Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--7

The Lebanese health economic evaluation guideline

BACKGROUND

Economic evaluation guidelines (EEGs) serve as a valuable tool to assist appraisers in making consistent and transparent recommendations, standardize EE studies, enhance their quality, and minimize methodological uncertainties. As other LMICs, Lebanon aims for UHC where EEG is a necessity. This paper aims to report on the Lebanese health EEG (LEEG) and its reference case, including the intermediate results leading to the final decisions.‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬.

RESEARCH DESIGN AND METHODS

The LEEG followed a structured, systematic, and transparent process: (1) identifying the rationale and the guideline scope; (2) establishing the Guideline Development Group; (3) searching the evidence; (4) planning the development process; (5) selecting the panel for the deliberative process; (6) surveying Lebanese stakeholders; (7) deliberating on the results; (8) drafting the guideline; and (9) consulting with international experts.

RESULTS

The LEEG includes three general characteristics, 19 key features, a reference case, and an action plan.

CONCLUSIONS

The LEEG is the first national EEG for health interventions. It will help decision-makers, researchers, and healthcare providers improve the quality and assessment of EE in Lebanon to identify the most cost-effective health interventions. Implementing LEEG is crucial to promoting an equitable, efficient, and high-quality health system with a more consistent decision-making process.

New IPECAD Open-Source Model Framework for the Health Technology Assessment of Early Alzheimer's Disease Treatment: Development and Use Cases

OBJECTIVES

Reimbursement decisions for new Alzheimer's disease (AD) treatments are informed by economic evaluations. An open-source model with intuitive structure for model cross-validation can support the transparency and credibility of such evaluations. We describe the new International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model framework (version 2) for the health-economic evaluation of early AD treatment and use it for cross-validation and addressing uncertainty.

METHODS

A cohort state-transition model using a categorized composite domain (cognition and function) was developed by replicating an existing reference model and testing it for internal validity. Then, features of existing Institute for Clinical and Economic Review (ICER) and Alzheimer's Disease Archimedes Condition-Event Simulator (AD-ACE) models assessing lecanemab treatment were implemented for model cross-validation. Additional uncertainty scenarios were performed on choice of efficacy outcome from trial, natural disease progression, treatment effect waning and stopping rules, and other methodological choices. The model is available open-source as R code, spreadsheet, and web-based version via https://github.com/ronhandels/IPECAD.

RESULTS

In the IPECAD model incremental life-years, quality-adjusted life-years (QALY) gains and cost savings were 21% to 31% smaller compared with the ICER model and 36% to 56% smaller compared with the AD-ACE model. IPECAD model results were particularly sensitive to assumptions on treatment effect waning and stopping rules and choice of efficacy outcome from trial.

CONCLUSIONS

We demonstrated the ability of a new IPECAD open-source model framework for researchers and decision makers to cross-validate other (Health Technology Assessment submission) models and perform additional uncertainty analyses, setting an example for open science in AD decision modeling and supporting important reimbursement decisions.

IPECAD Modeling Workshop 2023 Cross-Comparison Challenge on Cost-Effectiveness Models in Alzheimer's Disease

OBJECTIVES

Decision-analytic models assessing the value of emerging Alzheimer's disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD.

METHODS

A benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop.

RESULTS

Nine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (clinical dementia rating - sum of boxes, clinical dementia rating - global, mini-mental state examination, functional activities questionnaire) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6 to 5.2 years for control strategy and from 0.1 to 1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0 to 0.6 and incremental costs (excluding treatment costs) from -US$66 897 to US$11 896.

CONCLUSIONS

Trial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend (1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, (2) a standardized reporting table for model predictions, and (3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health-economic models.

Cost-Effectiveness Analysis of Idursulfase for the Long-Term Treatment of Hunter Syndrome Using a Partitioned-Survival Model Approach in R

OBJECTIVES

Hunter syndrome is among the costliest life-long genetic conditions associated with a substantial burden-of-illness and a significant impact on the health systems, families, and society. We estimated the cost-effectiveness of long-term enzyme replacement therapy with idursulfase versus the standard of care from a societal perspective using a streamlined modeling strategy in R.

METHODS

A de novo 4-state partitioned survival model was developed to compare lifetime cost and outcomes of 2 care models operationalized in R. The disease progression was based on independent survival modeling of relevant Kaplan-Meier data. The healthcare and out-of-pocket costs were drawn from the local setting. The quality of life was measured using the EQ5D5L and the time trade-off valuation of health-state vignettes that match the states in the model. Probabilistic and deterministic sensitivity analyses were conducted to test the uncertainty around the model results.

