Intensive care unit (ICU)-acquired bacteraemia and ICU mortality and discharge:Addressing time-varying confounding using appropriate methodology

Background: Studies often ignore time-varying confounding or may use inappropriate methodology to adjust for time-varying confounding. Aim: To estimate the effect of intensive care unit (ICU)-acquired bacteraemia on ICU mortality and discharge using appropriate methodology. Methods: Marginal structural models with inverse probability weighting were used to estimate the ICU mortality and discharge associated with ICU-acquired bacteraemia among patients who stayed more than two days at the general ICU of a London teaching hospital and remained bacteraemia-free during those first two days. For comparison, the same associations were evaluated with (i) a conventional Cox model, adjusting only for baseline confounders and (ii) a Cox model adjusting for baseline and time-varying confounders. Findings: Using the marginal structural model with inverse probability weighting, bacteraemia was associated with an increase in ICU mortality (cause-specific hazard ratio (CSHR): 1.29; 95% confidence interval (CI): 1.02-1.63)and a decrease in discharge (CSHR: 0.52; 95% CI: 0.45-0.60). By 60 days, among patients still in the ICU after two days and without prior bacteraemia, 8.0% of ICU deaths could be prevented by preventing all ICU-acquired bacteraemia cases. The conventional Cox model adjusting for time-varying confounders gave substantially different results [for ICU mortality, CSHR: 1.08 (95% CI: 0.88-1.32); for discharge, CSHR: 0.68 (95% CI: 0.60-0.77)]. Conclusion: In this study, even after adjusting for the timing of acquiring bacteraemia and time-varying confounding using inverse probability weighting for marginal structura

Comparison of contact patterns relevant for transmission of respiratory pathogens in Thailand and the Netherlands using respondent-driven sampling

Understanding infection dynamics of respiratory diseases requires the identification and quantification of behavioural, social and environmental factors that permit the transmission of these infections between humans. Little empirical information is available about contact patterns within real-world social networks, let alone on differences in these contact networks between populations that differ considerably on a socio-cultural level. Here we compared contact network data that were collected in the Netherlands and Thailand using a similar online respondent-driven method. By asking participants to recruit contact persons we studied network links relevant for the transmission of respiratory infections. We studied correlations between recruiter and recruited contacts to investigate mixing patterns in the observed social network components. In both countries, mixing patterns were assortative by demographic variables and random by total numbers of contacts. However, in Thailand participants reported overall more contacts which resulted in higher effective contact rates. Our findings provide new insights on numbers of contacts and mixing patterns in two different populations. These data could be used to improve parameterisation of mathematical models used to design control strategies. Although the spread of infections through populations depends on more factors, found similarities suggest that spread may be similar in the Netherlands and Thailand

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

The spread of infectious diseases crucially depends on the pattern of contacts among individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. Few empirical studies are however available that provide estimates of the number and duration of contacts among social groups. Moreover, their space and time resolution are limited, so that data is not explicit at the person-to-person level, and the dynamical aspect of the contacts is disregarded. Here, we want to assess the role of data-driven dynamic contact patterns among individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. We consider high resolution data of face-to-face interactions between the attendees of a conference, obtained from the deployment of an infrastructure based on Radio Frequency Identification (RFID) devices that assess mutual face-to-face proximity. The spread of epidemics along these interactions is simulated through an SEIR model, using both the dynamical network of contacts defined by the collected data, and two aggregated versions of such network, in order to assess the role of the data temporal aspects. We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation which retains only the topology of the contact network fails in reproducing the size of the epidemic. These results have important implications in understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics

The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?

Mathematical models are useful tools for understanding and predicting epidemics. A recent innovative modeling study by Stehle and colleagues addressed the issue of how complex models need to be to ensure accuracy. The authors collected data on face-to-face contacts during a two-day conference. They then constructed a series of dynamic social contact networks, each of which was used to model an epidemic generated by a fast-spreading airborne pathogen. Intriguingly, Stehle and colleagues found that increasing model complexity did not always increase accuracy. Specifically, the most detailed contact network and a simplified version of this network generated very similar results. These results are extremely interesting and require further exploration to determine their generalizability

Does appropriate empiric antibiotic therapy modify intensive care unit-acquired Enterobacteriaceae bacteraemia mortality and discharge?

