Characteristics and outcomes of patients with COVID-19 at high risk of disease progression receiving sotrovimab, oral antivirals, or no treatment: a retrospective cohort study.

BACKGROUND: The clinical benefit of coronavirus disease 2019 (COVID-19) treatments against new circulating variants remains unclear. We sought to describe characteristics and clinical outcomes of highest risk patients with COVID-19 receiving early COVID-19 treatments in Scotland. METHODS: Retrospective cohort study of non-hospitalized patients diagnosed with COVID-19 from December 1, 2021-October 25, 2022, using Scottish administrative health data. We included adult patients who met ≥ 1 of the National Health Service highest risk criteria for early COVID-19 treatment and received outpatient treatment with sotrovimab, nirmatrelvir/ritonavir or molnupiravir, or no early COVID-19 treatment. Index date was defined as the earliest of COVID-19 diagnosis or early COVID-19 treatment. Baseline characteristics and acute clinical outcomes in the 28 days following index were reported. Values of ≤ 5 were suppressed. RESULTS: In total, 2548 patients were included (492: sotrovimab, 276: nirmatrelvir/ritonavir, 71: molnupiravir, and 1709: eligible highest risk untreated). Patients aged ≥ 75 years accounted for 6.9% (n = 34/492), 21.0% (n = 58/276), 16.9% (n = 12/71) and 13.2% (n = 225/1709) of the cohorts, respectively. Advanced renal disease was reported in 6.7% (n = 33/492) of sotrovimab-treated and 4.7% (n = 81/1709) of untreated patients, and ≤ 5 nirmatrelvir/ritonavir-treated and molnupiravir-treated patients. All-cause hospitalizations were experienced by 5.3% (n = 25/476) of sotrovimab-treated patients, 6.9% (n = 12/175) of nirmatrelvir/ritonavir-treated patients, ≤ 5 (suppressed number) molnupiravir-treated patients and 13.3% (n = 216/1622) of untreated patients. There were no deaths in the treated cohorts; mortality was 4.3% (n = 70/1622) among untreated patients. CONCLUSIONS: Sotrovimab was often used by patients who were aged < 75 years. Among patients receiving early COVID-19 treatment, proportions of 28-day all-cause hospitalization and death were low.

Real-world severe COVID-19 outcomes associated with use of antivirals and neutralising monoclonal antibodies in Scotland.

We sought to investigate the incidence of severe COVID-19 outcomes after treatment with antivirals and neutralising monoclonal antibodies, and estimate the comparative effectiveness of treatments in community-based individuals. We conducted a retrospective cohort study investigating clinical outcomes of hospitalisation, intensive care unit admission and death, in those treated with antivirals and monoclonal antibodies for COVID-19 in Scotland between December 2021 and September 2022. We compared the effect of various treatments on the risk of severe COVID-19 outcomes, stratified by most prevalent sub-lineage at that time, and controlling for comorbidities and other patient characteristics. We identified 14,365 individuals treated for COVID-19 during our study period, some of whom were treated for multiple infections. The incidence of severe COVID-19 outcomes (inpatient admission or death) in community-treated patients (81% of all treatment episodes) was 1.2% (n = 137/11894, 95% CI 1.0-1.4), compared to 32.8% in those treated in hospital for acute COVID-19 (re-admissions or death; n = 40/122, 95% CI 25.1-41.5). For community-treated patients, there was a lower risk of severe outcomes (inpatient admission or death) in younger patients, and in those who had received three or more COVID-19 vaccinations. During the period in which BA.2 was the most prevalent sub-lineage in the UK, sotrovimab was associated with a reduced treatment effect compared to nirmaltrelvir + ritonavir. However, since BA.5 has been the most prevalent sub-lineage in the UK, both sotrovimab and nirmaltrelvir + ritonavir were associated with similarly lower incidence of severe outcomes than molnupiravir. Around 1% of those treated for COVID-19 with antivirals or neutralising monoclonal antibodies required hospital admission. During the period in which BA.5 was the prevalent sub-lineages in the UK, molnupiravir was associated with the highest incidence of severe outcomes in community-treated patients.

Association between antibody responses post-vaccination and severe COVID-19 outcomes in Scotland.

