Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).

OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

Acceptability of Using Real-World Data to Estimate Relative Treatment Effects in Health Technology Assessments: Barriers and Future Steps.

OBJECTIVES: Evidence about the comparative effects of new treatments is typically collected in randomized controlled trials (RCTs). In some instances, RCTs are not possible, or their value is limited by an inability to capture treatment effects over the longer term or in all relevant population subgroups. In these cases, nonrandomized studies (NRS) using real-world data (RWD) are increasingly used to complement trial evidence on treatment effects for health technology assessment (HTA). However, there have been concerns over a lack of acceptability of this evidence by HTA agencies. This article aims to identify the barriers to the acceptance of NRS and steps that may facilitate increases in the acceptability of NRS in the future. METHODS: Opinions of the authorship team based on their experience in real-world evidence research in academic, HTA, and industry settings, supported by a critical assessment of existing studies. RESULTS: Barriers were identified that are applicable to key stakeholder groups, including HTA agencies (eg, the lack of comprehensive methodological guidelines for using RWD), evidence generators (eg, avoidable deviations from best practices), and external stakeholders (eg, data controllers providing timely access to high-quality RWD). Future steps that may facilitate future acceptability of NRS include improvements in the quality, integration, and accessibility of RWD, wider use of demonstration projects to highlight the value and applicability of nonrandomized designs, living, and more detailed HTA guidelines, and improvements in HTA infrastructure relating to RWD. CONCLUSION: NRS can represent a crucial source of evidence on treatment effects for use in HTA when RCT evidence is limited.

Measuring quality of life of adults with intellectual disabilities: Psychometric evaluation of the personal outcomes scale in the United Kingdom.

BACKGROUND: The Personal Outcomes Scale (POS) is a scale developed to measure quality of life of adults (18+) with intellectual disability. Previous studies have reported good fit for Spanish and Portuguese language versions of POS. AIMS: This study aimed to evaluate the factor structure of the English language version of POS when used to measure the quality of life of adults (18+) with intellectual disability in the UK. MATERIALS AND METHODS: Analysis was conducted on POS data from 310 adults with an intellectual disability. First and second order factor models and multi-level models were used to assess fit. RESULTS: There was poor fit to the data for all tested models. We estimated that 23% of variance in POS scores was accounted for by interviewer cluster. DISCUSSION: This was the first UK-based evaluation of POS and our data did not confirm the factor structure of the POS measure. The identification of systematic variability within the dataset indicates that inter-rater reliability is a potential limitation of the POS tool. CONCLUSION: Further research is needed to investigate inter-rater reliability of POS interviewers and to explore factor structure.

Economic Evaluations of Digital Health Interventions for Children and Adolescents: Systematic Review.

BACKGROUND: Digital health interventions (DHIs) are defined as digital technologies such as digital health applications and information and communications technology systems (including SMS text messages) implemented to meet health objectives. DHIs implemented using various technologies, ranging from electronic medical records to videoconferencing systems and mobile apps, have experienced substantial growth and uptake in recent years. Although the clinical effectiveness of DHIs for children and adolescents has been relatively well studied, much less is known about the cost-effectiveness of these interventions. OBJECTIVE: This study aimed to systematically review economic evaluations of DHIs for pediatric and adolescent populations. This study also reviewed methodological issues specific to economic evaluations of DHIs to inform future research priorities. METHODS: We conducted a database search in PubMed from 2011 to 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. In total, 2 authors independently screened the titles and abstracts of the search results to identify studies eligible for full-text review. We generated a data abstraction procedure based on recommendations from the Panel on Cost-Effectiveness in Health and Medicine. The types of economic evaluations included in this review were cost-effectiveness analyses (costs per clinical effect), cost-benefit analyses (costs and effects expressed in monetary terms as net benefit), and cost-utility analyses (cost per quality-adjusted life year or disability-adjusted life year). Narrative analysis was used to synthesize the quantitative data because of heterogeneity across the studies. We extracted methodological issues related to study design, analysis framework, cost and outcome measurement, and methodological assumptions regarding the health economic evaluation. RESULTS: We included 22 articles assessing the cost-effectiveness of DHI interventions for children and adolescents. Most articles (14/22, 64%) evaluated interventions delivered through web-based portals or SMS text messaging, most frequently within the health care specialties of mental health and maternal, newborn, and child health. In 82% (18/22) of the studies, DHIs were found to be cost-effective or cost saving compared with the nondigital standard of care. The key drivers of cost-effectiveness included population coverage, cost components, intervention effect size and scale-up, and study perspective. The most frequently identified methodological challenges were related to study design (17/22, 77%), costing (11/22, 50%), and economic modeling (9/22, 41%). CONCLUSIONS: This is the first systematic review of economic evaluations of DHIs targeting pediatric and adolescent populations. We found that most DHIs (18/22, 82%) for children and adolescents were cost-effective or cost saving compared with the nondigital standard of care. In addition, this review identified key methodological challenges directly related to the conduct of economic evaluations of DHIs and highlighted areas where further methodological research is required to address these challenges. These included the need for measurement of user involvement and indirect effects of DHIs and the development of children-specific, generic quality-of-life outcomes.

