通过成年人体重指数的空间聚类确定肥胖环境。

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Kimberly Yuin Y'ng Wong, Foong Ming Moy, Aziz Shafie, Sanjay Rampal
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引用次数: 0

摘要

背景:马来西亚的肥胖症呈上升趋势,而缺乏环境对肥胖症影响的证据是站不住脚的。导致肥胖的环境因素往往是相邻地区之间共同的环境、人口或文化效应影响生活方式的结果。采用空间聚类的方法可以有效地阐明肥胖的地理分布,并确定潜在致肥环境的地区,从而为公共卫生干预措施提供信息,并对当地环境进行进一步的探索。本研究旨在确定马来西亚成年人体重指数(BMI)的空间聚类:本研究利用了 2014 年和 2015 年马来西亚半岛和东马来西亚全国健康与发病率调查(NHMS)中 18 至 59 岁受访者的信息。快餐店距离、地区人口密度和地区家庭收入中位数则来自其他来源。分析针对所有受访者,并按性别进行分层。采用多层次回归法得出一组变量的 BMI 估计值,并根据计数区的数据聚类进行调整。利用受访者的点位置和 8 千米欧氏半径或 5 个近邻的空间权重,分别使用全球莫兰 I 统计量和地方空间关联指标来评估 BMI 的总体聚类和空间聚类的位置:结果:BMI 的空间聚类与个人社会人口统计学无关,但具有显著性(p 结论:BMI 的空间聚类与个人社会人口统计学无关:在郊区发现了导致肥胖的环境,在某些地区男性和女性的空间聚类有所不同。今后有关创造更健康环境的研究和干预措施应具有地理针对性,并考虑性别差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying obesogenic environment through spatial clustering of body mass index among adults.

Background: The escalating trend of obesity in Malaysia is surmounting, and the lack of evidence on the environmental influence on obesity is untenable. Obesogenic environmental factors often emerge as a result of shared environmental, demographic, or cultural effects among neighbouring regions that impact lifestyle. Employing spatial clustering can effectively elucidate the geographical distribution of obesity and pinpoint regions with potential obesogenic environments, thereby informing public health interventions and further exploration on the local environments. This study aimed to determine the spatial clustering of body mass index (BMI) among adults in Malaysia.

Method: This study utilized information of respondents aged 18 to 59 years old from the National Health and Morbidity Survey (NHMS) 2014 and 2015 at Peninsular Malaysia and East Malaysia. Fast food restaurant proximity, district population density, and district median household income were determined from other sources. The analysis was conducted for total respondents and stratified by sex. Multilevel regression was used to produce the BMI estimates on a set of variables, adjusted for data clustering at enumeration blocks. Global Moran's I and Local Indicator of Spatial Association statistics were applied to assess the general clustering and location of spatial clusters of BMI, respectively using point locations of respondents and spatial weights of 8 km Euclidean radius or 5 nearest neighbours.

Results: Spatial clustering of BMI independent of individual sociodemographic was significant (p < 0.001) in Peninsular and East Malaysia with Global Moran's index of 0.12 and 0.15, respectively. High-BMI clusters (hotspots) were in suburban districts, whilst the urban districts were low-BMI clusters (cold spots). Spatial clustering was greater among males with hotspots located closer to urban areas, whereas hotspots for females were in less urbanized areas.

Conclusion: Obesogenic environment was identified in suburban districts, where spatial clusters differ between males and females in certain districts. Future studies and interventions on creating a healthier environment should be geographically targeted and consider gender differences.

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来源期刊
International Journal of Health Geographics
International Journal of Health Geographics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
10.20
自引率
2.00%
发文量
17
审稿时长
12 weeks
期刊介绍: A leader among the field, International Journal of Health Geographics is an interdisciplinary, open access journal publishing internationally significant studies of geospatial information systems and science applications in health and healthcare. With an exceptional author satisfaction rate and a quick time to first decision, the journal caters to readers across an array of healthcare disciplines globally. International Journal of Health Geographics welcomes novel studies in the health and healthcare context spanning from spatial data infrastructure and Web geospatial interoperability research, to research into real-time Geographic Information Systems (GIS)-enabled surveillance services, remote sensing applications, spatial epidemiology, spatio-temporal statistics, internet GIS and cyberspace mapping, participatory GIS and citizen sensing, geospatial big data, healthy smart cities and regions, and geospatial Internet of Things and blockchain.
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