Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey

Ropo E. Ogunsakin and Themba G. Ginindza
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Ropo E. Ogunsakin: Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa
Themba G. Ginindza: Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa

IJERPH, 2022, vol. 19, issue 15, 1-17

Abstract: Determining spatial links between disease risk and socio-demographic characteristics is vital in disease management and policymaking. However, data are subject to complexities caused by heterogeneity across host classes and space epidemic processes. This study aims to implement a spatially varying coefficient (SVC) model to account for non-stationarity in the effect of covariates. Using the South Africa general household survey, we study the provincial variation of people living with diabetes and hypertension risk through the SVC model. The people living with diabetes and hypertension risk are modeled using a logistic model that includes spatially unstructured and spatially structured random effects. Spatial smoothness priors for the spatially structured component are employed in modeling, namely, a Gaussian Markov random field (GMRF), a second-order random walk (RW2), and a conditional autoregressive (CAR) model. The SVC model is used to relax the stationarity assumption in which non-linear effects of age are captured through the RW2 and allow the mean effect to vary spatially using a CAR model. Results highlight a non-linear relationship between age and people living with diabetes and hypertension. The SVC models outperform the stationary models. The results suggest significant provincial differences, and the maps provided can guide policymakers in carefully exploiting the available resources for more cost-effective interventions.

Keywords: Bayesian inference; diabetes; hypertension; spatially varying coefficients; conditional autoregressive (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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