Spatial association of racial/ethnic disparities between late-stage diagnosis and mortality for female breast cancer: where to intervene?

Geographical Variation in Social Determinants of Female Breast Cancer Mortality Across US Counties

Importance

Breast cancer mortality is complex and traditional approaches that seek to identify determinants of mortality assume that their effects on mortality are stationary across geographic space and scales.

Objective

To identify geographic variation in the associations of population demographics, environmental, lifestyle, and health care access with breast cancer mortality at the US county-level.

Design, Setting, and Participants

This geospatial cross-sectional study used data from the Surveillance, Epidemiology, and End Results (SEER) database on adult female patients with breast cancer. Statistical and spatial analysis was completed using adjusted mortality rates from 2015 to 2019 for 2176 counties in the US. Data were analyzed July 2022.

Exposures

County-level population demographics, environmental, lifestyle, and health care access variables were obtained from open data sources.

Main Outcomes and Measures

Model coefficients describing the association between 18 variables and age-adjusted breast cancer mortality rate. Compared with a multivariable linear regression (OLS), multiscale geographically weighted regression (MGWR) relaxed the assumption of spatial stationarity and allowed for the magnitude, direction, and significance of coefficients to change across geographic space.

Results

Both OLS and MGWR models agreed that county-level age-adjusted breast cancer mortality rates were significantly positively associated with obesity (OLS: β, 1.21; 95% CI, 0.88 to 1.54; mean [SD] MGWR: β, 0.72 [0.02]) and negatively associated with proportion of adults screened via mammograms (OLS: β, -1.27; 95% CI, -1.70 to -0.84; mean [SD] MGWR: β, -1.07 [0.16]). Furthermore, the MGWR model revealed that these 2 determinants were associated with a stationary effect on mortality across the US. However, the MGWR model provided important insights on other county-level factors differentially associated with breast cancer mortality across the US. Both models agreed that smoking (OLS: β, -0.65; 95% CI, -0.98 to -0.32; mean [SD] MGWR: β, -0.75 [0.92]), food environment index (OLS: β, -1.35; 95% CI, -1.72 to -0.98; mean [SD] MGWR: β, -1.69 [0.70]), exercise opportunities (OLS: β, -0.56; 95% CI, -0.91 to -0.21; mean [SD] MGWR: β, -0.59 [0.81]), racial segregation (OLS: β, -0.60; 95% CI, -0.89 to -0.31; mean [SD] MGWR: β, -0.47 [0.41]), mental health care physician ratio (OLS: β, -0.93; 95% CI, -1.44 to -0.42; mean [SD] MGWR: β, -0.48 [0.92]), and primary care physician ratio (OLS: β, -1.46; 95% CI, -2.13 to -0.79; mean [SD] MGWR: β, -1.06 [0.57]) were negatively associated with breast cancer mortality, and that light pollution was positively associated (OLS: β, 0.48; 95% CI, 0.24 to 0.72; mean [SD] MGWR: β, 0.27 [0.04]). But in the MGWR model, the magnitude of effect sizes and significance varied across geographical regions. Inversely, the OLS model found that disability was not a significant variable for breast cancer mortality, yet the MGWR model found that it was significantly positively associated in some geographical locations.

Conclusions and Relevance

This cross-sectional study found that not all social determinants associated with breast cancer mortality are spatially stationary and provides spatially explicit insights for public health practitioners to guide geographically targeted interventions.

Racial/Ethnic Disparity in the Relationship of Mental and Physical Health With Colorectal Cancer Screening Utilization Among Breast and Prostate Cancer Survivors

PURPOSE

We examined colorectal cancer (CRC) screening utilization among non-Hispanic White, non-Hispanic Black (NHB), non-Hispanic other (NHO)/Hispanic cancer survivors. We also determined whether experiencing poor physical and/or mental health affects CRC screening utilization in breast and prostate cancers across different racial/ethnic groups.

METHODS

Data from years 2016, 2018, and 2020 of the Behavioral Risk Factor Surveillance System on 3,023 eligible treatment-utilizing cancer survivors with complete treatment were used. We performed descriptive statistics and multivariable logistic regression to examine the mentioned association.

RESULTS

Among 3,023 eligible survivors, 67.7% of NHO/Hispanic survivors demonstrated lower CRC screening use compared with non-Hispanic White (82%) and NHB (89%) survivors (P < .001). In multivariable analysis, having frequent (14-30 days) poor mental health was associated with lower odds of receiving CRC screening among NHB (odds ratio [OR], 0.32; 95% CI, 0.11 to 0.95) and NHO/Hispanic (OR, 0.39; 95% CI, 0.18 to 0.81) survivors. Similar results in physical health were also found in NHB (OR, 0.34; 95% CI, 0.13 to 0.91) and NHO/Hispanic (OR, 0.22; 95% CI, 0.05 to 0.91) groups. Among those experienced both frequent poor mental and physical health, NHB/NHO/Hispanic were less likely to be screened for CRC (OR, 0.05; 95% CI, 0.02 to 0.10).

