ABSTRACT
The ongoing COVID-19 pandemic has raised concerns about the risk of SARS-CoV-2 infection in patients with Crohn's disease (CD) and patients with ulcerative colitis (UC) taking immunosuppressants or biologics. We conducted a systematic review and meta-analysis to assess the risk of respiratory infections in patients with inflammatory bowel disease (IBD) treated with vedolizumab. We searched PubMed, EMBASE and Scopus to identify randomized controlled trials (RCT) comparing vedolizumab to placebo in patients with IBD. Outcomes were the rate of respiratory tract infections (RTI), upper respiratory tract infections (URTI) and lower respiratory tract infections (LRTI) among patients receiving vedolizumab as compared with placebo. Pooled rates were reported as Odds Ratios (OR) with 95% Confidence Interval (CI). Eight RCT involving 3,287 patients (1873 CD and 1415 UC) were analyzed; 2,493 patients received vedolizumab and 794 received placebo. The rates of RTI and URTI were statistically higher in vedolizumab-treated patients compared to placebo [OR = 1.63; 95% CI (1.07-2.49); OR = 1.64 95% CI (1.07-2.53) respectively]. UC patients, but not CD patients, receiving vedolizumab had a higher risk to develop RTI and URTI [OR = 1.98; 95% CI (1.41-2.77); OR = 2.02; 95% CI (1.42-2.87)] compared to placebo-treated patients. The number of LRTI was small in both treatment groups. Data confirm the good safety profile of vedolizumab even though RTI were more frequent in patients receiving vedolizumab and the risk of URTIs was significantly higher in patients with UC.
ABSTRACT
OBJECTIVES: A major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. METHODS: A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. RESULTS: A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture-recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. CONCLUSIONS: The proposed method answers to a fundamental open question: "How many undetected cases are going around?". CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.