Clustering patterns in Finnish type 1 diabetes patients: a nationwide register-based study.

PurposeAcute 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 spatial and spatiotemporal clustering of childhood leukemia using advanced methods and complete residential histories.MethodsWe 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 statistic, and Jacquez’s Q statistic.ResultsThe dataset included 1,626 childhood leukemia cases (median age 5.0 years, 54% male). The Knox test revealed no evidence of spatiotemporal clustering. However, the Cuzick-Edwards test revealed spatial clustering at diagnosis addresses for children under 1 year (OR 1.35, 95% CI 1.14–1.57). Further analysis with Jacquez’s Q test 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.ConclusionOverall, 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.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10552-025-01998-1.

BackgroundThe incidence of pediatric inflammatory bowel disease (PIBD) has increased dramatically during the past decades. This implies involvement of environmental factors in etiology but lends no clues about specific agents. We evaluated clustering in time and place of residence at PIBD onset using a case-control setting with comprehensive nationwide register data.MethodsWe included all PIBD cases diagnosed at ages < 18 years during 1992–2017 (3748 cases; median age of 14.6; 2316 (58%) with ulcerative colitis (UC), 1432 with Crohn’s, and 18,740 age- and sex-matched controls) and constructed complete residential histories (including coordinates) from the national database until the date of the diagnosis of the case assigned as index date for the controls. Using the coordinates of the addresses of the subjects and the diagnosis/index dates, we evaluated clustering in time and place using the Knox test. Four temporal (2, 4, 6, 12 months) and four distance (0.25, 0.5, 1, 5 km) thresholds were used, and results were calculated separately for Crohn´s disease and UC. Similar analyses were conducted using the addresses at birth and the addresses five years before the diagnosis or index date. Based on the threshold values displaying the most clustering in the Knox test, logistic regression models were built to identify whether sex, age at diagnosis or the year of diagnosis affected the probability of belonging to a cluster. To analyze clustering in time and place throughout the residential histories, we used Jacquez’s Q with an open-access python program pyjacqQ.ResultsThe mean number of residencies until the index date was 2.91 for cases and 3.05 for controls (p = 0.0003). Knox test indicated residential clustering for UC with thresholds of 500 m between locations and time-period of four months (p = 0.004). In the regression analysis, sex, age at diagnosis or year of UC diagnosis did not show differences between the clustered and other cases. Jacquez Q analyses showed higher than expected frequency of clustered cases throughout residential histories (p < 10− 8).ConclusionOur findings suggest that the incidence of PIBD, especially of UC, exhibits clustering in locations of residencies over time. For the clustered cases, environmental triggers warrant future studies.

BackgroundThe systemic form of porcine circovirus associated disease (PCVAD), also known as postweaning multisystemic wasting syndrome (PMWS) was initially detected in the early 1990s. Starting in 2004, the Canadian swine industry experienced considerable losses due to PCVAD, concurrent with a shift in genotype of porcine circovirus type 2 (PCV2). Objectives of the current study were to explore spatial characteristics of self-reported PCVAD distribution in Ontario between 2004 and 2008, and to investigate the existence and nature of local spread.ResultsThe study included 278 swine herds from a large disease-monitoring project that included porcine reproductive and respiratory syndrome (PRRS) virus-positive herds identified by the diagnostic laboratory, and PRRS virus-negative herds directly from the target population. Herds were included if they had growing pigs present on-site and available geographical coordinates for the sampling site. Furthermore, herds were defined as PCVAD-positive if a producer reported an outbreak of circovirus associated disease, or as PCVAD-negative if no outbreak was noted. Spatial trend was investigated using generalized additive models and time to PCVAD outbreak in a herd using Cox's proportional hazard model; spatial and spatio-temporal clustering was explored using K-functions; and location of most likely spatial and spatio-temporal clusters was investigated using scan statistics. Over the study period, the risk of reporting a PCVAD-positive herd tended to be higher in the eastern part of the province after adjustment for herd PRRS status (P = 0.05). This was partly confirmed for spread (Partial P < 0.01). Local spread also appeared to exist, as suggested by the tentative (P = 0.06) existence of spatio-temporal clustering of PCVAD and detection of a spatio-temporal cluster (P = 0.04).ConclusionsIn Ontario, PCVAD has shown a general trend, spreading from east-to-west. We interpret the existence of spatio-temporal clustering as evidence of spatio-temporal aggregation of PCVAD-positive cases above expectations and, together with the existence of spatio-temporal and spatial clusters, as suggestive of apparent local spread of PCVAD. Clustering was detected at small spatial and temporal scales. Other patterns of spread could not be detected; however, survival rates in discrete Ontario zones, as well as a lack of a clear spatial pattern in the most likely spatio-temporal clusters, suggest other between-herd transmission mechanisms.

