[Reduction of adverse outcome due to COVID-19 infection in a high-risk population: evaluation of an informative intervention through active call by General Practitioners]. / Riduzione degli outcome sfavorevoli dovuti all'infezione da COVID-19 su una popolazione ad alto rischio: valutazione di un intervento informativo mediante chiamata attiva da parte dei medici di medicina generale.

BACKGROUND: currently, individuals at risk of adverse outcomes for COVID-19 can access to vaccination and pharmacological interventions. But, during the first epidemic wave, there were no treatments or therapeutic strategies available to reduce adverse outcomes in patients at risk. OBJECTIVES: to assess the impact of an intervention at 15-month follow-up developed by the Agency for Health Protection of the Metropolitan Area of Milan (ATS Milan) based on telephone triage and consultation by the General Practitioners (GPs) for patient with high-risk for adverse outcomes. DESIGN: intervention on population. SETTING AND PARTICIPANTS: a total of 127,292 patients in the ATS aged ≥70 years and with comorbidities associated with an increased risk of dying from COVID-19 infection were identified. Using a specific information system, patients were assigned to their GPs for telephone triage and consultation. GPs inform them about the risks of the disease, non-pharmacological prevention measures, and precautions in contacts with family members and other persons. No specific clinical intervention was carried out, only an information/training intervention was performed. MAIN OUTCOME MEASURES: by the end of May 2020, 48.613 patients had been contacted and 78.679 had not been contacted. Hazard Ratios (HRs) of infection hospitalisation and death at 3 and 15 months were estimated using Cox regression models adjusted by confounder. RESULTS: no differences in gender, age class distribution, prevalence of specific diseases, and Charlson Index were found between the two groups (treated such as called patients and not called). Called patients had a higher propensity for influenza and antipneumococcal vaccination and have more comorbidities and greater access to pharmacological therapies. Non-called patients have a greater risk for COVID-19 infection: HR was 3.88 (95%CI 3.48-4.33) at 3 months and 1.28 (95%CI 1.23-1.33) at 15 months; for COVID-19 hospitalization HR was 2.66 (95%CI 2.39-2,95) at 3 months and 1.31 (95%CI 1.25-1.37) at 15 months; for overall mortality HR was 2,52 (95%CI 2.35-2:72) at 3 months and 1.23 (95%CI 1.19-1.27) at 15 months. CONCLUSIONS: the results of this study show a reduction in hospitalization and deaths and support, in case of pandemic events, the implementation of new care strategies based on adapted stratification systems in order to protect the population's health. This study presents some limits: it is not randomized; a selection bias is present (called patients were those most in contact with the GPs); the intervention is indication-based (on march 2020, the actual benefit of protection and distancing for high-risk groups was unclear), and the adjustment is not able to fully control for confounding. However, this study points out the importance to develop information systems and improve methods to best protect the health of the population in setting of territorial epidemiology.

[Description of excess of overall and cause-specific mortality in the Agency for Health Protection of the Metropolitan Area of Milan (Lombardy Region, Northern Italy) in 2020]. / Descrizione degli eccessi di mortalità per tutte le cause e specifici per causa nell'ATS di Milano nel 2020.

