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Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health

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  • Katherine L. Forthman

    (Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA
    Department of Mathematics, College of Engineering & Natural Sciences, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA)

  • Janna M. Colaizzi

    (Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA)

  • Hung-wen Yeh

    (Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA
    Division of Health Services and Outcomes Research, Children’s Mercy Hospital, 2401 Gillham Road, Kansas City, MO 64108, USA)

  • Rayus Kuplicki

    (Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA)

  • Martin P. Paulus

    (Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA)

Abstract

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.

Suggested Citation

  • Katherine L. Forthman & Janna M. Colaizzi & Hung-wen Yeh & Rayus Kuplicki & Martin P. Paulus, 2021. "Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health," IJERPH, MDPI, vol. 18(3), pages 1-19, January.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1202-:d:489398
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    References listed on IDEAS

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    1. Jessica A. Kelley-Moore & Kathleen A. Cagney & Kimberly A. Skarupski & Susan A. Everson-Rose & Carlos F. Mendes de Leon, 2016. "Do Local Social Hierarchies Matter for Mental Health? A Study of Neighborhood Social Status and Depressive Symptoms in Older Adults," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 71(2), pages 369-377.
    2. Arcaya, Mariana C. & Tucker-Seeley, Reginald D. & Kim, Rockli & Schnake-Mahl, Alina & So, Marvin & Subramanian, S.V., 2016. "Research on neighborhood effects on health in the United States: A systematic review of study characteristics," Social Science & Medicine, Elsevier, vol. 168(C), pages 16-29.
    3. Nicolle A Mode & Michele K Evans & Alan B Zonderman, 2016. "Race, Neighborhood Economic Status, Income Inequality and Mortality," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
    4. Malia Jones & Anne Pebley, 2014. "Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared," Demography, Springer;Population Association of America (PAA), vol. 51(3), pages 727-752, June.
    5. Marmot, Michael & Bell, Ruth, 2010. "Health Equity and Development: the Commission on Social Determinants of Health," European Review, Cambridge University Press, vol. 18(1), pages 1-7, February.
    6. Jang, Soong-Nang & Kawachi, Ichiro & Chang, Jiyeun & Boo, Kachung & Shin, Hyun-Gu & Lee, Hyejung & Cho, Sung-il, 2009. "Marital status, gender, and depression: Analysis of the baseline survey of the Korean Longitudinal Study of Ageing (KLoSA)," Social Science & Medicine, Elsevier, vol. 69(11), pages 1608-1615, December.
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