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To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.
Multicenter retrospective cohort study.
The study included 43 hospitals using a common infection prevention surveillance system.
A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.
We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.
Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.
We present a workflow to track icebergs in proglacial fjords using oblique time-lapse photos and the Lucas-Kanade optical flow algorithm. We employ the workflow at LeConte Bay, Alaska, where we ran five time-lapse cameras between April 2016 and September 2017, capturing more than 400 000 photos at frame rates of 0.5–4.0 min−1. Hourly to daily average velocity fields in map coordinates illustrate dynamic currents in the bay, with dominant downfjord velocities (exceeding 0.5 m s−1 intermittently) and several eddies. Comparisons with simultaneous Acoustic Doppler Current Profiler (ADCP) measurements yield best agreement for the uppermost ADCP levels (~ 12 m and above), in line with prevalent small icebergs that trace near-surface currents. Tracking results from multiple cameras compare favorably, although cameras with lower frame rates (0.5 min−1) tend to underestimate high flow speeds. Tests to determine requisite temporal and spatial image resolution confirm the importance of high image frame rates, while spatial resolution is of secondary importance. Application of our procedure to other fjords will be successful if iceberg concentrations are high enough and if the camera frame rates are sufficiently rapid (at least 1 min−1 for conditions similar to LeConte Bay).
Nanomedicine is yielding new and improved treatments and diagnostics for a range of diseases and disorders. Nanomedicine applications incorporate materials and components with nanoscale dimensions (often defined as 1-100 nm, but sometimes defined to include dimensions up to 1000 nm, as discussed further below) where novel physiochemical properties emerge as a result of size-dependent phenomena and high surface-to-mass ratio. Nanotherapeutics and in vivo nanodiagnostics are a subset of nanomedicine products that enter the human body. These include drugs, biological products (biologics), implantable medical devices, and combination products that are designed to function in the body in ways unachievable at larger scales. Nanotherapeutics and in vivo nanodiagnostics incorporate materials that are engineered at the nanoscale to express novel properties that are medicinally useful. These nanomedicine applications can also contain nanomaterials that are biologically active, producing interactions that depend on biological triggers. Examples include nanoscale formulations of insoluble drugs to improve bioavailability and pharmacokinetics, drugs encapsulated in hollow nanoparticles with the ability to target and cross cellular and tissue membranes (including the bloodbrain barrier) and to release their payload at a specific time or location, imaging agents that demonstrate novel optical properties to aid in locating micrometastases, and antimicrobial and drug-eluting components or coatings of implantable medical devices such as stents.
The Texas Department of State Health Services (DSHS) implemented an active mortality surveillance system to enumerate and characterize hurricane-related deaths during Hurricane Ike in 2008. This surveillance system used established guidelines and case definitions to categorize deaths as directly, indirectly, and possibly related to Hurricane Ike.
The objective of this study was to evaluate Texas DSHS' active mortality surveillance system using US Centers for Disease Control and Prevention's (CDC) surveillance system evaluation guidelines.
Using CDC's Updated Guidelines for Surveillance System Evaluation, the active mortality surveillance system of the Texas DSHS was evaluated. Data from the active mortality surveillance system were compared with Texas vital statistics data for the same time period to estimate the completeness of reported disaster-related deaths.
From September 8 through October 13, 2008, medical examiners (MEs) and Justices of the Peace (JPs) in 44 affected counties reported deaths daily by using a one-page, standardized mortality form. The active mortality surveillance system identified 74 hurricane-related deaths, whereas a review of vital statistics data revealed only four deaths that were hurricane-related. The average time of reporting a death by active mortality surveillance and vital statistics was 14 days and 16 days, respectively.
Texas's active mortality surveillance system successfully identified hurricane-related deaths. Evaluation of the active mortality surveillance system suggested that it is necessary to collect detailed and representative mortality data during a hurricane because vital statistics do not capture sufficient information to identify whether deaths are hurricane-related. The results from this evaluation will help improve active mortality surveillance during hurricanes which, in turn, will enhance preparedness and response plans and identify public health interventions to reduce future hurricane-related mortality rates.
Choudhary E, Zane DF, Beasley C, Jones R, Rey A, Noe RS, Martin C, Wolkin AF, Bayleyegn TM. Evaluation of active mortality surveillance system data for monitoring hurricane-related deaths, Texas, 2008. Prehosp Disaster Med. 2012;27(4):1-6.