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The Weathering Hypothesis on Racial Disparities in Maternal Health

Senan Ebrahim, MD, PhD
CEO at Delfina
25 Aug 2022 · 4 min read
Weathering Hypothesis

Academic studies increasingly indicate that childhood exposure to chronic stress permanently alters processes within the body, resulting in chronic physiological damage over time. It is currently hypothesized that individuals who have had Adverse Childhood Experiences (ACEs) are more susceptible to disease development (Hughes et al. 2017).

While the body of work on ACEs has typically focused on childhood abuse, neglect, and mental illness, Dr. Arline Geronimus extends this research with the “Weathering Hypothesis” to include the impact of structural racism. Structural racism is the “totality of ways in which societies foster racial discrimination through mutually reinforcing systems of housing, education, employment, earnings, benefits, credit, media, health care, and criminal justice” (Bailey et al. 2017). The Weathering Hypothesis attempts to explain how structural racism compounds with age to effect poor health outcomes. In her groundbreaking research, Dr. Geronimus attributes poor maternal health outcomes for Black women to a “cumulative impact of social inequality” that can be likened to premature aging from the stress of systemic racism (Geronimus et al. 2006).

Illustration of the Weathering Hypothesis

Illustration of the Weathering Hypothesis (Brisendine et al. 2017).

Scientists interested in studying these effects measure allostatic load, or “wear and tear” on the body, and have found that the Weathering Hypothesis can explain disparities in maternal and child health outcomes. Studies have shown significantly higher allostatic loads in Black women from ages 35-64 compared to Black men and white women of the same age (Geronimus et al. 2006). One recent intersectional demographic study demonstrated the correlation of gendered racism experienced by women of color with maternal mortality, with a 2-fold demonstrated increase in maternal mortality for Black women when controlling for age and care access (Patterson et al. 2022).

Another study used National Vital Statistics System Data from 2007 to 2014 to examine disparities in stillbirth among non-Hispanic Black patients as compared to non-Hispanic white ones. Non-Hispanic Black mothers were found to experience much higher rates of stillbirth, with Black women experiencing a 2.8-fold increased risk of stillbirth compared to non-Hispanic white women at 20 to 23 weeks gestation (Brisendine et al. 2017).

Racial disparities in stillbirth rates peaked at 35 years of age, and decreased at later ages, which suggests that the accelerated aging proposed by the Weathering Hypothesis is not fully explanatory of disparities at every age. In particular, research is still needed to account for why Black mothers over the age of 35 face relatively lower risk of stillbirth. One proposed explanation is that for white mothers older than 35, the accumulation of risk “catches up” due to a higher global likelihood of fertility challenges and fetal loss in the general population; another explanation is that increased access to medical and social resources over age 35 may differentially reduce risk for Black mothers (Brisendine et al. 2017).

At Delfina, we develop models that provide tailored risk predictions for pregnancy-related health outcomes to enable early intervention. Developing models that can accurately predict outcomes for individuals requires a lot of rich data – and a deep understanding of why certain biological, social, or demographic factors contribute to certain health outcomes! We work closely with clinicians and health disparities researchers to collate data on biological, social, demographic, and environmental factors that contribute to adverse health outcomes. We are also purposeful about collecting data from geographically, economically, racially, and ethnically diverse communities in the United States to ensure that all pregnant patients are represented in our developed models, such as in our NICHD-awarded hypertension prediction model. Our rich data sources enable us to better tailor our predictions, and collaborate with public health researchers to investigate the impacts of structural racism on pregnancy outcomes. Our ultimate goal is to support clinicians and patients by closing the gap in maternal health outcomes for pregnant women of color.

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