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Background <p>The United States leads developed nations in maternal morbidity, yet research on the literature surrounding severe maternal health in the context of natural disasters remains limited. Projections suggest that tropical cyclone (e.g., hurricane, typhoon, cyclone) intensity will continue to surge as global temperatures rise, and experts warn that they pose one of the most significant threats to global public health in the 21st century. </p> Objective <p>This study is the first to apply a spatial clustering approach to maternal health following exposure to a tropical cyclone in North Carolina. </p> Methods <p>We conducted an exploratory clustering analysis of hospitalizations for Severe Maternal Morbidity (SMM-21) using the Bernoulli-Kulldorff SaTScan statistic in the context of Hurricane Florence (2018). Multivariate logistic regression identified individual and contextual factors associated with high-risk clusters in the aftermath of Hurricane Florence (2018). </p> Results <p>All 28 FEMA disaster-declared counties had presence within an SMM spatial cluster, while individual factors (age ≥ 40) and contextual factors (racial segregation [ICE Race], reduced greenspace, and high-urbanity) were associated with residence in high-risk clusters. </p> Conclusion <p>Results indicate the importance of a spatial analytic approach following climate disasters to better identify characteristics of high-burden maternal populations for post-disaster relief and response.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Kristen Lysne,
  • Margaret Sugg,
  • Charlie Reed,
  • Jennifer Runkle,
  • Dennis Guignet,
  • L. Baker Perry

摘要

Background

The United States leads developed nations in maternal morbidity, yet research on the literature surrounding severe maternal health in the context of natural disasters remains limited. Projections suggest that tropical cyclone (e.g., hurricane, typhoon, cyclone) intensity will continue to surge as global temperatures rise, and experts warn that they pose one of the most significant threats to global public health in the 21st century.

Objective

This study is the first to apply a spatial clustering approach to maternal health following exposure to a tropical cyclone in North Carolina.

Methods

We conducted an exploratory clustering analysis of hospitalizations for Severe Maternal Morbidity (SMM-21) using the Bernoulli-Kulldorff SaTScan statistic in the context of Hurricane Florence (2018). Multivariate logistic regression identified individual and contextual factors associated with high-risk clusters in the aftermath of Hurricane Florence (2018).

Results

All 28 FEMA disaster-declared counties had presence within an SMM spatial cluster, while individual factors (age ≥ 40) and contextual factors (racial segregation [ICE Race], reduced greenspace, and high-urbanity) were associated with residence in high-risk clusters.

Conclusion

Results indicate the importance of a spatial analytic approach following climate disasters to better identify characteristics of high-burden maternal populations for post-disaster relief and response.