Preterm birth disparities between states in the United States: an opportunity for public health interventions.
Margaret H BublitzMarshall CarpenterGhada BourjeilyPublished in: Journal of psychosomatic obstetrics and gynaecology (2019)
Objective: To examine associations between statelevel characteristics and state-level preterm birth rates.Study design: We conducted a retrospective ecological cross-sectional study using statelevel data from 2013 to 2014 extracted from publicly available sources -the March of Dimes PeriStats database, the U.S. Census Bureau, the US Department of Education, and the US Department of Justice.Results: State-level preterm birth rates correlated with the following state characteristics: poverty rate, obesity rate, percentage of non-Hispanic Black women residents, smoking rate, percent of C - section deliveries, percent of births to women <20 years old, pregnancies receiving late/no prenatal care, and violent crimes per capita. Linear regression analysis found that only the percent of non-Hispanic Black women by state remained a significant predictor of state-level preterm birth rates after adjusting for other risk factors.Conclusions: States with higher percentages of non-Hispanic Black women had higher rates of preterm birth, even after adjusting for sociodemographic characteristics, prenatal care, and maternal health by state. These findings suggest that public health interventions that target contextual and environmental risk factors affecting non-Hispanic Black women may help to curb rising rates of preterm birth in the United States.
Keyphrases
- preterm birth
- public health
- gestational age
- low birth weight
- polycystic ovary syndrome
- pregnancy outcomes
- healthcare
- birth weight
- pregnant women
- risk factors
- insulin resistance
- palliative care
- type diabetes
- cervical cancer screening
- cross sectional
- breast cancer risk
- african american
- quality improvement
- physical activity
- mental health
- emergency department
- metabolic syndrome
- human health
- machine learning
- drinking water
- tertiary care
- weight gain
- adipose tissue
- pain management
- risk assessment
- climate change
- deep learning
- mental illness
- data analysis