Significant Clusters Research Articles

Pollen variability in Quercus L. species and relative systematic implications

This study aims to address one of the challenges related to the complexity of the Quercus L. genus, that is the identification of structural elements favouring the systematic identification of the oak pollen. Thus, in this contribution, we explored the variation of morphometric and chemical parameters in pollen samples collected from 47 different Quercus species and hybrids. Several qualitative (e.g., outline in polar view, class, aperture structures) and quantitative (e.g., diameter, exine and sporoderm thickness, autofluorescence, content in proteins and plant metabolites) features were evaluated by optic microscopy and spectrophotometric assays. Statistical analyses were also carried out to assess significant correlations and clustering effects among the studied taxa, based on phenotypical and biochemical data, to identify the parameters which could be useful for taxonomic discrimination at inter- and intra-specific level. Only few morphological traits showed the potentiality to be diagnostic, such as pollen diameter and outline in polar view. The intensity of pollen autofluorescence varied among the samples but it did not seem to correlate with protein, carotenoid, phenolic and flavonoid content. However, differences in protein and carotenoid levels were detected, suggesting them as possible taxonomic discriminants for oak pollen. Thus, our work represents a step forward in understanding morphology and biochemistry of oak pollen, constitutes an experimental set-up applicable in future systematic studies on other genera, and opens new perspectives for further molecular investigations on Quercus species.

Identifying influential nodes through an improved k-shell iteration factor model

Measuring the importance of nodes is a rapidly developing field in network science, with centrality measures playing a critical role in various applications, including epidemic control, influence maximization, and network integration. The commonly used coreness calculated by the k-core decomposition process for identifying influential spreaders has limitations, which fail to consider the topological position of nodes and lead to low node discrimination. To address these issues, we propose a novel approach called the structural iteration factor ranking method to identify the influential nodes in complex networks, which considers the remaining structural properties when implementing the k-core decomposition. Our method considers both local and global hierarchy information and selects the neighbor information attribute to avoid problems with overlapping neighbor information propagation caused by the significant clustering coefficient of networks. We consider ground-truth influence ranking based on the Susceptible–Infected–Recovered model, on which the performance of the proposed ranking method is verified. Our proposed method outperforms previous methods in calculating monotonicity, similarity, and accuracy. It provides a more comprehensive evaluation of nodes and enables more in-depth identification of critical nodes. These findings imply that considering the remaining structural properties when implementing the k-core decomposition dominates the spreading strength of nodes and catches more details for ranking the node influence in complex networks.

Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

PurposeDespite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development. MethodsWe performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters. ResultsThe most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters. ConclusionsResults provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.

Nigerian Maritime

Abstract Maritime industry clustering has in recent years gained attention as a tool for strategic global and regional economic competitiveness policies, owing to their co-location synergies and agglomeration economies. The Nigerian maritime sector is known to have a large industry, which comprises shipping and logistics firms, shipyards, ship consultants and a suite of other firms, institutions and associations with maritime functions. However, nothing is known about the structural characteristics and environmental dynamics of this significant cluster. This article, capitalising on a critical and meta-analytic review of studies and conceptual modelling, explores for the first time the Nigerian maritime industry from a cluster perspective, underpinned by the foundational competitiveness of Nigerian microeconomic performance. It further evaluates broadly the quality of the Nigerian business environment, highlighting the challenges and opportunities for strategic foresight in decision-making. Targeted recommendations predicated on cluster-specific characteristics are provided to improve cluster competitiveness.

Hotspot Analysis of Dengue Hemorrhagic Fever in Ho Chi Minh City, Vietnam

Background: Dengue Hemorrhagic Fever (DHF) in Vietnam remains a serious emerging arboviral disease, which generates significant concerns among international health authorities. Incidence rates of DHF have increased significantly during the last few years in many provinces and cities, especially Ho Chi Minh. The purpose of this study is to carried out a a study on analysis of hotspots of DHF in the Ho Chi Minh city. Methods: The Getis-Ord’s statistic-based hotspot analysis was employed to study spatial patterns (hotspots and coldspot) of DHF incidence. Results: a case study from a dataset of DHF collected in Ho Chi Minh city has indicated that a total of four hotspots of DHF were successfully detected. These hotspots were identified in Hoc Mon, District 12, Binh Tan, and District 7. In addition, 18 districts were not statistically significant at the level of 0.05. Conclusions: There was evidence for the existence of statistically significant clusters of DHF in Ho Chi Minh. The study results prove the effectiveness of Getis-Ord’s statistic-based hotspot analysis in studies of DHF. Findings in this study serves as a starting point for further research into the social and environmental factors influencing spatial patterns of DHF. Keywords: Hotspot analysis and Coldspot analysis, Dengue Hemorrhagic Fever, Getis-Ord’s G_i^* statistic, Ho Chi Minh, Vietnam.

Expression and Predictive Significance of FHL1 and SLIT3 in Surgically Resected Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common type of lung cancer. However, predictive biomarkers for early efficacy and prognosis evaluation in patients with surgically resected LUAD are not completely explained. Differentially expressed genes (DEGs), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were identified by RNA sequencing (RNA-Seq) between thirteen LUAD tissues and five normal lung tissues. The expression of DEGs was confirmed by qRT-PCR and a validated cohort from GEPIA. Protein-protein interaction (PPI) network of the top 5% DEGs was constructed by STRING and visualized in Cytoscape. Immunofluorescence results were acquired from clinical specimens from LUAD patients. The expression of FHL1 was analyzed by ImageJ. Survival analysis was performed using the GEPIA dataset. Consistent with the RNA-Seq data, validation of DEGs expression by qRT-PCR and GEPIA cohort showed that FHL1 and SLIT3 were down-regulated in LUAD patient tissues compared with non-tumor tissues. Moreover, FHL1 was significantly reduced in LUAD cell lines compared to the bronchial epithelium cell line (P < 0.01). However, SLIT3 was elevated in A549 and H1299 cells (wide type EGFR) (P < 0.05) while decreased in HCC827 and PC9 cells (mutant EGFR) compared to BESA-2B cells (P < 0.01). PPI network revealed the most significant cluster with 10 nodes and 43 edges. Immunofluorescent staining also showed that the expression of FHL1 was lower in LUAD tissues compared with that in normal lung tissues (P < 0.01). The expressions of SLIT3 and FHL1 were positively correlated. Specifically, the higher expression level of SLIT3 and FHL1 independently predicted a better prognosis (P < 0.01 or P < 0.05). Our findings provide two novel candidates, FHL1 and SLIT3, for prognostic evaluation and treatments after surgery.

Can you find it? Novel oddity detection task for the early detection of Alzheimer's disease.

We aimed to develop a measure to specifically assess the functioning of the perirhinal cortex (PRC), a brain structure affected very early in Alzheimer's disease (AD) pathology. In this novel task, participants were shown arrays of six complex figures and had to identify the "odd-one." The pilot study included 50 normal controls (NCs) and 50 patients in very early stages of AD. Participants completed the task and received MRI scanning. Best differentiating items were determined and applied in a validation study including 25 NCs, 27 early-stage AD patients, and 26 patients with major depression. Logistic regression models investigated if task performance predicted group membership. Task performance was then related to whole-brain gray matter integrity. As proof of concept, cortical thickness values of four regions of interest (ROIs; e.g., medial PRC and entorhinal cortex [ERC]) were compared between the groups. The associations of task performance and cortical thickness of the ROIs were investigated using linear models. Task performance showed good discriminative ability between early-stage AD patients and NCs. Whole-brain analyses revealed four significant clusters (p < .001) with peak voxels in parahippocampal regions including PRC and ERC. ROI analyses showed distinctly reduced cortical thickness in the AD group compared to both other groups in the medial PRC and ERC (p ≤ .001). Task performance modeled by ROI cortical thickness did not achieve significant results. Although further validation is needed, especially with age-matched participant groups, these findings indicate that the task detects early cognitive impairment related to AD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sporadic Shiga Toxin-Producing Escherichia coli-Associated Pediatric Hemolytic Uremic Syndrome, France, 2012-2021.

Shiga toxin-producing Escherichia coli-associated pediatric hemolytic uremic syndrome (STEC-HUS) remains an important public health risk in France. Cases are primarily sporadic, and geographic heterogeneity has been observed in crude incidence rates. We conducted a retrospective study of 1,255 sporadic pediatric STEC-HUS cases reported during 2012-2021 to describe spatiotemporal dynamics and geographic patterns of higher STEC-HUS risk. Annual case notifications ranged from 109 to 163. Most cases (n = 780 [62%]) were in children <3 years of age. STEC serogroups O26, O80, and O157 accounted for 78% (559/717) of cases with serogroup data. We identified 13 significant space-time clusters and 3 major geographic zones of interest; areas of southeastern France were included in >5 annual space-time clusters. The results of this study have numerous implications for outbreak detection and investigation and research perspectives to improve knowledge of environmental risk factors associated with geographic disparities in STEC-HUS in France.

Association of Intrinsic Functional Connectivity between the Locus Coeruleus and Salience Network with Attentional Ability.

The locus coeruleus (LC) is a brainstem region associated with broad neural arousal because of norepinephrine production, but it has increasingly been associated with specific cognitive processes. These include sustained attention, with deficits associated with various neuropsychological disorders. Neural models of attention deficits have focused on interrupted dynamics between the salience network (SAL) with the frontoparietal network, which has been associated with task-switching and processing of external stimuli, respectively. Conflicting findings for these regions suggest the possibility of upstream signaling leading to attention dysfunction, and recent research suggests LC involvement. In this study, resting-state functional connectivity and behavioral performance on an attention task was examined within 584 individuals. Analysis revealed significant clusters connected to LC activity in the SAL. Given previous findings that attention deficits may be caused by SAL network switching dysfunctions, findings here further suggest that dysfunction in LC-SAL connectivity may impair attention.

Identification of cognitive load-dependent activation patterns using working memory task-based fMRI at various levels of difficulty

Working memory, which is regarded as the foundation of cognitive processes, is a system that stores, processes, and manipulates information in short intervals of time that are actually needed for daily functioning. This study aimed to assess the brain activity of healthy controls (HC) while performing the N-back task, which is one of the most popularly used tests for evaluating working memory along with functional magnetic resonance imaging (fMRI). In this regard, we collected fMRI data from right-handed individuals in a 3.0 T scanner during the Persian version of the visual variant N-back task performance with three levels of complexity varied throughout the experiment (1, 2, and 3-back conditions) to increase the cognitive demands. The statistical parametric mapping (SPM12) software was used to analyze fMRI data for the identification of cognitive load-dependent activation patterns based on contrast images obtained from different levels of task difficulty. Our findings showed that as cognitive complexity increased, the number of significant activation clusters and cluster extent increased in several areas distributed in the cerebellum, frontoparietal lobes, insula, SMA, and lenticular nucleus, the majority of which are recognized for their role in working memory. Furthermore, deactivation patterns during 1-, 2-, and 3-back vs. 0-back contrasts revealed significant clusters in brain regions that are mostly described as being part of the default mode network (DMN). Based on previous research, our results supported the recognized involvement of the mentioned cortical and subcortical areas in various types or levels of N-back tasks. This study found that altering activation patterns by increasing task difficulty could aid in evaluating the various stages of cognitive dysfunction in many brain diseases such as multiple sclerosis (MS) and Alzheimer's disease by comparing controls in future studies to apply early appropriate treatment strategies.

Open innovation and firm performance in emerging economies: a bibliometric and TCCM analysis review

PurposeCompared to their counterparts in developed economies, businesses established in emerging economies continuously struggle due to resource and time constraints. Open innovation (OI) allows these firms to bridge the gap and advance towards technological advancements; however, the scholarly knowledge on the subject is not systematized. Thus, this study synthesizes the extant literature, proposes a framework and highlights future research avenues for domain advancements.Design/methodology/approachBased on the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) framework, the study evaluates 79 journal publications from Scopus and Web of Science (WoS). The bibliometric analysis highlights annual publication trends and research clusters, whereas TCCM analysis provides deep analysis into applied theories, context and characteristics, i.e. OI–performance linkage, barriers, mediators and moderators, followed by employed methodologies in the domain.FindingsThe bibliometric results showcase a rising publication trend, significant research clusters and trends, whereas the content analysis via TCCM framework identifies theories, contexts and methodologies employed in the domain. In terms of characteristics, the OI–performance relation and barriers at diverse levels, followed by the moderating and mediating mechanism, are further explained in detail.Originality/valueThis is the first study to examine OI and firm performance in the context of emerging economies and employ rigorous frame-based bibliometric and content analysis measures, establishing the foundations for a comprehensive understanding.

