Bayesian random effects modelling with application to childhood anaemia in Malawi

Epidemiological studies in Malawi on child anaemia have neglected the community spatial effect to childhood anaemia. Neglecting the community spatial effect in the model ignores the influence of unobserved or unmeasured contextual variables, and at the same time the resultant model may under estimate model parameter standard errors which can result in erroneous significance of covariates. We aimed at investigating risk factors of childhood anaemia in Malawi with focus on geographical spatial effect.

We adopted a Bayesian random effect model for child anaemia with district as spatial effect using the 2010 Malawi demographic healthy survey data. We fitted the binary logistic model for the two categories outcome (anaemia (Hb < 11), and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized splines and spatial effects were smoothed by the two dimensional spline.

Residual spatial patterns reveal Nsanje, Chikhwawa, Salima, Nkhota-kota, Mangochi and Machinga increasing the risk of childhood anaemia. Karonga, Chitipa, Rumphi, Mzimba, Ntchisi, and Chiradzulu reduce the risk of childhood anaemia. Known determinants such as maternal anaemia, child stunting, and child fever, have a positive effect on child anaemia. Furthermore childhood anaemia decreases with child age. It also decreases with wealth index. There is a U relationship between child anaemia and mother age.

Strategies in childhood anaemia control should be tailored to local conditions, taking into account the specific etiology and prevalence of anaemia.