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Purpose We estimated the cost-effectiveness of 4 radiotherapy modalities to treat early breast cancer in the UK. In a subgroup of patients eligible for all modalities, we compared whole-breast (WB) and partial breast (PB) radiotherapy delivered in either 15 (WB15F, PB15F) or 5 fractions (WB5F, PB5F). In a subgroup ineligible for PB radiotherapy, we compared WB15F to WB5F. Methods We developed a Markov cohort model to simulate lifetime healthcare costs and quality-adjusted life years (QALYs) for each modality. This was informed by the clinical analysis of two non-inferiority trials (FAST Forward and IMPORT LOW) and supplemented with external literature. The primary analysis assumed that radiotherapy modality influences health only through its impact on locoregional recurrence and radiotherapy-related adverse events. Results In the primary analysis, PB5F had the least cost and greatest expected QALYs. WB5F had the least cost and the greatest expected QALYs in those only eligible for WB radiotherapy. Applying a cost-effectiveness threshold of £15,000/QALY, there was a 62% chance that PB5F was the cost-effective alternative in the PB eligible group, and there was a 100% chance that WB5F was cost-effective in the subgroup ineligible for PB radiotherapy. Conclusions Hypofractionation to 5 fractions and partial breast radiotherapy modalities offer potentially important benefits to the UK health system.

Objective A ‘lite’ version of the EQ-5D-5L valuation protocol, which requires a smaller sample by collecting more data from each participant, was proposed and used to develop an EQ-5D-5L value set for Uganda. Methods Adult respondents from the general Ugandan population were quota sampled based on age and sex. Eligible participants were asked to complete 20 composite time trade-off tasks in the tablet-assisted personal interviews using the offline EuroQol Portable Valuation Technology software under routine quality control. No discrete choice experiment task was administered. The composite time trade-off data were modelled using four additive and two multiplicative regression models. Model performance was evaluated based on face validity, prediction accuracy in cross-validation and in predicting mild health states. The final value set was generated using the best-performing model. Results A representative sample (N = 545) participated in this study. Responses to composite time trade-off tasks from 492 participants were included in the primary analysis. All models showed face validity and generated comparable prediction accuracy. The Tobit model with constrained intercepts and corrected for heteroscedasticity was considered the preferred model for the value set on the basis of better performance. The value set ranges from − 1.116 (state 55555) to 1 (state 11111) with ‘pain/discomfort’ as the most important dimension. Conclusions This is the first EQ-5D-5L valuation study using a ‘lite’ protocol involving composite time trade-off data only. Our results suggest its feasibility in resource-constrained settings. The established EQ-5D-5L value set for Uganda is expected to be used for economic evaluations and decision making in Uganda and the East Africa region.

The Social Cash Transfer Programme (SCTP) in Malawi is a cross-sectoral policy with impacts on health, education, nutrition, agriculture and welfare. Implementation of the SCTP requires collaboration across sectors and across national and international stakeholders. Economic evaluation can inform investment by indicating whether benefits exceed costs, but economic evaluations that provide an overall benefit–cost ratio typically assume a common agreed objective and agreed set of value judgements. In reality, the various stakeholders involved in the delivery of the SCTP may have different remits and objectives and may differ in how they value the impacts of the programme. We use the SCTP as a case study to illustrate a cross-sectoral analytical framework that accounts for these differences. The stakeholders that contribute to the SCTP include the Ministry of Gender, Ministry of Finance, Ministry of Economic Planning and Development and Global Fund. We estimate how the SCTP changes outcomes in education, health, net production and poverty, and distinguish outcomes in three groups: SCTP recipients; population in Malawi not eligible for the SCTP and population in other countries. After estimating the direct effects and opportunity costs from investing in the SCTP, we summarize the results according to different perspectives. The SCTP is estimated to provide benefits in excess of costs from the perspective of national stakeholders. From the perspective of an international donor interested in health outcomes, its health benefits do not outweigh the opportunity costs unless health improvement in SCTP recipients is valued at 18 times that of other potential spending beneficiaries or the donor values broader outcomes than health alone. This work illustrates the potential of a cross-sectoral economic evaluation to guide debate about stakeholder contributions to the SCTP, and the value judgements required to favour the SCTP above other policy options.

