See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/40870723 Epidemiologic Mapping of Florida Childhood Cancer Clusters Article  in  Pediatric Blood & Cancer · April 2010 DOI: 10.1002/pbc.22403 · Source: PubMed CITATIONS READS 44 3,071 5 authors, including: Raid Amin Alexander Bohnert University of West Florida Friedrich-Alexander-University of Erlangen-Nürnberg 98 PUBLICATIONS   1,642 CITATIONS    21 PUBLICATIONS   120 CITATIONS    SEE PROFILE SEE PROFILE Laurens Holmes Chatchawin Assanasen University of Delaware University of Texas Health Science Center at San Antonio 122 PUBLICATIONS   1,722 CITATIONS    24 PUBLICATIONS   302 CITATIONS    SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Epigenomic Basis of Sub-population Disparities in Opiates Additcion Following Chronic Pain Management View project sharks and sharksuckers View project All content following this page was uploaded by Chatchawin Assanasen on 16 October 2017. The user has requested enhancement of the downloaded file. Pediatr Blood Cancer Epidemiologic Mapping of Florida Childhood Cancer Clusters Raid Amin, PhD, 1,2 * Alexander Bohnert,1 Laurens Holmes, PhD, DrPH,2,3 Ayyappan Rajasekaran, and Chatchawin Assanasen, MD2,4** Background. Childhood cancer remains the leading cause of disease-related mortality for children. Whereas, improvement in care has dramatically increased survival, the risk factors remain to be fully understood. The increasing incidence of childhood cancer in Florida may be associated with possible cancer clusters. We aimed, in this study, to identify and confirm possible childhood cancer clusters and their subtypes in the state of Florida. Methods. We conducted purely spatial and space–time analyzes to assess any evidence of childhood malignancy clusters in the state of Florida using SaTScanTM. Data from the Florida Association of Pediatric Tumor Programs (FAPTP) for the period 2000–2007 were used in this analysis. Results. In the purely spatial analysis, the relative risks (RR) of overall childhood cancer persisted after controlling for confounding factors in south Florida (SF) (RR ¼ 1.36, P ¼ 0.001) and northeastern Florida (NEF) Key words: PhD, 2 (RR ¼ 1.30, P ¼ 0.01). Likewise, in the space–time analysis, there was a statistically significant increase in cancer rates in SF (RR ¼ 1.52, P ¼ 0.001) between 2006 and 2007. The purely spatial analysis of the cancer subtypes indicated a statistically significant increase in the rate of leukemia and brain/CNS cancers in both SF and NEF, P < 0.05. The space–time analysis indicated a statistically significant sizable increase in brain/CNS tumors (RR ¼ 2.25, P ¼ 0.02) for 2006–2007. Conclusions. There is evidence of spatial and space–time childhood cancer clustering in SF and NEF. This evidence is suggestive of the presence of possible predisposing factors in these cluster regions. Therefore, further study is needed to investigate these potential risk factors. Pediatr Blood Cancer ß 2010 Wiley-Liss, Inc. cancer cluster; childhood neoplasm; cluster analysis; epidemiology; florida INTRODUCTION Cancer remains the leading cause of disease-related death among children in the United States despite progress in clinical trials and significant improvements in survival rates [1]. Over the past 20 years in the United States, increases in the incidence of childhood cancer have also been observed from 11.5 cases per 100,000 children in 1975 to 14.8 per 100,000 children in 2004 [2]. In 2009, approximately 10,730 children under the age of 15 will be diagnosed with cancer and about 1,480 are projected to die from the disease [3]. Despite the burden of childhood cancer and the many years of epidemiologic investigations, its causes remain largely unknown but have been linked in small percentages to certain genetic predispositions and exposures to chemotherapy agents and ionizing radiation [4–7]. A number of studies continue to examine the complexities of other possible risk factors for childhood cancers [8– 11]. These include early-life exposures to infectious agents; parental, fetal, or childhood exposures to environmental toxins; parental occupational exposures to radiation or chemicals; parental medical conditions during pregnancy or before conception; maternal diet during pregnancy; early postnatal feeding patterns and diet; and maternal reproductive history [12–24]. Environmental factors may play an important etiologic role in childhood malignancies and can be evidenced by excessive numbers of cases in a defined geographic area relative to other areas, termed clusters. A cancer cluster can be defined as the occurrence of a