DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.

INTRODUCTION: Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. METHODS AND ANALYSIS: A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.

Prevalence and risk factors for long COVID among adults in Scotland using electronic health records: a national, retrospective, observational cohort study.

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.

Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: a cohort study of 23.5 million people in England.

OBJECTIVE: To determine whether periods of disruption were associated with increased 'avoidable' hospital admissions and wider social inequalities in England. DESIGN: Observational repeated cross-sectional study. SETTING: England (January 2019 to March 2022). PARTICIPANTS: With the approval of NHS England we used individual-level electronic health records from OpenSAFELY, which covered ~40% of general practices in England (mean monthly population size 23.5 million people). PRIMARY AND SECONDARY OUTCOME MEASURES: We estimated crude and directly age-standardised rates for potentially preventable unplanned hospital admissions: ambulatory care sensitive conditions and urgent emergency sensitive conditions. We considered how trends in these outcomes varied by three measures of social and spatial inequality: neighbourhood socioeconomic deprivation, ethnicity and geographical region. RESULTS: There were large declines in avoidable hospitalisations during the first national lockdown (March to May 2020). Trends increased post-lockdown but never reached 2019 levels. The exception to these trends was for vaccine-preventable ambulatory care sensitive admissions which remained low throughout 2020-2021. While trends were consistent by each measure of inequality, absolute levels of inequalities narrowed across levels of neighbourhood socioeconomic deprivation, Asian ethnicity (compared with white ethnicity) and geographical region (especially in northern regions). CONCLUSIONS: We found no evidence that periods of healthcare disruption from the COVID-19 pandemic resulted in more avoidable hospitalisations. Falling avoidable hospital admissions has coincided with declining inequalities most strongly by level of deprivation, but also for Asian ethnic groups and northern regions of England.

Handling Class Imbalance in Machine Learning-based Prediction Models: A Case Study in Asthma Management.

A data-driven prediction tool has the potential to provide early warning of an asthma attack and improve asthma management and outcomes. Most previous machine learning (ML)-based studies for asthma attack prediction have reported a severe class imbalance, with major implications for model performance. We aimed to undertake a systematic comparison of several class imbalance handling techniques in the context of risk prediction models for asthma prognosis. We used data from 9,835 asthma patients extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database and deployed five class imbalance handling methods based on synthetic minority oversampling technique (SMOTE) and cost function customisation. We then compared their performances in improving two-class classifier models developed using logistic regression (LR) and extreme gradient boosting (XGBoost) for three different prediction tasks with varying severity of class imbalance (proportion of majority class ranging from 90.86% to 98.98%). The cost function customisation technique substantially outperformed the SMOTE-based methods in all tasks. XGBoost combined with cost function customisation achieved the highest prediction performance for the outcome with the most extreme class imbalance ratio (AUC = 0.72). Our findings suggest that the cost function customisation-based approach to tackle class imbalance provides substantially better performance compared to oversampling in the context of asthma management.Clinical Relevance- This study underscores the challenge of class imbalance in the context of prediction tools to improve asthma management and outcomes and provides a methodological solution that addresses the challenge. Accurate asthma prediction tools can provide early warning and potentially prevent deterioration thereby improving the quality of life of patients with asthma.

Predictors of incomplete COVID-19 vaccine schedule among adults in Scotland: Two retrospective cohort analyses of the primary schedule and third dose.

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.

Home monitoring with connected mobile devices for asthma attack prediction with machine learning.

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Effectiveness of mRNA boosters after homologous primary series with BNT162b2 or ChAdOx1 against symptomatic infection and severe COVID-19 in Brazil and Scotland: A test-negative design case-control study.

BACKGROUND: Brazil and Scotland have used mRNA boosters in their respective populations since September 2021, with Omicron's emergence accelerating their booster program. Despite this, both countries have reported substantial recent increases in Coronavirus Disease 2019 (COVID-19) cases. The duration of the protection conferred by the booster dose against symptomatic Omicron cases and severe outcomes is unclear. METHODS AND FINDINGS: Using a test-negative design, we analyzed national databases to estimate the vaccine effectiveness (VE) of a primary series (with ChAdOx1 or BNT162b2) plus an mRNA vaccine booster (with BNT162b2 or mRNA-1273) against symptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and severe COVID-19 outcomes (hospitalization or death) during the period of Omicron dominance in Brazil and Scotland compared to unvaccinated individuals. Additional analyses included stratification by age group (18 to 49, 50 to 64, ≥65). All individuals aged 18 years or older who reported acute respiratory illness symptoms and tested for SARS-CoV-2 infection between January 1, 2022, and April 23, 2022, in Brazil and Scotland were eligible for the study. At 14 to 29 days after the mRNA booster, the VE against symptomatic SARS-CoV-2 infection of ChAdOx1 plus BNT162b2 booster was 51.6%, (95% confidence interval (CI): [51.0, 52.2], p < 0.001) in Brazil and 67.1% (95% CI [65.5, 68.5], p < 0.001) in Scotland. At ≥4 months, protection against symptomatic infection waned to 4.2% (95% CI [0.7, 7.6], p = 0.02) in Brazil and 37.4% (95% CI [33.8, 40.9], p < 0.001) in Scotland. VE against severe outcomes in Brazil was 93.5% (95% CI [93.0, 94.0], p < 0.001) at 14 to 29 days post-booster, decreasing to 82.3% (95% CI [79.7, 84.7], p < 0.001) and 98.3% (95% CI [87.3, 99.8], p < 0.001) to 77.8% (95% CI [51.4, 89.9], p < 0.001) in Scotland for the same periods. Similar results were obtained with the primary series of BNT162b2 plus homologous booster. Potential limitations of this study were that we assumed that all cases included in the analysis were due to the Omicron variant based on the period of dominance and the limited follow-up time since the booster dose. CONCLUSIONS: We observed that mRNA boosters after a primary vaccination course with either mRNA or viral-vector vaccines provided modest, short-lived protection against symptomatic infection with Omicron but substantial and more sustained protection against severe COVID-19 outcomes for at least 3 months.

Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease.

BACKGROUND: Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. OBJECTIVE: In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). MATERIALS AND METHODS: STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. RESULTS: Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. CONCLUSION: LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.

Severe COVID-19 outcomes after full vaccination of primary schedule and initial boosters: pooled analysis of national prospective cohort studies of 30 million individuals in England, Northern Ireland, Scotland, and Wales.

BACKGROUND: Current UK vaccination policy is to offer future COVID-19 booster doses to individuals at high risk of serious illness from COVID-19, but it is still uncertain which groups of the population could benefit most. In response to an urgent request from the UK Joint Committee on Vaccination and Immunisation, we aimed to identify risk factors for severe COVID-19 outcomes (ie, COVID-19-related hospitalisation or death) in individuals who had completed their primary COVID-19 vaccination schedule and had received the first booster vaccine. METHODS: We constructed prospective cohorts across all four UK nations through linkages of primary care, RT-PCR testing, vaccination, hospitalisation, and mortality data on 30 million people. We included individuals who received primary vaccine doses of BNT162b2 (tozinameran; Pfizer-BioNTech) or ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines in our initial analyses. We then restricted analyses to those given a BNT162b2 or mRNA-1273 (elasomeran; Moderna) booster and had a severe COVID-19 outcome between Dec 20, 2021, and Feb 28, 2022 (when the omicron (B.1.1.529) variant was dominant). We fitted time-dependent Poisson regression models and calculated adjusted rate ratios (aRRs) and 95% CIs for the associations between risk factors and COVID-19-related hospitalisation or death. We adjusted for a range of potential covariates, including age, sex, comorbidities, and previous SARS-CoV-2 infection. Stratified analyses were conducted by vaccine type. We then did pooled analyses across UK nations using fixed-effect meta-analyses. FINDINGS: Between Dec 8, 2020, and Feb 28, 2022, 16 208 600 individuals completed their primary vaccine schedule and 13 836 390 individuals received a booster dose. Between Dec 20, 2021, and Feb 28, 2022, 59 510 (0·4%) of the primary vaccine group and 26 100 (0·2%) of those who received their booster had severe COVID-19 outcomes. The risk of severe COVID-19 outcomes reduced after receiving the booster (rate change: 8·8 events per 1000 person-years to 7·6 events per 1000 person-years). Older adults (≥80 years vs 18-49 years; aRR 3·60 [95% CI 3·45-3·75]), those with comorbidities (≥5 comorbidities vs none; 9·51 [9·07-9·97]), being male (male vs female; 1·23 [1·20-1·26]), and those with certain underlying health conditions-in particular, individuals receiving immunosuppressants (yes vs no; 5·80 [5·53-6·09])-and those with chronic kidney disease (stage 5 vs no; 3·71 [2·90-4·74]) remained at high risk despite the initial booster. Individuals with a history of COVID-19 infection were at reduced risk (infected ≥9 months before booster dose vs no previous infection; aRR 0·41 [95% CI 0·29-0·58]). INTERPRETATION: Older people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalisation and death after the initial vaccine booster and should, therefore, be prioritised for additional boosters, including novel optimised versions, and the increasing array of COVID-19 therapeutics. FUNDING: National Core Studies-Immunity, UK Research and Innovation (Medical Research Council), Health Data Research UK, the Scottish Government, and the University of Edinburgh.