RESULTS

The lifetime incremental quality-adjusted life years were 4.1 years (95% CI, 2.37-5.68). Incremental costs were estimated to be $9.5 million (95% CI, 9.0 million-10.0 million), which primarily consists of drug costs (99%). The incremental costs per quality-adjusted life year were estimated to be approximately $2.4 million (95% CI, 1.7 million-3.8 million). Sensitivity analyses showed that the key drivers of incremental cost-effectiveness ratio were quality of life in the preprogression state and differential discounting approach, besides the acquisition cost of enzyme replacement therapy of idursulfase.

CONCLUSIONS

The incremental cost-effectiveness ratios were beyond any conventionally used cost-effectiveness threshold in all cases. At the current price, there is a significant discrepancy between the therapy's funding decision and the cost-effectiveness assessment as a basis for guiding healthcare prioritization in Malaysia.

Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report

OBJECTIVES

To provide an introduction to the uses of generative artificial intelligence (AI) and foundation models, including large language models, in the field of health technology assessment (HTA).

METHODS

We reviewed applications of generative AI in 3 areas: systematic literature reviews, real-world evidence, and health economic modeling.

RESULTS

(1) Literature reviews: generative AI has the potential to assist in automating aspects of systematic literature reviews by proposing search terms, screening abstracts, extracting data, and generating code for meta-analyses; (2) real-world evidence: generative AI can facilitate automating processes and analyze large collections of real-world data, including unstructured clinical notes and imaging; (3) health economic modeling: generative AI can aid in the development of health economic models, from conceptualization to validation. Limitations in the use of foundation models and large language models include challenges surrounding their scientific rigor and reliability, the potential for bias, implications for equity, as well as nontrivial concerns regarding adherence to regulatory and ethical standards, particularly in terms of data privacy and security. Additionally, we survey the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.

CONCLUSIONS

Although generative AI technology holds promise with respect to HTA applications, it is still undergoing rapid developments and improvements. Continued careful evaluation of their applications to HTA is required. Both developers and users of research incorporating these tools, should familiarize themselves with their current capabilities and limitations.

A Systematic Literature Review of Modelling Approaches to Evaluate the Cost Effectiveness of PET/CT for Therapy Response Monitoring in Oncology

BACKGROUND AND OBJECTIVE

This systematic literature review addresses model-based cost-effectiveness studies for therapy response monitoring with positron emission tomography (PET) generally combined with low-dose computed tomography (CT) for various cancer types. Given the known heterogeneity in therapy response events, studies should consider patient-level modelling rather than cohort-based modelling because of its flexibility in handling these events and the time to events. This review aims to identify the modelling methods used and includes a systematic assessment of the assumptions made in the current literature.

METHODS

This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Information sources included electronic bibliographic databases, reference lists of review articles and contact with experts in the fields of nuclear medicine, health technology assessment and health economics. Eligibility criteria included peer-reviewed scientific publications and published grey literature. Literature searches, screening and critical appraisal were conducted by two reviewers independently. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to assess the methodological quality. The Bias in Economic Evaluation (ECOBIAS) checklist was used to determine the risk of bias in the included publications.

RESULTS

The search results included 2959 publications. The number of publications included for data extraction and synthesis was ten, representing eight unique studies. These studies addressed patients with lymphoma, advanced head and neck cancers, brain tumours, non-small cell lung cancer and cervical cancer. All studies addressed response to chemotherapy. No study evaluated response to immunotherapy. Most studies positioned PET/CT as an add-on modality and one study positioned PET/CT as a replacement for conventional imaging (X-ray and contrast-enhanced CT). Three studies reported decision-tree structures, four studies reported cohort-level state-transition models and one study reported a partitioned survival model. No patient-level models were reported. The simulation horizons adopted ranged from 1 year to lifetime. Most studies reported a probabilistic analysis, whereas two studies reported a deterministic analysis only. Two studies conducted a value of information analysis. Multiple studies did not adequately discuss model-specific aspects of bias. Most importantly and regularly observed were a high risk of structural assumptions bias, limited simulation horizon bias and wrong model bias.

CONCLUSIONS

Model-based cost-effectiveness analysis for therapy response monitoring with PET/CT was based on cohorts of patients instead of individual patients in the current literature. Therefore, the heterogeneity in therapy response events was commonly not addressed appropriately. Further research should include more advanced and patient-level modelling approaches to accurately represent the complex context of clinical practice and, therefore, to be meaningful to support decision making.

REGISTRATION

This review is registered in PROSPERO, the international prospective register of systematic reviews funded by the National Institute for Health Research, with CRD42023402581.

