Clonal VGII subtypes (outbreak strains) of Cryptococcus gattii have caused an outbreak in the US Pacific Northwest since 2004. Outbreak-associated infections occur equally in male and female patients (median age 56 years) and usually cause pulmonary disease in persons with underlying medical conditions. Since 2009, a total of 25 C. gattii infections, 23 (92%) caused by non–outbreak strain C. gattii, have been reported from 8 non–Pacific Northwest states. Sixteen (64%) patients were previously healthy, and 21 (84%) were male; median age was 43 years (range 15–83 years). Ten patients who provided information reported no past-year travel to areas where C. gattii is known to be endemic. Nineteen (76%) patients had central nervous system infections; 6 (24%) died. C. gattii infection in persons without exposure to known disease-endemic areas suggests possible endemicity in the United States outside the outbreak-affected region; these infections appear to differ in clinical and demographic characteristics from outbreak-associated C. gattii. Clinicians outside the outbreak-affected areas should be aware of locally acquired C. gattii infection and its varied signs and symptoms.
As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.
Background Methods of measuring influenza vaccination of healthcare personnel (HCP) vary substantially, including which groups of HCP are included in measurements. Thus, comparison of vaccination rates across healthcare facilities is difficult. Purpose The goal of the study was to determine the feasibility of implementing a standardized measure for reporting HCP influenza vaccination data in various types of healthcare facilities. Methods A total of 318 facilities recruited in four U.S. jurisdictions agreed to participate in the evaluation, including hospitals, long-term care facilities, dialysis clinics, ambulatory surgery centers, and physician practices. HCP in participating facilities were categorized as employees, credentialed non-employees, or other non-employees using standard definitions. Data were gathered using cross-sectional web-based surveys completed at three intervals between October 2010 and May 2011 and analyzed in February 2012. Results 234 facilities (74%) completed all three surveys. Most facilities could report on-site employee vaccination; almost one third could not provide complete data on HCP vaccinated outside the facility, contraindications, or declinations, primarily due to missing non-employee data. Inability to determine vaccination status of credentialed and other non-employees was cited as a major barrier to measure implementation by 24% and 27% of respondents, respectively. Conclusions Using the measure to report employee vaccination status was feasible for most facilities; tracking non-employee HCP was more challenging. Based on evaluation findings, the measure was revised to limit the types of non-employees included. Although the revised measure is less comprehensive, it is more likely to produce valid vaccination coverage estimates. Use of this standardized measure can inform quality improvement efforts and facilitate comparison of HCP influenza vaccination among facilities.
Introduction Following a declaration by the World Health Organization that Liberia had successfully interrupted Ebola virus transmission on May 9th, 2015; the country entered a period of enhanced surveillance. The number of cases had significantly reduced prior to the declaration, leading to closure of eight out of eleven Ebola testing laboratories. Enhanced surveillance led to an abrupt increase in demand for laboratory services. We report interventions, achievements, lessons learned and recommendations drawn from enhancing laboratory capacity. Methods Using archived data, we reported before and after interventions that aimed at increasing laboratory capacity. Laboratory capacity was defined by number of laboratories with Ebola Virus Disease (EVD) testing capacity, number of competent staff, number of specimens tested, specimen backlog, daily and surge testing capacity, and turnaround time. Using Stata 14 (Stata Corporation, College Station, TX, USA), medians and trends were reported for all continuous variables. Results Between May and December 2015, interventions including recruitment and training of eight staff, establishment of one EVD laboratory facility, implementation of ten Ebola GeneXpert diagnostic platforms, and establishment of working shifts yielded an 8-fold increase in number of specimens tested, a reduction in specimens backlog to zero, and restoration of turn-around time to 24 hours. This enabled a more efficient surveillance system that facilitated timely detection and containment of two EVD clusters observed thereafter. Conclusion Effective enhancement of laboratory services during high demand periods requires a combination of context-specific interventions. Building and ensuring sustainability of local capacity is an integral part of effective surveillance and disease outbreak response efforts.
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