BACKGROUND: as a result of the SARS-CoV-2 pandemic, a generalised mortality excess was recorded in 2020. However, the mortality for COVID-19 cannot fully explain the observed excesses. The analysis of cause-specific mortality could contribute to estimate the direct and indirect effects of the SARS-CoV-2 outbreak and to the monitoring mortality trends. OBJECTIVES: to describe the impact of the SARS-CoV-2 epidemic in overall and cause-specific mortality in population residing in the Agency for Health Protection (ATS) of Milan. Descriptive analysis of cause-specific mortality within thirty days of SARS-COV-2 infection. DESIGN: descriptive analysis of overall and cause-specific mortality in the ATS of Milan area in 2020 and comparison with a reference period (2015-2019). SETTING AND PARTICIPANTS: overall deaths in ATS of Milan in 2020 were collected, using the Local Registry of Causes of Death, and were classified according to the ICD-10 codes. MAIN OUTCOME MEASURES: total and weekly overall and cause-specific mortality, by age. RESULTS:  in 2020, 44,757 deaths for all causes were observed in people residing in the ATS of Milan with percentage change of 35%. The leading cause of death in 2020 were cardiovascular disease and neoplasm; COVID-19 infection was the third cause. An excess of mortality was observed for most of all causes of deaths. Starting from 40-49-year age group, an increase of mortality was observed; the largest increase was observed in the group 70+ years. The largest increases were observed for endocrine, respiratory, and hypertensive diseases. On the contrary, for neoplasm, infectious (not COVID-19) diseases, traffic-related mortality, and cerebrovascular disease and ictus, a decrease of mortality was observed. The greater mortality increase was observed during the first pandemic wave. The leading cause of death after positive swab was COVID-19 infection, with little variation with age class. Other frequent causes of death were respiratory diseases, cardiovascular diseases, and neoplasm. CONCLUSIONS: the study showed a generalised increase for most causes of death; observed mortality trends may indicate delay in access to health care system, in diagnosis and treatment.

[Identifying deaths due to or involving COVID-19 in absence of the death certificate]. / Identificare i decessi riconducibili al COVID-19 in assenza del certificato di morte.

BACKGROUND: during 2020, Italy was one of the first nation hit by SARS-CoV-2, but it was not the hardest-hit country in terms of deaths. In absence of the death certificate, the burden of COVID-19 on mortality is usually calculated from overall deaths or from deaths of patients tested positive for COVID-19. However, these measures do not express the real burden of the disease on the population. OBJECTIVES: identify deaths due to or involving COVID-19 in absence of the death certificates. DESIGN: deaths for all causes, cause-specific deaths, COVID-19 hospitalization and COVID-19 confirmed cases between 01.01.2020 and 31.12.2021 observed in subjects residing in the territory of the ATS of Milan. Potential deaths due to or involving COVID-19 as those occurring in an optimal time period between the date of death and the date of positive swab and/or COVID-19 hospitalization, were identified. Optimal time period was defined maximizing sensitivity and specificity, comparing potential COVID-19 deaths with 2020 cause-specific mortality as gold standard, stratifying results by time of deaths, age, and number of comorbidities. Then, this method was further validated using a time-series approach to estimate the excess mortality during the COVID-19 outbreak in comparison with the pre-outbreak period 2015-2019. Accuracy of predictions was evaluated with the Root Mean Square Error (RMSE) between observed and predicted values. SETTING AND PARTICIPANTS: 78,202 deaths for all causes, of which 8,815 due to or involving COVID-19 as classified by the Milan Register of Death Causes for 2020. MAIN OUTCOME MEASURES: all-cause mortality, cause-specific mortality. RESULTS: from the beginning of the epidemic, 30% (23,495) died in the first semester of 2020, 26% (19,988) in the second semester of 2020, 23% (18,189) in the first semester of 2021, and 21% (16,530) in the second semester of 2021. COVID-19 hospitalizations were 13.826 (17%), while confirmed COVID-19 cases were 17,548 (22%). The optimal time intervals capable to identify a potential death due to or involving COVID-19 were 0-61 between the date of death and the date of positive swab and 0-11 between the date of death and the date of COVID-19 hospitalization, with an overall sensitivity of 90%, a specificity of 95%, and a RMSE of 3.6. Comparing the method proposed with the time-series approach, a RMSE in 2021 of 15.8 was found. Results showed different optimal time intervals for 2021 vs 2020 and by years of age and comorbidities. CONCLUSIONS: this study found that deaths due to or involving COVID-19 could be sensitively identified from the date of positive swab and/or COVID-19 hospitalization. This method can be used for public health interventions which provided so far measures in terms of total deaths instead of real numbers of COVID-19 death, in particular those involving the effective reproduction number usually calculated from overall mortality.

The impact of school closure intervention during the third wave of the COVID-19 pandemic in Italy: Evidence from the Milan area.

