Understanding interventions delivered in the emergency department targeting improved asthma outcomes beyond the emergency department: an integrative review

OBJECTIVES

The emergency department (ED) represents a place and moment of opportunity to provide interventions to improve long-term asthma outcomes, but feasibility, effectiveness and mechanisms of impact are poorly understood. We aimed to review the existing literature on interventions that are delivered in the ED for adults and adolescents, targeting asthma outcomes beyond the ED, and to code the interventions according to theory used, and to understand the barriers and facilitators to their implementation.

METHODS

We systematically searched seven electronic databases and research registers, and manually searched reference lists of included studies and relevant reviews. Both quantitative and qualitative studies that reported on interventions delivered in the ED which aimed to improve asthma outcomes beyond management of the acute exacerbation, for adolescents or adults were included. Methodological quality was assessed using the Mixed Methods Appraisal Tool and informed study interpretation. Theory was coded using the Theoretical Domains Framework. Findings were summarised by narrative synthesis.

RESULTS

12 articles were included, representing 10 unique interventions, including educational and medication-based changes (6 randomised controlled trials and 4 non-randomised studies). Six trials reported statistically significant improvements in one or more outcome measures relating to long-term asthma control, including unscheduled healthcare, asthma control, asthma knowledge or quality of life. We identified limited use of theory in the intervention designs with only one intervention explicitly underpinned by theory. There was little reporting on facilitators or barriers, although brief interventions appeared more feasible.

CONCLUSION

The results of this review suggest that ED-based asthma interventions may be capable of improving long-term outcomes. However, there was significant variation in the range of interventions, reported outcomes and duration of follow-up. Future interventions would benefit from using behaviour change theory, such as constructs from the Theoretical Domains Framework.

PROSPERO REGISTRATION NUMBER

CRD 42020223058.

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.

Patient and public involvement in research: the need for budgeting PPI staff costs in funding applications

BACKGROUND

Patient and Public Involvement (PPI) groups are becoming more established as collaborators with academic researchers and institutions to ensure that research is important and relevant to end users, and to identify areas that might have ethical considerations, as well as to advise on solutions. The National Institute for Health and Care Research UK Standards for Public Involvement in Research embody best practice for PPI, including support and learning opportunities that build confidence and skills for members of the public to play an invaluable and mutually productive role in research. However, the pivotal role of research and professional services (management and administrative) staff within academic institutions for sustaining and making this involvement successful is often overlooked.

MAIN BODY

It takes significant effort to develop and sustain effective PPI in research. The six UK Standards for Public Involvement highlight the need for consistent, inclusive, well-governed and mutually respectful working relationships to sustain effective PPI contributions in health research. Productivity across a team of lay and academic members requires organisation and experience of implementing these standards by a dedicated PPI team, yet advice on PPI finances is usually focused on costs for patient panel members, and budgets in funding applications rarely consider the wider PPI team behind this involvement. As an exemplar, we reflect on how the Asthma UK Centre for Applied Research (AUKCAR) has developed a dedicated PPI Platform, with guidance for how PPI should be embedded throughout the research lifecycle, and detailed information to support the costing of PPI in funding applications. AUKCAR's work with established researchers, as well as Early Career Researchers and PhD students, is at the heart of a campaign to raise awareness of the importance of PPI in effective research planning.

CONCLUSION

Focusing attention on the staff behind best practice involvement in health research may stimulate a much-needed discussion to ensure flourishing PPI capacity, with significant patient and public benefit. With adaptation, the PPI expertise within AUKCAR can be translated more widely.

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.

Electronic reminders and rewards to improve adherence to inhaled asthma treatment in adolescents: a non-randomised feasibility study in tertiary care

OBJECTIVE

To test the feasibility and acceptability of a short-term reminder and incentives intervention in adolescents with low adherence to asthma medications.

METHODS

Mixed-methods feasibility study in a tertiary care clinic. Adolescents recruited to a 24-week programme with three 8-weekly visits, receiving electronic reminders to prompt inhaled corticosteroid (ICS) inhalation through a mobile app coupled with electronic monitoring devices (EMD). From the second visit, monetary incentives based on adherence of ICS inhalation: £1 per dose, maximum £2 /day, up to £112/study, collected as gift cards at the third visit. End of study interviews and questionnaires assessing perceptions of asthma and ICS, analysed using the Perceptions and Practicalities Framework.

PARTICIPANTS

Adolescents (11-18 years) with documented low ICS adherence (<80% by EMD), and poor asthma control at the first clinic visit.

