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GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities.
Ogunsakin, Ropo Ebenezer; Babalola, Bayowa Teniola; Olusola, Johnson Adedeji; Joshua, Ayodele Oluwasola; Okpeku, Moses.
Afiliación
  • Ogunsakin RE; School of Health Systems and Public Health, University of Pretoria Faculty of Health Sciences, Pretoria, South Africa. oreropo@gmail.com.
  • Babalola BT; Department of Statistics, Ekiti State University, Ado Ekiti, Nigeria.
  • Olusola JA; Department of Geography and Planning Science, Ekiti State University, Ado Ekiti, Nigeria.
  • Joshua AO; Department of Mathematical Sciences, Science and Technology, Bamidele Olumilua University of Education, Ikere Ekiti, Nigeria.
  • Okpeku M; Discipline of Genetics, School of Life Sciences, University of Kwa-Zulu Natal, Westville, Durban, South Africa.
Parasitol Res ; 123(7): 262, 2024 Jul 06.
Article en En | MEDLINE | ID: mdl-38970660
ABSTRACT
Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sistemas de Información Geográfica / Análisis Espacio-Temporal / Malaria Límite: Humans País/Región como asunto: Africa Idioma: En Revista: Parasitol Res Asunto de la revista: PARASITOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Sudáfrica Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sistemas de Información Geográfica / Análisis Espacio-Temporal / Malaria Límite: Humans País/Región como asunto: Africa Idioma: En Revista: Parasitol Res Asunto de la revista: PARASITOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Sudáfrica Pais de publicación: Alemania