Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands.
UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Gatton 4343 QLD, Australia.
Int J Environ Res Public Health. 2019 Jan 9;16(2):176. doi: 10.3390/ijerph16020176.
Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables.
空间建模研究中的不确定性与血吸虫病（SCH）相关，对于可靠识别高危人群至关重要。当进行个体层面推断时，进行生态或群体层面分析，例如特定行政级别上的空间聚集，就会出现生态谬误。这可能导致高危人群的识别不可靠，并导致药物分配策略及其成本效益产生谬误。一种特定形式的生态谬误是纯粹的规范偏差。本研究旨在量化其对用作 SCH 感染驱动因素的各种环境协变量参数估计的影响。这是通过以下步骤实现的：(i) 使用去除纯粹规范偏差的空间卷积模型；(ii) 估计群体和个体层面协变量回归参数；以及 (iii) 量化卷积和生态模型的参数估计与疾病结果预测之间的差异。我们使用群体层面的健康结果数据和城市层面的环境数据（代表个体层面的暴露）来模拟日本血吸虫病的流行率。我们纳入了环境数据，如水和植被指数、到水体的距离、白天和夜间陆地表面温度以及海拔。我们使用贝叶斯统计方法估计和比较卷积和生态模型的参数估计。卷积和生态模型的协变量参数估计之间存在差异，从最接近水体的距离（NDWB）的 0.03 到归一化差异水指数（NDWI）的 0.28。卷积模型在大多数参数估计中表现出较低的不确定性，除了 NDWB。夜间陆地表面温度（0.23）和海拔（0.13）的不确定性差异较大。两种模型的预测值及其不确定性之间没有发现显著差异。所提出的卷积模型能够通过呈现不确定性较小的参数估计来纠正纯粹的规范偏差。它对于平均流行率值和阳性感染者数量具有良好的预测性能。需要进一步研究以更好地理解空间范围和支持分析，从而可靠地探索环境变量的作用。
