Elsevier

Health & Place

Volume 17, Issue 1, January 2011, Pages 289-299
Health & Place

Neighborhood archetypes for population health research: Is there no place like home?

https://doi.org/10.1016/j.healthplace.2010.11.002Get rights and content

Abstract

This study presents a new, latent archetype approach for studying place in population health. Latent class analysis is used to show how the number, defining attributes, and change/stability of neighborhood archetypes can be characterized and tested for statistical significance. The approach is demonstrated using data on contextual determinants of health for US neighborhoods defined by census tracts in 1990 and 2000. Six archetypes (prevalence 13–20%) characterize the statistically significant combinations of contextual determinants of health from the social environment, built environment, commuting and migration patterns, and demographics and household composition of US neighborhoods. Longitudinal analyses based on the findings demonstrate notable stability (76.4% of neighborhoods categorized as the same archetype ten years later), with exceptions reflecting trends in (ex)urbanization, gentrification/downgrading, and racial/ethnic reconfiguration. The findings and approach is applicable to both research and practice (e.g. surveillance) and can be scaled up or down to study health and place in other geographical contexts or historical periods.

Introduction

Research on neighborhoods and health is motivated by the idea that we live in places that represent more than physical locations. They are also the manifestation of the social, cultural, political and geographic cleavages that shape a constellation of health-related risks and resources. Research on neighborhood effects has reconnected public health with its earlier population foundations—showing that social ecology and built environments are important “upstream” determinants of health. This work documents how social and built environments structure opportunities and barriers to more proximal social and material determinants of health (Sampson et al., 2002, Cummins et al., 2007).

Neighborhoods and health research draws heavily on theory and methodologies from Chicago School factorial social ecology (e.g., Janson, 1980, Schwirian, 1983; for critique see Sampson et al., 2002). This approach conceptualized four primary axes of neighborhood structure—class, race/ethnicity, density, and life-course stage, measured primarily with Census data using factor analysis. The theory and methods also informed the most commonly employed measures of context for neighborhoods and health research (Sampson et al., 2002).

In this paper, we reconsider the models and measures of neighborhoods that emerged from the Chicago School factorial social ecology and explore whether there have been changes in the ecological context of neighborhoods since the four primary cleavages were identified. We address questions raised in literature reviews on the characteristics of US neighborhoods, the relevance of the built environment, and the dynamics of neighborhoods over time (Diez Roux, 2001, Sampson et al., 2002, Robert et al., 2010). We develop a new, complementary theoretical and methodological approach to study neighborhoods that employs archetypes to characterize neighborhoods and assess stability or change. In so doing, we produce a reliable measure of U.S. neighborhood archetypes that can be employed in future research on neighborhoods and health.

Researchers have identified how economic, social, demographic, geographic, structural, and institutional conditions of a neighborhood coalesce to influence physical and mental wellbeing. While some studies highlight specific neighborhood characteristics—e.g. neighborhood poverty (Haan et al., 1987), racial and ethnic concentration (Collins and Williams, 1999), or urbanization (Galea and Vlahov, 2005)—most indicate that multiple factors affect neighborhood characteristics (e.g., Sampson et al., 2002, Cummins et al., 2007). Latent construct approaches are advocated for the measurement and characterization of social, cultural, political and geographic cleavages that entail correlated and overlapping constructs that are typically not well captured by any one individual indicator (e.g., Weden et al., 2008; see also Janson, 1980, Schwirian, 1983).

Early and most commonly used measures of neighborhood conditions rate neighborhoods on a single continuous scale (e.g., using factor-based scales commonly developed from sociodemographic indicators like local rates of poverty, public assistance, female headed households, unemployment, and African American residents; for review see Browning and Cagney, 2002). The most common of these scales extends to a neighborhood-level the integrated assessment of class, status, and prestige through neighborhood socioeconomic status (NSES). Recent studies have focused on the multi-dimensionality of place-based social stratification. For example, Weden et al. (2008) develop a two-dimensional latent model for neighborhood-based social stratification that demonstrates the independent relevance of neighborhood disadvantage and neighborhood affluence to individual health. This is consistent with other work highlighting the relationship between structural aspects of neighborhoods (e.g., boarded-buildings, vacancy rates, and residential turnover; Wilson and Kelling, 1982) and social disorganization that emerges from a long history of research (beginning with seminal works by Durkheim and Simmel) that relates rural–urban differentials and industrialization to social disorganization, and therein, physical and mental health (e.g., see review by Vlahov and Galea, 2002).

