Date: Monday, November 9, 2020
Session Type: Poster Session D
Session Time: 9:00AM-11:00AM
Background/Purpose: IgA vasculitis (IgAV) is the most common childhood vasculitis, which etiology seems to be related to the combination of genetic as well as environmental factors. The application of geostatistical analyses in medicine was mostly focused on the analysis of infectious diseases spreading showing the significance of spatiotemporal trends in diseases spreading. Unfortunately, there is only a small amount of research on the application of geostatistical analyses on rheumatic diseases. The aim of this research was to investigate whether geospatial analytical tools can be applied in characterizing the spatial distribution of rheumatic diseases, especially IgAV.
Methods: A retrospective database was created containing the data from patients diagnosed with IgAV in five tertiary hospitals in Croatia, over a ten years period between 2009 and 2019. The average annual incidence of IgAV based on the population census data from 2011. Descriptive choropleth maps were created to observe the spatial distributions of raw and Bayesian adjusted incidence data. Three geospatial methods: spatial-empirical Bayesian smoothing, local spatial autocorrelations assessed with Moran’s I, and local identifiers of spatial autocorrelations (LISA) were applied to make inferences about the spatial incidence distribution of IgaV.
Results: 596 patients, of which 52.52% male, and 47.48% females, were included in the study with a median age of 6.42 (4.42 – 8.84). The estimated average annual incidence was 7.47 with a 95% confidence interval between 6.88 and 8.98 per 100 000 children. The raw data showed the highest number of cases in the cities with a higher population count. However, spatial empirical Bayes smoothed average annual incidenes were clustered similarly around these areas. Three statistically significant clusters were found: two in the Mediterranean, and one in the continental part of Croatia. The obtained Morran’s I autocorrelation coefficient was 0.493, which showed significant positive spatial autocorrelation of IgAV. LISA analysis further identified those areas as statistically significant higher incidence clusters with an estimated average annual incidence above 13 per 100 000 children.
Conclusion: This study shows that IgAV exhibits spatial clustering features. While applying modern analytical methods on pediatric IgAV, this research shows the potential application of geostatistical analysis in identifying clusters in noncommunicable diseases that were previously not known. Further research is needed to investigate the importance and relationship of temporal trends in spatial clustering.
SUPPORT: Croatian Science Foundation project IP-2019-04-8822.
To cite this abstract in AMA style:Sapina M, Frkovic M, Sestan M, Srsen S, Ovuka A, Batnozic Varga M, Cekada N, Kramaric K, Brdaric D, Milas K, Gagro A, Jelusic M. Applied Geostatistics in Pediatric Rheumatology – Spatial Clustering of IgA Vasculitis [abstract]. Arthritis Rheumatol. 2020; 72 (suppl 10). https://acrabstracts.org/abstract/applied-geostatistics-in-pediatric-rheumatology-spatial-clustering-of-iga-vasculitis/. Accessed November 26, 2020.
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/applied-geostatistics-in-pediatric-rheumatology-spatial-clustering-of-iga-vasculitis/