Journal article
A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US
Health & place, v 86, p103209
25 Feb 2024
PMID: 38408408
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Neighborhoods are complex and multi-faceted. Analytic strategies used to model neighborhoods should reflect this complexity, with the potential to better understand how neighborhood characteristics together impact health. We used latent profile analysis (LPA) to derive a residential neighborhood typology applicable for census tracts across the US.
From tract-level 2015-2019 American Community Survey (ACS) five-year estimates, we selected five indicators that represent four neighborhood domains: demographic composition, commuting, socioeconomic composition, and built environment. We compared model fit statistics for up to eight profiles to identify the optimal number of latent profiles of the selected neighborhood indicators for the entire US. We then examined differences in national tract-level 2019 prevalence estimates of physical and mental health derived from CDC's PLACES dataset between derived profiles using one-way analysis of variance (ANOVA).
The 6-profile LPA model was the optimal categorization of neighborhood profiles based on model fit statistics and interpretability. Neighborhood types were distinguished most by demographic composition, followed by commuting and built environment domains. Neighborhood profiles were associated with meaningful differences in the prevalence of health outcomes. Specifically, tracts characterized as "Less educated non-immigrant racial and ethnic minority active transiters" (n = 3,132, 4%) had the highest poor health prevalence (Mean poor physical health: 18.6 %, SD: 4.30; Mean poor mental health: 19.6 %, SD: 3.85), whereas tracts characterized as "More educated metro/micropolitans" (n = 15, 250, 21%) had the lowest prevalence of poor mental and physical health (Mean poor physical health: 10.6 %, SD: 2.41; Mean poor mental health: 12.4 %, SD: 2.67; p < 0.001).
LPA can be used to derive meaningful and standardized profiles of tracts sensitive to the spatial patterning of social and built conditions, with observed differences in mental and physical health by neighborhood type in the US.
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Details
- Title
- A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US
- Creators
- Hiwot Y Zewdie - University of WashingtonJamaica R RobinsonMarc A Adams - Arizona State UniversityAnjum Hajat - University of WashingtonJana A Hirsch - Drexel UniversityBrian E SaelensStephen J Mooney - University of Washington
- Publication Details
- Health & place, v 86, p103209
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:001194091400001
- Scopus ID
- 2-s2.0-85186751421
- Other Identifier
- 991021855559904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health