Healthcare workers and health care-associated infections: knowledge, attitudes, and behavior in emergency departments in Italy

<p>Abstract</p> <p>Background</p> <p>This survey assessed knowledge, attitudes, and compliance regarding standard precautions about health care-associated infections (HAIs) and the associated determinants among healthcare workers (HCWs) in emergency departments in Italy.</p> <p>Methods</p> <p>An anonymous questionnaire, self-administered by all HCWs in eight randomly selected non-academic acute general public hospitals, comprised questions on demographic and occupational characteristics; knowledge about the risks of acquiring and/or transmitting HAIs from/to a patient and standard precautions; attitudes toward guidelines and risk perceived of acquiring a HAI; practice of standard precautions; and sources of information.</p> <p>Results</p> <p>HCWs who know the risk of acquiring Hepatitis C (HCV) and Human Immunodeficiency Virus (HIV) from a patient were in practice from less years, worked fewer hours per week, knew that a HCW can transmit HCV and HIV to a patient, knew that HCV and HIV infections can be serious, and have received information from educational courses and scientific journals. Those who know that gloves, mask, protective eyewear, and hands hygiene after removing gloves are control measures were nurses, provided care to fewer patients, knew that HCWs' hands are vehicle for transmission of nosocomial pathogens, did not know that a HCW can transmit HCV and HIV to a patient, and have received information from educational courses and scientific journals. Being a nurse, knowing that HCWs' hands are vehicle for transmission of nosocomial pathogens, obtaining information from educational courses and scientific journals, and needing information were associated with a higher perceived risk of acquiring a HAI. HCWs who often or always used gloves and performed hands hygiene measures after removing gloves were nurses, provided care to fewer patients, and knew that hands hygiene after removing gloves was a control measure.</p> <p>Conclusions</p> <p>HCWs have high knowledge, positive attitudes, but low compliance concerning standard precautions. Nurses had higher knowledge, perceived risk, and appropriate HAIs' control measures than physicians and HCWs answered correctly and used appropriately control measures if have received information from educational courses and scientific journals.</p

Predicting Hospital-Acquired Infections by Scoring System with Simple Parameters

BACKGROUND: Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings

Advances in methods for detection of anaerobic ammonium oxidizing (anammox) bacteria

Anaerobic ammonium oxidation (anammox), the biochemical process oxidizing ammonium into dinitrogen gas using nitrite as an electron acceptor, has only been recognized for its significant role in the global nitrogen cycle not long ago, and its ubiquitous distribution in a wide range of environments has changed our knowledge about the contributors to the global nitrogen cycle. Currently, several groups of methods are used in detection of anammox bacteria based on their physiological and biochemical characteristics, cellular chemical composition, and both 16S rRNA gene and selective functional genes as biomarkers, including hydrazine oxidoreductase and nitrite reductase encoding genes hzo and nirS, respectively. Results from these methods coupling with advances in quantitative PCR, reverse transcription of mRNA genes and stable isotope labeling have improved our understanding on the distribution, diversity, and activity of anammox bacteria in different environments both natural and engineered ones. In this review, we summarize these methods used in detection of anammox bacteria from various environments, highlight the strengths and weakness of these methods, and also discuss the new development potentials on the existing and new techniques in the future