Malaria is a major global health hazard, particularly in developing countries such as Ethiopia, where it contributes to high morbidity and mortality rates. According to reports from the South Omo Zone Health Bureau, despite various interventions such as insecticide-treated bed nets and indoor residual spraying, the incidence of malaria has increased in recent years. Therefore, this study aimed to assess the spatial, temporal, and spatiotemporal variation in malaria incidence in the South Omo Zone, Southwest Ethiopia. A retrospective study was conducted using 4 years of malaria data from the South Omo Zone District Health Information Software (DHIS). The incidence rate of malaria per 1,000 people was calculated using Microsoft Excel software. Kulldorff SaTScan software with a discrete Poisson model was used to identify statistically significant spatial, temporal, and spatiotemporal malaria clusters. Graduated color maps depicting the incidence of malaria were generated using ArcGIS 10.7 software. Spatial clusters were identified in the districts of Dasenech (RR = 2.06, p < 0.0001), Hamer (RR = 1.90, p < 0.0001), Salamago (RR = 2.00, p < 0.0001), Bena Tsemay (RR = 1.71, p < 0.0001), Malie (RR = 1.50, p < 0.0001), Nyngatom (RR = 1.91, p < 0.0001) and North Ari (RR = 1.05, p < 0.0001) during the period from 08th July 2019 to 07th July 2023. A temporal cluster was identified as the risk period across all districts between 08th July 2022 and 07th July 2023 (RR = 1.59, p = 0.001). Spatiotemporal clusters were detected in Dasenech (RR = 2.26, p < 0.001) Salamago, (RR = 2.97, p < 0.001) Hamer (RR = 1.95, p < 0.001), Malie (RR = 2.03, p < 0.001), Bena Tsemay (RR = 1.80, p < 0.001), Nyngatom (RR = 2.65, p < 0.001), North Ari (RR = 1.50, p < 0.001), and Jinka town (RR = 1.19, p < 0.001). Significant spatial, temporal, and spatiotemporal clusters in malaria incidence were identified in the South Omo Zone. To better understand the factors contributing to these high-risk areas, further research is needed to explore individual, household, geographical, and climatic factors. Targeted interventions based on these findings could help reduce malaria incidence and associated risks in the region.

Variable spatio-temporal clustering of microseismicity in the Hellenic Subduction Zone as possible indicator for fluid migration

Spatio-Temporal Clustering of Childhood Leukemia Relative to Population Mixing in Finland: A Nationwide Register-Based Study

Middle East respiratory syndrome coronavirus (MERS-CoV) is a great public health concern globally. Although 83% of the globally confirmed cases have emerged in Saudi Arabia, the spatiotemporal clustering of MERS-CoV incidence has not been investigated. This study analysed the spatiotemporal patterns and clusters of laboratory-confirmed MERS-CoV cases reported in Saudi Arabia between June 2012 and March 2019. Temporal, seasonal, spatial and spatiotemporal cluster analyses were performed using Kulldorff’s spatial scan statistics to determine the time period and geographical areas with the highest MERS-CoV infection risk. A strongly significant temporal cluster for MERS-CoV infection risk was identified between April 5 and May 24, 2014. Most MERS-CoV infections occurred during the spring season (41.88%), with April and May showing significant seasonal clusters. Wadi Addawasir showed a high-risk spatial cluster for MERS-CoV infection. The most likely high-risk MERS-CoV annual spatiotemporal clusters were identified for a group of cities (n = 10) in Riyadh province between 2014 and 2016. A monthly spatiotemporal cluster included Jeddah, Makkah and Taif cities, with the most likely high-risk MERS-CoV infection cluster occurring between April and May 2014. Significant spatiotemporal clusters of MERS-CoV incidence were identified in Saudi Arabia. The findings are relevant to control the spread of the disease. This study provides preliminary risk assessments for the further investigation of the environmental risk factors associated with MERS-CoV clusters.

BackgroundOutbreaks of measles re-emerged in Guangxi province during 2013–2014, where measles again became a major public health concern. A better understanding of the patterns of measles cases would help in identifying high-risk areas and periods for optimizing preventive strategies, yet these patterns remain largely unknown. Thus, this study aimed to determine the patterns of measles clusters in space, time and space-time at the county level over the period 2004–2014 in Guangxi.MethodsAnnual data on measles cases and population sizes for each county were obtained from Guangxi CDC and Guangxi Bureau of Statistics, respectively. Epidemic curves and Kulldorff’s temporal scan statistics were used to identify seasonal peaks and high-risk periods. Tango’s flexible scan statistics were implemented to determine irregular spatial clusters. Spatio-temporal clusters in elliptical cylinder shapes were detected by Kulldorff’s scan statistics. Population attributable risk percent (PAR%) of children aged ≤24 months was used to identify regions with a heavy burden of measles.ResultsSeasonal peaks occurred between April and June, and a temporal measles cluster was detected in 2014. Spatial clusters were identified in West, Southwest and North Central Guangxi. Three phases of spatio-temporal clusters with high relative risk were detected: Central Guangxi during 2004–2005, Midwest Guangxi in 2007, and West and Southwest Guangxi during 2013–2014. Regions with high PAR% were mainly clustered in West, Southwest, North and Central Guangxi.ConclusionsA temporal uptrend of measles incidence existed in Guangxi between 2010 and 2014, while downtrend during 2004–2009. The hotspots shifted from Central to West and Southwest Guangxi, regions overburdened with measles. Thus, intensifying surveillance of timeliness and completeness of routine vaccination and implementing supplementary immunization activities for measles should prioritized in these regions.

The development of visual tools for the timely identification of spatio-temporal clusters will assist in implementing control measures to prevent further damage. From January 2015 to June 2020, a total number of 1463 avian influenza outbreak farms were detected in Taiwan and further confirmed to be affected by highly pathogenic avian influenza subtype H5Nx. In this study, we adopted two common concepts of spatio-temporal clustering methods, the Knox test and scan statistics, with visual tools to explore the dynamic changes of clustering patterns. Since most (68.6%) of the outbreak farms were detected in 2015, only the data from 2015 was used in this study. The first two-stage algorithm performs the Knox test, which established a threshold of 7 days and identified 11 major clusters in the six counties of southwestern Taiwan, followed by the standard deviational ellipse (SDE) method implemented on each cluster to reveal the transmission direction. The second algorithm applies scan likelihood ratio statistics followed by AGC index to visualize the dynamic changes of the local aggregation pattern of disease clusters at the regional level. Compared to the one-stage aggregation approach, Knox-based and AGC mapping were more sensitive in small-scale spatio-temporal clustering.

