Project description:Patient days and days present were compared to directly measured person time to quantify how choice of different denominator metrics may affect antimicrobial use rates. Overall, days present were approximately one-third higher than patient days. This difference varied among hospitals and units and was influenced by short length of stay.Infect Control Hosp Epidemiol 2018;39:612-615.
Project description:We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.
Project description:Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.
Project description:ObjectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused devastation in over 200 countries. Italy, Spain, and the United States (US) were most severely affected by the first wave of the pandemic. The reasons why some countries were more strongly affected than others remain unknown. We identified the most-affected and less-affected countries and states and explored environmental, host, and infrastructure risk factors that may explain differences in the SARS-CoV-2 mortality burden.MethodsWe identified the top 10 countries/US states with the highest deaths per population until May 2020. For each of these 10 case countries/states, we identified 6 control countries/states with a similar population size and at least 3 times fewer deaths per population. We extracted data for 30 risk factors from publicly available, trusted sources. We compared case and control countries/states using the non-parametric Wilcoxon rank-sum test, and conducted a secondary cluster analysis to explore the relationship between the number of cases per population and the number of deaths per population using a scalable EM (expectation-maximization) clustering algorithm.ResultsStatistically significant differences were found in 16 of 30 investigated risk factors, the most important of which were temperature, neonatal and under-5 mortality rates, the percentage of under-5 deaths due to acute respiratory infections (ARIs) and diarrhea, and tuberculosis incidence (p < 0.05).ConclusionCountries with a higher burden of baseline pediatric mortality rates, higher pediatric mortality from preventable diseases like diarrhea and ARI, and higher tuberculosis incidence had lower rates of coronavirus disease 2019-associated mortality, supporting the hygiene hypothesis.
Project description:In this study we profiled 288 new serum proteomics samples measured at admission from patients hospitalized within the Mount Sinai Health System with positive SARS-CoV-2 infection. We first computed Th1 and Th2 pathway enrichment scores by gene set variation analysis and then compared the differences in Th2 and Th1 pathway scores between patients that died compared to those that survived.
Project description:Physical distancing has been argued as one of the effective means to combat the spread of COVID-19 before a vaccine or therapeutic drug becomes available. How far people can be spatially separated is partly behavioral but partly constrained by population density. Most models developed to predict the spread of COVID-19 in the U.S. do not include population density explicitly. This study shows that population density is an effective predictor of cumulative infection cases in the U.S. at the county level. Daily cumulative cases by counties are converted into 7-day moving averages. Treating the weekly averages as the dependent variable and the county population density levels as the explanatory variable, both in logarithmic scale, this study assesses how population density has shaped the distributions of infection cases across the U.S. from early March to late May, 2020. Additional variables reflecting the percentages of African Americans, Hispanic-Latina, and older adults in logarithmic scale are also included. Spatial regression models with a spatial error specification are also used to account for the spatial spillover effect. Population density alone accounts for 57% of the variation (R-squared) in the aspatial models and up to 76% in the spatial models. Adding the three population subgroup percentage variables raised the R-squared of the aspatial models to 72% and the spatial model to 84%. The influences of the three population subgroups were substantial, but changed over time, while the contributions of population density have been quite stable after the first several weeks, ascertaining the importance of population density in shaping the spread of infection in individual counties, and in their neighboring counties. Thus, population density and sizes of vulnerable population subgroups should be explicitly included in transmission models that predict the impacts of COVID-19, particularly at the sub-county level.
Project description:BackgroundAs governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change.MethodsWe build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R 0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R 0 is estimated through the model.FindingsThe changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R 0.InterpretationOur model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R 0 cause small errors to have extensive effects on the predicted outcome.
Project description:Ecologic studies investigating COVID-19 mortality determinants, used to make predictions and design public health control measures, generally focused on population-based variable counterparts of individual-based risk factors. Influenza is not causally associated with COVID-19, but shares population-based determinants, such as similar incidence/mortality trends, transmission patterns, efficacy of non-pharmaceutical interventions, comorbidities and underdiagnosis. We investigated the ecologic association between influenza mortality rates and COVID-19 mortality rates in the European context. We considered the 3-year average influenza (2014-2016) and COVID-19 (31 May 2020) crude mortality rates in 34 countries using EUROSTAT and ECDC databases and performed correlation and regression analyses. The two variables - log transformed, showed significant Spearman's correlation ? = 0.439 (P = 0.01), and regression coefficients, b = 0.743 (95% confidence interval, 0.272-1.214; R2 = 0.244; P = 0.003), b = 0.472 (95% confidence interval, 0.067-0.878; R2 = 0.549; P = 0.02), unadjusted and adjusted for confounders (population size and cardiovascular disease mortality), respectively. Common significant determinants of both COVID-19 and influenza mortality rates were life expectancy, influenza vaccination in the elderly (direct associations), number of hospital beds per population unit and crude cardiovascular disease mortality rate (inverse associations). This analysis suggests that influenza mortality rates were independently associated with COVID-19 mortality rates in Europe, with implications for public health preparedness, and implies preliminary undetected SARS-CoV-2 spread in Europe.