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1.
J Correct Health Care ; 28(3): 155-163, 2022 06.
Article in English | MEDLINE | ID: mdl-35263181

ABSTRACT

On April 6, 2020, a confirmed COVID-19 case in a correctional facility employee (Staff A) was reported to the Vermont Department of Health (VDH). Staff A worked in the facility while symptomatic, without reporting symptoms, for 10 days. VDH and the facility conducted two facility-wide testing events, implemented symptom monitoring, and initiated contact tracing. All 197 incarcerated persons and 115 (71%) staff were tested for SARS-CoV-2; 45 (23%) incarcerated persons and 17 (10%) staff had positive results (confirmed case), of whom 37 (82%) incarcerated persons and 1 (6%) staff had asymptomatic infections. Case detection enabled isolation of incarcerated persons and staff, work exclusion of staff with COVID-19, and quarantine of staff and incarcerated persons who had close contact with persons with COVID-19. Broad-based SARS-CoV-2 testing identified more cases than symptom monitoring.


Subject(s)
COVID-19 , COVID-19 Testing , Disease Outbreaks , Humans , Prisons , SARS-CoV-2 , Vermont/epidemiology
2.
J Am Geriatr Soc ; 69(10): 2708-2715, 2021 10.
Article in English | MEDLINE | ID: mdl-34235743

ABSTRACT

COVID-19 has exacted a disproportionate toll on the health of persons living in nursing homes. Healthcare providers and other decision-makers in those settings must refer to multiple evolving sources of guidance to coordinate care delivery in such a way as to minimize the introduction and spread of the causal virus, SARS-CoV-2. It is essential that guidance be presented in an accessible and usable format to facilitate its translation into evidence-based best practice. In this article, we propose the Haddon matrix as a tool well-suited to this task. The Haddon matrix is a conceptual model that organizes influencing factors into pre-event, event, and post-event phases, and into host, agent, and environment domains akin to the components of the epidemiologic triad. The Haddon matrix has previously been applied to topics relevant to the care of older persons, such as fall prevention, as well as to pandemic planning and response. Presented here is a novel application of the Haddon matrix to pandemic response in nursing homes, with practical applications for nursing home decision-makers in their efforts to prevent and contain COVID-19.


Subject(s)
COVID-19 , Civil Defense/organization & administration , Evidence-Based Practice , Homes for the Aged/organization & administration , Infection Control , Models, Organizational , Nursing Homes/organization & administration , Aged , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Disease Transmission, Infectious/prevention & control , Evidence-Based Practice/methods , Evidence-Based Practice/trends , Health Services for the Aged/organization & administration , Health Services for the Aged/standards , Health Services for the Aged/trends , Humans , Infection Control/methods , Infection Control/organization & administration , Infection Control/standards , Organizational Innovation , SARS-CoV-2 , United States
3.
Expert Rev Anti Infect Ther ; 18(10): 1055-1062, 2020 10.
Article in English | MEDLINE | ID: mdl-32552054

ABSTRACT

OBJECTIVE: This study presents trends in organism isolation and antimicrobial resistance in routine microbiology test results from acute-care hospital microbiology laboratories in Vermont. METHODS: Organism identifications and antimicrobial susceptibility test results were captured from acute-care hospital laboratories to monitor geographic and temporal trends in resistance and emerging microbial threats with the free WHONET software. RESULTS: Data were provided from 12 acute care hospital laboratories from 2011 through 2018 for 318,833 isolates from 148,994 patients (70% female, 74% outpatient, and 63% urine). Significant differences (p < 0.05) in age, gender, and antimicrobial susceptibility results (e.g. Escherichia coli and levofloxacin) between outpatient and inpatient isolates were identified with temporal increases in certain species (e.g. Aerococcus urinae) and resistance (e.g. Streptococcus pneumoniae and erythromycin). The use of multi-resistance phenotypes demonstrated significant heterogeneity (p < 0.05) in MRSA strains between facilities, for example Staphylococcus aureus resistant to six priority antimicrobials were found in no critical access hospitals (fewer than 25 inpatient beds) but in all non-critical access hospitals. CONCLUSIONS: Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging community and healthcare resistance threats at the local and state level.


Subject(s)
Anti-Bacterial Agents/pharmacology , Bacteria/drug effects , Bacterial Infections/drug therapy , Adolescent , Adult , Aged , Bacteria/isolation & purification , Bacterial Infections/epidemiology , Bacterial Infections/microbiology , Child , Child, Preschool , Drug Resistance, Bacterial , Female , Humans , Infant , Infant, Newborn , Male , Microbial Sensitivity Tests , Middle Aged , Population Surveillance , Vermont/epidemiology , Young Adult
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