BACKGROUND: Conflicting results have been found regarding outcomes of intensive care unit (ICU)-acquired Enterobacteriaceae bacteraemia and the potentially modifying effect of appropriate empiric antibiotic therapy. AIM: To evaluate these associations while adjusting for potential time-varying confounding using methods from the causal inference literature. METHODS: Patients who stayed more than two days in two general ICUs in England between 2002 and 2006 were included in this cohort study. Marginal structural models with inverse probability weighting were used to estimate the mortality and discharge associated with Enterobacteriaceae bacteraemia and the impact of appropriate empiric antibiotic therapy on these outcomes. FINDINGS: Among 3411 ICU admissions, 195 (5.7%) ICU-acquired Enterobacteriaceae bacteraemia cases occurred. Enterobacteriaceae bacteraemia was associated with an increased daily risk of ICU death [cause-specific hazard ratio (HR): 1.48; 95% confidence interval (CI): 1.10-1.99] and a reduced daily risk of ICU discharge (HR: 0.66; 95% CI: 0.54-0.80). Appropriate empiric antibiotic therapy did not significantly modify ICU mortality (HR: 1.08; 95% CI: 0.59-1.97) or discharge (HR: 0.91; 95% CI: 0.63-1.32). CONCLUSION: ICU-acquired Enterobacteriaceae bacteraemia was associated with an increased daily risk of ICU mortality. Furthermore, the daily discharge rate was also lower after acquiring infection, even when adjusting for time-varying confounding using appropriate methodology. No evidence was found for a beneficial modifying effect of appropriate empiric antibiotic therapy on ICU mortality and discharge

Six challenges in measuring contact networks for use in modelling.

Contact networks are playing an increasingly important role in epidemiology. A contact network represents individuals in a host population as nodes and the interactions among them that may lead to the transmission of infection as edges. New avenues for data collection in recent years have afforded us the opportunity to collect individual- and population-scale information to empirically describe the patterns of contact within host populations. Here, we present some of the current challenges in measuring empirical contact networks. We address fundamental questions such as defining contact; measurement of non-trivial contact properties; practical issues of bounding measurement of contact networks in space, time and scope; exploiting proxy information about contacts; dealing with missing data. Finally, we consider the privacy and ethical issues surrounding the collection of contact network data

Minimal models for spatially resolved population dynamics : applications to coexistence in multi – trait models

Spatial resolution is relevant for many processes in population dynamics because it may give rise to heterogeneity. Simulating the effect of space in two or three dimensions is computationally costly. Furthermore, in Euclidean space, the notion of heterogeneity is complemented by neighbourhood correlations. In this paper, we use an infinite-dimensional simplex as a minimal model of space in which heterogeneity is realized, but neighbourhood is trivial and study the coexistence of viral traits in a SIRS - model. As a function of the migration parameter, multiple regimes are observed. We further discuss the relevance of minimal models for decision support

A mechanistic model of infection: why duration and intensity of contacts should be included in models of disease spread

<p>Abstract</p> <p>Background</p> <p>Mathematical models and simulations of disease spread often assume a constant per-contact transmission probability. This assumption ignores the heterogeneity in transmission probabilities, e.g. due to the varying intensity and duration of potentially contagious contacts. Ignoring such heterogeneities might lead to erroneous conclusions from simulation results. In this paper, we show how a mechanistic model of disease transmission differs from this commonly used assumption of a constant per-contact transmission probability.</p> <p>Methods</p> <p>We present an exposure-based, mechanistic model of disease transmission that reflects heterogeneities in contact duration and intensity. Based on empirical contact data, we calculate the expected number of secondary cases induced by an infector (i) for the mechanistic model and (ii) under the classical assumption of a constant per-contact transmission probability. The results of both approaches are compared for different basic reproduction numbers <it>R</it>₀.</p> <p>Results</p> <p>The outcomes of the mechanistic model differ significantly from those of the assumption of a constant per-contact transmission probability. In particular, cases with many different contacts have much lower expected numbers of secondary cases when using the mechanistic model instead of the common assumption. This is due to the fact that the proportion of long, intensive contacts decreases in the contact dataset with an increasing total number of contacts.</p> <p>Conclusion</p> <p>The importance of highly connected individuals, so-called super-spreaders, for disease spread seems to be overestimated when a constant per-contact transmission probability is assumed. This holds particularly for diseases with low basic reproduction numbers. Simulations of disease spread should weight contacts by duration and intensity.</p

Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics