Abstract
BackgroundThe 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.MethodsWe 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.ResultsIncreasing 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.ConclusionsInclusion 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.
Highlights
The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors
Modelling Schistosoma japonicum infection under the MAUP Convolution model We modelled human S. japonicum infection at the five increasing spatial support of analysis (SSA) using a convolution model that accounts for pure specification bias [27]
Modelling Schistosoma japonicum infection under the MAUP Convolution model Our findings show that normalized difference vegetation index (NDVI) has a non-significant effect on the prevalence of SCH infection for all SSA, except for SSA = 1 km (Additional file 3: Table S1, Fig. 3a)
Summary
The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. SCH mapping uses geographical information systems (GIS), global positioning systems and remotely sensed environmental data [9, 10] Modelling those infections using various statistical methods have enabled the study of the distribution of populations at-risk [9, 10], and the role of the environmental variation on the geographical heterogeneity of infection burden (i.e. prevalence or intensity of infection) [11]. Statistical modelling of SCH quantifies empirical relationships between indirect morbidity indicators of public health significance and environmental risk factors. Those could be extracted from Earth Observation (EO) data such as monitor sites or satellite imagery. EO data help to interpolate the level of infection towards unsampled locations [12,13,14]
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