ArticleTropical parasitology
Malaria transmission trends and its lagged association with climatic factors in the highlands of Plateau State, Nigeria.
Article in Tropical parasitology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Current Status of Malaria Control and Elimination in Africa: Epidemiology, Diagnosis, Treatment, Progress and Challenges.Journal of epidemiology and global health · 2024Review
- Do socio-demographic factors modify the effect of weather on malaria in Kanungu District, Uganda?Malaria journal · 2022Article
- Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria.International journal of environmental research and public health · 2020Article
- Mapping malaria seasonality in Madagascar using health facility data.BMC medicine · 2020Article
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6 authors.
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Abstract
backgroundMalaria is a serious disease and still remains a public health problem in many parts of Nigeria.
objectivesThe aim of this study was to describe malaria transmission trends and analyzed the impact of climatic factors on malaria transmission in the highlands of Plateau State, Central Nigeria.
methodsThe study was a retrospective survey which used archival data of climate parameters and medical case records on malaria. Rainfall, relative humidity, and temperature data were obtained from the nearest weather stations to the study locations from 1980 to 2015. Data on reported malaria cases were collected from general hospitals in the selected local government areas (LGAs) from 2003 to 2015. Generalized Additive Models were used to model trends in malaria incidences over time, and it is lagged association with climatic factors.
resultsThe results show a significant cyclical trend in malaria incidence in all the study areas (
conclusionOur findings suggest that climatic factors are among the major determinants of malaria transmission in the highlands of Plateau state except in Jos-North LGA where the low model deviance explained (35.4%) could mean that there are other important factors driving malaria transmission in the area other than climatic factors.
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