An eye on equity: faricimab-driven health equity improvements in diabetic macular oedema using a distributional cost-effectiveness analysis from a UK societal perspective.

BACKGROUND/OBJECTIVES: Diabetic macular oedema (DMO) is a leading cause of blindness in developed countries, with significant disease burden associated with socio-economic deprivation. Distributional cost-effectiveness analysis (DCEA) allows evaluation of health equity impacts of interventions, estimation of how health outcomes and costs are distributed in the population, and assessments of potential trade-offs between health maximisation and equity. We conducted an aggregate DCEA to determine the equity impact of faricimab. METHODS: Data on health outcomes and costs were derived from a cost-effectiveness model of faricimab compared with ranibizumab, aflibercept and off-label bevacizumab using a societal perspective in the base case and a healthcare payer perspective in scenario analysis. Health gains and health opportunity costs were distributed across socio-economic subgroups. Health and equity impacts, measured using the Atkinson inequality index, were assessed visually on an equity-efficiency impact plane and combined into a measure of societal welfare. RESULTS: At an opportunity cost threshold of £20,000/quality-adjusted life year (QALY), faricimab displayed an increase in net health benefits against all comparators and was found to improve equity. The equity impact increased the greater the concerns for reducing health inequalities over maximising population health. Using a healthcare payer perspective, faricimab was equity improving in most scenarios. CONCLUSIONS: Long-acting therapies with fewer injections, such as faricimab, may reduce costs, improve health outcomes and increase health equity. Extended economic evaluation frameworks capturing additional value elements, such as DCEA, enable a more comprehensive valuation of interventions, which is of relevance to decision-makers, healthcare professionals and patients.

Distributional Cost-Effectiveness Analysis of Treatments for Non-Small Cell Lung Cancer: An Illustration of an Aggregate Analysis and its Key Drivers.

BACKGROUND AND OBJECTIVE: Distributional cost-effectiveness analysis (DCEA) facilitates quantitative assessments of how health effects and costs are distributed among population subgroups, and of potential trade-offs between health maximisation and equity. Implementation of DCEA is currently explored by the National Institute for Health and Care Excellence (NICE) in England. Recent research conducted an aggregate DCEA on a selection of NICE appraisals; however, significant questions remain regarding the impact of the characteristics of the patient population (size, distribution by the equity measure of interest) and methodologic choices on DCEA outcomes. Cancer is the indication most appraised by NICE, and the relationship between lung cancer incidence and socioeconomic status is well established. We aimed to conduct an aggregate DCEA of two non-small cell lung cancer (NSCLC) treatments recommended by NICE, and identify key drivers of the analysis. METHODS: Subgroups were defined according to socioeconomic deprivation. Data on health benefits, costs, and target populations were extracted from two NICE appraisals (atezolizumab versus docetaxel [second-line treatment following chemotherapy to represent a broad NSCLC population] and alectinib versus crizotinib [targeted first-line treatment to represent a rarer mutation-positive NSCLC population]). Data on disease incidence were derived from national statistics. Distributions of population health and health opportunity costs were taken from the literature. A societal welfare analysis was conducted to assess potential trade-offs between health maximisation and equity. Sensitivity analyses were conducted, varying a range of parameters. RESULTS: At an opportunity cost threshold of £30,000 per quality-adjusted life-year (QALY), alectinib improved both health and equity, thereby increasing societal welfare. Second-line atezolizumab involved a trade-off between improving health equity and maximising health; it improved societal welfare at an opportunity cost threshold of £50,000/QALY. Increasing the value of the opportunity cost threshold improved the equity impact. The equity impact and societal welfare impact were small, driven by the size of the patient population and per-patient net health benefit. Other key drivers were the inequality aversion parameters and the distribution of patients by socioeconomic group; skewing the distribution to the most (least) deprived quintile improved (reduced) equity gains. CONCLUSION: Using two illustrative examples and varying model parameters to simulate alternative decision problems, this study suggests that key drivers of an aggregate DCEA are the opportunity cost threshold, the characteristics of the patient population, and the level of inequality aversion. These drivers raise important questions in terms of the implications for decision making. Further research is warranted to examine the value of the opportunity cost threshold, capture the public's views on unfair differences in health, and estimate robust distributional weights incorporating the public's preferences. Finally, guidance from health technology assessment organisations, such as NICE, is needed regarding methods for DCEA construction and how they would interpret and incorporate those results in their decision making.

