Original articleNeighborhood archetypes and breast cancer survival in California
Introduction
Breast cancer is the most common cancer and the second-leading cause of cancer deaths among women in the United States (U.S.).1 Neighborhoods shape individuals’ exposures to health-related risks and access to resources; they have an additional and distinct effect on cancer outcomes apart from individual-level characteristics.[2], [3], [4], [5] The impact of neighborhoods on cancer mortality is of increasing importance, since while cancer mortality has declined steadily over the past three decades, socioeconomic and geographic disparities in mortality have increased.6 Thus, there is a growing interest in health disparities research for more sophisticated neighborhood measures and a more thorough understanding of how and why neighborhoods matter to individuals’ health.[7], [8], [9]
Most published studies of neighborhoods and breast cancer survival have reported associations of lower neighborhood socioeconomic status (nSES) or greater neighborhood deprivation with risk of death, independent of patient tumor and sociodemographic characteristics.5,[10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] More recent work, including work from our group, has considered additional domains of neighborhood built and social environments (e.g., food environment, walkability, and ethnic enclave).5,18,[21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] Although few of these measures have been independently associated with overall or breast cancer-specific survival, a joint effect for ethnic enclave and nSES has been observed, which highlights the importance of considering interactions among neighborhood domains.26,27 Accordingly, understanding the full impact of neighborhoods on breast cancer survival necessitates an approach that informs how these and other often highly correlated neighborhood attributes, measured as individual indicators (e.g., poverty) or unidimensional indices (e.g., nSES or walkability) interact.[34], [35], [36], [37], [38] Consequently, we suggest reconsidering how neighborhoods are assessed in epidemiologic studies to fully capture, and ultimately more effectively intervene on, their potential health impact.39
Combining multiple neighborhood characteristics into archetypes is conceptually a more meaningful approach to identifying neighborhoods where residents have lower survival and to inform contextually-mediated interventions. Latent variable models, including latent class analysis (LCA), provide a statistically rigorous methodological approach to measuring archetypes40 as they allow for the assessment of potential interactions between many measures and summarize those measures into a more practical number of inter-relationships, apart from their impact on any given health outcome. Like principal component analysis for continuous variables and cluster analysis for categorical variables, LCA is a data reduction technique, but it has the additional benefits (shared by other latent variable methods for continuous measures like factor analysis and structural equation modeling) that it also addresses uncertainty, bias, and potential attenuation due to systematic and stochastic error in the measurement of variables.39 Nevertheless, the majority of studies that have used latent variable models to characterize neighborhoods utilized multiple measures of a single neighborhood domain (e.g., built environment attributes to describe neighborhood physical activity) and so were not designed to observe important interactions among social and built environment domains.[41], [42], [43], [44], [45], [46], [47], [48], [49] A study from Weden et al. identified 6 neighborhood archetypes with measures across multiple neighborhood domains for 1990 and 2000 census tracts across the U.S., but these archetypes were not used to study a health behavior or outcome.39
In this study, we used an analogous approach to Weden et al. with a broad set of social and built environment attributes across several neighborhood domains to define neighborhood archetypes in California (CA) and to examine associations between neighborhood archetypes and breast cancer survival. Moreover, literature to date on the neighborhood environment and breast cancer survival stresses that the relative importance of neighborhoods may depend on resident characteristics, including race/ethnicity,33 so we examine interactions between archetypes and race/ethnicity in their associations with survival.
Section snippets
Neighborhood data
Neighborhood data were from the California Neighborhoods Data System (CNDS).50 The CNDS is a geospatial dataset that compiles data on the social and built environment attributes for small areas (i.e., block groups and census tracts) using multiple sources of data. Thirty-nine indicator variables characterizing several domains of neighborhood social and built environments (i.e., socioeconomic status, urbanicity, demographics, housing, land use, commuting and traffic, residential mobility, and
Results
Labels and descriptions for the 9 different classes, or types, of neighborhoods characterized are in Table 1. Descriptions are based on the prevailing characteristics that the LCA model identified for each class. For example, suburban pioneer and city pioneer neighborhoods are both located in cities, but not right in downtown, have racially/ethnically diverse populations, and lots of mixed land use, but contrast in that suburban pioneer neighborhoods have more families and home owners and city
Discussion
Neighborhood archetypes provide a novel approach to assess the impact of multiple neighborhood factors on breast cancer survival. We present 9 neighborhood archetypes for census tracts in CA for the year 2000 developed with a broad suite of variables representing several domains of neighborhood social and built environments. These archetypes can be interpreted as (1) the most likely combinations of neighborhood characteristics observed in CA and (2) the most common forms of potential
Acknowledgments
(Andrew Hertz) We thank Mr. Andrew Hertz from the Cancer Prevention Institute of California for his contributions to this study.
This work was supported by the National Cancer Institute at the National Institutes of Health (1R21CA174469 to S.S.M.) The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103,885; Centers for Disease Control and Prevention's (CDC) National Program of
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Competing Interests: Authors have no conflicts of interest to declare.