BACKGROUND: Differences in late‐stage cancer risk between urban and rural residents are a key component of cancer disparities. Using data from the Illinois State Cancer Registry from 1998 through 2002, the authors investigated the rural‐urban gradient in late‐stage cancer risk for 4 major types of cancer: breast, colorectal, lung, and prostate.METHODS: Multilevel modeling was used to evaluate the role of population composition and area‐based contextual factors in accounting for rural‐urban variation. Instead of a simple binary rural‐urban classification, a finer grained classification was used that differentiated the densely populated City of Chicago from its suburbs and from smaller metropolitan areas, large towns, and rural settings.RESULTS: For all 4 cancers, the risk was highest in the most highly urbanized area and decreased as rurality increases, following a J‐shaped progression that included a small upturn in risk in the most isolated rural areas. For some cancers, these geographic disparities were associated with differences in population age and race; for others, the disparities remained after controlling for differences in population composition, zip code socioeconomic characteristics, and spatial access to healthcare.CONCLUSIONS: The observed pattern of urban disadvantage emphasized the need for more extensive urban‐based cancer screening and education programs. Cancer 2009. © 2009 American Cancer Society.

Recent scholarship points to a protective association between green space and birth outcomes as well a positive relationship between blue space and wellbeing. We add to this body of literature by exploring the relationship between expectant mothers’ exposure to green and blue spaces and adverse birth outcomes in New York City. The Normalized Difference Vegetation Index (NDVI), the NYC Street Tree Census, and access to major green spaces served as measures of greenness, while proximity to waterfront areas represented access to blue space. Associations between these factors and adverse birth outcomes, including preterm birth, term birthweight, term low birthweight, and small for gestational age, were evaluated via mixed-effects linear and logistic regression models. The analyses were conducted separately for women living in deprived neighborhoods to test for differential effects on mothers in these areas. The results indicate that women in deprived neighborhoods suffer from higher rates adverse birth outcomes and lower levels of residential greenness. In adjusted models, a significant inverse association between nearby street trees and the odds of preterm birth was found for all women. However, we did not identify a consistent significant relationship between adverse birth outcomes and NDVI, access to major green spaces, or waterfront access when individual covariates were taken into account.

Geographies of home and work have changed as public investment has favored central and distant suburban locations and as income inequality has increased. These changes result in shifting geographies of advantage that (dis)benefit gender and racial/ethnic groups unevenly. We examine commuting differentials by gender and race/ethnicity based on combinations of wages and commute times using data for the New York region.We find that Black, Asian, and Hispanic women and men are concentrated in jobs that have long commutes and low-wages, and Black and Hispanic workers’ concentrations increased from 2000–2010.Although Asian men and women remain overrepresented in that category, their share decreased in the 2000's.The urban core has become a region of heightened advantage, as White men, and an increasing share of White women, commute short times to well-paid jobs. Disadvantage has expanded for Black and Latina women whose long commutes are not compensated by well-paid employment.

Methods for detecting spatial and spatiotemporal clusters of health and disease have advanced significantly in the past decade. This article reviews recent advances in four areas: spatial search processes, network-based methods, statistical analysis and modelling of local clusters and space-time cluster detection. I then turn to a more critical discussion of the implications of hotspot mapping for public health policy and intervention, highlighting the need to incorporate process-based understandings that impact spatial and social inequalities in ill health for particular health issues in particular geographic contexts.

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.