Background: The COVID-19 disease has overwhelmed and disrupted healthcare services worldwide, particularly healthcare workers (HCW). HCW are essential workers performing any job in a healthcare setting who are potentially directly or indirectly exposed to infectious materials. Our retrospective cohort study aimed to determine the prevalence of COVID-19 infections among HCW in Jakarta and neighbouring areas during the first three months of the pandemic.
Methods: Nasopharyngeal/oropharyngeal swab specimens from HCW working at private and public hospitals in Jakarta and neighbouring areas were screened for SARS-CoV-2 between March and May 2020. Data on demography, clinical symptoms, contact history, and personal protective equipment (PPE) use were collected using standardised forms.
Results: Among 1201 specimens, 7.9% were confirmed positive for SARS-CoV-2 with the majority coming from medical doctors (48.4%) and nurses (44.2%). 64.2% of the positive cases reported to have contact with suspect/confirmed COVID-19 cases, including 32 (52.2%) with patient and 3 (6.6%) with co-worker. The symptomatic HCW had a significantly lower median Ct value as compared to their asymptomatic counterpart ( < .001). Tendency to have a higher prevalence of pneumonia was observed in the age group of 40 - 49 and ≥50 years old.
Conclusion: Our findings highlighted the necessity to implement proper preventive and surveillance strategies for this high-risk population including adherence to strict PPE protocol and appropriate training.Key MessageHealthcare workers (HCW), defined as those handling any job in a healthcare setting, are at the frontline of risk of infection as SARS-CoV-2 is easily transmitted through airborne droplets and direct contact with contaminated surfaces. The aim of our study is to attain a more comprehensive and accurate picture of the impact of COVID-19 on HCW during the earlier phase of the outbreak in Indonesia to develop effective strategies that protect the health and safety of this workforce. Our findings highlighted that COVID-19 infections in HCW were mostly acquired in healthcare settings, with significant consequences of pneumonia and hospitalisation occurring across all age groups.
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http://dx.doi.org/10.1080/07853890.2021.1975309 | DOI Listing |
Clin Pediatr Endocrinol
January 2025
Indonesian Pediatric Society, Jakarta, Indonesia.
Type 1 diabetes mellitus (T1DM) is a lifelong disorder that affects all aspects of the lives of children and their families. A Health Needs Assessment (HNA) survey was conducted at two diabetes camps in Batu, East Java, and Parung, West Java, to evaluate the challenges and burdens faced by families of children living with T1DM in Indonesia. A total of forty-one respondents, comprising parents/caregivers, participated in the HNA.
View Article and Find Full Text PDFSpat Spatiotemporal Epidemiol
November 2024
Australian Centre for Health Services Innovation & Centre for Healthcare Transformation, Queensland University of Technology, Australia.
F1000Res
November 2024
Department of Medical Pharmacy, Faculty of Medicine, University of Indonesia, Jakarta, Jakarta, 10430, Indonesia.
Neurosurg Rev
October 2024
Department of Neurosurgery, Universitas Pelita Harapan, Tangerang, Banten, Indonesia.
Introduction: Delineating subthalamic nucleus (STN) boundaries using microelectrode recordings (MER) and trajectory history is a valuable resource for neurosurgeons, aiding in the accurate and efficient positioning of deep brain stimulation (DBS) electrodes within the STN. Here, we aimed to assess the application of artificial intelligence, specifically Hidden Markov Models (HMM), in the context of STN localization.
Methods: A comprehensive search strategy was employed, encompassing electronic databases, including PubMed, EuroPMC, and MEDLINE.
Food Chem
November 2024
Department of Agricultural Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand. Electronic address:
This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed method simultaneously incorporates single or multiple-preprocessing steps and tunes hyperparameters to determine the best model performance for FT-NIR and Micro-NIR spectral data of coconut milk adulteration with distilled water and mature coconut water in the range of 0%-50%. Computational experiments were conducted using nine single preprocessing types, three types of ML classifier (linear discriminant analysis (LDA), k-nearest neighbour (KNN), multilayer perceptron (MLP)) and three types of ML regressor (partial least squares (PLS), KNN, MLP).
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