The Borderplex region has been profoundly impacted by the COVID-19 pandemic. Borderplex residents live in low socioeconomic (SES) neighborhoods and lack access to COVID-19 testing. The purpose of this study was two-fold: first, to implement a COVID-19 testing program in the Borderplex region to increase the number of residents tested for COVID-19, and second, to administer a community survey to identify trusted sources of COVID-19 information and factors associated with COVID-19 vaccine uptake. A total of 4071 community members were tested for COVID-19, and 502 participants completed the survey. COVID-19 testing resulted in 66.8% ( = 2718) positive cases. The community survey revealed that the most trusted sources of COVID-19 information were doctors or health care providers (67.7%), government websites (e.g., CDC, FDA, etc.) (41.8%), and the World Health Organization (37.8%). Logistic regression models revealed several statistically significant predictors of COVID-19 vaccine uptake such as having a trusted doctor or health care provider, perceiving the COVID-19 vaccine to be effective, and perceiving that the COVID-19 vaccine does not cause side-effects. Findings from the current study highlight the need for utilizing an integrated, multifactorial approach to increase COVID-19 testing and to identify factors associated with COVID-19 vaccine uptake in underserved communities.
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http://dx.doi.org/10.3390/ijerph20065076 | DOI Listing |
JMIR Public Health Surveill
January 2025
Center for Global Health, University of New Mexico Health Sciences Center, Albuquerque, NM, United States.
Background: Numerous studies have assessed the risk of SARS-CoV-2 exposure and infection among health care workers during the pandemic. However, far fewer studies have investigated the impact of SARS-CoV-2 on essential workers in other sectors. Moreover, guidance for maintaining a safely operating workplace in sectors outside of health care remains limited.
View Article and Find Full Text PDFVet Med Sci
March 2025
Department of Veterinary Medicine, National Chiayi University, Chiayi City, Taiwan.
This case report highlights a potential vaccine safety concern associated with the Pseudorabies virus (PRV) live vaccine, which warrants further investigation for comprehensive understanding. Vaccine-induced immune thrombotic thrombocytopenia (VITT), a novel syndrome of adverse events following adenovirus vector COVID-19 vaccines, was observed after vaccination with Zoetis PR-VAC PLUS. This led to a 100% morbidity and high mortality among PRV-free Danish purebred pigs from Danish Genetics Co.
View Article and Find Full Text PDFVet Med Sci
March 2025
College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA.
Local health departments can play a critical role in zoonoses surveillance at the human-domestic animal interface, especially when existing public health services and close relationships with community groups can be leveraged. Investigators at Harris County Veterinary Public Health employed a community-based surveillance tool for identifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in dogs and cats in June--December 2021. Diagnosis was made using both RT-qPCR testing of oral and nasal swabs and plaque reduction neutralization testing of serum samples.
View Article and Find Full Text PDFJAMA Intern Med
January 2025
Research and Development, Veterans Affairs Puget Sound Health Care System, Seattle, Washington.
Importance: SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) contribute to many hospitalizations and deaths each year. Understanding relative disease severity can help to inform vaccination guidance.
Objective: To compare disease severity of COVID-19, influenza, and RSV among US veterans.
ACS Sens
January 2025
Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.
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