greater than expected number of cases of a malignancy within a group of people, a geographic area, or a period of time. There exist various definitions of the terms ‘‘cluster’’ and ‘‘clustering’’ in the context of spatial epidemiology and cancer research, respectively [25,26]. Identification of space–time variations in incidence rate patterns can provide important clues for further in-depth studies into the etiology and control of cancer [27]. Spatial clustering is defined as a general irregular spatial distribution of cases that is not confined to one particular small area. Space–time cancer clustering is observed when an excess number of cases occur within a geographical location over very limited periods of time and cannot be explained in terms of general excesses in these locations or time frames. Regional, national, and international registries have been utilized to ß 2010 Wiley-Liss, Inc. DOI 10.1002/pbc.22403 Published online in Wiley InterScience (www.interscience.wiley.com) investigate possible spatial and space–time clustering and any associated risks of cancer predisposition [21–24,28–31]. In Florida, overall cancer statistics are similar to the rest of the United States. From 1981 to 2000, 10,238 new cases of cancer were diagnosed among Florida children and adolescents, representing 0.7% of all cancer cases diagnosed in the state [28]. The Florida Association of Pediatric Tumor Programs (FAPTP) was founded in 1970 as a statewide network of children’s cancer programs under the auspices of the Florida Regional Medical Program (FRMP). The Florida legislature established a pediatric hematology/oncology program within Children’s Medical Services (CMS) and FAPTP that was given the responsibility and authority to monitor and evaluate pediatric cancer care statewide. This reporting system provides the state and the public with data on cancer incidence, clinical trial participation, and survivorship. The Statewide Patient Information Recording System (SPIRS) registers patients from the 16 pediatric hematology/oncology centers statewide. In addition, the Florida Cancer Data System (FCDS) captures the data from patients treated outside the FAPTP system and can be linked with SPIRS data to study the larger patient data base. — ————— 1 Department of Mathematics and Statistics, University of West Florida, Pensacola, Florida; 2Nemours Center for Childhood Cancer Research, Wilmington, Delaware; 3Department of Orthopedics, Alfred I duPont Hospital for Children, Wilmington, Delaware; 4Nemours Children’s Clinic, Pensacola, Florida Conflict of interest: Nothing to report. Grant sponsor: Nemours Children’s Clinic, Pensacola; Grant sponsor: Nemours Foundation; Grant sponsor: Caitlin Robb Foundation. *Correspondence to: Raid Amin, Department of Mathematics and Statistics, University of West Florida, 11000 University Parkway, Pensacola, FL 32514. E-mail: ramin@uwf.edu **Correspondence to: Chatchawin Assanasen, Nemours Center for Childhood Cancer Research, Nemours Children’s Clinic—Pensacola, 5153 North 9th Avenue, Pensacola, FL 32504. E-mail: cassanas@nemours.org Received 8 September 2009; Accepted 17 November 2009 2 Amin et al. The recently founded Nemours Center for Childhood Cancer Research (NCCCR) has three of its oncology clinics in Jacksonville, Orlando, and Pensacola, Florida as well as one in Wilmington, Delaware. One of the initial goals of this center was to evaluate pediatric cancer epidemiology data in the states of Florida and Delaware. In 2008, the Delaware childhood cancer rates were evaluated by NCCCR in collaboration with Delaware Department of Health and Social Services for possible childhood cancer clusters. This assessment failed to confirm clusters probably due to small number of cases as well as absence of clusters. The current study was initiated about 2 years ago in collaboration with the University of West Florida. We sought to identify and confirm overall childhood cancer clusters as well as to determine whether or not clusters could be confirmed by cancer subtypes. We utilized the data from FAPTP and modeled our analysis using SaTScanTM to test the following null hypotheses: (1) The pediatric cancer rate of all cancer types is randomly distributed over space in Florida from 2000 to 2007, (2) The pediatric cancer rate of all cancer types is randomly distributed over time and space in Florida from 2000 to 2007, (3) The rates for specific pediatric cancer types are randomly distributed over space in Florida from 2000 to 2007, and (4) The rates for specific pediatric cancer types are randomly distributed over time and space in Florida from 2000 to 2007. MATERIALS AND METHODS We conducted purely spatial and space–time analyzes to assess the evidence of childhood cancer clusters in the state of Florida using SaTScanTM. Study Area and Population We identified 67 counties and 972 zip code areas in Florida in the year 2000. While the clustering evaluations could have been based on Florida counties, we decided to obtain more detailed information by using the zip code areas. The statistical analysis used in this study requires that the geographic information for each zip code area be represented by some form of a centroid. To obtain the geographical centroid of each zip code area and to create maps with information on the cancer clusters, the geographical information system ArcGIS was utilized. We used consistent geographical data for zip code areas, Zip Code Tabulation Area (ZCTA), from the year 2000 from the Florida Geographic Data Library (FGDL). For zip code areas that were created after 2000 with an identified cancer case, we manually assigned in the software package ArcGIS a zip code based on its position in the 2000 geographical data set. A marginal number of cases for which we could not determine their position relative to the 2000 zip code area file were discarded. The study population included the entire population of children 0– 19 years of age in the state of Florida during the time period 2000– 2007. These included children with and without the diagnosis of a childhood cancer. During this time, there were 4,591 cases of pediatric cancer diagnosed, of which 1,254 (27%) had leukemia, 839 (18%) had brain/central nervous system (CNS) cancer, and 252 (5.5%) had lymphoma. Data Sources The data for this study were available from FAPTP, an existing de-identified dataset, that is, publicly available. FAPTP has been Pediatr Blood Cancer DOI 10.1002/pbc shown to be a valid and reliable source for pediatric cancer incidence data in Florida [28,32,33]. The dataset included information on cancer cases such as the diagnosis code for the study period 2000– 2007 designated by the International Classification for Childhood Cancer (ICCC) [34], incorporating the new codes introduced in ICD-O-2 and the updated ICD-O-3. Demographic information was also included, such as date of birth, age at cancer diagnosis, sex, and zip code of residence. This study involved age-adjusted data. We obtained Florida demographic population data such as age and race/ ethnicity from the 2000 census. For each ZCTA, we obtained the total population at risk, stratified by age, sex, and race/ethnicity. Data Analyzes Clusters have been analyzed previously using several statistical and epidemiologic approaches [35]. In this study, we used SaTScanTM. The software package SaTScanTM [26] uses spatial scan statistics to identify and test for the significance of cancer clusters. The incidence counts in each zip code area are used either in two dimensions for a purely spatial analysis or in a threedimensional setting for a space–time analysis with the additional dimension representing time. We assumed that the incidence of cancer in each zip code area is distributed according to a Poisson model [36,37]. This method tests the null hypothesis that the ageadjusted risk of cancer incidence is the same for all zip code areas. With the covariates included in the model, we tested the null hypothesis that within any age group, the risk of cancer incidence is the same for the entire area covered in this study [37]. To include the effect of urbanicity in our analysis, we used the population density information for postal code level [37] that is available through the Florida Geographic Data Library (FGDL). Possible associations to socioeconomic status (SES) were investigated by using the economics wealth index by Woods & Poole Economics Inc., which we obtained from the HAAS Business Center at the University of West Florida [38]. Since neither of these two covariates resulted in any changes in the SaTScanTM computations, results on population density or the socioeconomic status are not presented. The spatial scan statistics in SaTScanTM identifies clusters by imposing a window that moves over a map, including different sets of neighboring zip code areas represented by their corresponding centroids [29]. If the window includes the centroid of a specific zip code area, then this zip code area is included in the window. As suggested by Kulldorff et al. [29], the center of the window is positioned only at the 972 zip code centroids. For each window, the spatial scan statistic tests the null hypothesis of equal risk of childhood cancer incidence for all zip code areas against the alternative hypothesis that there exists an elevated risk of childhood cancer incidence within the scan window when compared with areas outside the window. The likelihood function for the Poisson model can be shown to be proportional to n n N n N n I ðn > EÞ E N E where n is the number of cancer incidences within the scan window, N is the total number of incidences in Florida, and E is the expected number of cancer incidences under the null hypothesis [29,37]. Since we are using a one-tailed test that rejects the null hypothesis if there exists elevated cancer risk, an indicator function I is used such that I ¼ 1 when the scan window has a larger number of cancer incidences than expected if the null hypothesis were true, and zero Florida Childhood Cancer Clusters otherwise. It can be shown that for a given N and E, the likelihood increases as the number of incidences, n, increase in the scan window. How the spatial scan statistic within SaTScanTM actually identifies cancer clusters is described elsewhere in detail [37]. By a Monte Carlo simulation, we generated 999 random replications of the data set to obtain the statistical stability for the identified cancer clusters in the program SaTScanTM. The Monte Carlo’s test also allows for the simultaneous controlling of multiple confounders such as age, sex, race, income level, etc. The identified cancer clusters are listed by SaTScanTM in order of significance such that the P-value for each cluster is compared with a pre-set significance level of 0.05. There exist different types of the spatial scan statistics. Circular or elliptical windows can be used to identify circular clusters and elliptical shaped clusters, respectively. Both approaches were used, and we arrived at virtually identical cluster results. In this study, we present only the cancer clusters identified by circular windows. While the spatial scan statistic requires specification of the underlying distribution of the data used in SaTScanTM, making it a parametric statistical method, a non-parametric smoothing method was also used to check whether similar or identical cluster results would be obtained. In particular, we used a weighted Head Banging algorithm based on median smoothing which removes the background noise of random variability so that the underlying spatial pattern becomes more clear [39–41]. Both parametric and non-parametric methods were used for the purpose of results validation. In this study, Head Bang was used to statistically double check the results from SaTScanTM by removing local variations in cancer incidence age-adjusted rates for the 972 zip code areas. This particular smoother retains the important features, such as edges, but smoothes out unreliable data points and spikes for low population areas based on the chosen weights in the algorithm. To ensure adequate statistical power, all cancer cases for the period 2000– 2007 were used to perform a purely spatial analysis. For the space– time analysis, which is a temporal extension of the spatial analysis, the algorithm searches within 2000–2007 for time periods in which clusters appear. RESULTS The SaTScanTM purely spatial analysis of the FAPTP data revealed two significant clusters in the state, one in southern Florida and the other in northeastern Florida (NEF). The south Florida (SF) cluster encompasses the southwest, south central and southeast regions. The NEF cluster incorporates areas of the northeast and north central regions. After adjusting for age, sex, and race as covariates, a total of 4,181 cases were identified with a corresponding incidence rate of 14.4 average annual cases per 100,000. In SF, there were 465 observed cases and 352 expected cases, with a relative risk of 1.36, implying that compared with the state, there is a statistically significant 36% increased risk of childhood cancer (P ¼ 0.001). In the NEF cluster, there were 466 and 375 observed and expected cases, respectively. This region appears to be smaller in size, although it may represent a more densely populated area. A similar increase in the rates of childhood cancer was identified with a RR ¼ 1.30 (P ¼ 0.01). In addition, a third overall childhood cancer cluster was identified in a small area of central Florida in which the observed number of cases was 31 as compared to 11 expected cases. The rates were statistically significantly higher in this area relative to the state with a RR ¼ 2.82 (P ¼ 0.008), which implies that Pediatr Blood Cancer DOI 10.1002/pbc 3 Fig. 1. Purely spatial analysis of FAPTP database for all cancer types 2000–2007. Clustering