate model showed significantly higher odds of late-stage diagnosis among Hispanics (n=6053, OR=1.07;95% CI= 1.00–1.15) and NH-Black patients (n=3318, OR=1.29 95% CI= 1.19–1.40) relative to NHW (n=27,470); adjustment for SDOH was associated with a partial decrease in the high odds of late-stage diagnosis in Hispanic and NH Black patients. Interaction terms between race-ethnicity and poverty were not significant. Racial disparities in late-stage breast and colorectal cancer diagnoses remained after adjusting for SDOH and other clinically-relevant factors, underscoring needs for studies on optimizing access to screening and timely cancer treatments in racial/ethnic minorities.

A Scoping Review of Factors Contributing to Late-Stage Diagnosis of Breast Cancer in Racial and Ethnic Minority (African American and Hispanic) Women

This study aimed to examine the factors contributing to the late-stage diagnosis of breast cancer in African American and Hispanic women. Databases were electronically searched using Academic Search Complete; Alt Health Watch; APA PsycInfo; CINAHL with Full Text; Health and Psychosocial Instruments; Health Source—Consumer Edition; Health Source: Nursing/Academic Edition; and MEDLINE. Arksey and O’Malley’s methodological framework was followed. The five levels of the socio-ecological model were used as theoretical guidelines. Social determinants of health (at the individual, interpersonal, organizational, community, and policy levels) limited the adherence to breast cancer screening and led to the late-stage diagnosis of breast cancer, which impacted the survival rates. Multi-level efforts are required to address the social determinants of health, which impede care accessibility, and improve health outcomes for women experiencing health inequalities.

Effects of housing quality, housing stability, and contemporary mortgage lending bias on breast cancer stage at diagnosis among older women in the United States.

e18557 Background: The purpose of this study was to examine the association between measures of housing quality, stability, and access on breast cancer stage at diagnosis among older women living in the United States. Methods: This study included 67,588 women aged 66-90 with data from the SEER-Medicare linked database. The primary outcome was breast cancer stage at diagnosis. Multinomial regression models were performed using a three-category outcome (stage 0, early-stage (I-II), late-stage (III-IV)). The key independent variables were median housing value, percentage living in the same house as the previous year, percentage owner occupied homes, and an index of contemporary mortgage lending bias (redlining). Results: In adjusted models, higher contemporary mortgage lending bias was significantly associated with later-stage diagnosis (RR = 1.10 1.02-1.20; RR = 1.31, 95% CI 1.16-1.49; RR = 1.41, 95% CI 1.24-1.60 for Least to High, respectively). Median housing value was inversely associated with later-stage diagnosis, but to a lesser degree than mortgage lending bias (RR = 0.88, 95%CI 0.80-0.96; RR = 0.77, 95% CI 0.68-0.88 for second and third tertiles, respectively). Owner occupancy and tenure were not significantly associated with late-stage diagnosis in adjusted models. Conclusions: Contemporary mortgage lending bias demonstrated a significant dose-response relationship with later stage at diagnosis of breast cancer in this cohort of elderly women. Policy interventions aimed at reducing the effects of redlining with the goal of decreasing late-stage breast cancer diagnosis to improve prognosis should be considered. Table. Relative risk of late stage breast cancer diagnosis based on measures of housing quality and stability, as well as redlining. Risk is relative to the base outcome, stage 0. Values for the first tertile of housing quality and stability as well as the “least” category for redlining are not shown in this table as they are the base outcome which the other values are compared to. Standard error was adjusted for MSA clustering effects in all models.[Table: see text]

Timing of Medicaid Enrollment, Late-Stage Breast Cancer Diagnosis, Treatment Delays, and Mortality.