BACKGROUND: In February 2021, the spread of a new variant of SARS-CoV-2 in the Lombardy Region, Italy caused concerns about school-aged children as a source of contagion, leading local authorities to adopt an extraordinary school closure measure. This generated a debate about the usefulness of such an intervention in light of the trade-off between its related benefits and costs (e.g. delays in educational attainment, impact on children and families' psycho-physical well-being). This article analyses the epidemiological impact of the school closure intervention in the Milan metropolitan area. METHODS: Data from the Agency for Health Protection of the Metropolitan City of Milan allowed analysing the trend of contagion in different age classes before and after the intervention, adopting an interrupted times series design, providing a quasi-experimental counterfactual scenario. Segmented Poisson regression models of daily incident cases were performed separately for the 3-11-year-old, the 12-19-year-old, and the 20+-year-old age groups, examining the change in the contagion curves after the intervention, adjusting for time-varying confounders. Kaplan-Meier survival curves and Cox regression were used to assess the equality of survival curves in the three age groups before and after the intervention. RESULTS: Net of time-varying confounders, the intervention produced a daily reduction of the risk of contagion by 4% in those aged 3-11 and 12-19 (IRR = 0·96) and by 3% in those aged 20 or more (IRR = 0·97). More importantly, there were differences in the temporal order of contagion decrease between the age groups, with the epidemic curve lowering first in the school-aged children directly affected by the intervention, and only subsequently in the adult population, which presumably indirectly benefitted from the reduction of contagion among children. CONCLUSION: Though it was not possible to completely discern the effect of school closures from concurrent policy measures, a substantial decrease in the contagion curves was clearly detected after the intervention. The extent to which the slowdown of infections counterbalanced the social costs of the policy remains unclear.

Boosters and time from the last anti-COVID-19 vaccine dose: lead public health choices by real-time epidemiological assessment.

BACKGROUND: the levels of anti-SARS-CoV-2 antibodies after the second vaccine dose decline in the following months; the administration of an additional vaccine dose (booster) is able to restore the immune system in the short period significantly reducing the risk of a severe disease. In the winter of 2021, a new particularly infectious variant caused the urgent need to increase the coverage of the booster dose. OBJECTIVES: to present, using real data, an evaluation of the efficacy of the booster dose in reducing the severe disease of SARS-CoV-2 infection in terms of hospital admissions, intensive care and death from all causes. DESIGN: descriptive study of vaccine adherence; associative study of the factors linked with adherence of vaccination and COVID-19 symptoms; associative study of vaccine effectiveness against hospital admission and mortality. SETTING AND PARTICIPANTS: population-based study in the Milan and Lodi provinces (Lombardy Region, Northern Italy) with subjects aged >=19 years alive at 01.10.2021, not residing in a nursery home, followed up to 31.12.2021. MAIN OUTCOME MEASURES: COVID-19 symptoms, hospitalization for COVID-19, intensive care hospitalization, and all-cause mortality in the period 01.10.2021-31.12.2021. RESULTS: the cohort included 2,936,193 patients at 01.10.2021: at the end of the follow-up period (31.12.2021), 378,616 (12.9%) had no vaccine, 128,879 (4.3%) had only 1 dose, 412,227 (14.0%) had a 2nd dose given since less than 4 months, 725. 806 (25%) had a 2nd dose given since 4-7 months, 74,152 (2.5%) had a 2nd dose given since 7+ months, 62,614 (2.1%) had a 2nd dose and have had the disease, and 1,153,899 (39.3%) received the booster. In the study period (01.10.2021-31.12.2021), characterized by a very high prevalence of the omicron variant, 121,620 cases (antigenic/molecular buffer positive), 3,661 hospitalizations for COVID-19, 162 ICU hospitalizations, and 7,508 deaths from all causes were identified. Compared to unvaccinated people, subjects who had the booster dose had half the risk of being symptomatic, in particular for asthenia, muscle pain, and dyspnoea which are the most commons COVID-19 symptoms. In comparison with the subjects who had the booster dose, the unvaccinated had a 10-fold risk of hospitalization for COVID-19, a 9-fold risk of intensive care, and a 3-fold risk of dying. CONCLUSIONS: this work highlights the vaccination efficacy in reducing serious adverse events for those who undergo the booster and the need to implement specific engagement policies to bring to a booster those who had taken the second dose since the longest time.

