Geographic distribution of vestibular schwannomas in West Scotland between 2000-2015

Abstract

BackgroundAlthough the natural history of vestibular schwannomas (VS) has been previously studied, few studies have investigated associated epidemiological factors, primarily because of the lack of large available cohorts.ObjectiveThe objective of this study was to perform a multi-scale geographical analysis of the period prevalence of VS in West Scotland from 2000 to 2015.MethodsAdults diagnosed with sporadic VS were identified through the National Health Services of West Scotland database and geocoded to the unit postcode. To assess whether the cohort of VS cases could be pooled into a period prevalence measure, the locations of VS cases were analyzed by sex using Cross-L and Difference-K functions. VS period prevalence was examined at two aggregate spatial scales: the postcode district and a coarser scale of NHS Health Boards. The spatial structure of period prevalence within each level of spatial aggregation was measured using univariate global and local Moran’s I. Bivariate local Moran’s I was used to examine the between-scale variability in period prevalence from the postcode district level to the NHS Health Boards levels. Prior to spatial autocorrelation analyses, the period prevalence at the postcode district was tested for stratified spatial heterogeneity within the NHS Health Boards using Wang’s q-Statistic.ResultsA total of 512 sporadic VS were identified in a population of over 3.1 million. Between 2000 and 2015, VS period prevalence was highest within the NHS Health Boards of Greater Glasgow and Clyde, Ayrshire and Arran and the Western Isles. However, at the NHS scale, period prevalence exhibited no spatial autocorrelation globally or locally. At the district scale, Highland exhibited the most unusual local spatial autocorrelation. Bivariate local Moran’s I results indicated general stability of period prevalence across the postcode district to Health Boards scales. However, locally, some postcode districts in Greater Glasgow and Clyde, Ayrshire and Arran exhibited unusually low district to zone spatial autocorrelation in period prevalence, as did the southern parts of the Western Isles. Some unusually high period prevalence values between the postcode district to Health Board scale were found in Tayside, Forth Valley and Dumfries and Galloway.ConclusionGeographic variability in VS in West Scotland was identified in this patient population, showing that there are areas, even remote, with unusually high or low period prevalence. This can be partially attributed to links between primary and tertiary care. Potential genetic or environmental risk factors that may contribute to geographic variation in this disease within Scotland are also a possibility but require further investigation.

Association between socioeconomic indicators and geographic distribution of vestibular schwannomas in West Scotland: a 15-year review

“…The geographic analysis used to establish the geographic variability of vestibular schwannomas in West Scotland has been reported elsewhere. 15 Mapping and data preparation were conducted with ArcGIS desktop 10.4.1 (Redlands, Environmental Systems Research Institute, USA). The period prevalence was calculated as the number of vestibular schwannoma cases over the 15-year period, divided by the population for each aggregated spatial unit (district and zone).…”

“…Recent investigations have demonstrated the geographic variability of vestibular schwannomas in West Scotland, with areas of unusually high or low period prevalence of vestibular schwannomas. 15 Socio-economic risk factors may contribute to geographic variation in this disease within Scotland but socioeconomic inequalities in vestibular schwannomas have yet to be described in the UK.…”

Modelling the potential distribution of arbovirus vector Aedes aegypti under current and future climate scenarios in Taiwan, China

“…To avoid the possible confounding caused by spatial stratified heterogeneity (SSH), we calculated Wang's q‐Statistic by GeoDetector to test the presence of SSH in our sample . Town level occurrences were paired with integer codes representing the county level administrative district to which they belong . The q‐Statistic test indicates no significant stratified spatial heterogeneity exists ( q = 0.124; p = 0.728).…”

Higher Education Hot Spots: Using Local Indicators of Spatial Association

“…The latter is the default method for calculating pseudo p-values in ArcGIS and GeoDa, while the "localmoran" function in the "spdep" package in R returns standardized I i statistics and p-values (Bivand & Wong, 2018). As consensus among most spatial researchers is to shy away from distributional assumptions about I i , Caulley et al (2017) implemented a Monte Carlo simulation-based function for calculating local Moran' s I i in R. The next section in this chapter puts to practice methodological details discussed so far using administrative data from a college in southern California.…”

“…For k-NN = 9, a global Moran' s I index of 0.01 rejected the null hypothesis of spatial independence in total GPA variables at α = 0.05 based on 5,000 permutations (pseudo-p = 0.023). Though such a small I value might not warrant consideration of spatial autocorrelation in further estimation of regression models (i.e., global Moran' s I can range from −1 to 1), I nonetheless proceeded with calculating local Moran' s I i statistics from GPA variables using the Monte Carlo function outlined by Caulley et al (2017). Local Moran's I i Results.…”