Our latest research, all in one place. Browse our collection of journal articles, reports and conference proceedings to see how we’re contributing to HEOR research. Remember to:
OBJECTIVES: Climate breakdown is affecting human health globally. The National Health Service (NHS) generates 26 million tonnes of CO2e per annum equivalent to the size of Croatia's annual emissions. Healthcare suppliers possess a sizeable opportunity to support health technology environmental sustainability (HTES) underpinned by leaner pathways and resource optimization. HTA is developing approaches to include environmental sustainability (ES). Reporting a broad range of environmental outcomes is important, but there is a lack of published guidance. This study aims to develop recommendations for decision makers on how broader environmental criteria should be included in HTA, based on the technical validation of an environmental model of Aurora EV-ICD by Medtronic.
METHODS: The Medtronic environmental model estimates the impact of the device on CO2e, water usage and waste volumes over a 10-year time horizon. A multi-step process was used to internally validate the model, using a specifically designed checklist for HTES models being reported as an information conduit. Recommendations were provided to support appropriate reporting of the data to the NHS.
RESULTS: The model structure was deemed appropriate and suitable for submission to the NHS. Recommendations for reporting ES to the NHS include: 1) using and referencing multiple environmental management guidelines for reporting CO2e to ensure transparency and reproducibility; 2) applying the same principles to quantify other environmental outcomes that will enable a more holistic evaluation of unintended consequences, including human health impact, resource use and biodiversity loss; 3) using life cycle assessment software (such as OpenLCA) to overcome data challenges and to facilitate reporting appropriate and comprehensive environmental endpoint categories.
CONCLUSIONS: This multi-perspective collaboration between industry and researchers supports ES in HTA framework development. These recommendations can be used by decision makers to aid the inclusion of environmental models that supports reporting a broader range of environmental outcomes over a long-term time horizon.
BACKGROUND: The EARLY-AF (NCT02825979), STOP AF First (NCT03118518), and Cryo-FIRST (NCT01803438) randomised controlled trials (RCTs) demonstrated that cryoballoon pulmonary vein isolation reduces atrial fibrillation (AF) recurrence compared with antiarrhythmic drugs (AADs) in patients with symptomatic paroxysmal atrial fibrillation (PAF). The present study developed a cost-effectiveness model (CEM) of first-line cryoablation compared with first-line AADs for PAF, from the Canadian health care payer's perspective.
METHODS: Data from the 3 RCTs were analysed to estimate key CEM parameters. The model structure used a decision tree for the first 12 months and a Markov model with a 3-month cycle length for the remaining lifetime time horizon. Costs were set at 2023 Canadian dollars, health benefits were expressed as quality-adjusted life years (QALYs), and both were discounted 3% annually. Probabilistic sensitivity analysis (PSA) considered parameter uncertainty.
RESULTS: The statistical analysis estimated that first-line cryoablation generates a 47% reduction (P < 0.001) in the rate of AF recurrence, a 73% reduction in the rate of subsequent ablation (P < 0.001), and a 4.3% (P = 0.025) increase in health-related quality of life, compared with first-line AADs. The PSA indicates that an individual treated with first-line cryoablation accrues less costs (-$3,862) and more QALYs (0.19) compared with first-line AADs. Cryoablation is cost-saving in 98.4% of PSA iterations and has a 99.9% probability of being cost-effective at a cost-effectiveness threshold of $50,000 per QALY gained. Cost-effectiveness results were robust to changes in key model parameters. CONCLUSIONS: First-line cryoballoon ablation is cost-effective when compared with AADs for patients with symptomatic PAF.
OBJECTIVES: Diabetes is a chronic condition that incurs substantial costs on health systems. The increasing prevalence of diabetes means that costs will likely increase exponentially over the next few decades, with devastating effects on healthcare funding. This study reports on the increased costs, as well as the potential savings should there be a slowing down in the number of new cases.
METHODS: A recent study has reported that the annual cost of diabetes (including Type 1, Type 2 and gestational diabetes) in the United Kingdom is £10.7 billion, with an additional £3.3 billion indirect costs. We use current prevalence forecasts to estimate the future costs under a series of different assumptions for the change in prevalence rates. The estimates assume that there will be no changes to care management.
RESULTS: Using current forecast estimates, the total annual cost of diabetes is estimated to increase to £17.9 billion within fifteen years (an increase of 68%). If the rate of growth in prevalence could be reduced by just 1% (i.e. from 0.200% to 0.198%), then this would result in annual savings of £67.9 million by year 15. A 10% reduction in the growth of prevalence would results in savings of £678.7 million, whilst a 50% reduction in the growth of prevalence would save £3.4 billion annually.
CONCLUSIONS: Even very small changes in the annual increase in prevalence of diabetes can lead to substantial cost savings that can be reinvested in the health system. Larger reductions will have exponentially increased impact, potentially saving billions of pounds for the health system. This suggests that public health efforts to prevent diabetes should be prioritized in order to avoid devastating impacts on health system funding.
OBJECTIVES: It is common for the uncertainty in an economic evaluation to be 'tested' using one-way sensitivity analysis. A common method for reporting such analysis is to present a 'tornado diagram'. This research explores how useful this method is, and whether alternative approaches should be recommended.
METHODS: We examined the results sections of recent technology appraisals from the National Institute for Health and Care Excellence (NICE) in the United Kingdom. We recorded whether one-way sensitivity analysis was undertaken and, if so, how it was reported.