Several population-level studies have described individual clinical risk factors associated with suboptimal antibody responses following COVID-19 vaccination, but none have examined multimorbidity. Others have shown that suboptimal post-vaccination responses offer reduced protection to subsequent SARS-CoV-2 infection; however, the level of protection from COVID-19 hospitalisation/death remains unconfirmed. We use national Scottish datasets to investigate the association between multimorbidity and testing antibody-negative, examining the correlation between antibody levels and subsequent COVID-19 hospitalisation/death among double-vaccinated individuals. We found that individuals with multimorbidity ( ≥ five conditions) were more likely to test antibody-negative post-vaccination and 13.37 [6.05-29.53] times more likely to be hospitalised/die from COVID-19 than individuals without conditions. We also show a dose-dependent association between post-vaccination antibody levels and COVID-19 hospitalisation or death, with those with undetectable antibody levels at a significantly higher risk (HR 9.21 [95% CI 4.63-18.29]) of these serious outcomes compared to those with high antibody levels.

Cause of death coding in asthma.

BACKGROUND: While clinical coding is intended to be an objective and standardized practice, it is important to recognize that it is not entirely the case. The clinical and bureaucratic practices from event of death to a case being entered into a research dataset are important context for analysing and interpreting this data. Variation in practices can influence the accuracy of the final coded record in two different stages: the reporting of the death certificate, and the International Classification of Diseases (Version 10; ICD-10) coding of that certificate. METHODS: This study investigated 91,022 deaths recorded in the Scottish Asthma Learning Healthcare System dataset between 2000 and 2017. Asthma-related deaths were identified by the presence of any of ICD-10 codes J45 or J46, in any position. These codes were categorized either as relating to asthma attacks specifically (status asthmatic; J46) or generally to asthma diagnosis (J45). RESULTS: We found that one in every 200 deaths in this were coded as being asthma related. Less than 1% of asthma-related mortality records used both J45 and J46 ICD-10 codes as causes. Infection (predominantly pneumonia) was more commonly reported as a contributing cause of death when J45 was the primary coded cause, compared to J46, which specifically denotes asthma attacks. CONCLUSION: Further inspection of patient history can be essential to validate deaths recorded as caused by asthma, and to identify potentially mis-recorded non-asthma deaths, particularly in those with complex comorbidities.

Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.

Prognostic models hold great potential for predicting asthma exacerbations, providing opportunities for early intervention, and are a popular area of current research. However, it is unclear how models should be compared and contrasted, given their differences in both design and performance, particularly with a view to potential implementation in routine practice. This systematic review aimed to identify novel predictive models of asthma attacks in adults and compare differences in construction related to populations, outcome definitions, prediction time horizons, algorithms, validation, and performance estimation. Twenty-five studies were identified for comparison, with varying definitions of asthma attacks and prediction event time horizons ranging from 15 days to 30 months. The most commonly used algorithm was logistic regression (20/25 studies); however, none of the six which tested multiple algorithms identified it as highest performing algorithm. The effect of various study design characteristics on performance was evaluated in order to provide context to the limitations of highly performing models. Models used a variety of constructs, which affected both their performance and their viability for implementation in routine practice. Consultation with stakeholders is necessary to identify priorities for model refinement and to create a benchmark of acceptable performance for implementation in clinical practice.

Validation of general pain scores from multidomain assessment tools in stroke.

Purpose: We describe how well general pain reported in multidomain assessment tools correlated with pain-specific assessment tools; associations between general pain, activities of daily living and independence after stroke. Materials and methods: Analyses of individual participant data (IPD) from the Virtual International Stroke Trials Archive (VISTA) described correlation coefficients examining (i) direct comparisons of assessments from pain-specific and multidomain assessment tools that included pain, (ii) indirect comparisons of pain assessments with the Barthel Index (BI) and modified Rankin Scale (mRS), and (iii) whether pain identification could be enhanced by accounting for reported usual activities, self-care, mobility and anxiety/depression; factors associated with pain. Results: European Quality of Life 3- and 5-Level (EQ-5D-3L and EQ-5D-5L), RAND 36 Item Health Survey 1.0 (SF-36) or the 0-10 Numeric Pain Rating Scale (NPRS) were available from 10/94 studies (IPD = 10,002). The 0-10 NPRS was the only available pain-specific assessment tool and was a reference for comparison with other tools. Pearson correlation coefficients between the 0-10 NPRS and (A) the EQ-5D-3L and (B) EQ5D-5 L were r = 0.572 (n = 436) and r = 0.305 (n = 1,134), respectively. mRS was better aligned with pain by EQ-5D-3L (n = 8,966; r = 0.340) than by SF-36 (n = 623; r = 0.318). BI aligned better with pain by SF-36 (n = 623; r = -0.320). Creating a composite score using the EQ-5D 3 L and 5 L comprising pain, mobility, usual-activities, self-care and anxiety/depression did not improve correlation with the 0-10 NPRS. Discussion: The EQ-5D-3L pain domain aligned better than the EQ-5D-5L with the 0-10 NPRS and may inform general pain description where resources and assessment burden hinder use of additional, pain-specific assessments.

Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: a national linked data study.

BACKGROUND: We compared the quality of ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other National Health Service datasets, with the 2011 Scottish Census. METHODS: Measures of quality included the level of missingness and misclassification. We examined the impact of misclassification using Cox proportional hazards to compare the risk of severe coronavirus disease (COVID-19) (hospitalization & death) by ethnic group. RESULTS: Misclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared with the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was highest among the White Other British group [39%] and lowest among the Pakistani group [17%]. PHS-EL data often underestimated severe COVID-19 risk compared with Census data. e.g. in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared with the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL. CONCLUSIONS: Poor quality ethnicity coding in health records can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.

External validation of the QCovid 2 and 3 risk prediction algorithms for risk of COVID-19 hospitalisation and mortality in adults: a national cohort study in Scotland.

OBJECTIVE: The QCovid 2 and 3 algorithms are risk prediction tools developed during the second wave of the COVID-19 pandemic that can be used to predict the risk of COVID-19 hospitalisation and mortality, taking vaccination status into account. In this study, we assess their performance in Scotland. METHODS: We used the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 national data platform consisting of individual-level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR virology testing, hospitalisation and mortality data. We assessed the discrimination and calibration of the QCovid 2 and 3 algorithms in predicting COVID-19 hospitalisations and deaths between 8 December 2020 and 15 June 2021. RESULTS: Our validation dataset comprised 465 058 individuals, aged 19-100. We found the following performance metrics (95% CIs) for QCovid 2 and 3: Harrell's C 0.84 (0.82 to 0.86) for hospitalisation, and 0.92 (0.90 to 0.94) for death, observed-expected ratio of 0.24 for hospitalisation and 0.26 for death (ie, both the number of hospitalisations and the number of deaths were overestimated), and a Brier score of 0.0009 (0.00084 to 0.00096) for hospitalisation and 0.00036 (0.00032 to 0.0004) for death. CONCLUSIONS: We found good discrimination of the QCovid 2 and 3 algorithms in Scotland, although performance was worse in higher age groups. Both the number of hospitalisations and the number of deaths were overestimated.

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care.

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85-0.87 and 1.00, 95% CI 0.95-1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83-0.88, and calibration slope 1.22, 95% CI 1.09-1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Prevalence, Trajectory, and Predictors of Poststroke Pain: Retrospective Analysis of Pooled Clinical Trial Data Set.

BACKGROUND: Poststroke pain remains underdiagnosed and inadequately managed. To inform the optimum time to initiate interventions, we examined prevalence, trajectory, and participant factors associated with poststroke pain. METHODS: Eligible studies from the VISTA (Virtual International Stroke Trials Archives) included an assessment of pain. Analyses of individual participant data examined demography, pain, mobility, independence, language, anxiety/depression, and vitality. Pain assessments were standardized to the European Quality of Life Scale (European Quality of Life 5 Dimensions 3 Level) pain domain, describing no, moderate, or extreme pain. We described pain prevalence, associations between participant characteristics, and pain using multivariable models. RESULTS: From 94 studies (n>48 000 individual participant data) in VISTA, 10 (n=10 002 individual participant data) included a pain assessment. Median age was 70.0 years (interquartile range [59.0-77.1]), 5560 (55.6%) were male, baseline stroke severity was National Institutes of Health Stroke Scale score 10 (interquartile range [7-15]). Reports of extreme pain ranged between 3% and 9.5% and were highest beyond 2 years poststroke (31/328 [9.5%]); pain trajectory varied by study. Poorer independence was significantly associated with presence of moderate or extreme pain (5 weeks-3 months odds ratio [OR], 1.5 [95% CI, 1.4-1.6]; 4-6 months OR, 1.7 [95% CI, 1.3-2.1]; >6 months OR, 1.5 [95% CI, 1.2-2.0]), and increased severity of pain (5 weeks-3 months: OR, 1.2 [95% CI, 1.1-1.2]; 4-6 months OR, 1.1 [95% CI, 1.1-1.2]; >6 months, OR, 1.2 [95% CI, 1.1-1.2]), after adjusting for covariates. Anxiety/depression and lower vitality were each associated with pain severity. CONCLUSIONS: Between 3% and 9.5% of participants reported extreme poststroke pain; the presence and severity of pain were independently associated with dependence at each time point. Future studies could determine whether and when interventions may reduce the prevalence and severity of poststroke pain.