representation of SaTScanTM purely spatial analysis is illustrated utilizing zip code data with age, sex, and race as covariates. Clusters are represented in colors. The red area represents the South Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (26.3N, 81.3W)/101.6 km, Population 294,119, Observed cases ¼ 465, Expected Cases ¼ 352. The orange area represents the North Central Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (29.9N, 82.4W)/95.8 km, Population 375,761, Observed cases ¼ 530, Expected cases ¼ 420. The yellow area represents the Central Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (28.2N, 81.5W)/13.4 km, Population 9,213, Observed cases ¼ 31, Expected cases ¼ 11. compared with the state of Florida, those in this area are almost three times as likely to be diagnosed with childhood cancer (Fig. 1). Since a purely spatial analysis for the period 2000–2007 does not indicate when the cluster appeared, a space–time analysis was performed, assessing these clusters using the Poisson model within SaTScanTM. We observed that the spatial dimensions of the clusters persisted during these periods. SF emerged as the most likely temporal cluster with elevated risk during 2006–2007 (Fig. 2). Whereas the observed cases were 403, the expected were 274, RR ¼ 1.52, P ¼ 0.001, implying a significant 52% increase in childhood cancer rate in SF compared with the state of Florida. Similarly, the NEF emerged as a secondary temporal cluster for 2001–2004, with the observed and expected cases as 136 and 87 respectively, RR ¼ 1.59, P ¼ 0.06. This suggested a 59% increase in the rate of overall childhood cancer in NEF relative to the state, but the increase was not statistically significant. To confirm the clusters, we compared cancer rates within SF to the state. The cancer rates of the state for this time period was 14.1 per 100,000 in 2005 and increased slightly to 16.4 per 100,000 and 15.7 per 100,000 for 2006 and 2007, respectively. By contrast, from 2000 to 2007, the SF cancer rates have been consistently higher than the corresponding Florida rates. In particular, the rates computed for 2006 and 2007 increased significantly from 13.8 per 100,000 in 2005 to 23.9 and 21.1 per 100,000, in 2006 and 2007, respectively. 4 Amin et al. TABLE I. Childhood Age and Sex Adjusted Cancer Incidence Rates for Florida, SF Cluster, and Florida Without SF Cluster Area FL FL w/o SF SF Year Rate 95% CI Rate ratio 2006 2007 Aggregate 2006 2007 Aggregate 2006 2007 Aggregate 16.4 15.7 16.05 14.8 14.5 14.65 23.9 21.1 22.5 15.1, 17.6 14.5, 16.9 14.8, 17.3 13.5, 16.1 13.2, 15.8 13.2, 15.7 20.3, 27.5 17.7, 24.4 19.2, 26.1 1.0 1.0 1.0 0.9024 0.9236 0.9128 1.4573 1.3439 1.4019 Incidence counts were utilized directly to compute incidence rates using the FAPTP Dataset for 2000–2007 and Florida population statistics for 2000. Confidence intervals are provided, as uncertainty still exists within ideal registry datasets and computed cancer statistics (United States Cancer Statistics: 1999 incidence). Aggregate refers to the rates for 2006 and 2007 combined. The state of Florida is the reference group, hence the ratio is 1.0 for FL, Florida. SF is the southern Florida cluster. The time frame for the SF cluster was noted to be January 1, 2006 to December 31, 2007. CI, Confidence intervals. Fig. 2. Space–time analysis of FAPTP database for all cancer types 2000–2007. Clustering representation of SaTScanTM space–time analysis is illustrated utilizing zip code data with age, sex, and race as covariates. Clusters are represented in colors. Spatial representations were not affected significantly however time frame results for the Southern Florida (SF) cluster (2006–2007) are noted to be representative of a recent surge in incidence rates. The red area represents the South Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (26.0N, 81.4W)/121.1 km, Time frame ¼ January 1, 2006 to December 31, 2007, Population 963,643, Observed cases ¼ 403, Expected cases ¼ 274. The orange area represents the North Central Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (29.5N, 82.0W)/65.9 km, Time frame ¼ January 1, 2001 to December 31, 2004, Population 155,681, Observed cases ¼ 136, Expected cases ¼ 87. However, when we excluded the SF cases from the overall Florida cancer cases, the rates in Florida significantly decreased (Table I, Fig. 3). Purely spatial analysis of leukemia rates identified two regions of Florida (during the period of 2000–2007) similar to the cluster areas identified when all cancer types were combined. A total of 1,254 leukemia cases in the state were identified and utilized in