Objective To determine whether horses with Corynebacterium pseudotuberculosis infections that were examined at a veterinary medical teaching hospital between July 1, 1992, and June 30, 1994 had patterns of temporal or spatial clustering. Animals 134 case and 800 control horses randomly selected from all non-case horses admitted during the study period. Procedures Admission date and geographic location were determined. Scan, Cuzick &amp; Edwards’, and Knox tests were applied to determine whether case horses had patterns of temporal or spatial clustering. Results For all windows ≥ 3 days (134 case horses) and ≥ 7 days (subset of 69 case horses), results of the Scan test were significant. Results of Cuzick &amp; Edwards’ test were significant for all data sets. A significant spatial cluster of case horses was observed for October, November, and December 1992. Results of the Knox test were significant for temporal intervals between 7 and 56 days and spatial intervals between 4.3 and 6.5 km. Higher Knox(x) proportions were observed for temporal intervals of 0 to 7, 8 to 14, 22 to 28, and 29 to 35 days. Conclusions Significant spatial and temporal clustering of horses with C pseudotuberculosis infection was detected. Clinical Relevance Analysis of the results strongly indicates that this disease is directly or indirectly (ie, short distance and time) transmitted. In addition, data analyses indicated an incubation period of 3 to 4 weeks. The disease could be transmitted through horse-to-horse contact or from infected to susceptible horses via insects, other vectors, or contaminated soil. (Am J Vet Res 1999;60:284–291)

Type 2 diabetes mellitus (T2DM) and hypertension (HT) often coexist and contribute to left atrial (LA) functional abnormalities. The aim of the present study was to explore whether there is a potential interaction effect between T2DM and HT on LA function. A total of 135 patients (45 with T2DM only, 45 with HT only, and 45 with both T2DM and HT) were enrolled and compared to 45 age- and sex-matched controls. LA volume fraction, including LA ejection fraction (LAEF), LA expansion index (LAEI), LA passive emptying fraction (LAPEF), and LA active emptying fraction (LAAEF), and strain parameters, including LA reservoir longitudinal strain (LASr), LA conduit longitudinal strain (LAScd), and LA contraction longitudinal strain (LASct), were obtained using three-dimensional echocardiography (3DE). Patients with T2DM had significantly more impaired LA reservoir and conduit functions compared to those without T2DM (P<0.05), and patients with HT had a significantly more impaired LA reservoir function, conduit function, and booster pump function compared to those without HT (P<0.05). Two-way analysis of variance showed that there were significant additive interaction effects between T2DM and HT with respect to LASr (PT2DM + HT =0.002) and LAScd (PT2DM + HT =0.001). Generalized linear model demonstrated that T2DM + HT had a greater relative contribution than either T2DM or HT alone to the LA strain indexes, even after adjustment for other confounders (LASr, βT2DM + HT =-3.931, 95% CI: -6.237 to -1.624, P=0.001; LAScd, βT2DM + HT=-3.781, 95% CI: -5.653 to -1.908, P<0.001). Both T2DM and HT had an adverse effect on LA function. The coexistence of both conditions further impaired LA performance in an additive interaction fashion.

Despite advances in the medical management of type 1 diabetes mellitus (T1DM), for many, glycaemic control remains substandard. Other factors are clearly important in determining success, or lack thereof, with diabetes management. With this in mind, we have investigated whether family CHAOS may provide a novel tool to identify when environmental confusion could impact on diabetes management and subsequent glycaemic control. A case-control study of children and adolescents with established T1DM and age-/sex-matched controls was conducted. Demographic information, both maternal and paternal CHAOS scores, and HbA1c were collected. Statistical analysis was undertaken to explore associations between T1DM and CHAOS and between CHAOS and HbA1c. Data on 65 children with T1DM and 60 age-/sex-matched controls were obtained. There was no evidence of group differences for maternal CHAOS (p=0.227), but paternal CHAOS scores were higher for the T1DM group (p=0.041). Greater maternal and paternal CHAOS scores were both associated with higher HbA1c (p≤0.027). The maternal association remained after controlling for diabetes duration, SMBG frequency, and insulin therapy. In children with T1DM, there appears to be a negative association between increased environmental confusion, as rated by CHAOS, and glycaemic control. In addition, when compared to controls, fathers of children and adolescents with T1DM appear to experience CHAOS differently to mothers. These findings contribute to the growing body of literature exploring psychosocial factors in T1DM. Continuing efforts are required to fully understand how the family and psychosocial environment interact with diabetes to impact on long-term health outcomes.