Ethnic inequalities in positive SARS-CoV-2 tests, infection prognosis, COVID-19 hospitalisations and deaths: analysis of 2 years of a record linked national cohort study in Scotland.

BACKGROUND: This study aims to estimate ethnic inequalities in risk for positive SARS-CoV-2 tests, COVID-19 hospitalisations and deaths over time in Scotland. METHODS: We conducted a population-based cohort study where the 2011 Scottish Census was linked to health records. We included all individuals ≥ 16 years living in Scotland on 1 March 2020. The study period was from 1 March 2020 to 17 April 2022. Self-reported ethnic group was taken from the census and Cox proportional hazard models estimated HRs for positive SARS-CoV-2 tests, hospitalisations and deaths, adjusted for age, sex and health board. We also conducted separate analyses for each of the four waves of COVID-19 to assess changes in risk over time. FINDINGS: Of the 4 358 339 individuals analysed, 1 093 234 positive SARS-CoV-2 tests, 37 437 hospitalisations and 14 158 deaths occurred. The risk of COVID-19 hospitalisation or death among ethnic minority groups was often higher for White Gypsy/Traveller (HR 2.21, 95% CI (1.61 to 3.06)) and Pakistani 2.09 (1.90 to 2.29) groups compared with the white Scottish group. The risk of COVID-19 hospitalisation or death following confirmed positive SARS-CoV-2 test was particularly higher for White Gypsy/Traveller 2.55 (1.81-3.58), Pakistani 1.75 (1.59-1.73) and African 1.61 (1.28-2.03) individuals relative to white Scottish individuals. However, the risk of COVID-19-related death following hospitalisation did not differ. The risk of COVID-19 outcomes for ethnic minority groups was higher in the first three waves compared with the fourth wave. INTERPRETATION: Most ethnic minority groups were at increased risk of adverse COVID-19 outcomes in Scotland, especially White Gypsy/Traveller and Pakistani groups. Ethnic inequalities persisted following community infection but not following hospitalisation, suggesting differences in hospital treatment did not substantially contribute to ethnic inequalities.

Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions.

BACKGROUND: Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study. METHODS: Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared. RESULTS: Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures. CONCLUSIONS: We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.

Derivation of asthma severity from electronic prescription records using British thoracic society treatment steps.

BACKGROUND: Asthma severity is typically assessed through a retrospective assessment of the treatment required to control symptoms and to prevent exacerbations. The joint British Thoracic Society and Scottish Intercollegiate Guidelines Network (BTS/SIGN) guidelines encourage a stepwise approach to pharmacotherapy, and as such, current treatment step can be considered as a severity categorisation proxy. Briefly, the steps for adults can be summarised as: no controller therapy (Step 0), low-strength Inhaled Corticosteroids (ICS; Step 1), ICS plus Long-Acting Beta-2 Agonist (LABA; Step 2), medium-dose ICS + LABA (Step 3), and finally either an increase in strength or additional therapies (Step 4). This study aimed to investigate how BTS/SIGN Steps can be estimated from across a large cohort using electronic prescription records, and to describe the incidence of each BTS/SIGN Step in a general population. METHODS: There were 41,433,707 prescriptions, for 671,304 individuals, in the Asthma Learning Health System Scottish cohort, between 1/2009 and 3/2017. Days on which an individual had a prescription for at least one asthma controller (preventer) medication were labelled prescription events. A rule-based algorithm was developed for extracting the strength and volume of medication instructed to be taken daily from free-text data fields. Asthma treatment regimens were categorised by the combination of medications prescribed in the 120 days preceding any prescription event and categorised into BTS/SIGN treatment steps. RESULTS: Almost 4.5 million ALHS prescriptions were for asthma controllers. 26% of prescription events had no inhaled corticosteroid prescriptions in the preceding 120 days (Step 0), 16% were assigned to BTS/SIGN Step 1, 7% to Step 2, 21% to Step 3, and 30% to Step 4. The median days spent on a treatment step before a step-down in treatment was 297 days, whereas a step-up only took a median of 134 days. CONCLUSION: We developed a reproducible methodology enabling researchers to estimate BTS/SIGN asthma treatment steps in population health studies, providing valuable insights into population and patient-specific trajectories, towards improving the management of asthma.

