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Analysis of the spatial variation of hospitalization admissions for hypertension disease in Shenzhen, China.

Wang Z, Du Q, Liang S, Nie K, Lin DN, Chen Y, Li JJ - Int J Environ Res Public Health (2014)

Bottom Line: In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters.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.

View Article: PubMed Central - PubMed

Affiliation: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. wangzhens@whu.edu.cn.

ABSTRACT
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.

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Related in: MedlinePlus

These maps illustrate the spatial variation of relative risk: (1) a map of the SR; (2) a map of the smoothing SR.
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ijerph-11-00713-f005: These maps illustrate the spatial variation of relative risk: (1) a map of the SR; (2) a map of the smoothing SR.

Mentions: The map of the SR illustrates that the relative risk varied throughout Shenzhen (Figure 5(1)). The sub-district Lianhua had the highest relative risk with an SR of 3.53, whereas in Longhua, this risk was only 0.40. The results of Moran’s I showed that the cluster pattern was statistically significant in the standardized ratios of adjacent sub-districts with a p-value < 0.01 and a z-score of 2.60. By using the General G-statistic, a high-value cluster was significant with a z-score of 2.46. Then, a hot spot analysis based on the local G-statistic (Gi*) was used to show where the clusters of high values or low values were, and results were summarized in Table 1. A group of sub-districts with high Gi* values indicated a concentration of sub-districts with a high SR as a hot spot; conversely, a group of sub-districts with low Gi* values indicated a cold spot (Figure 6(2)).


Analysis of the spatial variation of hospitalization admissions for hypertension disease in Shenzhen, China.

Wang Z, Du Q, Liang S, Nie K, Lin DN, Chen Y, Li JJ - Int J Environ Res Public Health (2014)

These maps illustrate the spatial variation of relative risk: (1) a map of the SR; (2) a map of the smoothing SR.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924470&req=5

ijerph-11-00713-f005: These maps illustrate the spatial variation of relative risk: (1) a map of the SR; (2) a map of the smoothing SR.
Mentions: The map of the SR illustrates that the relative risk varied throughout Shenzhen (Figure 5(1)). The sub-district Lianhua had the highest relative risk with an SR of 3.53, whereas in Longhua, this risk was only 0.40. The results of Moran’s I showed that the cluster pattern was statistically significant in the standardized ratios of adjacent sub-districts with a p-value < 0.01 and a z-score of 2.60. By using the General G-statistic, a high-value cluster was significant with a z-score of 2.46. Then, a hot spot analysis based on the local G-statistic (Gi*) was used to show where the clusters of high values or low values were, and results were summarized in Table 1. A group of sub-districts with high Gi* values indicated a concentration of sub-districts with a high SR as a hot spot; conversely, a group of sub-districts with low Gi* values indicated a cold spot (Figure 6(2)).

Bottom Line: In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters.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.

View Article: PubMed Central - PubMed

Affiliation: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. wangzhens@whu.edu.cn.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus