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

Jiao Yu, Thomas K.M. Cudjoe, Walter S. Mathis and Xi Chen
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Jiao Yu: Yale University
Thomas K.M. Cudjoe: Johns Hopkins University
Walter S. Mathis: Yale University

No 18251, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: inverse probability weighting; machine learning; metabolic and inflammation biomarkers; neighborhood disorder; latent class analysis (search for similar items in EconPapers)
JEL-codes: I12 I14 I18 J14 R20 (search for similar items in EconPapers)
Date: 2025-11
New Economics Papers: this item is included in nep-age and nep-big
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