Impact of the COVID-19 lockdown on psychological health and nutritional habits in Italy: results from the #PRESTOinsieme study.

OBJECTIVES: The present work aims to present the results of the 'PRESTOinsieme' (which is 'we will be together soon' in English). The web-based survey (www.prestoinsieme.com) describes changes in lifestyle habits and symptoms of psychological discomfort in the Italian population during the COVID-19 lockdown. DESIGN: Cross-sectional online survey disseminated by messaging apps (ie, WhatsApp and Telegram) and social networks (ie, Instagram, Facebook and LinkedIn). SETTING: Italy. PARTICIPANTS: Italian population older than 16 years of age. EXPOSURE: COVID-19 lockdown. MAIN OUTCOMES AND MEASURES: Survey respondents filled out a set of validated questionnaires aimed at assessing lifestyle habits and psychological health, that is, the General Health Questionnaire (GHQ-12) to screen for psychological distress, the Impact of Event Scale-Revised (IES-R) to screen for post-traumatic stress and the Center for Epidemiologic Studies Depression Scale (CES-D). RESULTS: Survey respondents totalled 5008. Moderate or severe psychological distress was reported in 25.5% and 22% of survey respondents, respectively. Lower age, female gender, being unemployed (OR 1.57, 95% CI 1.22 to 2.02) or being a student (OR 1.73, 95% CI 1.31 to 2.28) were predictors of more severe depressive symptoms. CONCLUSIONS: The present study is one of the largest population-based surveys conducted in Italy during the first COVID-19 lockdown, providing valuable data about the Italian population's psychological health. Further studies should be conducted to understand whether psychological distress persists after the end of the lockdown.

Psychological Effects of Lockdown Measures for the COVID-19 Outbreak in Patients with Systemic Lupus Erythematosus.

OBJECTIVE: To compare the psychological impact of the lockdown measures contrasting the COVID-19 outbreak between systemic lupus erythematosus (SLE) and general population. PATIENTS AND METHODS: From July 15th to August 15th 2020, a retrospective survey referring to the period March 9th to May 18th 2020 was administered to SLE patients and the results of the survey, called LEPRE (Lupus Erythematosus PREsto) study, were compared with those from the PRESTO (imPact of quaRantine mEasures againST cOvid19) project, the same survey provided to the general population. Consecutive patients >18 years old affected by SLE and regularly followed in a single rheumatologic centre were involved. Primary outcome was to compare the scores of the Impact of Events Scale-Revised (IES-R), the General Health Questionnaire 12 (GHQ-12) and the Center for Epidemiological Depression Scale (CES-D) between patients and general population. RESULTS: A total of 64 patients completed the survey. After a propensity score matching, they were compared to 128 people from PRESTO project. The median age among patients was 43 years (I-III interquartile range 35-54.5), 88% were female and 100% Caucasian. IES-R [(score>23: 57% (34) vs 49% (58)], GHQ-12 [(score>13: 85% (52) vs 88% (106)], and CES-D [(score>15: 45% (28) vs 40% (46)] scores were not statistically different between patients and controls (p>0.05). CONCLUSION: Restrictive measures for COVID-19 pandemic had no greater impact on patients with SLE than in the general population. Strategy for coping to the SLE might be useful during lockdown measures and may be helpful for other chronic conditions.

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol.

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.