Structural Racism and Perinatal Health Disparities
by Center for Justice Research
Oct. 12, 5 a.m. EDT
Briefing Document
Source: Project overview document for “Structural Racism, Disparities, and Perinatal Health” research project, a collaboration between the Center for Justice Research at Texas Southern University (TSU) and Baylor College of Medicine.
Main Theme: This research project investigates the pervasive impacts of structural racism on racial and ethnic disparities observed in maternal and infant health outcomes.
Key Objectives :
- Isolate and evaluate the individual effects of key domains of structural racism on perinatal health, such as criminal justice, environmental pollution, economic segregation, housing discrimination, and food insecurity.
- Assess the cumulative burden of exposure to multiple forms of structural racism on perinatal health disparities.
- Leverage machine learning to identify the primary drivers of adverse outcomes for Black, Hispanic, and White women in Houston.
Methodology :
- Mixed-methods approach: Combines quantitative data analysis with qualitative insights.
- Data sources: Criminal justice data, environmental pollution indices, socioeconomic indicators, housing data, and food access metrics.
- Analytical approaches: Statistical analysis and machine learning techniques.
Key Questions :
- How does structural racism manifest across different sectors and impact maternal and infant health?
- What is the cumulative impact of exposure to multiple domains of structural racism on perinatal health?
- Which factors are most strongly associated with adverse outcomes for different racial/ethnic groups?
Expected Outcomes :
- Comprehensive reports detailing the impact of structural racism on maternal and infant health.
- Evidence-based policy recommendations to reduce health disparities.
- Increased awareness among stakeholders about the role of structural racism in public health.
Innovation :
- The project is unique in its comprehensive examination of multiple domains of structural racism concurrently.
- Machine learning methods will be utilized to uncover complex interactions that contribute to health disparities.
Challenges & Mitigation :
- Data accessibility: Collaboration with local health departments and community organizations will be crucial.
- Community engagement: Targeted outreach efforts will build trust and encourage participation from affected communities.
Alignment with CJR Mission:
This project directly aligns with the Center for Justice Research’s mission to reduce mass incarceration as it tackles the root causes of health disparities linked to structural racism, contributing to broader public health goals.
Quote: “This project is innovative in its comprehensive approach to examining multiple domains of structural racism simultaneously. By employing machine learning methods, it aims to uncover complex interactions that contribute to health disparities.”
Significance: This research has the potential to significantly advance our understanding of how structural racism operates and impacts the health of mothers and infants. The findings will be instrumental in informing policies and interventions that promote health equity.
FAQ: Structural Racism and Perinatal Health Disparities
This research project investigates the effects of structural racism on racial and ethnic disparities in adverse maternal and infant health outcomes. It aims to understand how different aspects of structural racism contribute to these disparities.
The research examines various domains of structural racism, including:
- Criminal Justice: Mass incarceration rates and their impact on communities of color.
- Environmental Pollution: Disproportionate exposure to environmental hazards in minority communities.
- Racialized Economic Segregation: Unequal access to resources and opportunities based on race and ethnicity.
- Housing Discrimination: Biased practices in housing that limit access for certain racial/ethnic groups.
- Food Deserts: Lack of access to healthy and affordable food options in predominantly minority neighborhoods.
The project utilizes a mixed-methods approach, combining:
- Data collection: Gathering data from various sources related to the domains of structural racism listed above.
- Statistical analysis: Using statistical methods to evaluate the relationship between structural racism and health outcomes.
- Machine learning: Employing advanced computational techniques to identify key drivers of adverse outcomes among different racial and ethnic groups.
The project aims to produce:
- Comprehensive reports: Detailing the impact of structural racism on maternal and infant health disparities.
- Evidence-based recommendations: Providing policymakers with actionable strategies to reduce health disparities.
- Increased awareness: Educating stakeholders about the role of structural racism in public health.
This project is innovative in its comprehensive approach to examining multiple domains of structural racism simultaneously. It also utilizes machine learning methods to uncover complex interactions contributing to health disparities.
Challenges include:
- Data accessibility: Ensuring access to relevant data from various sources will be addressed through collaborations with local health departments and community organizations.
- Community engagement: Building trust with affected communities is crucial. Targeted outreach efforts will encourage participation and ensure the research reflects community needs.
The project aligns with the Center for Justice Research's mission to reduce mass incarceration through targeted, culturally responsive solutions. By addressing the root causes of health disparities linked to structural racism, it supports broader public health goals.
The project is a collaboration between the Center for Justice Research at Texas Southern University (TSU) and Baylor College of Medicine, leveraging expertise in public health research and community engagement.