RESULTS: One-way sensitivity analysis was undertaken in all appraisals. In the vast majority of cases, the results were reported using tornado diagrams. In most cases, the ranges used were based on observed uncertainty for the parameter inputs (e.g. standard errors or confidence intervals) and in a small proportion, they were based on arbitrary ranges. However, in most cases, the diagram or table only showed the key output (e.g. incremental cost-effectiveness ratio or net health benefit) for the extreme values in the range, and no information was provided for intermediate values.
CONCLUSIONS: One-way sensitivity analysis is a tool to help decision makers understand the degree of uncertainty associated with model inputs, and how that uncertainty affects the model's outcomes. Showing the results only for the extreme values deprives the decision maker of useful information. For example, where a 95% confidence interval is used, the decision maker is only able to see the impact of the changes for outcomes that have a 2.5% likelihood of occurring (i.e. at each end of the range). Instead, tornado diagrams could take a number of alternative approaches to show the impact of different values along the plausible ranges. This could include, for example, decile values, inter-quartile ranges and threshold points. This would allow decision makers to have more constructive discussions around the impact of uncertainty.
OBJECTIVES: Publications that report early Kaplan Meier (KM) from a clinical trial are useful for understanding how accurate trends seen in immature data are to that of end-of-trial publications. This immature data may not be reported or available. This de novo approach aimed to recreate early trial data from full published KM. If successful, this approach may be used to adjust trial evidence to reflect an earlier timepoint, allowing for before and after comparisons so that analysts can observe predictive data trends and characteristics.
METHODS: Published five-year KM from the ZUMA-1 trial for axicabtagene ciloleucel CAR-T therapy in refractory large B-cell lymphoma was digitised and pseudo individual participant data (IPD) was generated using the Guyot algorithm. The aim was to produce IPD that reflected 12-month, 18-month and 24-month minimum follow-ups, with the proportion censored at a given time informed by earlier ZUMA-1 trial publications.
The censoring indicator was changed until the desired proportion censored was reached, starting with the latest reported events in the five-year dataset. A truncated normal distribution was assumed when assigning the relevant follow-up time for the newly censored points. Generated early datasets were plotted and summary statistics produced, which were compared to equivalent outputs of early ZUMA-1 publications.
RESULTS: The generated truncated datasets resulted in KM that visually matched the KM reported in early ZUMA-1 publications. Median survival was not met in either dataset. The total number of events is 49 in the generated truncated 24-months minimum follow-up dataset and 50 in the ZUMA-1 early publication with 27-month median follow-up.
CONCLUSIONS: Publications reporting trial KM can be truncated to recreate earlier datasets to a reasonably accurate degree. A key feature of the published KM that allowed for this truncation was the small range from minimum to maximum follow-up timepoint, as to narrow the applied censoring to a more accurate range.
OBJECTIVES: There is a lack of consensus and transparency on using real-world evidence (RWE) in health technology assessment (HTA). In 2022, the National Institute for Health and Care Excellence (NICE) developed an RWE framework. This study explores the use of RWE databases in NICE technology appraisals (TAs) from April 2019.
METHODS: The NICE website was searched to source TAs from April 2019 to May 2024 that mentioned at least 1 of 10 identified RWE databases. Information on how RWE was used and how it was viewed by the External Assessment Group (EAG) and NICE committee were extracted.
RESULTS: 13 TAs mentioned at least 1 of the databases: Clinical Practice Research Datalink (12 TAs), OpenSAFELY (3 TAs), The Health Improvement Network (1 TA) and General Practice Extraction Service Data for Pandemic Planning and Research (1 TA). In 12 TAs, RWE data were used to inform or validate model parameters. In all TAs screened, the committee and EAG did not criticize the use of RWE data extracted from databases. Moreover, in three cases the EAG utilized RWE data to inform alternative base case parameter estimates. In two cases, the committee recommended the manufacturer use RWE data to validate a model assumption. Comments from the committee focused on the relevance and applicability of the RWE to the decision problem.
CONCLUSIONS: It is evident that decision makers respond positively to RWE when it has been correctly employed. For RWE to aid the decision-making process, data must be applicable and relevant to the population of interest. Correct utilization of RWE yields the potential to reduce parameter uncertainty. This study supports the use of RWE in HTA and demonstrates that a standard framework can ensure appropriate use of RWE. Future research could explore RWE use in other HTA agencies to strengthen the current study and broaden its applicability.
OBJECTIVES: The aim of this study was to investigate whether it is possible to elicit how United Kingdom (UK) public citizens might trade off human health and environmental outcomes.
METHODS: Using a representative adult population, a discrete choice experiment (DCE) was conducted on criteria impacting trade-off decisions between human health and environmental outcomes. Respondents were asked to make twelve choices that included four attributes: the impact on UK life expectancy, the impact on biodiversity, the impact on UK carbon emissions and location of environmental impacts. Data were analyzed using a conditional logit regression model.
RESULTS: 508 respondents completed the survey. A DCE found UK public citizens are willing to forgo human health to reduce environmental harm.
CONCLUSIONS: This research demonstrated it is possible to elicit the public’s view about trade-offs between health and the environment. Moreover, the public is willing to forgo human health to reduce environmental impact, propounding the importance of healthcare sustainability.