End-of-life care costs and place of death across health and social care sectors.

OBJECTIVES: This study explores the relationship between end-of-life care costs and place of death across different health and social care sectors. METHODS: We used a linked local government and health data of East London residents (n=4661) aged 50 or over, deceased between 2016 and 2020. Individuals who died in hospital were matched to those who died elsewhere according to a wide range of demographic, socioeconomic and health factors. We reported mean healthcare costs and 95% CIs by care sectors over the 12-month period before death. Subgroup analyses were conducted to investigate if the role of place of death differs according to long-term conditions and age. RESULTS: We found that mean difference in total cost between hospital and non-hospital decedents was £4565 (95% CI £3132 to £6046). Hospital decedents were associated with higher hospital cost (£5196, £4499 to £5905), higher mental healthcare cost (£283, £78 to £892) and lower social care cost (-£838, -£1,209 to -£472), compared with individuals who died elsewhere. Subgroup analysis shows that the association between place of death and healthcare costs differs by age and long-term conditions, including cancer, mental health and cardiovascular diseases. CONCLUSION: This study suggests that trajectories of end-of-life healthcare costs vary by place of death in a differential way across health and social care sectors. High hospital burden for cancer patients may be alleviated by strengthening healthcare provision in less cost-intensive settings, such as community and social care.

Addressing missing data in the estimation of time-varying treatments in comparative effectiveness research.

Comparative effectiveness research is often concerned with evaluating treatment strategies sustained over time, that is, time-varying treatments. Inverse probability weighting (IPW) is often used to address the time-varying confounding by re-weighting the sample according to the probability of treatment receipt at each time point. IPW can also be used to address any missing data by re-weighting individuals according to the probability of observing the data. The combination of these two distinct sets of weights may lead to inefficient estimates of treatment effects due to potentially highly variable total weights. Alternatively, multiple imputation (MI) can be used to address the missing data by replacing each missing observation with a set of plausible values drawn from the posterior predictive distribution of the missing data given the observed data. Recent studies have compared IPW and MI for addressing the missing data in the evaluation of time-varying treatments, but they focused on missing confounders and monotone missing data patterns. This article assesses the relative advantages of MI and IPW to address missing data in both outcomes and confounders measured over time, and across monotone and non-monotone missing data settings. Through a comprehensive simulation study, we find that MI consistently provided low bias and more precise estimates compared to IPW across a wide range of scenarios. We illustrate the implications of method choice in an evaluation of biologic drugs for patients with severe rheumatoid arthritis, using the US National Databank for Rheumatic Diseases, in which 25% of participants had missing health outcomes or time-varying confounders.

Emotion regulation in children (ERiC): A protocol for a randomised clinical trial to evaluate the clinical and cost effectiveness of Mentalization Based Treatment (MBT) vs Treatment as Usual for school-age children with mixed emotional and behavioural difficulties.

BACKGROUND: The majority of children referred to Child and Adolescent Mental Health Services (CAMHS) in the UK will present with mixed emotional and behavioural difficulties, but most mental health treatments are developed for single disorders. There is a need for research on treatments that are helpful for these mixed difficulties, especially for school-age children. Emotion Regulation (ER) difficulties present across a wide range of mental health disorders and mentalizing may help with regulation. The ability to mentalize one's own experiences and those of others plays a key role in coping with stress, regulation of emotions, and the formation of stable relationships. Mentalization Based Therapy (MBT) is a well-evidenced therapy that aims to promote mentalization, which in turn increases ER capacities, leading to decreased emotional and behavioural difficulties. The aim of this study is to test the clinical- and cost-effectiveness of MBT compared to treatment as usual for school age children with emotional and behavioural difficulties. If effective, we hope this approach can become available to the growing number of children presenting to mental health services with a mix of emotional and behavioural difficulties. MATERIALS AND METHODS: Children referred to CAMHS aged 6-12 with mixed mental health problems (emotional and behavioural) as primary problem can take part with their parent/carers. Children will be randomly allocated to receive either MBT or treatment as usual (TAU) within the CAMHS clinic they have been referred to. MBT will be 6-8 sessions offered fortnightly and can flexibly include different family members. TAU is likely to include CBT, parenting groups, and/or children's social skills groups. Parent/carers and children will be asked to complete outcome assessments (questionnaires and tasks) online at the start of treatment, mid treatment (8 weeks), end of treatment (16 weeks) and at follow up (40 weeks). TRIAL REGISTRATION: Clinical trial registration: ISRCTN 11620914.

