Detecting multiple spatial disease clusters: information criterion and scan statistic approach
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2020) 19:33
International Journal of Health Geographics Open Access
METHODOLOGY
Detecting multiple spatial disease clusters: information criterion and scan statistic approach Kunihiko Takahashi1* and Hideyasu Shimadzu2,3
Abstract Background: Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specific epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods. Methods: A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters. Results: A case study and simulation studies conducted both confirmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity. Conclusions: We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches. Keywords: Scan statistic, Information criteria, Generalized linear model, Cluster detection test, Multiple clustering Introduction In the middle of the 19th century, a deadly cholera outbreak affected the Soho area of London, UK. John Snow, a British physician, plotted the cases of cholera victims on a map and identified many victims within a short distance of a water pump on Broad Street. The disease map led him to a historic landmark, with the water from the pump identified as the source of cholera [1]. However, what if other cholera victims had also clustered around *Correspondence: [email protected] 1 Department of Biostatistics, M&D Data Science Center, Tokyo Medical and Dental University, 1‑5‑45, Yushima, Bunkyo‑ku, Tokyo 113‑8510, Japan Full list of author information is available at the end of the article
another pump just 200 yards away? Would this still be considered as a single cluster or preferably another cluster with a different epicenter? Although the cause of disease or incident cannot be determined only by mapping the victims, disease maps are useful in initial investigations of disease causes. Whether the cases of diseases are scattered randomly or clustered around multiple specific centers is a long-standing question in epidemiological studies [2]. To date, detecting the tende
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