Social inequalities in health are known to be influenced by the socioeconomic status of the territory in which people live. In the context of the ongoing coronavirus disease 2019 (COVID-19) pandemic, this study is aimed at assessing the role of 5 area-level indicators in shaping the risk of contagion in the provinces of Milan and Lodi (Lombardy, Italy), namely: educational disadvantage, unemployment, housing crowding, mobility, and population density. The study area includes the municipalities at the origin of the first Italian epidemic outbreak. Data on COVID-19 patients from the Integrated Datawarehouse for COVID Analysis in Milan were used and matched with aggregate-level data from the National Institute of Statistics Italy (Istat). Multilevel logistic regression models were used to estimate the association between the census block-level predictors and COVID-19 infection, independently of age, sex, country of birth, and preexisting health conditions. All the variables were significantly associated with the outcome, with different effects before and after the lockdown and according to the province of residence. This suggests a pattern of socioeconomic inequalities in the outbreak, which should be taken into account in the eventuality of future epidemics to contain their spread and its related disparities.

In July 2018, a large outbreak of Legionnaires’ disease (LD) caused by Legionella pneumophila serogroup 1 (Lp1) occurred in Bresso, Italy. Fifty-two cases were diagnosed, including five deaths. We performed an epidemiological investigation and prepared a map of the places cases visited during the incubation period. All sites identified as potential sources were investigated and sampled. Association between heavy rainfall and LD cases was evaluated in a case-crossover study. We also performed a case–control study and an aerosol dispersion investigation model. Lp1 was isolated from 22 of 598 analysed water samples; four clinical isolates were typed using monoclonal antibodies and sequence-based typing. Four Lp1 human strains were ST23, of which two were Philadelphia and two were France-Allentown subgroup. Lp1 ST23 France-Allentown was isolated only from a public fountain. In the case-crossover study, extreme precipitation 5–6 days before symptom onset was associated with increased LD risk. The aerosol dispersion model showed that the fountain matched the case distribution best. The case–control study demonstrated a significant eightfold increase in risk for cases residing near the public fountain. The three studies and the matching of clinical and environmental Lp1 strains identified the fountain as the source responsible for the epidemic.

BackgroundIn the winter of 2016–2017, the number of deaths recorded in the north-west Europe was significantly higher than that in previous years. This spike in mortality was attributed principally to an influenza epidemic, but the contribution of air pollution and cold temperature has not been investigated. Information on the combined effect of low temperatures, influenza epidemic, and air pollution on mortality is inadequate. The objective of this study was to estimate the excess mortality in the winter of 2016–2017 in the metropolitan area of Milan, and to evaluate the independent short-term effect of 3 risk factors: low temperatures, the influenza epidemic, and air pollution.MethodsWe used a case-crossover, time-stratified study design. Mortality data were collected on all people aged > 65 years who died of natural causes, due to respiratory diseases or cardiovascular diseases, between December 1, 2016 and February 15, 2017. Environmental data were extracted from the Regional Environmental Protection Agency. The National Surveillance Network provided data on influenza epidemic.ResultsAmong the 7590 natural deaths in people aged > 65 years, 965 (13%) were caused by respiratory conditions, and 2688 (35%) were caused by cardiovascular conditions. There were statistically significant associations between the minimum recorded temperature and deaths due to natural causes (OR = 0.966, 95% CI: 0.944–0.989), and cardiovascular conditions (OR = 0.961, 95% CI: 0.925–0.999). There were also statistically significant association between the influenza epidemic and deaths due to natural causes (OR = 1.198, 95% CI: 1.156–1.241), cardiovascular conditions (OR = 1.153, 95% CI: 1.088–1.223), and respiratory conditions (OR = 1.303, 95% CI: 1.166–1.456). High levels of PM10 (60 and 70 μg/m3) were associated with a statistically significant increase in natural and cause-specific mortality. There were statistically significant interactions between PM10 and influenza for cardiovascular-related mortality, and between influenza and temperature for deaths due to natural causes.ConclusionsExcess of mortality in Milan during winter 2016–2017 was associated with influenza epidemic and concomitant environmental exposures, specifically, the combined effect of air pollution and low temperatures. Policies mitigating the effects of environmental risk factors should be implemented to prevent future excess mortality.

Background COVID-19 epidemic has paralleled with the so called infodemic, where countless pieces of information have been disseminated on putative risk factors for COVID-19. Among those, emerged the notion that people suffering from autoimmune diseases (AIDs) have a higher risk of SARS-CoV-2 infection. Methods The cohort included all COVID-19 cases residents in the Agency for Health Protection (AHP) of Milan that, from the beginning of the outbreak, developed a web-based platform that traced positive and negative cases as well as related contacts. AIDs subjects were defined ad having one the following autoimmune disease: rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, Sjogren disease, ankylosing spondylitis, myasthenia gravis, Hashimoto’s disease, acquired autoimmune hemolytic anemia, and psoriatic arthritis. To investigate whether AID subjects are at increased risk of SARS-CoV-2 infection, and whether they have worse prognosis than AIDs-free subjects once infected, we performed a combined analysis of a test-negative design case–control study, a case–control with test-positive as cases, and one with test-negative as cases (CC-NEG). Results During the outbreak, the Milan AHP endured, up to April 27th 2020, 20,364 test-positive and 34,697 test-negative subjects. We found no association between AIDs and being positive to COVID-19, but a statistically significant association between AIDs and being negative to COVID-19 in the CC-NEG. If, as likely, test-negative subjects underwent testing because of respiratory infection symptoms, these results imply that autoimmune diseases may be a risk factor for respiratory infections in general (including COVID-19), but they are not a specific risk factor for COVID-19. Furthermore, when infected by SARS-CoV-2, AIDs subjects did not have a worse prognosis compared to non-AIDs subjects. Results highlighted a potential unbalance in the testing campaign, which may be correlated to the characteristics of the tested person, leading specific frail population to be particularly tested. Conclusions Lack of availability of sound scientific knowledge inevitably lead unreliable news to spread over the population, preventing people to disentangle them form reliable information. Even if additional studies are needed to replicate and strengthen our results, these findings represent initial evidence to derive recommendations based on actual data for subjects with autoimmune diseases.

Given empirical evidence for the dependence of an outcome variable on an exposure variable, we can typically only provide bounds for the "probability of causation" in the case of an individual who has developed the outcome after being exposed. We show how these bounds can be adapted or improved if further information becomes available. In addition to reviewing existing work on this topic, we provide a new analysis for the case where a mediating variable can be observed. In particular we show how the probability of causation can be bounded when there is no direct effect and no confounding.