ObjectiveTo evaluate the cost-effectiveness of a personalised medicine strategy for Crohn’s disease in the UK, using early targeted top-down therapy compared to standard of care.Materials & MethodsA decision tree leading into a Markov state-transition model was constructed, allowing comparison of two treatment approaches: 1) standard of care therapy following established UK clinical guidelines (‘step-up’ treatment) and 2) a personalised medicine strategy in which patients identified as high-risk of subsequent relapse using a prognostic biomarker receive ‘top-down’ anti-TNF treatment at diagnosis. The model facilitated comparison of both costs and Quality Adjusted Life Years (QALYs) in a hypothetical cohort of newly diagnosed Crohn’s disease patients with sensitivity analyses undertaken to model the impact of key assumptions.ResultsEarly personalised treatment with anti-TNF based combination therapy resulted in an incremental cost-effectiveness ratio (ICER) of £2,176 per quality-adjusted life year (QALY), with £717 incremental costs and 0.330 incremental QALYs, substantially below the NICE cost-effectiveness threshold of between £20,000 and £30,000 per QALY. Additional costs relating to earlier biologic use were offset by incremental QALYS and reductions in costs driven by fewer disease flares and hospitalisations. Sensitivity analysis across a wide range of parameter assumptions did not impact on the model’s conclusion.ConclusionA personalised medicine strategy using anti-TNF therapy at diagnosis in Crohn’s disease to patients at high risk of subsequent relapse is highly likely to be a cost-effective use of resources in the UK National Health Service.KEY SUMMARYEstablished KnowledgeCurrently there are no validated prognostic test that can stratify IBD patients based on long term outcomes at the point of diagnosis used routinely in the UKIt therefore remains unclear which patients with Crohn’s disease should be treated with early anti-TNF based therapy as part of a ‘top-down’ regimen.As a consequence, the majority of IBD patients in the UK are currently treated with an accelerated step-up approachSignificant new findingsWe show here that the use of biomarkers at diagnosis to guide personalised use of such treatment is a cost-effective approach for treatment of Crohn’s disease.Use of a prognostic test to deliver personalised medicine for Crohn’s disease results in positive QALY of 0.330The approach is cost effective with an incremental cost of £717 and an ICER of 2,176The model’s conclusions were unaffected by a wide range of sensitivity analyses

Cost-effectiveness analysis, routinely used in health care to inform funding decisions, can be extended to consider impact on health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates socioeconomic differences in model parameters to capture how an intervention would affect both overall population health and differences in health between population groups. In DCEA, uncertainty analysis can consider the decision uncertainty around on both impacts (i.e., the probability that an intervention will increase overall health and the probability that it will reduce inequality). Using an illustrative example assessing smoking cessation interventions (2 active interventions and a “no-intervention” arm), we demonstrate how the uncertainty analysis could be conducted in DCEA to inform policy recommendations. We perform value of information (VOI) analysis and analysis of covariance (ANCOVA) to identify what additional evidence would add most value to the level of confidence in the DCEA results. The analyses were conducted for both national and local authority-level decisions to explore whether the conclusions about decision uncertainty based on the national-level estimates could inform local policy. For the comparisons between active interventions and “no intervention,” there was no uncertainty that providing the smoking cessation intervention would increase overall health but increase inequality. However, there was uncertainty in the direction of both impacts when comparing between the 2 active interventions. VOI and ANCOVA show that uncertainty in socioeconomic differences in intervention effectiveness and uptake contributes most to the uncertainty in the DCEA results. This suggests potential value of collecting additional evidence on intervention-related inequalities for this evaluation. We also found different levels of decision uncertainty between settings, implying that different types and levels of additional evidence are required for decisions in different localities.

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]

Public health decision makers value interventions for their effects on overall health and health inequality. Distributional cost-effectiveness analysis (DCEA) incorporates health inequality concerns into economic evaluation by accounting for how parameters, such as effectiveness, differ across population groups. A good understanding of how and when accounting for socioeconomic differences between groups affects the assessment of intervention impacts on overall health and health inequality could inform decision makers where DCEA would add most value. We interrogated 2 DCEA models of smoking and alcohol policies using first national level and then local authority level information on various socioeconomic differences in health and intervention use. Through a series of scenario analyses, we explored the impact of altering these differences on the DCEA results. When all available evidence on socioeconomic differences was incorporated, provision of a smoking cessation service was estimated to increase overall health and increase health inequality, while the screening and brief intervention for alcohol misuse was estimated to increase overall health and reduce inequality. Ignoring all or some socioeconomic differences resulted in minimal change to the estimated impact on overall health in both models; however, there were larger effects on the estimated impact on health inequality. Across the models, there were no clear patterns in how the extent and direction of socioeconomic differences in the inputs translated into the estimated impact on health inequality. Modifying use or coverage of either intervention so that each population group matched the highest level improved the impacts to a greater degree than modifying intervention effectiveness. When local level socioeconomic differences were considered, the magnitude of the impacts was altered; in some cases, the direction of impact on inequality was also altered.

In low- and middle-income countries (LMICs), making the best use of scarce resources is essential to achieving universal health coverage. The design of health benefits packages creates the opportunity to select interventions on the basis of explicit objectives. Distributional cost-effectiveness analysis (DCEA) provides a framework to evaluate interventions based on two objectives: increasing population health and reducing health inequality. We conduct aggregate DCEA of potential health benefits package interventions to demonstrate the feasibility of this approach in LMICs, using the case of the Malawian health benefits package. We use publicly available survey and census data common to LMICs and describe what challenges we encountered and how we addressed them. We estimate that diseases targeted by the health benefits package are most prevalent in the poorest population quintile and least prevalent in the richest quintile. The survey data we use indicate socioeconomic patterns in intervention uptake that diminish the population health gain and inequality reduction from the package. We find that a similar set of interventions would be prioritized when impact on health inequality is incorporated alongside impact on overall population health. However, conclusions about the impact of individual interventions on health inequalities are sensitive to assumptions regarding the health opportunity cost, the utilization of interventions, the distribution of diseases across population groups and the level of aversion to inequality. Our results suggest that efforts to improve access to the Essential Health Package could be targeted to specific interventions to improve the health of the poorest fastest but that identifying these interventions is uncertain. This exploratory work has shown the potential for applying the DCEA framework to inform health benefits package design within the LMIC setting and to provide insight into the equity impact of a health benefits package.