A population-based matched cohort study of early pregnancy outcomes following COVID-19 vaccination and SARS-CoV-2 infection.

Data on the safety of COVID-19 vaccines in early pregnancy are limited. We conducted a national, population-based, matched cohort study assessing associations between COVID-19 vaccination and miscarriage prior to 20 weeks gestation and, separately, ectopic pregnancy. We identified women in Scotland vaccinated between 6 weeks preconception and 19 weeks 6 days gestation (for miscarriage; n = 18,780) or 2 weeks 6 days gestation (for ectopic; n = 10,570). Matched, unvaccinated women from the pre-pandemic and, separately, pandemic periods were used as controls. Here we show no association between vaccination and miscarriage (adjusted Odds Ratio [aOR], pre-pandemic controls = 1.02, 95% Confidence Interval [CI] = 0.96-1.09) or ectopic pregnancy (aOR = 1.13, 95% CI = 0.92-1.38). We undertook additional analyses examining confirmed SARS-CoV-2 infection as the exposure and similarly found no association with miscarriage or ectopic pregnancy. Our findings support current recommendations that vaccination remains the safest way for pregnant women to protect themselves and their babies from COVID-19.

Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

INTRODUCTION: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. METHODS AND ANALYSIS: A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. ETHICS AND DISSEMINATION: Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

BNT162b2 COVID-19 vaccination uptake, safety, effectiveness and waning in children and young people aged 12-17 years in Scotland.

Background: The two-dose BNT162b2 (Pfizer-BioNTech) vaccine has demonstrated high efficacy against COVID-19 disease in clinical trials of children and young people (CYP). Consequently, we investigated the uptake, safety, effectiveness and waning of the protective effect of the BNT162b2 against symptomatic COVID-19 in CYP aged 12-17 years in Scotland. Methods: The analysis of the vaccine uptake was based on information from the Turas Vaccination Management Tool, inclusive of Mar 1, 2022. Vaccine safety was evaluated using national data on hospital admissions and General Practice (GP) consultations, through a self-controlled case series (SCCS) design, investigating 17 health outcomes of interest. Vaccine effectiveness (VE) against symptomatic COVID-19 disease for Delta and Omicron variants was estimated using a test-negative design (TND) and S-gene status in a prospective cohort study using the Scotland-wide Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) surveillance platform. The waning of the VE following each dose of BNT162b2 was assessed using a matching process followed by conditional logistic regression. Findings: Between Aug 6, 2021 and Mar 1, 2022, 75.9% of the 112,609 CYP aged 16-17 years received the first and 49.0% the second COVID-19 vaccine dose. Among 237,681 CYP aged 12-15 years, the uptake was 64.5% and 37.2%, respectively. For 12-17-year-olds, BNT162b2 showed an excellent safety record, with no increase in hospital stays following vaccination for any of the 17 investigated health outcomes. In the 16-17-year-old group, VE against symptomatic COVID-19 during the Delta period was 64.2% (95% confidence interval [CI] 59.2-68.5) at 2-5 weeks after the first dose and 95.6% (77.0-99.1) at 2-5 weeks after the second dose. The respective VEs against symptomatic COVID-19 in the Omicron period were 22.8% (95% CI -6.4-44.0) and 65.5% (95% CI 56.0-73.0). In children aged 12-15 years, VE against symptomatic COVID-19 during the Delta period was 65.4% (95% CI 61.5-68.8) at 2-5 weeks after the first dose, with no observed cases at 2-5 weeks after the second dose. The corresponding VE against symptomatic COVID-19 during the Omicron period were 30.2% (95% CI 18.4-40.3) and 81.2% (95% CI 77.7-84.2). The waning of the protective effect against the symptomatic disease began after five weeks post-first and post-second dose. Interpretation: During the study period, uptake of BNT162b2 in Scotland has covered more than two-thirds of CYP aged 12-17 years with the first dose and about 40% with the second dose. We found no increased likelihood of admission to hospital with a range of health outcomes in the period after vaccination. Vaccination with both doses was associated with a substantial reduction in the risk of COVID-19 symptomatic disease during both the Delta and Omicron periods, but this protection began to wane after five weeks. Funding: UK Research and Innovation (Medical Research Council); Research and Innovation Industrial Strategy Challenge Fund; Chief Scientist's Office of the Scottish Government; Health Data Research UK; National Core Studies - Data and Connectivity.

