Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method. Issue 11 (2nd November 2019)

• Record Type: Journal Article
• Authors: Li, Meifang; Shi, Xun; Li, Xia; Ma, Wenjun; He, Jianfeng; Liu, Tao
• Is Part Of: International journal of geographical information science. Volume 33:Issue 11(2019)
• Page Start: 2125
• Page End: 2152
• Publication Date: 2019-11-02
• Subjects: Spatial scan statistic -- spatial scale -- data aggregation -- cluster detection -- dengue fever; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1616741
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method's sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method's sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.