Governments and health technology assessment agencies are putting greater focus on and efforts in understanding and addressing health inequities. Cost-effectiveness analyses are used to evaluate the costs and health gains of different interventions to inform the decision-making process on funding of new treatments. Distributional cost-effectiveness analysis (DCEA) is an extension of cost-effectiveness analysis that quantifies the equity impact of funding new treatments. Key challenges for the routine and consistent implementation of DCEA are the lack of clearly defined equity concerns from decision makers and endorsed measures to define equity subgroups and the availability of evidence that allows analysis of differences in data inputs associated with the equity characteristics of interest. In this article, we detail the data gaps and challenges to build robust DCEA analysis routinely in health technology assessment and suggest actions to overcome these hurdles.

Microinjected transgenes, including bacterial artificial chromosomes (BACs), insert randomly in the mouse genome. Traditional methods of mapping a transgene are challenging, thus complicating breeding strategies and the accurate interpretation of phenotypes, particularly when a transgene disrupts critical coding or noncoding sequences. Here, we introduce CRISPR-Cas9 long-read sequencing (CRISPR-LRS) to ascertain transgene integration locus and estimated copy number. This method revealed integration loci for both a BAC and Cre-driver line, and estimated the copy numbers for two other BAC mouse lines. CRISPR-LRS offers an easy approach to establish robust breeding practices and accurate phenotyping of most any transgenic mouse line.

Background Following the publication of the National Stroke Audit in 2015 with below the national average rates of thrombolysis – 3.3% versus 11% - there has been a number of initiatives launched to improve stroke care in this hospital. In 2018 we enrolled in the National Quality Improvement Project for the care of Patients with Acute Ischaemic Stroke run by the RCSI. A stroke steering committee was established consisting of a multi-disciplinary group encompassing all areas of acute stroke care. Methods Acute stroke care practice and factors causing sub-optimal management were examined by the committee and compared with national standards. A ‘3 jobs’ proforma for management and communication of Fast positive cases was designed to address the difficulties identified in stroke care and tailored to the resources available in this hospital. These simplified and standardised roles for all staff members involved, many of whom were unfamiliar with the practical delivery of thrombolysis and thrombectomy. Educational sessions were initiated for all those involved in acute stroke management. The acute stroke program was implemented as a 6 month pilot before official launch in April 2019 with necessary changes assessed weekly by the stroke committee. Stroke data is continually audited with the National Stroke Register. Key Performance Indicators (KPI’s) in the pilot were thrombolysis/thrombectomy rates and door to needle time. Results There were 164 patients admitted to the stroke unit in 2018. In 2018 prior to this initiative, the hospital had a 3% thrombolysis rate in 2018 and a 1% thrombectomy rate. Amongst FAST positive patients during the pilot period, 10/68 patients were thombolysed (15%) and 6/68 thrombectomies (9%). Average door to needle time for patients was 96 minutes. Conclusion The redesigning of the acute stroke care program has led to significant improvements in the identified KPI’s although door to needle times remain below the national target of 30 minutes.

In most societies, resources are distributed by individuals acting in markets and by governments through some form of collective decision-making process. Economic evaluation offers a set of tools to inform collective decisions by examining the resource requirements and outcomes of alternative policies. The ‘societal perspective’ has been advocated, but less consideration has been given to what this should include and its practical implementation. This paper presents a framework for economic evaluation of policies with costs and outcomes falling on different sectors (e.g. health, criminal justice, education) and involving different decision makers. It extends the ‘impact inventory’ developed by the Second Panel on Cost-Effectiveness in Health and Medicine by considering all affected individuals and reflecting how outcomes attributed to an intervention can be compared with outcomes forgone as a result of resources not being available for other purposes. The framework sets out the series of assessments to be made, distinguishing points at which value judgements feed into the evaluation, and the implications of alternative judgements. These assessments reflect the institutional arrangements of public bodies, for example, their funding, the outcomes they consider important and their relative valuations of these outcomes. By avoiding the use of an abstract ‘societal perspective’, the contribution of the framework is to inform multiple decision makers with different objectives and provide practical guidance on overall societal impact. Electronic supplementary material The online version of this article (10.1007/s40258-019-00481-8) contains supplementary material, which is available to authorized users.

