ABSTRACT
Introduction: Technologies such as electronic prescribing, clinical decision support systems, and electronic medication dispensers, are increasingly being introduced into healthcare. Existing toolkits focus on the implementation of such technologies, rather than identifying and mitigating any unintended consequences technologies may have on patient safety. They also focus on high-level organisational issues instead of those faced by end-users: frontline staff, patients and carers. Aim(s): To identify and classify types of unintended consequences that may be encountered by technologies' end-users, in order to inform development of a tool for identifying and preventing technology-related risks on patient safety. Method(s): Five focus group discussions with healthcare professionals, patients and carers were conducted through video-conferencing. Healthcare professionals with experience of using/implementing healthcare technologies and known to the research team via professional networks were invited to participate. Patient/carer participants were recruited using convenience sampling via an external research-participant organisation. Participants were asked to identify patient safety risks associated with the use of healthcare technologies, drawn from their own experiences and through consideration of hypothetical risks. Discussions were audio-recorded and transcribed verbatim. Transcripts were analysed by two researchers (NVivo R1.6) and two public partners (manually), using iterative inductive thematic analysis. Result(s): Eleven healthcare professionals and 29 patients and carers participated in focus groups. Three focus groups comprised patients, carers, and healthcare professionals. Two involved solely patients and carers. Analysis revealed five areas of unintended consequences (Table 1). Most unintended consequences identified by patients and carers were similar to those described by healthcare professionals. Healthcare professionals also described additional unintended consequences associated with technologies designed specifically for their use. Conclusion(s): A limitation of the study is that many of the unintended consequences identified related to virtual health technologies used during the COVID-19 pandemic, which may affect generalisability. A strength is that unintended consequences that have been overlooked in existing literature, such as the build-up of patients' psychological dependence on technologies, were identified. Developing a tool based on these will allow implementers and users of healthcare technologies to consider such issues and address the potential risks they may have on patient safety before healthcare technologies are fully implemented in practice.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus responsible for the COVID-19 pandemic that can lead to severe respiratory distress requiring hospitalization and can be fatal. Media have reported that various dietary supplements (DS) or their combination with different medications can prevent infection or decrease disease severity. Here, we analyzed data collected from 15,830 patient follow-up telephone interviews from the University of Arkansas for Medical Sciences COVID-19 testing sites from March 15 to August 1, 2020. Within the REDCap database, we recorded patient demographics and DS and medication use. In total, data on DS and medication use was available for 8,150 study participants, of whom 21.9% and 4.1% reported using DS or medications, respectively, to either prevent or treat COVID-19. The majority of respondents were female (64%) and non-Hispanic whites (44.5%). Most individuals (64.5%) who took DS were younger than 50 years of age. Products such as vitamin C (1,013, 33.2%), multivitamins (722, 23.6%), and vitamin D (294, 9.6%) were the most commonly used DS among the responders. Analysis of the DS use and symptom scores association did not provide a strong evidence of beneficial health effects of DS. The results of this study demonstrate that a significantly higher proportion of study participants considered usage of DS to mitigate or prevent COVID-19-related symptoms compared to those who preferred medications. However, lack of observable health benefits associated with ingestion of DS suggests that more rigorous research is needed to substantiate the label claims.
ABSTRACT
Introduction: Housebound patients may face challenges to their medicines management due to reduced household mobility and potential lack of access to healthcare services. Previous literature has explored the medication-related needs of housebound patients from pharmacists' perspectives (1-2). However little work has focussed on the patient/family perspective. In this study, we used data obtained from those staying at home as much as possible during the COVID-19 pandemic to fill this gap. Aim: To explore home medicine practices and safety for people who were housebound during the COVID19 pandemic and to create guidance, from the patient/family perspective, for enabling pharmacists to facilitate safe medicine practices for this population. Methods: Interviews were carried out with people who were taking at least one long term medication and met the criteria for ?shielding' and/or were over 70 years of age during the first wave of the COVID-19 pandemic in the UK and/or their family carers. Respondents were recruited through patient and public involvement representatives, the research team's networks, and support groups. Potential participants were approached via personal contact and social media. Interviews were conducted by telephone or video conferencing and participants asked about their medicines management while staying at home. Inductive thematic analysis was carried out. Patient and public involvement representatives were involved in the data analysis alongside the researchers. Results: Fifty people were interviewed (16 males, 34 females;mean age 68 years, range 26-93 years). Interview data suggested diversity of experiences of medicines management while staying at home. Some respondents reported no or little change, others an initial crisis followed by re-stabilisation, and others that the pandemic was a tipping point, exacerbating underlying challenges and having negative effects on their health and wellbeing. Medicine safety issues reported included omitted doses and less-effective formulations being used. Participants also described experiencing high levels of anxiety related to obtaining medicines, monitoring medicines and feeling at risk of contracting COVID-19 while accessing medicine-related healthcare services. Key factors identified as facilitating a smooth transition included patients' own agency, support from family, friends and community, good communication with pharmacy staff, continuity of pharmacy services and synchronisation of medicines supply so that a maximum of one collection/delivery was required each month. Conclusion: The study findings that we have presented relate to the UK only;this may limit the generalisability of our findings to other countries. Findings from Ireland are in the process of being analysed and will provide a basis of comparison. In addition, more females took part than males, despite efforts to address this. However, our findings suggest pharmacy staff can support medicines management for people who are housebound by synchronisation of medicines supply, delivering medicines where possible, developing/raising awareness of alternative means of communication, providing continuity of pharmacy services and signposting any community support available.