Estimating Medication Adherence from Electronic Health Records Using Rolling Averages of Single Refill-based Estimates.

Medication adherence is usually defined as the manner in which a patient takes their medication, in relation to the regimen agreed to with their healthcare provider. Electronic Health Records (EHRs) can be used to estimate adherence in a cost-effective and non-invasive manner across large-scale populations, although there is no universally agreed optimal approach to doing so. We sought to explore patterns of asthma ICS prescription refills in a large EHR dataset, and to evaluate the use of rolling-average based measures towards short-term adherence estimation. Over 1.6 million asthma controllers were prescribed for our cohort of 91,332 individuals, between January 2009 and March 2017. The Continuous Single interval measures of medication Availability (CSA) and Gaps (CSG) were calculated for individual prescriptions, as well as rolling-average adherence measures of the CSA over 3, 5, or 10 past prescription intervals. 16.7% of the study population had only a single prescription during their follow-up (a median duration of 7.1 years). 51% of prescriptions were refilled before (or on the day that) supply was exhausted, and for 19% of prescription refills, the amount of medication dispensed should have lasted at least twice as long as the duration before the next refill was filled. The rolling average measures had statistically strong associations (Spearman |R|>0.7) with the estimate for the subsequent prescription refill. Rolling averages of multiple individual refill-level adherence estimates provide a novel and simple way to crudely smoothen estimates from individual prescription refills, which are strongly influenced by common (and adherent) real-world behaviors, for more meaningful and effective trend detection. Clinical Relevance- This demonstrates a novel methodology for estimating medication adherence which can detect recent changes in trends.

Effectiveness of mobile health interventions to improve nasal corticosteroid adherence in allergic rhinitis: A systematic review.

BACKGROUND: Mobile health interventions (MHI) offer the potential to help improve nasal corticosteroid (NCS) adherence in allergic rhinitis (AR). The aim of this systematic review was to summarise the current evidence on the effectiveness of MHI for improving NCS adherence in AR. METHODS: We systematically searched MEDLINE, Embase and the Cochrane Central register of Controlled Trials (CENTRAL) for randomised controlled trials filtered for publication dates between 2010 and 2021. We evaluated the effects of MHI aiming to improve NCS adherence on self-management outcomes in AR and comorbid conditions. Two reviewers independently screened potential studies, extracted study characteristics and outcomes from eligible papers and assessed risk of bias using the Cochrane Risk of Bias tool 2.0. High heterogeneity precluded meta-analysis. Data were descriptively and narratively synthesised. RESULTS: Our searches identified 776 individual studies of which 4 met the inclusion criteria. These studies were heterogeneous with respect to participant, intervention and outcome characteristics. We considered all outcome-specific overall risk of bias assessments to be of high risk of bias except for two studies examining NCS adherence which received 'some concern' grades. The three studies which reported on NCS adherence found that MHI were associated with improvement in NCS adherence. Significant MHI-associated improvement in symptoms or disease-specific quality of life was found in one study each, whilst no study reported significant differences in nasal patency. CONCLUSIONS: Whilst MHI showed potential to improve NCS adherence, their effect on clinical outcomes varied. Furthermore, robust studies with longer intervention durations are needed to adequately assess effects of MHI and their individual features on NCS adherence and clinical outcomes.

Health information technology and digital innovation for national learning health and care systems.

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Hormone Replacement Therapy and Risk of Severe Asthma Exacerbation in Perimenopausal and Postmenopausal Women: 17-Year National Cohort Study.

