Geographic and Demographic Disparities in Late-stage Breast and Colorectal Cancer Diagnoses Across the US

Geographic Disparities in Late-Stage Breast Cancer Diagnosis Rates and Their Persistence Over Time

Background: Other than skin cancer, breast cancer is the most common cancer in the United States. Lower uptake of mammography screening is associated with higher rates of late-stage breast cancers. This study aims to show geographic patterns in the United States, where rates of late-stage breast cancer are high and persistent over time, and examines factors associated with these patterns. Materials and Methods: We examined all primary breast cancers diagnosed among all counties in 43 U.S. states with available data. We used spatial cluster analysis to identify hot spots (i.e., spatial clusters with above average late-stage diagnosis rates among counties). Demographic and socioeconomic characteristics were compared between persistent hot spots and those counties that were never hot spots. Results: Of the 2,599 counties examined in 43 states, 219 were identified as persistent hot spots. Counties with persistent hot spots (compared with counties that were never hot spots) were located in more deprived areas with worse housing characteristics, lower socioeconomic status, lower levels of health insurance, worse access to mammography, more isolated American Indian/Alaska Native, Black, or Hispanic neighborhoods, and larger income disparity. In addition, persistent hot spots were significantly more likely to be observed among poor, rural, African American, or Hispanic communities, but not among poor, rural, White communities. This analysis includes a broader range of socioeconomic conditions than those included in previous literature. Conclusion: We found geographic disparities in late-stage breast cancer diagnosis rates, with some communities experiencing persistent disparities over time. Our findings can guide public health efforts aimed at reducing disparities in stage of diagnosis for breast cancer.

A Bivariate Mapping Tutorial for Cancer Control Resource Allocation Decisions and Interventions

Bivariate choropleth mapping is a straightforward but underused method for displaying geographic health information to use in public health decision making. Previous studies have recommended this approach for state comprehensive cancer control planning and similar efforts. In this method, 2 area-level variables of interest are mapped simultaneously, often as overlapping quantiles or by using other classification methods. Variables to be mapped may include area-level (eg, county level) measures of disease burden, health care use, access to health care services, and sociodemographic characteristics. We demonstrate how geographic information systems software, specifically ArcGIS, can be used to develop bivariate choropleth maps to inform resource allocation and public health interventions. We used 2 types of county-level public health data: South Carolina’s Behavioral Risk Factor Surveillance System estimates of ever having received cervical cancer screening, and a measure of availability of cervical cancer screening providers that are part of South Carolina’s Breast and Cervical Cancer Early Detection Program. Identification of counties with low screening rates and low access to care may help inform where additional resources should be allocated to improve access and subsequently improve screening rates. Similarly, identifying counties with low screening rates and high access to care may help inform where educational and behavioral interventions should be targeted to improve screening in areas of high access.

Multilevel analysis in rural cancer control: A conceptual framework and methodological implications

Rural populations experience a myriad of cancer disparities ranging from lower screening rates to higher cancer mortality rates. These disparities are due in part to individual-level characteristics like age and insurance status, but the physical and social context of rural residence also plays a role. Our objective was two-fold: 1) to develop a multilevel conceptual framework describing how rural residence and relevant micro, macro, and supra-macro factors can be considered in evaluating disparities across the cancer control continuum and 2) to outline the unique considerations of multilevel statistical modeling in rural cancer research. We drew upon several formative frameworks that address the cancer control continuum, population-level disparities, access to health care services, and social inequities. Micro-level factors comprised individual-level characteristics that either predispose or enable individuals to utilize health care services or that may affect their cancer risk. Macro-level factors included social context (e.g. domains of social inequity) and physical context (e.g. access to care). Rural-urban status was considered a macro-level construct spanning both social and physical context, as "rural" is often characterized by sociodemographic characteristics and distance to health care services. Supra-macro-level factors included policies and systems (e.g. public health policies) that may affect cancer disparities. Our conceptual framework can guide researchers in conceptualizing multilevel statistical models to evaluate the independent contributions of rural-urban status on cancer while accounting for important micro, macro, and supra-macro factors. Statistically, potential collinearity of multilevel model predictive variables, model structure, and spatial dependence should also be considered.

Investigating the Impact of Geographic Location on Colorectal Cancer Stage at Diagnosis: A National Study of the SEER Cancer Registry

BACKGROUND

Early detection of colorectal cancer (CRC) is associated with decreased mortality and potential avoidance of chemotherapy. CRC screening rates are lower in rural communities and patient outcomes are poorer. This study examines the extent to which United States' rural residents present at a more advanced stage of CRC compared to nonrural residents.

METHODS

Using the 2010-2014 Surveillance, Epidemiology and End Results Incidence data, 132,277 patients with CRC were stratified using their county of residence and urban influence codes into 5 categories (metro, adjacent micropolitan, nonadjacent micropolitan, small rural, and remote small rural). Logistic regression was used to investigate the relationship between late stage at diagnosis and county-level characteristics including level of rurality, persistent poverty, low education and low employment, and patient characteristics.

RESULTS

In the adjusted analysis the rate of stage 4 CRC at diagnosis differed across geographic classification, with patients living in remote small rural counties having the highest rate of stage 4 disease (range: 19.2% in nonadjacent micropolitan counties to 22.7% in remote small rural counties). Other factors, such as patient characteristics, insurance status, and regional practice variation were also significantly associated with late-stage CRC diagnosis.

CONCLUSIONS

Geographic residence is associated with the rate of stage 4 disease at presentation. Additional patient factors are associated with stage 4 CRC disease at diagnosis. Cancer outcomes are worse for rural patients, and late stage at diagnosis may partially account for this disparity. These differences have persisted over time and suggest areas for further research, patient engagement, and education.

Contextual effects and cancer outcomes in the United States: a systematic review of characteristics in multilevel analyses

PURPOSE

There is increasing call for the utilization of multilevel modeling to explore the relationship between place-based contextual effects and cancer outcomes in the United States. To gain a better understanding of how contextual factors are being considered, we performed a systematic review.

METHODS

We reviewed studies published between January 1, 2002 and December 31, 2016 and assessed the following attributes: (1) contextual considerations such as geographic scale and contextual factors used; (2) methods used to quantify contextual factors; and (3) cancer type and outcomes. We searched PubMed, Scopus, and Web of Science and initially identified 1060 studies. One hundred twenty-two studies remained after exclusions.

RESULTS

Most studies utilized a two-level structure; census tracts were the most commonly used geographic scale. Socioeconomic factors, health care access, racial/ethnic factors, and rural-urban status were the most common contextual factors addressed in multilevel models. Breast and colorectal cancers were the most common cancer types, and screening and staging were the most common outcomes assessed in these studies.

CONCLUSIONS

Opportunities for future research include deriving contextual factors using more rigorous approaches, considering cross-classified structures and cross-level interactions, and using multilevel modeling to explore understudied cancers and outcomes.