Background: Long COVID is a debilitating multisystem condition. The objective of this study was to estimate the prevalence of long COVID in the adult population of Scotland, and to identify risk factors associated with its development. Methods: In this national, retrospective, observational cohort study, we analysed electronic health records (EHRs) for all adults (≥18 years) registered with a general medical practice and resident in Scotland between March 1, 2020, and October 26, 2022 (98-99% of the population). We linked data from primary care, secondary care, laboratory testing and prescribing. Four outcome measures were used to identify long COVID: clinical codes, free text in primary care records, free text on sick notes, and a novel operational definition. The operational definition was developed using Poisson regression to identify clinical encounters indicative of long COVID from a sample of negative and positive COVID-19 cases matched on time-varying propensity to test positive for SARS-CoV-2. Possible risk factors for long COVID were identified by stratifying descriptive statistics by long COVID status. Findings: Of 4,676,390 participants, 81,219 (1.7%) were identified as having long COVID. Clinical codes identified the fewest cases (n = 1,092, 0.02%), followed by free text (n = 8,368, 0.2%), sick notes (n = 14,469, 0.3%), and the operational definition (n = 64,193, 1.4%). There was limited overlap in cases identified by the measures; however, temporal trends and patient characteristics were consistent across measures. Compared with the general population, a higher proportion of people with long COVID were female (65.1% versus 50.4%), aged 38-67 (63.7% versus 48.9%), overweight or obese (45.7% versus 29.4%), had one or more comorbidities (52.7% versus 36.0%), were immunosuppressed (6.9% versus 3.2%), shielding (7.9% versus 3.4%), or hospitalised within 28 days of testing positive (8.8% versus 3.3%%), and had tested positive before Omicron became the dominant variant (44.9% versus 35.9%). The operational definition identified long COVID cases with combinations of clinical encounters (from four symptoms, six investigation types, and seven management strategies) recorded in EHRs within 4-26 weeks of a positive SARS-CoV-2 test. These combinations were significantly (p < 0.0001) more prevalent in positive COVID-19 patients than in matched negative controls. In a case-crossover analysis, 16.4% of those identified by the operational definition had similar healthcare patterns recorded before testing positive. Interpretation: The prevalence of long COVID presenting in general practice was estimated to be 0.02-1.7%, depending on the measure used. Due to challenges in diagnosing long COVID and inconsistent recording of information in EHRs, the true prevalence of long COVID is likely to be higher. The operational definition provided a novel approach but relied on a restricted set of symptoms and may misclassify individuals with pre-existing health conditions. Further research is needed to refine and validate this approach. Funding: Chief Scientist Office (Scotland), Medical Research Council, and BREATHE.
Background: In this priority-setting exercise, we sought to identify leading research priorities needed for strengthening future pandemic preparedness and response across countries. Methods: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals. Results: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences. Conclusions: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.
COVID-19 , Pandemic Preparedness , Child , Humans , Consensus , Research Design , COVID-19/epidemiology , COVID-19/prevention & control , Child Health
BACKGROUND: Vaccination continues to be the key public health measure for preventing severe COVID-19 outcomes. Certain groups may be at higher risk of incomplete vaccine schedule, which may leave them vulnerable to COVID-19 hospitalisation and death. AIM: To identify the sociodemographic and clinical predictors for not receiving a scheduled COVID-19 vaccine after previously receiving one. METHODS: We conducted two retrospective cohort studies with ≥3.7 million adults aged ≥18 years in Scotland. Multivariable logistic regression was used to estimate adjusted odds ratios (aOR) of not receiving a second, and separately a third dose between December 2020 and May 2022. Independent variables included sociodemographic and clinical factors. RESULTS: Of 3,826,797 people in the study population who received one dose, 3,732,596 (97.5%) received two doses, and 3,263,153 (86.5%) received all doses available during the study period. The most strongly associated predictors for not receiving the second dose were: being aged 18-29 (reference: 50-59 years; aOR:4.26; 95% confidence interval (CI):4.14-4.37); hospitalisation due to a potential vaccine related adverse event of special interest (AESI) (reference: not having a potential AESI, aOR:3.78; 95%CI: 3.29-4.35); and living in the most deprived quintile (reference: least deprived quintile, aOR:3.24; 95%CI: 3.16-3.32). The most strongly associated predictors for not receiving the third dose were: being 18-29 (reference: 50-59 years aOR:4.44; 95%CI: 4.38-4.49), living in the most deprived quintile (reference: least deprived quintile aOR:2.56; 95%CI: 2.53-2.59), and Black, Caribbean, or African ethnicity (reference: White ethnicity aOR:2.38; 95%CI: 2.30-2.46). Pregnancy, previous vaccination with mRNA-1273, smoking history, individual and household severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity, and having an unvaccinated adult in the household were also associated with incomplete vaccine schedule. CONCLUSION: We observed several risk factors that predict incomplete COVID-19 vaccination schedule. Vaccination programmes must take immediate action to ensure maximum uptake, particularly for populations vulnerable to severe COVID-19 outcomes.
