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Pathogenic Vibrio spp., including V. cholerae and V. vulnificus, are commonly found along the estuaries of the south-east United States; however, it is often difficult to recover these species directly from environmental samples. Pre-enrichment assays are commonly used to improve the detection of pathogenic vibrios from environmental sources. Here, we evaluated a novel enrichment procedure using freshly collected and autoclaved natural estuarine water amended with 1% peptone (designated as estuarine peptone water, EPW) and compared it to traditional alkaline peptone water (APW) for detection by PCR of V. cholerae and V. vulnificus. Of the 50 samples collected in total, V. cholerae DNA was detected in APW 10% of the time and in EPW 40% of the time. Likewise, the cholera toxin gene ( ctxA) was detected in 4 vs 18% of the samples using APW and EPW, respectively. Conversely, APW showed improved recovery for V. vulnificus relative to EPW with respective detection frequencies of 46 and 20%. Results showed similar patterns across different sample types (water and plankton). While enrichment in traditional APW was adequate for the recovery of Vibrio vulnificius, use of sterile estuarine water amended with peptone significantly improved the detection of V. cholerae and the virulence gene ctxA from estuarine sources. [ABSTRACT FROM AUTHOR]

Background: Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results., Methods: From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution., Results: Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool., Conclusion: Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level., Competing Interests: Declarations of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Resuscitation of cardiac arrest in coronavirus disease 2019 (COVID-19) patients places the healthcare staff at higher risk of exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Unfortunately, COVID-19 status is unknown in most patients presenting to the emergency department (ED), and therefore special attention must be given to protect the healthcare staff along with the other patients. This is particularly true for out-of-hospital cardiac arrest patients who are transported to the ED. Based on the current data available on transmissibility of SARS-CoV-2, we have proposed a protocolized approach to out-of-hospital cardiac arrests to limit risk of transmission.

Prospective hospital reimbursement based on Diagnosis Related Groups (DRGs) began in 1983 for Medicare patients, and many states are adopting similar systems for Medicaid recipients in an attempt to curb rising health care costs. Because of their unprecedented intrusiveness compared to previous cost-containment measures and because they explicitly introduce financial incentives to reduce services, DRGs have great potential to affect health care delivery. To determine the effects of DRGs on hospitals and physicians, six months of ethnographic fieldwork was carried out on the medicine and pediatrics services of a university-affiliated hospital during the first year of DRG-based reimbursement. Observations and interviews were used to discern institutional responses to DRGs and physician knowledge of, experience with, and reactions to this cost-containment effort. Our findings indicate that the hospital instituted many changes to protect its interests. Data gathered from patients' abstracts suggest providers are successfully dealing with the new system; the average length of stay for Medicare patients was reduced by 38% in the first year of prospective reimbursement, compared with a 15% reduction for other patients (P less than 0.05). As a group, the physicians made no organized effort to educate themselves about the new cost-containment regulations. Their knowledge of DRGs was vague and included many misconceptions. Their response was not a coherent one taking broad social concerns into account. Cost-containment was viewed negatively, as a threat to the financial integrity of the hospital, patient care, and professional autonomy.(ABSTRACT TRUNCATED AT 250 WORDS)