The National Institute for Health and Care Excellence (NICE) invited Pfizer, the manufacturer of inotuzumab ozogamicin (henceforth inotuzumab), to submit clinical- and cost-effectiveness evidence for inotuzumab as part of NICE's single technology appraisal process. The Centre for Reviews and Dissemination and the Centre for Health Economics, both at the University of York, were commissioned as the independent evidence review group (ERG). The clinical-effectiveness data were from a multicentre randomised controlled trial that compared inotuzumab with standard of care (SoC), where SoC was the investigator's choice of chemotherapy. Inotuzumab demonstrated statistically significant improvements in response rates or in the proportion of patients progressing to haematopoietic stem cell transplant (HSCT) but failed to meet the second primary objective of longer overall survival. Treatment-emergent adverse events were more frequent in the SoC arm, except veno-occlusive disease, which was more frequent in the inotuzumab arm. The company's economic model split patients into three post-hoc subgroups and used a partitioned survival approach within each group, with a cure assumption 3 years after receiving HSCT. In contrast with the trial results, the economic model estimated substantial improvement in survival with inotuzumab compared with SoC, providing an additional 5.2 life-years and 2.2 quality-adjusted life-years (QALYs) using a discount rate of 1.5% per annum. The ERG's critique highlighted a number of concerns, including the use of a post-hoc post-randomisation patient subset for extrapolation, the choice of a 1.5% discount rate, the complexity of the parametric modelling, the assumption of further treatment benefit post-HSCT, the nature of the cure assumption, and the length of inpatient stay while receiving treatment. The combination of the ERG's adjustments resulted in an incremental cost-effectiveness ratio (ICER) of £122,174 per QALY gained using Kaplan-Meier survival estimates and £114,078 per QALY gained with parametric survival models fit to the trial data. The final determination of the appraisal followed four NICE Appraisal Committee meetings, an appeal by the company and other stakeholders, two patient access schemes, and a company response to each appraisal consultation. The final ICER post-consultation was between £33,749 and £37,497 per QALY gained compared with SoC (excluding the confidential discount for blinatumomab received as subsequent therapy). The Appraisal Committee concluded that the ICER for inotuzumab was within the range usually considered cost effective for end-of-life care and recommended inotuzumab within its licensed indication.

Background High-throughput non-invasive prenatal testing (NIPT) for fetal Rhesus D (RhD) status could avoid unnecessary treatment with anti-D immunoglobulin for RhD-negative women found to be carrying an RhD-negative fetus. We aimed to assess the diagnostic accuracy of high-throughput NIPT for fetal RhD status in RhD-negative women not known to be sensitized to the RhD antigen, by performing a systematic review and meta-analysis. Methods Prospective cohort studies of high-throughput NIPT used to determine fetal RhD status were included. The eligible population were pregnant women who were RhD negative and not known to be sensitized to RhD antigen. The index test was high-throughput, NIPT cell-free fetal DNA tests of maternal plasma used to determine fetal RhD status. The reference standard considered was serologic cord blood testing at birth. Databases including MEDLINE, EMBASE, and Science Citation Index were searched up to February 2016. Two reviewers independently screened titles and abstracts and assessed full texts identified as potentially relevant. Risk of bias was assessed using QUADAS-2. The bivariate and hierarchical summary receiver-operating characteristic (HSROC) models were fitted to calculate summary estimates of sensitivity, specificity, false positive and false negative rates, and the associated 95% confidence intervals (CIs). Results A total of 3921 references records were identified through electronic searches. Eight studies were included in the systematic review. Six studies were judged to be at low risk of bias. The HSROC models demonstrated high diagnostic performance of high-throughput NIPT testing for women tested at or after 11 weeks gestation. In the primary analysis for diagnostic accuracy, women with an inconclusive test result were treated as having tested positive. The false negative rate (incorrectly classed as RhD negative) was 0.34% (95% CI 0.15 to 0.76) and the false positive rate (incorrectly classed as RhD positive) was 3.86% (95% CI 2.54 to 5.82). There was limited evidence for non-white women and multiple pregnancies. Conclusions High-throughput NIPT is sufficiently accurate to detect fetal RhD status in RhD-negative women and would considerably reduce unnecessary treatment with routine anti-D immunoglobulin. The applicability of these findings to non-white women and women with multiple pregnancies is uncertain. Electronic supplementary material The online version of this article (10.1186/s12916-019-1254-4) contains supplementary material, which is available to authorized users.

BackgroundOne-fifth of conveyances to the emergency department (ED) are due to acute-on-chronic breathlessness. Paramedic breathlessness management may ease distress quicker and/or reduce ED conveyances. We evaluated the feasibility of a full trial of a paramedic delivered intervention to reduce avoidable conveyances (recruitment, randomisation, consent, training and intervention acceptability, adherence, data quality, best primary outcome, sample size estimation). The intervention comprised evidence-based non-drug techniques and a self-management booklet.MethodsThis mixed-methods feasibility cluster randomised controlled trial (ISRCTN80330546) with embedded qualitative study about trial processes, training and intervention delivery, randomised paramedics to usual care or to intervention+usual care. Retrospective patient consent to use call-out data and prospective patient/carer consent for follow-up was sought. Potential primary outcomes were breathlessness intensity (numerical rating scale) and ED conveyance. Follow-up included an interview for patients/carers and questionnaires at 14 days, 1 and 6 months and paramedic focus groups and survey.ResultsRecruitment was during the COVID-19 pandemic, leading to high demands on paramedics and fewer call-outs by eligible patients. We enrolled 29 paramedics; 9 withdrew. Randomisation/trial procedures were acceptable. Paramedics recruited 13 patients; 8 were followed up. Data quality was good. The intervention did not extend call-out time, was delivered with fidelity and no contamination and was acceptable to patients, carers, and paramedics. There were no repeat call-outs < 48 hours. Recruitment stop-go criteria were not met. We had insufficient data for sample size estimation.ConclusionsA full trial in the same circumstances is not feasible. However, valuable information was gained on recruitment, attrition, consent, training and intervention acceptability and adherence, and patient-reported data collection.

