Identifying the most vulnerable subjects is crucial for the effectiveness of health interventions aimed at limiting the adverse consequences of high temperatures. We conducted a case crossover study aimed at assessing whether suffering from mental health disorders modifies the effect of high temperatures on mortality. We included all deaths occurred in the area of Bologna Local Health Trust during the summers 2004–2017. Subjects with mental disorders were identified by using the local Mental Health Registry. A conditional logistic model was applied, and a z-test was used to study the effect modification. Several models were estimated stratifying by subjects’ characteristics. For every 1 °C above 24 °C, mortality among people without mental disorders increased by 1.9% (95% CI 1.0–2.6, p < 0.0001), while among mental health service users, mortality increased by 5.5% (95% CI 2.4–8.6, p < 0.0001) (z-test equal to p = 0.0259). The effect modification varied according to gender, residency and cause of death. The highest probability of dying due to an increase in temperature was registered in patients with depression and cognitive decline. In order to reduce the effects of high temperatures on mortality, health intervention strategies should include mental health patients among the most vulnerable subjects taking account of their demographic and clinical characteristics.

The aim of the study is to evaluate the association between summer temperatures and emergency department visits (EDVs) in Bologna (Italy) and assess whether this association varies across areas with different socioeconomic and microclimatic characteristics. We included all EDVs within Bologna residences during the summers of 2010–2019. Each subject is attributed a deprivation and a microclimatic discomfort index according to the residence. A time-stratified case-crossover design was conducted to estimate the risk of EDV associated with temperature and the effect modification of deprivation and microclimatic characteristics. In addition, a spatial analysis of data aggregated at the census block level was conducted by applying a Poisson and a geographically weighted Poisson regression model. For each unit increase in temperature above 26 °C, the risk of EDV increases by 0.4% (95%CI: 0.05–0.8). The temperature–EDV relationship is not modified by the microclimatic discomfort index but rather by the deprivation index. The spatial analysis shows that the EDV rate increases with deprivation homogeneously, while it diminishes with increases in median income and microclimatic discomfort, with differences across areas. In conclusion, in Bologna, the EDV risk associated with high temperatures is not very relevant overall, but it tends to increase in areas with a low socioeconomic level.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

Various measures have been taken by the Italian Government to contain and mitigate the COVID-19 outbreak and on March 11th a decree called "I stay at home" put the whole nation under lockdown. Our aim is to describe sociodemographic and transmission profile of COVID-19 cases that were transmitted before and after the introduction of the decree in the Bologna Local Health Authority. Cases were classified as transmitted before or after the decree according to the date of last contact with a COVID-19 case or, if this date was unavailable, we used the date of onset of symptoms considering the incubation period. Sociodemographic, clinical and epidemiological information was collected by using the infectious disease monitoring database, hospital discharge, deprivation index and long term care facility databases. In the period after the publication of the decree, there were more elderly, females, strangers, retired, residents in nursing homes and deprived people than in the first period. There were also more health care personnel and less professionals/managers, sales or office workers. In both phases, family is mentioned as the first community attended although less frequently in the second group. The profile of the new COVID-19 cases changed during the outbreak suggesting a differential effect of lockdown measures on the population. An equity lens should be used when analyzing the impact of pandemia and the measures taken to curb it.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring SARS-CoV-2 prevalence in a city in Northern Italy from October 2021 to May 2023. Starting from a widely applied deterministic model, this study proposed a variation to take into account population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated with healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred in the considered period, demonstrating the model’s capacity to predict relevant fluctuations in the number of cases. Population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied for comparing virus load values across cities with varying population demographics and sewer network structures, enhancing the comparability of WBE data for effective surveillance and intervention strategies.