Key result
Spatial Bayesian modeling identifies geographic clusters of increased hypertension hospital admission risk in southeastern Shenzhen.
Observational
Yes
In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China's aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe the spatial variation of the geo-referenced data on hypertension disease over an urban area of China. In this study, we applied hierarchical Bayesian models to explore the spatial heterogeneity of the relative risk for hypertension admissions throughout Shenzhen in 2011. The final model specification includes an intercept and spatial components (structured and unstructured). Although the road density could be used as a covariate in modeling, it is an indirect factor on the relative risk. In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters. The results showed that the relative risk for hospital admission for hypertension has high-value clusters in the south and southeastern Shenzhen. This study aimed to identify some specific regions with high relative risk, and this information is useful for the health administrators. Further research should address more-detailed data collection and an explanation of the spatial patterns.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2014) conducted an observational in Hypertension. Geographic location and road density in Shenzhen was evaluated on Relative risk for hospital admission for hypertension. Spatial Bayesian modeling in 2011 identified high-value clusters of relative risk for hypertension hospital admissions in south and southeastern Shenzhen.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: