Discipline of Public Health Medicine, School of Nursing and Public Health, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa.
Cancer & Infectious Diseases Epidemiology Research Unit (CIDERU), College of Health Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa.
Int J Environ Res Public Health. 2022 Jul 22;19(15):8886. doi: 10.3390/ijerph19158886.
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.
确定疾病风险与社会人口特征之间的空间联系对于疾病管理和政策制定至关重要。然而，数据受到宿主分类和空间流行过程异质性引起的复杂性的影响。本研究旨在实施空间变化系数（SVC）模型来解释协变量的非平稳性。我们使用南非综合家庭调查数据，通过 SVC 模型研究了糖尿病和高血压风险人群在省级层面的变化。糖尿病和高血压风险人群使用包含空间非结构化和空间结构化随机效应的逻辑模型进行建模。在建模中使用空间结构化分量的空间平滑先验，即高斯马尔可夫随机场（GMRF）、二阶随机游走（RW2）和条件自回归（CAR）模型。SVC 模型用于放松平稳性假设，通过 RW2 捕捉年龄的非线性效应，并使用 CAR 模型允许均值效应在空间上变化。结果突出了年龄与糖尿病和高血压患者之间的非线性关系。SVC 模型优于固定效应模型。结果表明存在显著的省级差异，提供的地图可以指导政策制定者谨慎利用现有资源，以实现更具成本效益的干预措施。
