Clustering of childhood acute leukemia in Finland: a nationwide register-based study

Clustering of childhood acute leukemia in Finland: a nationwide register-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clustering of childhood acute leukemia in Finland: a nationwide register-based study Julia Ventelä, Mia Korja, Anssi Auvinen, Olli Lohi, Atte Nikkilä This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4848058/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Cancer Causes & Control → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Acute leukemia is the most common childhood malignancy, with suspected contributions from environmental factors and immune responses to common pathogens. A recent meta-analysis indicated possible spatiotemporal clustering, though the findings were hindered by data quality limitations. We investigated spatiotemporal clustering of childhood leukemia using advanced methods and complete residential histories. Methods We included patients aged 0–17 years diagnosed in 1990–2019, using data from the Finnish Cancer Registry. A 1:3 age- and sex-matched case-control design was employed and residential history data with exact coordinates was collected. Clustering was evaluated using the Cuzick-Edwards test, Knox test, Kulldorff's scan, and Jacquez's Q statistic. Results The dataset included 1,626 childhood leukemia cases (median age 5.0 years, 54% male). The Knox test revealed no evidence of clustering. However, the Cuzick-Edwards test revealed spatial clustering at diagnosis addresses for females (Observed/Expected [Obs/Exp] ratio 1.08, 95% CI 1.01–1.15) and for children under 1 year (Obs/Exp 1.35, 95% CI 1.15–1.57). Further analysis with Jacquez's Q using complete residential histories, identified significant spatiotemporal clustering in young children (ages 1.5–5.99 years) with acute lymphoblastic leukemia (ALL, p = 0.037). We also tested for co-incidence between leukemia and type 1 diabetes but found no clustering. Conclusion Overall, we found limited evidence for clustering. In the subgroup analyses, significant spatiotemporal clustering in acute lymphoblastic leukemia cases among children aged 1.5–5.99 years was observed, coinciding with the peak incidence in early childhood. Previous research has shown that this age group has distinct genetic characteristics and may possess a unique etiology. Acute leukemia Clustering Pediatric Type 1 Diabetes Epidemiology Figures Figure 1 Figure 2 Background Acute leukemia is the most common malignancy in children, yet its etiology remains largely unknown. Several somatic changes are required for the development of leukemia, and according to a prevailing hypothesis, the first often occurs during fetal development. One or several factors triggering secondary mutations are needed, but their significance is still poorly understood. [ 1 ] These secondary mutations have been suggested to arise in connection with an abnormal immune reaction during childhood exposure to a delayed infection due to e.g. specific population mixing [ 2 – 4 ]. In addition to infections, other factors such as environmental or social factors could contribute to the disease etiology as well as well [ 5 ]. The first reports of epidemic-type clustering of leukemia were published in the 1960s [ 6 ]. Subsequent studies, and a recent meta-analysis, investigating the spatiotemporal clustering of childhood leukemia have produced mixed results, with some indicating moderate clustering. [ 7 , 8 ] Type 1 diabetes is an autoimmune disease leading to destruction of the insulin-producing β-cells in the pancreas. Despite distinct pathogeneses, evidence suggesting similar environmental exposures in etiology of the two diseases has been reported. Notably, both diseases show higher incidence in developed countries, with one of the highest incidences of type 1 diabetes globally in Finland, at 56.8 per 100,000 person-years during 2000–2022 [ 9 ]. These parallels indicate potential shared environmental triggers [ 10 ]. Previous studies examining the co-clustering of these diseases have found correlated incidence patterns and similar large-scale distributions [ 11 – 13 ]. The aim of this study was to further investigate the spatiotemporal clustering of childhood acute leukemia and to investigate whether it shares spatiotemporal patterns with type 1 diabetes. Materials and Methods Data We identified childhood acute leukemia cases diagnosed in Finland from the Finnish Cancer Registry using International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) codes M9800–M9948. Diabetes cases were identified from the Social Insurance Institution of Finland (Kela) using special reimbursements for diabetes medication. Our inclusion criteria encompassed ages from 0 to 18 years and diagnosis between 1990 and 2019. For each leukemia case, three sex- and age- matched controls were randomly sampled from the Population Information System of Finland. The age matching criterion was the same birth year. Each control was assigned a reference date corresponding to the age of their respective case at the time of his/her diagnosis. The conclusion of the exposure period was defined as the diagnosis date for cases and the reference date for the controls. Residential histories from birth to the diagnosis or reference date for the study subjects were retrieved from the Digital and Population Data Services Agency (DVV). The data included the start and end dates of the residence periods, municipality, zip code, address and North and East coordinates in ETRS-TM35FIN. The residential histories were restricted to addresses between birth and the date of leukemia diagnosis or the reference date for controls, resulting in 14,223 residencies. The residential periods lacking start or end dates were removed. Among leukemia cases, 5.1% (n = 83) lacked coordinates for some of their residential periods, comparable to 5.9% (n = 286) of controls. Consequently, we excluded 439 instances of residential data: 308 periods abroad, 125 domestic periods not registered in the Population Information System, and 6 domestic periods registered in the Population Information but lacking documentation. This process yielded a dataset of 13,784 rows of residential data, with 3,426 rows allocated to 1,617 cases and 10,358 to 4,865 controls (Fig. 1 ). For 1,512 cases (93.0%) and 4,445 controls (91.1%), the residential history was complete with no missing data. Statistical analysis Statistical analyses were conducted using R (v. 4.0.5, R Core Team, 2021, Vienna), Spacestat (BioMedware Inc., Ann Arbor, MI, v. 4.0.21), and SaTScan (v. 10.1.2) [ 14 , 15 ]. All the analyses were performed in a secure remote environment provided by the Finnish Social and Health Data Permit Authority, Findata. The analyses were conducted using four distinct approaches: full residential histories, the addresses at the time of diagnosis, the addresses one year preceding the diagnosis or the addresses at birth. A significance threshold of p < 0.05 was applied to determine statistically significant clustering, while 0.05 < p < 0.1 was considered indicative of suggestive clustering. We used Benjamini-Hochberg (BH) method to correct for multiple testing [ 16 ]. Cuzick-Edwards’ test The Cuzick-Edwards’ test evaluates the number and proportion of cases within the k nearest neighbors (kNNs) and relies solely on spatial analysis, utilizing coordinates of both cases and controls [ 17 ]. In our study, we adapted this test also to address co-clustering of acute leukemia and diabetes, a method originally utilized in a Danish case-control study investigating co-clustering of the two diseases in Denmark [ 13 ]. Shortly, individuals with leukemia were considered as cases and type 1 diabetes cases served as controls. Rejecting the null hypothesis with a smaller test statistic than expected indicates leukemia cases exhibiting an excess of diabetes cases nearby. Additionally, we conducted the test also treating diabetes cases as cases and leukemia cases as their controls. Cuzick-Edwards’ test was performed using the ZceTk() function from the nnspat package (version 0.1.2). Knox test We utilized the Knox test to examine the spatiotemporal clustering patterns of cases with predefined thresholds: distance (0.25, 0.5, 1, 5, 10 km) and time (2, 6, 12, 18, 24 months) [ 18 ]. This test was implemented using the knox() function from the surveillance package (version 1.20.0) in the R