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STS489 Danielle J.R. et al.






















            Figure 3: Estimated posterior means of the structured spatial effect (left) and the
            unstructured spatial effect (right) on the log-odds of anaemia (criss-cross pattern
            indicates water bodies; diagonal lines indicates districts with no data).

            4. Discussion and Conclusion
                Anaemia control measures need to account for the spatial heterogeneity
            that  is  evident  in  these  countries,  as  well  as  take  into  consideration  the
            potential factors and type of factors contributing to the spatial heterogeneity.
            Kenya and Malawi districts showed negative (low) structured spatial effects.
            On the hand, districts in Uganda and Tanzania displayed a mix of positive and
            negative  structured  and  unstructured  spatial  effects,  with  the  unstructured
            spatial effect being more prominent. Accordingly, efforts in assessing the local
            district-specific drivers of childhood anaemia within each country should be
            focused on.

            References
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                 (2018). The prevalence and determinants of anaemia in Jordan. East
                 Mediterr Health J, In press.
            2.  Besag, J., York, J., and Mollie, A. (1991). Bayesian image restoration, with
                 two applications in spatial statistics. Ann Inst Stat Math, 343:1–20.
            3.  Kandala, N.-B. and Madise, N. (2004). The Spatial Epidemiology of
                 Childhood Diseases in Malawi and Zambia. African Population Studies,
                 Supplement B, 191–218.
            4.  Lang, S. and Brezger, A. (2004). Bayesian P-Splines. J Comput Graphical
                 Statist, 13:183–212. Mainardi, S. (2012) Modelling spatial heterogeneity
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