Introduction This study examines the cost-effectiveness of PredictSURE in guiding the early use of biologic therapy in newly diagnosed CD patients, at high-risk of requiring early and frequent treatment escalations in the UK. PredictSURE IBD™ is a 17-gene, whole blood-based qPCR-based classifier that predicts long-term outcome in IBD, enabling early personalised treatment strategies through the early use of biologics in high-risk patients. Methods A decision tree leading into a Markov state-transition model was constructed in MS Excel to compare two treatment approaches: 1) standard of care therapy following established UK clinical guidelines, consisting of sequences of immunomodulator followed by biologic upon relapse (‘step-up’ treatment), 2) targeted therapy guided by PredictSURE, whereby patients identified as high-risk receive sequences of anti-TNF biologic treatment followed by other biologic classes upon relapse (‘top-down’ treatment), figure 1. Parameters were informed by patient data from PredictSURE clinical studies and the literature. Results Top-down treatment guided by PredictSURE resulted in an incremental cost-effectiveness ratio (ICER) of £7,179 per quality-adjusted life year (QALY), with £1,852 incremental costs and 0.258 incremental QALYs vs. standard of care generated over a 15-year time horizon. Additional costs relating to earlier biologic use were offset by reductions in the costs of flares, hospitalisations and surgery. Incremental QALYs were driven by increased time spent in remission and improved quality of life from reduced flares and surgery. The model was most sensitive to the time horizon, rates of mucosal healing on top-down vs. step-up therapy, the costs of hospitalisation and the costs and quality of life in the severe disease health state. Conclusion Modelling shows that upfront use of biologic guided by PredictSURE could substantially improve clinical outcomes for high-risk patients by increasing remission rates and reducing flares, surgery and treatment escalations. The ICER for PredictSURE was well below the £20-£30 k/QALY threshold used by the UK National Institute for Health and Care Excellence (NICE). Top-down treatment guided by PredictSURE would not only represent a treatment paradigm shift for CD patients but would also be a highly cost-effective use of resources in the UK National Health Service.

INTRODUCTION: As well as improving population health, promoting equity in health is one of the key goals of health policy in low- and middle-income countries (LMICs). However, it is less clear how equity is defined, or how it may impact on resource allocation decisions. We investigated the degree to which health inequalities have been considered in economic evaluation of health interventions in LMICs, and what demographic or socioeconomic characteristics were used to define equity relevant subgroups. AREAS COVERED: We reviewed publications since 2010 from three main databases following the search strategy developed by including the key terms 'health inequalities/health disparities/health equity,' 'economics' and 'low- and middle-income countries' in the title or abstract. Twelve studies were identified, mainly focusing on interventions for the more vulnerable groups such as children and women. EXPERT OPINION: Some attempts have been made to assess interventions' impact on health inequality and there is increasing interest in evaluating it, although research in this area is lacking. Population subgroups highlighted in the included studies were those differing in socioeconomic status. Most studies reported the results across subgroups to illustrate inequality impact, and the newly developed methods, extended cost-effectiveness analysis and distributional cost-effectiveness analysis, have also been applied.

Cost-effectiveness analyses of health care programs often focus on maximizing health and ignore nonhealth impacts. Assessing the cost-effectiveness of public health interventions from a narrow health care perspective would likely underestimate their full impact, and potentially lead to inefficient decisions about funding. The aim of this study is to provide a practical application of a recently proposed framework for the economic evaluation of public health interventions, evaluating an intervention to reduce alcohol misuse in criminal offenders. This cross-sectoral analysis distinguishes benefits and opportunity costs for different sectors, makes explicit the value judgments required to consider alternative perspectives, and can inform heterogeneous decision makers with different objectives in a transparent manner. Three interventions of increasing intensity are compared: client information leaflet, brief advice, and brief lifestyle counseling. Health outcomes are measured in quality-adjusted life-years and criminal justice outcomes in reconvictions. Costs considered include intervention costs, costs to the NHS and costs to the criminal justice system. The results are presented for four different perspectives: "narrow" health care perspective; criminal justice system perspective; "full" health care perspective; and joint "full" health and criminal justice perspective. Conclusions and recommendations differ according to the normative judgment on the appropriate perspective for the evaluation.

This chapter illustrates how value of information analysis can be applied to standard results of meta-analysis to inform a number of important policy questions, using a case study of corticosteroids after traumatic brain injury. Value of information analysis provides a framework to assess the need for additional evidence, the consequences of uncertainty, and the relative priority of alternative topics for research prioritization decisions. It can be used to establish: (1) whether the evidence currently available is sufficient to support the use of the intervention in practice, i.e., whether on balance the evidence from the meta-analysis suggests that the intervention achieves better outcomes than the available alternatives; (2) whether additional evidence might be needed to resolve uncertainty in the range of plausible values; (3) what type and design of research might resolve this uncertainty; (4) whether it would be better to withhold the intervention until additional evidence becomes available; and (5) whether any proposed research is considered a priority relative to other topics competing for the same resources.

