BACKGROUND The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. METHODS We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. RESULTS Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. CONCLUSIONS Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programmes by providing reliable parameter estimates at the same spatial support and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

中文翻译：

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模拟 MAUP 对日本血吸虫流行环境驱动因素的影响。

背景技术当空间变量的支持大小影响患病率和环境风险因素之间的关系时，就会出现可修改面积单位问题（MAUP）。它对血吸虫病模型研究的影响可能导致参数估计不可靠。本研究旨在通过以下方式量化 MAUP 对日本血吸虫感染环境驱动因素的影响：(i) 将所有协变量置于相同的空间支持下，(ii) 估计 30 m、90 m、250 m、500 m 和1 km 空间支持，以及 (iii) 使用五个模型量化参数估计之间的差异。方法 我们利用各省份的健康结果数据和像素级环境数据对日本血吸虫的流行情况进行了建模。我们使用贝叶斯统计估计并比较了卷积模型的回归系数。结果将空间支持增加到 500 m 逐渐增加参数估计及其相关的不确定性。参数估计值的突然变化发生在 1 km 空间支持处，导致几乎所有协变量失去显着性。五个模型的预测值及其不确定性之间没有发现显着差异。我们提供建议来定义适当的空间数据结构进行建模，从而提供更可靠的参数估计以及风险因素与疾病之间的明确关系。结论 在这项血吸虫病研究中纳入量化的 MAUP 效应非常重要。这将通过在相同的空间支持下提供可靠的参数估计并建议使用适当的空间数据结构来支持蠕虫控制计划，以生成可以指导有效的大规模药物管理活动的可靠地图。