Disparities in Female Breast Cancer Stage at Diagnosis in New Jersey: A Spatial-Temporal Analysis

Medicaid Expansion Associated With Reduction in Geospatial Breast Cancer Stage at Diagnosis Disparities

CONTEXT

Prior studies demonstrate that Medicaid expansion has been associated with earlier-stage breast cancer diagnosis among women with low income, likely through increased access to cancer screening services. However, how this policy change has impacted geospatial disparities in breast cancer stage at diagnosis is unclear.

OBJECTIVE

To examine whether there were reductions in geospatial disparities in advanced stage breast cancer at diagnosis in Ohio after Medicaid expansion.

DESIGN

The study included 33 537 women aged 40 to 64 years diagnosed with invasive breast cancer from the Ohio Cancer Incidence Surveillance System between 2010 and 2017. The space-time scan statistic was used to detect clusters of advanced stage at diagnosis before and after Medicaid expansion. Block group variables from the Census were used to describe the contextual characteristics of detected clusters.

RESULTS

The percentage of local stage diagnosis among women with breast cancer increased from 60.2% in the pre-expansion period (2010-2013) to 62.6% in the post-expansion period (2014-2017), while the uninsured rate among those women decreased from 13.7% to 7.5% during the same period. Two statistically significant ( P < .05) and 6 nonsignificant spatial clusters ( P > .05) of advanced stage breast cancer cases were found in the pre-expansion period, while none were found in the post-expansion period. These clusters were in the 4 largest metropolitan areas in Ohio, and individuals inside the clusters were more likely to be disadvantaged along numerous socioeconomic factors.

CONCLUSIONS

Medicaid expansion has played an important role in reducing geospatial disparities in breast cancer stage at diagnosis, likely through the reduction of advanced stage disease among women living in socioeconomically disadvantaged communities.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

The use of machine learning methods of CART and random forest together with geographic methods offers a promising avenue for future disparities research.

IMPACT

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.

A space-time analysis of disparities in age at diagnosis of autism spectrum disorder: environmental and socioeconomic risk factors

INTRODUCTION

Early diagnosis of autism is a critical step for gaining early intervention. The earlier interventions begin, the greater chance to reduce symptoms of autism over the lifespan. Despite the improvement in early diagnosis, age at diagnosis varies by residential locations. In order to improve early screening services, this study aims to identify geographic clusters of early and late diagnosis of autism, in addition, it is aimed to compare cases inside the clusters with the rest of the province on characteristics and socioeconomic factors.

MATERIALS AND METHODS

Survey data were collected from 163 autistics born from 1996 to 2011 in Isfahan Province, Iran. As this study found diagnosis of autism occur at an earlier age among children, who on average every 2.5 months increased for each year of age, distance from regression line has been used to determine how early a case was diagnosed compared to other identified cases. After dividing cases into 5 classes based on their distances from the regression line, the ordinal based space-time scan statistic in SaTScan was used to identify geographic areas within specific time periods that have significantly elevated proportions of autistic children who received diagnosis at the earlier or later stages.

RESULTS

The space-time analysis identified two geographic areas that age of diagnosis was inconsistent with the overall study area, the first area has an early diagnosis in central part of Isfahan megacity between 1998 and 2006 (P = .001), the second area shows to have a late diagnosis centered by Najafabad from 2010 through 2015 (P = .007).

CONCLUSIONS

The result of our spatial analysis can be used to evaluate the performance of diagnosis services and additionally provide information to target specific at-risk population for further interventions.

Impact of social determinants of health on cancer care: a survey of community oncologists

OBJECTIVE

Cancer survival rates have improved over the past few decades, yet socioeconomic disparities persist. Social determinants of health (SDOH) have consistently been shown to correlate with health outcomes. The objective of this study was to characterise oncologists' perceptions of the impact of SDOH on their patients, and their opinions on how these effects could be remediated.

DESIGN

Cross-sectional survey of physicians.

SETTING

Web-based survey completed prior to live meetings held between February and April 2020.

PARTICIPANTS

Oncologists/haematologists from across the USA.

EXPOSURE

Clinical practice in a community-based or hospital-based setting.

MAIN OUTCOME AND MEASURE

Physician responses regarding how SDOH affected their patients, which factors represented the most significant barriers to optimal health outcomes and how the impact of SDOH could be mitigated through assistance programmes.

RESULTS

Of the 165 physicians who completed the survey, 93% agreed that SDOH had a significant impact on their patients' health outcomes. Financial security/lack of insurance and access to transportation were identified most often as the greatest barriers for their patients (83% and 58%, respectively). Eighty-one per cent of physicians indicated that they and their staff had limited time to spend assisting patients with social needs, and 76% reported that assistance programmes were not readily accessible. Government organisations, hospitals, non-profit organisations and commercial payers were selected by 50% or more of oncologists surveyed as who should be responsible for delivering assistance programmes to patients with social needs; 42% indicated that pharmaceutical manufacturers should also be responsible.

CONCLUSION

Our survey found that most oncologists were aware of the impact of SDOH on their patients but were constrained in their time to assist patients with social needs. The physicians in our study identified a need for more accessible assistance programmes and greater involvement from all stakeholders in addressing SDOH to improve health outcomes.

