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open-access-imgOpen AccessLatent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health
Katherine L Forthman,
Janna M. Colaizzi,
Hung-Wen Yeh,
Rayus Kuplicki,
Martin P. Paulus
Publication year2021
Publication title
international journal of environmental research and public health
Resource typeJournals
Neighborhood characteristics can have profound impacts on resident mental health, but the wide variability in methodologies used across studies makes it difficult to reach a consensus as to the implications of these impacts. The aim of this study was to simplify the assessment of neighborhood influence on mental health. We used a factor analysis approach to reduce the multi-dimensional assessment of a neighborhood using census tracts and demographic data available from the American Community Survey (ACS). Multivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011–2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2014–2015 data (2017 release). A five-factor model provided the best fit for the data. Each factor represents a complex multi-dimensional construct. However, based on heuristics and for simplicity we refer to them as (1) Affluence, (2) Singletons in Tract, (3) African Americans in Tract, (4) Seniors in Tract, and (5) Hispanics or Latinos in Tract. African Americans in Tract (with loadings showing larger numbers of people who are black, single moms, and unemployed along with fewer people who are white) and Affluence (with loadings showing higher income, education, and home value) were strongly associated with poor mental health (R 2 = 0.67,R 2 = 0.83). These findings demonstrate the utility of this factor model for future research focused on the relationship between neighborhood characteristics and resident mental health.
Subject(s)american community survey , census , census tract , computer science , construct (python library) , demography , environmental health , gerontology , heuristics , mathematics , medicine , mental health , multivariate analysis , multivariate statistics , operating system , population , programming language , psychiatry , psychology , sociology , statistics , structural equation modeling
Keyword(s) neighborhood , factor analysis , mental health
SCImago Journal Rank0.747

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