Acquisition of extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-PE) carriage after exposure to systemic antimicrobials during travel: systematic review and meta-analysis

BACKGROUND: International travel is an important risk factor for colonization with extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-PE). Antimicrobial use during travel likely amplifies this risk, yet to what extent, and whether it varies by antimicrobial class, has not been established. METHODS: We conducted a systematic review that included prospective cohorts reporting both receipt of systemic antimicrobials and acquired ESBL-PE isolated from stool or rectum during international travel. We performed a random effects meta-analysis to estimate odds of acquiring ESBL-PE due to antimicrobials during travel, overall and by antimicrobial class. RESULTS: Fifteen studies were included. The study population was mainly female travellers from high income countries recruited primarily from travel clinics. Participants travelled most frequently to Asia and Africa with 10% reporting antimicrobial use during travel. The combined odds ratio (OR) for ESBL-PE acquisition during travel was 2.37 for antimicrobial use overall (95% confidence interval [CI], 1.69 to 3.33), but there was substantial heterogeneity between studies. Fluoroquinolones were the antibiotic class associated with the highest combined OR of ESBL-PE acquisition, compared to no antimicrobial use (OR 4.68, 95% CI, 2.34 to 9.37). CONCLUSIONS: The risk of ESBL-PE colonization during travel is increased substantially with exposure to antimicrobials, especially fluoroquinolones. While a small proportion of colonized individuals will develop a resistant infection, there remains the potential for onward spread among returning travellers. Public health efforts to decrease inappropriate antimicrobial usage during travel are warranted

Forecasting trachoma control and identifying transmission-hotspots

Background: Tremendous progress towards elimination of trachoma as a public health problem has been made. However, there are areas where the clinical indicator of disease, trachomatous inflammation—follicular (TF), remains prevalent. We quantify the progress that has been made, and forecast how TF prevalence will evolve with current interventions. We also determine the probability that a district is a transmission-hotspot based on its TF prevalence (ie, reproduction number greater than one). Methods: Data on trachoma prevalence come from the GET2020 global repository organized by the World Health Organization and the International Trachoma Initiative. Forecasts of TF prevalence and the percent of districts with local control is achieved by regressing the coefficients of a fitted exponential distribution for the year-by-year distribution of TF prevalence. The probability of a district being a transmission-hotspot is extrapolated from the residuals of the regression. Results: Forecasts suggest that with current interventions, 96.5% of surveyed districts will have TF prevalence among children aged 1–9 years Conclusions: Sustainable control of trachoma appears achievable. However there are transmission-hotspots that are not responding to annual mass drug administration of azithromycin and require enhanced treatment in order to reach local control.</p

Forecasting Trachoma Control and Identifying Transmission-Hotspots.

BackgroundTremendous progress towards elimination of trachoma as a public health problem has been made. However, there are areas where the clinical indicator of disease, trachomatous inflammation-follicular (TF), remains prevalent. We quantify the progress that has been made, and forecast how TF prevalence will evolve with current interventions. We also determine the probability that a district is a transmission-hotspot based on its TF prevalence (ie, reproduction number greater than one).MethodsData on trachoma prevalence come from the GET2020 global repository organized by the World Health Organization and the International Trachoma Initiative. Forecasts of TF prevalence and the percent of districts with local control is achieved by regressing the coefficients of a fitted exponential distribution for the year-by-year distribution of TF prevalence. The probability of a district being a transmission-hotspot is extrapolated from the residuals of the regression.ResultsForecasts suggest that with current interventions, 96.5% of surveyed districts will have TF prevalence among children aged 1-9 years &lt;5% by 2030 (95% CI: 86.6%-100.0%). Districts with TF prevalence &lt; 20% appear unlikely to be transmission-hotspots. However, a district having TF prevalence of over 28% in 2016-2019 corresponds to at least 50% probability of being a transmission-hotspot.ConclusionsSustainable control of trachoma appears achievable. However there are transmission-hotspots that are not responding to annual mass drug administration of azithromycin and require enhanced treatment in order to reach local control

Forecasting Trachoma Control and Identifying Transmission-Hotspots.

BackgroundTremendous progress towards elimination of trachoma as a public health problem has been made. However, there are areas where the clinical indicator of disease, trachomatous inflammation-follicular (TF), remains prevalent. We quantify the progress that has been made, and forecast how TF prevalence will evolve with current interventions. We also determine the probability that a district is a transmission-hotspot based on its TF prevalence (ie, reproduction number greater than one).MethodsData on trachoma prevalence come from the GET2020 global repository organized by the World Health Organization and the International Trachoma Initiative. Forecasts of TF prevalence and the percent of districts with local control is achieved by regressing the coefficients of a fitted exponential distribution for the year-by-year distribution of TF prevalence. The probability of a district being a transmission-hotspot is extrapolated from the residuals of the regression.ResultsForecasts suggest that with current interventions, 96.5% of surveyed districts will have TF prevalence among children aged 1-9 years &lt;5% by 2030 (95% CI: 86.6%-100.0%). Districts with TF prevalence &lt; 20% appear unlikely to be transmission-hotspots. However, a district having TF prevalence of over 28% in 2016-2019 corresponds to at least 50% probability of being a transmission-hotspot.ConclusionsSustainable control of trachoma appears achievable. However there are transmission-hotspots that are not responding to annual mass drug administration of azithromycin and require enhanced treatment in order to reach local control

Practical considerations for measuring the effective reproductive number, R-t

Estimation of the effective reproductive number R-t is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using R-t to assess the effectiveness of interventions and to inform policy. However, estimation of R-t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R-t, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting R-t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R-t estimation.Author summaryThe effective reproductive number R-t is a key epidemic parameter used to assess whether an epidemic is growing, shrinking, or holding steady. R-t estimates can be used as a near real-time indicator of epidemic growth or to assess the effectiveness of interventions. But due to delays between infection and case observation, estimating R-t in near real time, and correctly inferring the timing of changes in R-t, is challenging. Here, we provide an overview of challenges and best practices for accurate and timely R-t estimation.Development and application of statistical models for medical scientific researc

Practical considerations for measuring the effective reproductive number, Rt

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation

Single-Server Queues with Spatially Distributed Arrivals

Consider a queueing system where customers arrive at a circle according to a homogeneous Poisson process. After choosing their positions on the circle, according to a uniform distribution, they wait for a single server who travels on the circle. The server's movement is modelled by a Brownian motion with drift. Whenever the server encounters a customer, he stops and serves this customer. The service times are independent, but arbitrarily distributed. The model generalizes the continuous cyclic polling system (the diffusion coefficient of the Brownian motion is zero in this case) and can be interpreted as a continuous version of a Markov polling system. Using Tweedie's lemma for positive recurrence of Markov chains with general state space, we show that the system is stable if and only if the traffic intensity is less than one. Moreover, we derive a stochastic decomposition result which leads to equilibrium equations for the stationary configuration of customers on the circle. Steady-state performance characteristics are determined, in particular the expected number of customers in the system as seen by a travelling server and at an arbitrary point in time