ABSTRACT
In Semester 1 of the 2020 academic year, face-to-face higher education students in many institutions were instructed to not attend classes or lectures on campus soon after the semester commenced, due to precautions put in place to limit the spread of Covid-19 in institutions across Australia. To sustain education and course progression, students were rapidly transitioned to learning-platforms, and synchronous or asynchronous online instruction. Although this action was needed to help ensure undisrupted learning, little consideration was given to the impact this would have on the students who had chosen to study in the face-to-face mode. The instrumental case study reported in this paper sought to capture the lived experiences of students enrolled in initial teacher education (ITE) programs in mathematics, science, and technology (STEM) units in on-campus, face-to-face mode as they moved to emergency fully online instruction. An initial online survey, constructed in Qualtrics and using a 4-point Likert scale, was sent to these students in Semester 2, and this was followed by semi-structured interviews with those who indicated their willingness to participate. Thirty-two students participated in the survey and 11 in the interviews, and these data were examined through the lens of self-determination theory. The majority of participants preferred the face-to-face mode, yet some were surprised about the affordances of fully online. Although the respondent group was small, the insights gained are of interest to educators in higher education and have the potential to inform new ways of designing and delivering authentic and engaging online and blended learning in these programs.
ABSTRACT
Objectives: To estimate the impact of the COVID-19 pandemic on cardiovascular disease (CVD) and CVD management using routinely collected medication data as a proxy. Design: Descriptive and interrupted time series analysis using anonymised individual-level population-scale data for 1.32 billion records of dispensed CVD medications across 15.8 million individuals in England, Scotland and Wales. Setting: Community dispensed CVD medications with 100% coverage from England, Scotland and Wales, plus primary care prescribed CVD medications from England (including 98% English general practices). Participants: 15.8 million individuals aged 18+ years alive on 1st April 2018 dispensed at least one CVD medicine in a year from England, Scotland and Wales. Main outcome measures: Monthly counts, percent annual change (1st April 2018 to 31st July 2021) and annual rates (1st March 2018 to 28th February 2021) of medicines dispensed by CVD/ CVD risk factor; prevalent and incident use. Results: Year-on-year change in dispensed CVD medicines by month were observed, with notable uplifts ahead of the first (11.8% higher in March 2020) but not subsequent national lockdowns. Using hypertension as one example of the indirect impact of the pandemic, we observed 491,203 fewer individuals initiated antihypertensive treatment across England, Scotland and Wales during the period March 2020 to end May 2021 than would have been expected compared to 2019. We estimated that this missed antihypertension treatment could result in 13,659 additional CVD events should individuals remain untreated, including 2,281 additional myocardial infarctions (MIs) and 3,474 additional strokes. Incident use of lipid-lowering medicines decreased by an average 14,793 per month in early 2021 compared with the equivalent months prior to the pandemic in 2019. In contrast, the use of incident medicines to treat type-2 diabetes (T2DM) increased by approximately 1,642 patients per month. Conclusions: Management of key CVD risk factors as proxied by incident use of CVD medicines has not returned to pre-pandemic levels in the UK. Novel methods to identify and treat individuals who have missed treatment are urgently required to avoid large numbers of additional future CVD events, further adding indirect cost of the COVID-19 pandemic.
