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Objective: This study leveraged data from 11 independent international diabetes models to evaluate the impact of unrelated future medical costs on the outcomes of health economic evaluations in diabetes mellitus., Methods: Eleven models simulated the progression of diabetes and occurrence of its complications in hypothetical cohorts of individuals with type 1 (T1D) or type 2 (T2D) diabetes over the remaining lifetime of the patients to evaluate the cost effectiveness of three hypothetical glucose improvement interventions versus a hypothetical control intervention. All models used the same set of costs associated with diabetes complications and interventions, using a United Kingdom healthcare system perspective. Standard utility/disutility values associated with diabetes-related complications were used. Unrelated future medical costs were assumed equal for all interventions and control arms. The statistical significance of changes on the total lifetime costs, incremental costs and incremental cost-effectiveness ratios (ICERs) before and after adding the unrelated future medical costs were analysed using t-test and summarized in incremental cost-effectiveness diagrams by type of diabetes., Results: The inclusion of unrelated costs increased mean total lifetime costs substantially. However, there were no significant differences between the mean incremental costs and ICERs before and after adding unrelated future medical costs. Unrelated future medical cost inclusion did not alter the original conclusions of the diabetes modelling evaluations., Conclusions: For diabetes, with many costly noncommunicable diseases already explicitly modelled as complications, and with many interventions having predominantly an effect on the improvement of quality of life, unrelated future medical costs have a small impact on the outcomes of health economic evaluations., (© 2024. The Author(s).)

Objectives: The Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes., Methods: Ten independent modeling groups completed a blinded simulation exercise to estimate the cost-effectiveness of 3 interventions in 2 type 2 diabetes populations. Modeling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB) and the drivers of those differences., Results: Overall, modeling groups agreed which interventions had a positive NMB (ie, were cost-effective), Although estimates of NMB varied substantially-by up to £23 696 for 1 intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modeled health states was also a significant predictor of NMB., Conclusions: This exercise demonstrates that structural uncertainty between different health economic models affects cost-effectiveness estimates. Although it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimizing the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modeling., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section., (Copyright © 2024. Published by Elsevier Inc.)

Background: Excess weight is a major risk factor for severe disease after infection with SARS-CoV-2. However, the effect of BMI on COVID-19 hospital resource use has not been fully quantified. This study aimed to identify the association between BMI and hospital resource use for COVID-19 admissions with the intention of informing future national hospital resource allocation., Methods: In this community-based cohort study, we analysed patient-level data from 57 415 patients admitted to hospital in England with COVID-19 between April 1, 2020, and Dec 31, 2021. Patients who were aged 20-99 years, had been registered with a general practitioner (GP) surgery that contributed to the QResearch database for the whole preceding year (2019) with at least one BMI value measured before April 1, 2020, available in their GP record, and were admitted to hospital for COVID-19 were included. Outcomes of interest were duration of hospital stay, transfer to an intensive care unit (ICU), and duration of ICU stay. Costs of hospitalisation were estimated from these outcomes. Generalised linear and logit models were used to estimate associations between BMI and hospital resource use outcomes., Findings: Patients living with obesity (BMI >30·0 kg/m 2 ) had longer hospital stays relative to patients in the reference BMI group (18·5-25·0 kg/m 2 ; IRR 1·07, 95% CI 1·03-1·10); the reference group had a mean length of stay of 8·82 days (95% CI 8·62-9·01). Patients living with obesity were more likely to be admitted to ICU than the reference group (OR 2·02, 95% CI 1·86-2·19); the reference group had a mean probability of ICU admission of 5·9% (95% CI 5·5-6·3). No association was found between BMI and duration of ICU stay. The mean cost of COVID-19 hospitalisation was £19 877 (SD 17 918) in the reference BMI group. Hospital costs were estimated to be £2736 (95% CI 2224-3248) higher for patients living with obesity., Interpretation: Patients admitted to hospital with COVID-19 with a BMI above the healthy range had longer stays, were more likely to be admitted to ICU, and had higher health-care costs associated with hospital treatment of COVID-19 infection as a result. This information can inform national resource allocation to match hospital capacity to areas where BMI profiles indicate higher demand., Funding: National Institute for Health Research., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification targeting BMI and LDL in addition to HbA1c., Research Design and Methods: We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups., Results: Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% more and still be cost effective for data-driven and risk-driven subgroups, respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to 10-fold increases in QALYs gained., Conclusions: Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains., (© 2023 by the American Diabetes Association.)

