scholarly journals Bacteria Associated with Healthcare-Associated Infections on Environmental Samples Obtained from Two Fire Departments

2021 ◽  
Vol 18 (22) ◽  
pp. 11885
Author(s):  
Kelli L. Barr ◽  
Rodney X. Sturdivant ◽  
Denise N. Williams ◽  
Debra Harris
Keyword(s):  
Fire Department ◽  
Occupational Exposures ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Fire Departments ◽  
E Coli ◽  
Medical Emergencies ◽  
Fire Stations ◽  
Healthcare Associated ◽  
In Fire

(1) Background: Firefighters spend about 64% of their time responding to medical emergencies and providing medical care without a patient history, which can render them vulnerable to healthcare-associated infections (HAI). Infection prevention, control, and surveillance systems have been instituted at hospitals. However, the prevalence of firefighters’ exposure to HAI is unknown. The objective of this study was to document evidence of HAI on surfaces in fire stations and engines to inform disinfection procedures and identify which pathogens might contribute to occupational exposures. (2) Methods: High-touch or high-use surfaces of two fire departments were sampled during five separate occasions. One fire station from one fire department was sampled over a 4-week period, whereas four fire stations were sampled from a different fire department only once. Sampled surfaces included: entryway floor, washing machine, medical bag, back seat of engine, keyboard of reporting computer, engine console, and uniform pants. (3) Results: Multiple statistical models determined that bacterial contamination was similar between the two fire departments and their stations. Keyboards were the most contaminated surface for all fire stations and departments, E. coli was the most common bacteria detected, and C. difficile was the least detected bacteria. Adjustments for rates of contamination found that contamination rates varied between fire stations. (4) Conclusions: Comprehensive environmental sampling and clinical studies are needed to better understand occupational exposures of firefighters to HAI.

Surveillance for Healthcare associated Infections in Indian NICUs: Researchable Issues

2009 ◽  
Vol 23 (4) ◽  
pp. 331-336
Author(s):  
Ashwani Kumar ◽  
Praveen Kumar
Keyword(s):  
Developed Countries ◽  
Control Measures ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Infection Control Measures ◽  
Infection Surveillance ◽  
Healthcare Associated ◽  
Symptoms And Signs ◽  
Local Research ◽  
Underlying Risk

Systematic surveillance is the first and integral step of all infection control measures, especially in intensive care settings. Surveillance systems started evolving in developed countries nearly 40 years ago. With experience and wisdom gained, the surveillance methods have improved and become more standardized. It is now clearly recognized that all patients are not at equal risk. For fair comparisons over time within an unit and in between units, the denominator must take the underlying risk into account. Infection surveillance in the NICU presents a number of unique challenges regarding definitions and differing symptoms and signs in the neonate. Although the importance of surveillance is being increasingly recognized in our country and the methods of developed countries are being adopted, there are numerous issues which need local research. This is in view of the limited manpower and financial resources and different profile of organisms and their epidemiology.

scholarly journals 2467. Inferring Strain Type Attribution from Antibiotic Resistance Profiles among E. coli Causing Healthcare-Associated Infections in the United States, 2013–2017

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S854-S854
Author(s):  
Athena P Kourtis ◽  
Joseph D Lutgring ◽  
Edward Sheriff ◽  
Alison L Halpin ◽  
James Rasheed ◽  
...  
Keyword(s):  
United States ◽  
Laboratory Data ◽  
The United States ◽  
Targeted Prevention ◽  
Healthcare Associated Infections ◽  
E Coli ◽  
Laboratory Surveillance ◽  
Strain Type ◽  
Tract Infections ◽  
Healthcare Associated