Subject(s)
Myocardial Infarction , Cardiovascular Diseases , Diabetes Mellitus, Type 2 , Hypertension , COVID-19 , StrokeABSTRACT
BackgroundInnovation that improves the quality of care children and young people (CYP) receive is a major theme of the RCPCH 2040 vision. Paediatricians are being challenged to move beyond the virtual consultation adopted during the COVID-19 pandemic and utilise new technology to monitor, care for and treat CYP;empowering them to access services in a way that achieves this.Bradford Teaching Hospitals NHS Foundation Trust (BTHFT) is the first NHS site in the UK to pilot the use of a handheld Telehealth device called TytoCare. The device enables healthcare professionals, CYP or their carers to link with clinicians remotely, in real time or offline, to enable examination of the heart, lungs, skin, ears and throat.ObjectivesOur pilot aimed to assess the usability and potential benefit of TytoCare in CYP with chronic respiratory conditions like cystic fibrosis and primary ciliary dyskinesia to reduce face to face reviews by health care professionals, acute admissions and the burden of illness for these CYP and their families.MethodsStakeholder engagement with the BTHFT Executive, Paediatric Respiratory multi-disciplinary team, Informatics, Information Governance Department, Clinical Engineering and the Tytocare Company.Workflow design:Professional workflow: a device was utilised by our respiratory specialist nurse when a review from a senior doctor, other speciality or other allied health care professional was needed.Patient workflow: families were chosen according to where the clinical team felt there would be greatest benefit The device was used within an individualised care plan to assess acute or routine review with a member of the team from home.Feedback on the usability, workflow and key outcomes was gathered at various stages of the project:A feedback survey completed by the healthcare professional after each consult.Data was collected via the TytoCare system for each consultation.End of pilot surveys were completed by staff and families.Results48 consultations were undertaken using TytoCare during the pilot. We had healthcare professional feedback for 46 of them reporting the following impact: 100% of consultations felt to provide reassurance to families, 98% had a positive impact on the CYP. Two hospital assessments, 3 inpatient admissions, 13 face to face clinic appointments, 4 home visits, 23 face to face physiotherapy reviews and approximately 329miles were saved.ConclusionsIn this pilot the TytoCare device was found to be easy to use by professionals and carers and to be reliable and effective in providing safe and quality care for a select group of CYP at home. The pilot highlighted the impact technology can have in reducing the burden of chronic illness for families. It also demonstrated that technology could be used successfully to improve access to care for some of our most vulnerable families.
ABSTRACT
BackgroundTo externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England. MethodsPopulation-based cohort study using the ONS Public Health Linked Data Asset, a cohort based on the 2011 Census linked to Hospital Episode Statistics, the General Practice Extraction Service Data for pandemic planning and research, radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two time periods were used: (a) 24th January to 30th April 2020; and (b) 1st May to 28th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period. FindingsThe study comprises 34,897,648 adults aged 19-100 years resident in England. There were 26,985 COVID-19 deaths during the first time-period and 13,177 during the second. The algorithms had good calibration in the validation cohort in both time periods with close correspondence of observed and predicted risks. They explained 77.1% (95% CI: 76.9% to 77.4%) of the variation in time to death in men in the first time-period (R2); the D statistic was 3.76 (95% CI: 3.73 to 3.79); Harrells C was 0.935 (0.933 to 0.937). Similar results were obtained for women, and in the second time-period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first time period was 65.9% for men and 71.7% for women. People in the top 20% of predicted risks of death accounted for 90.8% of all COVID-19 deaths for men and 93.0% for women. InterpretationThe QCovid population-based risk algorithm performed well, showing very high levels of discrimination for COVID-19 deaths in men and women for both time periods. It has the potential to be dynamically updated as the pandemic evolves and therefore, has potential use in guiding national policy. FundingNational Institute of Health Research RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSPublic policy measures and clinical risk assessment relevant to COVID-19 need to be aided by rigorously developed and validated risk prediction models. A recent living systematic review of published risk prediction models for COVID-19 found most models are subject to a high risk of bias with optimistic reported performance, raising concern that these models may be unreliable when applied in practice. A population-based risk prediction model, QCovid risk prediction algorithm, has recently been developed to identify adults at high risk of serious COVID-19 outcomes, which overcome many of the limitations of previous tools. Added value of this studyCommissioned by the Chief Medical Officer for England, we validated the novel clinical risk prediction model (QCovid) to identify risks of short-term severe outcomes due to COVID-19. We used national linked datasets from general practice, death registry and hospital episode data for a population-representative sample of over 34 million adults. The risk models have excellent discrimination in men and women (Harrells C statistic>0.9) and are well calibrated. QCovid represents a new, evidence-based opportunity for population risk-stratification. Implications of all the available evidenceQCovid has the potential to support public health policy, from enabling shared decision making between clinicians and patients in relation to health and work risks, to targeted recruitment for clinical trials, and prioritisation of vaccination, for example.