Impact of fatigue as the primary determinant of functional limitations among patients with post-COVID-19 syndrome: a cross-sectional observational study.

OBJECTIVES: To describe self-reported characteristics and symptoms of treatment-seeking patients with post-COVID-19 syndrome (PCS). To assess the impact of symptoms on health-related quality of life (HRQoL) and patients' ability to work and undertake activities of daily living. DESIGN: Cross-sectional single-arm service evaluation of real-time user data. SETTING: 31 post-COVID-19 clinics in the UK. PARTICIPANTS: 3754 adults diagnosed with PCS in primary or secondary care deemed suitable for rehabilitation. INTERVENTION: Patients using the Living With Covid Recovery digital health intervention registered between 30 November 2020 and 23 March 2022. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the baseline Work and Social Adjustment Scale (WSAS). WSAS measures the functional limitations of the patient; scores of ≥20 indicate moderately severe limitations. Other symptoms explored included fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue), depression (Patient Health Questionnaire-Eight Item Depression Scale), anxiety (Generalised Anxiety Disorder Scale, Seven-Item), breathlessness (Medical Research Council Dyspnoea Scale and Dyspnoea-12), cognitive impairment (Perceived Deficits Questionnaire, Five-Item Version) and HRQoL (EQ-5D). Symptoms and demographic characteristics associated with more severe functional limitations were identified using logistic regression analysis. RESULTS: 3541 (94%) patients were of working age (18-65); mean age (SD) 48 (12) years; 1282 (71%) were female and 89% were white. 51% reported losing ≥1 days from work in the previous 4 weeks; 20% reported being unable to work at all. Mean WSAS score at baseline was 21 (SD 10) with 53% scoring ≥20. Factors associated with WSAS scores of ≥20 were high levels of fatigue, depression and cognitive impairment. Fatigue was found to be the main symptom contributing to a high WSAS score. CONCLUSION: A high proportion of this PCS treatment-seeking population was of working age with over half reporting moderately severe or worse functional limitation. There were substantial impacts on ability to work and activities of daily living in people with PCS. Clinical care and rehabilitation should address the management of fatigue as the dominant symptom explaining variation in functionality.

Distributional Cost-Effectiveness Analysis of Treatments for Non-Small Cell Lung Cancer: An Illustration of an Aggregate Analysis and its Key Drivers.

