Bayesian spatio-temporal modelling and mapping of malaria and anaemia among children between 0 and 59 months in Nigeria

Abstract

Background/m&m: A vital aspect of disease management and policy making lies in the understanding of the universal distribution of diseases. Nevertheless, due to differences all-over host groups and space-time outbreak activities, data are subject to intricacies. Herein, Bayesian spatio-temporal models were proposed to model and map malaria and anaemia risk ratio in space and time as well as to ascertain risk factors related to these diseases and the most endemic states in Nigeria. Parameter estimation was performed by employing the R-integrated nested Laplace approximation (INLA) package and Deviance Information Criteria were applied to select the best model.

Results: In malaria, model 7 which basically suggests that previous trend of an event cannot account for future trend i.e., Interaction with one random time effect (random walk) has the least deviance. On the other hand, model 6 assumes that previous event can be used to predict future event i.e., (Interaction with one random time effect (ar1)) gave the least deviance in anaemia.

Discussion: For malaria and anaemia, models 7 and 6 were selected to model and map these diseases in Nigeria, because these models have the capacity to receive strength from adjacent states, in a manner that neighbouring states have the same risk. Changes in risk and clustering with a high record of these diseases among states in Nigeria was observed. However, despite these changes, the total risk of malaria and anaemia for 2010 and 2015 was unaffected.

Conclusion: Notwithstanding the methods applied, this study will be valuable to the advancement of a spatio-temporal approach for analyzing malaria and anaemia risk in Nigeria.

Keywords: Bayesian Hierarchical; Deviance Information Criteria; Heterogeneity; Risk Ratio and R-integrated nested Laplace approximation (INLA); Spatio-temporal.

Fig. 1

Location map of Nigeria showing the 6 geopolitical zones and their 37 states including the capital territory

Fig. 2

Map of Nigeria showing state rates based on sampling weights of under 5 years old malaria prevalence

Fig. 3

Map of Nigeria showing state rates based on sampling weights of under 5 years old anaemia prevalence

Fig. 4

Maps displaying residual spatial effects of malaria in Nigeria for year 2010 and 2015 obtained from spatio-temporal interaction logistic regression model, i.e., Model 7

Fig. 5

Depicting estimated posterior relative risk of malaria for the logistic regression best fitting model

Fig. 6

Relationship between child’s age in months and AOR of malaria and anaemia

Fig. 7

Maps displaying residual spatial effects of anaemia in Nigeria for year 2010 and 2015 sprang from the spatio-temporal interaction logistic regression model i.e., Model 6

Fig. 8

Depicting estimated posterior relative risk of anaemia for the logistic regression best fitting model