Dataset Information

Water as a Source of Antimicrobial Resistance and Healthcare-Associated Infections.

ABSTRACT: Healthcare-associated infections (HAIs) are one of the most common patient complications, affecting 7% of patients in developed countries each year. The rise of antimicrobial resistant (AMR) bacteria has been identified as one of the biggest global health challenges, resulting in an estimated 23,000 deaths in the US annually. Environmental reservoirs for AMR bacteria such as bed rails, light switches and doorknobs have been identified in the past and addressed with infection prevention guidelines. However, water and water-related devices are often overlooked as potential sources of HAI outbreaks. This systematic review examines the role of water and water-related devices in the transmission of AMR bacteria responsible for HAIs, discussing common waterborne devices, pathogens, and surveillance strategies. AMR strains of previously described waterborne pathogens including Pseudomonas aeruginosa, Mycobacterium spp., and Legionella spp. were commonly isolated. However, methicillin-resistant Staphylococcus aureus and carbapenem-resistant Enterobacteriaceae that are not typically associated with water were also isolated. Biofilms were identified as a hot spot for the dissemination of genes responsible for survival functions. A limitation identified was a lack of consistency between environmental screening scope, isolation methodology, and antimicrobial resistance characterization. Broad universal environmental surveillance guidelines must be developed and adopted to monitor AMR pathogens, allowing prediction of future threats before waterborne infection outbreaks occur.

SUBMITTER: Hayward C

PROVIDER: S-EPMC7459458 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Water as a Source of Antimicrobial Resistance and Healthcare-Associated Infections.

Hayward Claire C Ross Kirstin E KE Brown Melissa H MH Whiley Harriet H

Pathogens (Basel, Switzerland) 20200818 8

Healthcare-associated infections (HAIs) are one of the most common patient complications, affecting 7% of patients in developed countries each year. The rise of antimicrobial resistant (AMR) bacteria has been identified as one of the biggest global health challenges, resulting in an estimated 23,000 deaths in the US annually. Environmental reservoirs for AMR bacteria such as bed rails, light switches and doorknobs have been identified in the past and addressed with infection prevention guideline ...[more]

PMID: 32824770

Similar Datasets

Project description:OBJECTIVE To describe pathogen distribution and antimicrobial resistance patterns for healthcare-associated infections (HAIs) reported to the National Healthcare Safety Network (NHSN) from pediatric locations during 2011-2014. METHODS Device-associated infection data were analyzed for central line-associated bloodstream infection (CLABSI), catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and surgical site infection (SSI). Pooled mean percentage resistance was calculated for a variety of pathogen-antimicrobial resistance pattern combinations and was stratified by location for device-associated infections (neonatal intensive care units [NICUs], pediatric intensive care units [PICUs], pediatric oncology and pediatric wards) and by surgery type for SSIs. RESULTS From 2011 to 2014, 1,003 hospitals reported 20,390 pediatric HAIs and 22,323 associated pathogens to the NHSN. Among all HAIs, the following pathogens accounted for more than 60% of those reported: Staphylococcus aureus (17%), coagulase-negative staphylococci (17%), Escherichia coli (11%), Klebsiella pneumoniae and/or oxytoca (9%), and Enterococcus faecalis (8%). Among device-associated infections, resistance was generally lower in NICUs than in other locations. For several pathogens, resistance was greater in pediatric wards than in PICUs. The proportion of organisms resistant to carbapenems was low overall but reached approximately 20% for Pseudomonas aeruginosa from CLABSIs and CAUTIs in some locations. Among SSIs, antimicrobial resistance patterns were similar across surgical procedure types for most pathogens. CONCLUSION This report is the first pediatric-specific description of antimicrobial resistance data reported to the NHSN. Reporting of pediatric-specific HAIs and antimicrobial resistance data will help identify priority targets for infection control and antimicrobial stewardship activities in facilities that provide care for children. Infect Control Hosp Epidemiol 2018;39:1-11.