Evaluation of the anti-COVID-19 vaccination campaign in the Metropolitan Area of Milan (Lombardy Region, Northern Italy).

OBJECTIVES: to present an evaluation of the campaign for vaccination against COVID-19 in the territory covered by the Agency for Health Protection of the Metropolitan Area of Milan from 01.01.2021 to 30.09.2021. DESIGN: descriptive study of vaccine adherence; predictive study of the factors associated with vaccine adherence, efficacy of vaccination in terms of hospitalization and mortality, and factors that increase the risk of hospital admission following full vaccination. SETTING AND PARTICIPANTS: population-based study with subjects aged >18 years eligible for vaccination (N. 2,981,997). An information system obtained by integrating various administrative healthcare sources made it possible to analyse socioeconomic characteristics, COVID-19 related hospitalizations, and general mortality in subjects eligible for vaccination. MAIN OUTCOME MEASURES: full vaccination (2 doses); COVID-19-related hospitalizations, COVID-19-related hospitalizations occurring more than 15 days after the second dose, general mortality. RESULTS: in the first nine months of the vaccination campaign, 74.7% of the subjects (N. 2,228,915) was fully vaccinated, whereas 15.6% (N. 465,829) did not even receive one dose. Women have a lower probability of getting vaccinated than men; the 50-59 years and 70+ years age groups emerge as the most problematic to reach, while the younger one (<40) is the most adherent. A social gradient emerged, with residents of more disadvantaged areas progressively less incline to get vaccinated than those living in more affluent areas. Adherence is greater in Italian citizenship and is likely to increase with an increase in the number of chronic conditions. Hospitalizations amounted to 1.22% (N. 5,672) in the unvaccinated population compared to 0.05% (N. 1,013) in the vaccinated population; general mortality was 4.51% (N. 15,198) in the unvaccinated population against 0.32% (N. 8.733) in the vaccinated population. Sociodemographic factors and the presence of previous health conditions are important predictors of hospitalization outcomes even within the fully vaccinated population. Specifically, the highest hazard ratios are found in subjects with heart failure (HR 2.15; 95%CI 1.83-2.53), in immunocompromised patients (HR 2.02; 95%CI 1.52-2.69), and in transplant recipients (HR 1.92; 95%CI 1.10-3.33). CONCLUSIONS: vaccination campaign adherence is affected by the sociodemographic characteristics of the population and is a determining factor in preventing hospitalizations for COVID-19 and death. The persistent higher risk of hospitalization in chronic subjects following the second dose emphasizes the need to direct booster doses to the more vulnerable. Information systems proved to be effective monitoring tools in the absence of specific trials.

Metabolic syndrome and risk of COVID-19-related hospitalization: a large, population-based cohort study carried out during the first European outbreak of SARS-CoV-2 infection in the Metropolitan area of Milan (Lombardy Region, Northern Italy).

BACKGROUND: since the beginning of the COVID-19 pandemic, specific characteristics of the infected subjects appeared to be associated with a severe disease, leading to hospitalization or death. OBJECTIVES: to evaluate the association between three components of the metabolic syndrome (diabetes mellitus, dyslipidaemia, and hypertension), alone and in combination, and risk of hospitalization in subjects with nasopharyngeal swab-confirmed COVID-19. DESIGN: cohort study. SETTING AND PARTICIPANTS: the study subjects were all COVID-19 cases diagnosed in the area of the Agency for Health Protection of the Metropolitan Area of Milan (Lombardy Region, Northern Italy) between 10.02.2020 and 25.04.2020, whose data were gathered with an ad hoc information system developed at the beginning of the pandemic. MAIN OUTCOME MEASURES: the association between metabolic syndrome components (alone and in combination) and hospitalization (both in any ward and in intensive care unit) was measured by means of cause-specific Cox models with gender, age, and comorbidities as potential confounders. RESULTS: the cohort included 15,162 subjects followed from diagnosis up to 20.07.2020. Adjusted hazard ratios (HRs) of hospitalization in any ward estimated by the Cox model were 1.26 for uncomplicated diabetes mellitus (95%CI 1.18-1.34); 1.21 for complicated diabetes mellitus (95%CI 1.05-1.39); 1.07 for dyslipidaemia (95%CI 1.00-1.14); and 1.11 for hypertension (95%CI 1.05-1.17). When all components coexisted in the same subject, the HR was 1.46 (95%CI 1.31-1.62). A significant increase in risk of hospitalization in intensive care unit was found for uncomplicated diabetes mellitus (HR 1.38; 95%CI 1.15-1.66). CONCLUSIONS: this population-based study confirms that metabolic syndrome components increase the risk of hospitalization for COVID-19. The HR increases in an additive manner when the three components are simultaneously present.

Algorithm for Individual Prediction of COVID-19-Related Hospitalization Based on Symptoms: Development and Implementation Study.

BACKGROUND: The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. OBJECTIVE: This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. METHODS: A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. RESULTS: The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept -0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. CONCLUSIONS: A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

Development of a multivariable model predicting mortality risk from comorbidities in an Italian cohort of 18,286 confirmed COVID-19 cases aged 40 years or older.

OBJECTIVES: to develop a risk prediction model for 30-day mortality from COVID­19 in an Italian cohort aged 40 years or older. DESIGN: a population-based retrospective cohort study on prospectively collected data was conducted. SETTING AND PARTICIPANTS: the cohort included all swab positive cases aged 40 years older (No. 18,286) among residents in the territory of the Milan's Agency for Health Protection (ATS-MI) up to 27.04.2020. Data on comorbidities were obtained from the ATS administrative database of chronic conditions. MAIN OUTCOME MEASURES: to predict 30-day mortality risk, a multivariable logistic regression model, including age, gender, and the selected conditions, was developed following the TRIPOD guidelines. Discrimination and calibration of the model were assessed. RESULTS: after age and gender, the most important predictors of 30-day mortality were diabetes, tumour in first-line treatment, chronic heart failure, and complicated diabetes. The bootstrap-validated c-index was 0.78, which suggests that this model is useful in predicting death after COVID-19 infection in swab positive cases. The model had good discrimination (Brier score 0.13) and was well calibrated (Index of prediction accuracy of 14.8%). CONCLUSIONS: a risk prediction model for 30-day mortality in a large COVID-19 cohort aged 40 years or older was developed. In a new epidemic wave, it would help to define groups at different risk and to identify high-risk subjects to target for specific prevention and therapeutic strategies.

Strategy to identify priority groups for COVID-19 vaccination: A population based cohort study.

BACKGROUND: Evidence from COVID-19 outbreak shows that individuals with specific chronic diseases are at higher risk of severe prognosis after infection. Public health authorities are developing vaccination programmes with priorities that minimize the risk of mortality and severe events in individuals and communities. We propose an evidence-based strategy that targets the frailest subjects whose timely vaccination is likely to minimize future deaths and preserve the resilience of the health service by preventing infections. METHODS: The cohort includes 146,087 cases with COVID-19 diagnosed in 2020 in Milan (3.49 million inhabitants). Individual level data on 42 chronic diseases and vital status updated as of January 21, 2021, were available in administrative data. Analyses were performed in three sub-cohorts of age (16-64, 65-79 and 80+ years) and comorbidities affecting mortality were selected by means of LASSO cross-validated conditional logistic regression. Simplified models based on previous results identified high-risk categories worth targeting with highest priority. Results adjusted by age and gender, were reported in terms of odds ratios and 95%CI. RESULTS: The final models include as predictors of mortality (7,667 deaths, 5.2%) 10, 12, and 5 chronic diseases, respectively. The older age categories shared, as risk factors, chronic renal failure, chronic heart failure, cerebrovascular disease, Parkinson disease and psychiatric diseases. In the younger age category, predictors included neoplasm, organ transplantation and psychiatric conditions. Results were consistent with those obtained on mortality at 60 days from diagnosis (6,968 deaths). CONCLUSION: This approach defines a two-level stratification for priorities in the vaccination that can easily be applied by health authorities, eventually adapted to local results in terms of number and types of comorbidities, and rapidly updated with current data. After the early phase of vaccination, data on effectiveness and safety will give the opportunity to revise prioritization and discuss the future approach in the remaining population.