COVID-19 Vaccines , COVID-19 , Female , Pregnancy , Adult , Humans , Adolescent , Retrospective Studies , COVID-19/epidemiology , COVID-19/prevention & control , SARS-CoV-2 , Scotland/epidemiology
Background: Pre-exposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) is a highly effective way to reduce virus transmission. There have been increasing calls to improve access to PrEP in Canada. One way to improve access is by having more prescribers available. The objective of this study was to determine target users' acceptance of a PrEP-prescribing service by pharmacists in Nova Scotia. Methods: A triangulation, mixed-methods study was conducted consisting of an online survey and qualitative interviews underpinned by the Theoretical Framework of Acceptability (TFA) constructs (affective attitude, burden, ethicality, intervention coherence, opportunity cost, perceived effectiveness and self-efficacy). Participants were those eligible for PrEP in Nova Scotia (men who have sex with men or transgender women, persons who inject drugs and HIV-negative individuals in serodiscordant relationships). Descriptive statistics and ordinal logistic regression were used to analyze survey data. Interview data were deductively coded according to each TFA construct and then inductively coded to determine themes within each construct. Results: A total of 148 responses were captured by the survey, and 15 participants were interviewed. Participants supported pharmacists' prescribing PrEP across all TFA constructs from both survey and interview data. Identified concerns related to pharmacists' abilities to order and view lab results, pharmacists' knowledge and skills for sexual health and the potential for experiencing stigma within pharmacy settings. Conclusion: A pharmacist-led PrEP-prescribing service is acceptable to eligible populations in Nova Scotia. The feasibility of PrEP prescribing by pharmacists should be pursued as an intervention to increase access to PrEP.
We reflect on our experiences of using Generative Pre-trained Transformer ChatGPT, a chatbot launched by OpenAI in November 2022, to draft a research article. We aim to demonstrate how ChatGPT could help researchers to accelerate drafting their papers. We created a simulated data set of 100 000 health care workers with varying ages, Body Mass Index (BMI), and risk profiles. Simulation data allow analysts to test statistical analysis techniques, such as machine-learning based approaches, without compromising patient privacy. Infections were simulated with a randomized probability of hospitalisation. A subset of these fictitious people was vaccinated with a fictional vaccine that reduced this probability of hospitalisation after infection. We then used ChatGPT to help us decide how to handle the simulated data in order to determine vaccine effectiveness and draft a related research paper. AI-based language models in data analysis and scientific writing are an area of growing interest, and this exemplar analysis aims to contribute to the understanding of how ChatGPT can be used to facilitate these tasks.
Software , Vaccine Efficacy , Humans , Computer Simulation , Confidentiality , Health Personnel
BACKGROUND: COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom's response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. OBJECTIVE: We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). METHODS: A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners' pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers' secure environments, and to support federated cohort discovery queries and meta-analysis. RESULTS: A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. CONCLUSIONS: CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.
