Impact of spatial aggregation error on the spatial scan analysis: a case study of colorectal cancer

Abstract

Abstract. The paper aims to estimate the level and impact of spatial aggregation error for spatial scan statistics where disaggregated data below the zip code level are not available. Data on colorectal cancer cases in Cook county, Illinois, USA with a 5-year interval were used. An innovative procedure using SAS and Java was designed to make SaTScan auto-run. Characteristics of clusters at each reference level were compared to those at zip code level to observe differences related to spatial aggregation. The comparison reveals that spatial scan statistic at the zip code level can generate reliable clusters in areas with a large number of cases, but fail to detect clusters in areas where there are a sparse number of cases, since the spatial aggregation error is minimised in areas with sizeable numbers of cases. Without localised cancer data, zip code level data can be used effectively to identify dominant clusters. However, smaller clusters located in low-density areas may be missed.

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

“…First, in combined analysis of geographically aggregated data, difficulties may arise when they are not grouped according to the same geographical boundaries (Blackley et al, 2012;Goovaerts, 2006a); second, analysis results of aggregated data should be considered true only at their scale of aggregation and should not be extrapolated to other aggregation or disaggregation levels (Fortunato et al, 2011) since inconsistencies in results obtained at different scales may arise Xiao, 2011, 2012); third, the spatial patterns obtained based on aggregated data can result from the level of aggregation chosen and not from the distribution of the phenomenon under review itself (Krewski et al, 2005); and fourth, data are often aggregated into geographical areas defined for political or administrative reasons , which may not always be the most appropriate for undertaking a particular study (Goovaerts, 2006a). If the areas' aggregation criteria does not take into account the area characteristics in terms of health, the modifiable areal unit problem (MAUP) may arise (Luo, 2013;Shi, 2009;Sloan et al, 2012) and the risk of aggregating areas with very different characteristics could emerge (Thompson et al, 2007).…”

The spatio-temporal distribution of Oncomelania hupensis along Yangtze river in Jiangsu Province, China after implementation of a new, integrated schistosomiasis control strategy

“…The hotspots of infected snails showed statistical significance in the midstream location. The absence of clusters in this area by SaTScan method could be due to the fact that SaTScan calculates the relative likelihood ratio with non-clustering area under different radii by Monte-Carlo iterative simulation (Chong et al, 2013;Luo, 2013;Sherman et al, 2014). The snail density clearly decreased significantly and it was eventually hard to find a specific area showing a cluster phenomenon.…”

Local spatial clustering in youths’ use of tobacco, alcohol, and marijuana in Boston

“…Future research on methodology should focus on identifying optimal cluster detection methods given the type and level of aggregation of available data and the spatial scale being analyzed. As previously mentioned, several studies have shown that aggregating data can negatively impact cluster detection (25, 26); and it is an axiom in geography that studying a phenomenon at different spatial scales often requires different analytical approaches.…”

“…Because our data are at the individual level, and to maximize spatial resolution, we did not aggregate the data into administrative levels (e.g., census tracts or counties in the U.S.). Empirical research has shown that aggregation of spatial units can reduce the ability to detect spatial clusters (25, 26), which is not surprising, as there can be much within-unit variation in larger spatial units.…”

“…As an example, ZIP code was the geographic unit of analysis in the study conducted by Chaney and Rojas-Guyler (2015) that examined spatial patterns of adolescent drug use in Cincinnati, Ohio. Using administrative neighborhoods minimizes variation between neighborhoods, and empirical research has shown that such aggregation can indeed reduce the power to detect spatial clusters (25, 26). In addition, using administrative neighborhood definitions can result in spatial misclassification and make it difficult to plan more localized interventions (27).…”