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

University of California, Los Angeles, USA
Health & Place (Impact Factor: 2.44). 11/2010; 17(1):289-299. DOI: 10.1016/j.healthplace.2010.11.002

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.

  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Understanding the determinants of Helicobacter pylori infection in adults is essential to predict the burden of H. pylori-related diseases. We aimed to estimate the prevalence and incidence of H. pylori infection and to identify its major sociodemographic correlates in an urban population from the North of Portugal. MATERIAL AND METHODS: A representative sample of noninstitutionalized adult inhabitants of Porto (n = 2067) was evaluated by ELISA (IgG) and a subsample (n = 412) was tested by Western Blot to assess infection with CagA-positive strains. Modified Poisson and Poisson regression models were used to estimate crude and sex-, age-, and education-adjusted prevalence ratios (PR) and incidence rate ratios (RR), respectively. RESULTS: The prevalence of H. pylori infection was 84.2% [95% confidence interval (95%CI): 82.4-86.1]. It increased across age-groups in the more educated subjects, (18-30 years: 72.6%; ≥71 years: 88.1%; p for trend <0.001) and decreased with education in the younger (≤4 schooling years: 100.0%; ≥10 schooling years: 72.6%; p for trend <0.001). Living in a more deprived neighborhood was associated with a higher prevalence of infection, only in the younger (PR = 1.20, 95%CI: 1.03-1.38) and more educated participants (PR = 1.15, 95%CI: 1.03-1.29). Among the infected, the proportion with CagA-positive strains was 61.7% (95%CI: 56.6-66.9). The incidence rate was 3.6/100 person-years (median follow-up: 3 years; 95%CI: 2.1-6.2), lower among the more educated (≥10 vs ≤9: RR = 0.25, 95%CI: 0.06-0.96). The seroreversion rate was 1.0/100 person-years (95%CI: 0.6-1.7). CONCLUSIONS: The prevalence of infection among adults is still very high in Portugal, suggesting that stomach cancer rates will remain high over the next few decades.
    Helicobacter 06/2013; DOI:10.1111/hel.12061 · 2.99 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Community-based approaches to preventing chronic diseases are attractive because of their broad reach and low costs, and as such, are integral components of health care reform efforts. Implementing community-based initiatives across Massachusetts' municipalities presents both programmatic and evaluation challenges. For effective delivery and evaluation of the interventions, establishing a community typology that groups similar municipalities provides a balanced and cost-effective approach. Through a series of key informant interviews and exploratory data analysis, we identified 55 municipal-level indicators of 6 domains for the typology analysis. The domains were health behaviors and health outcomes, housing and land use, transportation, retail environment, socioeconomics, and demographic composition. A latent class analysis was used to identify 10 groups of municipalities based on similar patterns of municipal-level indicators across the domains. Our model with 10 latent classes yielded excellent classification certainty (relative entropy = .995, minimum class probability for any class = .871), and differentiated distinct groups of municipalities based on health-relevant needs and resources. The classes differentiated healthy and racially and ethnically diverse urban areas from cities with similar population densities and diversity but worse health outcomes, affluent communities from lower-income rural communities, and mature suburban areas from rapidly suburbanizing communities with different healthy-living challenges. Latent class analysis is a tool that may aid in the planning, communication, and evaluation of community-based wellness initiatives such as Community Transformation Grants projects administrated by the Centers for Disease Control and Prevention.
    Preventing chronic disease 02/2014; 11:E21. DOI:10.5888/pcd11.130215 · 1.96 Impact Factor

Full-text (2 Sources)

Available from
May 20, 2014