This chapter introduces the basic measurement concepts needed to understand and critically appraise distributional cost-effective analysis (DCEA) findings about equity impacts and trade-offs. It starts by introducing the key questions that research users need to ask about DCEA findings, drawing on basic concepts from the literature on measuring inequality and social welfare. It then introduces two diagrams for summarizing trade-offs between equity and efficiency impacts. Close attention is paid to the value judgments underpinning different equity impact metrics, with a view to helping research users engage in well-informed conversations about those value judgments.

Distributional cost-effectiveness analysis (DCEA) aims to help healthcare and public health organizations make fairer decisions with better outcomes. Standard cost-effectiveness analysis provides information about total costs and effects. DCEA provides additional information about fairness in the distribution of costs and effects—who gains, who loses, and by how much. It can also provide information about the trade-offs that sometimes occur between efficiency objectives: improving total health and equity objectives, such as reducing unfair inequality in health. This chapter introduces DCEA and provides an overview of the book contents. After summarizing the motivation, aims, and scope of the book, it concludes with a section on how to use the book and which chapters are likely to be useful for which audiences. Chapters 1, 2, 3, and 4 introduce the basic concepts and are likely to be useful to research producers and research users alike, so we recommend reading these first before turning to specific methods chapters.

This chapter covers methods for describing how lack of knowledge impacts on the conduct and findings of distributional cost-effectiveness analysis (DCEA). It also sets out methods for describing how different value judgments can alter the findings. It explains why and how to distinguish uncertainty about facts from heterogeneity in values, and the role of each in informing decisions. It shows how the standard tools of uncertainty analysis in economic evaluation—including deterministic and probabilistic sensitivity analysis, and value of information analysis—can be applied to DCEA to provide information about uncertainty in the estimated health distributions and summary measures of equity impact. The chapter also shows how to use deterministic sensitivity analyses to investigate the implications of alternative value judgments and inequality metrics for DCEA findings and recommendations.

This chapter is a guide to designing a distributional cost-effective analysis (DCEA) for research commissioners and producers. It describes the main components of DCEA and how they can be combined in different ways to inform decision-making in various contexts. The appropriate design of a DCEA will depend on the objectives of the relevant decision-making authorities; the political, economic, and institutional environment, and the prevailing social norms in the population being served; as well as the resources available for the analysis. We start by reviewing key questions about the decision-making context and illustrate how the answers to those questions can shape the study design, using our training example of nicotine replacement therapy in England. We then present the equity-efficiency impact plane, a visual tool for illustrating the relationship between the impact of a decision on efficiency (e.g. net health benefit) and equity (e.g. reduction in unfair health inequality). We then go through the main components of DCEA, including simple, low-cost forms of equity analysis that may be useful when detailed bespoke modelling of distributional consequences is not a practical option.

Highlights Distributional Cost-Effectiveness Analysis (DCEA) provides information about the equity impacts of health technologies and programmes It can provide distributional breakdowns of effects and opportunity costs by equity-relevant social variables and/or disease categories It can also use equity weights to summarise equity impacts and analyse trade-offs between equity and efficiency This editorial describes recent developments in DCEA New training resources and professional networks are provided

Introduction\ud \ud Reduction of health inequality is a goal in health policy, but commissioners lack information on how policies change health inequality. This study illustrates how decision models can be readily extended to produce information on health inequality impacts as well as for population health, using the example of smoking cessation therapies.\ud \ud \ud Methods\ud \ud We retrospectively adapt a model developed for public health guidance to undertake distributional cost effectiveness analysis. We identify and incorporate evidence on how inputs vary by area-level deprivation. Therapies are evaluated in terms of total population health, extent of inequality, and a summary measure of equally distributed equivalent health based on a societal value for inequality aversion. Last, we examine how accounting for social variation in different sets of parameters affects our results.\ud \ud \ud Results\ud \ud All interventions increase population health and increase the slope index of inequality. At estimated levels of health inequality aversion for England, our results indicate that the increases in inequality are compensated by the health gains.\ud \ud \ud Discussion\ud \ud The inequality impacts are driven by higher benefits of quitting and higher intervention uptake amongst advantaged groups, despite the greater proportion of smokers in disadvantaged groups. Failure to account for differential effects between groups leads to different conclusions about health inequality impact but does not alter conclusions about value for money.

