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A Spatial Analytic Approach to Maternal Health Following Hurricane Florence (2018).

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Severe maternal morbidity clusters were identified after Hurricane Florence, highlighting the need for spatial analysis in disaster response. Factors like age, racial segregation, and urbanity were linked to higher-risk areas.

Keywords:
Hurricane florenceMaternal healthNorth CarolinaSaTScanSevere maternal morbidityTropical cyclones

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Area of Science:

  • Environmental Health
  • Maternal Health
  • Spatial Epidemiology

Background:

  • US maternal morbidity rates are high, with limited research on severe maternal health during natural disasters.
  • Increasing tropical cyclone intensity due to climate change poses a significant public health threat.
  • Severe maternal morbidity (SMM) research is crucial in disaster contexts.

Purpose of the Study:

  • This study pioneered a spatial clustering approach to examine maternal health outcomes after a tropical cyclone in North Carolina.
  • To identify high-risk areas for severe maternal morbidity (SMM) following Hurricane Florence.

Main Methods:

  • Exploratory spatial clustering analysis of Severe Maternal Morbidity (SMM-21) hospitalizations using the Bernoulli-Kulldorff SaTScan statistic.
  • Multivariate logistic regression was employed to identify factors associated with SMM clusters post-Hurricane Florence.

Main Results:

  • All 28 FEMA disaster-declared counties were part of an SMM spatial cluster.
  • Individual factors (age ≥ 40) and contextual factors (racial segregation, reduced greenspace, high urbanity) were associated with high-risk clusters.

Conclusions:

  • Spatial analysis is vital for identifying high-burden maternal populations in the wake of climate-related disasters.
  • Findings emphasize the need for targeted post-disaster relief and response strategies informed by spatial epidemiology.