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Spatial Epidemiology: Spatial Clustering and Vulnerability

The two learning activities outlined in this PowerPoint will aid in developing students’ understanding of spatial epidemiology and the intersectionality of socio-economic and environmental factors. Task 1 involves a short presentation on the software SaTScan covering cluster analysis and including the data types needed for the analysis. Following this, students receive a scenario-based task using a pre-designed hypothetical dataset of the spread of a contagion in the UK. Students input the appropriate text files, developed from contagion datasets, into SaTScan to produce a cluster analysis of unusually high rates of contagion in the region. This task allows for many different manipulations and outputs from the analysis by changing the parameters, such as cluster size and type of analysis in the software. Guidance on the use of SaTScan for the lesson is provided for teachers to help students understand and interpret output using multiple parameter settings. In brief, SaTScan tests the null hypothesis that cases of disease are randomly distributed. Statistical significance suggests that unusual spatial clustering is unlikely to have occurred by chance. This method has been used previously to identify clusters of contagious diseases, such as malaria,23 HIV,24 tuberculosis,25 as well as chronic diseases.26 The output of this session will be a cluster analysis Keyhole Markup Language (.kml) file which shall be used in task 2.Task 2 utilises the cluster outputs produced in task 1. Students then import the .kml cluster analysis layer produced by SaTScan into ArcGIS (or QGIS if preferred) and overlay this layer over a publicly available dataset that contains multiple spatial indexes. This socio-economic dataset uses real-world data on a region of the UK. This process allows students to visually explore the possible characteristics of a region that may explain where clusters fall. For example, students can choose to layer a measure of deprivation (The Index of Multiple Deprivation) over cluster output to visually examine the socio-economic characteristics of individual clusters. The assessment for this task is a student-led presentation and discussion based on their own critical thinking about which factors may predict cluster membership as well as maps that reflect these ideas. This activity is designed to help students develop their visual presentation and interpretation skills and become familiar with linking spatial factors to epidemiological trends.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

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