Endocrinology for the hepatologist.

Introduction: Patients with type 1 diabetes mellitus (T1DM) have a higher lifetime fracture risk. The rising obesity incidence in T1DM is causing hybrid diabetes. There is scarce data of bone health and body composition in T1DM from India. This South Indian study compared bone health and body composition of patients with T1DM to healthy age- and sex-matched controls. Methods: Fifty-one adults with T1DM and 52 healthy controls were enrolled and underwent dual-energy X-ray absorptiometry (DXA) for bone mineral density (BMD) and body composition. Bone turnover markers—C-terminal telopeptide of type 1 collagen (CTX) and procollagen type 1 N pro-peptide (P1NP), parathormone (PTH) and 25 hydroxy-vitamin D levels were measured in patients. Results: The mean age of patients with T1DM was 27 years, the median duration of illness was 15 years and the median glycated haemoglobin (HbA1c) was 8%. BMD at the lumbar spine and hip were similar among patients and healthy controls. Patients with T1DM had decreased levels of CTX compared to controls (cases 0.347 (0.233–0.463) ng/mL, controls 0.440 (0.320–0.684) ng/mL, P = 0.005), whereas both had similar levels of P1NP. One-third of patients with T1DM were obese. Patients with T1DM with disease duration more than 10 years showed higher abdomen fat% (&lt;10 years 20.4% [14–39.5], 10–20 years 37.2% [30.1–41.9], &gt;20 years 41.5% [36.4–42.7] P = 0.013) and trunk fat% (&lt;10 years 22.6% [14.8–37.2], 10–20 years 36.7% [30.1–40.5], &gt;20 years 37.6% [33.55–40.5]). Conclusion: Young adults with T1DM have normal BMD and low bone resorption markers compared to healthy controls, whereas truncal obesity increases with a longer duration of illness. This may indicate a change in T1DM bone health and body composition characters, probably due to better glycaemic management.

Objective To explore the association between sleep quality and the increasing risk of type 2 diabetes mellitus (T2DM). Methods A total of 771 patients aged 25-70 years living in Xuzhou City of Jiangsu Province for at least 5 years were enrolled for the survey of risk factor related noninfectious chronic disease in 2013. In this investigation, those who suffered from other types of diabetes, neuropathy, other endocrine disease, cardiovascular, renal and hepatic dysfunction, dyspnea or cancer were excluded. To reduce the influence of confounding factors, another 771 participants were enrolled as controls. Each case was arranged to have a control who was matched in age (difference not more than 3 years), gender, residence and family history. All the participants were interviewed with self-designed questionnaire, and sleep quality was measured by Pittsburgh Sleep Quality Index (PSQI) questionnaire. Student's t test, Chi-square and multivariate logistic regression were used for data analysis. Results The PSQI score in the T2DM patients vs. the controls were 5.15±2.40 vs. 2.71±1.93 (t=21.96, P<0.01). The scores of sleep-related factors, including subjective poor sleep quality, bedtime resistance, short sleep duration, sleep efficiency, sleep disturbance, use of sleep medication and daytime dysfunction, of the T2DM patients were higher than those of the controls. The proportion of sleep related behaviors of the T2DM patients was higher, except for early awakening, cold feeling and nightmare. Poor sleep quality was associated with the increasing risk of T2DM (odds ratio 2.06, 95% CI 1.69-2.52). In multivariate logistic regression, when adjusted for confounding factors, the risk of T2DM was still increased (odds ratio 1.72, 95% CI 1.62-1.83). Sleep-related factors (e.g. subjective poor sleep quality, bedtime resistance, short sleep duration, sleep efficiency and sleep disturbance) were correlated with the risk of T2DM (odds ratio was 3.34, 1.63, 1.10, 1.87 and 3.89, respectively). Conclusion Low quality of sleep may be strongly associated with an increased risk of T2DM. Key words: Diabetes mellitus, type 2; Sleep initiation and maintenance disorders; Risk factors; Case-control studies