Dynamic modeling of the Italians' attitude towards Covid-19.

We analyze repeated cross-sectional survey data collected by the Institute of Global Health Innovation, to characterize the perception and behavior of the Italian population during the Covid-19 pandemic, focusing on the period that spans from April 2020 to July 2021. To accomplish this goal, we propose a Bayesian dynamic latent-class regression model, that accounts for the effect of sampling bias including survey weights into the likelihood function. According to the proposed approach, attitudes towards covid-19 are described via ideal behaviors that are fixed over time, corresponding to different degrees of compliance with spread-preventive measures. The overall tendency toward a specific profile dynamically changes across survey waves via a latent Gaussian process regression, that adjusts for subject-specific covariates. We illustrate the evolution of Italians' behaviors during the pandemic, providing insights on how the proportion of ideal behaviors has varied during the phases of the lockdown, while measuring the effect of age, sex, region and employment of the respondents on the attitude toward covid-19.

Are empathy profiles and perceived social support associated with depressive and grief-related symptoms in suicide survivors?

OBJECTIVE: To investigate the link between empathy, perceived social support, and depressive and grieving symptoms in suicide survivors. METHODS: Scores on the Beck Depression Inventory (BDI), Inventory of Complicated Grief (ICG), Prolonged Grief Disorder (PGD), Interpersonal Reactivity Index (IRI), and the Social Support section of the Interpersonal Questionnaire were collected from 265 survivors. Relations were tested via multivariate regression models. RESULTS: Lower Perspective Taking (PT) was related with higher levels of BDI score, and higher Personal Distress (PD) was associated with higher BDI, ICG, and PGD scores. Higher levels of Social Support were related with higher BDI and ICG (but not PGD) scores. CONCLUSION: Empathic PD and PT, and perceived social support are differently associated with depression and grief-related symptoms. Empathy-focused psychotherapies and empowerment of social support may reduce symptoms in suicide survivors.

When does morbidity start? An analysis of changes in morbidity between 2013 and 2019 in Italy.

Morbidity is one of the key aspects for assessing populations' well-being. In particular, chronic diseases negatively affect the quality of life in the old age and the risk that more years added to lives are years of disability and illness. Novel analysis, interventions and policies are required to understand and potentially mitigate this issue. In this article, we focus on investigating whether in Italy the compression of morbidity is in act in the recent years, parallely to an increase of life expectancy. Our analysis rely on large repeated cross-sectional data from the national surveillance system passi, providing deep insights on the evolution of morbidity together with other socio-demographical variables. In addition, we investigate differences in morbidity across subgroups, focusing on disparities by gender, level of education and economic difficulties, and assessing the evolution of these differences across the period 2013-2019.

Projected t-SNE for batch correction.

MOTIVATION: Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data. RESULTS: The proposed methods are based on linear algebra and constrained optimization, leading to efficient algorithms and fast computation in many high-dimensional settings. Results on artificial single-cell transcription profiling data show that the proposed procedure successfully removes multiple batch effects from t-SNE embeddings, while retaining fundamental information on cell types. When applied to single-cell gene expression data to investigate mouse medulloblastoma, the proposed method successfully removes batches related with mice identifiers and the date of the experiment, while preserving clusters of oligodendrocytes, astrocytes, and endothelial cells and microglia, which are expected to lie in the stroma within or adjacent to the tumours. AVAILABILITY AND IMPLEMENTATION: Source code implementing the proposed approach is available as an R package at https://github.com/emanuelealiverti/BC_tSNE, including a tutorial to reproduce the simulation studies. CONTACT: aliverti@stat.unipd.it.

COMPOSITE MIXTURE OF LOG-LINEAR MODELS WITH APPLICATION TO PSYCHIATRIC STUDIES.

Psychiatric studies of suicide provide fundamental insights on the evolution of severe psychopathologies, and contribute to the development of early treatment interventions. Our focus is on modelling different traits of psychosis and their interconnections, focusing on a case study on suicide attempt survivors. Such aspects are recorded via multivariate categorical data, involving a large numbers of items for multiple subjects. Current methods for multivariate categorical data-such as penalized log-linear models and latent structure analysis-are either limited to low-dimensional settings or include parameters with difficult interpretation. Motivated by this application, this article proposes a new class of approaches, which we refer to as Mixture of Log Linear models (mills). Combining latent class analysis and log-linear models, mills defines a novel Bayesian approach to model complex multivariate categorical data with flexibility and interpretability, providing interesting insights on the relationship between psychotic diseases and psychological aspects in suicide attempt survivors.

Removing the influence of group variables in high-dimensional predictive modelling.

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors, or sampling bias. Without explicit adjustment, machine learning algorithms trained using these data can produce poor out-of-sample predictions which propagate these undesirable correlations. We propose a method to pre-process the training data, producing an adjusted dataset that is statistically independent of the nuisance variables with minimum information loss. We develop a conceptually simple approach for creating an adjusted dataset in high-dimensional settings based on a constrained form of matrix decomposition. The resulting dataset can then be used in any predictive algorithm with the guarantee that predictions will be statistically independent of the group variable. We develop a scalable algorithm for implementing the method, along with theory support in the form of independence guarantees and optimality. The method is illustrated on some simulation examples and applied to two case studies: removing machine-specific correlations from brain scan data, and removing race and ethnicity information from a dataset used to predict recidivism. That the motivation for removing undesirable correlations is quite different in the two applications illustrates the broad applicability of our approach.