Strategy to reduce adverse health outcomes in subjects highly vulnerable to COVID-19: results from a population-based study in Northern Italy.

OBJECTIVES: This study describes a new strategy to reduce the impact of COVID-19 on the elderly and other clinically vulnerable subjects, where general practitioners (GPs) play an active role in managing high-risk patients, reducing adverse health outcomes. DESIGN: Retrospective cohort study. SETTING: Population-based study including subjects resident in the province of Milan and Lodi. PARTICIPANTS: 127 735 residents older than 70 years, with specific chronic conditions. INTERVENTIONS: We developed a predictive algorithm for overall mortality risk based on demographic and clinical characteristics. All residents older than 70 years were classified as being at low or high risk of death from COVID-19 infection according to the algorithm. The high-risk group was assigned to their GPs for telephone triage and consultation. The high-risk cohort was divided into two groups based on GP intervention: patients who were not contacted and patients who were contacted by their GPs. OUTCOME MEASURES: Overall mortality, COVID-19 morbidity and hospitalisation. RESULTS: Patients with increased risk of death from COVID-19 were 127 735; 495 669 patients were not at high risk and were not included in the intervention. Out of the high-risk subjects, 79 110 were included but not contacted by their GPs, while 48 625 high-risk subjects were included and contacted. Overall mortality, morbidity and hospitalisation was higher in high-risk patients compared with low-risk populations. High-risk patients contacted by their GPs had a 50% risk reduction in COVID-19 mortality, and a 70% risk reduction in morbidity and hospitalisation for COVID-19 compared with non-contacted patients. CONCLUSIONS: The study showed that, during the COVID-19 outbreak, involvement of GPs and changes in care management of high-risk groups produced a significant reduction in all adverse health outcomes.

Assessing the Impact of Individual Characteristics and Neighborhood Socioeconomic Status During the COVID-19 Pandemic in the Provinces of Milan and Lodi.

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.

Trend of pneumonia diagnosis in emergency departments as a COVID-19 surveillance system: a time series study.

OBJECTIVE: In Italy, the first diagnosis of COVID-19 was confirmed on 20 February 2020 in the Lombardy region. Given the rapid spread of the infection in the population, it was suggested that in Europe, and specifically in Italy, the virus had already been present in the last months of 2019. In this paper, we aim to evaluate the hypothesis on the early presence of the virus in Italy by analysing data on trends of access to emergency departments (EDs) of subjects with a diagnosis of pneumonia during the 2015-2020 period. DESIGN: Time series cohort study. SETTING: We collected data on visits due to pneumonia between 1 October 2015 and 31 May 2020 in all EDs of the Agency for Health Protection of Milan (ATS of Milan). Trend in the winter of 2019-2020 was compared with those in the previous 4 years in order to identify unexpected signals potentially associated with the occurrence of the pandemic. Aggregated data were analysed using a Poisson regression model adjusted for seasonality and influenza outbreaks. PRIMARY OUTCOME MEASURES : Daily pneumonia-related visits in EDs. RESULTS : In the studied period, we observed 105 651 pneumonia-related ED visits. Compared with the expected, a lower occurrence was observed in January 2020, while an excess of pneumonia visits started in the province of Lodi on 21 February 2020, and almost 10 days later was observed in the remaining territory of the ATS of Milan. Overall, the peak in excess was found on 17 March 2020 (369 excess visits compared with previous years, 95% CI 353 to 383) and ended in May 2020, the administrative end of the Italian lockdown. CONCLUSIONS : An early warning system based on routinely collected administrative data could be a feasible and low-cost strategy to monitor the actual situation of the virus spread both at local and national levels.