Mapping analysis to predict SF-6D utilities from health outcomes in people with focal epilepsy.

BACKGROUND: Focal-onset seizures (FOS) are commonly experienced by people with epilepsy and have a significant impact on quality of life (QoL). This study aimed to develop a mapping algorithm to predict SF-6D values in adults with FOS for use in economic evaluations of a new treatment, cenobamate. METHODS: An online survey, including questions on disease history, SF-36, and an epilepsy-specific measure (QOLIE-31-P) was administered to people with FOS in the UK, France, Italy, Germany, and Spain. A range of regression models were fitted to SF-6D scores including direct and response mapping approaches. RESULTS: 361 individuals were included in the analysis. In the previous 28 days, the mean number of FOS experienced was 3, (range 0-43) and the mean longest period of consecutive days without experiencing a seizure was 14 days (range 1-28 days or more). Mean responses on all SF-36 dimensions were lower than general population norms. Mean SF-6D and QOLIE-31-P scores were 0.584 and 45.72, respectively. The best performing model was the ordinary least squares (OLS), with root mean squared error and mean absolute error values of 0.0977 and 0.0742, respectively. Explanatory variables which best predicted SF-6D included seizure frequency, severity, freedom, and age. CONCLUSION: People with uncontrolled FOS have poor QoL. The mapping algorithm enables the prediction of SF-6D values from clinical outcomes in people with FOS. It can be applied to outcome data from clinical trials to facilitate cost-utility analysis.

Distributional Cost-Effectiveness Analysis of Health Technologies: Data Requirements and Challenges.

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.

Cost Consequence Analysis of the Management of Hyperkalemia by Patiromer and Optimization of Renin-Angiotensin-Aldosterone System Inhibitors Therapy in Chronic Kidney Disease Patients in Saudi Arabia.

Renin-angiotensin-aldosterone system inhibitors (RAASi) have been shown to improve outcomes in chronic kidney disease (CKD) patients but are associated with an increased risk of hyperkalemia in this vulnerable population. Hyperkalemia often leads to patients' downtitrating or discontinuing RAASi which can result in sub-optimal health outcomes. The objective is to evaluate the cost and health benefits of maintaining normokalemia using patiromer, an oral potassium binder while optimizing RAASi therapy in CKD patients in the Kingdom of Saudi Arabia. The medium-to long-term costs and health outcomes of patients with CKD stage 3-4 and raised serum potassium levels (≥5.5 mmol/L) at baseline were estimated, from a Saudi Arabia payer perspective, using a Markov state-transition model simulating the natural progression of CKD depending on patients' serum potassium level and usage of RAASi at different dosages. The analysis demonstrated that appropriate management of hyperkalemia, enabling optimization of RAASi, leads to cost and health benefits. The cost of patiromer is offset by 68% due to a reduction in management costs associated with CKD progression, hyperkalemia-related hospitalization, and cardiovascular (CV) events. Over a 10-year time horizon, a pool of 300 patients treated with patiromer experience increased life-expectancy [+3.78 life-years (LYs)] and slower disease progression, with decreased time spent in end-stage renal disease (-9.59 LYs). Patiromer may deliver value to both CKD patients and payers in Saudi Arabia, leading to better health outcomes for the former and reduced cost of management of CKD progression and CV events at low additional costs for the latter.

Indirect and Direct Mapping of the Cancer-Specific EORTC QLQ-C30 onto EQ-5D-5L Utility Scores.