COVID-19 , Humans , COVID-19/epidemiology , Pandemics , United Kingdom/epidemiology
According to the College of Podiatry, footprints rank among the most frequent forms of evidence found at crime scenes, and the recent ascension of forensic podiatry reflects the importance of footwear and barefoot traces in contemporary forensic practice. In this context, this pilot study focused on whether it is possible to distinguish between walking and running states using parameters derived from two-dimensional foot or shoe prints. Eleven subjects moved along four tracks (barefoot walking; barefoot running; footwear walking; footwear running) while having their bare feet or footwear stained with artificial blood and their footstep patterns recorded. Contact stains and associated bloodstain patterns were collected, and body movements were recorded through three-dimensional motion capture. Barefoot walking prints were found to be larger than barefoot static prints (1.789±0.481cm; p<0.001) and barefoot running prints (0.635±0.405cm; p=0.006). No correlation was observed for footwear prints. Running trials were more associated with the presence of both passive and cast off stains than walking trials, and the quantity of additional associated stains surrounding individual foot and shoe prints was also higher in running states. Furthermore, a previously proposed equation predicted speed with a high degree of accuracy (within 6%) and may be used for clinical assessment of walking speed. Contact stains, associated bloodstain patterns and stride length measurements may serve to ascertain state of motion in real crime scene scenarios, and future studies may be capable of designing statistical frameworks which could be used in courts of law.
Foot , Running , Shoes , Walking , Biomechanical Phenomena , Blood Stains , Female , Forensic Sciences/methods , Humans , Male , Pilot Projects , Statistics as Topic , Walking Speed , Young Adult
BACKGROUND: The aim of this study was to determine whether toe sliding is more likely to cause knee injuries than flatfoot sliding in curling. METHODS: Twelve curlers participated in the study, each delivering 12 stones. Six stones per volunteer were delivered using a flatfoot slide and six were delivered using a toe slide. The Pedar-X in-shoe pressure system recorded the plantar pressure during each of the slides, while a sagittal plane digital video recorded the body position of the curler. Measurements were taken from the video recordings using a software overlay program (MB Ruler), and this, combined with the Pedar-X data, gave the overall joint force in the tuck knee. RESULTS: The knee joint force for toe sliding was more than double that of flatfoot sliding (p<0.05). There was a strong correlation between the increase in knee joint force and the increase in the moment arm of the ground reaction force. Images produced using the three-dimensional Vicon system confirm that toe sliding produces a larger moment arm than flatfoot sliding. CONCLUSION: Injuries are more likely to occur in toe sliding, compared with flatfoot sliding, due to the increase in force and moment, pushing the weight of the curler forward over the knee, which could make the adopted position less stable. Curlers might consider avoiding toe sliding to reduce the risk of knee injuries if the two types of delivery could be performed equally well.
Good quality acoustical experiments are needed to measure microbubble behavior. An absolute calibration of the transmitted ultrasound field is possible using a calibrated hydrophone, but characterization of the received ultrasound beam is a more elaborate process and is not described in the literature. A new system based on a hydrodynamically focused flow has been used to measure echoes from single microbubbles at well specified positions in the ultrasonic field. An experimental set-up was built around a commercial scanner (Sonos 5500, Philips Medical Systems) to measure the scatter from solid spheres with radii between 30 to 60 microm. The behavior of these linear scatterers is accurately predicted by theory and software was produced to incorporate a simulation of the experimental conditions. The calibration of a phased array transducer was achieved by quantifying the receiver's spectral sensitivity for the range of receive frequencies (1.2 to 4.5 MHz). Examples of echoes from the microbubble agent Definity are used to illustrate the implementation of the calibration technique.
Microbubbles , Ultrasonics , Algorithms , Calibration , Contrast Media , Copper , Equipment Design , Fluorocarbons , Microspheres , Scattering, Radiation , Transducers
The general Keller-Herring equation for free gas bubbles is augmented by specific terms to describe the elasticity, viscosity and thickness of the encapsulating shell in ultrasound contrast agent microbubbles. A numerical investigation that analyses the acoustic backscatter from bubbles is employed to identify resonance frequencies that can be compared, for increasing driving pressure amplitude, with linear approximations obtained via analytical considerations. Calculations for bubbles of the size employed in diagnostic ultrasound, between 2 and 6 mum diameter, that are immersed in water and blood and exposed to monochromatic insonation, causing the bubbles to undergo stable cavitation, reveal that the resonance frequency diverges from the linear approximation as the pressure amplitude is increased. The shift in resonance, to lower frequency values, is found to be more pronounced for larger bubbles with the calculated value differing by up to 40% from the linear approximation. The results of this simulation might be potentially useful in preparation of formulations of ultrasound contrast agents with the specifically desired features, such as for instance resonance frequency.