BACKGROUND: We aimed to identify a five-fraction schedule of adjuvant radiotherapy (radiation therapy) delivered in 1 week that is non-inferior in terms of local cancer control and is as safe as an international standard 15-fraction regimen after primary surgery for early breast cancer. Here, we present 5-year results of the FAST-Forward trial.METHODS: FAST-Forward is a multicentre, phase 3, randomised, non-inferiority trial done at 97 hospitals (47 radiotherapy centres and 50 referring hospitals) in the UK. Patients aged at least 18 years with invasive carcinoma of the breast (pT1-3, pN0-1, M0) after breast conservation surgery or mastectomy were eligible. We randomly allocated patients to either 40 Gy in 15 fractions (over 3 weeks), 27 Gy in five fractions (over 1 week), or 26 Gy in five fractions (over 1 week) to the whole breast or chest wall. Allocation was not masked because of the nature of the intervention. The primary endpoint was ipsilateral breast tumour relapse; assuming a 2% 5-year incidence for 40 Gy, non-inferiority was predefined as ≤1·6% excess for five-fraction schedules (critical hazard ratio [HR] of 1·81). Normal tissue effects were assessed by clinicians, patients, and from photographs. This trial is registered at isrctn.com, ISRCTN19906132.FINDINGS: Between Nov 24, 2011, and June 19, 2014, we recruited and obtained consent from 4096 patients from 97 UK centres, of whom 1361 were assigned to the 40 Gy schedule, 1367 to the 27 Gy schedule, and 1368 to the 26 Gy schedule. At a median follow-up of 71·5 months (IQR 71·3 to 71·7), the primary endpoint event occurred in 79 patients (31 in the 40 Gy group, 27 in the 27 Gy group, and 21 in the 26 Gy group); HRs versus 40 Gy in 15 fractions were 0·86 (95% CI 0·51 to 1·44) for 27 Gy in five fractions and 0·67 (0·38 to 1·16) for 26 Gy in five fractions. 5-year incidence of ipsilateral breast tumour relapse after 40 Gy was 2·1% (1·4 to 3·1); estimated absolute differences versus 40 Gy in 15 fractions were -0·3% (-1·0 to 0·9) for 27 Gy in five fractions (probability of incorrectly accepting an inferior five-fraction schedule: p=0·0022 vs 40 Gy in 15 fractions) and -0·7% (-1·3 to 0·3) for 26 Gy in five fractions (p=0·00019 vs 40 Gy in 15 fractions). At 5 years, any moderate or marked clinician-assessed normal tissue effects in the breast or chest wall was reported for 98 of 986 (9·9%) 40 Gy patients, 155 (15·4%) of 1005 27 Gy patients, and 121 of 1020 (11·9%) 26 Gy patients. Across all clinician assessments from 1-5 years, odds ratios versus 40 Gy in 15 fractions were 1·55 (95% CI 1·32 to 1·83, pINTERPRETATION: 26 Gy in five fractions over 1 week is non-inferior to the standard of 40 Gy in 15 fractions over 3 weeks for local tumour control, and is as safe in terms of normal tissue effects up to 5 years for patients prescribed adjuvant local radiotherapy after primary surgery for early-stage breast cancer.FUNDING: National Institute for Health Research Health Technology Assessment Programme.

Background. A common aim of health expenditure is to reduce unfair inequalities in health. Although previous research has attempted to estimate the total health effects of changes in health expenditure, little is known about how changes affect different groups in the population. Methods. We propose a general framework for disaggregating the total health effects of changes in health expenditure by social groups. This can be performed indirectly when the estimate of the total health effect has first been disaggregated by a secondary factor (e.g., disease area) that can be linked to social characteristics. This is illustrated with an application to the English National Health Service. Evidence on the health effects of expenditure across 23 disease areas is combined with data on the distribution of disease-specific hospital utilization by age, sex, and area-level deprivation. Results. We find that the health effects from NHS expenditure changes are produced largely through disease areas in which individuals from more deprived areas account for a large share of health care utilization, namely, respiratory and neurologic disease and mental health. We estimate that 26% of the total health effect from a change in expenditure would accrue to the fifth of the population living in the most deprived areas, compared with 14% to the fifth living in the least deprived areas. Conclusions. Our approach can be useful for evaluating the health inequality impacts of changing health budgets or funding alternative health programs. However, it requires robust estimates of how health expenditure affects health outcomes. Our example analysis also relied on strong assumptions about the relationship between health care utilization and health effects across population groups.

Alternative strategies can reduce road vehicle emissions, with differential effects on exposure across population groups. We compare alternative strategies in West Yorkshire using a framework for economic evaluation that considers multiple perspectives and that takes account of the distribution of health outcomes. Exposure to pollutants by area is converted, via dose response relationships, into disease averted. Health benefits and National Health Service costs from diseases are estimated conditional on population demographics and index of multiple deprivation. The net health benefits from alternative strategies are expressed as distributions of quality-adjusted life expectancy (QALE), which are compared using dominance criteria and societal aversion to health inequality. Net production is estimated from intervention costs and the effects of health improvement on production and consumption. Social care outcomes are estimated from health improvement among care recipients and changes in care expenditure. A switch to less polluting private vehicles is dominant in terms of the distribution of QALE and social care outcomes but not consumption. Inclusion of health inequality aversion alters the rank order compared with prioritisation on health maximisation. The results were sensitive to the magnitude of health opportunity costs, the level of inequality aversion, and the proportion of intervention cost that generates health opportunity cost.

Purpose The purpose of this paper is to determine the impact of having a patient-designated caregiver remain overnight with ambulatory extended recovery patients on early postoperative clinical outcomes. Design/methodology/approach This was a retrospective cohort study of patients undergoing surgery requiring overnight stay in a highly resourced free-standing oncology ambulatory surgery center. Postoperative outcomes in patients who had caregivers stay with them overnight were compared with outcomes in those who did not. All other care was standardized. Primary outcomes were postoperative length of stay, hospital readmission rates, urgent care center (UCC) visits within 30 days and perioperative complication rates. Findings Among patients staying overnight, 2,462 (57 percent) were accompanied by overnight caregivers. In this group, time to discharge was significantly lower. Readmissions (though rare) were slightly higher, though the difference was not statistically significant (p=0.059). No difference in early ( Originality/value This study is unique in its evaluation of the clinical impact of having a caregiver stay overnight with ambulatory surgery patients. Little research has focused on the direct impact of the caregiver on patient outcomes, especially in the ambulatory setting. With increased adoption of minimally invasive surgical techniques and enhanced recovery pathways, a larger number of patients are eligible for short-stay ambulatory surgery. Factors that impact discharge and early postoperative complications are important.

