Background <p>Structural racism is a key driver of health inequities among Black Americans, yet its measurement remains inconsistent. This study introduces a multigenerational model of exposure to structural racism across five domains—criminal justice, economic opportunity, educational resources, political participation, and residential segregation—among Black women in South Carolina from 1989 to 2020. It addresses key gaps by applying a lifecourse perspective, identifying consistent indicators over time and space, and using latent variables for assessment across two generations.</p> Methods <p>We developed a multigenerational dataset by linking South Carolina birth certificates along the maternal line (1989–2020), focusing on first births among Black mothers with complete structural racism data (<i>n</i> = 75,088 births; 37,544 family trees). Twenty-two indicators were drawn from national and state sources, merged at the county level. Confirmatory factor analysis (CFA) was used to construct latent variables representing structural racism. Measurement invariance testing assessed consistency across generations.</p> Results <p>The model showed good fit for four domains—criminal justice, economic opportunity, educational resources, and residential segregation. Political participation was excluded due to poor fit. Economic opportunity indicators were significant for mothers but not grandmothers, highlighting generational shifts in indicator relevance.</p> Conclusions <p>Four key dimensions of structural racism were observed across two generations in Black families in South Carolina, though indicator relevance varied. These findings emphasize the importance of context-specific, temporally aware measures. Future research should link these models to health outcomes to deepen understanding of how structural racism shapes health over time.</p>

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Developing an Intergenerational Model of Structural Racism Exposure among Women in Black Families in South Carolina, 1989–2020

  • Annie Ro,
  • Yanmei Xie,
  • Woo Jung Lee,
  • Kristi L. Allgood,
  • Andrea K. Henderson,
  • Michael P. Huynh,
  • Claire E. Margerison,
  • Belinda L. Needham,
  • Anton M. Palma,
  • Michael G. Smith,
  • Catherine Vander Woude,
  • Nancy L. Fleischer

摘要

Background

Structural racism is a key driver of health inequities among Black Americans, yet its measurement remains inconsistent. This study introduces a multigenerational model of exposure to structural racism across five domains—criminal justice, economic opportunity, educational resources, political participation, and residential segregation—among Black women in South Carolina from 1989 to 2020. It addresses key gaps by applying a lifecourse perspective, identifying consistent indicators over time and space, and using latent variables for assessment across two generations.

Methods

We developed a multigenerational dataset by linking South Carolina birth certificates along the maternal line (1989–2020), focusing on first births among Black mothers with complete structural racism data (n = 75,088 births; 37,544 family trees). Twenty-two indicators were drawn from national and state sources, merged at the county level. Confirmatory factor analysis (CFA) was used to construct latent variables representing structural racism. Measurement invariance testing assessed consistency across generations.

Results

The model showed good fit for four domains—criminal justice, economic opportunity, educational resources, and residential segregation. Political participation was excluded due to poor fit. Economic opportunity indicators were significant for mothers but not grandmothers, highlighting generational shifts in indicator relevance.

Conclusions

Four key dimensions of structural racism were observed across two generations in Black families in South Carolina, though indicator relevance varied. These findings emphasize the importance of context-specific, temporally aware measures. Future research should link these models to health outcomes to deepen understanding of how structural racism shapes health over time.