Abstract Background E. coli is a leading cause of healthcare-associated infections; clonal group ST131, which has expanded worldwide with notable increased severity of infections, is commonly resistant to extended-spectrum cephalosporins (ESC) and fluoroquinolones (FQ). Herein, we relate ESC and FQ resistance profiles from CDC’s National Healthcare Safety Network (NHSN) with specific strain types from CDC laboratory surveillance collections. Methods NHSN isolate and antibiotic susceptibility testing data were collected from all E. coli associated with central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated events, or surgical site infections from 2013–2017. Resistance was scored as non-susceptibility to at least one drug per class [susceptible (S); resistant (R)]. ESC and FQ susceptibilities and multilocus sequence types (ST) using the Achtman 7 loci scheme were determined for a contemporaneous set of E. coli isolates collected through CDC laboratory surveillance. Results Of 96,672 E. coli infections reported to NHSN, 13% were ESC-R/FQ-R, 23% ESC-S/FQ-R, 4% ESC-R/FQ-S, and 60% were ESC-S/FQ-S. Among 105 ESC-R/FQ-R and 21 ESC-S/FQ-R laboratory isolates, the majority (67.6% and 52.4%, respectively) were ST131, whereas of 38 ESC-R/FQ-S and 53 ESC-S/FQ-S isolates, ST131 was a minority (18.4% and 7.5%, respectively). The odds of an isolate being ST131 were 10.5 if FQ-R (P < 0.001), 3.4 if ESC-R (P < 0.001), and 6.0 if ESC-R/FQ-R (P < 0.001). Using the national distribution of resistance combinations from NHSN, and assuming static ST-resistance distribution, we can infer that ST131 was responsible for 25.8% (95% CI, 23.9%-27.6%) of all E.coli healthcare-associated infections in the United States in 2013–2017. Conclusion Molecular inferences generated by applying laboratory data to resistance signature data in reportable datasets may make national E. coli ST burden estimates possible. Further characterization of resistance combinations with strain type, infection rates, and clinical outcomes may inform targeted prevention strategies at the local/regional level. Disclosures All authors: No reported disclosures.

scholarly journals Disinfectant Activity of A Portable Ultraviolet C Equipment

2019 ◽  
Vol 16 (23) ◽  
pp. 4747 ◽  
Author(s):  
Andrea Guridi ◽  
Elena Sevillano ◽  
Iñigo de la Fuente ◽  
Estibaliz Mateo ◽  
Elena Eraso ◽  
...  
Keyword(s):  
Antimicrobial Activities ◽  
Pathogen Transmission ◽  
Hospital Environment ◽  
Healthcare Associated Infections ◽  
Promising Alternative ◽  
Major Health ◽  
Initial Inoculum ◽  
E Coli ◽  
Ultraviolet C ◽  
Healthcare Associated

Healthcare-associated infections (HAIs) can be caused by microorganisms present in common practice instruments generating major health problems in the hospital environment. The aim of this work was to evaluate the disinfection capacity of a portable ultraviolet C equipment (UV Sanitizer Corvent® -UVSC-) developed to disinfect different objects. For this purpose, six pathogens causing HAIs: Acinetobacter baumannii, Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Candida albicans, were inoculated on slides and discs of different biomaterials (borosilicate, polycarbonate, polyurethane, silicone, Teflon and titanium) and exposed to ultraviolet C radiation. UVSC disinfection was compared with ethanol and chlorhexidine antimicrobial activities following the standards EN14561 and EN14562. Disinfection, established as a reduction of five logarithms from the initial inoculum, was achieved with the UVSC at 120 s of exposure time, with and without the presence of organic matter. The disinfectant effect was observed against S. aureus, P. aeruginosa, E. coli, B. subtilis and C. albicans (reduction >99.999%). Disinfection was also achieved with 70% ethanol and 2% chlorhexidine. As conclusion, UVSC was effective disinfecting the most contaminated surfaces assayed, being a promising alternative for disinfecting hospital materials and inanimate objects that cannot be immersed in liquid biocides, reducing the risk of pathogen transmission.

scholarly journals Impact of Healthcare-Associated Infections on Length of Stay: A Study in 68 Hospitals in China

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Huixue Jia ◽  
Liuyi Li ◽  
Weiguang Li ◽  
Tieying Hou ◽  
Hongqiu Ma ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Bacterial Infections ◽  
Matched Control ◽  
Multidrug Resistant ◽  
Healthcare Associated Infections ◽  
E Coli ◽  
Significant Difference ◽  
Multidrug Resistant Pathogens ◽  
Healthcare Associated ◽  
Different Levels