Background: An automated virtual reality cognitive therapy (gameChange) has demonstrated its effectiveness to treat agoraphobia in patients with psychosis, especially for high or severe anxious avoidance. Its economic value to the health care system is not yet established., Objective: In this study, we aimed to estimate the potential economic value of gameChange for the UK National Health Service (NHS) and establish the maximum cost-effective price per patient., Methods: Using data from a randomized controlled trial with 346 patients with psychosis (ISRCTN17308399), we estimated differences in health-related quality of life, health and social care costs, and wider societal costs for patients receiving virtual reality therapy in addition to treatment as usual compared with treatment as usual alone. The maximum cost-effective prices of gameChange were calculated based on UK cost-effectiveness thresholds. The sensitivity of the results to analytical assumptions was tested., Results: Patients allocated to gameChange reported higher quality-adjusted life years (0.008 QALYs, 95% CI -0.010 to 0.026) and lower NHS and social care costs (-£105, 95% CI -£1135 to £924) compared with treatment as usual (£1=US $1.28); however, these differences were not statistically significant. gameChange was estimated to be worth up to £341 per patient from an NHS and social care (NHS and personal social services) perspective or £1967 per patient from a wider societal perspective. In patients with high or severe anxious avoidance, maximum cost-effective prices rose to £877 and £3073 per patient from an NHS and personal social services perspective and societal perspective, respectively., Conclusions: gameChange is a promising, cost-effective intervention for the UK NHS and is particularly valuable for patients with high or severe anxious avoidance. This presents an opportunity to expand cost-effective psychological treatment coverage for a population with significant health needs., Trial Registration: ISRCTN Registry ISRCTN17308399; https://www.isrctn.com/ISRCTN17308399., International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2019-031606., (©James Altunkaya, Michael Craven, Sinéad Lambe, Ariane Beckley, Laina Rosebrock, Robert Dudley, Kate Chapman, Anthony Morrison, Eileen O'Regan, Jenna Grabey, Aislinn Bergin, Thomas Kabir, Felicity Waite, Daniel Freeman, José Leal. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.11.2022.)

Background: Policy evaluations often focus on ex post estimation of causal effects on short-term surrogate outcomes. The value of such information is limited for decision making, as the failure to reflect policy-relevant outcomes and disregard for opportunity costs prohibits the assessment of value for money. Further, these evaluations do not always consider all relevant evidence, other courses of action, or decision uncertainty., Methods: In this article, we explore how policy evaluation could better meet the needs of decision making. We begin by defining the evidence required to inform decision making. We then conduct a literature review of challenges in evaluating policies. Finally, we highlight potential methods available to help address these challenges., Results: The evidence required to inform decision making includes the impacts on the policy-relevant outcomes, the costs and associated opportunity costs, and the consequences of uncertainty. Challenges in evaluating health policies are described using 8 categories: 1) valuation space; 2) comparators; 3) time of evaluation; 4) mechanisms of action; 5) effects; 6) resources, constraints, and opportunity costs; 7) fidelity, adaptation, and level of implementation; and 8) generalizability and external validity. Methods from a broad set of disciplines are available to improve policy evaluation, relating to causal inference, decision-analytic modeling, theory of change, realist evaluation, and structured expert elicitation., Limitations: The targeted review may not identify all possible challenges, and the methods covered are not exhaustive., Conclusions: Evaluations should provide appropriate evidence to inform decision making. There are challenges in evaluating policies, but methods from multiple disciplines are available to address these challenges., Implications: Evaluators need to carefully consider the decision being informed, the necessary evidence to inform it, and the appropriate methods.[Box: see text].

