Bayesian Spatial Modeling of Diabetes and Hypertension
Results from the South Africa General Household Survey
Bibliographic Data
| ID | 15484124 |
|---|---|
| Authors | Ropo Ebenezer Ogunsakin (0000-0003-3853-3382, University of KwaZulu-Natal, corresponding author), Themba G Ginindza (0000-0002-0993-8308, University of KwaZulu-Natal) |
| Year | 2022 |
| Volume | 19 |
| Issue | 15 |
| Pages | 8886-8886 |
| Publication date | 2022-07-22 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19158886 |
| PMID | 35897258 |
| OpenAlex | W4286462010 |
| Language | EN |
| References cited | 52 |
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
Autoregressive model · Bayesian probability · Covariate · Econometrics · Estimator · Generalized linear mixed model · Logistic regression · Markov chain Monte Carlo · Random effects model · Small area estimation · Statistics · Computer Science · Data-Driven Disease Surveillance · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine · Spatial and Panel Data Analysis
Monitoring and surveillance of chronic non-communicable diseases
Bayesian image restoration, with two applications in spatial statistics
Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
Approximate Inference in Generalized Linear Mixed Models
Jags
Bayesian Spatial Modelling with R - Inla
Burden of Undiagnosed Hypertension in Sub-Saharan Africa
Spatial Interaction and the Statistical Analysis of Lattice Systems
The burden of non-communicable diseases in South Africa
Non-communicable diseases in sub-Saharan Africa
Bayesian Measures of Model Complexity and Fit
Spatial pattern and determinants of diagnosed diabetes in southern India
Spatial analysis of gender variation in the prevalence of hypertension among the middle-aged and elderly population in Zhejiang Province, China
| Highly cited | No |
|---|