this analysis. There was a statistically significant cluster in SF (RR ¼ 1.53, P ¼ 0.001) (Fig. 4). A second cluster was identified in the north central region of the state, shifting somewhat from the NEF cluster and was statistically significant as well, RR ¼ 1.45, P ¼ 0.03. Likewise, in the space–time analysis of leukemia cases, there was a statistically significant cluster in SF (RR ¼ 1.74, P ¼ 0.05) (Fig. 5). The time period identified for the peak rate of the cluster was 2000– 2002. During this time period, the number of observed cancer cases was 105 while the expected number of cases was 63. While the space–time analysis points to 2000–2002 as the time of the peak in leukemia rates, the purely spatial analysis indicated that leukemia rates in the SF cluster area remained elevated throughout the entire period (2000–2007), when compared to the state. A purely spatial analysis of brain/CNS cancer identified one area in southern Florida. Of the 839 cases identified in the state, there Pediatr Blood Cancer DOI 10.1002/pbc were 60 observed and 33 expected cases in this region. The relative risk comparing Florida to SF was not statistically significant, RR ¼ 1.86, P ¼ 0.07 (Fig. 6). A space–time analysis (52 observed cases and 24 expected cases) for the brain/CNS cancer identified a cluster corresponding to the SF cluster, with a statistically significant increased incidence rate RR ¼ 2.25, P ¼ 0.02, implying that children in SF were two times as likely to develop brain/CNS cancer when compared with children in the state of Florida. The time period identified for this cluster was 2006–2007 (Fig. 7). In contrast, lymphoma rates were not statistically significant probably due to small numbers. Fig. 3. Age-adjusted pediatric cancer incidence rates 2000–2007. Incidence counts were utilized directly to compute incidence rates using FAPTP Dataset for 2000–2007 and Florida population statistics for 2000. Southern Florida cluster (SF) is shown in comparison to rates for the entire state of Florida and to rates for the state of Florida excluding the influence of the SF. Differences between these rates during 2006 and 2007 suggest that the rise in Florida rates during this period was influenced by the surge in incidence rates in the SF cluster. Florida Childhood Cancer Clusters 5 Fig. 4. Purely spatial analysis of leukemia cases 2000–2007. Clustering representation of SaTScanTM purely spatial analysis is illustrated utilizing FAPTP zip code data with age and sex covariates. Clusters are represented in colors. The SF cluster remains durable and significant with respect to the specific leukemia cases in Florida. The red area represents the South Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (26.2N, 81.7W)/141.6 km, Population 417,327, Observed cases ¼ 190, Expected cases ¼ 131. The orange area represents the North Central Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (29.1N, 82.7W)/120.1 km, Population 435,669, Observed cases ¼ 190, Expected cases ¼ 138. Fig. 5. Space–time analysis of leukemia cases 2000–2007. Clustering representation of SaTScanTM Space–time analysis is illustrated utilizing FAPTP zip code data with age and sex as covariates. Space– time clusters were statistically significant. Time frame results for the Southern Florida (SF) cluster were noted to be 2000–2002. The yellow area represents the South Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (26.0N, 81.6W)/128.5 km, Time frame ¼ January 1, 2000 to December 31, 2002, Population 553,592, Observed cases ¼ 105, Expected cases ¼ 63. DISCUSSION clusters, in the human population [29–31,35,36]. By utilizing the data from FAPTP, we ensured the accuracy and reliability of the data used. FAPTP routinely reviews the cancer data for discrepancies including duplications and provides the most comprehensive incidence data of childhood cancer in Florida. Thus, FAPTP facilitates assessment of patterns of cancer rates and geographical trends within the state of Florida. Whereas the limitations addressed in previous studies on clusters could not be avoided completely, our chances of repeating similar methodologic issues were substantially minimized as described below. The large sample of cases with overall childhood cancer as well as significant cases in cancer subtypes should ensure a sufficiently high statistical power. It has been shown in a simple power study [36] for the likelihood ratio test used in SaTScanTM that a relative risk of 1.35 can result in an estimated power (1 b > 0.80) to detect the differences in cancer cases between the clusters and non-cluster areas (in the state of Florida), if