CONCLUSION

NHO/Hispanic survivors demonstrated lower CRC screening use. Frequent poor mental and/or physical health was strongly associated with lower CRC screening use among NHB and NHO/Hispanic survivors. Our study suggests that cancer survivorship care considering mental and physical health status may improve adherence to CRC screening recommendation (for secondary cancer prevention) for NHB, NHO, and Hispanic survivors.

Variation in and Factors Associated With US County-Level Cancer Mortality, 2008-2019

IMPORTANCE

The association between cancer mortality and risk factors may vary by geography. However, conventional methodological approaches rarely account for this variation.

OBJECTIVE

To identify geographic variations in the association between risk factors and cancer mortality.

DESIGN, SETTING, AND PARTICIPANTS

This geospatial cross-sectional study used county-level data from the National Center for Health Statistics for individuals who died of cancer from 2008 to 2019. Risk factor data were obtained from County Health Rankings & Roadmaps, Health Resources and Services Administration, and Centers for Disease Control and Prevention. Analyses were conducted from October 2021 to July 2022.

MAIN OUTCOMES AND MEASURES

Conventional random forest models were applied nationwide and by US region, and the geographical random forest model (accounting for local variation of association) was applied to assess associations between a wide range of risk factors and cancer mortality.

RESULTS

The study included 7 179 201 individuals (median age, 70-74 years; 3 409 508 women [47.5%]) who died from cancer in 3108 contiguous US counties during 2008 to 2019. The mean (SD) county-level cancer mortality rate was 177.0 (26.4) deaths per 100 000 people. On the basis of the variable importance measure, the random forest models identified multiple risk factors associated with cancer mortality, including smoking, receipt of Supplemental Nutrition Assistance Program (SNAP) benefits, and obesity. The geographical random forest model further identified risk factors that varied at the county level. For example, receipt of SNAP benefits was a high-importance factor in the Appalachian region, North and South Dakota, and Northern California; smoking was of high importance in Kentucky and Tennessee; and female-headed households were high-importance factors in North and South Dakota. Geographic areas with certain high-importance risk factors did not consistently have a corresponding high prevalence of the same risk factors.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, the associations between cancer mortality and risk factors varied by geography in a way that did not correspond strictly to risk factor prevalence. The degree to which other place-specific characteristics, observed and unobserved, modify risk factor effects should be further explored, and this work suggests that risk factor importance may be a preferable paradigm for selecting cancer control interventions compared with risk factor prevalence.

"I Struggle with Breast Cancer and I Struggle with God": Insights from African American Breast Cancer Survivors

PURPOSE

Recognizing that spiritual and religious beliefs are personal and vary within communities, the purpose of this qualitative study was to explore the influence of these beliefs on experiences with breast cancer care and social support among African American Christian breast cancer survivors.

METHODS

Forty-seven African American breast cancer survivors participated in focus groups (n = 7) in three northeastern urban cities. We used thematic analyses to identify major themes.

RESULTS

Three themes emerged relating to how spirituality influenced participants' cancer journeys: (1) struggling with God, (2) reclaiming my power, and (3) needing religious social support. Participants described the rhythmic flow of their spiritual beliefs as they navigated their lived experiences during diagnosis, treatment, and post-treatment. Spirituality was intimately intertwined with their illness experience as they grappled with their health and well-being.

CONCLUSIONS

Participants used spirituality as an avenue to cope and navigate through their diagnosis and treatment. These spiritual relationships created "church families" and provided the survivors' access to cancer support groups, financial support, and therapeutic support. Our findings support faith-based approaches to health promotion and call for more studies to understand the influence of religion on health.

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 hot spots of breast cancer mortality in the United States: place matters for Blacks and Hispanics

PURPOSE

The goals of this study were to identify geographic and racial/ethnic variation in breast cancer mortality, and evaluate whether observed geographic differences are explained by county-level characteristics.

METHODS

We analyzed data on breast cancer deaths among women in 3,108 contiguous United States (US) counties from years 2000 through 2015. We applied novel geospatial methods and identified hot spot counties based on breast cancer mortality rates. We assessed differences in county-level characteristics between hot spot and other counties using Wilcoxon rank-sum test and Spearman correlation, and stratified all analysis by race/ethnicity.