BACKGROUND: The impact of hormone replacement therapy (HRT) on clinical outcomes in menopausal women is uncertain. OBJECTIVE: To investigate the association between use of HRT and severe asthma exacerbation in perimenopausal and postmenopausal women with asthma. METHODS: We used the Optimum Patient Care Research Database, a population-based longitudinal primary care database in the United Kingdom, to construct a 17-year (January 1, 2000, to December 31, 2016) cohort of perimenopausal and postmenopausal (46-70 years, N = 31,656) women. We defined use of HRT, its subtypes, and duration of HRT use. Severe asthma exacerbation was defined as an asthma-related hospitalization, emergency department visits due to asthma, and/or prescription of oral corticosteroids. Analyses were undertaken using multilevel mixed-effects Poisson regression. RESULTS: At baseline, 22% of women were using any HRT, 11% combined HRT, and 11% estrogen-only HRT. Previous, but not current, use of any (incidence rate ratio [IRR]: 1.24, 95% confidence interval [CI]: 1.22-1.26), combined (IRR: 1.28, 95% CI: 1.25-1.31), and estrogen-only HRT (IRR: 1.18, 95% CI: 1.14-1.21), and longer duration (1-2 years: IRR: 1.16, 95% CI: 1.13-1.19; 3-4 years: IRR: 1.43, 95% CI: 1.38-1.48; 5+ years: IRR: 1.32, 95% CI: 1.28-1.36) of HRT use were associated with increased risk of severe asthma exacerbation compared with nonuse. The risk estimates were greater among lean women (body mass index [BMI] <25 kg/m2) than among heavier women (BMI 25-29.9 kg/m2 and ≥30 kg/m2) and higher among smokers than nonsmokers. CONCLUSION: Use of HRT and subtypes, particularly previous, but not current, use and use for more than 2 years, is associated with an increased risk of severe asthma exacerbation in perimenopausal/postmenopausal women with established asthma. Lean women and smokers are at greater risk than heavier women and nonsmokers, respectively.

Outcomes of Victorian Prevention and Recovery Care Services: A matched pairs comparison.

OBJECTIVE: In Victoria, Prevention and Recovery Care Services have been established to provide a partial alternative to inpatient admissions through short-term residential mental health care in the community. This study set out to determine whether Prevention and Recovery Care Services are achieving their objectives in relation to reducing service use and costs, fostering least restrictive care and leading to positive clinical outcomes. METHODS: We matched 621 consumers whose index admission in 2014 was to a Prevention and Recovery Care ('PARCS consumers') with 621 similar consumers whose index admission in the same year was to an acute inpatient unit and who had no Prevention and Recovery Care stays for the study period ('inpatient-only consumers'). We used routinely collected data to compare them on a range of outcomes. RESULTS: Prevention and Recovery Care Services consumers made less subsequent use of acute inpatient services and, on balance, incurred costs that were similar to or lower than inpatient-only consumers. They were also less likely to spend time on an involuntary treatment order following their index admission. Prevention and Recovery Care Services consumers also experienced positive clinical outcomes over the course of their index admission, but the magnitude of this improvement was not as great as for inpatient-only consumers. This type of clinical improvement is important for Prevention and Recovery Care Services, but they may place greater emphasis on personal recovery as an outcome. CONCLUSION: Prevention and Recovery Care Services can provide an alternative, less restrictive care option for eligible consumers who might otherwise be admitted to an acute inpatient unit and do so at no greater cost.

Eyes and Ears on Patient Safety: Sources of Notifications About the Health, Performance, and Conduct of Health Practitioners.

OBJECTIVE: The aim of the study was to describe the sources of notifications of concern ("notifications") regarding the health, performance, and conduct of health practitioners from 14 registered professions in Australia. METHODS: This retrospective cohort study analyzed 43,256 notifications lodged with the Australian Health Practitioner Regulation Agency and the Health Professional Councils Authority between 2011 and 2016. We used descriptive statistical analysis to describe the characteristics of these notifications, including their source, issue and domain, and subject. RESULTS: Patients and their relatives lodged more than three-quarters (78%) of notifications regarding clinical performance, including diagnosis, treatment, and communication. Fellow practitioners were a common source of notifications about advertising and titles. Self-reports commonly related to health impairments, such as mental illness or substance use. Other agencies played a role in reporting concerns about prescribing or supply of medicines. CONCLUSIONS: Various actors in the healthcare system play different roles in sketching the picture of healthcare quality and safety that notifications present to regulators. Improved understanding of which sources are most likely to raise which concerns may enhance regulators' ability to identify and respond to patient safety risks.