one does exist. For example, from 2006 to 2007, the observed cases were 403 in SF, which is a large sample for comparison between areas with and without clusters (Fig. 2). Because we used cancer data from a highly reliable source (FAPTP), both selection and misclassification biases were dramatically minimized in our study. The observed clusters in SF and NEF are not driven by improved case ascertainment following the increased childhood cancers in certain geographic areas in Florida. In addition, because this study started 2 years ago, it is highly unlikely that our findings are influenced by other recent studies on Florida clusters. These purely spatial and space–time clustering studies of childhood cancer in Florida were conducted using data from FAPTP and the Census data of 2000. The accuracy of case ascertainment is high with FAPTP and has been described and validated elsewhere [28,32,33]. This epidemiologic mapping study of Florida reveals three major findings. First, childhood cancer clusters were identified in SF and NEF. Second, the childhood cancer clusters persisted after controlling for age, sex, and race/ethnicity. Third, whereas significant increase in cancer rates was observed in leukemia and brain/CNS cancer, there was no significant increase in the lymphoma rate among children in SF and NEF. There are several methodologic issues in identification and confirmation of childhood cancer clusters, especially leukemia [42]. In general, these studies are limited by low statistical power [43]. Therefore, the identification of cancer clusters may be driven by bias such as the practice of defining geographical boundaries of the cluster and improved case ascertainment in the areas suspected of having clusters, as well as error, namely, random variation [44]. Cancer cluster studies utilizing multiple comparisons over a small period of time or different methods have shown false positive results [45]. Further, population density, age, migration, sex, and race/ ethnicity are potential confounding elements affecting childhood cancer cluster confirmation [46,47]. This study utilized statistical software (SaTScanTM) that is reliable in the assessment of cancer clusters, as well as other disease Pediatr Blood Cancer DOI 10.1002/pbc 6 Amin et al. Fig. 6. Purely spatial analysis of brain/CNS Tumor cases 2000–2007. Clustering representation of SaTScanTM purely spatial analysis is illustrated utilizing FAPTP zip code data with age and sex covariates. The SF cluster significant although size of area is altered with respect to prior cluster maps identified in Florida. The red area represents the South Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (26.0N, 80.4 W)/15.6 km, Population 157,361, Observed cases ¼ 60, Expected cases ¼ 33. To better understand the increased cancer rates in SF, it is important to consider changes in the population for that region as well. Otherwise, the possible environmental factors affecting cancer rates could be confounded with population migrations and increases. While estimates for the pediatric population counts for all ages in each of the Florida zip code areas were not available for the period 2001–2007, we utilized population estimates for the pediatric population by county for 2001 and 2007 from the Florida Legislature [48] and the estimates of the pediatric population for a 3year-period 2005–2007 from the American Community Survey of the Census Bureau [49]. Considering the relative annual population increase, defined by the ratio r as follows: r¼ pediatric pop 2007 pediatric pop 2000 pediatric pop 2000 where the change in the pediatric population count in 2007 is obtained relative to the pediatric population count in 2000, we compared the average values for the ratio r for the SF area with the corresponding annual relative population increase for the rest of Florida. Similarly, we also obtained a ratio based on the 2005–2007 estimates. Our results indicated that relative population increases in the SF cluster area are not significantly different from the rest of the state. It is also possible that zip code population shifts over time could have altered the results between 2000 and 2007. Such shifts could result in an apparently elevated cancer rate when using 2000 as the population standard. Using population estimates for larger areas such as counties would limit the effects of such small-area migrations on the cancer rates. Florida county population estimates Pediatr Blood Cancer DOI 10.1002/pbc Fig. 7. Space–time analysis of brain/CNS Tumor Cases 2000–2007. Clustering representation of SaTScanTM Space–time