Unfair differences in healthcare access, utilisation, quality or health outcomes exist between and within countries around the world. Improving health equity is a stated objective for many governments and international organizations. We provide an overview of the major tools that have been developed to measure, evaluate and promote health equity, along with the data required to operationalise them.Methods are organised into four key policy questions facing decision-makers: (i) what is the current level of inequity in health; (ii) does government health expenditure benefit the worst-off; (iii) can government health expenditure more effectively promote equity; and (iv) which interventions provide the best value for money in reducing inequity.Benefit incidence analysis can be used to estimate the distribution of current public health sector expenditure, with geographical resource allocation formulae and health system reform being the main government policy levers for improving equity. Techniques from the economic evaluation literature, such as extended and distributional cost-effectiveness analysis can be used to identify ‘best buy’ interventions from a health equity perspective. A range of inequality metrics, from gap measures and slope indices to concentration indices and regression analysis, can be applied to these approaches to evaluate changes in equity.Methods from the economics literature can provide policymakers with a toolkit for addressing multiple aspects of health equity, from outcomes to financial protection, and can be adapted to accommodate data commonly available in low- and middle-income settings.

The Centre for Reviews and Dissemination and Centre for Health Economics Technology Assessment Group at the University of York was commissioned by the National Institute for Health and Care Excellence (NICE) Highly Specialised Technologies (HST) programme to act as the independent Evidence Review Group (ERG) for an appraisal of Strimvelis ®, a gene therapy treatment for adenosine deaminase deficiency-severe combined immunodeficiency (ADA-SCID). This paper describes the manufacturing company's submission of clinical and economic evidence, the ERG's review and the resulting NICE guidance. For Strimvelis ® compared with haematopoietic stem cell transplant (HSCT) from a matched unrelated donor (MUD) and HSCT from a haploidentical donor, the company base-case deterministic incremental cost-effectiveness ratios (ICERs) were £36,360 and £14,645 per quality-adjusted life-year (QALY) gained, respectively (using a discount rate of 1.5%). Although overall survival in patients receiving Strimvelis ® was substantially higher than historical comparator data on HSCT from a MUD or haploidentical donor, the ERG was concerned that the estimated treatment benefit remained highly uncertain. The ERG critiqued some assumptions in the cost-effectiveness model, including that all patients return to general population mortality and morbidity after a successful procedure; that all patients receive a matched sibling donor following an unsuccessful engraftment; and that differences in wait times exist between the treatments. Incorporating a number of changes to the model, the ERG's base-case ICERs were £86,815 per QALY gained for Strimvelis ® compared with HSCT from a MUD and £16,704 per QALY gained compared with HSCT from a haploidentical donor (using a discount rate of 1.5%). The ICER for Strimvelis ® compared with HSCT from a MUD was highly sensitive to the difference in procedural mortality and could exceed NICE's £100,000 per QALY gained threshold for HSTs, if HSCT survival rates have improved since the most recent data. The evaluation committee concluded that the most plausible ICERs were lower than £100,000 per QALY gained and that Strimvelis ® should be recommended for treatment of ADA-SCID where a matched related donor is unavailable.

OBJECTIVE: To evaluate the cost-effectiveness of high-throughput, non-invasive prenatal testing (HT-NIPT) for fetal RhD genotype to guide antenatal prophylaxis with anti-D immunoglobulin compared to routine antenatal anti-D immunoglobulin prophylaxis (RAADP). DESIGN: Cost-effectiveness decision-analytic modelling. SETTING: Primary care. PARTICIPANTS: A simulated population of 100,000 RhD negative women not known to be sensitised to the RhD antigen. METHODS: A decision tree model was used to characterise the antenatal care pathway in England and the long-term consequences of sensitisation events. The diagnostic accuracy of HT-NIPT was derived from a systematic review and bivariate meta-analysis; estimates of other inputs were derived from relevant literature sources and databases. Women in whom the HT-NIPT was positive or inconclusive continued to receive RAADP, while women with a negative result received none. Five alternative strategies in which the use of HT-NIPT may affect the existing post-partum care pathway were considered. MAIN OUTCOME MEASURES: Costs expressed in 2015GBP and impact on health outcomes expressed in terms of quality adjusted life years (QALYs) over a lifetime. RESULTS: The results suggested that HT-NIPT appears cost saving but also less effective than current practice, irrespective of the post-partum strategy evaluated. A post-partum strategy in which inconclusive test results are distinguished from positive results performed best. HT-NIPT is only cost-effective when the overall test cost is £26.60 or less. CONCLUSIONS: HT-NIPT would reduce unnecessary treatment with routine anti-D immunoglobulin and is cost saving when compared to current practice. The extent of any savings and cost-effectiveness is sensitive to the overall test cost. This article is protected by copyright. All rights reserved.