programming environment, with 10,000 iterations for leukemia cases, adjusted according to dataset size and computational capacity. To assess the statistical significance of the observed clustering patterns, Monte Carlo permutation tests were employed to derive p-values. Kulldorff’s scan Kulldorff's spatial scan statistic, implemented using the SaTScan program, was utilized to investigate spatiotemporal clustering [ 19 , 20 ]. For each location, a series of circles with a given radius was constructed. These circles encompass neighboring study subjects falling within their boundaries. The parameters were configured to allow for a maximum spatial cluster size equivalent to 25% of the population at risk within ellipses with a radius of 10 km. Due to excessively long computing times, the case accrual was aggregated into three-month intervals. A Bernoulli probabilistic model, comparing cases and controls as binary variables, was utilized. To assess statistical significance, p-values were derived from Monte Carlo simulations, consisting of 999 replications. Jacquez’s Q statistic We employed Jacquez's Q statistic, utilizing the commercial Spacestat program, developed by BioMedware to analyze spatiotemporal clustering with full residential histories[ 21 ]. Fifteen nearest neighbors were analyzed (k = 15). This statistical approach incorporates both spatial and temporal dimensions, providing insights into the likelihood of individuals belonging to clusters at various times and locations. The statistic is recalculated each time a study subject moves, allowing for a dynamic understanding of clustering patterns over individual's study period from birth to diagnosis/reference date. To assess the statistical significance of the Q-statistics, we conducted Monte Carlo simulations with 599–999 iterations. Jacquez’s Q yields four main indicators: the global Q, Q i , Q t , and Q it . The global Q represents the average clustering across all individuals over time, while Q i evaluates clustering around individual cases or controls throughout their life course. Q t assesses global clustering at a specific time point, and Q it examines clustering around an individual at a given time. Particularly interesting are the Q it values whose cases have significant Q i values. This combination indicates specific locations and times where cases with significant lifetime clustering are part of geographically localized clusters. Subgroups The data were stratified by sex, age, and type of leukemia (acute lymphoblastic leukemia and acute myeloid leukemia). Due to the low number of B-cell (n = 226) and T-cell (n = 70) ALL, we could not include those more specific subtypes for subgroup analyses. The age groups considered for the leukemia dataset were derived a priori from its incidence distribution: 0–0.99, 1–9.99 and 10–17.99. Additionally, for ALL cases, the incidence peak age group 1.5–5.99 years was further evaluated. The aforementioned statistical analyses were conducted independently for each subgroup. Additionally, the Knox test was performed for two distinct scenarios: cases with only a single residence prior to the diagnosis date and for varying time periods categorized by the diagnosis date 1990–1999, 2000–2009, 2010–2019). This approach allowed for a comprehensive exploration of spatial clustering patterns across different demographic and temporal strata. Results Study subjects and dataset In total, 1,762 leukemia cases were identified from the Finnish Cancer Registry. After excluding duplicates, we retained a cohort of 1,626 leukemia cases along with 4,877 controls. The median age at leukemia diagnosis was 4.97 years (IQR 2.85–10.33). The age distribution revealed an expected peak incidence of leukemia within the age range of 1.5 to 5.99 years old. Among the leukemia cases, a slight majority were male, constituting 53.9% (n = 876). As anticipated, ALL was the predominant type of leukemia, accounting for 83.0% (n = 1,350) of all leukemia cases, with a median age of 4.86 years (IQR 2.98–9.53) (Table 1 ). Table 1 Characteristics of the study subjects. Cases Controls Total, N 1,626 4,877 Sex, N (%) Female 750 (46.1) 2,250 (46.1) Male 876 (53.9) 2,627 (53.9) Age, N (%) 0–0.99 years 78 (4.8) 234 (4.8) 1–9.99 years 1,127 (69.3) 3,380 (69.3) 10–17.99 years 421 (25.9) 1,263 (25.9) Type of leukemia, N (%) ALL 1,350 (83.0) ALL, 1.5–5.99 years 732 (45.0) AML 224 (13.8) Others 52 (3.2) Residential history information, N (%) Complete 1,512 (93.0) 4,445 (91.1) Residence at birth 1,577 (97.0) 4,701 (96.4) Residence at one year prior to diagnosis 1,600 (98.4) 4,816 (98.7) Residence at diagnosis 1,612 (99.1) 4,859 (99.6) Abbreviations: ALL, Acute lymphoblastic leukemia; AML, Acute myeloid leukemia There was no significant difference between leukemia cases and controls regarding the number of residencies before the reference date (mean for leukemia cases 2.12, and mean for controls 2.13; p=0.618, Wilcoxon rank sum test). Leukemia clustering Leukemia clustering was assessed using various statistical methods. Initially, we applied the Cuzick-Edwards test, which detects solely spatial clustering by analyzing the proportion of cases among the k nearest neighbors for all instances. By examining the five nearest neighbors at different time points, diagnosis date, one year preceding diagnosis, and birth date, we observed signs of clustering among all leukemia cases based on their residential addresses at birth (Observed/Expected [Obs/Exp] ratio=1.05, 95% CI 1.00–1.10, p=0.045) (Table S1). In subgroup analyses, clustering was found among females at the time of diagnosis (Obs/E = 1.08, 95% CI 1.01–1.15, p=0.024), and among children aged under 1 year both at the diagnosis date (Obs/Exp=1.35, 95% CI 1.14-1.57, p=0.00098) and birth date (Obs/Exp=1.22, 95% CI 1.01-1.43, p=0.040) (Table S1). For evaluation of spatiotemporal clustering, we utilized the Knox test, which quantifies, using prespecified thresholds, whether two diagnoses being close in time increases the probability of being close also in distance. Overall, subsequent analyses considering different types of leukemia or various time periods did not yield statistically significant clustering patterns (Table S2 and Table S3). Kulldorff’s scan, which identifies clusters by scanning the study area with varying-sized windows to detect higher-than-expected case densities, found no significant clusters in the analysis of all study subjects. Likewise, the subgroup analyses revealed no significant clustering, as all potential clusters detected had a p-value greater than 0.1. The method that uses the entire residential histories of both leukemia cases and their controls, Jacquez’s Q statistic, identified significant clustering among the 15 nearest neighbors for the subgroup comprising ALL cases aged 1.5 to 5.99 years. The results for this common ALL age group indicated clustering around cases throughout their lifespan (Q i , n=60, p=0.008). Furthermore, significant Q it statistics (n=96, p=0.037) were observed, indicating clustering at specific time points within this subgroup. To illustrate the locations of these significant clusters, we plotted the study subjects' locations on a map of Finland, summarized from years 1990-2019 (Figure 2). The significant clusters were disproportionately located in southwestern Finland, within a 50km radius of the city of Turku, accounting for 24% (n=23) of the significant Q it values. In comparison, this region encompasses approximately 2.3% of Finland's total land area and contains 5.5% of the country's population. However, no significant temporal intervals were detected. The presence of significant Q it values that were also part of significant Q i clusters (n=74, p=0.011) underscored the statistical weight of these findings. Similar analyses for other subgroups yielded non-significant results. Co-clustering of leukemia and type I diabetes Given the potential links between childhood acute leukemia and type 1 diabetes, with shared environmental triggers and abnormal immune responses suggested by recent studies, we examined the spatiotemporal co-clustering of these diseases. To this end, we used a cohort of 16,307 cases diagnosed with type 1 diabetes and a dataset comprising 38,625 rows of residential data (Ventelä et al., manuscript in preparation). This analysis employed a modified version of the Cuzick-Edwards test, comparing 1,565 leukemia cases to 16,142 diabetes cases