A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH International Journal of Health Geographics Pub Date : 2023-06-21 DOI:10.1186/s12942-023-00336-5
Maria Safura Mohamad, Khairul Nizam Abdul Maulud, Christel Faes
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Abstract

Background: National prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5-17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates.

Results: The spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight.

Conclusion: This study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east-west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.

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用INLA对不连贯区域的空间平滑方法的实际说明:马来西亚超重和肥胖的空间调查。
背景:国家流行率可以掩盖疾病发生的次国家异质性,疾病制图是说明疾病空间格局的重要工具。然而,在涉及非连通区域的疾病制图中,关于条件自回归模型规范的技术信息有限。本研究探讨了为不相连地区编制区级患病率估算的可用技术,并以马来西亚儿童超重为例,马来西亚由南中国海分隔的半岛和婆罗洲地区组成。我们使用了2015年马来西亚国家健康和发病率调查的数据。本研究采用贝叶斯分层模型,利用r软件中的集成嵌套拉普拉斯近似(INLA)程序,对位于两个不连通地区的144个地区的6301名5-17岁儿童的超重空间分布进行了建模。我们展示了不同类型的空间模型,用于在不连通的地区绘制患病率地图,考虑到调查设计并调整了地区层面的人口和社会经济协变量。结果:具有分裂随机效应和共同截距的空间模型具有最低的偏差和渡边信息标准。有证据表明,各地区儿童超重患病率存在空间格局。从半岛和婆罗洲地区的东部向西部移动时,观察到超重的平滑流行率呈上升趋势。该地区土著民族比例与儿童超重呈显著负相关:该地区土著民族比例越高,儿童超重患病率越低。结论:本研究说明了不同的可用技术来绘制跨地区的流行病学在不连通的地区使用调查数据。这些技术可用于对由互不相连的区域组成的任何地区进行可靠的次国家估计。通过这个例子,我们了解到最适合的模型是考虑了单个区域的单独变化。我们发现,马来西亚儿童超重的发生遵循一个东西梯度趋势的空间模式,我们确定了超重高发的地区。这些信息可以帮助决策者做出明智的决定,以便在高风险地区采取有针对性的公共卫生干预措施。
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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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