Abstract
Although malaria burden has declined globally following scale up of intervention, the disease has remained a leading cause of hospitalization and deaths among children aged under-5 years in Nigeria. Malaria is known to be related to climate and environmental conditions. Previous research has usually studied the effects of these factors, neglecting possible correlation between them, high correlation among variables is a source of multicollinearity that induces overfitting in regression modelling. In this paper, a factor analysis was first introduced to circumvent the issue of multicollinearity and a Generalized Additive Model (GAM) was subsequently explored to identify the important risk factors that might influence the prevalence of childhood malaria in Nigeria. The GAM incorporated the complexity of the survey data, while simultaneously modelling the nonlinear and spatial random effects to allow a more precise identification of the major malaria risk factors that influence the geographical distribution of the disease. From our findings, the three latent factor components (constituted by humidity, precipitation, potential evapotranspiration, and wet days/maximum and minimum temperature/proximity to permanent waters, respectively) were significantly associated with malaria prevalence. Our analysis also detected statistically significant and nonlinear effect of altitude: the risk of malaria increased with lower values but declined sharply with higher values. A significant spatial variability in under-5 malaria prevalence across the survey clusters was also observed; malaria burden was higher in the northern part of Nigeria. Investigating the impact of important risk factors and geographical location on childhood malaria is of high relevance for the sustainable development goals (SDGs) 2015–2030 Agenda on malaria eradication, and we believe that the information obtained from this study and the generated risk maps can be useful to effectively target intervention efforts to high-risk areas based on climate and environmental context.
Highlights
Malaria disease is majorly caused by the protozoan of the Plasmodium types, which comprise the Plasmodium malariae species, the Plasmodium falciparum species, the Plasmodium ovale species and the Plasmodium vivax species, usually transmitted via the infected female Anopheles mosquitoes [1,2,3,4,5,6]
A factor analysis was first introduced to circumvent the issue of multicollinearity and a Generalized Additive Model (GAM) was subsequently explored to identify the important risk factors that might influence the prevalence of childhood malaria in Nigeria
Investigating the impact of important risk factors and geographical location on childhood malaria is of high relevance for the sustainable development goals (SDGs) 2015–2030 Agenda on malaria eradication, and we believe that the information obtained from this study and the generated risk maps can be useful to effectively target intervention efforts to high-risk areas based on climate and environmental context
Summary
Malaria disease is majorly caused by the protozoan of the Plasmodium types, which comprise the Plasmodium malariae species, the Plasmodium falciparum species, the Plasmodium ovale species and the Plasmodium vivax species, usually transmitted via the infected female Anopheles mosquitoes [1,2,3,4,5,6]. The global burden of the deadly P. falciparum malaria has declined greatly, but the decline has not been universal, and areas of higher burden persist in many African countries [3]. According to the 2017 World malaria report, the P. falciparum was the most prevalent species of malaria parasite in the African region, which accounted for approximately 99.8% of the estimated severe malaria cases, followed by the South-Eastern Asia 62.8%, the Eastern Mediterranean 69% and the Western Pacific regions 71.9% [3]. Among the sub-Saharan African countries, Nigeria has the highest share of the global burden of malaria disease [3]. More than 95% of the malaria cases in Nigeria are caused by P. falciparum [10,11,12], mostly occurring in children under the age of 5 years [3,10,12]. An important partway to understanding malaria distribution patterns and planning effective intervention strategies is the identification of important influencing factors to malaria prevalence and transmission [8,15]
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