Italians locked down: people's responses to early COVID-19 pandemic public health measures

Patterns of psychiatric admissions across two major health crises: L' Aquila earthquake and COVID-19 pandemic lockdown

BACKGROUND

This study examined psychiatric hospitalisation patterns in San Salvatore Hospital in L' Aquila (Italy), during two major crises: the 2009 earthquake and the COVID-19 pandemic lockdown. The investigation spans two four-year periods, from 2008 to 2011 and from 2019 to 2022, with a focus on the trimester around the earthquake and the first wave/lockdown of the pandemic.

METHODS

We analysed weekly psychiatric unit admissions of adults diagnosed with schizophrenia spectrum disorder, major depression, bipolar disorder, and alcohol/substance use disorder. Four-year periods around the Earthquake and COVID-19 Lockdown were divided into sixteen trimesters, and Generalised linear models were used to analyse the relationship between weekly hospitalisation frequency and trimesters by diagnosis using a Poisson distribution.

RESULTS

A total of 1195 and 1085 patients were admitted to the psychiatric ward in the 2008-2011 and 2019-2022 periods, respectively. Weekly hospitalisations in the earthquake trimester were lower than during the previous one for all diagnoses (schizophrenia spectrum: -41.9%, p = 0.040; major depression: -56.7%, p = 0.046; bipolar disorder: -69.1%, p = 0.011; alcohol/substance use disorder: -92.3%, p = 0.013). This reduction persisted for 21, 18, and 33 months after the earthquake for schizophrenia spectrum, bipolar, and alcohol/substance use disorders, respectively. Contrarily, patterns of weekly admissions around the COVID-19 lockdown remained substantially stable in the short term. However, a consistent long-term hospitalisation increase for all diagnoses characterised the first half of 2022 (the cessation of anti-COVID-19 measures; schizophrenia spectrum: +68.6%, p = 0.014; major depression: +133.3%, p = 0.033; bipolar disorder: +180.0%, p = 0.034; alcohol/substance use disorder: +475.0%, p = 0.001).

CONCLUSIONS

The present study indicated that exposure to major health crises can have both short- and long-term effects on psychiatric ward admission, holding significant implications for current and future major health emergency management strategies.

Quality of sleep after COVID-19 infection: a cross-sectional study in the Southern Italy

Introduction

This study investigated the quality of sleep in a sample of individuals from Southern Italy after the major waves of the COVID-19 pandemic, with the aim of evaluating how sleep patterns changed.

Methods

A cross-sectional study was conducted between March 2022 and January 2023 and involved adults who had a COVID-19 infection, who were invited to complete a self-administered online questionnaire.

Results

A total of 408 individuals participated in the survey. Overall, 66.4% had a reduction in social relations; 72.1% had an increase in the use of social media; and 86%, 77.2%, and 71.1% reported an extremely severe level of anxiety, stress, and depression, respectively. Almost all of the respondents had a Pittsburgh Sleep Quality Index score (PSQI) ≥5, indicating poor sleep quality. Subjects with a severe or extremely severe depression score, a severe or extremely severe stress score, who had a job, and who had someone close who died because of a COVID-19 infection were more likely to have a high PSQI global score. The use of sleep medication in the past months was significantly higher in those who were older, who had a job, who had a COVID-19 infection in the first and second waves, who had someone close who died from COVID-19, and who did not have changes in social relationships during the pandemic. Moreover, participants with severe or extremely severe depression scores, with severe or extremely severe stress scores, who were women, and who were older had troubles staying awake while engaging in social activities during the past month.

Conclusion

The results bring to light the high prevalence of poor sleep quality among individuals who were infected with SARS-CoV-2. Future research is needed to understand whether these disturbances are still present in the endemic period and whether it is necessary to investigate further determinants that have affected and/or are affecting sleep quality.

Prosocial Behaviour and Antibiotic Resistance: Evidence from a Discrete Choice Experiment

INTRODUCTION

The health of a community depends on the health of its individuals; therefore, individual health behaviour can implicitly affect the health of the entire community. This is particularly evident in the case of infectious diseases. Because the level of prosociality in a community might determine the effectiveness of health programmes, prosocial behaviour may be a crucial disease-control resource. This study aimed to extend the literature on prosociality and investigate the role of altruism in antibiotic decision making.

METHODS

A discrete choice experiment was conducted to assess the influence of altruism on the general public's preferences regarding antibiotic treatment options. The survey was completed by 378 Swedes. Latent class analysis models were used to estimate antibiotic treatment characteristics and preference heterogeneity. A three-class model resulted in the best model fit, and altruism significantly impacted preference heterogeneity.

RESULTS

Our findings suggest that people with higher altruism levels had more pronounced preferences for treatment options with lower contributions to antibiotic resistance and a lower likelihood of treatment failure. Furthermore, altruism was statistically significantly associated with sex, education, and health literacy.

CONCLUSIONS

Antibiotic awareness, trust in healthcare systems, and non-discriminatory priority setting appear to be structural elements conducive to judicious and prosocial antibiotic behaviour. This study suggests that prosocial messages could help to decrease the demand for antibiotic treatments.

A humanistic model of corporate social responsibility in e-commerce with high-tech support in the artificial intelligence economy

This paper aims to develop a humanistic model of corporate social responsibility in e-commerce, relying on high technology in an artificial intelligence economy. The research is based on the experience of the top 30 publicly traded e-commerce companies, the 16 most responsible companies in the retail industry in the USA, and the leading global and Russian e-commerce business structures in 2020-2021. Based on econometric modeling, it is substantiated that the humanization (qualitative criterion) of jobs provides an increase in revenues of e-commerce businesses to a greater extent than an increase in the number (quantitative criterion) of jobs. The high technology of the artificial intelligence economy (AI economy) makes it possible to maximize the contribution of responsible HRM of the e-commerce business in increasing its revenues. For this purpose, a humanistic model of corporate social responsibility in e-commerce based on high technology in the AI economy has been developed. The theoretical significance lies in proving the need to humanize jobs in e-commerce and revealing the essence of this process. The practical significance lies in the fact that the developed humanistic model will increase the profitability and, consequently, the resilience of businesses to future economic crises that arise against the backdrop of the COVID-19 pandemic.

How the COVID-19 Pandemic Affected Attendance at a Tertiary Orthopedic Center Emergency Department: A Comparison between the First and Second Waves

Italy was the first European country to face the SARS-CoV-2 virus (COVID-19) pandemic in 2020. The country quickly implemented strategies to contain contagions and re-organize medical resources. We evaluated the COVID-19 effects on the activity of a tertiary-level orthopedic emergency department (ED) during the first and second pandemic waves. We retrospectively collected and compared clinical radiological data of ED admissions during four periods: period A, first pandemic wave; period B, second pandemic wave; period C, three months before the COVID-19 outbreak; period D, same timeframe of the first wave but in 2019. During period A, we found a reduction in ED admissions (-68.2% and -59.9% compared with periods D and C) and a decrease in white codes (non-urgent) (-7.5%) compared with pre-pandemic periods, with a slight increase for all other codes: +6.3% green (urgent, not critical), +0.8% yellow (moderately critical) and +0.3% red (highly urgent, risk of death). We observed an increased rate of fracture diagnosis in period A: +14.9% and +13.3% compared with periods D and C. Our study shows that the COVID-19 pandemic caused a drastic change in the ED patient flow and clinical radiological activity, with a marked reduction in admissions and an increased rate of more severe triage codes and diagnosed fractures.