Targeting of insulin receptor endocytosis as a treatment to insulin resistance

BackgroundInsulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for degradation or for re-use. ObjectivesOur goal was to discover insulin-resistance-related genes that play key roles in endocytosis which could serve as potential biological targets to enhance insulin sensitivity. MethodsThe gene mutations related to insulin resistance were elucidated from ClinVar. These were used as the seed set. Using the GeneFriends program, the genes associated with this set were elucidated and used as an enriched set for the next step. The enriched gene set network was visualized by Cytoscape. After that, using the VisANT program, the most significant cluster of genes was identified. With the help of the DAVID program, the most important KEGG pathway corresponding to the gene cluster and insulin resistance was found. Eleven genes part of the KEGG endocytosis pathway were identified. Finally, using the ChEA3 program, seven transcription factors managing these genes were defined. ResultsThirty-two genes of pathogenic significance in insulin resistance were elucidated, and then co-expression data for these genes were utilized. These genes were organized into clusters, one of which was singled out for its high node count of 58 genes and low p-value (p = 4.117 × 10−7). DAVID Pathways, a functional annotation tool, helped identify a set of 11 genes from a single cluster associated with the endocytosis pathway related to insulin resistance. These genes (AMPH, BIN1, CBL, DNM1, DNM2, DNM3, ITCH, SH3GL1, SH3GL2, SH3GL3, and SH3KBP1) are all involved in either clathrin-mediated endocytosis of the insulin receptor (IR) or clathrin-independent endocytosis of insulin-resistance-related G protein-coupled receptors (GPCR). They represent prime therapeutic targets to improve insulin sensitivity through modulation of transmembrane cell signaling. Using the ChEA3 database, we also found seven transcription factors (REST, MYPOP, CAMTA2, MYT1L, ZBTB18, NKX6–2, and CXXC5) that control the expression of these 11 genes. Inhibiting these key transcription factors would be another strategy to downregulate endocytosis. ConclusionWe believe that delaying removal of insulin receptors from the cell surface would prolong signaling of glucose uptake and counteract the symptoms of insulin resistance.

Genetic basis and spatial distribution of glucose-6-phosphate dehydrogenase deficiency in ecuadorian ethnic groups: a malaria perspective

BackgroundGlucose-6-phosphate dehydrogenase deficiency (G6PDd) is an X-linked disorder affecting over 400 million people worldwide. Individuals with molecular variants associated with reduced enzymatic activity are susceptible to oxidative stress in red blood cells, thereby increasing the risk of pathophysiological conditions and toxicity to anti-malarial treatments. Globally, the prevalence of G6PDd varies among populations. Accordingly, this study aims to characterize G6PDd distribution within the Ecuadorian population and to describe the spatial distribution of reported malaria cases.MethodsMolecular variants associated with G6PDd were genotyped in 581 individuals from Afro-Ecuadorian, Indigenous, Mestizo, and Montubio ethnic groups. Additionally, spatial analysis was conducted to identify significant malaria clusters with high incidence rates across Ecuador, using data collected from 2010 to 2021.ResultsThe A- c.202G > A and A- c.968T > C variants underpin the genetic basis of G6PDd in the studied population. The overall prevalence of G6PDd was 4.6% in the entire population. However, this frequency increased to 19.2% among Afro-Ecuadorian people. Spatial analysis revealed 12 malaria clusters, primarily located in the north of the country and its Amazon region, with relative risks of infection of 2.02 to 87.88.ConclusionsThe findings of this study hold significant implications for public health interventions, treatment strategies, and targeted efforts to mitigate the burden of malaria in Ecuador. The high prevalence of G6PDd among Afro-Ecuadorian groups in the northern endemic areas necessitates the development of comprehensive malaria eradication strategies tailored to this geographical region.

RNA-seq Analysis Reveals Potential Synergic Effects of Acetate and Cold Exposure on Interscapular Brown Adipose Tissue in Mice.