BackgroundDisrupted and delayed Medicaid coverage has been consistently associated with lower rates of cancer screening and early-stage cancer diagnosis compared with continuous coverage. However, the relationships between Medicaid coverage timing, breast cancer treatment delays, and survival are less clear.MethodsUsing the linked Missouri Cancer Registry-Medicaid claims data, we identified 4583 women diagnosed with breast cancer between 2007 and 2016. We used logistic regression to estimate odds ratios (ORs) of late-stage diagnosis and treatment delays for prediagnosis (>30 days, >90 days, and >1 year before diagnosis) vs peridiagnosis enrollment. Cox proportional hazards models were used to estimate the hazard ratio (HR) of breast cancer-specific mortality for pre- vs postdiagnosis enrollment.ResultsPatients enrolled in Medicaid more than 30 days before diagnosis were less likely to be diagnosed at a late stage compared with those enrolled in Medicaid peridiagnosis (OR = 0.69, 95% confidence interval [CI] = 0.60 to 0.79). This result persisted using enrollment 90-day (OR = 0.64, 95% CI = 0.56 to 0.74) and 1-year thresholds (OR = 0.55, 95% CI = 0.47 to 0.65). We did not observe a difference in the likelihood of treatment delays between the 2 groups. After adjustment for sociodemographic factors, there was no statistically significant difference in the risk of breast cancer mortality for patients enrolled more than 30 days prediagnosis relative to patients enrolled peridiagnosis (HR = 0.98, 95% CI = 0.83 to 1.14), but a lower risk was observed for patients enrolled prediagnosis when using 90 days (HR = 0.85, 95% CI = 0.72 to 0.999) or 1 year (HR = 0.79, 95% CI = 0.66 to 0.96) as the threshold.ConclusionsWomen with breast cancer who enroll in Medicaid earlier may benefit from earlier diagnoses, but only longer-term enrollment may have survival benefits.

Abstract P3-04-04: Multi-institutional perspective on screening mammography and breast cancer stage at diagnosis during the COVID-19 pandemic

Abstract Background: During the SARS-CoV-2 pandemic in 2020, the use of routine screening mammography (SM) and diagnostic mammography (DM) was limited for several months in order to reduce patient exposure and redeploy medical personnel. Previous studies suggest such delays result in more late-stage breast cancer diagnoses. We hypothesized that this impact would vary between institutions depending on regional variations in shutdown periods and the ability and willingness of patients to resume screening. Methods: Patients diagnosed with invasive breast cancers from 2016-2020 were identified using the Beth Israel Deaconess Medical Center (BIDMC) and the Duke University Medical Center (DUMC) cancer registries. Rates of mammography were ascertained from billing data. Baseline patient characteristics, demographics, and clinical information were gathered and cross-referenced with the electronic medical record. Late-stage was defined as Anatomic Stage III-IV disease (AJCC 8th edition). Chi-squared analysis was used to examine monthly distributions in stage at presentation for diagnosis in 2016-2019 compared to in 2020 at each institution. Results: There were 5907 patients diagnosed with invasive breast cancer between 2016-2019 (1597 at BIDMC and 4310 at DUMC) and 1075 in 2020 (333 and 742, respectively). Mammography was limited from 3/16/20-6/8/20 at BIDMC and from 3/16/20-4/20/20 at DUMC. There were fewer SM at each institution during their respective shutdown periods in 2020 than in the same months in 2019: BIDMC 1713 versus 8566 (80% reduction) and at DUMC 1649 versus 5698 (71% reduction). Following the pandemic shutdown, SM volume increased in July-December 2020 compared to July-December 2019 (108% at BIDMC and 116% at DUMC). The proportion of patients diagnosed with late-stage disease at BIDMC was greater in 2020 than in 2016-2019, at 12.6% and 6.6%, respectively (p &amp;lt; 0.001); 86% of late-stage diagnoses and 68% of all diagnoses in 2020 at BIDMC occurred from July-December following the initial shutdown period. The proportion of patients diagnosed with late-stage disease at DUMC in these two cohorts were 14.3% in 2020 and 16.2%% in 2016-2019 (p = 0.1); 50% of late-stage diagnoses and 51% of all diagnoses in 2020 at DUMC occurred in the period following the initial shutdown from July-December. Conclusion: We identified variation between two large academic medical centers in the impact of the SARS-CoV-2 pandemic shutdown on the proportion of late-stage breast cancer diagnoses. These dissimilar outcomes may be the result of differences in referral patterns as well as regional differences in the approach to SM during the pandemic. In particular, a shorter closure time and substantial increase in SM volume following the initial shutdown period in the Southeast region may have prevented an increase in late-stage diagnoses. Further information and analysis may help suggest additional strategies to minimize adverse effects of reduced cancer screening in future public-health emergencies. Table 1.Proportion of SM in 2020 compared to 2019 and proportion of late disease per month in 2020BIDMC 2020JanFebMarAprMayJunJulAugSepOct NovDecSM, %96109480034104106112105113112Late Disease, n(%)0 (0)2 (10)1 (3)0 (0)0 (0)3 (21)1 (7)9 (21)10 (15)10 (19)5 (12)1 (8)DUMC 2020JanFebMarAprMayJunJulAugSepOctNovDecSM, %565860040123111109127116120119Late Disease, n(%)12 (11)13 (19)14 (19)4 (18)4 (12)6 (11)12 (13)10 (13)6 (10)12 (19)7 (13)5 (13) Citation Format: Sarah J Stephens, Maxwell R Lloyd, Julian C Hong, Tejas Mehta, Ted A James, Rachel Blitzblau, Abram Recht, Daphna Y Spiegel. Multi-institutional perspective on screening mammography and breast cancer stage at diagnosis during the COVID-19 pandemic [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-04-04.

