A Cluster Analysis of Pediatric Cancer Incidence Rates in Florida: 2000–2010

Abstract

This study uses disease surveillance cluster analysis methods to identify clusters for the three most common pediatric cancers in Florida (brain tumors, leukemia, lymphoma). In addition to a univariate purely spatial analysis, a space-time analysis is done, followed by a nonparametric permutation test for space-time interaction. This is followed by a multivariate spatial analysis for the three pediatric cancer types together. The main findings include a significant spatial cluster for each of the three cancer types covered in this study, in addition to identifying two significant clusters for all pediatric cancer types combined.

Modeling pediatric tumor risks in Florida with conditional autoregressive structures and identifying hot-spots

“…An estimated temporal autocorrelation parameter suggests that we need to incorporate temporal dependence in the model. Estimates of relative risks by WRSEL could capture hotspot areas which is similar to the results obtained by the other researchers (Amin et al, 2014;Wang and Rodriguez, 2014;Zhang et al, 2014). While hot-spot regions by the Poisson ICAR model under WRSEL substantially vary over the years, those by spatio-temporal model do not.…”

“…This phenomenon was also captured by Wang and Rodriguez (2014) by having high but not the highest in large areas of north Florida. Amin et al (2014) also found that their approach for each cancer type among brain cancer, leukemia and lymphoma makes a cluster in south Florida and includes Miami in common. Results of all pediatric cancer types by Amin et al (2014) give the main significant regions as a cluster in southwest Florida which is slightly different from our outcomes but the cluster size is big enough to include Miami.…”

“…Amin et al (2014) also found that their approach for each cancer type among brain cancer, leukemia and lymphoma makes a cluster in south Florida and includes Miami in common. Results of all pediatric cancer types by Amin et al (2014) give the main significant regions as a cluster in southwest Florida which is slightly different from our outcomes but the cluster size is big enough to include Miami. The significant secondary regions are located in north central Florida which are mapped with orange colors for the year 2001-2004 in Figure 3.4.…”

“…The race as a covariate strongly impacts on high risk and younger population (under 19 years old) is also recognized for being exposed to the high risk. Amin et al (2014) employed the software SaTScan to identify surveillance clusters based on hypothesis testing for three common pediatric cancers (brain tumors, leukemia and lymphoma). They also conducted nonparametric permutation tests for each cancer in spatiotemporal analysis as well as multivariate analysis for three cancer types together.…”

Analysis of spatial-temporal clusters of childhood cancer incidence in the province of Córdoba, Argentina (2004-2013)

“…Although they cannot be directly compared, those studies found, as in our study, clusters within the large groups of total tumors, leukemias, tumors of the central nervous system, and lymphomas. 7,[24][25][26][27][28][29][30][31] However, those studies failed to establish a conclusive relationship between results and a socioenvironmental factor, even though the presence of clusters is probably an evidence of the fact that environmental factors are related to the presence and development of cancer. [32][33][34] Apart from ionizing radiation and some congenital genetic syndromes, there is little evidence -and lots of suspicion-regarding other factors that may influence the clustering of childhood cancer.…”