Brown adipose tissue (BAT) exhibits remarkable morphological and functional plasticity in response to environmental (e.g., cold exposure) and nutrient (e.g., high-fat diet) stimuli. Notably, a number of studies have showed that acetate, the main fermentation product of dietary fiber in gut, profoundly influences the differentiation and activity of BAT. However, the potential synergic or antagonistic effects of acetate and cold exposure on BAT have not been well examined. In the present study, the C57BL/6J mice were treated with acetate at the systemic level before a short period of cold exposure. Physiological parameters including body weight, blood glucose, and Respiratory Exchange Ratio (RER) were monitored, and thermal imaging of body surface temperature was captured. Moreover, the transcriptome profiles of interscapular BAT were also determined and analyzed afterwards. The obtained results showed that acetate treatment prior to cold exposure could alter the gene expression profile, as evidenced by significant differential clusters between the two groups. GO analysis and KEGG analysis further identified differentially expressed genes being mainly enriched for a number of biological terms and pathways related to lipid metabolism and brown adipose activity such as "G-protein-coupled receptor activity", "cAMP metabolic process", "PPAR signaling pathway", and "FoxO signaling pathway". GSEA analysis further suggested that activation status of key pathways including "PPAR signaling pathway" and "TCA cycle" were altered upon acetate treatment. Taken together, our study identified the potential synergistic effect of acetic acid with cold exposure on BAT, which highlighted the positive dietary and therapeutic aspects of acetate.

Spatiotemporal Distribution and Driving Force Analysis of the Ecosystem Service Value: A Typical Case Study of the Coastal Zone, Eastern China

Identifying and assessing the drivers of change in ecosystem service value (ESV) is critical for integrated management and human well-being in coastal zone areas. This paper took a typical coastal zone in eastern China as the research object. Based on five periods of remote sensing monitoring data of land use status from 2000 to 2020, the ESV of Yancheng was estimated by adopting the equivalence factor method. Spatial statistical analysis and GeoDetector were applied to reveal the heterogeneous features of ESV and its driving mechanism. The results suggested that: (1) From 2000–2020, each land use type in the study area changed to different degrees, among which the most drastic change was in the construction land, which increased continuously by 962.69 km2, the cultivated land area decreased continuously by 784.1 km2, and the area of water body decreased by 163.34 km2. (2) ESV experienced a process of increasing and then decreasing, from 547.84 × 108 yuan to 570.86 × 108 yuan and then decreasing to 507.62 × 108 yuan, with farmland ecosystems having the largest ESV, accounting for more than 60%. Regulatory services were the core function of ecosystem services, accounting for more than 60%. (3) There was significant spatial-temporal differentiation in ESV, with extremely low ESV expanding in rapidly developing urbanized areas. The ESV distribution exhibited significant spatial autocorrelation and local spatial clustering, with the gravity center showing a general trend from north to southwest. (4) The ESV spatial and temporal evolutionary characteristics were the result of a multifactorial integration of land use, topography, socio-economics, and climate factors. The explanatory power of each factor in descending order was LDI &gt; DEM &gt; POP &gt; GDP &gt; RAI &gt; TEM, and any two-factor interaction had higher explanatory power than the single factor.

Patent analysis based technology innovation assessment with the lens of disruptive innovation theory: A case of blockchain technological trajectories

The technological innovation period has reduced drastically in the current era giving rise to constantly new disruptive technologies, which may seem discreet; however, their evolution is also derived from the previous technological path. This study aims to identify the technology trajectory and evolution phases of disruptive technology, Blockchain, with respect to its predecessor technologies. Within the context of disruptive innovation theory, patent citation analysis employing the key-route main path method was utilized for this study. The data collection was based on keywords and IPC codes to retrieve US patents from the webpat database. The data obtained included 10,919 initial patents and 6206 final patents after simple family merge. The results acquired ranged from the year 1974 to 2021. The study identified five significant technology clusters based on the key-route main path analysis and framed the evolution path of the technology. The findings reveal the technological path dependence and knowledge flow of technological innovation. The novelty of this study lies in its approach to mapping DI theory characteristics with patent analysis to identify the path dependence of disruptive innovations, which aids researchers and decision-makers in understanding and assessing their innovation strategies.

