A Partnership and Data Integration Approach for Investigating Rural Birthing Unit Closures
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Published on: January 12, 2018
Heather Rouse1, Sharon Zanti2, Todd Abraham1
1Iowa's Integrated Data System for Decision-Making (I2D2), Department of Human Development and Family Studies, Iowa State University, Ames, IA, USA.
Background:
Rural birthing unit closures have raised concerns about access to prenatal care and maternal and infant outcomes. Iowa has experienced 41 closures since 2000, leaving a majority of counties without obstetric services. This study examines the impacts of these closures through a four-phase mixed-methods study conducted in partnership with Iowa State University, Iowa Department of Public Health, and Maternal, Infant, and Early Childhood Home Visiting.
Methods:
We replicated aggregate analyses used by state partners, then analyzed 681,187 individual birth records from 1999 to 2017 linked to county-level closure data. Hierarchical linear and logistic regression models estimated effects of closures on prenatal care and birth outcomes, stratified by maternal education, WIC receipt, and Medicaid coverage. We conducted qualitative interviews with 17 home visiting staff across eight counties impacted by closures and engaged partners in deliberating findings.
Results:
Closures were associated with significant differences in prenatal care for mothers without a high school degree, including later initiation (0.30 months later when giving birth pre-closure, p < 0.05); fewer visits (1.53 fewer pre-closure, p < 0.05), and higher odds of inadequate prenatal care (OR = 5.7 pre-closure, p < 0.05). However, WIC and Medicaid buffered against these disruptions. Qualitative data highlighted increased travel burden, reduced choice, and weakened provider relationships.
Conclusions:
Closing rural birthing units disrupts prenatal care access, disproportionately affecting mothers without a high school degree. A cyclical, partnership-engaged approach using integrated data revealed impacts masked in aggregate analyses and offers a model for generating timely, actionable insights to guide rural maternal health policy and practice.
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