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<p>This study presents a gender-sensitive spatial vulnerability assessment of malaria in Kebbi State, Nigeria, using a Composite Vulnerability Index (CVI) constructed from five geospatial indicators: malaria incidence, mortality, insecticide-treated net (ITN) usage, travel time to health facilities, and antenatal care (ANC) non-attendance. ANC non-attendance serves as a proxy for women’s healthcare access, revealing gendered disparities driven by cost constraints, limited autonomy, and sociocultural norms. Indicators were weighted using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), and vulnerability hotspots were identified through GIS-based analysis and Getis-Ord Gi* statistics. Results reveal pronounced spatial heterogeneity, with central-western Local Government Areas (LGAs), such as Maiyama, Dandi, and Koko/Besse, exhibiting high malaria burden, poor healthcare access, and high ANC non-attendance. These “Gendered Mismatch Zones” highlight the intersection of disease risk and structural gender inequities. The study underscores the importance of incorporating gender-sensitive proxies into spatial models, especially in data-scarce settings, to guide targeted, equitable malaria interventions, such as mobile health services and gender-focused programming. The findings support inclusive policy planning aligned with global health and gender equity goals.</p>

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Gendered implications of malaria prevention and healthcare accessibility in Kebbi State, Nigeria: a spatial composite vulnerability analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Ayobami Atijosan,
  • Abimbola Atijosan

摘要

This study presents a gender-sensitive spatial vulnerability assessment of malaria in Kebbi State, Nigeria, using a Composite Vulnerability Index (CVI) constructed from five geospatial indicators: malaria incidence, mortality, insecticide-treated net (ITN) usage, travel time to health facilities, and antenatal care (ANC) non-attendance. ANC non-attendance serves as a proxy for women’s healthcare access, revealing gendered disparities driven by cost constraints, limited autonomy, and sociocultural norms. Indicators were weighted using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), and vulnerability hotspots were identified through GIS-based analysis and Getis-Ord Gi* statistics. Results reveal pronounced spatial heterogeneity, with central-western Local Government Areas (LGAs), such as Maiyama, Dandi, and Koko/Besse, exhibiting high malaria burden, poor healthcare access, and high ANC non-attendance. These “Gendered Mismatch Zones” highlight the intersection of disease risk and structural gender inequities. The study underscores the importance of incorporating gender-sensitive proxies into spatial models, especially in data-scarce settings, to guide targeted, equitable malaria interventions, such as mobile health services and gender-focused programming. The findings support inclusive policy planning aligned with global health and gender equity goals.