Background: Patients undergoing contrast-enhanced computed tomography (CECT) imaging in a non-emergency outpatient setting often lack a recent estimated Glomerular Filtration Rate measurement. This may lead to inefficiencies in the CECT pathway. The use of point-of-care (POC) creatinine tests to evaluate kidney function in these patients may provide a safe and cost-effective alternative to current practice, as these can provide results within the same CECT appointment., Methods: A decision tree model was developed to characterise the diagnostic pathway and patient management (e.g., intravenous hydration) and link these to adverse renal events associated with intravenous contrast media. Twelve diagnostic strategies including three POC devices (i-STAT, ABL800 Flex and StatSensor), risk factor screening and laboratory testing were compared with current practice. The diagnostic accuracy of POC devices was derived from a systematic review and meta-analysis; relevant literature sources and databases informed other parameters. The cost-effective strategy from a health care perspective was identified based on highest net health benefit (NHB) which were expressed in quality-adjusted life years (QALYs) at £20,000/QALY., Results: The cost-effective strategy, with a NHB of 9.98 QALYs and a probability of being cost-effective of 79.3%, was identified in our analysis to be a testing sequence involving screening all individuals for risk factors, POC testing (with i-STAT) on those screening positive, and performing a confirmatory laboratory test for individuals with a positive POC result. The incremental NHB of this strategy compared to current practice, confirmatory laboratory test, is 0.004 QALYs. Results were generally robust to scenario analysis., Conclusions: A testing sequence combining a risk factor questionnaire, POC test and confirmatory laboratory testing appears to be cost-effective compared to current practice. The cost-effectiveness of POC testing appears to be driven by reduced delays within the CECT pathway. The contribution of intravenous contrast media to acute kidney injury, and the benefits and harms of intravenous hydration remain uncertain., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Healthcare decision makers require accurate long-term economic models to evaluate the cost-effectiveness of new mental health interventions., Aims: To assess the suitability of current patient-level economic models to estimate long-term economic outcomes in severe mental illness., Method: We undertook pre-specified systematic searches in MEDLINE, Embase and PsycINFO to identify reviews and stand-alone publications of economic models of interventions for schizophrenia, bipolar disorder and major depressive disorder (PROSPERO: CRD42020158243). We screened paper titles and abstracts to identify unique patient-level economic models. We conducted a structured extraction of identified models, recording the presence of key predefined model features. Model quality and validation were appraised using the 2014 ISPOR and 2016 AdViSHE model checklists., Results: We identified 15 unique patient-level models for psychosis and major depressive disorder from 1481 non-duplicate records. Models addressed schizophrenia (n = 6), bipolar disorder (n = 2) and major depressive disorder (n = 7). The predominant model type was discrete event simulation (n = 9). Model complexity and incorporation of patient heterogeneity varied considerably, and only five models extrapolated costs and outcomes over a lifetime horizon. Key model parameters were often based on low-quality evidence, and checklist quality assessment revealed weak model verification procedures., Conclusions: Existing patient-level economic models of interventions for severe mental illness have considerable limitations. New modelling efforts must be supplemented by the generation of good-quality, contemporary evidence suitable for model building. Combined effort across the research community is required to build and validate economic extrapolation models suitable for accurately assessing the long-term value of new interventions from short-term clinical trial data.