Project description:BackgroundHealthcare-associated infections (HCAI) place a significant burden on healthcare systems globally. This systematic review and meta-analysis aimed to investigate the prevalence, risk factors, and aetiologic agents of endemic HCAI in Africa.MethodsMEDLINE/PubMed, CINAHL, and Global Health databases (EBSCOhost interface) were searched for studies published in English and French describing HCAI in Africa from 2010 to 2022. We extracted data on prevalence of HCAI, risk factors, aetiologic agents, and associated antimicrobial resistance patterns. We used random-effects models to estimate parameter values with 95% confidence intervals for risk factors associated with HCAI. This study was registered in PROSPERO (CRD42022374559) and followed PRISMA 2020 guidelines.ResultsOf 2541 records screened, 92 were included, comprising data from 81,968 patients. Prevalence of HCAI varied between 1.6 and 90.2% with a median of 15% across studies. Heterogeneity (I2) varied from 93 to 99%. Contaminated wound (OR: 1.75, 95% CI: 1.31-2.19), long hospital stay (OR: 1.39, 95% CI: 0.92-1.80), urinary catheter (OR: 1.57, 95% CI: 0.35-2.78), intubation and ventilation (OR: 1.53, 95% CI: 0.85-2.22), vascular catheters (OR: 1.49, 95% CI: 0.52-2.45) were among risk factors associated with HCAI. Bacteria reported from included studies comprised 6463 isolates, with E. coli (18.3%, n = 1182), S. aureus (17.3%, n = 1118), Klebsiella spp. (17.2%, n = 1115), Pseudomonas spp. (10.3%, n = 671), and Acinetobacter spp. (6.8%, n = 438) being most common. Resistance to multiple antibiotics was common; 70.3% (IQR: 50-100) of Enterobacterales were 3rd -generation cephalosporin resistant, 70.5% (IQR: 58.8-80.3) of S. aureus were methicillin resistant and 55% (IQR: 27.3-81.3) Pseudomonas spp. were resistant to all agents tested.ConclusionsHCAI is a greater problem in Africa than other regions, however, there remains a paucity of data to guide local action. There is a clear need to develop and validate sustainable HCAI definitions in Africa to support the implementation of routine HCAI surveillance and inform implementation of context appropriate infection prevention and control strategies.

Project description:BackgroundAntimicrobial-resistant (AMR) bacterial infection is a significant global threat to the healthcare systems. Pseudomonas aeruginosa, the leading infectious agent in the healthcare setting is now one of the major threats due to AMR. A comprehensive understanding of the magnitude of AMR, particularly highly public health important pathogens such as P. aeruginosa, is necessary for the management of infections based on local information.ObjectiveThis systematic review and meta-analysis aimed to determine the country-wide AMR of P. aeruginosa.MethodsSystematic searches were performed to retrieve articles from PubMed, Scopus, Web of Science, ScienceDirect electronic databases, Google Scholar search engine, and repository registrars from 2015 to 31st December 2023. Twenty-three studies that provided important data on AMR in P. aeruginosa were systematically reviewed and analyzed to determine the country-wide magnitude of P. aeruginosa AMR profile from healthcare-associated infections. AMR of P. aeruginosa to 10 different antibiotics were extracted separately into Microsoft Excel and analyzed using STATA 17.0. Cohen's kappa was computed to determine the agreement between reviewers, the Inverse of variance (I2) was used to evaluate heterogeneity across studies, and Egger's test to identify publication bias. A random effect model was used to determine the pooled resistance to each antibiotic. Subgroup analysis was performed by infection type and year of publication.ResultsThis systematic review and meta-analysis revealed that the pooled prevalence of P. aeruginosa in clinical specimens associated with HAI was 4.38%(95%CI: 3.00-5.76). The pooled prevalence of AMR in P. aeruginosa for different antibiotics varies, ranging from 20.9% (95%CI: 6.2-35.8) for amikacin to 98.72% (95%CI: 96.39-101.4) for ceftriaxone. The pooled resistance was higher for ceftriaxone (98.72%), Trimethoprim-sulfamethoxazole (75.41), and amoxicillin-clavulanic acid (91.2). In contrast relatively lower AMR were observed for amikacin (20.9%) and meropenem (28.64%). The pooled multi-drug resistance (MDR) in P. aeruginosa was 80.5% (95%CI: 66.25-93.84). Upon subgroup analysis by infection types and year of publication, P. aeruginosa isolated from healthcare-associated infections exhibited higher resistance to ceftazidime (94.72%) compared to isolates from mixed types of healthcare-associated infections (70.84%) and surgical site infections (57.84%). Antimicrobial resistance in gentamicin was higher during the periods of 2018-2020 (73.96%), while comparatively lower during 2021-2023 (42.69%) and 2015-2017 (29.82%).ConclusionsSignificantly high AMR and MDR were observed from this systematic review and meta-analysis. AMR obtained from this systematic review and meta-analysis urges the need for improved infection control, antimicrobial stewardship practices, and strengthened surveillance systems to control the spread of AMR and ensure effective treatment of P. aeruginosa infections.Protocol registrationThis systematic review and meta-analysis was registered on PROSPERO (Registration ID: CRD42024518145).

Dataset Information

Water as a Source of Antimicrobial Resistance and Healthcare-Associated Infections.

Publications

Water as a Source of Antimicrobial Resistance and Healthcare-Associated Infections.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets