Tipre M, Bolaji B, Blanchard C, Harrelson A, Szychowski J, Sinkey R, Julian Z, Tita A, Baskin ML. Relationship Between Neighborhood Socioeconomic Disadvantage and Severe Maternal Morbidity and Maternal Mortality.
Ethn Dis 2022;
32:293-304. [PMID:
36388861 PMCID:
PMC9590600 DOI:
10.18865/ed.32.4.293]
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Abstract
Background
Rates of severe maternal morbidity and maternal mortality (SMM/MM) in the United States are rising. Disparities in SMM/MM persist by race, ethnicity and geography, and could partially be attributed to social determinants of health.
Purpose
Utilizing data from the largest, statewide referral hospital in Alabama, we investigated the relationship between residence in disadvantaged neighborhoods and SMM/MM.
Methods
Data on all pregnancies between 2010 and 2020 were included; SMM/MM cases were identified using CDC definitions. Area deprivation index (ADI) available at the census-block group was geographically linked to individual records and categorized using quintile cutoffs; higher ADI score indicated higher socioeconomic disadvantage. Generalized estimating equation models were used to adjust for spatial autocorrelation and ORs were computed to evaluate the relationship between ADI and SMM/MM, adjusted for covariates including age, race, insurance, residence in medically underserved areas/population (MUAP), and urban/rural residence.
Results
Overall, 32,909 live-birth deliveries were identified, with a prevalence of 9.8% deliveries with SMM/MM with blood transfusion and 5.3% without blood transfusion, respectively. Increased levels of ADI were associated with increased odds of SMM/MM. Compared to women in the lowest quintile, the adjusted OR for SMM/MM among women in highest quintile was 1.78 (95%CI, 1.22-2.59, P=.0027); increasing age, non-Hispanic Black, government insurance and residence in MUAP were also significantly associated with increased odds of SMM/MM.
Conclusion
Our results suggest that residence within disadvantaged neighborhoods may contribute to SMM/MM even after adjusting for patient-level factors. Measures such as ADI can help identify the most vulnerable populations and provide points to intervene.
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