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.

Purpose The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies. Method The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes. Results The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care. Conclusion In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.

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