RESULTS

Among all women, 80 of 3,108 (2.57%) contiguous US counties were deemed hot spots for breast cancer mortality with the majority located in the southern region of the US (72.50%, p value < 0.001). In race/ethnicity-specific analyses, 119 (3.83%) hot spot counties were identified for NH-Black women, with the majority being located in southern states (98.32%, p value < 0.001). Among Hispanic women, there were 83 (2.67%) hot spot counties and the majority was located in the southwest region of the US (southern = 61.45%, western = 33.73%, p value < 0.001). We did not observe definitive geographic patterns in breast cancer mortality for NH-White women. Hot spot counties were more likely to have residents with lower education, lower household income, higher unemployment rates, higher uninsured population, and higher proportion indicating cost as a barrier to medical care.

CONCLUSIONS

We observed geographic and racial/ethnic disparities in breast cancer mortality: NH-Black and Hispanic breast cancer deaths were more concentrated in southern, lower SES counties.

Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality

In the United States, early detection methods have contributed to the reduction of overall breast cancer mortality but this pattern has not been observed uniformly across all racial groups. A vast body of research literature shows a set of health care, socio-economic, biological, physical, and behavioural factors influencing the mortality disparity. In this paper, we review the modelling frameworks, statistical tests, and databases used in understanding influential factors, and we discuss the factors documented in the modelling literature. Our findings suggest that disparities research relies on conventional modelling and statistical tools for quantitative analysis, and there exist opportunities to implement data-based modelling frameworks for (1) exploring mechanisms triggering disparities, (2) increasing the collection of behavioural data, and (3) monitoring factors associated with the mortality disparity across time.

Does Survival Vary for Breast Cancer Patients in the United States? A Study from Six Randomly Selected States

BACKGROUND

Breast cancer is the most common cancer in women. Disparities in some characteristics of breast cancer patients and their survival data for six randomly selected states in the US were examined.

MATERIALS AND METHODS

A probability random sampling method was used to select the records of 2,000 patients from each of six randomly selected states. Demographic and disease characteristics were extracted from the Surveillance Epidemiology and End Results (SEER) database. To evaluate relationships between variables, we employed a Cox Proportional Regression to compare survival times in the different states.

RESULTS

Iowa had the highest mean age of diagnosis at 64.14 years (SE = 0.324) and Georgia had the lowest at 57.97 years (SE = 0.313). New Mexico had the longest mean survival time of 189.09 months (SE = 20.414) and Hawaii the shortest at 119.01 (SE = 5.394) months, a 70.08-month difference (5.84 years). Analysis of stage of diagnosis showed that the highest survival times for Whites and American Indians/Alaska Natives were for stage I cancers. The highest survival times for Blacks varied. Stage IV cancer consistently showed the lowest survival times.

CONCLUSIONS

Differences in breast cancer characteristics across states highlight the need to understand differences between the states that result in variances in breast cancer survival.

Racial disparities in survival outcomes by breast tumor subtype among African American women in Memphis, Tennessee

Racial disparities in survival among African American (AA) women in the United States have been well documented. Breast cancer mortality rates among AA women is higher in Memphis, Tennessee as compared to 49 of the largest US cities. In this study, we investigated the extent to which racial/ethnic disparities in survival outcomes among Memphis women are attributed to differences in breast tumor subtype and treatment outcomes. A total of 3527 patients diagnosed with stage I-IV breast cancer between January 2002 and April 2015 at Methodist Health hospitals and West Cancer Center in Memphis, TN were included in the analysis. Kaplan-Meier survival curves were generated and Cox proportional hazards regression were used to compare survival outcomes among 1342 (38.0%) AA and 2185 (62.0%) non-Hispanic White breast cancer patients by race and breast tumor subtype. Over a mean follow-up time of 29.9 months, AA women displayed increased mortality risk [adjusted hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.35-2.03] and were more likely to be diagnosed at advanced stages of disease. AA women with triple-negative breast cancer (TNBC) had the highest death rate at 26.7% compared to non-Hispanic White women at 16.5%. AA women with TNBC and luminal B/HER2- breast tumors had the highest risk of mortality. Regardless of race, patients who did not have surgery had over five times higher risk of dying compared to those who had surgery. These findings provide additional evidence of the breast cancer disparity gap between AA and non-Hispanic White women and highlight the need for targeted interventions and policies to eliminate breast cancer disparities in AA populations, particularly in Memphis, TN.

Influence of clinical, societal, and treatment variables on racial differences in ER-/PR- breast cancer survival

BACKGROUND

African American (AA) women with breast cancer have persistently higher mortality compared to whites. We evaluated racial disparities in mortality among women with estrogen receptor (ER)/progesterone receptor (PR)-negative breast cancer.

