Poor black women who lose a mother or sister during pregnancy are more likely to have preterm births

Aim: We conducted a systematic review evaluating race/ethnicity representation in DNA methylomic studies of preterm birth. Data sources: PubMed, EMBASE, CINHAL, Scopus and relevant citations from 1 January 2000 to 30 June 2019. Study appraisal & synthesis methods: Two authors independently identified abstracts comparing DNA methylomic differences between term and preterm births that included race/ethnicity data. Results: 16 studies were included. Black and non-Hispanic Black deliveries were well represented (28%). However, large studies originating from more than 95% White populations were excluded due to unreported race/ethnicity data. Most studies were cross-sectional, allowing for reverse causation. Most studies were also racially/ethnically homogeneous, preventing direct comparison of DNA methylomic differences across race/ethnicities. Conclusion: In DNA methylomic studies, Black women and infants were well represented. However, the literature has limitations and precludes drawing definitive conclusions.

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County Socioeconomic Deprivation and Preterm Birth Risk Between White and Black Mothers in Georgia, USA

Innovations in Global Maternal Health ◽

10.4018/978-1-7998-2351-3.ch008 ◽

2020 ◽

pp. 182-200

Author(s):

Wei Tu

Keyword(s):

Black Women ◽

Odds Ratio ◽

Preterm Births ◽

Vital Statistics ◽

Racial Groups ◽

County Level ◽

Black Mothers ◽

Logistic Regression Models ◽

Individual Level ◽

Random Intercept

Past studies have reported the association between living in a socioeconomically deprived neighborhood and elevated preterm births (PtB) risk after adjusting certain individual level confounders. This article examined the association between county-level deprivation and PtB risk of three stratified racial groups, white, black, and the others. The author built two level random intercept logistic regression models using 112,589 single live births retrieved from vital statistics record in Georgia, USA in 2010. Although county level deprivation was found to be insignificant for PtB risk for the entire study population, it had a significant yet modest effect on magnifying the PtB risk of black women (The odds ratio (OR) = 1.063,95% CI = 1.02, 1.12). In addition, the Median Odds Ratio (MOR) (1.229) indicated a weak neighborhood effect on PtB risk and the Interval Odds Ratio (IOR)-80% (0.68-1.49) suggested large unaccounted county-level heterogeneity. Future research will include more confounders at both levels in analysis as well as addressing the uncertain geographic context problem (UGCoP).

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County Socioeconomic Deprivation and Preterm Birth Risk Between White and Black Mothers in Georgia, USA

International Journal of Applied Geospatial Research ◽

10.4018/ijagr.2018070102 ◽

2018 ◽

Vol 9 (3) ◽

pp. 18-30

Author(s):

Wei Tu

Keyword(s):

Black Women ◽

Odds Ratio ◽

Preterm Births ◽

Vital Statistics ◽

Future Research ◽

Racial Groups ◽

County Level ◽

Entire Study ◽

Logistic Regression Models ◽

Individual Level

Past studies have reported the association between living in a socioeconomically deprived neighborhood and elevated preterm births (PtB) risk after adjusting certain individual level confounders. This article examined the association between county-level deprivation and PtB risk of three stratified racial groups, white, black, and the others. The author built two level random intercept logistic regression models using 112,589 single live births retrieved from vital statistics record in Georgia, USA in 2010. Although county level deprivation was found to be insignificant for PtB risk for the entire study population, it had a significant yet modest effect on magnifying the PtB risk of black women (The odds ratio (OR) = 1.063,95% CI = 1.02, 1.12). In addition, the Median Odds Ratio (MOR) (1.229) indicated a weak neighborhood effect on PtB risk and the Interval Odds Ratio (IOR)-80% (0.68-1.49) suggested large unaccounted county-level heterogeneity. Future research will include more confounders at both levels in analysis as well as addressing the uncertain geographic context problem (UGCoP).

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