Healthcare-associated infections (HAIs) not only bring additional medical cost to the patients but also prolong the length of stay (LOS). 2119 HAI case-patients and 2119 matched control-patients were identified in 68 hospitals in 14 primary sampling provinces of 7 major regions of China. The HAI caused an increase in stay of 10.4 days. The LOS due to HAI increased from 9.7 to 10.9 days in different levels of hospitals. There was no statistically significant difference in the increased LOS between different hospital levels. The increased LOS due to HAI in different regions was 8.2 to 12.6 days. Comparing between regions, we found that the increased LOS due to HAI in South China is longer than other regions except the Northeast. The gastrointestinal infection (GI) caused the shortest extra LOS of 6.7 days while the BSI caused the longest extra LOS of 12.8 days. The increased LOS for GI was significantly shorter than that of other sites. Among 2119 case-patients, the non-multidrug-resistant pathogens were detected in 365 cases. The average increased LOS due to these bacterial infections was 12.2 days. E. coli infection caused significantly shorter LOS. The studied MDROs, namely, MRSA, VRE, ESBLs-E. coli, ESBLs-KP, CR-E. coli, CR-KP, CR-AB, and CR-PA were detected in 381 cases (18.0%). The average increased LOS due to these MDRO infections was 14 days. Comparing between different MDRO infections, we found that the increased LOS due to HAI caused by CR-PA (26.5 days) is longer than other MDRO infections (shorter than 19.8 days).

scholarly journals Artificial Intelligence-based tools to control healthcare associated infections: where do we stand

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
A Scardoni ◽  
F Balzarini ◽  
F Cabitza ◽  
A Odone
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Performance Measures ◽  
Performance Outcomes ◽  
Health Concern ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Public Health Action ◽  
Practice Performance ◽  
Healthcare Associated

Abstract Background Control of Healthcare associated infections (HAI) is a key public health concern in Europe. Current HAI surveillance systems are based on manual medical records review, vulnerable to misclassification and expensive. Artificial intelligence (AI) offers great potential to public health action and, specifically, to HAI control. Still, scant evidence is available on both its practice and impact. Methods As part of a broader multidisciplinary project, we conducted a systematic review to retrieve, pool and critically apprize all the available evidence on practice, performance and impact of AI-based HAI control programmes. We followed PRISMA guidelines and searched the Medline and Embase databases for relevant studies. Included studies were stratified by HAI type and outcomes of interest, including all possible performance measures, clinical, organizational and economic outcomes. Results We screened 2873 records, resulting in 27 papers included in the review. Studies were carried out in 9 countries, the majority in the US (56%), 18.5% in EU countries, 25.9% published in 2018. Two thirds of studies focused on selected types of infections. Study designs were very diverse and performance observed for HAI detection were very heterogeneous, precluding pooled calculation of summary diagnostic accuracy estimates in most instances, but generally higher than non AI-based models. The highest performance outcomes were Specificity and Negative Predictive Value. Overall performance measures of AI algorithms were: sensitivity range 19%-92%, specificity range 64%-96%, accuracy 70.2%-96.1%. Conclusions Use of AI algorithms for HAI surveillance of HAI has increased reliability compared to traditional surveillance or to automated surveillance models. With ongoing improvements in information technology, implementation of AI models will improve the quality and capacity of surveillance will support hospital HAI surveillance. Key messages Artificial Intelligence (AI) offer great potential to healthcare associated infections (HAI) control. Preliminary evidence show AI-based models have perform better than manual or automated models for HAIs detection.

Clinical and Molecular Epidemiology of Healthcare-Associated Infections Due to Extended-Spectrumβ-Lactamase (ESBL)–Producing Strains ofEscherichia coliandKlebsiella pneumoniaeThat Harbor Multiple ESBL Genes

2008 ◽  
Vol 29 (11) ◽  
pp. 1026-1034 ◽  
Author(s):  
Anucha Apisarnthanarak ◽  
Pattarachai Kiratisin ◽  
Linda M. Mundy
Keyword(s):  
Confidence Interval ◽  
Antimicrobial Therapy ◽  
Odds Ratio ◽  
Healthcare Associated Infections ◽  
Healthcare Associated Infection ◽  
Middle Income ◽  
E Coli ◽  
Esbl Gene ◽  
Empirical Antimicrobial Therapy ◽  
Healthcare Associated