METHODS

The study population included 542 women (45% AA) diagnosed with ER/PR-negative Stage I through III breast cancer treated at the Henry Ford Health System (HFHS) between 1996 and 2005. Linked datasets from HFHS, Metropolitan Detroit Cancer Surveillance System, and the U.S. Census Bureau were used to obtain demographic, socioeconomic, and clinical information. Economic deprivation was categorized using a previously validated deprivation index, which included 5 categories based on the quintile of census tract socioeconomic deprivation. Cox proportional hazards models were used to assess the relationship between race and mortality.

RESULTS

AA women were more likely to have larger tumors, have higher Charlson Comorbidity Indices (CCI), and to reside in economically deprived areas. In an unadjusted analysis, AA women demonstrated a significantly higher risk of death compared to whites [hazard ratio (HR) 1.47, 95% confidence interval (CI) 1.09-2.00]. Following adjustment for clinical factors (age, stage, CCI) and treatment (radiation and chemotherapy), AA race continued to have a significant impact on mortality (HR 1.51, CI 1.10-2.08 and HR 1.63, CI 1.20-2.21). Only after adjusting for deprivation was race no longer significant (HR 1.26, CI 0.84-1.87).

CONCLUSIONS

Social determinants of health play a large role in explaining racial disparities in breast cancer outcomes, especially among women with aggressive subtypes.

Psychosocial correlates of cervical cancer screening among older Hispanic women

Early detection through screening can reduce mortality rates of cervical cancer, and yet Hispanic women who have incidence rates higher than their non-Hispanic White counterparts are least likely to participate in cancer screening initiatives. This study utilized data from the 2008 wave of the Health and Retirement Study to investigate the psychosocial correlates associated with older Hispanic women's participation in cervical cancer screening services. Logistic regression models were used. Findings indicated that greater life satisfaction and religiosity were associated with a greater likelihood of participating in cervical cancer screening. Despite ongoing national conversations, evidence indicates there is agreement that underserved women need to be screened, particularly the older Hispanic population.

Geographic disparities in late stage breast cancer incidence: results from eight states in the United States

Background

Late stage of cancer at diagnosis is an important predictor of cancer mortality. In many areas worldwide, cancer registry systems, available data and mapping technologies can provide information about late stage cancer by geographical regions, offering valuable opportunities to identify areas where further investigation and interventions are needed. The current study examined geographical variation in late stage breast cancer incidence across eight states in the United States with the objective to identify areas that might benefit from targeted interventions.

Methods

Data from the Surveillance Epidemiology and End Results Program on late stage breast cancer incidence was used as dependent variable in regression analysis and certain factors known to contribute to high rates of late stage cancer (socioeconomic characteristics, health insurance characteristics, and the availability and utilization of cancer screening) as covariates. Geographic information systems were used to map and highlight areas that have any combination of high late stage breast cancer incidence and significantly associated risk factors.

Results

The differences in mean rates of late stage breast cancer between eight states considered in this analysis are statistically significant. Factors that have statistically negative association with late stage breast cancer incidence across the eight states include: density of mammography facilities, percent population with Bachelor’s degree and English literacy while percent black population has statistically significant positive association with late stage breast cancer incidence.

Conclusions

This study describes geographic disparities in late stage breast cancer incidence and identifies areas that might benefit from targeted interventions. The results suggest that in the eight US states examined, higher rates of late stage breast cancer are more common in areas with predominantly black population, where English literacy, percentage of population with college degree and screening availability are low. The approach described in this work may be utilized both within and outside US, wherever cancer registry systems and technologies offer the same opportunity to identify places where further investigation and interventions for reducing cancer burden are needed.

An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models

Disparities in cancer risk exist between ethnic groups in the United States. These disparities often result from differential access to healthcare, differences in socioeconomic status and differential exposure to carcinogens. This study uses cancer incidence data from the population based Texas Cancer Registry to investigate the disparities in digestive and respiratory cancers from 2000 to 2008. A Bayesian hierarchical regression approach is used. All models are fit using the INLA method of Bayesian model estimation. Specifically, a spatially varying coefficient model of the disparity between Hispanic and Non-Hispanic incidence is used. Results suggest that a spatio-temporal heterogeneity model best accounts for the observed Hispanic disparity in cancer risk. Overall, there is a significant disadvantage for the Hispanic population of Texas with respect to both of these cancers, and this disparity varies significantly over space. The greatest disparities between Hispanics and Non-Hispanics in digestive and respiratory cancers occur in eastern Texas, with patterns emerging as early as 2000 and continuing until 2008.

Racial differences in breast cancer survival in a large urban integrated health system

BACKGROUND

African American (AA) women are known to have poorer breast cancer survival than whites, and the differences may be related to underlying disparities in their clinical presentation or access to care. This study evaluated the relationship between demographic, treatment, and socioeconomic factors and breast cancer survival among women in southeast Michigan.

