ObjectivesResearch on spatial variability of the incidence of IgA vasculitis (IgAV) in children and its potential implications for elucidation of the multifactorial aetiology and pathogenesis is limited. We intended to observe spatial variability of the incidence of IgAV and IgA vasculitis-associated nephritis (IgAVN) using modern geostatistical methods, and hypothesised that their spatial distribution may be spatially clustered.MethodsPatients' data were retrospectively collected from 2009 to 2019 in five Croatian University Hospital Centres for paediatric rheumatology, and census data were used to calculate the incidence of IgAV. Using spatial empirical Bayesian smoothing, local Morans’ I and local indicator of spatial autocorrelation (LISA), we performed spatial statistical analysis.Results596 children diagnosed with IgAV were included in this study, of which 313 (52.52%) were male. The average annual incidence proportion was estimated to be 6.79 per 100 000 children, and the prevalence of IgAVN was 19.6%. Existence of spatial autocorrelation was observed in both IgAV and IgAVN; however, clustering distribution differed. While IgAV showed clustering in Mediterranean and west continental part around cities, IgAVN was clustered in the northern Mediterranean and eastern continental part, where a linear cluster following the Drava and Danube river was observed.ConclusionIgAV incidence in Croatia is similar to other European countries. Spatial statistical analysis showed a non-random distribution of IgAV and IgAVN. Although aetiological associations cannot be inferred, spatial analytical techniques may help in investigating and generating new hypotheses in non-communicable diseases considering possible environmental risk factors and identification of potential genetic or epigenetic diversity.
We aim at detecting stress in newborns by observing heart rate variability (HRV). The HRV features nonlinearities. Fractal dynamics is a usual way to model them and the Hurst exponent summarizes the fractal information. In our framework, we have observations of short duration, for which usual estimators of the Hurst exponent, like detrended fluctuation analysis (DFA), are not adapted. Moreover, we observe that the Hurst exponent does not vary much between stress and rest phases, but its decomposition in memory and underlying properties of the probability distribution leads to satisfactory diagnostic tools. This decomposition of the Hurst exponent is in addition embedded in a mean-reverting model. The resulting model is a mean-reverting fractional Lévy stable motion (FLSM). We estimate it and use its parameters as diagnostic tools of neonatal stress. Indeed, the value of the speed of reversion parameter is a significant indicator of stress. The evolution of both parameters in which the Hurst exponent is decomposed provides us with significant indicators as well. On the contrary, the Hurst exponent itself does not bear useful information.
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