Evidence map›Paper›PMID 42056388›Full record

ArticleMaternal and child health journal2026

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

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

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Article in Maternal and child health journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Kristen LysneDepartment of Geography and Planning, Appalachian State University, Boone, NC, USA.
Margaret SuggDepartment of Geography and Planning, Appalachian State University, Boone, NC, USA. kovachmm@appstate.edu.ORCID http://orcid.org/0000-0002-0315-4860
Charlie ReedNorth Carolina Institute for Climate Studies, North Carolina State University, Asheville, NC, USA.
Jennifer RunkleNorth Carolina Institute for Climate Studies, North Carolina State University, Asheville, NC, USA.
Dennis GuignetDepartment of Economics, Appalachian State University, Boone, NC, USA.
L Baker PerryDepartment of Geography and Planning, Appalachian State University, Boone, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

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

methodsWe 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).

resultsAll 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.

conclusionResults 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.

Indexed as

Cyclonic StormsHospitalizationMaternal HealthSpatial AnalysisAdultCluster AnalysisDisastersFemaleHumansNorth CarolinaPregnancyHurricane florenceMaternal healthNorth CarolinaSaTScanSevere maternal morbidityTropical cyclones

Identifiers

PMID42056388
PMCPMC13190442

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