METHODS

The population included 2387 women (34% AA) with American Joint Committee on Cancer stage I to III breast cancer who were treated at the Henry Ford Health System (HFHS) from 1996 through 2005. Linked data sets from the HFHS, the Metropolitan Detroit Cancer Surveillance System, and the US Census Bureau were used to obtain demographic and clinical information. Comorbidities were classified with the modified Charlson comorbidity index (CCI). Economic deprivation was categorized with a census tract-based deprivation index (DI), which was stratified into 5 quintiles of increasing socioeconomic disadvantage.

RESULTS

Compared with whites, AA women were significantly more likely to have larger, hormone receptor-negative tumors and more comorbidities and to reside in an economically deprived area. In an unadjusted analysis, AAs had a significantly higher risk of death (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.16-1.59); however, after adjustments for clinical (age, stage, hormone receptor, and CCI) and societal factors (DI), the effect of race was not significant (HR, 1.13 [95% CI, 0.96-1.34] , and HR, 0.97 [0.80-1.19] respectively).

CONCLUSIONS

Racial differences in breast cancer survival can be explained by clinical and socioeconomic factors. Nonetheless, AA women with breast cancer remain disproportionately affected by unfavorable tumor characteristics and economic deprivation, which likely contribute to their increased overall mortality.

The effects of navigation and types of neighborhoods on timely follow-up of abnormal mammogram among black women

BACKGROUND

Despite the availability of relatively simple and inexpensive screening tools, minority women are more often diagnosed at a late stage of breast cancer, in part due to delays in follow-up of abnormal screening result. One of the key factors for timely follow-up of abnormal mammogram may be neighborhood characteristics. Patient Navigation (PN) programs aim to diminish barriers, but its differential effects by neighborhood have not been fully examined. The current study examines the effect of types of neighborhoods on time to follow-up of abnormal mammogram, and the differential effects of PN by neighborhood characteristics.

METHODS

We examined data from a total of 1,696 randomized patients from a randomized controlled trial, "the Patient Navigation in Medically Underserved Areas" study that explored the effect of navigation on breast health outcomes. We categorized participants' neighborhoods into three categories and compared the effect of navigation between these neighborhood types.

RESULTS

Navigated women in mixed race neighborhoods had a shorter time to follow-up compared with non-navigated women in the neighborhoods. Black women living in mixed neighborhoods had a significant longer time to follow-up of abnormal mammogram, compared with black women living in middle class black neighborhoods.

CONCLUSION

Patient navigation interventions improve timely follow-up of abnormal mammogram. Patient navigation may be particularly beneficial for minority women who reside in racially heterogeneous neighborhoods which may be less likely to have access to affordable health clinics and social services. Health policies concerning breast cancer early detection for minority women need to pay further attention to those who might potentially be excluded from health services due to the characteristics of neighborhoods. Socioeconomic conditions of neighborhood may affect individual health through multiple interlinked mechanisms. Neighborhood characteristics, such as poverty, segregation, access to resources, and social cohesion, cannot be fully understood with simplistic measures of neighborhood disadvantage.

Spatial analysis and correlates of county-level diabetes prevalence, 2009-2010

Introduction

Information on the relationship between diabetes prevalence and built environment attributes could allow public health programs to better target populations at risk for diabetes. This study sought to determine the spatial prevalence of diabetes in the United States and how this distribution is associated with the geography of common diabetes correlates.

Methods

Data from the Centers for Disease Control and Prevention and the US Census Bureau were integrated to perform geographically weighted regression at the county level on the following variables: percentage nonwhite population, percentage Hispanic population, education level, percentage unemployed, percentage living below the federal poverty level, population density, percentage obese, percentage physically inactive, percentage population that cycles or walks to work, and percentage neighborhood food deserts.

Results

We found significant spatial clustering of county-level diabetes prevalence in the United States; however, diabetes prevalence was inconsistently correlated with significant predictors. Percentage living below the federal poverty level and percentage nonwhite population were associated with diabetes in some regions. The percentage of population cycling or walking to work was the only significant built environment–related variable correlated with diabetes, and this association varied in magnitude across the nation.

Conclusion

Sociodemographic and built environment–related variables correlated with diabetes prevalence in some regions of the United States. The variation in magnitude and direction of these relationships highlights the need to understand local context in the prevention and maintenance of diabetes. Geographically weighted regression shows promise for public health research in detecting variations in associations between health behaviors, outcomes, and predictors across geographic space.

Elderly breast and colorectal cancer patients' clinical course: patient and contextual influences

BACKGROUND

The social and medical environments that surround people are each independently associated with their cancer course. The extent to which these characteristics may together mediate patients' cancer care and outcomes is not known.