Comparison of Early- and Late-Stage Breast and Colorectal Cancer Diagnoses During vs Before the COVID-19 Pandemic

Comparison of Early- and Late-Stage Breast and Colorectal Cancer Diagnoses During vs Before the COVID-19 Pandemic

Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach.

Disparities in the stage at diagnosis for breast cancer have been independently associated with various contextual characteristics. Understanding which combinations of these characteristics indicate highest risk, and where they are located, is critical to targeting interventions and improving outcomes for patients with breast cancer. The study included women diagnosed with invasive breast cancer between 2009 and 2018 from 680 U.S. counties participating in the Surveillance, Epidemiology, and End Results program. We used a machine learning approach called Classification and Regression Tree (CART) to identify county "phenotypes," combinations of characteristics that predict the percentage of patients with breast cancer presenting with late-stage disease. We then mapped the phenotypes and compared their geographic distributions. These findings were further validated using an alternate machine learning approach called random forest. We discovered seven phenotypes of late-stage breast cancer. Common to most phenotypes associated with high risk of late-stage diagnosis were high uninsured rate, low mammography use, high area deprivation, rurality, and high poverty. Geographically, these phenotypes were most prevalent in southern and western states, while phenotypes associated with lower percentages of late-stage diagnosis were most prevalent in the northeastern states and select metropolitan areas. The use of machine learning methods of CART and random forest together with geographic methods offers a promising avenue for future disparities research. Local interventions to reduce late-stage breast cancer diagnosis, such as community education and outreach programs, can use machine learning and geographic modeling approaches to tailor strategies for early detection and resource allocation.

Factors associated with Late-Stage Diagnosis of Breast Cancer among Egyptian Women.

BackgroundDetecting Breast Cancer (BC) at earlier stages comes with a better prognosis, while diagnosis at late stages has poor outcomes and escalating mortality rates from the disease. The study aims to understand the factors associated with the late-stage diagnosis of BC in Egypt.Design and MethodsA sample of 400 women with a pathologically confirmed BC were enrolled from one of the main tertiary cancer hospitals in Egypt. A cross-sectional study design was conducted. The collected data included: clinical characteristics of the tumor, socio-demographic characteristics of the studied women, reproductive and medical history, screening practices, and the time from symptom onset to definite diagnosis as suspected predictors to the stage of BC at diagnosis. Data was analyzed by crude odds ratios (95% confidence interval) and multivariate logistic regression analysis.ResultsThe study revealed that 47.5% were diagnosed at late stages (40% at stage III/ 7.5% at stage IV), while (52.5%) were diagnosed at early stages (6.5% at stage I/46% at stage II). A binary logistic regression model showed that unmarried females (p=0.012), had non-luminal molecular subtype of BC including HER2 enriched and triple-negative tumors (p<0.001), presentation with breast changes and a non-palpable lump (p=0.024) or non-breast symptoms (P=0.002), a delay longer than 3 months to the first presentation by patients (p<0.001), and a delay to definite diagnosis longer than 1 month by providers (p<0.001) were significant risk factors of late-stage diagnosis of BC.ConclusionsLate-stage diagnosis of BC in Egypt is associated with the aggressiveness of some molecular subtypes and other important modifiable factors that should be addressed.