ABSTRACT
This study focuses on using wastewater-based epidemiology to provide early warnings of the second COVID-19 wave in the Detroit metropolitan area of Michigan. SARS-CoV-2 RNA from untreated wastewater samples was compared to reported public health records. Untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) Water Resource Recovery Facility (WRRF), located in southeast Michigan, between August 6, 2020 and December 14, 2020. The WRRF receives wastewater from its service area via three main interceptors: the Detroit River Interceptor (DRI), the North Interceptor-East Arm (NIEA), and the Oakwood-Northwest-Wayne County Interceptor (ONWI). A total of 144 untreated wastewater samples were collected (45, 48, and 51 for ONWI, NIEA, and DRI, respectively) at the point of intake into the WRRF. Virus-selective sampling was conducted, and viruses were isolated from wastewater using electropositive NanoCeram column filters. For each sample, an average of 33 L of wastewater was passed through Nano Ceram electropositive cartridge filters at an average rate of 11 L/min. Viruses were eluted and concentrated, and the SARS-CoV-2 RNA concentrations were quantified with RT-qPCR. SARS-CoV-2 RNA was detected in 98% of the samples, and measured concentrations were in the range of 4.45x10(4) to 5.30x10(6) genomic copies/L. Early warnings of COVID-19 peaks were observed approximately 4 weeks prior to reported publicly available clinical data. (C) 2021 American Society of Civil Engineers.
ABSTRACT
Untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) Water Resource Recovery Facility (WRRF) located in southeast Michigan between April 8 and May 26, 2020. The WRRF is the largest single-site wastewater treatment facility in the US, and it receives wastewater from its service area via three main interceptors: Detroit River Interceptor (DRI), North Interceptor-East Arm (NI-EA), and Oakwood-Northwest-Wayne County Interceptor (O-NWI). A total of 54 untreated wastewater samples were collected (18 per interceptor) at the point of intake into the WRRF. Viruses were isolated from wastewater using electropositive NanoCeram column filters (Argonide, Sanford, Florida). For each sample, an average of 45 L of wastewater was passed through NanoCeram electropositive cartridge filters at a rate of no more than 11.3 L/m 11.3 L/m. Viruses were eluted and concentrated and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations were quantified with reverse transcription quantitative polymerase chain reaction (RT-qPCR). SARS-CoV-2 was detected in 100% of samples, and measured concentrations were in the range of 10 4 104-10 5 105 genomic copies/L. Quantification of concentrations of human viruses, such as SARS-CoV-2, in wastewater is a critical first step in the development of wastewater-based epidemiology predictive methods. However, accurate prediction involves the incorporation of multiple other measurements, data, and processes, such as the estimation of fate and detention times of viruses in the sewer collection network, estimation of contributing population, incorporation of disease characteristics based on anthropometric data, and others. A viral disease prediction model (Viral PD) that incorporates all these other inputs is currently being developed for COVID-19 in Detroit, Michigan.
ABSTRACT
Untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) Water Resource Recovery Facility (WRRF) located in southeast Michigan between April 8 and May 26, 2020. The WRRF is the largest single-site wastewater treatment facility in the US, and it receives wastewater from its service area via three main interceptors: Detroit River Interceptor (DRI), North Interceptor-East Arm (NI-EA), and Oakwood-Northwest-Wayne County Interceptor (O-NWI). A total of 54 untreated wastewater samples were collected (18 per interceptor) at the point of intake into the WRRF. Viruses were isolated from wastewater using electropositive NanoCeram column filters (Argonide, Sanford, Florida). For each sample, an average of 45 L of wastewater was passed through NanoCeram electropositive cartridge filters at a rate of no more than 11.3 L/m. Viruses were eluted and concentrated and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations were quantified with reverse transcription quantitative polymerase chain reaction (RT-qPCR). SARS-CoV-2 was detected in 100% of samples, and measured concentrations were in the range of 104-105 genomic copies/L. Quantification of concentrations of human viruses, such as SARS-CoV-2, in wastewater is a critical first step in the development of wastewater-based epidemiology predictive methods. However, accurate prediction involves the incorporation of multiple other measurements, data, and processes, such as the estimation of fate and detention times of viruses in the sewer collection network, estimation of contributing population, incorporation of disease characteristics based on anthropometric data, and others. A viral disease prediction model (Viral PD) that incorporates all these other inputs is currently being developed for COVID-19 in Detroit, Michigan. This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https:// creativecommons.org/licenses/ by/4.0/.