BACKGROUND AND OBJECTIVE: Distributional cost-effectiveness analysis (DCEA) facilitates quantitative assessments of how health effects and costs are distributed among population subgroups, and of potential trade-offs between health maximisation and equity. Implementation of DCEA is currently explored by the National Institute for Health and Care Excellence (NICE) in England. Recent research conducted an aggregate DCEA on a selection of NICE appraisals; however, significant questions remain regarding the impact of the characteristics of the patient population (size, distribution by the equity measure of interest) and methodologic choices on DCEA outcomes. Cancer is the indication most appraised by NICE, and the relationship between lung cancer incidence and socioeconomic status is well established. We aimed to conduct an aggregate DCEA of two non-small cell lung cancer (NSCLC) treatments recommended by NICE, and identify key drivers of the analysis. METHODS: Subgroups were defined according to socioeconomic deprivation. Data on health benefits, costs, and target populations were extracted from two NICE appraisals (atezolizumab versus docetaxel [second-line treatment following chemotherapy to represent a broad NSCLC population] and alectinib versus crizotinib [targeted first-line treatment to represent a rarer mutation-positive NSCLC population]). Data on disease incidence were derived from national statistics. Distributions of population health and health opportunity costs were taken from the literature. A societal welfare analysis was conducted to assess potential trade-offs between health maximisation and equity. Sensitivity analyses were conducted, varying a range of parameters. RESULTS: At an opportunity cost threshold of £30,000 per quality-adjusted life-year (QALY), alectinib improved both health and equity, thereby increasing societal welfare. Second-line atezolizumab involved a trade-off between improving health equity and maximising health; it improved societal welfare at an opportunity cost threshold of £50,000/QALY. Increasing the value of the opportunity cost threshold improved the equity impact. The equity impact and societal welfare impact were small, driven by the size of the patient population and per-patient net health benefit. Other key drivers were the inequality aversion parameters and the distribution of patients by socioeconomic group; skewing the distribution to the most (least) deprived quintile improved (reduced) equity gains. CONCLUSION: Using two illustrative examples and varying model parameters to simulate alternative decision problems, this study suggests that key drivers of an aggregate DCEA are the opportunity cost threshold, the characteristics of the patient population, and the level of inequality aversion. These drivers raise important questions in terms of the implications for decision making. Further research is warranted to examine the value of the opportunity cost threshold, capture the public's views on unfair differences in health, and estimate robust distributional weights incorporating the public's preferences. Finally, guidance from health technology assessment organisations, such as NICE, is needed regarding methods for DCEA construction and how they would interpret and incorporate those results in their decision making.

Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene).

INTRODUCTION: Inherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients by molecular genetic testing is the first step towards effective care and patient management. However, genetic diagnosis is currently slow, expensive and not widely accessible. The aim of the current project is to address the evidence gap in IRD diagnosis with an AI algorithm, Eye2Gene, to accelerate and democratise the IRD diagnosis service. METHODS AND ANALYSIS: The data-only retrospective cohort study involves a target sample size of 10 000 participants, which has been derived based on the number of participants with IRD at three leading UK eye hospitals: Moorfields Eye Hospital (MEH), Oxford University Hospital (OUH) and Liverpool University Hospital (LUH), as well as a Japanese hospital, the Tokyo Medical Centre (TMC). Eye2Gene aims to predict causative genes from retinal images of patients with a diagnosis of IRD. For this purpose, 36 most common causative IRD genes have been selected to develop a training dataset for the software to have enough examples for training and validation for detection of each gene. The Eye2Gene algorithm is composed of multiple deep convolutional neural networks, which will be trained on MEH IRD datasets, and externally validated on OUH, LUH and TMC. ETHICS AND DISSEMINATION: This research was approved by the IRB and the UK Health Research Authority (Research Ethics Committee reference 22/WA/0049) 'Eye2Gene: accelerating the diagnosis of IRDs' Integrated Research Application System (IRAS) project ID: 242050. All research adhered to the tenets of the Declaration of Helsinki. Findings will be reported in an open-access format.

ATOM Calibration Framework: Interaction and Visualization Functionalities.

Robotic systems are evolving to include a large number of sensors and diverse sensor modalities. In order to operate a system with multiple sensors, the geometric transformations between those sensors must be accurately estimated. The process by which these transformations are estimated is known as sensor calibration. Behind every sensor calibration approach is a formulation and a framework. The formulation is the method by which the transformations are estimated. The framework is the set of operations required to carry out the calibration procedure. This paper proposes a novel calibration framework that gives more flexibility, control and information to the user, enhancing the user interface and the user experience of calibrating a robotic system. The framework consists of several visualization and interaction functionalities useful for a calibration procedure, such as the estimation of the initial pose of the sensors, the data collection and labeling, the data review and correction and the visualization of the estimation of the extrinsic and intrinsic parameters. This framework is supported by the Atomic Transformations Optimization Method formulation, referred to as ATOM. Results show that this framework is applicable to various robotic systems with different configurations, number of sensors and sensor modalities. In addition to this, a survey comparing the frameworks of different calibration approaches shows that ATOM provides a very good user experience.

Does a working day keep the doctor away? A critical review of the impact of unemployment and job insecurity on health and social care utilisation.

While the negative impact of unemployment on health is relatively well established, the extent to which that impact reflects on changes in health and social care utilisation is not well understood. This paper critically reviews the direction, magnitude and drivers of the impact of unemployment and job insecurity on health and social care utilisation across different care settings. We identified 28 relevant studies, which included 79 estimates of association between unemployment/job insecurity and healthcare utilisation. Positive associations dominated mental health services (N = 8 out of 11), but not necessarily primary care (N = 25 out of 43) or hospital care (N = 5 out of 22). We conducted a meta-analysis to summarise the magnitude of the impact and found that unemployed individuals were about 30% more likely to use health services compared to those employed, although this was largely driven by mental health service use. Key driving factors included financial pressure, health insurance, social network, disposable time and depression/anxiety. This review suggests that unemployment is likely to be associated with increased mental health service use, but there is considerable uncertainty around primary and hospital care utilisation. Future work to examine the impact across other settings, including community and social care, and further explore non-health determinants of utilisation is needed. The protocol was registered in PROSPERO (CRD42020177668).

The Impact of Digital Health Interventions for the Management of Type 2 Diabetes on Health and Social Care Utilisation and Costs: A Systematic Review.

BACKGROUND: Digital health interventions such as smartphone applications (mHealth) or Internet resources (eHealth) are increasingly used to improve the management of chronic conditions, such as type 2 diabetes mellitus. These digital health interventions can augment or replace traditional health services and may be paid for using healthcare budgets. While the impact of digital health interventions for the management of type 2 diabetes on health outcomes has been reviewed extensively, less attention has been paid to their economic impact. OBJECTIVE: This study aims to critically review existing literature on the impact of digital health interventions for the management of type 2 diabetes on health and social care utilisation and costs. METHODS: Studies that assessed the impact on health and social care utilisation of digital health interventions for type 2 diabetes were included in the study. We restricted the digital health interventions to information provision, self-management and behaviour management. Four databases were searched (MEDLINE, EMBASE, PsycINFO and EconLit) for articles published between January 2010 and March 2021. The studies were analysed using a narrative synthesis approach. The risk of bias and reporting quality were appraised using the ROBINS-I checklist. RESULTS: The review included 22 studies. Overall, studies reported mixed evidence on the impact of digital health interventions on health and social care utilisation and costs, and suggested this impact differs according to the healthcare utilisation component. For example, digital health intervention use was associated with lower medication use and fewer outpatient appointments, whereas evidence on general practitioner visits and inpatient admissions was mixed. Most reviewed studies focus on a single component of healthcare utilisation. CONCLUSIONS: The review shows no clear evidence of an impact of digital health interventions on health and social care utilisation or costs. Further work is needed to assess the impact of digital health interventions across a broader range of care utilisation components and settings, including social and mental healthcare services. CLINICAL TRIAL REGISTRATION: The study protocol was registered on PROSPERO before searches began in April 2021 (registration number: CRD42020172621).

Rapid waning of protection induced by prior BA.1/BA.2 infection against BA.5 infection.

SARS-CoV-2 omicron subvariants BA.1 and BA.2 became dominant in many countries in early 2022. These subvariants are now being displaced by BA.4 and BA.5. While natural infection with BA.1/BA.2 provides some protection against BA.4/BA.5 infection, the duration of this protection remains unknown. We used the national Portuguese COVID-19 registry to investigate the waning of protective immunity conferred by prior BA.1/BA.2 infection towards BA.5. We divided the individuals infected during the period of BA.1/BA.2 dominance (>90% of sample isolates) in successive 15-day intervals and determined the risk of subsequent infection with BA.5 over a fixed period. Compared with uninfected people, one previous infection conferred substantial protection against BA.5 re-infection at 3 months (RR=0.12; 95% CI: 0.11-0.12). However, although still significant, the protection was reduced by two-fold at 5 months post-infection (RR=0.24; 0.23-0.24). These results should be interpreted in the context of vaccine breakthrough infections, as the vaccination coverage in the individuals included in the analyses is >98% since the end of 2021. This waning of protection following BA.1/BA.2 infection highlights the need to assess the stability and durability of immune protection induced with the adapted vaccines (based on BA.1) over time.

Unmeasured confounding in nonrandomized studies: quantitative bias analysis in health technology assessment.

Evidence generated from nonrandomized studies (NRS) is increasingly submitted to health technology assessment (HTA) agencies. Unmeasured confounding is a primary concern with this type of evidence, as it may result in biased treatment effect estimates, which has led to much criticism of NRS by HTA agencies. Quantitative bias analyses are a group of methods that have been developed in the epidemiological literature to quantify the impact of unmeasured confounding and adjust effect estimates from NRS. Key considerations for application in HTA proposed in this article reflect the need to balance methodological complexity with ease of application and interpretation, and the need to ensure the methods fit within the existing frameworks used to assess nonrandomized evidence by HTA bodies.