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Background: The unwillingness to share contacts is one of the least explored aspects of the COVID-19 pandemic. Here we report the factors associated with resistance to collaborate on contact tracing, based on the results of a nation-wide survey conducted in Italy in January-March 2021. Methods and findings: The repeated cross-sectional on-line survey was conducted among 7,513 respondents (mean age 45.7, 50.4% women) selected to represent the Italian adult population 18–70 years old. Two groups were defined based on the direct question response expressing (1) unwillingness or (2) willingness to share the names of individuals with whom respondents had contact. We selected 70% of participants (training data set) to produce several multivariable binomial generalized linear models and estimated the proportion of variation explained by the model by McFadden R2, and the model's discriminatory ability by the index of concordance. Then, we have validated the regression models using the remaining 30% of respondents (testing data set), and identified the best performing model by removing the variables based on their impact on the Akaike information criterion and then evaluating the model predictive accuracy. We also performed a sensitivity analysis using principal component analysis. Overall, 5.5% of the respondents indicated that in case of positive SARS-CoV-2 test they would not share contacts. Of note, this percentage varied from 0.8% to 46.5% depending on the answers to other survey questions. From the 139 questions included in the multivariable analysis, the initial model proposed 20 independent factors that were reduced to the 6 factors with only modest changes in the model performance. The 6-variables model demonstrated good performance in the training (c-index 0.85 and McFadden R2 criteria 0.25) and in the testing data set (93.3% accuracy, AUC 0.78, sensitivity 30.4% and specificity 97.4%). The most influential factors related to unwillingness to share contacts were the lack of intention to perform the test in case of contact with a COVID-19 positive individual (OR 5.60, 95% CI 4.14 to 7.58, in a fully adjusted multivariable analysis), disagreement that the government should be allowed to force people into self-isolation (OR 1.79, 95% CI 1.12 to 2.84), disagreement with the national vaccination schedule (OR 2.63, 95% CI 1.86 to 3.69), not following to the preventive anti-COVID measures (OR 3.23, 95% CI 1.85 to 5.59), the absence of people in the immediate social environment who have been infected with COVID-19 (1.66, 95% CI 1.24 to 2.21), as well as difficulties in finding or understanding the information about the infection or related recommendations. A limitation of this study is the under-representation of persons not participating in internet-based surveys and some vulnerable groups like homeless people, persons with disabilities or migrants. Conclusions: Our analysis revealed several groups that expressed unwillingness to collaborate on contact tracing. The identified patterns may play a principal role not only in the COVID-19 epidemic but also be important for possible future public health threats, and appropriate interventions for their correction should be developed and ready for the implementation. [ABSTRACT FROM AUTHOR]

Introduction: In mental healthcare, one area of major concern identified by health information systems is variability in antipsychotic prescribing. While most studies have investigated patient- and prescriber-related factors as possible reasons for such variability, no studies have investigated facility-level characteristics. The present study ascertained whether staffing level is associated with antipsychotic prescribing in community mental healthcare. Methods: A cross-sectional analysis of data extracted from the Italian national mental health information system was carried out. For each Italian region, it collects data on the availability and use of mental health facilities. The rate of individuals exposed to antipsychotic drugs was tested for evidence of association with the rate of mental health staff availability by means of univariate and multivariate analyses. Results: In Italy there were on average nearly 60 mental health professionals per 100,000 inhabitants, with wide regional variations (range 21 to 100). The average rate of individuals prescribed antipsychotic drugs was 2.33%, with wide regional variations (1.04% to 4.01%). Univariate analysis showed that the rate of individuals prescribed antipsychotic drugs was inversely associated with the rate of mental health professionals available in Italian regions (Kendall's tau -0.438, p = 0.006), with lower rates of antipsychotic prescriptions in regions with higher rates of mental health professionals. After adjustment for possible confounders, the total availability of mental health professionals was still inversely associated with the rate of individuals exposed to antipsychotic drugs. Discussion: The evidence that staffing level was inversely associated with antipsychotic prescribing indicates that any actions aimed at decreasing variability in antipsychotic prescribing need to take into account aspects related to the organization of the mental health system. [ABSTRACT FROM AUTHOR]

Purpose: During economic recession people with mental health problems have higher risk of losing their job. This paper analyses the issue by considering the Italian rates of unemployment amongst individuals with and without mental health problems in 2005 and 2013, that is prior and during the economic crisis. Methods: We used data from the National surveys on “Health conditions and use of health services” carried out by the Italian National Institute of Statistics (ISTAT) for the years 2005 and 2013. The surveys collected information on the health status and socioeconomic conditions of the Italian population. Self-reported unemployment status was analysed amongst individuals with and without reported mental health problems. In addition, descriptive statistics were performed in order to detect possible differences in the risk of unemployment within different regional contexts characterised by different socio-economic conditions. Results: The recession determined increased disparities in unemployment rates between people with and without mental health problems. Regardless to the presence of mental health problems, young people were more likely to be unemployed. Among people who reported mental health problems, males were more likely to be unemployed than females. People with low education level were more likely to be unemployed, particularly during the recession and in presence of mental health problems. Changes in unemployment rates due to the crisis showed different patterns across different regions of the Country. Conclusions: These analyses confirm that in periods of economic crisis people with mental health problems are at risk of experiencing exclusion from labour market. In addition, the impact is even worse within the group with low education and younger age. These findings emphasise the importance of specific interventions aimed at promoting labour market participation and reintegration for people with mental health problems. [ABSTRACT FROM AUTHOR]