Spatial distribution of advanced stage diagnosis and mortality of breast cancer: Socioeconomic and health service offer inequalities in Brazil

Breast cancer presents high incidence and mortality rates, being considered an important public health issue. Analyze the spatial distribution pattern of late stage diagnosis and mortality for breast cancer and its correlation with socioeconomic and health service offer-related population indicators. Ecological study, developed with 161 Intermediate Region of Urban Articulation (IRUA). Mortality data were collected from the Mortality Information System (MIS). Tumor staging data were extracted from the Hospital Cancer Registry (HCR). Socioeconomic variables were obtained from the Atlas of Human Development in Brazil; data on medical density and health services were collected from the National Registry of Health Institutions (NRHI) and Supplementary National Health Agency. Global Moran's Index and Local Indicator of Spatial Association (LISA) were utilized to verify the existence of territorial clusters. Multivariate analysis used models with global spatial effects. The proportion of late stage diagnosis of breast cancer was 39.7% (IC 39.4–40.0). The mean mortality rate for breast cancer, adjusted by the standard world population was 10.65 per 100,000 women (± 3.12). The proportion of late stage diagnosis presented positive spatial correlation with Gini’s Index (p = 0.001) and negative with the density of gynecologist doctors (p = 0.009). The adjusted mortality rates presented a positive spatial correlation with the Human Development Index (p<0.001) and density of gynecologist doctors (p<0.001). Socioeconomic and health service offer-related inequalities of the Brazilian territory are determinants of the spatial pattern of breast cancer morbimortality in Brazil.

Do mobile units contribute to spatial accessibility to mammography for uninsured women?

Limited spatial accessibility to mammography, and socioeconomic barriers (e.g., being uninsured), may contribute to rural disparities in breast cancer screening. Although mobile mammography may contribute to population-level access, few studies have investigated this relationship. We measured mammography access for uninsured women using the variable two-step floating catchment area (V2SFCA) method, which estimates access at the local level using estimated potential supply and demand. Specifically, we measured supply with mammography machine certifications in 2014 from FDA and brick-and-mortar and mobile facility data from the community-based Breast Screening and Patient Navigation (BSPAN) program. We measured potential demand using Census tract-level estimates of female residents aged 45-74 from 5-year 2012-2016 American Community Survey data. Using the sign test, we compared mammography access estimates based on 3 facility groupings: FDA-certified, program brick-and-mortar only, and brick-and-mortar plus mobile. Using all mammography facilities, accessibility was high in urban Dallas-Ft. Worth, low for the ring of adjacent counties, and high for rural counties outlying this ring. Brick-and-mortar-based estimates were lower for the outlying ring, and mobile-unit contribution to access was observed more in urban tracts. Weak mobile-unit contribution across the study area may indicate suboptimal dispatch of mobile units to locations. Geospatial methods could identify the optimal locations for mobile units, given existing brick-and-mortar facilities, to increase access for underserved areas.

Breast Cancer Incidence and Mortality by Molecular Subtype: Statewide Age and Racial/Ethnic Disparities in New Jersey

The objective of this study was to assess breast cancer incidence and mortality rates by molecular subtype for cases diagnosed in New Jersey. Data on all primary, histologically confirmed, invasive breast cancers diagnosed among women between January 1, 2008 and December 31, 2013 were retrieved from the New Jersey State Cancer Registry. Age-adjusted incidence rates were calculated for each subtype, by ageandrace/ethnicity. Logistic regression models, Cox proportional hazards models, and Kaplan Meier curves were used to describe the relative risks for breast cancer incidence, mortality, and survival, respectively. In this population-based sample of 32,770 breast cancer cases, non-Hispanic Blacks (NHBs) had the highest triple-negative breast cancer (TNBC) incidence rate (17.8 per 100,000, 95% CI 16.5-19.2) compared to other races/ethnicities. NHBs had also higher odds of TNBC (OR 2.1, 95% CI 1.95-2.36) and higher hazards of death when diagnosed with TNBC (HR 1.28, 95% CI 1.05-1.56), luminal A (HR 1.64, 95% CI 1.41-1.91), or luminal B (HR 1.54, 95% CI 1.10-2.15) than non-Hispanic Whites (NHWs). Younger women (20-39 years) had higher odds of TNBC (OR 1.77, 95% CI 1.54-2.02) and luminal B (OR 1.56, 95% CI 1.35-1.80) compared to women 50-64 years; minority women had higher odds of non-luminal HER2-expressing and lower odds of luminal A than NHWs. TNBC was associated with the poorest survival rates. These findings highlight a need for enhanced screening to promote earlier diagnosis and improve breast cancer outcomes, particularly in minorities and younger women, which will be essential for achieving health equity.

Mapping Variation in Breast Cancer Screening: Where to Intervene?

Small geographic areas with lower mammography screening participation rates may reflect gaps in screening efforts. Our objective was to use spatial analyses to understand disparities in mammography screening use and to identify factors to increase its uptake in areas that need it in Lyon metropolitan area, France. Data for screened women between the ages of 50 and 74 were analyzed. Census blocks of screened and non screened women were extracted from the mammography screening programme 2015-2016 dataset. We used spatial regression models, within a generalized additive framework to determine clusters of census blocks with significantly higher prevalence of non-participation of mammography screening. Smoothed risk maps were crude and adjusted on the following covariates: deprivation index and opportunistic screening. Among 178,002 women aged 50 to 74, 49.9% received mammography screening. As hypothesized, women living in highly deprived census blocks had lower participation rates compared to less deprived blocks, 45.2% vs. 51.4% p < 0.001. Spatial analyses identified four clusters, one located in an urban area and three in suburban areas. Moreover, depending on the location of the cluster, the influence came from different variables. Knowing the impact of site-specific risk factors seems to be important for implementing an appropriate prevention intervention.