Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.
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http://dx.doi.org/10.3389/fmicb.2024.1493511 | DOI Listing |
Viruses
January 2025
Département de Virologie, Institut Pasteur de Dakar, Dakar BP 220, Senegal.
Despite extensive experience with influenza surveillance in humans in Senegal, there is limited knowledge about the actual situation and genetic diversity of avian influenza viruses (AIVs) circulating in the country, hindering control measures and pandemic risk assessment. Therefore, as part of the "One Health" approach to influenza surveillance, we conducted active AIV surveillance in two live bird markets (LBMs) in Dakar to better understand the dynamics and diversity of influenza viruses in Senegal, obtain genetic profiles of circulating AIVs, and assess the risk of emergence of novel strains and their transmission to humans. Cloacal swabs from poultry and environmental samples collected weekly from the two LBMs were screened by RT-qPCR for H5, H7, and H9 AIVs.
View Article and Find Full Text PDFMicroorganisms
January 2025
Department of Virology, National Institute of Public Health NIH-National Research Institute, 00-791 Warsaw, Poland.
Respiratory Syncytial Virus (RSV) is a prevalent pathogen of the respiratory tract, posing a significant threat to individuals with compromised immune systems, particularly the elderly and neonates in hospital settings. The primary objective of this study was to identify a specific period within the epidemic season during which healthcare providers can anticipate an increased incidence of RSV infections and characterize the epidemic season in Poland. Molecular biology techniques were employed to diagnose samples at Sanitary Stations and the National Institute of Public Health (NIC) in Warsaw.
View Article and Find Full Text PDFCancer Epidemiol
January 2025
Division of Hematology/Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX USA.
Background: Birth defects are associated with childhood cancer, but little is known regarding pediatric carcinomas, a group of especially rare tumors.
Methods: We used Cox proportional hazards regression to estimate the hazard ratio (HR) and 95 % confidence interval (CI) for any carcinoma, as well as thyroid, hepatocellular, and renal carcinoma specifically, up to 18 years of age among children with major, non-syndromic anomalies or chromosomal/genetic syndromes, relative to unaffected children.
Results: Our registry-linkage study included nine states and 21,933,476 children between 1990 and 2018: 641,827 with non-syndromic anomalies, and 49,619 with syndromes.
JMIR Res Protoc
January 2025
Clinical Informatics and Health Outcomes Research Group, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
Background: There are gaps in our understanding of the clinical characteristics and disease burden of the respiratory syncytial virus (RSV) among community-dwelling adults. This is in part due to a lack of routine testing at the point of care. More data would enhance our assessment of the need for an RSV vaccination program for adults in the United Kingdom.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Importance: Secondary lymphedema is a common, harmful side effect of breast cancer treatment. Robust risk models that are externally validated are needed to facilitate clinical translation. A published risk model used 5 accessible clinical factors to predict the development of breast cancer-related lymphedema; this model included a patient's mammographic breast density as a novel predictive factor.
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