Abstract

OBJECTIVES: When modelling the impact of an intervention and comparator, they usually have the same population. However, interventions may alter the clinical pathway, which can impact the target population. This can be a challenge to model, for example, the underlying prevalence of the condition explored might be altered in the new population. We present possible approaches to address this using a case study involving the introduction of a home testing kit for sexually transmitted infections (STI).

METHODS: The home testing kit would reach a much larger population with differing risks. An economic model was developed to investigate the health and cost impact of implementing home testing kits compared to standard in-clinic testing for STIs. The key model outcomes were total incremental cost and the number of complications averted from excess cases being detected through home testing.

Data on prevalence was used to inform comparator population prevalence of STIs, test uptake, and positive results. A meta-analysis of 7 studies estimated that home testing increased positive tests by 71%. For the intervention, evidence on increase in positive tests was leveraged to explore scenarios where prevalence estimates were non-equivalent to the comparator population.

RESULTS: The use of the home test is estimated to be cost-effective with an ICER of £3,865 when compared with standard STI testing assuming equivalent prevalence. The inclusion of a differential increase in completed tests and positive diagnoses resulted in a larger ICER (£3,865 and £13,877 for the optimistic scenario and the lower risk scenario, respectively).

CONCLUSIONS: Modelling different populations between the intervention and comparator can be challenging. This case study outlines an approach to addressing this by leveraging evidence to draw assumptions and guide scenario analyses.

Abstract

BACKGROUND: A virtual ward (VW) supports patients who would otherwise need hospitalization by providing acute care, remote monitoring, investigations, and treatment at home. By March 2024, the VW programme had treated 10 950 patients across six speciality VWs, including heart failure (HF). This evaluation presents the economic assessment of the Liverpool HF VW.

METHODS AND RESULTS: A comprehensive economic cost comparison model was developed by the York Health Economics Consortium (University of York) to compare the costs of the VW to standard hospital inpatient care [standard care (SC)]. The model included direct VW costs and additional costs across the care pathway. Costs and resource use for 648 patients admitted to the HF VW were calculated for 30 days post-discharge and total cohort costs were extrapolated to a full year. Primary outcomes included costs related to length of stay, readmissions, and NHS 111 contact. The total cost for the HF VW pathway, including set-up costs, was £467 524. This results in an incremental net cost benefit of £735 512 compared with the total SC cost of £1 203 036, indicating a substantial net cost benefit of £1135 per patient per episode (PPPE). This advantage remains despite initial setup expenses and ongoing costs such as home visits, virtual consultations, point-of-care testing, and home monitoring equipment.

CONCLUSION: Our HF VW model offers a substantial net cost benefit, driven by reduced hospital stays, fewer emergency department visits, and lower readmission rates. The study highlights the importance of considering system-wide impacts and continuous monitoring of VWs as they develop.

Abstract

BACKGROUND: AcuPebble SA100 ('AcuPebble') is a novel wearable medical device to diagnose obstructive sleep apnoea (OSA). This paper investigates the potential economic impact of the technology in the UK through cost savings analysis, and the redirection of savings into further diagnoses.

METHODS: A cost comparison study was conducted, comparing AcuPebble to the standard diagnostic approach of home respiratory polygraphy (HRP) and in-clinic polysomnography (PSG), estimating the net benefit value (NBV) and return on investment (ROI). Cohort size was varied to model the effects of volume discounted pricing and staff training costs. To demonstrate the potential for cost savings, data on the healthcare costs of undiagnosed OSA patients were used to quantify the benefit of increased OSA diagnosis rates, as facilitated by AcuPebble.

RESULTS: For 500 uses of AcuPebble, the NBV in the diagnostic pathway over one year would be in excess of £101,169, increasing to £341,665 for 1,500 uses, £1,263,993 for 5,000 uses, and to £2,628,198 for 10,000 uses, with ROIs of 2.02, 3.03, 5.05, and 6.56, respectively. Given an initial cohort of 1,500 patients, 4,555 extra AcuPebble studies could be completed by redirecting resources from HRP/PSG. Direct cost savings to the NHS from resultant lower undiagnosed rates could be between £24,147 and £4,707,810, based on the cost per use and the percentage of tests that result in a positive diagnosis (varied from 25 to 75% positives).

CONCLUSIONS: AcuPebble presents an opportunity for substantial healthcare savings, enabling an increase in the number of people tested, diagnosed and treated for OSA.

Abstract

OBJECTIVES: The level of an individual's physical activity has a clear and strong association with physical and mental health status. Those with sedentary lifestyles or with low levels of physical activity are at a higher risk of many conditions including coronary heart disease, stroke, cancer and depression. We aimed to estimate the health economic impact associated with different levels of increases in physical activity for people with limited mobility.

METHODS: We used national health survey data to estimate the current level of physical activity in people with limited mobility (n=1113), and how these levels changed after an intervention was introduced. Limited mobility was defined as having either 'Some problems in walking about' or 'Confined to bed' on the mobility component of the EQ-5D questionnaire. The level of physical activity was measured using metabolic equivalent time (MET) minutes, a measure that combines the time spent exercising with the level of vigour involved. We used information from published studies to estimate the risk of developing five health conditions (breast and colon cancer, diabetes, stroke and coronary heart disease), depending on MET level. Costs, utilities and mortality rates were assigned to each of these conditions, to estimate the lifetime costs and quality-adjusted life years for a range of scenarios.

RESULTS: Interventions that cost £25, £100 and £250 per person would need to increase physical activity by approximately 4, 16 and 41 MET minutes per week respectively to be deemed cost effective. Sensitivity analysis showed that changes to cost, utility and mortality input parameters had minimal impact on these results.

CONCLUSIONS: Even small increases in physical activity can generate substantial health gains in a population with limited mobility. Whilst the benefits need to be weighed against the implementation costs, it is likely that most public health strategies to facilitate activity in this population will be cost effective.

Abstract

OBJECTIVES: The UK NHS is committed to achieving net zero direct carbon emissions by 2040. Nitrous oxide (N2O) is a greenhouse gas used for pain relief during contractions in labor. Destruction technology (DT) can help the NHS achieve its target by breaking N2O down into nitrogen and oxygen. This study evaluated the impact of introducing N2O DT in labou=r wards on NHS emissions and costs.

METHODS: A cost-consequence model was developed to estimate the per delivery incremental cost, and NHS cost impact, of implementing DT. A pragmatic literature search was conducted to identify all model inputs. N2O emissions were monetized by converting into CO2 equivalents (CO2e) and using the 2022 marginal abatement CO2e costs. Scenario analysis explored the impact with varying rates of N2O use and one-way deterministic sensitivity analysis was used to identify the main drivers of results.

RESULTS: N2O DT is estimated to be cost saving to the NHS when valuing emissions monetarily. Per-delivery savings vary from £14.09 to £135.20 for emergency caesarean section labors (which use less N2O) and labors using N2O for every contraction, respectively. The per-delivery cost of mobile DT units is £5.57, and £10.10 for central units. It would cost the NHS £22,512,210 and £40,765,953 to purchase and maintain mobile and central DT units, respectively, for all UK consultant-led labor wards. Sensitivity analysis shows the amount of N2O used, the carbon value, and the effectiveness of DT impact results most, though DT remains cost saving in all scenarios.

CONCLUSIONS: The reduction in emissions offsets the cost increase of DT when these emissions are monetized. This raises the wider question of whether the environmental impact of technologies should be formally quantified in cost-effectiveness analyses because it may be a key driver of the conclusions. This study also adds to evidence from European studies to support DT implementation.

Abstract

OBJECTIVES: Single-arm trials (SATs) are becoming more common in an era of precision medicines. SATs typically lack trial randomization due to small sample sizes and limited statistical power, but health technology assessment bodies like The National Institute for Health and Care Excellence (NICE) require comparative effectiveness estimates for reimbursement decision making. Matching-adjusted indirect comparisons (MAICs) are one statistical method used to indirectly compare single-arm evidence to relevant comparators. This research aims to quantify the impact of MAIC parameter inclusion and sample size on propagated economic model uncertainty.

METHODS: We used a NICE technology appraisal (TA781) and supporting publications to create a simulated individual participant data (SIPD) set, conduct MAICs using the published comparator data, calculate parametric survival analysis and populate a partitioned-survival model. Two cuts of the SIPD were selected, the 'full' sample size (n=174) and an arbitrarily selected smaller subset ('small', n=30). For both cuts, MAICs were conducted using all variables (ALL) and high priority variables (HP) only.

RESULTS: For the full [small] samples, the effective sample size (ESS) reduced by 40% [46%] (ALL) and 25% [18%] (HP). The full sample MAICs displayed similar levels of uncertainty in the probabilistic economic model. The HP small sample MAIC displayed more uncertainty than both full sample MAICs. The small sample ALL MAIC displayed the most uncertainty, with probabilistic iterations spread across all four quadrants of the cost-effectiveness plane.

CONCLUSIONS: Where uncertainty is driven by low ESS, we recommend consideration of whether a MAIC is truly informative. If a MAIC is deemed necessary, we recommend preserving ESS by only including HP variables whilst transparently outlining the impact this could have on the MAIC quality and supplementing with analyses, such as naïve comparison and qualitative discussion. We suggest these analyses are presented to decision makers so the original shape of iterations is known.

Abstract

OBJECTIVES: Generalised gamma (GG) is one of six probability distributions recommended by the National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) for survival analysis. A persistent issue was investigated, where some survival models fitted using the GG distribution would produce deterministic results that functioned appropriately, however, the mean survival would be overestimated in the probabilistic sensitivity analysis (PSA). The objective of this analysis was to identify the cause of this overestimation.

METHODS: The reconstructed individual participant data (IPD) were used to provide example survival analysis inputs. The GG parametric survival models were fitted using the R package 'flexsurv'. Extrapolation and PSA were performed in both Microsoft Excel and R to control for any differences between the two.

RESULTS: The formulae used in Excel and R for extrapolation were found to be identical. The cause of the overestimation was identified as PSA samples where extrapolated survival was constant at 100% due to high parameter variance and covariance (particularly of the shape parameter 'Q') in the GG model. This is statistically and clinically implausible. The potential for survival overestimation in the PSA is not observable unless the survival analysis coefficients are applied probabilistically. The erroneous PSA samples only occurred when the GG distribution was used. This issue was observed in both Excel and R, and in raw and reconstructed IPD.

CONCLUSIONS: When reporting a GG model, it is recommended to check for: high variance and covariance in the survival model parameters; PSA samples with 100% survival; incongruence between mean survival in the PSA and deterministic estimates. If these elements are present, the GG model is inappropriate due to the inadvertent inclusion of statistically and clinically implausible survival probabilities in the PSA and the high levels of overall uncertainty this represents.