Assessment of the Overall Mortality during the COVID-19 Outbreak in the Provinces of Milan and Lodi (Lombardy Region, Northern Italy).

OBJECTIVES: to describe the overall mortality increase in the provinces of Milan and Lodi - area covered by the Agency for Health Protection of Milan - during the COVID-19 epidemic in the first four months of 2020, compare it with the same time period in the years 2016-2019, and evaluate to what extent the mortality can be directly attributed to the outbreak. DESIGN: cohort study. SETTING AND PARTICIPANTS: using a new information system developed during the pandemic, we gathered data on the number of daily deaths in the population residing in the provinces of Milan and Lodi by Local Health Unit (ASST) and age groups. To describe the case fatality of COVID-19, we performed a record linkage with a database specially constructed during the epidemic to identify deaths that occurred in confirmed cases. MAIN OUTCOME MEASURES: mortality and excess mortality were analysed by comparing the number of observed deaths in the first 4 months of 2020 with the average deaths of the years 2016-2019 in the same calendar period and with expected deaths, estimated using a Poisson model. Furthermore, a measure of relative risk was calculated as observed/expected ratio with a 95% confidence interval. RESULTS: the increase in mortality for all causes occurring in the study population in the first 4 months of 2020 was 48.8%, 30.8% for ages between 60 and 69, 43.9% for ages between 70 and 79, and 56.7% for subjects above 80 years of age. Focusing on the epidemic period, from 1 March to 30 April, the excess is quantifiable as more than 2-fold and mainly concerns the population over 60 years of age. The excess mortality was observed in all local health units (ASSTs). The highest increments were in the province of Lodi and the North-East of Milan (ASST Nord). In the ASSTs of Lodi and Melegnano-Martesana the mortality excess was detectable from March 15th, while for the other ASSTs the increase began in the first week of April. CONCLUSIONS: evaluation of overall mortality in the provinces of Milan and Lodi during the first wave of the Covid-19 epidemic showed a significant excess compared to the first 4 months of the years 2016-2019, mainly in the population over 60 years of age. However, this excess cannot be completely attributed directly to COVID-19 itself. This phenomenon was more intense in the Lodi ASST, with daily deaths up to 5 times higher than expected.

Describing the epidemic trends of COVID-19 in the area covered by Agency for Health Protection of the Metropolitan Area of Milan.

OBJECTIVES: to describe the epidemic trends of COVID-19 over time and by area in the territory covered by Milan's Agency for Health Protection (ATS-MI) from February to May 2020. DESIGN: descriptive study of COVID-19 cases. SETTING AND PARTICIPANTS: a new information system was developed to record COVID-19 cases with positive nasopharyngeal swab. Patients resident in the area covered by ATS-MI with symptom onset between February and May 2020 were selected. Different epidemic periods were considered based on the timeline of the various regional and national containment measures. MAIN OUTCOME MEASURES: case fatality ratios, incidence rates, and reproduction number by epidemic period and sub-area of ATS-MI. RESULTS: a total of 27,017 swab-positive COVID-19 cases were included. Mean age was 65 years and males were 45%. Incidence in the ATS-MI area was 776 per 100,000 population. The number of deaths was 4,660, the crude case fatality ratio was 17.3%, higher in males (21.2%) than in females (14.0%). The estimated reproduction number registered its peak (3.0) in the early stages of the epidemic and subsequently decreased. Territorial differences were observed in the epidemic spread, with a higher incidence in the Lodi area. CONCLUSIONS: estimated incidence and case fatality ratios were higher than national estimates for Italy. Each ATS-MI area had different epidemic spread patterns.

Association between autoimmune diseases and COVID-19 as assessed in both a test-negative case-control and population case-control design.

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.