Oluyemi A Okunlola1, Oyetunde T Oyeyemi2, Adewale F Lukman3. 1. Department of Mathematics, University of Medical Sciences, Ondo, Ondo State, Nigeria. 2. Department of Biological Sciences, University of Medical Sciences, Ondo, Ondo State, Nigeria. 3. Department of Physical Sciences, Landmark University, Omu-Aran, Kwara State, Nigeria.
Abstract
Objectives: To evaluate malaria transmission in relation to insecticide-treated nets (ITN) coverage in Nigeria. Methods: We used exploratory analysis approach to evaluate the variation of malaria transmission and ITN distribution in 1325 demographic and health survey (DHS) clusters in Nigeria. A Bayesian Spatial Generalized Linear Mixed Model (BSGLMM) with Leroux conditional autoregressive prior for the random effects was used to model the spatial and contextual variation in malaria prevalence and ITN distribution after adjusting for environmental variables. Results: Spatial smoothed maps showed a nationwide distribution of malaria and ITN. The distribution of ITN varied significantly across the six geopolitical zones (P<0.05). The North-East had the least ITN distribution (0.196±0.071) while ITN distribution was highest in the South-South (0.309±0.075). ITN coverage was also higher in the rural (0.281±0.074) than in the urban areas (0.240±0.096) (P<0.05). The Bayesian hierarchical regression results showed a non-significant negative relationship between malaria prevalence and ITN coverage but a significant spatial structured random effect and unstructured random effect. The correlates of malaria transmission include; rainfall, maximum temperature and proximity to water. Conclusion: Reduction in malaria transmission was not significantly related to ITN coverage though much could be achieved in an attempt to curtail malaria transmission with an enhanced ITN coverage. A multi and integrated approach to malaria control is strongly advocated.
Objectives: To evaluate malaria transmission in relation to insecticide-treated nets (ITN) coverage in Nigeria. Methods: We used exploratory analysis approach to evaluate the variation of malaria transmission and ITN distribution in 1325 demographic and health survey (DHS) clusters in Nigeria. A Bayesian Spatial Generalized Linear Mixed Model (BSGLMM) with Leroux conditional autoregressive prior for the random effects was used to model the spatial and contextual variation in malaria prevalence and ITN distribution after adjusting for environmental variables. Results: Spatial smoothed maps showed a nationwide distribution of malaria and ITN. The distribution of ITN varied significantly across the six geopolitical zones (P<0.05). The North-East had the least ITN distribution (0.196±0.071) while ITN distribution was highest in the South-South (0.309±0.075). ITN coverage was also higher in the rural (0.281±0.074) than in the urban areas (0.240±0.096) (P<0.05). The Bayesian hierarchical regression results showed a non-significant negative relationship between malaria prevalence and ITN coverage but a significant spatial structured random effect and unstructured random effect. The correlates of malaria transmission include; rainfall, maximum temperature and proximity to water. Conclusion: Reduction in malaria transmission was not significantly related to ITN coverage though much could be achieved in an attempt to curtail malaria transmission with an enhanced ITN coverage. A multi and integrated approach to malaria control is strongly advocated.