treated as controls. The co-clustering of the diseases was examined among the five nearest neighbors. The results suggested no evidence of co-clustering of the diseases (Table S4). We compiled summaries of the cluster analysis results by method and place of residence at three specific time points (Table 2). Table 2. Summary of cluster analysis results by method and place of residence. Discussion This study investigated the spatial and temporal clustering of childhood leukemia and its potential co-occurrence with type 1 diabetes in Finland. We employed a combination of established methods to analyze residential histories spanning birth to diagnosis (or reference date for controls). Overall, considering leukemia independently and in parallel with type 1 diabetes, we found limited evidence for clustering. In the subgroup analyses, our findings revealed significantly elevated spatiotemporal clustering for ALL in young children (1.5-5.99 years), coinciding with the peak incidence age in this cohort. This aligns with the known age distribution of ALL subtypes, particularly ETV6-RUNX1 and high hyperdiploidy, which are commonly prevalent in early childhood ALL [22]. A prior Swiss study investigating childhood leukemia clustering and potential clinical characteristics associated with it, reported a higher prevalence of the ETV6-RUNX1 gene translocation among leukemia cases born in close spatial and temporal proximity [23]. Employing the Cuzick-Edwards method, we identified statistically significant spatial clustering for female leukemia cases at diagnosis and for cases diagnosed under one year old. Investigating residential addresses at birth for the entire study population yielded borderline results (Obs/Exp = 1.05, 95% CI 1.00–1.10, p=0.045). Notably, the results based on the other spatial analysis methods lacked concordance with these findings, suggesting a low overall weight of evidence. A previous pooled analysis using the Knox test, summarizing prior research on leukemia clustering in individuals under 15 years old, also indicated borderline results (Obs/Exp=1.01, p=0.054). However, this analysis demonstrated statistically significant clustering specifically within the youngest age group, under 5 years old [7]. This finding aligns with our result of significant clustering among the ALL subgroup of young children aged 1.5-5.99 years which was located disproportionately in southwestern Finland. Our analysis using the Cuzick-Edwards test did not yield significant evidence for co-clustering of type 1 diabetes and acute childhood leukemia. Only a few studies have explored the co-clustering of childhood acute leukemia and type 1 diabetes. The first suggestion of shared spatiotemporal clustering of the diseases emerged in the early 2000s when a UK study observed a positive spatial correlation between the diseases and a further extension of the study considering the temporal variation supported it: a positive correlation of at least 0.7 between diseases was observed across all time periods (from 1978 to 2003) [11, 12]. Also, a previous Danish case-control study identified co-clustering with the Cuzick-Edwards method for the ALL age group of 2–6 years old with type 1 diabetes, at both the place of diagnosis and when considering residential mobility [13]. This study leverages high-quality data from well-established Finnish registries, encompassing a large population of roughly 1,1 million children over three decades with full residential histories. A key strength lies in employing a diverse array of analytical methods and conducting comprehensive data analysis looking at all timepoints of interest. To analyze clustering with full residential histories, we utilized the Jacquez's Q statistics, thereby providing a more nuanced understanding of potential spatiotemporal clustering compared to methods focused on a single time point. The study has several limitations. The clustering analysis methods depend on user-defined thresholds, which may influence the sensitivity in detecting subtle clustering patterns. Conversely, the potentially low specificity of the models could lead to false positive results. Another significant constraint of the analyses was the limited computational power, as multicore processing was not possible with the software available. Additionally, the dataset size, while adequate for this study, is relatively small on a global scale. Our findings indicate spatiotemporal clustering of ALL among young children, which is especially interesting as this subgroup has been hypothesized to have a distinct etiology. Subsequent studies could delve deeper into these identified clusters to identify common factors especially near the Turku area. The evidence for co-clustering of leukemia and type 1 diabetes remains inconclusive. Future investigations should aim to validate our finding on the clustering of ALL in its incidence peak, stratify these analyses by specific ALL subtypes and to look for explanations for these clusters. Declarations Funding This work was supported by Väre Foundation for Pediatric Research and Competitive State Research Funding. Competing interests The authors have no competing interests to disclose. Author contributions All authors contributed to the study conception and design. Julia Ventelä applied for data permit and registry data from Findata and performed data analysis. The first draft of the manuscript was written by Julia Ventelä and Mia Korja. All authors read and approved the final manuscript. Data Availability Research data are not publicly accessible in accordance with Finnish legislation as the Secondary Use Act prohibits sharing routinely compiled healthcare data. Ethics approval According to Finnish research legislation, ethical approval was not required for this study as it was based on registry data. This study has the research permission from Finnish Social and Health Data Permit Authority, Findata. References Greaves M (3/2006) Infection, immune responses and the aetiology of childhood leukaemia. Nat Rev Cancer 6:193–203. https://doi.org/10.1038/nrc1816 Smith M (1997) Considerations on a possible viral etiology for B-precursor acute lymphoblastic leukemia of childhood. J Immunother 20:89–100. https://doi.org/10.1097/00002371-199703000-00001 Greaves MF (1988) Speculations on the cause of childhood acute lymphoblastic leukemia. Leukemia 2:120–125 Kinlen L (1988) Evidence for an infective cause of childhood leukaemia: comparison of a Scottish new town with nuclear reprocessing sites in Britain. Lancet 2:1323–1327. https://doi.org/10.1016/s0140-6736(88)90867-7 McNally RJQ, Eden TOB (2004) An infectious aetiology for childhood acute leukaemia: a review of the evidence. Br J Haematol 127:243–263. https://doi.org/10.1111/j.1365-2141.2004.05166.x Kessler II, Lilienfeld AM (1969) Perspectives in the epidemiology of leukemia. Adv Cancer Res 12:225–302. 10.1016/s0065-230x(08)60332-2 Kreis C, Doessegger E, Lupatsch JE, Spycher BD (2019) Space-time clustering of childhood cancers: a systematic review and pooled analysis. Eur J Epidemiol 34:9–21. https://doi.org/10.1007/s10654-018-0456-y Nikkilä A, Raitanen J, Lohi O, Auvinen A (2019) Spatio-Temporal Clustering of Childhood Leukemia Relative to Population Mixing in Finland: A Nationwide Register-Based Study. Blood 134:5070–5070. https://doi.org/10.1182/blood-2019-125930 Hormazábal-Aguayo I, Ezzatvar Y, Huerta-Uribe N et al (2024) Incidence of type 1 diabetes mellitus in children and adolescents under 20 years of age across 55 countries from 2000 to 2022: A systematic review with meta-analysis. Diabetes Metab Res Rev 40:e3749. https://doi.org/10.1002/dmrr.3749 Hemminki K, Houlston R, Sundquist J et al (2012) Co-Morbidity between Early-Onset Leukemia and Type 1 Diabetes – Suggestive of a Shared Viral Etiology? PLoS ONE 7:e39523. https://doi.org/10.1371/journal.pone.0039523 Feltbower RG, Manda SOM, Gilthorpe MS et al (2005) Detecting small-area similarities in the epidemiology of childhood acute lymphoblastic leukemia and diabetes mellitus, type 1: a Bayesian approach. Am J Epidemiol 161:1168–1180. https://doi.org/10.1093/aje/kwi146 Manda SOM, Feltbower RG, Gilthorpe MS (2009) Investigating spatio-temporal similarities in the epidemiology of childhood leukaemia and diabetes. Eur J Epidemiol 24:743–752. https://doi.org/10.1007/s10654-009-9391-2 Schmiedel S, Jacquez GM, Blettner M, Schüz J (2011) Spatial clustering of leukemia and type 1 diabetes