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"Spatial analysis"
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Influence of geolocation and ethnicity on the phenotypic expression of primary Sjögren's syndrome at diagnosis in 8310 patients: a cross-sectional study from the Big Data Sjögren Project Consortium
ObjectivesTo analyse the influence of geolocation and ethnicity on the clinical presentation of primary Sjögren's syndrome (SjS) at diagnosis.MethodsThe Big Data Sjögren Project Consortium is an international, multicentre registry designed in 2014. By January 2016, 20 centres from five continents were participating. Multivariable logistic regression analyses were performed.ResultsWe included 7748 women (93%) and 562 men (7%), with a mean age at diagnosis of primary SjS of 53 years. Ethnicity data were available for 7884 patients (95%): 6174 patients (78%) were white, 1066 patients (14%) were Asian, 393 patients (5%) were Hispanic, 104 patients (1%) were black/African-American and 147 patients (2%) were of other ethnicities. SjS was diagnosed a mean of 7 years earlier in black/African-American compared with white patients; the female-to-male ratio was highest in Asian patients (27:1) and lowest in black/African-American patients (7:1); the prevalence of sicca symptoms was lowest in Asian patients; a higher frequency of positive salivary biopsy was found in Hispanic and white patients. A north-south gradient was found with respect to a lower frequency of ocular involvement in northern countries for dry eyes and abnormal ocular tests in Europe (OR 0.46 and 0.44, respectively) and Asia (OR 0.18 and 0.49, respectively) compared with southern countries. Higher frequencies of antinuclear antibodies (ANAs) were reported in northern countries in America (OR=1.48) and Asia (OR=3.80) while, in Europe, northern countries had lowest frequencies of ANAs (OR=0.67) and Ro/La (OR=0.69).ConclusionsThis study provides the first evidence of a strong influence of geolocation and ethnicity on the phenotype of primary SjS at diagnosis.
Journal Article
Spatial and Spatio-temporal Bayesian Models with R - INLA
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
eBook
Applied spatial statistics for public health data
While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of \"data breaks\") Exercises based on data analyses reinforce concepts
eBook
Comparison of univariate and multivariate spatial scan statistics in detecting geographic clusters of croup cases in Alberta
Background
Croup is a common pediatric respiratory illness primarily affecting infants and toddlers. While understanding the geographic distribution and spatial clustering of croup healthcare encounters is crucial for public health planning and resource allocation, this area remains understudied. This study aimed to identify geographic clusters of croup healthcare encounters and areas of higher healthcare demand in Alberta, Canada, and compare univariate and multivariate spatial scan statistics using emergency department (ED) visits and physician claims data.
Methods
We analyzed administrative health data for children aged
2 years in 70 sub-regional health authorities (sRHAs) in Alberta from 2017/18 to 2022/23. Data were aggregated by sRHA and year. Kulldorff’s spatial scan statistics were used to identify croup clusters adjusted for sex distribution for each year separately. Analyses were conducted on each data source separately (univariate) and for both data sources simultaneously (multivariate).
Results
During the study period, there were 32,740 ED visits and 49,389 physician claims for croup, with males accounting for 63.5% and 62.2% of the encounters, respectively. Overall, 59.8% of patients with physician claims and 82.8% of ED patients (n=21,688) accessed both healthcare settings during the study period. Significant spatial clustering was consistently identified, with 1-4 clusters annually in univariate, and 2-5 clusters in multivariate analyses. Certain northern areas appeared most consistently in all years and methods. The COVID-19 pandemic year (2020/21) showed unique patterns with the highest relative risks. ED visits data demonstrated wider geographic coverage with consistent major metropolitan area involvement, while physician claims data showed frequent clustering in different metropolitan areas. Multivariate and univariate analyses showed overlapping but distinct findings, with multivariate analysis identifying two clusters where only physician claims data showed significantly higher case numbers than expected.
Conclusions
Significant geographic clustering of croup healthcare encounters exists in Alberta, with northern regions most consistently identified as areas of higher healthcare utilization. Both univariate and multivariate spatial scan statistics detected significant clusters, with multivariate analysis providing additional insights by simultaneously analyzing ED visits and physician claims data. The distinct clustering patterns between data sources indicate different healthcare utilization behaviours and demonstrate the value of multivariate approaches for comprehensive spatial epidemiological analysis.
Journal Article
Spatial transcriptomics at subspot resolution with BayesSpace
Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Here, we introduce BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. We benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, we show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. Our results illustrate BayesSpace’s utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.
BayesSpace increases the resolution of spatial transcriptomics by using neighborhood information.
Journal Article