Studies have found a variety of evidence regarding the association between residential segregation measures and health outcomes in the United States. Some have focused on any individuals living in residentially segregated places, whereas others have examined whether persons of specific races or ethnicities living in places with high segregation of their own race or ethnicity have differential outcomes. This article compares and contrasts these two approaches in the study of predictors of late-stage colorectal cancer (CRC) diagnoses in a cross-national study. We argue that it is very important when interpreting results from studies like this to carefully consider the geographic scope of the analysis, which can significantly change the context and meaning of the results. We use US Cancer Statistics Registry data from 40 states to identify late-stage diagnoses among over 500,000 CRC cases diagnosed during 2004-2009. We pool data over the states and estimate a multilevel model with person, county, and state levels and a random intercepts specification to ensure robust effect estimates. The isolation index of residential segregation is defined for racial and ethnic groups at the county level using Census 2000 data. The association between isolation indices and late-stage CRC diagnosis was measured by (1) anyone living in minority-segregated areas (place-centered approach) and by (2) individuals living in areas segregated by one's own racial or ethnic peers (person-centered approach). Findings from the place-centered approach suggest that living in a highly segregated African American community is associated with lower likelihood of late-stage CRC diagnosis, whereas the opposite is true for people living in highly segregated Asian communities, and living in highly segregated Hispanic communities has no significant association. Using the person-centered approach, we find that living in places segregated by one's racial or ethnic peers is associated with lower likelihood of late-stage CRC diagnosis. In a model that covers a large geographic area across the nation, the place-centered approach is most likely picking up geographic disparities that may be deepened by targeted interventions in minority communities. By contrast, the person-centered approach provides a national average estimate suggesting that residential isolation may confer community cohesion or support that is associated with better CRC prevention.

We determined whether there were disparities in the likelihood of being diagnosed at a late stage for breast cancer (BC) or colorectal cancer (CRC) in each of 40 states, using the recently available US Cancer Statistics (USCS) database.

Spatial analyses of HIV/AIDS related outcomes are growing in popularity as a tool to understand geographic changes in the epidemic and inform the effectiveness of community-based prevention and treatment programs. The Urban Health Study was a serial, cross-sectional epidemiological study of injection drug users (IDUs) in San Francisco between 1987 and 2005 (N = 29,914). HIV testing was conducted for every participant. Participant residence was geocoded to the level of the United States Census tract for every observation in dataset. Local indicator of spatial autocorrelation (LISA) tests were used to identify univariate and bivariate Census tract clusters of HIV positive IDUs in two time periods. We further compared three tract level characteristics (% poverty, % African Americans, and % unemployment) across areas of clustered and non-clustered tracts. We identified significant spatial clustering of high numbers of HIV positive IDUs in the early period (1987–1995) and late period (1996–2005). We found significant bivariate clusters of Census tracts where HIV positive IDUs and tract level poverty were above average compared to the surrounding areas. Our data suggest that poverty, rather than race, was an important neighborhood characteristic associated with the spatial distribution of HIV in SF and its spatial diffusion over time.

In the US, about one-third of new breast cancers (BCs) are diagnosed at a late stage, where morbidity and mortality burdens are higher. Health outcomes research has focused on the contribution of measures of social support, particularly the residential isolation or segregation index, on propensity to utilize mammography and rates of late-stage diagnoses. Although inconsistent, studies have used various approaches and shown that residential segregation may play an important role in cancer morbidities and mortality. Some have focused on any individuals living in residentially segregated places (place-centered), while others have focused on persons of specific races or ethnicities living in places with high segregation of their own race or ethnicity (person-centered). This paper compares and contrasts these two approaches in the study of predictors of late-stage BC diagnoses in a cross-national study. We use 100% of U.S. Cancer Statistics (USCS) Registry data pooled together from 40 states to identify late-stage diagnoses among ~1 million new BC cases diagnosed during 2004–2009. We estimate a multilevel model with person-, county-, and state-level predictors and a random intercept specification to help ensure robust effect estimates. Person-level variables in both models suggest that non-White races or ethnicities have higher odds of late-stage diagnosis, and the odds of late-stage diagnosis decline with age, being highest among the <age 50 group. After controlling statistically for all other factors, we examine place-centered isolation and find for anyone living in an isolated Asian community there is a large beneficial association (suggesting lower odds of late-stage diagnosis) while for anyone living in an isolated White community there is a large detrimental association (suggesting greater odds of late-stage diagnosis). By contrast, living in neighborhoods among others of one’s own race or ethnicity (person-centered isolation) is associated with greater odds of late-stage diagnosis, as this measure is dominated by Whites (the majority). At the state level, living in a state that allows unfettered access to a specialist is associated with a somewhat lower likelihood of being diagnosed at a late stage of BC. Geographic factors help explain the likelihood of late-stage BC diagnosis, which varies considerably across the U.S. as heterogeneous compositional and contextual factors portray very different places and potential for improving information and outcomes. The USCS database is expanding to cover more states and is expected to be a valuable resource for ongoing and future place-based cancer outcomes research.

Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer, almost always diagnosed at late stage where mortality outcomes and morbidity burdens are known to be worse. Missed by mammography screening, IBC progresses rapidly and reaches late stage by the time of diagnosis. With an unknown etiology and poor prognosis, it is crucial to evaluate the distribution of the disease in the population as well as identify area social and economic contextual risk factors that may be contributing to the observed patterns of IBC incidence. In this study, we identified spatial clustering of county-based IBC rates among US females and examined the underlying community characteristics associated with the clusters. IBC accounted for ~1.25% of all primary breast cancers diagnoses in 2004–2012 and was defined by the Collaborative Stage (CS) Extension code 710 and 730. Global and local spatial clusters of IBC rates were identified and mapped. The Mann-Whitney U test was used to compare median differences in key contextual variables between areas with high and low spatial clusters of IBC rates. High clusters are counties and their neighbors that all exhibit above average rates, clustered together in a fashion that would be extremely unlikely to be observed by chance, and conversely for low clusters. There was statistically significant evidence of spatial clustering into high and low rate clusters. The average rate in the high rate clusters (n = 46) was approximately 12 times the average rate in low rate clusters (n = 126), and 2.2 times the national average across all counties. Significant differences were found in the medians of the underlying race, poverty, and urbanicity variables when comparing the low cluster counties with the high cluster counties (p < 0.05). Cluster analysis confirms that IBC rates differ geographically and may be influenced by social and economic environmental factors. Particular attention may need to be paid to race, urbanicity and poverty when considering risk factors for IBC and when developing interventions and alternative prevention strategies.