OBJECTIVE: The aim of this study was to develop a response mapping algorithm to predict EQ-5D-5L utilities from European Organisation for Research and Treatment Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) scores and compare performance with direct mapping approaches to identify the best performing algorithm. METHODS: The Multi-Instrument Comparison dataset contains responses to both the EQ-5D-5L and QLQ-C30 questionnaires from 692 individuals with a broad range of cancers. Response mapping was conducted, fitting ordered logistic regressions to predict response levels for each of the five EQ-5D dimensions and utilities were predicted using the US and Japanese EQ-5D-5L value sets to test the algorithm performance. Various direct mapping models were fitted: ordinary least squares, tobit, two-part (TPM), adjusted limited dependent variable mixture and beta mixture models. Model assessment and recommendations regarding the best mapping algorithm was based on goodness-of-fit statistics, predictive ability (measures of error, distribution of predicted utilities) and in sample cross-validation. RESULTS: The response mapping model performed well in terms of predictive ability and measurement error using the US or Japanese value set, with mean absolute error ranging from 0.0708 to 0.0988, and comparably to the TPM, which was the best performing direct algorithm. CONCLUSION: The developed mapping algorithms enable the prediction of EQ-5D-5L utilities from QLQ-C30 scores when EQ-5D-5L data have not been directly collected in clinical trials. The response mapping model offers the possibility of predicting EQ-5D-5L utility values using any national value set and can be generalised to multiple countries and oncology settings.

HTA methodology and value frameworks for evaluation and policy making for cell and gene therapies.

This last decade has been marked by significant advances in the development of cell and gene (C&G) therapies, such as gene targeting or stem cell-based therapies. C&G therapies offer transformative benefits to patients but present a challenge to current health technology decision-making systems because they are typically reviewed when clinical efficacy data are very limited and when there is uncertainty about the long-term durability of outcomes. These challenges are not unique to C&G therapies, but they face more of these barriers, reflecting the need for adapting existing value assessment frameworks. Still, C&G therapies have the potential to be cost-effective even at very high price points. The impact on healthcare budgets will depend on the success rate of pipeline assets and on the extent to which C&G therapies will expand to wider pathologies beyond rare or ultra-rare diseases. Getting pricing and reimbursement models right is important for incentivising research and development investment while not jeopardising the sustainability of healthcare systems. Payers and manufacturers therefore need to acknowledge each other's constraints-limitations in the evidence generation on the manufacturer side, budget considerations on the payer side-and embrace innovative thinking and approaches to ensure timely delivery of therapies to patients. Several experts in health technology assessment and clinical experts have worked together to produce this publication and identify methodological and policy options to improve the assessment of C&G therapies, and make it happen better, faster and sustainably in the coming years.

DNA adduct formation among workers in a Thai industrial estate and nearby residents.

The genotoxic effects of air pollutant exposures have been studied in people living and working in Map Ta Phut, Rayong province, Thailand, a site where is located the Map Ta Phut Industrial Estate (MIE) one of the largest steel, refinery and petrochemical complex in the South-Eastern Asia. This was done by the conduction of a transversal study aimed to compare the prevalence of bulky DNA adducts in groups of subjects experiencing various degree of air pollution. DNA adduct analysis was performed in the leukocytes of 201 volunteers by the (32)P-postlabelling assay: 79 were workers in the MIE complex, including 24 refinery workers, 40 steel workers and 15 tinplate workers, 72 were people residing downwind in the MIE area and 50 were residents in a control district of the same Rayong province but without industrial exposures. The groups of workers were analyzed separately to evaluate if DNA adduct formation differs by the type of industry. The levels of bulky DNA adducts were 1.17+/-0.17 (SE) adducts/10(8) nucleotides in refinery workers, 1.19+/-0.19 (SE) in steel workers, 0.87+/-0.17 (SE) in tinplate workers, 0.85+/-0.07 (SE) in MIE residents and 0.53+/-0.05 (SE) in district controls. No effects of smoking habits on DNA adducts was found. The multivariate regression analysis shows that the levels of DNA adducts were significantly increased among the individuals living near the MIE industrial complex in respect to those resident in a control district (p<0.05). In the groups of occupationally exposed workers, the highest levels of DNA adducts were found among the workers experiencing an occupational exposure to polycyclic aromatic hydrocarbons, e.g. the steel factory and refinery workers. When we have evaluated if the levels of DNA adducts of the PAH exposed workers were different from those of the MIE residents, a statistical significantly difference was found (p<0.05). Our present study indicates that people living near point sources of industrial air pollution can experiment an excess of DNA adduct formation. The emissions from the MIE complex are the main source of air pollution in this area and can be the cause of such increment in the levels of DNA damage.