A public early intervention approach to first-episode psychosis: Treated incidence over 7 years in the Emilia-Romagna region

AimTo estimate the treated incidence of individuals with first-episode psychosis (FEP) who contacted the Emilia-Romagna public mental healthcare system (Italy); to examine the variability of incidence and user characteristics across centres and years. MethodsWe computed the raw treated incidence in 2013-2019, based on FEP users aged 18-35, seen within or outside the regional program for FEP. We modelled FEP incidence across 10 catchment areas and 7 years using Bayesian Poisson and Negative Binomial Generalized Linear Models of varying complexity. We explored associations between user characteristics, study centre and year comparing variables and socioclinical clusters of subjects. ResultsThousand three hundred and eighteen individuals were treated for FEP (raw incidence: 25.3 / 100.000 inhabitant year, IQR: 15.3). A Negative Binomial location-scale model with area, population density and year as predictors found that incidence and its variability changed across centres (Bologna: 36.55; 95% CrI: 30.39-43.86; Imola: 3.07; 95% CrI: 1.61-4.99) but did not follow linear temporal trends or density. Centers were associated with different user age, gender, migrant status, occupation, living conditions and cluster distribution. Year was associated negatively with HoNOS score (R = -0.09, p < .001), duration of untreated psychosis (R = -0.12, p < .001) and referral type. ConclusionsThe Emilia-Romagna region presents a relatively high but variable incidence of FEP across areas, but not in time. More granular information on social, ethnic and cultural factors may increase the level of explanation and prediction of FEP incidence and characteristics, shedding light on social and healthcare factors influencing FEP

The socio-economical burden of schizophrenia: A simulation of cost-offset of early intervention program in Italy

Schizophrenia is associated with a high familiar, social and economic burden. During the recent years early and specific intervention for first psychotic episodes has been suggested to improve the long term outcome of the disease. Despite the promising results obtained so far, early intervention is still scarcely applied. One major problem arises from the translation of research findings into stakeholder policies. In fact very few analyses of cost reductions obtained with early intervention have been reported. In the present paper we present a simulation of direct cost reduction that can be obtained with early intervention programmes. We based our analysis on available data about schizophrenia care costs in Italy and the expected cost reduction with the use of early intervention. We observed that the increase in costs due to the more intensive early intervention is largely compensated by the reduction of inpatient admissions with a reduction of direct costs of 6.01%. Despite the apparently small economic gain, early ntervention offers more clinical and social benefits as it seems to be effective also in decreasing relapse rates, in improving the patients\u2019 quality of life and disability associated with psychosis and in increasing employment rates. Those indirect costs however are difficult to estimate and were not included in our model. In conclusion, our study supports the use of early intervention in schizophrenia, which could allow an outcome improvement with ower direct and indirect costs

A first episode psychosis case-control genetic association study

Background:GAP(genesandpsychosis) is a case-control studyof first episode psychosis conducted in London and Cambridge, which aims to identify genes conferring susceptibility to psychosis, and associated phenotypes including cognitive dysfunction and cerebral morphology. Methods:First episode psychosis cases have been recruited in South London and Maudsley NHS Trust and in Cambridge. A variety of demographic and clinical data have been collected. In a subset of these, neurocognitive assessments and MRIs have been performed. Samples have been taken for DNA, and in a subset for RNA and proteomic analysis. Genetic association analysis is being undertaken using a candidate gene approach. The genes chosen for the first wave of analysis include the current most promising candidates for suscept-ibility to psychosis (NRG1, dysbindin, DISC1, G72, etc) as well as candidates for susceptibility to cannabis misuse (COMT), cognitive dysfunction, dysregulation of brain morphology or susceptibility to bipolar disorder (e.g. LIS1), and candidates in the dopamine and serotonin neurotransmitter systems. Results:DNA has been collected from 302 patients to date. Of these, 72%aremale, and themeanage is 25 years; 187 areCaucasian; 115 are of black origin; and the rest are of other ormixed ethnicity. Genotyping is being undertaken in this sample and in matched controls. Conclusions:Data is being reported separately for a number of phenotypes forwhich there is already somedata. This presentationwill report the overall genetic association results

Stability in GRN inference

Reconstructing a gene regulatory network from one or more sets of omics measurements has been a major task of computational biology in the last twenty years. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in a ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We introduce here empirical stability, which is induced by variability of reconstruction as a function of data subsampling. By evaluating differences between networks that are inferred using different subsets of the same data we obtain quantitative indicators of the robustness of the algorithm, of the noise level affecting the data, and, overall, of the reliability of the reconstructed graph. We show that empirical stability can be used whenever no ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subsets, where the chosen metric is the Hamming-Ipsen-Mikhailov (HIM) distance evaluating dissimilarity of graph topologies with shared nodes. Operatively, the NetSI family is demonstrated here on synthetic and high-throughput datasets, inferring graphs at different resolution levels (topology, direction, weight), showing how the stability indicators can be effectively used for the quantitative comparison of the stability of different reconstruction algorithms

Congenital syphilis in Italy: a multicentre study

OBJECTIVE: To study the prevalence of congenital syphilis and its risk factors in Italy. STUDY DESIGN: Prospective study from 1 July 2006 to 30 June 2007. Data on mother-child pairs were collected for every syphilis seropositive mother. RESULTS: Maternal syphilis seroprevalence at delivery was 0.17%. 207 infants were born to 203 syphilis seropositive mothers. In 25 newborns it was possible to diagnose congenital syphilis (20/100,000 live births). Maternal risk factors included age <20 years, no antenatal care and no adequate treatment. The infected babies were more often preterm or weighed <2000 g at birth. DISCUSSION: Many syphilis seropositive mothers were foreign born but the risk of an infected newborn was not higher in foreign-born than in Italian seropositive women. The significant factors were lack of antenatal screening and inadequate maternal treatment. CONCLUSION: Syphilis is a re-emerging infection in Italy. Prevention strategies should include antenatal serological tests for all pregnant women and treatment for infected mothers