analysis is illustrated utilizing FAPTP zip code data with age and sex as covariates. Clusters are represented in colors. The red area represents a Northeastern Florida cluster. SaTScanTM computed results include: Coordinates/ radius ¼ (30.1N, 81.8W)/20 km, Time frame ¼ January 1, 2005 to December 31, 2007, Population 111,133, Observed cases ¼ 29, Expected cases ¼ 9. The orange area represents the South Florida cluster. SaTScanTM computed results include: Coordinates/radius ¼ (26.3N, 81.3W)/105.2 km, Time frame ¼ January 1, 2006 to December 31, 2007, Population 455,519, Observed cases ¼ 52, Expected cases ¼ 24. between 2005 and 2007 were available for 53 counties in Florida with populations greater than 20,000. We analyzed purely spatial and space–time SaTScanTM results for these 53 counties from 2000 to 2007 and found that the brain tumor cluster persisted. Analysis of leukemia clusters persisted during the space–time analysis but not for the purely spatial analysis. While our initial analysis was based on zip codes, limited analysis based on counties indirectly suggests that population shifts did not play a significant role in altering the cancer clusters. Thus, it is highly unlikely that our findings of childhood cancer clusters are driven primarily by migration since population changes in these geographic areas were non-differential, thus minimizing any misclassification bias and confounding from the observed clusters. In this study, we have shown that there is a relative increase in childhood cancer crude incidence rate in SF and NEF during the years 2000–2007. Since this finding might have been influenced by potential confounders of childhood cancer [44], we adjusted for age at diagnosis, sex,and race/ethnicity and still observed a statistically significant relative increase in SF and NEF compared with the state of Florida. Therefore, given these adjustments, it is possible to suspect geographic variation as the potential risk variable for the clusters. Although the cluster areas identified are quite large geographically, it is possible that localized environmental factors or person-to-person spread of viral or bacterial pathogen [12,13,21–24], may be involved in these suspected Florida Childhood Cancer Clusters geographic areas. Finally, despite these adjustments, we cannot rule out unmeasured confounding elements as a possible explanation of the observed clusters. Furthermore, residual confounding elements may influence this confirmation especially by race/ethnicity, since this information may have suffered from misclassification bias. Therefore, statistical modeling cannot completely remove the effect of confounding [27,50]. Our study found the crude incidence rate of childhood leukemia and brain/CNS cancers to be significantly higher in the SF and NEF clusters when compared with the state of Florida. As described earlier, these findings are unlikely to be driven by non-factual attributes of cancer clusters but are suggestive of environmental factors or common risk factors in the areas. Consequently, these findings could be etiologically driven, indicating the need for further investigation to identify the potential risk factors in the observed leukemia and brain/CNS cancer clusters in these areas. We did not find spatial or space–time clustering with lymphoma in the adjusted models. The negative finding with lymphoma may be due to the small number of cases in this subset, which limits the statistical power to detect significant clusters with these data [47] or due to the lack of a lymphoma cluster. Despite the strengths of this article, there are also some limitations. First, we used a preexisting dataset that may be associated with information and selection bias, thus influencing the validity of our findings. However, since the FAPTP data are highly reliable, it is unlikely that our confirmation of cancer clusters in SF is driven solely by information or selection bias. Second, confounding elements such as race/ethnicity and age may very well influence our results. But this is unlikely since we focused on childhood malignancy with no reference to adult tumors. Finally, as with all epidemiologic studies, unmeasured and residual confounding elements may also partly influence the findings reported. In summary, we found evidence of spatial and space–time childhood cancer clustering in SF and NEF. Statistically significant cancer subtype clustering was found for leukemia and brain/CNS cancer but not for lymphomas, which may be due to low statistical power of our study to detect smaller clusters. 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