A spatiotemporal mixed model to assess the influence of environmental and socioeconomic factors on the incidence of hand, foot and mouth disease.
A spatiotemporal mixed model to assess the influence of environmental and socioeconomic factors on the incidence of hand, foot and mouth disease.
复制标题
评估环境和社会经济因素对手足口病发病影响的时空混合模型
DOI：
10.1186/s12889-018-5169-3
复制
发表时间：
2018-02-20
影响因子：
4.5
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Li L;Qiu W;Xu C;Wang J
手足口病是一种常见的传染病，受多种环境和社会经济因素的影响，发病机制复杂。此外，手足口病的传播具有较强的空间聚集性和自相关性，如果不考虑空间自相关性，经典的统计方法可能会有偏差。在本文中，我们建议将空间特征嵌入时空加性模型，以改善手足口病发病率评估。利用中国山东省137个监测区的6439个样本的发病率数据，沿着气象、环境和社会经济空间和时空协变量数据，我们提出了一个时空混合模型来估计手足口病发病率。采用地理加性回归分析方法，对单变量和多变量模型中协变量对手足口病发病风险的非线性影响进行建模。此外，空间效应的构建，以捕捉空间自相关在次区域尺度上，和集群（高风险的热点）生成使用时空扫描统计作为预测。线性和非线性效应进行了比较，以说明非线性关联的有用性。探讨了空间效应和聚类的模式，以说明手足口病发病率在地理次区域之间的变化。为了验证我们的方法，进行了10倍交叉验证。结果表明，时间指数、时空气象因素、空间环境和社会经济因素与手足口病发病率存在显著的非线性关系。此外，手足口病的发病率有很强的空间自相关性和集群。时空气象参数、归一化植被指数（NDVI）、时间指数、时空聚类和空间效应在多变量模型中起着重要的预测作用。使用我们的方法获得了Efron的交叉验证R2为0.83。空间效应占R2的23%，并且捕获了后验空间效应的显著模式。我们开发了一个地理-可加性混合时空模型来评估气象、环境和社会经济因素对手足口病发病率的影响，并探索了这种发病率的时空模式。我们的方法在交叉验证中取得了有竞争力的表现，并揭示了手足口病发病率的强空间模式，说明了手足口病流行病学的重要意义。本文的在线版本（10.1186/s12889-018-5169-3）包含补充材料，可供授权用户使用。
As a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. Using incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron’s cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured. We developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD. The online version of this article (10.1186/s12889-018-5169-3) contains supplementary material, which is available to authorized users.
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