Molecular Epidemiology and Risk Factors for Extended-Spectrum β-Lactamase–Producing Enterobacterales in Long-Term Care Residents

Elderly people confined to chronic care facilities face an increased risk of acquiring infections by multidrug-resistant organisms (MDROs). This review presents the current knowledge of the prevalence and risk factors for colonization by MDROs in long-term care facilities (LTCF), thereby providing a useful reference to establish objectives for implementing successful antimicrobial stewardship programs (ASPs). We searched in PubMed and Scopus for studies examining the prevalence of MDROs and/or risk factors for the acquisition of MDROs in LTCF. One hundred and thirty-four studies published from 1987 to 2020 were included. The prevalence of MDROs in LTCF varies between the different continents, where Asia reported the highest prevalence of extended-spectrum ß-lactamase (ESBL) Enterobacterales (71.6%), carbapenem resistant (CR) Enterobacterales (6.9%) and methicillin-resistant Staphylococcus aureus (MRSA) (25.6%) and North America the highest prevalence to MDR Pseudomonas aeruginosa (5.4%), MDR Acinetobacter baumannii (15.0%), vancomycin-resistant Enterococcus spp. (VRE) (4.0%), and Clostridioides difficile (26.1%). Furthermore, MDRO prevalence has experienced changes over time, with increases in MDR P. aeruginosa and extended spectrum ß-lactamase producing Enterobacterales observed starting in 2015 and decreases of CR Enterobacterales, MDR A. baumannii, VRE, MRSA and C. difficile. Several risk factors have been found, such as male sex, chronic wounds, the use of medical devices, and previous antibiotic use. The last of these aspects represents one of the most important modifiable factors for reducing colonization with MDROs through implementing ASPs in LTCF.

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Colonization with Extended-Spectrum β-Lactamase-ProducingEscherichia coliandKlebsiellaSpecies in Long-Term Care Facility Residents

Infection Control and Hospital Epidemiology ◽

10.1086/664055 ◽

2012 ◽

Vol 33 (3) ◽

pp. 302-304 ◽

Cited By ~ 21

Author(s):

Ebbing Lautenbach ◽

Jennifer Han ◽

Evelyn Santana ◽

Pam Tolomeo ◽

Warren B. Bilker ◽

...

Keyword(s):

Risk Factors ◽

Fecal Incontinence ◽

Long Term Care ◽

Care Facility ◽

Infect Control Hosp ◽

Term Care ◽

Term Care Facility ◽

Long Term Care Facility ◽

Extended Spectrum

We describe the prevalence of and risk factors for colonization with extended-spectrum (3-lactamase-producing Enterobacteriaceae (ESBL-EB) in the long-term care facility (LTCF) setting. Colonization prevalence differed significantly across the 3 LTCFs evaluated in the study, with recent use of levofloxacin and fecal incontinence demonstrating borderline significant associations with ESBL-EB colonization.Infect Control Hosp Epidemiol2012;33(3):302-304

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Design and Development of a Web-based Delirium Preventive Application for Long-term Care Facilities: A Methodological Study (Preprint)

10.2196/preprints.26092 ◽

2020 ◽

Author(s):

Kyoung Ja Moon ◽

Chang-Sik Son ◽

Jong-Ha Lee ◽

Mina Park

Keyword(s):

Risk Factors ◽

Long Term Care ◽

Cloud System ◽

Evidence Based ◽

Methodological Study ◽

Term Care ◽

Web Based ◽

Care Facilities ◽

Delirium Prevention

BACKGROUND Long-term care facilities demonstrate low levels of knowledge and care for patients with delirium and are often not properly equipped with an electronic medical record system, thereby hindering systematic approaches to delirium monitoring. OBJECTIVE This study aims to develop a web-based delirium preventive application (app), with an integrated predictive model, for long-term care (LTC) facilities using artificial intelligence (AI). METHODS This methodological study was conducted to develop an app and link it with the Amazon cloud system. The app was developed based on an evidence-based literature review and the validity of the AI prediction model algorithm. Participants comprised 206 persons admitted to LTC facilities. The app was developed in 5 phases. First, through a review of evidence-based literature, risk factors for predicting delirium and non-pharmaceutical contents for preventive intervention were identified. Second, the app, consisting of several screens, was designed; this involved providing basic information, predicting the onset of delirium according to risk factors, assessing delirium, and intervening for prevention. Third, based on the existing data, predictive analysis was performed, and the algorithm developed through this was calculated at the site linked to the web through the Amazon cloud system and sent back to the app. Fourth, a pilot test using the developed app was conducted with 33 patients. Fifth, the app was finalized. RESULTS We developed the Web_DeliPREVENT_4LCF for patients of LTC facilities. This app provides information on delirium, inputs risk factors, predicts and informs the degree of delirium risk, and enables delirium measurement or delirium prevention interventions to be immediately implemented with a verified tool. CONCLUSIONS This web-based application is evidence-based and offers easy mobilization and care to patients with delirium in LTC facilities. Therefore, the use of this app improves the unrecognized of delirium and predicts the degree of delirium risk, thereby helping initiatives for delirium prevention and providing interventions. This would ultimately improve patient safety and quality of care. CLINICALTRIAL none

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Risk factors for COVID-19 diagnosis, hospitalization, and subsequent all-cause mortality in Sweden: a nationwide study

European Journal of Epidemiology ◽

10.1007/s10654-021-00732-w ◽

2021 ◽

Vol 36 (3) ◽

pp. 287-298

Author(s):

Jonathan Bergman ◽

Marcel Ballin ◽

Anna Nordström ◽

Peter Nordström

Keyword(s):

Risk Factors ◽

Down Syndrome ◽

Long Term Care ◽

Care Facility ◽

Prescription Medication ◽

Term Care ◽

Term Care Facility ◽

Long Term Care Facility ◽

All Cause Mortality

AbstractWe conducted a nationwide, registry-based study to investigate the importance of 34 potential risk factors for coronavirus disease 2019 (COVID-19) diagnosis, hospitalization (with or without intensive care unit [ICU] admission), and subsequent all-cause mortality. The study population comprised all COVID-19 cases confirmed in Sweden by mid-September 2020 (68,575 non-hospitalized, 2494 ICU hospitalized, and 13,589 non-ICU hospitalized) and 434,081 randomly sampled general-population controls. Older age was the strongest risk factor for hospitalization, although the odds of ICU hospitalization decreased after 60–69 years and, after controlling for other risk factors, the odds of non-ICU hospitalization showed no trend after 40–49 years. Residence in a long-term care facility was associated with non-ICU hospitalization. Male sex and the presence of at least one investigated comorbidity or prescription medication were associated with both ICU and non-ICU hospitalization. Three comorbidities associated with both ICU and non-ICU hospitalization were asthma, hypertension, and Down syndrome. History of cancer was not associated with COVID-19 hospitalization, but cancer in the past year was associated with non-ICU hospitalization, after controlling for other risk factors. Cardiovascular disease was weakly associated with non-ICU hospitalization for COVID-19, but not with ICU hospitalization, after adjustment for other risk factors. Excess mortality was observed in both hospitalized and non-hospitalized COVID-19 cases. These results confirm that severe COVID-19 is related to age, sex, and comorbidity in general. The study provides new evidence that hypertension, asthma, Down syndrome, and residence in a long-term care facility are associated with severe COVID-19.

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