A healthy migrant effect? Estimating health outcomes of the undocumented immigrant population in the United States using machine learning

Simon A. Ruhnke, Megan M. Reynolds, Fernando A. Wilson, James P Stimpson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper investigated whether the commonly observed immigrant health advantage persists among undocumented immigrants in the U.S. and provides nationally representative evidence on the health of this vulnerable population. Data were derived from pooled cross-sections of the National Health Interview Survey (NHIS, 2000–2018). The legal status of foreign-born NHIS respondents is imputed using a non-parametric machine learning model built based on information from the 2004, 2008 and 2014 cohorts of the Survey of Income and Program Participation (SIPP). Multivariate logistic regression analysis indicated that, despite exposure to numerous additional risk factors, the undocumented population experienced a more pronounced Healthy Migrant Effect, with lower odds of reporting fair or poor self-rated health, any physician-diagnosed chronic conditions or being obese. The observed patterns in undocumented health outcomes may be related to the additional challenges and exclusionary policies associated with undocumented migration that could in turn lead to a more pronounced selection of healthy and resilient individuals.

Original languageEnglish (US)
Article number115177
JournalSocial Science and Medicine
Volume307
DOIs
StatePublished - Aug 2022
Externally publishedYes

Keywords

  • Chronic disease
  • Demography
  • Immigrant
  • Marching learning
  • Obesity
  • Population health
  • Undocumented immigrants
  • United States

ASJC Scopus subject areas

  • Health(social science)
  • History and Philosophy of Science

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