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香港一个城市社区跌倒情况的空间分析。

Spatial analysis of falls in an urban community of Hong Kong.

作者信息

Lai Poh C, Low Chien T, Wong Martin, Wong Wing C, Chan Ming H

机构信息

Department of Geography, The University of Hong Kong, Hong Kong.

出版信息

Int J Health Geogr. 2009 Mar 17;8:14. doi: 10.1186/1476-072X-8-14.

DOI:10.1186/1476-072X-8-14
PMID:19291326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2666650/
Abstract

BACKGROUND

Falls are an issue of great public health concern. This study focuses on outdoor falls within an urban community in Hong Kong. Urban environmental hazards are often place-specific and dependent upon the built features, landscape characteristics, and habitual activities. Therefore, falls must be examined with respect to local situations.

RESULTS

This paper uses spatial analysis methods to map fall occurrences and examine possible environmental attributes of falls in an urban community of Hong Kong. The Nearest neighbour hierarchical (Nnh) and Standard Deviational Ellipse (SDE) techniques can offer additional insights about the circumstances and environmental factors that contribute to falls. The results affirm the multi-factorial nature of falls at specific locations and for selected groups of the population.

CONCLUSION

The techniques to detect hot spots of falls yield meaningful results that enable the identification of high risk locations. The combined use of descriptive and spatial analyses can be beneficial to policy makers because different preventive measures can be devised based on the types of environmental risk factors identified. The analyses are also important preludes to establishing research hypotheses for more focused studies.

摘要

背景

跌倒问题是一个备受公众健康关注的问题。本研究聚焦于香港一个城市社区内的户外跌倒情况。城市环境危害通常具有地点特异性,且取决于建筑特征、景观特点和日常活动。因此,必须结合当地情况对跌倒进行研究。

结果

本文运用空间分析方法绘制跌倒发生地点的地图,并研究香港一个城市社区中跌倒可能存在的环境属性。最近邻层次分析法(Nnh)和标准差椭圆法(SDE)能够为导致跌倒的情况和环境因素提供更多见解。结果证实了特定地点和特定人群组中跌倒的多因素性质。

结论

用于检测跌倒热点区域的技术产生了有意义的结果,能够识别高风险地点。描述性分析和空间分析的结合使用对政策制定者有益,因为可以根据所识别的环境风险因素类型制定不同的预防措施。这些分析也是为更具针对性的研究建立研究假设的重要前奏。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9ee/2666650/ab7f681939e4/1476-072X-8-14-7.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9ee/2666650/03fdf658fbd6/1476-072X-8-14-1.jpg
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