METHODS

Using multilevel methods and data, we studied elderly breast and colorectal cancer patients (level I) within urban social (level II: ZIP code tabulation area) and health care (level III: hospital service area) contexts. We sought to determine (1) which, if any, observable social and medical contextual attributes were associated with patient cancer outcomes after controlling for observable patient attributes, and (2) the magnitude of residual variation in patient cancer outcomes at each level.

RESULTS

Numerous patient attributes and social area attributes, including poverty, were associated with unfavorable patient cancer outcomes across the full clinical cancer continuum for both cancers. Health care area attributes were not associated with patient cancer outcomes. After controlling for observable covariates at all 3 levels, there was substantial residual variation in patient cancer outcomes at all levels.

CONCLUSIONS

After controlling for patient attributes known to confer risk of poor cancer outcomes, we find that neighborhood socioeconomic disadvantage exerts an independent and deleterious effect on residents' cancer outcomes, but the area supply of the specific types of health care studied do not. Multilevel interventions targeted at cancer patients and their social areas may be useful. We also show substantial residual variation in patient outcomes across social and health care areas, a finding potentially relevant to traditional small area variation research methods.

Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S

This study identified geographic disparities in breast cancer mortality across the U.S. using kriging to overcome unavailability of data because of confidentiality and reliability concerns. A structured additive regression model was used to detect where breast cancer mortality rates were elevated across nine divisions with 3109 U.S. counties during 1982-2004. Our analysis identified at least 25.8% of counties where breast cancer mortality rates were elevated. High-risk counties compared to lower-risk counties had higher relative risks for African American women than for White women. Greater geographic disparities more likely present in African American women and younger women. To sum up, our statistical approach reduced the impact of unavailable data, and identified the number and location of counties with high breast cancer mortality risk by race and age across the U.S.

The joint effects of census tract poverty and geographic access on late-stage breast cancer diagnosis in 10 US States

This study evaluated independent and joint effects of census tract (CT) poverty and geographic access to mammography on stage at diagnosis for breast cancer. The study included 161,619 women 40+ years old diagnosed with breast cancer between 2004 -2006 in ten participating US states. Multilevel logistic regression was used to estimate the odds of late-stage breast cancer diagnosis for the entire study population and by state. Poverty was independently associated with late-stage in the overall population (poverty rates >20% OR=1.30, 95% CI=1.26- 1.35) and for 9 of the 10 states. Geographic access was not associated with late-stage diagnosis after adjusting for CT poverty. State-specific analysis provided little evidence that geographic access was associated with breast cancer stage at diagnosis, and after adjusting for poverty, geographic access mattered in only 1 state. Overall, compared to women with private insurance, the adjusted odds ratios for late stage at diagnosis among women with either no insurance, Medicaid, or Medicare were 1.80 (95% CI = 1.65, 1.96), 1.75 (95% CI = 1.68, 1.84), and 1.05 (95% CI 1.01, 1.08), respectively. Although geographic access to mammography was not a significant predictor of late-stage breast cancer diagnosis, women in high poverty areas or uninsured are at greatest risk of being diagnosed with late-stage breast cancer regardless of geographic location and may benefit from targeted interventions.

Impact of chronic conditions on the cost of cancer care for Medicaid beneficiaries

BACKGROUND

No study has assessed the cost of treating adult Medicaid cancer patients with preexisting chronic conditions. This information is essential for understanding the cost of cancer care to the Medicaid program above that expended for other chronic conditions, given the increasing prevalence of chronic conditions among cancer patients.

RESEARCH DESIGN

We used administrative data from 3 state Medicaid programs' linked cancer registry data to estimate cost of care during the first 6 months following cancer diagnosis for beneficiaries with 4 preexisting chronic conditions: cardiac disease, respiratory diseases, diabetes, and mental health disorders. Our base cohort consisted of 6,212 Medicaid cancer patients aged 21 to 64 years (cancer diagnosed during 2001-2003) who were continuously enrolled in fee-for-service Medicaid for 6 months after diagnosis. A subset of these patients who did not die during the 6-month follow-up (n=4,628), were matched with 2 non-cancer patients each (n=8,536) to assess incremental cost of care.

RESULTS

The average cost of care for cancer patients with the chronic conditions studied was higher than for cancer patients without any of these conditions. The increase in cancer treatment cost associated with the chronic conditions ranged from $4,385 for cardiac disease to $11,009 for mental health disorders.

CONCLUSIONS

Chronic conditions, especially the presence of multiple conditions, are associated with a higher cost of care among Medicaid cancer patients, and these increased costs should be reflected in projections of future Medicaid cancer care costs. The implementation of better care-management processes for cancer patients with preexisting chronic conditions may be one way to reduce these costs.

Socioeconomic determinants of geographic disparities in campylobacteriosis risk: a comparison of global and local modeling approaches

Background

Socioeconomic factors play a complex role in determining the risk of campylobacteriosis. Understanding the spatial interplay between these factors and disease risk can guide disease control programs. Historically, Poisson and negative binomial models have been used to investigate determinants of geographic disparities in risk. Spatial regression models, which allow modeling of spatial effects, have been used to improve these modeling efforts. Geographically weighted regression (GWR) takes this a step further by estimating local regression coefficients, thereby allowing estimations of associations that vary in space. These recent approaches increase our understanding of how geography influences the associations between determinants and disease. Therefore the objectives of this study were to: (i) identify socioeconomic determinants of the geographic disparities of campylobacteriosis risk (ii) investigate if regression coefficients for the associations between socioeconomic factors and campylobacteriosis risk demonstrate spatial variability and (iii) compare the performance of four modeling approaches: negative binomial, spatial lag, global and local Poisson GWR.

Methods

Negative binomial, spatial lag, global and local Poisson GWR modeling techniques were used to investigate associations between socioeconomic factors and geographic disparities in campylobacteriosis risk. The best fitting models were identified and compared.

Results

Two competing four variable models (Models 1 & 2) were identified. Significant variables included race, unemployment rate, education attainment, urbanicity, and divorce rate. Local Poisson GWR had the best fit and showed evidence of spatially varying regression coefficients.

Conclusions

The international significance of this work is that it highlights the inadequacy of global regression strategies that estimate one parameter per independent variable, and therefore mask the true relationships between dependent and independent variables. Since local GWR estimate a regression coefficient for each location, it reveals the geographic differences in the associations. This implies that a factor may be an important determinant in some locations and not others. Incorporating this into health planning ensures that a needs-based, rather than a “one-size-fits-all”, approach is used. Thus, adding local GWR to the epidemiologists’ toolbox would allow them to assess how the impacts of different determinants vary by geography. This knowledge is critical for resource allocation in disease control programs.

Do socially deprived urban areas have lesser supplies of cancer care services?

PURPOSE

Area social deprivation is associated with unfavorable health outcomes of residents across the full clinical course of cancer from the stage at diagnosis through survival. We sought to determine whether area social factors are associated with the area health care supply.

PATIENTS AND METHODS

We studied the area supply of health services required for the provision of guideline-recommended care for patients with breast cancer and colorectal cancer (CRC) in each of the following three distinct clinical domains: screening, treatment, and post-treatment surveillance. We characterized area social factors in 3,096 urban zip code tabulation areas by using Census Bureau data and the health care supply in the corresponding 465 hospital service areas by using American Hospital Association, American Medical Association, and US Food and Drug Administration data. In two-level hierarchical models, we assessed associations between social factors and the supply of health services across areas.

RESULTS

We found no clear associations between area social factors and the supply of health services essential to the provision of guideline recommended breast cancer and CRC care in urban areas. The measures of health service included the supply of physicians who facilitate screening, treatment, and post-treatment care and the supply of facilities required for the same services.

CONCLUSION

Because we found that the supply of types of health care required for the provision of guideline-recommended cancer care for patients with breast cancer and CRC did not vary with markers of area socioeconomic disadvantage, it is possible that previously reported unfavorable breast cancer and CRC outcomes among individuals living in impoverished areas may have occurred despite an apparent adequate area health care supply.

Spatial inequalities in colorectal and breast cancer survival: premature deaths and associated factors

This study examines the influence of cancer stage, distance to treatment facilities and area disadvantage on breast and colorectal cancer spatial survival inequalities. We also estimate the number of premature deaths after adjusting for cancer stage to quantify the impact of spatial survival inequalities. Population-based descriptive study of residents aged <90 years in Queensland, Australia diagnosed with primary invasive breast (25,202 females) or colorectal (14,690 males, 11,700 females) cancers during 1996-2007. Bayesian hierarchical models explored relative survival inequalities across 478 regions. Cancer stage and disadvantage explained the spatial inequalities in breast cancer survival, however spatial inequalities in colorectal cancer survival persisted after adjustment. Of the 6,019 colorectal cancer deaths within 5 years of diagnosis, 470 (8%) were associated with spatial inequalities in non-diagnostic factors, i.e. factors beyond cancer stage at diagnosis. For breast cancers, of 2,412 deaths, 170 (7%) were related to spatial inequalities in non-diagnostic factors. Quantifying premature deaths can increase incentive for action to reduce these spatial inequalities.

Robust assessment of spatial non-stationarity in model associations related to pediatric mortality due to diarrheal disease in Brazil

Approximately 1.5 million people, mostly children, die annually due to disease attributed to diarrhea reflecting urgent needs for improved understanding of associations between the disease and potential risk factors. Numerous epidemiological studies found spatially varying (non-stationary) disease associations attributable to changing geographic or demographic context. Spatial non-stationarity implies that average relationships from statistical models fitted to the whole study area might be inappropriate since they do not reflect local conditions. Spatial modeling techniques such as geographically weighted regression (GWR) have limitations in providing statistically robust analysis of spatial non-stationarity. Thus, there is a need for development or expansion of modeling techniques to address this issue. Using data for pediatric diarrheal mortality in Brazil in 2000, and different risk factors, we develop an analytical framework to determine regions of similar (stationary) local associations by combining GWR and max-p regionalization. We fit statistical models to these regions, and compare goodness-of-fit and regionally varying coefficients to the national-scale model measures. The proposed framework allows us to examine (a) impact of non-stationarity for regions of different geographic extent with acceptable statistical power, (b) the explanatory power of each risk factor in each region, and (c) if these regions reflect changing data quality or truly existing variations in putative associations.

Impact of Hispanic ethnic concentration and socioeconomic status on obesity prevalence in Texas counties

The purpose of this study is to determine whether Hispanic ethnic concentration is associated with a higher prevalence of obesity and, if this relationship exists, whether it is affected by the socioeconomic environment. The study uses the Texas Behavioral Risk Factor Surveillance System (BRFSS) linked to 2000 census data to access the relationship between prevalence of obesity, Hispanic ethnic concentration, poverty and level of education at a county-level. The findings suggest that the association of Hispanic ethnic concentration and obesity varies by socioeconomic environment. Although little influence was observed for % poverty, the relationship between Hispanic ethnic concentration and obesity differed by county-level educational attainment. High proportion of residents with a bachelor's degree is associated with a low prevalence of obesity; counties with both high % Hispanic and high % with Bachelor's degrees had the lowest prevalence of obesity. Our results suggest that promoting and improving education, perhaps including training on healthful living, may serve as an effective means of curbing current obesity trends and associated health problems in Hispanic and possibly other ethnic communities.

Spatial analysis of county-level breast cancer mortality in Texas

OBJECTIVE

The objectives of the study were to detect high-risk areas and to examine how racial and ethnic status affect the geographic distribution of female breast cancer mortality in Texas. Analyses were based on county-level data for the years from 2000 to 2008.

MATERIALS AND METHODS

Breast cancer mortality data were obtained from the Texas Cancer Registry, and the Spatial Scan Statistics method was used to run Purely Spatial Analyses using the Discrete Poisson, Bernoulli, and Multinomial models.

RESULTS AND CONCLUSIONS

Highest rates of female breast cancer mortality in Texas have shifted over time from southeastern areas towards northern and eastern areas, and breast cancer mortality at the county level is distributed heterogeneously based on racial/ethnic status. Non-Hispanic blacks were at highest risk in the northeastern region and lowest risk in the southern region, while Hispanics were at highest risk in the southern region along the border with Mexico and lowest risk in the northeastern region.

Identifying risk factors for disparities in breast cancer mortality among African-American and Hispanic women

BACKGROUND

This study evaluated the risk factors associated with racial disparities in female breast cancer mortality for African-American and Hispanic women at the census tract level in Texas from 1995 to 2005.

METHODS

Data on female breast cancer cases were obtained from the Texas Cancer Registry. Socioeconomic and demographic data were collected from Census 2000. Network distance and driving times to mammography facilities were estimated using Geographic Information System techniques. Demographic, poverty and spatial accessibility factors were constructed using principal component analysis. Logistic regression models were developed to predict the census tracts with significant racial disparities in breast cancer mortality based on racial disparities in late-stage diagnosis and structured factors from the principal component analysis.

RESULTS

Late-stage diagnosis, poverty factors, and demographic factors were found to be significant predictors of a census tract showing significant racial disparities in breast cancer mortality. Census tracts with higher poverty status were more likely to display significant racial disparities in breast cancer mortality for both African Americans (odds ratio [OR], 2.43; 95% confidence interval [CI], 1.95-3.04) and Hispanics (OR, 5.30; 95% CI, 4.26-6.59). Spatial accessibility was not a consistent predictor of racial disparities in breast cancer mortality for African-American and Hispanic women.

CONCLUSION

Physical access to mammography facilities does not necessarily reflect a greater utilization of mammogram screening, possibly owing to financial constraints. Therefore, a metric measuring access to health care facilities is needed to capture all aspects of access to preventive care. Despite easier physical access to mammography facilities in metropolitan areas, great resources and efforts should also be devoted to these areas where racial disparities in breast cancer mortality are often found.