This project investigates public attitudes towards sharing confidential personal health information held in electronic health records (EHRs). The project uses computer assisted telephone interviewing (CATI) to conduct a quantitative national survey of the attitudes of New Zealanders towards access to their personal health information using vignettes. Respondents are presented with vignettes which describe ways in which their health information might be used, and asked about their attitude to and consent for each type of access. The project outcome will be a specification of requirements for an e-consent model meeting the needs of most New Zealanders, thus enabling the potential benefits of electronically sharing confidential health information from EHRs. This article presents preliminary results from the first 1828 respondents. Respondents were most willing to share their information for the purpose of providing care. However, removing their name and address greatly increased the acceptability of sharing information for other purposes.
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http://dx.doi.org/10.1177/1460458209337435 | DOI Listing |
Viruses
December 2024
Wadsworth Center, David Axelrod Institute, New York State Department of Health, Albany, NY 12208, USA.
A historical perspective of more than one hundred years of influenza surveillance in New York State demonstrates the progression from anecdotes and case counts to next-generation sequencing and electronic database management, greatly improving pandemic preparedness and response. Here, we determined if influenza virologic surveillance at the New York State public health laboratory (NYS PHL) tests sufficient specimen numbers within preferred confidence limits to assess situational awareness and detect novel viruses that pose a pandemic risk. To this end, we analyzed retrospective electronic data on laboratory test results for the influenza seasons 1997-1998 to 2021-2022 according to sample sizes recommended in the Influenza Virologic Surveillance Right Size Roadmap issued by the Association of Public Health Laboratories and Centers for Disease Control and Prevention.
View Article and Find Full Text PDFVaccines (Basel)
November 2024
IRD Global, 16 Raffles Quay, Singapore 049145, Singapore.
Background/objectives: Full immunization coverage in Pakistan remains suboptimal at 66%. An in-depth assessment is needed to understand the long-term trends in immunization and identify the extent of defaulters and associated risk factors of them being left uncovered by the immunization system.
Methods: We conducted a 5-year analysis using the Government's Provincial Electronic Immunization Registry data for the 2018-2023 birth cohorts in Sindh province.
Vaccines (Basel)
November 2024
School of Public Health, Centre of Postgraduate Medical Education of Warsaw, Kleczewska 61/63, 01-826 Warsaw, Poland.
Background/objectives: Human papillomavirus (HPV) vaccination programs play a critical role in the primary prevention of HPV-related diseases, including cervical cancer. However, the principles governing the implementation of these programs vary across European Union (EU) countries. The objective of this study was to analyze and compare the strategies for implementing HPV vaccination programs across the EU, with a focus on access, vaccine selection, and procurement processes.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.
This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). The system consists of four modules and is based on a virtual humanoid robot that is meant to motivate and encourage children in their rehabilitation programs. The efficiency of the developed system was tested on two children with CP.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.
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