RESULTS

10 out of 12 adolescents approached were recruited (7 males, 3 females, 12-16 years). Eight participants provided adherence measures up to the fourth visits and received rewards. Mean study duration was 281 days, with 7/10 participants unable to attend their fourth visit due to COVID-19 lockdown. Only 3/10 participants managed to pair the app/EMD up to the fourth visit, which was associated with improved ICS adherence (from 0.51, SD 0.07 to 0.86, SD 0.05). Adherence did not change in adolescents unable to pair the app/EMD. The intervention was acceptable to participants and parents/guardians. Exit interviews showed that participants welcomed reminders and incentives, though expressed frustration with app/EMD technological difficulties. Participants stated the intervention helped through reminding ICS doses, promoting self-monitoring and increasing motivation to take inhalers.

CONCLUSIONS

An intervention using electronic reminders and incentives through an app coupled with an EMD was feasible and acceptable to adolescents with asthma. A pilot randomised controlled trial is warranted to better estimate the effect size on adherence, with improved technical support for the EMD.

Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

INTRODUCTION

Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning.

METHODS AND ANALYSIS

Current prognostic tools use logistic regression to develop a risk scoring model for asthma attacks. We propose to build on this by systematically applying various well-known machine learning techniques to a large longitudinal deidentified primary care database, the Optimum Patient Care Research Database, and comparatively evaluate their performance with the existing logistic regression model and against each other. Machine learning algorithms vary in their predictive abilities based on the dataset and the approach to analysis employed. We will undertake feature selection, classification (both one-class and two-class classifiers) and performance evaluation. Patients who have had actively treated clinician-diagnosed asthma, aged 8-80 years and with 3 years of continuous data, from 2016 to 2018, will be selected. Risk factors will be obtained from the first year, while the next 2 years will form the outcome period, in which the primary endpoint will be the occurrence of an asthma attack.

ETHICS AND DISSEMINATION

We have obtained approval from OPCRD's Anonymous Data Ethics Protocols and Transparency (ADEPT) Committee. We will seek ethics approval from The University of Edinburgh's Research Ethics Group (UREG). We aim to present our findings at scientific conferences and in peer-reviewed journals.

Behavioural difficulties in early childhood and risk of adolescent injury

OBJECTIVE

To evaluate long-term associations between early childhood hyperactivity and conduct problems (CP), measured using Strengths and Difficulties Questionnaire (SDQ) and risk of injury in early adolescence.

DESIGN

Data linkage between a longitudinal birth cohort and routinely collected electronic health records.

SETTING

Consenting Millennium Cohort Study (MCS) participants residing in Wales and Scotland.

PATIENTS

3119 children who participated in the age 5 MCS interview.

MAIN OUTCOME MEASURES

Children with parent-reported SDQ scores were linked with hospital admission and Accident & Emergency (A&E) department records for injuries between ages 9 and 14 years. Negative binomial regression models adjusting for number of people in the household, lone parent, residential area, household poverty, maternal age and academic qualification, child sex, physical activity level and country of interview were fitted in the models.

RESULTS

46% of children attended A&E or were admitted to hospital for injury, and 11% had high/abnormal scores for hyperactivity and CP. High/abnormal or borderline hyperactivity were not significantly associated with risk of injury, incidence rate ratio (IRR) with 95% CI of the high/abnormal and borderline were 0.92 (95% CI 0.74 to 1.14) and 1.16 (95% CI 0.88 to 1.52), respectively. Children with borderline CP had higher injury rates compared with those without CP (IRR 1.31, 95% CI 1.09 to 1.57).

CONCLUSIONS

Children with high/abnormal hyperactivity or CP scores were not at increased risk of injury; however, those with borderline CP had higher injury rates. Further research is needed to understand if those with difficulties receive treatment and support, which may reduce the likelihood of injuries.

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model

INTRODUCTION

Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.

METHODS AND ANALYSIS

We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprising 500 000 individuals across 75 Scottish general practices, with linked longitudinal primary care prescribing records, primary care Read codes, accident and emergency records, hospital admissions and deaths. Models will be compared on a partition of the dataset reserved for validation, and the final model will be tested in both an unseen partition of the derivation dataset and an external dataset from the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study.

ETHICS AND DISSEMINATION

Permissions for the ALHS project were obtained from the South East Scotland Research Ethics Committee 02 [16/SS/0130] and the Public Benefit and Privacy Panel for Health and Social Care (1516-0489). Permissions for the SIVE II project were obtained from the Privacy Advisory Committee (National Services NHS Scotland) [68/14] and the National Research Ethics Committee West Midlands-Edgbaston [15/WM/0035]. The subsequent research paper will be submitted for publication to a peer-reviewed journal and code scripts used for all components of the data cleaning, compiling, and analysis will be made available in the open source GitHub website (https://github.com/hollytibble).