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Recent literature on the mental health consequences of social distancing measures has found a substantial increase in self-reported sleep disorders, anxiety and depressive symptoms during lockdown periods. We investigate this issue with data on monthly purchases of psychotropic drugs from the universe of Italian pharmacies during the first wave of the COVID-19 pandemic and find that purchases of mental health-related drugs have increased with respect to 2019. However, the excess volumes do not match the massive increase in anxiety and depressive disorders found in survey-based studies. We also study the interplay between mobility, measured with anonymized mobile phone data, and mental health and report no significant effect of mobility restrictions on antidepressants and anxiolytics purchases during 2020. We provide three potential mechanisms that could drive the discrepancy between self-reported mental health surveys and psychotropic drugs prescription registries: (1) stockpiling practices in the early phases of the pandemic; (2) the adoption of compensatory behavior and (3) unexpressed and unmet needs due to both demand- and supply-side shortages in healthcare services. [ABSTRACT FROM AUTHOR]

Italy has been severely affected by the COVID-19 pandemic, reporting the highest death toll in Europe as of April 2020. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. On March 12, the government imposed a national lockdown. To aid the evaluation of the impact of interventions, we present daily time-series of three different aggregated mobility metrics: the origin-destination movements between Italian provinces, the radius of gyration, and the average degree of a spatial proximity network. All metrics were computed by processing a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users before and during the outbreak, at the sub-national scale. This dataset can help to monitor the impact of the lockdown on the epidemic trajectory and inform future public health decision making. Measurement(s) mobility Technology Type(s) GPS navigation system Factor Type(s) temporal interval Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Italy Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12515256 [ABSTRACT FROM AUTHOR]

BACKGROUND Social media in scientific research offers a unique digital observatory of human behaviours and hence great opportunities to conduct research at large scale, answering complex sociodemographic questions. We focus on the identification and assessment of biases in social-media-administered surveys. OBJECTIVE This study aims to shed light on population, self-selection, and behavioural biases, empirically comparing the consistency between self-reported information collected traditionally versus social-media-administered questionnaires, including demographic and psychometric attributes. METHODS We engaged a demographically representative cohort of young adults in Italy (approximately 4,000 participants) in taking a traditionally administered online survey and then, after one year, we invited them to use our ad hoc Facebook application (988 accepted) where they filled in part of the initial survey. We assess the statistically significant differences indicating population, self-selection, and behavioural biases due to the different context in which the questionnaire is administered. RESULTS Our findings suggest that surveys administered on Facebook do not exhibit major biases with respect to traditionally administered surveys in terms of neither demographics nor personality traits. Loyalty, authority, and social binding values were higher in the Facebook platform, probably due to the platform's intrinsic social character. CONCLUSION We conclude that Facebook apps are valid research tools for administering demographic and psychometric surveys, provided that the entailed biases are taken into consideration. CONTRIBUTION We contribute to the characterisation of Facebook apps as a valid scientific tool to administer demographic and psychometric surveys, and to the assessment of population, self-selection, and behavioural biases in the collected data. [ABSTRACT FROM AUTHOR]

Background: Nearly every year Influenza affects most countries worldwide and the risk of a new pandemic is always present. Therefore, influenza is a major concern for public health. School-age individuals are often the most affected group, suggesting that the inclusion in preparedness plans of school closure policies may represent an option for influenza mitigation. However, their applicability remains uncertain and their implementation should carefully be weighed on the basis of cost-benefit considerations., Methods: We developed an individual-based model for influenza transmission integrating data on sociodemography and time use of the Italian population, face-to-face contacts in schools, and influenza natural history. The model was calibrated on the basis of epidemiological data from the 2009 influenza pandemic and was used to evaluate the effectiveness of three reactive school closure strategies, all based on school absenteeism., Results: In the case of a new influenza pandemic sharing similar features with the 2009 H1N1 pandemic, gradual school closure strategies (i.e., strategies closing classes first, then grades or the entire school) could lead to attack rate reduction up to 20-25 % and to peak weekly incidence reduction up to 50-55 %, at the cost of about three school weeks lost per student. Gradual strategies are quite stable to variations in the start of policy application and to the threshold on student absenteeism triggering class (and school) closures. In the case of a new influenza pandemic showing different characteristics with respect to the 2009 H1N1 pandemic, we found that the most critical features determining the effectiveness of school closure policies are the reproduction number and the age-specific susceptibility to infection, suggesting that these two epidemiological quantities should be estimated early on in the spread of a new pandemic for properly informing response planners., Conclusions: Our results highlight a potential beneficial effect of reactive gradual school closure policies in mitigating influenza spread, conditioned on the effort that decision makers are willing to afford. Moreover, the suggested strategies are solely based on routinely collected and easily accessible data (such as student absenteeism irrespective of the cause and ILI incidence) and thus they appear to be applicable in real world situations.