Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Institute for Mathematical Stochastics, University of Goettingen, Germany.
Spat Spatiotemporal Epidemiol. 2020 Feb;32:100319. doi: 10.1016/j.sste.2019.100319. Epub 2019 Dec 11.
The main goal of disease mapping is to estimate disease risk and identify high-risk areas. Such analyses are hampered by the limited geographical resolution of the available data. Typically the available data are counts per spatial unit and the common approach is the Besag-York-Mollié (BYM) model. When precise geocodes are available, it is more natural to use Log-Gaussian Cox processes (LGCPs). In a simulation study mimicking childhood leukaemia incidence using actual residential locations of all children in the canton of Zürich, Switzerland, we compare the ability of these models to recover risk surfaces and identify high-risk areas. We then apply both approaches to actual data on childhood leukaemia incidence in the canton of Zürich during 1985-2015. We found that LGCPs outperform BYM models in almost all scenarios considered. Our findings suggest that there are important gains to be made from the use of LGCPs in spatial epidemiology.
疾病制图的主要目的是估计疾病风险并识别高风险区域。这种分析受到可用数据的地理分辨率有限的限制。通常，可用的数据是每个空间单位的计数，常见的方法是 Besag-York-Mollié (BYM) 模型。当有精确的地理编码可用时，使用对数高斯 Cox 过程 (LGCP) 更为自然。在一项使用瑞士苏黎世州所有儿童的实际居住地点模拟儿童白血病发病率的模拟研究中，我们比较了这些模型识别风险表面和高风险区域的能力。然后，我们将这两种方法应用于 1985-2015 年苏黎世州儿童白血病发病率的实际数据。我们发现，LGCP 在几乎所有考虑的情况下都优于 BYM 模型。我们的研究结果表明，在空间流行病学中使用 LGCP 可以带来重要的收益。
