A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US

Hiwot Y Zewdie; Jamaica R Robinson; Marc A Adams; Anjum Hajat; Jana A Hirsch; Brian E Saelens; Stephen J Mooney

doi:10.1016/j.healthplace.2024.103209

A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US

Health Place. 2024 Mar:86:103209. doi: 10.1016/j.healthplace.2024.103209. Epub 2024 Feb 25.

Authors

Hiwot Y Zewdie¹, Jamaica R Robinson², Marc A Adams³, Anjum Hajat⁴, Jana A Hirsch⁵, Brian E Saelens⁶, Stephen J Mooney⁴

Affiliations

¹ Department of Epidemiology, University of Washington School of Public Health, USA. Electronic address: hzewdie@uw.edu.
² Department of Oncology, School of Medicine, Wayne State University, USA; Population Studies and Disparities Research group, Karmanos Cancer Institute, USA.
³ College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
⁴ Department of Epidemiology, University of Washington School of Public Health, USA.
⁵ Urban Health Collaborative and Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, USA.
⁶ Department of Pediatrics, University of Washington, USA; Seattle Children's Research Institute, USA.

PMID: 38408408
PMCID: PMC10998688 (available on 2025-03-01)
DOI: 10.1016/j.healthplace.2024.103209

Abstract

Introduction: 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.

Methods: 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).

Results: 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).

Conclusion: 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.

Keywords: Census data; Latent profile analysis; Neighborhoods; Population health.

MeSH terms

Ethnicity*
Humans
Minority Groups*
Racial Groups
Residence Characteristics

Abstract

MeSH terms

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