Objectives.To characterize healthcare-associated infections due to extended-spectrumβ-lactamase (ESBL)-producing strains ofEscherichia coliandKlebsiella pneumoniaethat harbor multiple ESBL genes, as opposed to a single ESBL gene.Methods.All patients with a confirmed healthcare-associated infection due to an ESBL-producing strain ofE. coliorK. pneumoniaewere enrolled in the study. Molecular typing of isolates was performed, and the comparative risks and outcomes of patients were analyzed.Results.Among 71 patients with healthcare-associated infection due to an ESBL-producing strain ofE. coliorK. pneumoniae, the gene for CTX-M, with or without other ESBL genes, was identified in all 51 (100%) of the patients infected with anE. colistrain and in 18 (90%) of the 20 patients infected with aK. pneumoniaestrain. Of these 71 patients, 17 (24%) met the definition of healthcare-associated infection due to an ESBL-producing strain that harbored multiple genes; in multivariate analysis, previous exposure to 3 or more classes of antibiotics (adjusted odds ratio, 4.5 [95% confidence interval, 1.7-75.2]) was the sole risk factor for healthcare-associated infection due to an ESBL-producing strain that harbored multiple ESBL genes. Isolates recovered from patients with healthcare-associated infection due to an ESBL-producing strain that harbored multiple ESBL genes were more resistant to various antibiotic classes, and, compared with patients with healthcare-associated infection due to an ESBL-producing strain that harbored a single ESBL gene, they were more likely to have ineffective initial empirical antimicrobial therapy (52% vs 94%; odds ratio, 5.1 [95% confidence interval, 1.04-14.5]).Conclusions.CTX-M ESBL is highly prevalent in Thailand. Patients with healthcare-associated infection due to an ESBL-producing strain that harbored multiple ESBL genes were more likely to have had ineffective initial empirical antimicrobial therapy, and, given that antibiotic selection pressure was the only associated risk, we suggest focused antimicrobial stewardship programs to limit the emergence and spread of healthcare-associated infection due to ESBL-producing strains in this middle-income country.

scholarly journals Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
H Roel A Streefkerk ◽  
Roel PAJ Verkooijen ◽  
Wichor M Bramer ◽  
Henri A Verbrugh
Keyword(s):  
Systematic Review ◽  
Control Programme ◽  
Mature Stage ◽  
Surveillance Systems ◽  
Healthcare Associated Infections ◽  
Medical Specialties ◽  
Specificity And Sensitivity ◽  
Healthcare Associated ◽  
Traditional Surveillance

Background Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.

scholarly journals Antimicrobial resistance pattern in healthcare-associated infections: investigation of in-hospital risk factors

2021 ◽  
Author(s):  
Mohammad Masoud Emami Meybodi ◽  
Abbas Rahimi Foroushani ◽  
Masoome Zolfaghari ◽  
Alireza Abdollahi ◽  
Abbas Alipour ◽  
...  
Keyword(s):  
Risk Factors ◽  
Antimicrobial Resistance ◽  
Antimicrobial Agents ◽  
Resistance Pattern ◽  
Healthcare Associated Infections ◽  
Efficient Treatment ◽  
E Coli ◽  
Causative Agents ◽  
Healthcare Associated ◽  
Hospital Wards

Background and Objectives: Antimicrobial resistance (AMR) is an increasing threat for efficient treatment of infections. Determining the epidemiology of healthcare-associated infections and causative agents in various hospital wards helps appropriate selection of antimicrobial agents. Materials and Methods: This retrospective study was performed by analyzing antibiograms from March 2017 to March 2018 among patients admitted to the different wards of Imam Khomeini Hospital Complex in Tehran, Iran. Results: Among 2440 hospital acquired infections, 59.3% were Gram-negative bacilli: E. coli (n = 469, 22.2%), K. pneumoniae (n = 457, 21.7%), Acinetobacter spp. (n = 282, 13.4%), P. aeruginosa (n = 139, 6.6%) and important Gram-positive bacteria were Enterococcus spp. (n = 216, 10.2%), S. aureus (n = 148, 7%), S. epidermidis (n = 118, 5.6). Generally, there was a high antimicrobial resistance in bacterial isolates in this study. Methicillin resistant Staphylococcus aureus (MRSA) was 56.3 % and MRSE 62.9 %. Vancomycin resistant enterococci (VRE) was 60.7%. K. pneumoniae- ESBL was 79.6% and its resistance to carbapenem was 38.4%. E. coli-ESBL was 42% and its resistance to carbapenems was 2.3%. P. aeruginosa resistance to ceftazidime was 74.4%, to fluroquinolones 63.3%, to aminoglycosides 64.8%, to piperacillin tazobactam 47.6% and to carbapenems 62.1%. Acinetobacter baumannii resistance to ceftazidime was 98.7%, to fluroquinolones 97%, to aminoglycosides 95.9%, to ampicillin sulbactam 84%, to carbapenems 96.4% and to colistin 4%. Conclusion: The study revealed an alarming rate of resistance to the commonly used antimicrobial agents used in treating HAIs. Also the relationship between AMR and some risk factors and thus taking steps towards controlling them have been shown.

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