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

Abstract

ProblemIn 2009, breast cancer was the most common cancer in women, and colorectal cancer was the third most common cancer in both men and women. Currently, the majority of colorectal and almost 1/3 of breast cancers are diagnosed at an advanced stage in the US, which results in higher morbidity and mortality than would obtain with earlier detection. The incidence of late-stage cancer diagnoses varies considerably across the US, and few analyses have examined the entire US.PurposeUsing the newly available US Cancer Statistics database representing 98% of the US population, we perform multilevel analysis of the incidence of late-stage cancer diagnoses and translate the findings via bivariate mapping, answering questions related to both Why and Where demographic and geographic disparities in these diagnoses are observed.MethodsTo answer questions related to Why disparities are observed, we utilize a three-level, random-intercepts model including person-, local area-, and region- specific levels of influence. To answer questions related to Where disparities are observed, we generate county level robust predictions of late-stage cancer diagnosis rates and map them, contrasting counties ranked in the upper and lower quantiles of all county predicted rates. Bivariate maps are used to spatially translate the geographic variation among US counties in the distribution of both BC and CRC late-stage diagnoses.ConclusionsEmpirical modeling results show demographic disparities, while the spatial translation of empirical results shows geographic disparities that may be quite useful for state cancer control planning. Late stage BC and CRC diagnosis rates are not spatially random, manifesting as place-specific patterns that compare counties in individual states to counties across all states. Providing a relative comparison that enables assessment of how results in one state compare with others, this paper is to be disseminated to all state cancer control and central cancer registry program officials.

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

“…Previous geographic studies, which have ranked states in terms of late-stage breast cancer diagnosis rates, examined the geospatial predictors of late-stage rates in multilevel models, and provided data about places where the predicted rates are higher (in the upper quantile) across the United States. 5,7 We focus on late-stage (regional and distant stage combined) diagnosis rates and identify U.S. counties where rates of late-stage breast cancers are high and persistent over time during two periods (2004-2009 and 2010-2014). We used actual, not predicted, rates to define these areas across most of the United States.…”

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

“…This approach uses choropleth mapping (ie, a thematic map where areas are colored to represent data values) to display 2 variables simultaneously among geographic units such as states or counties by creating "n × n" groupings where values of both variables intersect. State-level studies have used bivariate mapping to examine lung cancer mortality relative to access to lung cancer screening, racial disparities in 2 types of cancer screening, late-stage rates of 2 cancers, and other cancer-related data (6,(10)(11)(12)(13). Overall, bivariate mapping is underused in cancer control, especially at the sub-state level (eg, counties).…”

“…Data can be mapped in several ways for cancer prevention and control purposes by using public health surveillance, programming, policy, and other data ( Table 1). Such data may include availability of public health programming locations and of screening and/or safety net providers, state-level policies, populationlevel rates of screening use, cancer incidence, staging rates, and/or mortality (7,10,14,15). Such data are generally accessible to state and local public health departments from sources like the Behavioral Risk Factor Surveillance System (BRFSS), cancer registries, Robert Wood Johnson Foundation County Health Rankings, and vital statistics systems.…”

Spatiotemporal correlation analysis of colorectal cancer late-stage incidence, mortality, and survival

“…For both measures, the observed number of late-stage CRC cases is used as the numerator, but one uses the CRC cases diagnosed at any stage in the denominator while the other uses the population at risk for developing CRC. However, while mapping rates of late-stage cancer are relatively common in the disease-mapping literature (42)(43)(44)(45)(46)(47)(48), the geographic pattern of late-stage rates will be different depending on whether it is calculated relative to the number of cases diagnosed at any stage or the number of people at risk of being diagnosed.…”

“…However, this literature does not differentiate between these two ways of measuring late-stage disease. In this study, we tested which way of measuring late-stage CRC was a better predictor of CRC mortality.While mapping rates of late-stage cancer are relatively common in the diseasemapping literature(42)(43)(44)(45)(46)(47)(48), the geographic pattern of late-stage rates will be different depending on whether it is calculated relative to the number of cases diagnosed at any stage or the number of people at risk of being diagnosed. Since people diagnosed with late-stage cancer are more likely to die from the disease than those diagnosed at an earlier stage, we expect that populations with high rates of late-stage colorectal cancer (CRC) have higher rates of CRC mortality than populations with low rates of late-stage CRC.Furthermore, since diagnosis precedes death, often by several years, we expect a temporal lag in the rates of late-stage CRC and CRC mortality.We measure rates of late-stage CRC in two ways.…”