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Uncovering the Biological Toll of Neighborhood Disorder Trajectories: New Evidence Using Machine Learning Methods and Biomarkers in Older Adults

Author

Listed:
  • Yu, Jiao

    (Yale University)

  • Cudjoe, Thomas K.M.

    (Johns Hopkins University)

  • Mathis, Walter S.

    (Yale University)

  • Chen, Xi

    (Yale University)

Abstract

This study examined the link between neighborhood disorder trajectories and metabolic and inflammatory biomarkers in U.S. older adults. We analyzed data from community-dwelling Medicare beneficiaries in the National Health and Aging Trends Study. Neighborhood physical disorder was assessed annually through interviewer observations over six years. Latent class analysis was used to identify exposure trajectory subgroups. Machine learning based inverse probability weighted (IPW) regression models were conducted to estimate associations with five biomarkers, including body mass index (BMI), waist circumference, hemoglobin A1C (HbA1c), high-sensitivity C-reactive protein (hsCRP), and interleukin-6 (IL-6). Compared to the stable low exposure group, older adults with increased exposure, decreased exposure, and stable high exposure exhibited higher levels of HbA1c. Only stable high exposure was associated with increased hsCRP. No significant associations were found for other biomarkers. Residential environments play an important role in shaping the biological risk of aging. Incorporating routine screening for neighborhood environmental risks and implementing community-level interventions are pivotal in promoting healthy aging in place.

Suggested Citation

  • Yu, Jiao & Cudjoe, Thomas K.M. & Mathis, Walter S. & Chen, Xi, 2025. "Uncovering the Biological Toll of Neighborhood Disorder Trajectories: New Evidence Using Machine Learning Methods and Biomarkers in Older Adults," IZA Discussion Papers 18251, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp18251
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    References listed on IDEAS

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    1. Robert, Stephanie A. & Reither, Eric N., 2004. "A multilevel analysis of race, community disadvantage, and body mass index among adults in the US," Social Science & Medicine, Elsevier, vol. 59(12), pages 2421-2434, December.
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    3. Gustafsson, P.E. & Miguel, S.S. & Janlert, U. & Theorell, T. & Westerlund, H. & Hammarström, A., 2014. "Life-Course accumulation of neighborhood disadvantage and allostatic load: Empirical integration of three social determinants of health frameworks," American Journal of Public Health, American Public Health Association, vol. 104(5), pages 904-910.
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    Keywords

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    JEL classification:

    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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