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The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82–0.87) and 0.67 (95% CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22–1.53 vs. 1.20 95% CI 1.14–1.25; HRs for mortality: 3.96 95% CI 1.82–8.60 vs. 1.71 95% CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48–79.87 vs. 3.91 95% CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88–1.12) and mortality (MRR: 0.89 95% CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41–2.11 among HDC vs. 1.41 95% CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

Objective We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.Design and setting Retrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.Participants 450 patients discharged from the ED with diagnosis of COVID-19.Main outcome measure ED readmission within 14 days, followed by hospitalisation/death.Results Of the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.Conclusions A velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.

Objectives To determine the timing of viral clearance (first negative RT-PCR on nasopharyngeal swab) and the probability of viral clearance confirmation (two consecutive negative swabs) in COVID-19 patients and to identify related determinants.Design Population-based prospective cohort study on archive data.Setting Preventive services and hospital care in the Reggio Emilia province, northern Italy.Participants All 1162 subjects testing positive to RT-PCR on nasopharyngeal swabs and diagnosed with COVID-19 in the Reggio Emilia province with at least 30 days of follow-up by 22 April 2020.Main outcome measures Median times from diagnosis and from symptom onset to viral clearance with IQR assessed using the Kaplan–Meier estimator, stratified by included characteristics. The probability of viral clearance confirmation, stratified by time from diagnosis and putative determinants assessed using a multivariate logistic regression model.Results Viral clearance was achieved by 60.6% (704/1162) of patients, with a median time of 30 days from diagnosis (IQR 23–40) and 36 days from symptom onset (IQR 28–45). Of those negative and retested, 78.7% (436/554) had viral clearance confirmation, suggesting one in five false negative tests. The time from symptom onset to viral clearance slightly increased with age, from 35 (IQR 26–44) days under age 50 to 38 (IQR 28–44) in over age 80, and with disease severity, from 33 (IQR 25–41) days in non-hospitalised subjects to 38 (IQR 30–47) days in hospitalised patients. The probability of confirmed viral clearance reached 86.8% after 34 days from symptom onset and increased with time, even when adjusting for age and sex (OR 1.16 95% CI 1.06 to 1.26 per day from diagnosis).Conclusions Postponing follow-up testing of clinically recovered COVID-19 patients could increase the efficiency and performance of testing protocols. Understanding viral shedding duration also has implications for containment measures of paucisymptomatic subjects.