Space-time clustering of COVID-19 cases in the United States veteran population

To assess the distribution and clustering of coronavirus disease 2019 (COVID-19) testing and incidence over space and time, U.S. Department of Veteran's Affairs (VA) data were used to describe where and when veterans experienced highest proportions of test positivity. Data for 6,342,455 veterans who utilized VA services between January 1, 2018, and September 30, 2021, were assessed for COVID-19 testing and test positivity. Testing and positivity proportions by county were mapped and focused-cluster tests identified significant clustering around VA facilities. Spatial cluster analysis also identified where and when veterans experienced highest proportions of test positivity. Within the veterans study population and our time window, 21.3% received at least one COVID-19 test, and 20.4% of those tested had at least one positive test. There was statistically significant clustering of testing around VA facilities, revealing regional variation in testing practices. Veterans experienced highest test positivity proportions between November 2020 and January 2021 in a cluster of states in the Midwest, compared to those who received testing outside of the identified cluster (RR: 3.45). Findings reflect broad regional trends in COVID-19 positivity which can inform VA policy and resource allocation. Additional analysis is needed to understand patterns during Delta and Omicron variant periods.

ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations

Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.

Space-time clusters of cardiovascular mortality and the role of heatwaves and cold spells in the city of São Paulo, Brazil

The effects extreme air temperature events are related with an increase in cardiovascular mortality among vulnerable groups worldwide. Therefore, we identify spatiotemporal mortality clusters associated with diseases of the cardiovascular system among people ≥ 65 years in São Paulo, from 2006 to 2015, and investigate whether high-risk mortality clusters occurred during or following extreme air temperature events. To detect the clusters, we used daily mortality data and a retrospective space-time scan analysis with a discrete Poisson model. Extreme air temperature events were defined by daily mean temperatures, below the 10th percentile for cold spells and above the 90th percentile for heatwaves, with two or more consecutive days. We found statistically significant high-risk mortality clusters located in the peripheral areas. The spatiotemporal clusters of risk areas for cardiovascular and ischemic heart disease occurred during or following cold spell events, whereas those for stroke and ischemic stroke events were related to heatwaves.

Postmenopausal bleeding after COVID-19 vaccination

There is a growing literature base regarding menstrual changes following COVID-19 vaccination among premenopausal people. However, relatively little is known about uterine bleeding in postmenopausal people following COVID-19 vaccination. This study aimed to examine trends in incident postmenopausal bleeding diagnoses over time before and after COVID-19 vaccine introduction, and to describe cases of new-onset postmenopausal bleeding after COVID-19 vaccination. For postmenopausal bleeding incidence calculations, monthly population-level cohorts consisted of female Kaiser Permanente Northwest members aged ≥45 years. Those diagnosed with incident postmenopausal bleeding in the electronic medical record were included in monthly numerators. Members with preexisting postmenopausal bleeding or abnormal uterine bleeding, or who were at increased risk of bleeding due to other health conditions, were excluded from monthly calculations. We used segmented regression analysis to estimate changes in the incidence of postmenopausal bleeding diagnoses from 2018 through 2021 in Kaiser Permanente Northwest members meeting the inclusion criteria, stratified by COVID-19 vaccination status in 2021. In addition, we identified all members with ≥1 COVID-19 vaccination between December 14, 2020 and August 14, 2021, who had an incident postmenopausal bleeding diagnosis within 60 days of vaccination. COVID-19 vaccination, diagnostic procedures, and presumed bleeding etiology were assessed through chart review and described. A temporal scan statistic was run on all cases without clear bleeding etiology. In a population of 75,530 to 82,693 individuals per month, there was no statistically significant difference in the rate of incident postmenopausal bleeding diagnoses before and after COVID-19 vaccine introduction (P=.59). A total of 104 individuals had incident postmenopausal bleeding diagnosed within 60 days following COVID-19 vaccination; 76% of cases (79/104) were confirmed as postvaccination postmenopausal bleeding after chart review. Median time from vaccination to bleeding onset was 21 days (range: 2-54 days). Among the 56 postmenopausal bleeding cases with a provider-attributed etiology, the common causes of bleeding were uterine or cervical lesions (50% [28/56]), hormone replacement therapy (13% [7/56]), and proliferative endometrium (13% [7/56]). Among the 23 cases without a clear etiology, there was no statistically significant clustering of postmenopausal bleeding onset following vaccination. Within this integrated health system, introduction of COVID-19 vaccines was not associated with an increase in incident postmenopausal bleeding diagnoses. Diagnosis of postmenopausal bleeding in the 60 days following receipt of a COVID-19 vaccination was rare.