ABSTRACT
TeenCovidLife is part of Generation Scotland's CovidLife projects, a set of longitudinal observational studies designed to assess the psychosocial and health impacts of the COVID-19 pandemic. TeenCovidLife focused on how adolescents in Scotland were coping during the pandemic. As of September 2021, Generation Scotland had conducted three TeenCovidLife surveys. Participants from previous surveys were invited to participate in the next, meaning the age ranges shifted over time. TeenCovidLife Survey 1 consists of data from 5,543 young people age 12 to 17, collected from 22 May to 5 July 2020, during the first school closures period in Scotland. TeenCovidLife Survey 2 consists of data from 2,245 young people aged 12 to 18, collected from 18 August to 14 October 2020, when the initial lockdown measures were beginning to ease, and schools reopened in Scotland. TeenCovidLife Survey 3 consists of data from 597 young people age 12 to 19, collected from 12 May to 27 June 2021, a year after the first survey, after the schools returned following the second lockdown in 2021. A total of 316 participants took part in all three surveys. TeenCovidLife collected data on general health and well-being, as well as topics specific to COVID-19, such as adherence to COVID-19 health guidance, feelings about school closures, and the impact of exam cancellations. Limited work has examined the impact of the COVID-19 pandemic on young people. TeenCovidLife provides relevant and timely data to assess the impact of the pandemic on young people in Scotland. The dataset is available under authorised access from Generation Scotland; see the Generation Scotland website for more information.
ABSTRACT
CovidLife is a longitudinal observational study designed to investigate the impact of the COVID-19 pandemic on mental health, well-being and behaviour in adults living in the UK. In total, 18,518 participants (mean age = 56.43, SD = 14.35) completed the first CovidLife questionnaire (CovidLife1) between April and June 2020. To date, participants have completed two follow-up assessments. CovidLife2 took place between July and August 2020 (n = 11,319), and CovidLife3 took place in February 2021 (n = 10,386). A range of social and psychological measures were administered at each wave including assessments of anxiety, depression, well-being, loneliness and isolation. Information on sociodemographic, health, and economic circumstances was also collected. Questions also assessed information on COVID-19 infections and symptoms, compliance to COVID-19 restrictions, and opinions on the UK and Scottish Governments' handling of the pandemic. CovidLife includes a subsample of 4,847 participants from the Generation Scotland cohort (N~24,000, collected 2006-2011); a well-characterised cohort of families in Scotland with pre-pandemic data on mental health, physical health, lifestyle, and socioeconomic factors, along with biochemical and genomic data derived from biological samples. These participants also consented to their study data being linked to Scottish health records. CovidLife and Generation Scotland data can be accessed and used by external researchers following approval from the Generation Scotland Access Committee. CovidLife can be used to investigate mental health, well-being and behaviour during COVID-19; how these vary according to sociodemographic, health and economic circumstances; and how these change over time. The Generation Scotland subsample with pre-pandemic data and linkage to health records can be used to investigate the predictors of health and well-being during COVID-19 and the future health consequences of the COVID-19 pandemic.
ABSTRACT
RuralCovidLife is part of Generation Scotland's CovidLife project, investigating the impact of the COVID-19 pandemic and mitigation measures on people in Scotland. The RuralCovidLife project focuses on Scotland's rural communities, and how they have been impacted by the pandemic. During survey development, Generation Scotland consulted with people living or working in rural communities, and collaborated with a patient and public involvement and engagement (PPIE) group composed of rural community leaders. Through this consultation work, the RuralCovidLife survey was developed to assess the issues most pertinent to people in rural communities, such as mental health, employment, transport, connectivity, and local communities. Between 14th October and 30th November 2020, 3,365 participants from rural areas in Scotland took part in the survey. Participant ages ranged from 16 to 96 (mean = 58.4, standard deviation [SD] = 13.3), and the majority of the participants were female (70.5%). Over half (51.3%) had taken part in the original CovidLife survey. RuralCovidLife includes a subsample (n = 523) of participants from the Generation Scotland cohort. Pre-pandemic data on health and lifestyle, as well as biological samples, are available for these participants. These participants' data can also be linked to past and future healthcare records, allowing analysis of retrospective and prospective health outcomes. Like Generation Scotland, RuralCovidLife is designed as a resource for researchers. RuralCovidLife data, as well as the linked Generation Scotland data, is available for use by external researchers following approval from the Generation Scotland Access Committee. RuralCovidLife can be used to investigate mental health, well-being, and behaviour in participants living in rural areas during the COVID-19 pandemic, as well as comparisons with non-rural samples. Moreover, the sub-sample with full Generation Scotland data and linkage can be used to investigate the long-term health consequences of the COVID-19 pandemic in rural communities.
ABSTRACT
Background: Efficient generation of structured dose instructions that enable researchers to calculate drug exposure is central to pharmacoepidemiology studies. Our aim was to design and test an algorithm to codify dose instructions, applied to the NHS Scotland Prescribing Information System (PIS) that records about 100 million prescriptions per annum. Methods: A natural language processing (NLP) algorithm was developed that enabled free-text dose instructions to be represented by three attributes - quantity, frequency and qualifier - specified by three, three and two variables, respectively. A sample of 15 593 distinct dose instructions was used to test, validate and refine the algorithm. The final algorithm used a zero-assumption approach and was then applied to the full dataset. Results: The initial algorithm generated structured output for 13 152 (84.34%) of the 15 593 sample dose instructions, and reviewers identified 767 (5.83%) incorrect translations, giving an accuracy of 94.17%. Following subsequent refinement of the algorithm rules, application to the full dataset of 458 227 687 prescriptions (99.67% had dose instructions represented by 4 964 083 distinct instructions) generated a structured output for 92.3% of dose instruction texts. This varied by therapeutic area (from 86.7% for the central nervous system to 96.8% for the cardiovascular system). Conclusions: We created an NLP algorithm, operational at scale, to produce structured output that gives data users maximum flexibility to formulate, test and apply their own assumptions according to the medicines under investigation. Text mining approaches can provide a solution to the safe and efficient management and provisioning of large volumes of data generated through our health systems.
