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Bayesian Kernel Machine Regression for Social Epidemiologic Research.

Jemar R Bather1,2, Taylor J Robinson3,4,5, Melody S Goodman1,2

  • 1From the Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY.

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Social epidemiologic analysis can now examine multiple social variables as a mixture. Increased perceived discrimination and substance use were linked to higher psychological distress in individuals with police arrest histories.

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Area of Science:

  • Social epidemiology
  • Public Health
  • Statistical modeling

Background:

  • Limited research exists on analyzing multiple continuous social variables as a mixture in social epidemiology.
  • The Bayesian kernel machine regression framework is proposed to analyze univariate, bivariate, and overall exposure mixture effects.

Purpose of the Study:

  • To apply Bayesian kernel machine regression to investigate the relationship between a mixture of social and individual factors and psychological distress.
  • To identify specific factors contributing to psychological distress among individuals with a history of police arrest.

Main Methods:

  • Utilized data from the 2023 Survey of Racism and Public Health.
  • Employed Bayesian kernel machine regression to analyze exposure mixtures including racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use.
  • Complemented the primary analysis with unadjusted and adjusted linear regression models for each variable.

Main Results:

  • Higher self-reported discrimination and greater substance use were significantly associated with increased psychological distress (posterior inclusion probability = 1.00 for both).
  • These findings were consistent across unadjusted and adjusted linear regression models.
  • Perceived discrimination and substance use demonstrated a positive correlation with psychological distress.

Conclusions:

  • Novel statistical methods, such as Bayesian kernel machine regression, are crucial for advancing social epidemiology.
  • These advanced analytical approaches can identify complex exposure mixture associations.
  • This research highlights the importance of addressing the health needs of socially vulnerable populations by understanding multifaceted social exposures.