From a theoretical perspective, research in urban planning, urban studies and social ecology provide a foundation for linking social and physical dimensions of the neighborhood (e.g., see reviews by Vlahov and Galea, 2002; Corburn, 2004). Research on the neighborhood life cycle links shifts in the demographic composition of communities to changing land-use patterns (i.e. from residential to commercial; see Downs, 1981). Sociological research links residential turnover and deterioration of physical infrastructure to social disorganization (e.g., Sampson and Groves, 1989).

Recent studies refocused attention to built environment factors that support active life styles and reduce the risk of chronic disease, such as land use, commuting patterns and walkability (e.g., see reviews by Frumkin, 2003, Srinivasan et al., 2003, Galea and Vlahov, 2005). Yet few studies have reconsidered the linkages between neighborhood social ecology and built environment in light of current population health dynamics in chronic disease (e.g., see reviews by Diez Roux, 2001, Galea and Vlahov, 2005). One notable exception is research on social capital and the built environment as it relates to physical activity and obesity (e.g., Leyden, 2003, Poortinga, 2006, Wood and Giles-Corti, 2008, Cohen et al., 2008).

The first wave of studies on neighborhoods and health showed that ‘neighborhoods matter’ and have independent effects beyond individual socioeconomic characteristics (for review see Robert, 1999). These studies argued that neighborhoods influence health and behavior through mechanisms such as collective socialization, peer-group influence, and institutional capacity. The second wave of studies on neighborhoods and health evaluated these mechanisms with latent measures of neighborhood characteristics (such as level of segregation, collective social and economic capacity, or social disorganization) (Sampson et al., 2002). In this work, factor analysis or structural equation models are used to create scales for these characteristics and identify a continuum of sociodemographic disadvantage or affluence on which neighborhoods were located. We call this approach a ‘variable perspective’ to neighborhood research.

Although the variable perspective is useful for answering questions about the independent effect of specific neighborhood characteristics controlling for individual characteristics, it is not as well suited to studying how various aspects of neighborhoods combine to effect health and whether and how the effects differ over the life course. Rather than being defined by a single dimension, neighborhoods are the synthesis of different combinations of social, economic, demographic, structural and geographic conditions, which affect individuals’ lives and health. Theory on the multidimensional experience of local environments has been well developed by scholars of gender and race who employ the theoretical paradigm of “intersectionality” to describe the contingent and interacting dimensions of social stratification (e.g., Choo and Feree, 2010). And though direct attention to “intersectionality” has been addressed in only one previous known study on neighborhoods and health (Kershow and Forer, 2010), the potential importance of the concept is illustrated in previous literature. For example, the impact of neighborhood poverty depends on the community’s level of urbanization, age composition, and degree of segregation (Jargowsky, 1997, Boardman et al., 2005). Similarly neighborhood socioeconomic disadvantage is associated with and can be exacerbated by environmental risk factors including pollution and environmental hazards (Cutter et al., 2000, Ponce et al., 2005).

To date, most work has employed a ‘variable perspective’ to consider the multidimensionality of place based social stratification. For example, Boardman et al. (2005) model the potentially contingent role of neighborhood poverty based on racial segregation by exploring the role of an interaction between the two variables. The problem of data intensiveness required when taking a variable approach to multidimensionality becomes evident when additional axes are considered (e.g., a simple model with single dichotomous indicators for each of the four axes of the social ecology model would require 4 main effects and 12 interactions), and even more so when additional dimensions of the neighborhood (e.g. the built environment) are considered. Adequate statistical power for interactions between all of these neighborhood variables quickly becomes unattainable—an analytical problem analogous to that encountered by life course researchers seeking to assess interactions across multiple domains of individual experience (see Singer et al., 1998).

There are two areas of research on neighborhoods and health and our approach is well designed to extend. The first pertains to the interactions between different conceptual dimensions of the neighborhood. Interactions between conceptual dimensions can be studied by characterizing archetypes, and the empirical method of latent class analysis (LCA) designed for this purpose (e.g., Hagenaars and Halman, 1989) has been used extensively in social, behavioral, and health research (Bollen, 2002).1 The second area of research pertains to temporal dynamics including neighborhood change (e.g. gentrification, racial succession) and neighborhood life cycles (e.g., Schwirian, 1983, Sampson et al., 2002; Robert et al., 2010).