Background: Patients with low estimated glomerular filtration rates may be at higher risk of post-contrast acute kidney injury following contrast-enhanced computed tomography imaging. Point-of-care devices allow rapid measurement of estimated glomerular filtration rates for patients referred without a recent estimated glomerular filtration rate result., Objectives: To assess the clinical effectiveness and cost-effectiveness of point-of-care creatinine tests for outpatients without a recent estimated glomerular filtration rate measurement who need contrast-enhanced computed tomography imaging., Methods: Three systematic reviews of test accuracy, implementation and clinical outcomes, and economic analyses were carried out. Bibliographic databases were searched from inception to November 2018. Studies comparing the accuracy of point-of-care creatinine tests with laboratory reference tests to assess kidney function in adults in a non-emergency setting and studies reporting implementation and clinical outcomes were included. Risk of bias of diagnostic accuracy studies was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Probabilities of individuals having their estimated glomerular filtration rates correctly classified were estimated within a Bayesian framework and pooled using a fixed-effects model. A de novo probabilistic decision tree cohort model was developed to characterise the decision problem from an NHS and a Personal Social Services perspective. A range of alternative point-of-care testing approaches were considered. Scenario analyses were conducted., Results: Fifty-four studies were included in the clinical reviews. Twelve studies reported diagnostic accuracy for estimated glomerular filtration rates; half were rated as being at low risk of bias, but there were applicability concerns for most. i-STAT (Abbott Point of Care, Inc., Princeton, NJ, USA) and ABL (Radiometer Ltd, Crawley, UK) devices had higher probabilities of correctly classifying individuals in the same estimated glomerular filtration rate categories as the reference laboratory test than StatSensor ® devices (Nova Biomedical, Runcorn, UK). There was limited evidence for epoc ® (Siemens Healthineers AG, Erlangen, Germany) and Piccolo Xpress ® (Abaxis, Inc., Union City, CA, USA) devices and no studies of DRI-CHEM NX 500 (Fujifilm Corporation, Tokyo, Japan). The review of implementation and clinical outcomes included six studies showing practice variation in the management decisions when a point-of-care device indicated an abnormal estimated glomerular filtration rate. The review of cost-effectiveness evidence identified no relevant studies. The de novo decision model that was developed included a total of 14 strategies. Owing to limited data, the model included only i-STAT, ABL800 FLEX and StatSensor. In the base-case analysis, the cost-effective strategy appeared to be a three-step testing sequence involving initially screening all individuals for risk factors, point-of-care testing for those individuals with at least one risk factor, and including a final confirmatory laboratory test for individuals with a point-of-care-positive test result. Within this testing approach, the specific point-of-care device with the highest net benefit was i-STAT, although differences in net benefit with StatSensor were very small., Limitations: There was insufficient evidence for patients with estimated glomerular filtration rates < 30 ml/minute/1.73 m 2 , and on the full potential health impact of delayed or rescheduled computed tomography scans or the use of alternative imaging modalities., Conclusions: A three-step testing sequence combining a risk factor questionnaire with a point-of-care test and confirmatory laboratory testing appears to be a cost-effective use of NHS resources compared with current practice. The risk of contrast causing acute kidney injury to patients with an estimated glomerular filtration rate of < 30 ml/minute/1.73 m 2 is uncertain. Cost-effectiveness of point-of-care testing appears largely driven by the potential of point-of-care tests to minimise delays within the current computed tomography pathway., Future Work: Studies evaluating the impact of risk-stratifying questionnaires on workflow outcomes in computed tomography patients without recent estimated glomerular filtration rate results are needed., Study Registration: This study is registered as PROSPERO CRD42018115818., Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment ; Vol. 24, No. 39. See the NIHR Journals Library website for further project information., Competing Interests: Martine Harris has received point-of-care creatinine devices and consumables for use in research studies from Nova Biomedical (Runcorn, UK), Abbott Laboratories (Chicago, IL, USA) and Radiometer Ltd (Crawley, UK). She has co-authored academic papers in this area from 2016 to present and contributed (from August 2017 to January 2018) as an expert commentator for the National Institute for Health and Care Excellence’s Medtech innovation briefing number 136 (MIB136) entitled ‘Point-of-care creatinine tests before contrast-enhanced imaging’. James Altunkaya is funded via a National Institute for Health Research Research Methods Fellowship. Sofia Dias has received Medical Research Council funding.

Background: Excessive sedentary behaviour (sitting) is a risk factor for poor health in children and adults. Incorporating sit-stand desks in the classroom environment has been highlighted as a potential strategy to reduce children's sitting time. The primary aim of this study was to examine the feasibility of conducting a cluster randomised controlled trial (RCT) of a sit-stand desk intervention within primary school classrooms., Methods: We conducted a two-armed pilot cluster RCT involving 8 primary schools in Bradford, United Kingdom. Schools were randomised on a 1:1 basis to the intervention or usual practice control arm. All children (aged 9-10 years) in participating classes were eligible to take part. Six sit-stand desks replaced three standard desks (sitting 6 children) in the intervention classrooms for 4.5-months. Teachers were encouraged to use a rotation system to ensure all pupils were exposed to the sit-stand desks for > 1 h/day on average. Trial feasibility outcomes (assessed using quantitative and qualitative measures) included school and participant recruitment and attrition, intervention and outcome measure completion rates, acceptability, and preliminary effectiveness of the intervention for reducing sitting time. A weighted linear regression model compared changes in weekday sitting time (assessed using the activPAL accelerometer) between trial arms., Results: School and child recruitment rates were 33% (n = 8) and 75% (n = 176). At follow-up, retention rates were 100% for schools and 97% for children. Outcome measure completion rates ranged from 63 to 97%. A preliminary estimate of intervention effectiveness revealed a mean difference in change in sitting of - 30.6 min/day (95% CI: - 56.42 to - 4.84) in favour of the intervention group, after adjusting for baseline sitting and wear time. Qualitative measures revealed the intervention and evaluation procedures were acceptable to teachers and children, except for some problems with activPAL attachment., Conclusion: This study provides evidence of the acceptability and feasibility of a sit-stand desk intervention and evaluation methods. Preliminary evidence suggests the intervention showed potential in reducing children's weekday sitting but some adaptations to the desk rotation system are needed to maximize exposure. Lessons learnt from this trial will inform the planning of a definitive trial., Trial Registration: ISRCTN12915848 (registered: 09/11/16).