in children in Denmark. Cancer Causes Control 22:849–857. https://doi.org/10.1007/s10552-011-9755-2 R Core Team (2021) R A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org Kulldorff M, Information Management Services, Inc (2021) SaTScan v10, vol 0. Software for the spatial and space-time scan statistics Benjamini Y, Drai D, Elmer G et al (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125:279–284 Cuzick J, Edwards R (2018) Spatial Clustering for Inhomogeneous Populations. J R Stat Soc Ser B Stat Methodol 52:73–96. https://doi.org/10.1111/j.2517-6161.1990.tb01773.x Knox EG (1964) The Detection of Space-Time Interactions. J R Stat Soc Ser C Appl Stat 13:25–29. https://doi.org/10.2307/2985220 Kulldorff M (1997) A spatial scan statistic. Commun Stat - Theory Methods 26:1481–1496. https://doi.org/10.1080/03610929708831995 Kulldorff M, Huang L, Pickle L, Duczmal L (2006) An elliptic spatial scan statistic. Stat Med 25:3929–3943. https://doi.org/10.1002/sim.2490 Sloan CD, Jacquez GM, Gallagher CM et al (2012) Performance of cancer cluster Q-statistics for case-control residential histories. Spat Spatiotemporal Epidemiol 3:297–310. https://doi.org/10.1016/j.sste.2012.09.002 Lee SHR, Li Z, Tai ST et al (2021) Genetic Alterations in Childhood Acute Lymphoblastic Leukemia: Interactions with Clinical Features and Treatment Response. https://doi.org/10.3390/cancers13164068 . Cancers 13. Kreis C, Lupatsch JE, Niggli F et al (2017) Space-Time Clustering of Childhood Leukemia: Evidence of an Association with ETV6-RUNX1 (TEL-AML1) Fusion. PLoS ONE 12:e0170020. https://doi.org/10.1371/journal.pone.0170020 Additional Declarations No competing interests reported. Supplementary Files TableS1.docx TableS2.docx TableS3.docx TableS4.docx Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2025 Read the published version in Cancer Causes & Control → Version 1 posted Editorial decision: Revision requested 23 Feb, 2025 Reviews received at journal 23 Feb, 2025 Reviews received at journal 12 Feb, 2025 Reviewers agreed at journal 11 Jan, 2025 Reviewers agreed at journal 07 Jan, 2025 Reviewers invited by journal 07 Jan, 2025 Editor assigned by journal 04 Aug, 2024 Submission checks completed at journal 04 Aug, 2024 First submitted to journal 02 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4848058","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":340348688,"identity":"67ade375-1455-4d50-be96-6fe6b12f29d0","order_by":0,"name":"Julia Ventelä","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PsUoDMRzH8V84aJcrrg619wr/coMIfZgLwj1DB8EchbrU/QbxGTIpbgl/yCT4Cr29Q90qVDBXVHD517FDvkMuJHzy54BU6gRTJjPuZ+8wj58M2FYiUd9kADi8Uk9UK5Ee4ZeoJR2OWglkrW7cDjy5LBbOfTzu9fMwvrKWZrTa+BW4vFoOKn//RPplEYn4L5G4HLW2ISc3isQyhutjxO9R39pwtvWfDwdyfArnmFUUcvDI/IesOsNjmk1tqInHoSwtq0Yk07trft/Mzwti7rrNzeTCvrFvdhIx/Up/JxsBAIV4m0qlUqm+L7T/V886OuHJAAAAAElFTkSuQmCC","orcid":"","institution":"Faculty of Medicine and Health Technology","correspondingAuthor":true,"prefix":"","firstName":"Julia","middleName":"","lastName":"Ventelä","suffix":""},{"id":340348689,"identity":"3ab94463-3baa-4d14-8e38-92426417cf74","order_by":1,"name":"Mia Korja","email":"","orcid":"","institution":"Faculty of Medicine and Health Technology","correspondingAuthor":false,"prefix":"","firstName":"Mia","middleName":"","lastName":"Korja","suffix":""},{"id":340348690,"identity":"7d013dd5-2e7f-434f-8655-df16dc3084b8","order_by":2,"name":"Anssi Auvinen","email":"","orcid":"","institution":"Faculty of Social Sciences, Tampere University and Tampere University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anssi","middleName":"","lastName":"Auvinen","suffix":""},{"id":340348691,"identity":"563829c6-21d2-4c4e-acda-b35059519bc8","order_by":3,"name":"Olli Lohi","email":"","orcid":"","institution":"Tampere Center for Child, Adolescent, Maternal Health Research and Tays Cancer Center, Tampere University and Tampere University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Olli","middleName":"","lastName":"Lohi","suffix":""},{"id":340348692,"identity":"b69e9538-52ea-4bac-850f-7b2cfc567fcc","order_by":4,"name":"Atte Nikkilä","email":"","orcid":"","institution":"Tampere Center for Child, Adolescent, Maternal Health Research and Tays Cancer Center, Tampere University and Tampere University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Atte","middleName":"","lastName":"Nikkilä","suffix":""}],"badges":[],"createdAt":"2024-08-02 11:22:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4848058/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4848058/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10552-025-01998-1","type":"published","date":"2025-04-24T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64167997,"identity":"36cdd19e-a7a9-44e9-996a-ea2cb931464b","added_by":"auto","created_at":"2024-09-09 09:55:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56557,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population and their residential data.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e† \u003c/sup\u003eStudy cohort after excluding duplicate entries, along with their corresponding controls.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/f74a7ee3efda0da1d0812ab6.jpg"},{"id":64167324,"identity":"7112ba5c-3725-43cf-aa54-c7b4128dec48","added_by":"auto","created_at":"2024-09-09 09:47:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":506794,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map showing the locations of the identified significant Q\u003csub\u003eit\u003c/sub\u003e values (Jacquez’s Q) among the subgroup of ALL 1.5–5.99 years old summarized from years 1990-2019.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/2245d2b2a084775944a9b920.jpg"},{"id":81569671,"identity":"422f60c4-14d0-41e7-b027-b65b654d1e5f","added_by":"auto","created_at":"2025-04-28 16:09:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1082718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/9c91badc-efdc-4912-a0a3-57cd980cbc08.pdf"},{"id":64167319,"identity":"5ba83ae8-3092-4e4e-a089-8839fdf0e738","added_by":"auto","created_at":"2024-09-09 09:47:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22508,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/9b8575a293daddbeeac2ad43.docx"},{"id":64167320,"identity":"77fa319d-4c57-4309-8eb3-0714cf9108b0","added_by":"auto","created_at":"2024-09-09 09:47:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23304,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/cf171fffc7bcceee1cde52b3.docx"},{"id":64167323,"identity":"02c3eed0-439d-4c0e-a3ae-196f688af4bd","added_by":"auto","created_at":"2024-09-09 09:47:38","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":52562,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/baf0aaa6292ed1e9c7de7b93.docx"},{"id":64167322,"identity":"7a4fd7bf-9049-406f-a93e-fbca7d2d7dab","added_by":"auto","created_at":"2024-09-09 09:47:38","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21839,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4848058/v1/66d194d878d14ecb83d8b448.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clustering of childhood acute leukemia in Finland: a nationwide register-based study","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute leukemia is the most common malignancy in children, yet its etiology remains largely unknown. Several somatic changes are required for the development of leukemia, and according to a prevailing hypothesis, the first often occurs during fetal development. One or several factors triggering secondary mutations are needed, but their significance is still poorly understood. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] These secondary mutations have been suggested to arise in connection with an abnormal immune reaction during childhood exposure to a delayed infection due to e.g. specific population mixing [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to infections, other factors such as environmental or social factors could contribute to the disease etiology as well as well [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe first reports of epidemic-type clustering of leukemia were published in the 1960s [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Subsequent studies, and a recent meta-analysis, investigating the spatiotemporal clustering of childhood leukemia have produced mixed results, with some indicating moderate clustering. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eType 1 diabetes is an autoimmune disease leading to destruction of the insulin-producing β-cells in the pancreas. Despite distinct pathogeneses, evidence suggesting similar environmental exposures in etiology of the two diseases has been reported. Notably, both diseases show higher incidence in developed countries, with one of the highest incidences of type 1 diabetes globally in Finland, at 56.8 per 100,000 person-years during 2000\u0026ndash;2022 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These parallels indicate potential shared environmental triggers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies examining the co-clustering of these diseases have found correlated incidence patterns and similar large-scale distributions [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was to further investigate the spatiotemporal clustering of childhood acute leukemia and to investigate whether it shares spatiotemporal patterns with type 1 diabetes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData\u003c/p\u003e \u003cp\u003eWe identified childhood acute leukemia cases diagnosed in Finland from the Finnish Cancer Registry using International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) codes M9800\u0026ndash;M9948. Diabetes cases were identified from the Social Insurance Institution of Finland (Kela) using special reimbursements for diabetes medication. Our inclusion criteria encompassed ages from 0 to 18 years and diagnosis between 1990 and 2019.\u003c/p\u003e \u003cp\u003eFor each leukemia case, three sex- and age- matched controls were randomly sampled from the Population Information System of Finland. The age matching criterion was the same birth year. Each control was assigned a reference date corresponding to the age of their respective case at the time of his/her diagnosis. The conclusion of the exposure period was defined as the diagnosis date for cases and the reference date for the controls. Residential histories from birth to the diagnosis or reference date for the study subjects were retrieved from the Digital and Population Data Services Agency (DVV). The data included the start and end dates of the residence periods, municipality, zip code, address and North and East coordinates in ETRS-TM35FIN.\u003c/p\u003e \u003cp\u003eThe residential histories were restricted to addresses between birth and the date of leukemia diagnosis or the reference date for controls, resulting in 14,223 residencies. The residential periods lacking start or end dates were removed. Among leukemia cases, 5.1% (n\u0026thinsp;=\u0026thinsp;83) lacked coordinates for some of their residential periods, comparable to 5.9% (n\u0026thinsp;=\u0026thinsp;286) of controls.\u003c/p\u003e \u003cp\u003eConsequently, we excluded 439 instances of residential data: 308 periods abroad, 125 domestic periods not registered in the Population Information System, and 6 domestic periods registered in the Population Information but lacking documentation. This process yielded a dataset of 13,784 rows of residential data, with 3,426 rows allocated to 1,617 cases and 10,358 to 4,865 controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For 1,512 cases (93.0%) and 4,445 controls (91.1%), the residential history was complete with no missing data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using R (v. 4.0.5, R Core Team, 2021, Vienna), Spacestat (BioMedware Inc., Ann Arbor, MI, v. 4.0.21), and SaTScan (v. 10.1.2) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. All the analyses were performed in a secure remote environment provided by the Finnish Social and Health Data Permit Authority, Findata.\u003c/p\u003e \u003cp\u003eThe analyses were conducted using four distinct approaches: full residential histories, the addresses at the time of diagnosis, the addresses one year preceding the diagnosis or the addresses at birth. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied to determine statistically significant clustering, while 0.05\u0026thinsp;\u0026lt;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 was considered indicative of suggestive clustering. We used Benjamini-Hochberg (BH) method to correct for multiple testing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCuzick-Edwards\u0026rsquo; test\u003c/h2\u003e \u003cp\u003eThe Cuzick-Edwards\u0026rsquo; test evaluates the number and proportion of cases within the k nearest neighbors (kNNs) and relies solely on spatial analysis, utilizing coordinates of both cases and controls [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we adapted this test also to address co-clustering of acute leukemia and diabetes, a method originally utilized in a Danish case-control study investigating co-clustering of the two diseases in Denmark [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Shortly, individuals with leukemia were considered as cases and type 1 diabetes cases served as controls. Rejecting the null hypothesis with a smaller test statistic than expected indicates leukemia cases exhibiting an excess of diabetes cases nearby. Additionally, we conducted the test also treating diabetes cases as cases and leukemia cases as their controls. Cuzick-Edwards\u0026rsquo; test was performed using the ZceTk() function from the nnspat package (version 0.1.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eKnox test\u003c/h2\u003e \u003cp\u003eWe utilized the Knox test to examine the spatiotemporal clustering patterns of cases with predefined thresholds: distance (0.25, 0.5, 1, 5, 10 km) and time (2, 6, 12, 18, 24 months) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This test was implemented using the knox() function from the surveillance package (version 1.20.0) in the R programming environment, with 10,000 iterations for leukemia cases, adjusted according to dataset size and computational capacity. To assess the statistical significance of the observed clustering patterns, Monte Carlo permutation tests were employed to derive p-values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eKulldorff\u0026rsquo;s scan\u003c/h2\u003e \u003cp\u003eKulldorff's spatial scan statistic, implemented using the SaTScan program, was utilized to investigate spatiotemporal clustering [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For each location, a series of circles with a given radius was constructed. These circles encompass neighboring study subjects falling within their boundaries.\u003c/p\u003e \u003cp\u003eThe parameters were configured to allow for a maximum spatial cluster size equivalent to 25% of the population at risk within ellipses with a radius of 10 km. Due to excessively long computing times, the case accrual was aggregated into three-month intervals. A Bernoulli probabilistic model, comparing cases and controls as binary variables, was utilized. To assess statistical significance, p-values were derived from Monte Carlo simulations, consisting of 999 replications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eJacquez\u0026rsquo;s Q statistic\u003c/h2\u003e \u003cp\u003eWe employed Jacquez's Q statistic, utilizing the commercial Spacestat program, developed by BioMedware to analyze spatiotemporal clustering with full residential histories[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Fifteen nearest neighbors were analyzed (k\u0026thinsp;=\u0026thinsp;15). This statistical approach incorporates both spatial and temporal dimensions, providing insights into the likelihood of individuals belonging to clusters at various times and locations. The statistic is recalculated each time a study subject moves, allowing for a dynamic understanding of clustering patterns over individual's study period from birth to diagnosis/reference date.