Estimations of the number people with mental diseases associated with toxoplasmosis and identification of risk factors by continent.

Strong evidence exists based on metanalysis of the relationship between toxoplasmosis and many psychiatric diseases: schizophrenia, bipolar disorder, and suicidal behavior. Herein, we estimate the number of cases based on the attributable fraction due to toxoplasmosis on these diseases. The population attributable fraction of mental disease associated with toxoplasmosis was 20,4% for schizophrenia; 27,3% for bipolar disorder; and 0,29% for suicidal behavior (self-harm). The lower and upper estimated number of people with mental disease associated with toxoplasmosis was 4'816.491 and 5'564.407 for schizophrenia; 6'348.946 and 7'510.118,82 for bipolar disorder; and 24.310 and 28.151 for self-harm; for a global total lower estimated number of 11'189.748 and global total upper estimated number of 13'102.678 people with mental disease associated with toxoplasmosis for the year 2019. According to the prediction through the Bayesian model of risk factors for toxoplasmosis associated with mental disease, these varied in importance geographically; thus, in Africa, the most important risk factor was water contamination and in the European region, the cooking conditions of meats. Toxoplasmosis and mental health should be a research priority given the enormous potential impact of reducing this parasite in the general population.

A mixed-effect model for positive responses augmented by zeros.

In this research article, we propose a class of models for positive and zero responses by means of a zero-augmented mixed regression model. Under this class, we are particularly interested in studying positive responses whose distribution accommodates skewness. At the same time, responses can be zero, and therefore, we justify the use of a zero-augmented mixture model. We model the mean of the positive response in a logarithmic scale and the mixture probability in a logit scale, both as a function of fixed and random effects. Moreover, the random effects link the two random components through their joint distribution and incorporate within-subject correlation because of the repeated measurements and between-subject heterogeneity. A Markov chain Monte Carlo algorithm is tailored to obtain Bayesian posterior distributions of the unknown quantities of interest, and Bayesian case-deletion influence diagnostics based on the q-divergence measure is performed. We apply the proposed method to a dataset from a 24 hour dietary recall study conducted in the city of São Paulo and present a simulation study to evaluate the performance of the proposed methods.