Although latent measurement methods related to LCA have been developed to address issues of bias in population health research (e.g., see methods for addressing spatial clustering in the association between neighborhood deprivation and area-level health Congdon, 1997, Congdon, 1996a, Congdon, 1996b), to our knowledge, LCA has not been applied to study both the characterization and change in neighborhoods across the range of social and built environment domains relevant to population health. This is unfortunate, since the approach offers distinct analytical advantages to alternative methods that have predominated the literature (e.g., factor analytic methods, including structural equation modeling (SEM), and cluster analysis techniques). The advantages of LCA are reviewed elsewhere (Rapkin and Luke, 1993; Chow, 1998), and offer opportunities to advance research on neighborhoods and health. First, LCA can measure how constellations of characteristics capture distinct neighborhood archetypes. LCA, like other latent measurement methods, offers the advantage of addressing uncertainty, bias, and potential attenuation due to systematic and stochastic error in the measurement of variables. Additionally, LCA allows a researcher to explore the constellations of characteristics that would otherwise need to be modeled using ‘interactions’ between neighborhood dimensions in a model of neighborhood and health. Thus LCA allows a researcher to identify the most statistically robust set of interactions between dimensions as a constellation of characteristics that describe the places of interest, and to do so causally external from the impact of the neighborhood characteristics on health. Secondly, like factor analytic methods, LCA allows one to assess the stability or change of neighborhood archetypes over time using a temporally stable measurement methodology. These neighborhood archetypes provide a mechanism for studying outstanding questions about neighborhood life cycles. In summary, at its minimum, LCA is a data reduction mechanism similar to cluster analysis, but which offers the advantages of more fully addressing the potential biases of measurement error. In its full application, LCA becomes a powerful tool for the characterization of neighborhood archetypes and analysis of neighborhood change.

Section snippets

Data and sample

Data on U.S. neighborhoods come from a neighborhood characteristics database compiled and disseminated by RAND Corporation (http://www.rand.org/health/centers/pophealth/data.html). The database contains data from the 1990 to 2000 Decennial Census, the Census Topologically Integrated Geographic Encoding and Referencing (TIGER/Line) files, the Environmental Protection Agency Air Quality System, and the American Chamber of Commerce Research. U.S. neighborhoods are defined at the geographical level

How many neighborhood archetypes are there in 1990 and 2000?

Six neighborhood archetypes best summarize the combinations of characteristics from the built environment, migration and commuting, socioeconomic composition, and demographics and household composition in the U.S. in both 1990 and 2000 (see Table 1). Specifically, a six-class LCA model produces the best goodness of fit statistics when models are fit to the data for each year (see Weden et al., 2010 for statistical detail).

What are the defining characteristics of the neighborhood archetypes?

A summary of the combinations of characteristics for all of the

Discussion

The primary objective of this study was to study neighborhood characterization and neighborhood change using a neighborhood archetype approach and latent class analysis (LCA) methodology. We studied the structure and change of U.S. neighborhood archetypes between 1990 and 2000 as a demonstration of our approach, observing the following principal findings. There are six different archetypes that are characterized similarly across both years by distinct sets of characteristics in the social and

Conclusion

This study is designed to both demonstrate the flexibility of the neighborhood archetype approach to a large number of social and environmental indicators, and also to produce a construct which is a substantive contribution to the neighborhood and health literature. We underscore that the modeling conducted here is an illustration of the benefits and opportunities for further research made possible when taking a neighborhood archetypes approach. It is beyond the scope of this paper to develop

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    We acknowledge the contributions of Ricardo Basurto-Davila and Adria Dobkin for helping prepare the data; Paul Steinberg for editing; and Richard Carpiano, Stephanie Robert, Erin Ruel, and our reviewers for helpful suggestions on earlier versions of the manuscript. This work was supported by a grant from the National Institute on Environmental Health Sciences (P50 ES012383). The views expressed are solely those of the authors and do not necessarily represent those of the US Department of Health and Human Services or National Institute on Environmental Health Sciences.

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