Risk of COVID-19 hospitalizations among school-aged children in Scotland: A national incident cohort study.

Background: There is considerable policy, clinical and public interest about whether children should be vaccinated against SARS-CoV-2 and, if so, which children should be prioritised (particularly if vaccine resources are limited). To inform such deliberations, we sought to identify children and young people at highest risk of hospitalization from COVID-19. Methods: We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform to undertake a national incident cohort analysis to investigate the risk of hospitalization among 5-17 years old living in Scotland in risk groups defined by the living risk prediction algorithm (QCOVID). A Cox proportional hazard model was used to derive hazard ratios (HR) and 95% confidence intervals (CIs) for the association between risk groups and COVID-19 hospital admission. Adjustments were made for age, sex, socioeconomic status, co-morbidity, and prior hospitalization. Results: Between March 1, 2020 and November 22, 2021, there were 146 183 (19.4% of all 752 867 children in Scotland) polymerase chain reaction (PCR) confirmed SARS-CoV-2 infections among 5-17 years old. Of those with confirmed infection, 973 (0.7%) were admitted to hospital with COVID-19. The rate of COVID-19 hospitalization was higher in those within each QCOVID risk group compared to those without the condition. Similar results were found in age stratified analyses (5-11 and 12-17 years old). Risk groups associated with an increased risk of COVID-19 hospital admission, included (adjusted HR, 95% CIs): sickle cell disease 14.35 (8.48-24.28), chronic kidney disease 11.34 (4.61-27.87), blood cancer 6.32 (3.24-12.35), rare pulmonary diseases 5.04 (2.58-9.86), type 2 diabetes 3.04 (1.34-6.92), epilepsy 2.54 (1.69-3.81), type 1 diabetes 2.48 (1.47-4.16), Down syndrome 2.45 (0.96-6.25), cerebral palsy 2.37 (1.26-4.47), severe mental illness 1.43 (0.63-3.24), fracture 1.41 (1.02-1.95), congenital heart disease 1.35 (0.82-2.23), asthma 1.28 (1.06-1.55), and learning disability (excluding Down syndrome) 1.08 (0.82-1.42), when compared to those without these conditions. Although our Cox models were adjusted for a number of potential confounders, residual confounding remains a possibility. Conclusions: In this national study, we observed an increased risk of COVID-19 hospital admissions among school-aged children with specific underlying long-term health conditions compared with children without these conditions.

Deriving and validating a risk prediction model for long COVID-19: protocol for an observational cohort study using linked Scottish data.

INTRODUCTION: COVID-19 is commonly experienced as an acute illness, yet some people continue to have symptoms that persist for weeks, or months (commonly referred to as 'long-COVID'). It remains unclear which patients are at highest risk of developing long-COVID. In this protocol, we describe plans to develop a prediction model to identify individuals at risk of developing long-COVID. METHODS AND ANALYSIS: We will use the national Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) platform, a population-level linked dataset of routine electronic healthcare data from 5.4 million individuals in Scotland. We will identify potential indicators for long-COVID by identifying patterns in primary care data linked to information from out-of-hours general practitioner encounters, accident and emergency visits, hospital admissions, outpatient visits, medication prescribing/dispensing and mortality. We will investigate the potential indicators of long-COVID by performing a matched analysis between those with a positive reverse transcriptase PCR (RT-PCR) test for SARS-CoV-2 infection and two control groups: (1) individuals with at least one negative RT-PCR test and never tested positive; (2) the general population (everyone who did not test positive) of Scotland. Cluster analysis will then be used to determine the final definition of the outcome measure for long-COVID. We will then derive, internally and externally validate a prediction model to identify the epidemiological risk factors associated with long-COVID. ETHICS AND DISSEMINATION: The EAVE II study has obtained approvals from the Research Ethics Committee (reference: 12/SS/0201), and the Public Benefit and Privacy Panel for Health and Social Care (reference: 1920-0279). Study findings will be published in peer-reviewed journals and presented at conferences. Understanding the predictors for long-COVID and identifying the patient groups at greatest risk of persisting symptoms will inform future treatments and preventative strategies for long-COVID.

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.

Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

Impact of COVID-19 pandemic on asthma exacerbations: Retrospective cohort study of over 500,000 patients in a national English primary care database.

Background: Several countries reported a substantial reduction in asthma exacerbations associated with COVID-19 pandemic-related restrictions. However, it is not known if these early reported declines were short-term and if these have rebounded to pre-pandemic levels following easing of lockdown restrictions. Methods: We undertook a retrospective, cohort study of all asthma patients in a national primary care database of almost 10 million patients, Optimum Patient Care Database (OPCRD), identified from January 1, 2010, to December 31, 2015, using a previously validated algorithm. We subsequently followed the identified cohort of asthma patients from January 1, 2016, to October 3, 2021, and identified every asthma exacerbation episode with a validated algorithm. To quantify any pandemic-related change in exacerbations, we created a control time-series (mean of 2016-2019) and then compared the change in exacerbation rate in 2020-2021 over quarterly periods when compared with the control period (the pre-pandemic period). We undertook overall and stratified analyses by age group, sex, and English region. Findings: We identified 100,362 asthma patients (502,669 patient-years) from across England who experienced at least one exacerbation episode (298,390 exacerbation episodes during the entire follow-up). Except for the first quarter of 2020, the exacerbation rates were substantially lower (>25%) during all quarters in 2020-2021 when compared with the rates during 2016-2019 (39.7% (95% Confidence Interval (CI): 34.6, 44.9) in quarter-2, 2020; 46.5% (95%CI: 36.7, 56.4) in quarter-3, 2020; 56.3% (95%CI: 48.7, 63.9) in quarter-4, 2020; 63.2% (95%CI: 53.9, 72.5) in quarter-1, 2021; 57.7% (95%CI: 52.9, 62.4) in quarter-2, 2021; 53.3% (95%CI: 43.8, 62.8) in quarter-3, 2021). Interpretation: There was a substantial and persistent reduction in asthma exacerbations across England over the first 18 months after the first lockdown. This is unlikely to be adequately explained by changes in health-seeking behaviour, pandemic-related healthcare service disruption, or any air-quality improvements. Funding: Asthma UK, Health Data Research UK (HDR UK), Medical Research Council (MRC), National Institute for Health Research (NIHR).

Impact of first UK COVID-19 lockdown on hospital admissions: Interrupted time series study of 32 million people.

Background: Uncontrolled infection and lockdown measures introduced in response have resulted in an unprecedented challenge for health systems internationally. Whether such unprecedented impact was due to lockdown itself and recedes when such measures are lifted is unclear. We assessed the short- and medium-term impacts of the first lockdown measures on hospital care for tracer non-COVID-19 conditions in England, Scotland and Wales across diseases, sexes, and socioeconomic and ethnic groups. Methods: We used OpenSAFELY (for England), EAVEII (Scotland), and SAIL Databank (Wales) to extract weekly hospital admission rates for cancer, cardiovascular and respiratory conditions (excluding COVID-19) from the pre-pandemic period until 25/10/2020 and conducted a controlled interrupted time series analysis. We undertook stratified analyses and assessed admission rates over seven months during which lockdown restrictions were gradually lifted. Findings: Our combined dataset included 32 million people who contributed over 74 million person-years. Admission rates for all three conditions fell by 34.2% (Confidence Interval (CI): -43.0, -25.3) in England, 20.9% (CI: -27.8, -14.1) in Scotland, and 24.7% (CI: -36.7, -12.7) in Wales, with falls across every stratum considered. In all three nations, cancer-related admissions fell the most while respiratory-related admissions fell the least (e.g., rates fell by 40.5% (CI: -47.4, -33.6), 21.9% (CI: -35.4, -8.4), and 19.0% (CI: -30.6, -7.4) in England for cancer, cardiovascular-related, and respiratory-related admissions respectively). Unscheduled admissions rates fell more in the most than the least deprived quintile across all three nations. Some ethnic minority groups experienced greater falls in admissions (e.g., in England, unscheduled admissions fell by 9.5% (CI: -20.2, 1.2) for Whites, but 44.3% (CI: -71.0, -17.6), 34.6% (CI: -63.8, -5.3), and 25.6% (CI: -45.0, -6.3) for Mixed, Other and Black ethnic groups respectively). Despite easing of restrictions, the overall admission rates remained lower in England, Scotland, and Wales by 20.8%, 21.6%, and 22.0%, respectively when compared to the same period (August-September) during the pre-pandemic years. This corresponds to a reduction of 26.2, 23.8 and 30.2 admissions per 100,000 people in England, Scotland, and Wales respectively. Interpretation: Hospital care for non-COVID diseases fell substantially across England, Scotland, and Wales during the first lockdown, with reductions persisting for at least six months. The most deprived and minority ethnic groups were impacted more severely. Funding: This work was funded by the Medical Research Council as part of the Lifelong Health and Wellbeing study as part of National Core Studies (MC_PC_20030). SVK acknowledges funding from the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE - The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. BG has received research funding from the NHS National Institute for Health Research (NIHR), the Wellcome Trust, Health Data Research UK, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme.