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Infect Control Hosp Epidemiol ; : 1-21, 2022 Mar 30.
Article in English | MEDLINE | ID: covidwho-2323155


A comparison of computer-extracted and facility-reported counts of hospitalized COVID-19 patients for public health reporting at 36 hospitals found 42% of days with matching counts between the data sources. Mis-categorization of suspect cases was a primary driver of discordance. Clear reporting definitions and data validation facilitate emerging disease surveillance.

Infect Control Hosp Epidemiol ; : 1-24, 2022 Apr 05.
Article in English | MEDLINE | ID: covidwho-2258513


OBJECTIVE: To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on healthcare-associated infections (HAIs) reported from 128 acute care and 132 long-term care Veterans Affairs (VA) facilities. METHODS: Central line-associated bloodstream infections (CLABSIs), ventilator-associated events (VAEs), catheter-associated urinary tract infections (CAUTIs), and methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile infections and rates reported from each facility monthly to a centralized database before the pandemic (February 2019 through January 2020) and during the pandemic (July 2020 through June 2021) were compared. RESULTS: Nationwide VA COVID-19 admissions peaked in January 2021. Significant increases in the rates of CLABSIs, VAEs, and MRSA all-site HAIs (but not MRSA CLABSIs) were observed during the pandemic period in acute care facilities. There was no significant change in CAUTI rates and C. difficile rates significantly decreased. There were no significant increases in HAIs in long-term care facilities. CONCLUSIONS: The COVID-19 pandemic had a differential impact on HAIs of various types in VA acute care with many rates increasing. The decrease in CDI HAIs may be due, in part, to evolving diagnostic testing. The minimal impact of COVID-19 in VA long-term facilities may reflect differences in patient numbers and acuity and early recognition of the impact the pandemic had on nursing home residents leading to increased vigilance and optimization of infection prevention and control practices in that setting. These data support the need for building and sustaining conventional infection prevention and c ontrol strategies before and during a pandemic.

JMIR Public Health Surveill ; 7(3): e26719, 2021 03 24.
Article in English | MEDLINE | ID: covidwho-2197901


BACKGROUND: Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text. OBJECTIVE: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. METHODS: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy. RESULTS: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events. CONCLUSIONS: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

Communicable Diseases, Emerging/diagnosis , Electronic Health Records , Information Storage and Retrieval/methods , Public Health Surveillance/methods , Travel/statistics & numerical data , Algorithms , COVID-19/epidemiology , Communicable Diseases, Emerging/epidemiology , Feasibility Studies , Female , Humans , Machine Learning , Male , Middle Aged , Natural Language Processing , Reproducibility of Results , United States/epidemiology