\u003c/p\u003e \u003cp\u003eTo assess the statistical significance of the Q-statistics, we conducted Monte Carlo simulations with 599\u0026ndash;999 iterations. Jacquez\u0026rsquo;s Q yields four main indicators: the global Q, Q\u003csub\u003ei\u003c/sub\u003e, Q\u003csub\u003et\u003c/sub\u003e, and Q\u003csub\u003eit\u003c/sub\u003e. The global Q represents the average clustering across all individuals over time, while Q\u003csub\u003ei\u003c/sub\u003e evaluates clustering around individual cases or controls throughout their life course. Q\u003csub\u003et\u003c/sub\u003e assesses global clustering at a specific time point, and Q\u003csub\u003eit\u003c/sub\u003e examines clustering around an individual at a given time. Particularly interesting are the Q\u003csub\u003eit\u003c/sub\u003e values whose cases have significant Q\u003csub\u003ei\u003c/sub\u003e values. This combination indicates specific locations and times where cases with significant lifetime clustering are part of geographically localized clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSubgroups\u003c/h2\u003e \u003cp\u003eThe data were stratified by sex, age, and type of leukemia (acute lymphoblastic leukemia and acute myeloid leukemia). Due to the low number of B-cell (n\u0026thinsp;=\u0026thinsp;226) and T-cell (n\u0026thinsp;=\u0026thinsp;70) ALL, we could not include those more specific subtypes for subgroup analyses. The age groups considered for the leukemia dataset were derived a priori from its incidence distribution: 0\u0026ndash;0.99, 1\u0026ndash;9.99 and 10\u0026ndash;17.99. Additionally, for ALL cases, the incidence peak age group 1.5\u0026ndash;5.99 years was further evaluated. The aforementioned statistical analyses were conducted independently for each subgroup.\u003c/p\u003e \u003cp\u003eAdditionally, the Knox test was performed for two distinct scenarios: cases with only a single residence prior to the diagnosis date and for varying time periods categorized by the diagnosis date 1990\u0026ndash;1999, 2000\u0026ndash;2009, 2010\u0026ndash;2019). This approach allowed for a comprehensive exploration of spatial clustering patterns across different demographic and temporal strata.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects and dataset\u003c/h2\u003e \u003cp\u003eIn total, 1,762 leukemia cases were identified from the Finnish Cancer Registry. After excluding duplicates, we retained a cohort of 1,626 leukemia cases along with 4,877 controls. The median age at leukemia diagnosis was 4.97 years (IQR 2.85\u0026ndash;10.33). The age distribution revealed an expected peak incidence of leukemia within the age range of 1.5 to 5.99 years old. Among the leukemia cases, a slight majority were male, constituting 53.9% (n\u0026thinsp;=\u0026thinsp;876). As anticipated, ALL was the predominant type of leukemia, accounting for 83.0% (n\u0026thinsp;=\u0026thinsp;1,350) of all leukemia cases, with a median age of 4.86 years (IQR 2.98\u0026ndash;9.53) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750 \u003cem\u003e(46.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,250 (46.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e876 \u003cem\u003e(53.9)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,627 (53.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;0.99 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;9.99 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,127 (69.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,380 (69.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;17.99 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,263 (25.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of leukemia, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,350 (83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eALL, 1.5\u0026ndash;5.99 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e732 (45.0)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential history information, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,512 (93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,445 (91.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence at birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,577 (97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,701 (96.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence at one year prior to\u003c/p\u003e \u003cp\u003ediagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,600 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,816 (98.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,612 (99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,859 (99.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: ALL, Acute lymphoblastic leukemia; AML, Acute myeloid leukemia\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere was no significant difference between leukemia cases and controls regarding the number of residencies before the reference date (mean for leukemia cases 2.12, and mean for controls 2.13; p=0.618, Wilcoxon rank sum test).\u003c/p\u003e\n\u003cp\u003eLeukemia clustering\u003c/p\u003e\n\u003cp\u003eLeukemia clustering was assessed using various statistical methods. Initially, we applied the Cuzick-Edwards test, which detects solely spatial clustering by analyzing the proportion of cases among the k nearest neighbors for all instances. By examining the five nearest neighbors at different time points, diagnosis date, one year preceding diagnosis, and birth date, we observed signs of clustering among all leukemia cases based on their residential addresses at birth (Observed/Expected [Obs/Exp] ratio=1.05, 95% CI 1.00–1.10, p=0.045) (Table S1). In subgroup analyses, clustering was found among females at the time of diagnosis (Obs/E = 1.08, 95% CI 1.01–1.15, p=0.024), and among children aged under 1 year both at the diagnosis date (Obs/Exp=1.35, 95% CI 1.14-1.57, p=0.00098) and birth date (Obs/Exp=1.22, 95% CI 1.01-1.43, p=0.040) (Table S1).\u003c/p\u003e\n\u003cp\u003eFor evaluation of spatiotemporal clustering, we utilized the Knox test, which quantifies, using prespecified thresholds, whether two diagnoses being close in time increases the probability of being close also in distance. Overall, subsequent analyses considering different types of leukemia or various time periods did not yield statistically significant clustering patterns (Table S2 and Table S3).\u003c/p\u003e\n\u003cp\u003eKulldorff’s scan, which identifies clusters by scanning the study area with varying-sized windows to detect higher-than-expected case densities, found no significant clusters in the analysis of all study subjects. Likewise, the subgroup analyses revealed no significant clustering, as all potential clusters detected had a p-value greater than 0.1.\u003c/p\u003e\n\u003cp\u003eThe method that uses the entire residential histories of both leukemia cases and their controls, Jacquez’s Q statistic, identified significant clustering among the 15 nearest neighbors for the subgroup comprising ALL cases aged 1.5 to 5.99 years. The results for this common ALL age group indicated clustering around cases throughout their lifespan (Q\u003csub\u003ei\u003c/sub\u003e, n=60, p=0.008). Furthermore, significant Q\u003csub\u003eit\u0026nbsp;\u003c/sub\u003estatistics (n=96, p=0.037) were observed, indicating clustering at specific time points within this subgroup. To illustrate the locations of these significant clusters, we plotted the study subjects' locations on a map of Finland, summarized from years 1990-2019 (Figure 2). The significant clusters were disproportionately located in southwestern Finland, within a 50km radius of the city of Turku, accounting for 24% (n=23) of the significant Q\u003csub\u003eit\u003c/sub\u003e values. In comparison, this region encompasses approximately 2.3% of Finland's total land area and contains 5.5% of the country's population. However, no significant temporal intervals were detected. The presence of significant Q\u003csub\u003eit\u0026nbsp;\u003c/sub\u003evalues that were also part of significant Q\u003csub\u003ei\u003c/sub\u003e clusters (n=74, p=0.011) underscored the statistical weight of these findings. Similar analyses for other subgroups yielded non-significant results.\u003c/p\u003e\n\u003cp\u003eCo-clustering of leukemia and type I diabetes\u003c/p\u003e\n\u003cp\u003eGiven the potential links between childhood acute leukemia and type 1 diabetes, with shared environmental triggers and abnormal immune responses suggested by recent studies, we examined the spatiotemporal co-clustering of these diseases. To this end, we used a cohort of 16,307 cases diagnosed with type 1 diabetes and a dataset comprising 38,625 rows of residential data (Ventelä et al., manuscript in preparation). This analysis employed a modified version of the Cuzick-Edwards test, comparing 1,565 leukemia cases to 16,142 diabetes cases treated as controls. The co-clustering of the diseases was examined among the five nearest neighbors. The results suggested no evidence of co-clustering of the diseases (Table S4).\u003c/p\u003e\n\u003cp\u003eWe compiled summaries of the cluster analysis results by method and place of residence at three specific time points (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Summary of cluster analysis results by method and place of residence.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the spatial and temporal clustering of childhood leukemia and its potential co-occurrence with type 1 diabetes in Finland. We employed a combination of established methods to analyze residential histories spanning birth to diagnosis (or reference date for controls). Overall, considering leukemia independently and in parallel with type 1 diabetes, we found limited evidence for clustering. In the subgroup analyses, our findings revealed significantly elevated spatiotemporal clustering for ALL in young children (1.5-5.99 years), coinciding with the peak incidence age in this cohort. This aligns with the known age distribution of ALL subtypes, particularly ETV6-RUNX1 and high hyperdiploidy, which are commonly prevalent in early childhood ALL [22]. A prior Swiss study investigating childhood leukemia clustering and potential clinical characteristics associated with it, reported a higher prevalence of the ETV6-RUNX1 gene translocation among leukemia cases born in close spatial and temporal proximity [23].\u003c/p\u003e\n\u003cp\u003eEmploying the Cuzick-Edwards method, we identified statistically significant spatial clustering for female leukemia cases at diagnosis and for cases diagnosed under one year old. Investigating residential addresses at birth for the entire study population yielded borderline results (Obs/Exp = 1.05, 95% CI 1.00\u0026ndash;1.10, p=0.045). Notably, the results based on the other spatial analysis methods lacked concordance with these findings, suggesting a low overall weight of evidence. A previous pooled analysis using the Knox test, summarizing prior research on leukemia clustering in individuals under 15 years old, also indicated borderline results (Obs/Exp=1.01, p=0.054). However, this analysis demonstrated statistically significant clustering specifically within the youngest age group, under 5 years old [7]. This finding aligns with our result of significant clustering among the ALL subgroup of young children aged 1.5-5.99 years which was located disproportionately in southwestern Finland.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis using the Cuzick-Edwards test did not yield significant evidence for co-clustering of type 1 diabetes and acute childhood leukemia. Only a few studies have explored the co-clustering of childhood acute leukemia and type 1 diabetes. The first suggestion of shared spatiotemporal clustering of the diseases emerged in the early 2000s when a UK study observed a positive spatial correlation between the diseases and a further extension of the study considering the temporal variation supported it: a positive correlation of at least 0.7 between diseases was observed across all time periods (from 1978 to 2003) [11, 12]. Also, a previous Danish case-control study identified co-clustering with the Cuzick-Edwards method for the ALL age group of 2\u0026ndash;6 years old with type 1 diabetes, at both the place of diagnosis and when considering residential mobility [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study leverages high-quality data from well-established Finnish registries, encompassing a large population of roughly 1,1 million children over three decades with full residential histories. A key strength lies in employing a diverse array of analytical methods and conducting comprehensive data analysis looking at all timepoints of interest. To analyze clustering with full residential histories, we utilized the Jacquez\u0026apos;s Q statistics, thereby providing a more nuanced understanding of potential spatiotemporal clustering compared to methods focused on a single time point.\u003c/p\u003e\n\u003cp\u003eThe study has several limitations. The clustering analysis methods depend on user-defined thresholds, which may influence the sensitivity in detecting subtle clustering patterns. Conversely, the potentially low specificity of the models could lead to false positive results. Another significant constraint of the analyses was the limited computational power, as multicore processing was not possible with the software available. Additionally, the dataset size, while adequate for this study, is relatively small on a global scale.\u003c/p\u003e\n\u003cp\u003eOur findings indicate spatiotemporal clustering of ALL among young children, which is especially interesting as this subgroup has been hypothesized to have a distinct etiology. Subsequent studies could delve deeper into these identified clusters to identify common factors especially near the Turku area. The evidence for co-clustering of leukemia and type 1 diabetes remains inconclusive. Future investigations should aim to validate our finding on the clustering of ALL in its incidence peak, stratify these analyses by specific ALL subtypes and to look for explanations for these clusters.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Väre Foundation for Pediatric Research and Competitive State Research Funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Julia Ventelä applied for data permit and registry data from Findata and performed data analysis. The first draft of the manuscript was written by Julia Ventelä and Mia Korja. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch data are not publicly accessible in accordance with Finnish legislation as the Secondary Use Act prohibits sharing routinely compiled healthcare data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Finnish research legislation, ethical approval was not required for this study as it was based on registry data. This study has the research permission from Finnish Social and Health Data Permit Authority, Findata.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGreaves M (3/2006) Infection, immune responses and the aetiology of childhood leukaemia. Nat Rev Cancer 6:193\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrc1816\u003c/span\u003e\u003cspan address=\"10.1038/nrc1816\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith M (1997) Considerations on a possible viral etiology for B-precursor acute lymphoblastic leukemia of childhood. J Immunother 20:89\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/00002371-199703000-00001\u003c/span\u003e\u003cspan address=\"10.1097/00002371-199703000-00001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreaves MF (1988) Speculations on the cause of childhood acute lymphoblastic leukemia. Leukemia 2:120\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinlen L (1988) Evidence for an infective cause of childhood leukaemia: comparison of a Scottish new town with nuclear reprocessing sites in Britain. Lancet 2:1323\u0026ndash;1327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(88)90867-7\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(88)90867-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNally RJQ, Eden TOB (2004) An infectious aetiology for childhood acute leukaemia: a review of the evidence. Br J Haematol 127:243\u0026ndash;263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2141.2004.05166.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2141.2004.05166.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKessler II, Lilienfeld AM (1969) Perspectives in the epidemiology of leukemia. Adv Cancer Res 12:225\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0065-230x(08)60332-2\u003c/span\u003e\u003cspan address=\"10.1016/s0065-230x(08)60332-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreis C, Doessegger E, Lupatsch JE, Spycher BD (2019) Space-time clustering of childhood cancers: a systematic review and pooled analysis. Eur J Epidemiol 34:9\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-018-0456-y\u003c/span\u003e\u003cspan address=\"10.1007/s10654-018-0456-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikkil\u0026auml; A, Raitanen J, Lohi O, Auvinen A (2019) Spatio-Temporal Clustering of Childhood Leukemia Relative to Population Mixing in Finland: A Nationwide Register-Based Study. Blood 134:5070\u0026ndash;5070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1182/blood-2019-125930\u003c/span\u003e\u003cspan address=\"10.1182/blood-2019-125930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHormaz\u0026aacute;bal-Aguayo I, Ezzatvar Y, Huerta-Uribe N et al (2024) Incidence of type 1 diabetes mellitus in children and adolescents under 20 years of age across 55 countries from 2000 to 2022: A systematic review with meta-analysis. Diabetes Metab Res Rev 40:e3749. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/dmrr.3749\u003c/span\u003e\u003cspan address=\"10.1002/dmrr.3749\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemminki K, Houlston R, Sundquist J et al (2012) Co-Morbidity between Early-Onset Leukemia and Type 1 Diabetes \u0026ndash; Suggestive of a Shared Viral Etiology? PLoS ONE 7:e39523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0039523\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0039523\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeltbower RG, Manda SOM, Gilthorpe MS et al (2005) Detecting small-area similarities in the epidemiology of childhood acute lymphoblastic leukemia and diabetes mellitus, type 1: a Bayesian approach. Am J Epidemiol 161:1168\u0026ndash;1180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwi146\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwi146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManda SOM, Feltbower RG, Gilthorpe MS (2009) Investigating spatio-temporal similarities in the epidemiology of childhood leukaemia and diabetes. Eur J Epidemiol 24:743\u0026ndash;752. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-009-9391-2\u003c/span\u003e\u003cspan address=\"10.1007/s10654-009-9391-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmiedel S, Jacquez GM, Blettner M, Sch\u0026uuml;z J (2011) Spatial clustering of leukemia and type 1 diabetes in children in Denmark. Cancer Causes Control 22:849\u0026ndash;857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10552-011-9755-2\u003c/span\u003e\u003cspan address=\"10.1007/s10552-011-9755-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2021) R A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulldorff M, Information Management Services, Inc (2021) SaTScan v10, vol 0. Software for the spatial and space-time scan statistics\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Drai D, Elmer G et al (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125:279\u0026ndash;284\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuzick J, Edwards R (2018) Spatial Clustering for Inhomogeneous Populations. J R Stat Soc Ser B Stat Methodol 52:73\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2517-6161.1990.tb01773.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2517-6161.1990.tb01773.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnox EG (1964) The Detection of Space-Time Interactions. J R Stat Soc Ser C Appl Stat 13:25\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2985220\u003c/span\u003e\u003cspan address=\"10.2307/2985220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulldorff M (1997) A spatial scan statistic. Commun Stat - Theory Methods 26:1481\u0026ndash;1496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03610929708831995\u003c/span\u003e\u003cspan address=\"10.1080/03610929708831995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulldorff M, Huang L, Pickle L, Duczmal L (2006) An elliptic spatial scan statistic. Stat Med 25:3929\u0026ndash;3943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sim.2490\u003c/span\u003e\u003cspan address=\"10.1002/sim.2490\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSloan CD, Jacquez GM, Gallagher CM et al (2012) Performance of cancer cluster Q-statistics for case-control residential histories. Spat Spatiotemporal Epidemiol 3:297\u0026ndash;310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sste.2012.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.sste.2012.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SHR, Li Z, Tai ST et al (2021) Genetic Alterations in Childhood Acute Lymphoblastic Leukemia: Interactions with Clinical Features and Treatment Response. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers13164068\u003c/span\u003e\u003cspan address=\"10.3390/cancers13164068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cancers 13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKreis C, Lupatsch JE, Niggli F et al (2017) Space-Time Clustering of Childhood Leukemia: Evidence of an Association with ETV6-RUNX1 (TEL-AML1) Fusion. PLoS ONE 12:e0170020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0170020\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0170020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute leukemia, Clustering, Pediatric, Type 1 Diabetes, Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-4848058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4848058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAcute leukemia is the most common childhood malignancy, with suspected contributions from environmental factors and immune responses to common pathogens. A recent meta-analysis indicated possible spatiotemporal clustering, though the findings were hindered by data quality limitations. We investigated spatiotemporal clustering of childhood leukemia using advanced methods and complete residential histories.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe included patients aged 0\u0026ndash;17 years diagnosed in 1990\u0026ndash;2019, using data from the Finnish Cancer Registry. A 1:3 age- and sex-matched case-control design was employed and residential history data with exact coordinates was collected. Clustering was evaluated using the Cuzick-Edwards test, Knox test, Kulldorff's scan, and Jacquez's Q statistic.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe dataset included 1,626 childhood leukemia cases (median age 5.0 years, 54% male). The Knox test revealed no evidence of clustering. However, the Cuzick-Edwards test revealed spatial clustering at diagnosis addresses for females (Observed/Expected [Obs/Exp] ratio 1.08, 95% CI 1.01\u0026ndash;1.15) and for children under 1 year (Obs/Exp 1.35, 95% CI 1.15\u0026ndash;1.57). Further analysis with Jacquez's Q using complete residential histories, identified significant spatiotemporal clustering in young children (ages 1.5\u0026ndash;5.99 years) with acute lymphoblastic leukemia (ALL, p\u0026thinsp;=\u0026thinsp;0.037). We also tested for co-incidence between leukemia and type 1 diabetes but found no clustering.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOverall, we found limited evidence for clustering. In the subgroup analyses, significant spatiotemporal clustering in acute lymphoblastic leukemia cases among children aged 1.5\u0026ndash;5.99 years was observed, coinciding with the peak incidence in early childhood. Previous research has shown that this age group has distinct genetic characteristics and may possess a unique etiology.\u003c/p\u003e","manuscriptTitle":"Clustering of childhood acute leukemia in Finland: a nationwide register-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 09:47:33","doi":"10.21203/rs.3.rs-4848058/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-24T03:44:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-24T03:42:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-12T16:54:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87980933740158751719289575649006230588","date":"2025-01-11T14:53:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42814132317835963793016267852323283293","date":"2025-01-08T04:12:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-08T04:09:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-05T03:16:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-05T03:15:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2024-08-02T11:21:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f99d4e12-3960-41f8-a43f-5790a8a5c876","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:02:35+00:00","versionOfRecord":{"articleIdentity":"rs-4848058","link":"https://doi.org/10.1007/s10552-025-01998-1","journal":{"identity":"cancer-causes-and-control","isVorOnly":false,"title":"Cancer Causes \u0026 Control"},"publishedOn":"2025-04-24 15:57:51","publishedOnDateReadable":"April 24th, 2025"},"versionCreatedAt":"2024-09-09 09:47:33","video":"","vorDoi":"10.1007/s10552-025-01998-1","vorDoiUrl":"https://doi.org/10.1007/s10552-025-01998-1","workflowStages":[]},"version":"v1","identity":"rs-4848058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4848058","identity":"rs-4848058","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}