Translational Research in Cancer Screening: Long-Term Population-Action Bridges to Diffuse Adherence.

The population-level implementation of innovative, evidence-based medical recommendations for adopting health-behaviors depends on the last link in the translation chain: the users. “User-friendly” medical interventions aimed at engaging users to adopt recommended health behaviors are best developed in a collaborative bio-medical and social sciences setting. In the 1990s, National Breast and Colorectal Cancer Early Detection Programs were launched at the Israeli Department of Community Medicine and Epidemiology. Operating under the largest HMO (Health Maintenance Organization) in Israel (“Clalit Health Services”), the department had direct access to HMO community primary-care clinics’ teams, insured members, and medical records. Academically affiliated, the department engaged in translational research. In a decades-long translational process, this multi-disciplinary unit led a series of interventions built upon basic and applied behavioral/social science phenomena such as framing, “Implementation Intentions,” and “Question-Behavior-Effect”. A heterogeneous team of disciplinary specialists created an integrated scientific environment. In order to enhance screening, the team focused on the establishment of a systematic mechanism actively inviting programs’ “users” (average-risk targeted individuals on the national level), and continuously applied social and health psychology concepts to study individuals’ perceptions, expectations, and needs related to cancer screening. The increase in adherence to screening recommendations was slow and incremental. A decrease in late-stage breast and colorectal cancer diagnoses was observed nationally, but participation was lower than expected. This paper positions screening adherence as a unique challenge and proposes new social and network avenues to enhance future participation.

Is There a Relationship between County-Level Food Insecurity Rates and Breast Cancer Stage at Diagnosis?

The relationship between county food insecurity (FI) rate and breast cancer stage at diagnosis is not clear. Using 2010-2016 Surveillance Epidemiology and End Results (SEER) and Feeding America data we analyzed the association between county FI rate in quartiles (low, medium, high, very high) and breast cancer stage at diagnosis among adult females (≥18 years). We also analyzed the effect of insurance status and county poverty level on this relationship, and whether this relationship varies among non-elderly (<65 years) and elderly (≥ 65 years) individuals. Bivariate and multivariable multilevel logistic regression were used for analyses. Bivariate analysis showed increased likelihood of late-stage breast cancer with increasing county FI rate. This relationship persisted after adjusting for insurance status but was no longer significant after adjusting for county-level poverty rate. There was a statistically significant association between counties with very high food insecurity rates and late-stage breast cancer diagnosis (OR = 1.07; 95% CI = 1.00, 1.14) among the elderly population. Very high county food insecurity rate was associated with late-stage breast cancer among elderly women. Population-level interventions focused on counties with very high food insecurity rates could reduce disparities in stage at breast cancer diagnosis among elderly women.

180P Factors associated with late-stage diagnosis of breast cancer among Egyptian women

Tumor stage of breast cancer (BC) crucially determines its prognosis. Detecting BC at earlier stages comes with better prognosis, better response to treatment, and thereby higher survival rates, while diagnosis at late-stages (stage III & IV) has poor outcomes and responsible for the escalating mortality rates from the disease. Late-stage diagnosis of BC is a common problem in limited resources setting. The aim of the study was to understand the underlying factors associated with the late-stage diagnosis of BC in Egypt.