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INTRODUCTION: As the most internally rigorous design, randomized controlled trials (RCTs) are the gold standard for assessing the efficacy and safety profile of interventions. Increasingly, health technology assessment (HTA) considers evidence from non-randomized studies. Guidance recommends synthesizing different study designs separately due to their different inherent biases/limitations. But when authors or reviewers misclassify studies, this could affect which studies are included and therefore have an impact on review results.
METHODS: We are conducting a methods project to (i) identify a clear study design classification system, (ii) explore whether its use produces consistent study design categorizations among reviewers, and (iii) iteratively improve the classification system. We performed a pragmatic web-based search for study design categorization tools and used the resulting schemas to develop a clear algorithm for use by reviewers of all levels of experience, specifically in reviews of treatment interventions. Next, we tested tool consistency and user experience by web-based survey in a small internal sample of reviewers, each independently using the system to categorize 18 published studies.
RESULTS: A median of seven reviewers (range four to eight) categorized each study. Rater agreement using the chart varied widely, with 100 percent agreement on the designs of three studies (17%), and at least 75 percent of reviewers agreeing on one design for nine studies (50%). The most common agreement was reached on RCTs and non-randomized controlled trials. The most common sources of disagreement were between different types of cohort studies and between case series and controlled cohort studies, largely due to inconsistent reporting. We also identified several improvements: the wording of prompt questions, the ordering of designs, and the addition of new elements.
CONCLUSIONS: The classification system as initially designed led to too much variation in study design categorization to be useful. Consequently, we present a revised version that we now aim to evaluate in a larger sample of reviewers. Further research will also investigate whether using the tool would change the results of systematic reviews, using a small sample of published reviews.
AIMS: The direct cost of diabetes to the UK health system was estimated at around £10 billion in 2012. This analysis updates that estimate using more recent and accurate data sources.
METHODS: A pragmatic review of relevant data sources for UK nations was conducted, including population-level data sets and published literature, to generate estimates of costs separately for Type 1, Type 2 and gestational diabetes. A comprehensive cost framework, developed in collaboration with experts, was used to create a population-based cost of illness model.
The key driver of the analysis was prevalence of diabetes and its complications. Estimates were made of the excess costs of diagnosis, treatment and diabetes-related complications compared with the general UK population. Estimates of the indirect costs of diabetes focused on productivity losses due to absenteeism and premature mortality.
RESULTS: The direct costs of diabetes in 2021/22 for the UK were estimated at £10.7 billion, of which just over 40% related to diagnosis and treatment, with the rest relating to the excess costs of complications. Indirect costs were estimated at £3.3 billion.
CONCLUSIONS: Diabetes remains a considerable cost burden in the UK, and the majority of those costs are still spent on potentially preventable complications. Although rates of some complications are reducing, prevalence continues to increase and effective approaches to primary and secondary prevention continue to be needed. Improvements in data capture, data quality and reporting, and further research on the human and financial implications of increasing incidence of Type 2 diabetes in younger people are recommended.
INTRODUCTION: Technical and trade off issues to incorporating environmental data in HTA decision processes are known. Multi criteria decision analysis has been successfully used across disciplines, supporting multifaceted technology decision making. Challenges in prioritising environmental criteria, resource constraints, and other criteria to assess technologies can be overcome through the application of MCDA, also used in conjunction with other techniques in HTA.
METHODS: A systematic review of methods to evaluate environmental sustainability in HTA; conducted following the PRISMA guidelines. Using a comprehensive search strategy Ovid EMBASE; Web of Science (all databases); PubMed; EBSCO Host Greenfile; IEEE Xplore; International HTA database; Cochrane Library and grey literature were searched. The review analyzed the following broad themes; methods to overcome barriers including how methods can handle "trade off" issues between financial and environmental considerations , resource and expertise constraints and data quality issues. This review determined how comprehensive the methods are for assessing sustainability in HTA. Frameworks were also ranked based on their overall transparency and feasibility.
RESULTS:
This review identified ten key studies. Half of these studies outline MCDA models within their frameworks. All MCDA studies have been combined with other techniques to support sustainable technology decision making HTA. Analytical MCDA models with roots in mathematics are highlighted as reproducible techniques to support multifaceted decision making, particularly where there are conflicting criteria. Analytical hierarchy process (AHP) used in conjunction with a circular economy framework for health technology supports global health system net zero and wider sustainability goals. A performance matrix model elicits outcome trade off by assigning weights to costs, technical performance, and environmental outcomes.
CONCLUSIONS
The multidisciplinary findings of this research provide valuable insight in an area where there is currently limited research, confirming and expand on previous scoping reviews. The results highlight several comprehensive MCDA models supporting HTA sustainability framework development. Further research is warranted into the feasibility of the frameworks. Study selection and data extraction were undertaken independently due to project time constraints.
INTRODUCTION: Single-arm studies, particularly single-arm trials (SATs), are increasingly being used in submissions for marketing authorization and health technology assessment. As reviewers of evidence, we sought to better understand the validity of SATs, compared with observational single-arm studies (case series), and how to assess them in our reviews.
METHODS: We conducted a highly pragmatic literature review to create a convenience sample of recent systematic reviews published from January to July 2023 to establish the following: (i) what single-arm study designs are included; (ii) what quality assessment tools are used; and (iii) whether there is a difference in effect size and variability among different study designs. A single reviewer identified reviews of interventions that included single-arm studies and extracted information on the numbers of included SATs and case series, and the quality assessment tools used. Any misclassifications by review authors were identified. For meta-analyses, outcome data were extracted and a subgroup analysis comparing SATs and case series was conducted.
RESULTS: Work is still underway to complete this investigation. So far, it appears that a large proportion of systematic reviews misclassify SATs and case series studies and few use appropriate quality assessment tools. There is not yet any evidence of a systematic difference between SATs and case series in terms of effect size.
CONCLUSIONS: Findings suggest that there is poor understanding of SATs in the review community. There are limited specific quality assessment tools for SATs and review authors frequently use inappropriate tools to assess them. More research is likely to be needed to investigate the relative validity of SATs and single-arm observational studies.