Contagious acquisition of antimicrobial resistance is critical for explaining emergence in western Canadian feedlots-insights from an agent-based modelling tool

Introduction

Antimicrobial resistance (AMR) is a growing threat to the efficacy of antimicrobials in humans and animals, including those used to control bovine respiratory disease (BRD) in high-risk calves entering western Canadian feedlots. Successful mitigation strategies require an improved understanding of the epidemiology of AMR. Specifically, the relative contributions of antimicrobial use (AMU) and contagious transmission to AMR emergence in animal populations are unknown.

Materials and methods

A stochastic, continuous-time agent-based model (ABM) was developed to explore the dynamics of population-level AMR in Mannheimia haemolytica in pens of high-risk cattle on a typical western Canadian feedlot. The model was directly informed and parameterized with proprietary data from partner veterinary practices and AMU/AMR surveillance data where possible. Hypotheses about how AMR emerges in the feedlot environment were represented by model configurations in which detectable AMR was impacted by (1) only selection arising from AMU; (2) only transmission between animals in the same pen; and (3) both AMU-linked selection and transmission. Automated calibration experiments were used to estimate unknown parameters of interest for select antimicrobial classes. Calibrated parameter values were used in a series of Monte Carlo experiments to generate simulated outputs at both the pen and feedlot levels. Key model outputs included the prevalence of AMR by class at multiple time points across the feeding period. This study compared the relative performances of these model configurations with respect to reproducing empirical AMR data.

Results

Across all antimicrobial classes of interest, model configurations which included the potential for contagious acquisition of AMR offered stronger fits to the empirical data. Notably, sensitivity analyses demonstrated that model outputs were more robust to changes in the assumptions underscoring AMU than to those affecting the likelihood of transmission.

Discussion

This study establishes a feedlot simulation tool that can be used to explore questions related to antimicrobial stewardship in the context of BRD management. The ABM stands out for its unique hierarchical depiction of AMR in a commercial feedlot and its grounding in robust epidemiological data. Future experiments will allow for both AMU-linked selection and transmission of AMR and can accommodate parameter modifications as required.

Cost-effectiveness models of non-small cell lung cancer: A systematic literature review

BACKGROUND

Non-small cell lung cancer (NSCLC) presents a formidable global health challenge owing to significant morbidity, high mortality rates, and substantial economic burden. Recent advances in targeted therapies and immunotherapies have transformed NSCLC treatment, but efficacy varies across patients. Tailoring treatment to patients can improve outcomes and potentially improve cost-effectiveness (ie, value for money) as well. For NSCLC, cost-effectiveness must often be estimated using economic modeling, and estimates are only as good as the models. Existing cost-effectiveness models are not necessarily suitable for evaluating personalized medicines.

OBJECTIVE

To identify and assess cost-effectiveness models of NSCLC.

METHODS

We searched for studies indexed in PubMed and Embase from 2012 to October 2023 that described cost-effectiveness models of NSCLC. Study details were extracted, summarized, and evaluated for adherence to the Consolidated Health Economic Evaluation Reporting Standards.

RESULTS

We identified 237 unique models, 40% of which were published in 2022 or 2023. Despite cross-model heterogeneity, most models used the same 3 health states (progression-free survival, progressive disease, and death) combined with time-to-event equations that characterize risks. Thirty models included a diagnostic component, most of which considered guiding treatment selection using biomarkers. Adherence to the overall Consolidated Health Economic Evaluation Reporting Standards checklist was generally incomplete, and adherence to a subset of model-related questions even more so.

CONCLUSIONS

The large number of models that were found, almost half of which were published since 2022, underscores the importance of cost-effectiveness analysis in NSCLC. Variable adherence to best practices suggests opportunities for improvement, however, and making high-quality, open-source models available to researchers may be valuable.

Projecting the Population Level Burden of CKD Progression According to Urine Albumin-to-Creatinine Ratio Categories

Introduction

Urinary albumin-to-creatinine ratio (uACR) is an independent predictor of chronic kidney disease (CKD) progression. However there is limited evidence on the burden of CKD according to uACR categories at the population level. This study estimates future clinical and financial burden of CKD according to uACR categories using the Inside CKD microsimulation.

Methods

The Inside CKD model is an individual patient level microsimulation that emulates national populations based on demographic, epidemiological, and economic data. The analysis estimates clinical and economic outcomes over time according to the Kidney Disease: Improving Global Outcomes (KDIGO) uACR categories (A1-A3) at a population level for 31 countries and regions.

Results

CKD populations (diagnosed and undiagnosed individuals, stages G3-G5) were projected to be predominantly within uACR categories A1 and A2 in 2022. Projected cumulative incidence of CKD stage transitions (disease progression) and cardio-renal complications (heart failure, myocardial infarction, stroke, and all-cause mortality) occurred mostly in uACR categories A1 and A2 between 2022 and 2027. Patients in uACR categories A1 and A2, who represent the largest proportion of patients with CKD, were projected to incur most of the health care costs associated with CKD management and cardio-renal complications for the diagnosed population (prevalence 2027).

Conclusion

This study highlights the disproportionate population-level clinical and economic burden associated with individuals within KDIGO uACR categories A1 and A2, who represent most of the CKD population. This awareness will help health care decision makers to appropriately allocate resources and interventions to the CKD population, including those with mild to moderately increased albuminuria, to reduce clinical and economic burden associated with CKD.

Improving Transparency of Decision Models Through the Application of Decision Analytic Models with Omitted Objects Displayed (DAMWOOD)

The coronavirus disease 2019 (COVID-19) pandemic has increased public awareness of the influence of epidemiological and economic decision models on public policy decisions. Alongside this is an increased scrutiny on the development, analysis, reporting and utilisation of decision models for public policy making. Therefore, it is important that model developers can clearly explain and justify to all stakeholders what is included and excluded from a model developed to support decision-making, to both improve transparency and trust in decision-making. Our aim is to provide tools for improving communication between modellers and decision-makers, leading to improved transparency in decision-making. To do so, we extend the recently described directed acyclic graphs with omitted objects displayed (DAGWOOD) approach from Haber et al. (Ann Epidemiol 68:64-71, 2022) to decision analytic models, giving the decision analytic models with omitted objects displayed (DAMWOOD) approach. DAMWOOD is a framework for the identification of objects omitted from a decision model, as well as for consideration of the effects of omissions on model outcomes. Objects omitted from a decision model are classed as either an exclusion (known and unknown confounders), misdirection (alternative model pathways) or structure (e.g. model type, methods for estimating relationships between objects). DAMWOOD requires model developers to use explicit statements and provide illustration of included and omitted objects, supporting communication with model users and stakeholders, allowing them to provide input and feedback to modellers about which objects to include or omit in a model. In developing DAMWOOD, we considered two challenges we encountered in modelling for pandemic policy response. First, the scope of the decision problem is not always made sufficiently explicit by decision-makers, requiring modellers to intuit which policy options should be considered, and/or which outcomes should be considered in their evaluation. Second, there is rarely sufficient transparency to ensure stakeholders can see what is included in models and why. This limits stakeholders' ability to advocate to decision-makers for the prioritisation of specific outcomes and challenge the model results. To illustrate the application of DAMWOOD, we apply it to a previously published COVID-19 vaccine allocation optimisation model. The DAMWOOD diagrams illustrate the ways in which it is possible to improve the communication of model assumptions. The diagrams make explicit which outcomes are omitted and provide information on the expected impact of the omissions on model results. We discuss the usefulness of DAMWOOD for framing the decision problem, communicating the model structure and results and engaging with those making and affected by the decisions the model is developed to inform.

Cost-effectiveness of mass drug administration for control of scabies in Ethiopia: a decision-analytic model

Background

The strategies to control scabies in highly endemic populations include individual case/household management and mass drug administration (MDA). We used a decision-analytic model to compare ivermectin-based MDA and individual case/household management (referred to as "usual care") for control of scabies in Ethiopia at different prevalence thresholds for commencing MDA.

Methods

A decision-analytic model was based on a repeated population survey conducted in Northern Ethiopia in 2018-2020, which aimed to evaluate the secondary impact of single-dose ivermectin MDA for the control of onchocerciasis on scabies prevalence. The model estimates the number of scabies cases and costs of two treatment strategies (MDA and usual care) based on their effectiveness, population size, scabies prevalence, compliance with MDA, medication cost, and other parameters.

Results

In the base-case analysis with a population of 100,000 and scabies prevalence of 15%, the MDA strategy was both more effective and less costly than usual care. The probability of MDA being cost-effective at the current cost-effectiveness threshold (equivalent to the cost of usual care) was 85%. One-way sensitivity analyses showed that the MDA strategy remained dominant (less costly and more effective) in 22 out of 26 scenarios. MDA was not cost-effective at scabies prevalence <10%, MDA effectiveness <85% and population size <5,000. An increase in the cost of ivermectin from 0 (donated) to 0.54 US$/dose resulted in a decrease in the probability of MDA being cost-effective from 85% to 17%. At 0.25 US$/dose, the MDA strategy was no longer cost-effective.

Conclusions

The model provides robust estimates of the costs and outcomes of MDA and usual care and can be used by decision-makers for planning and implementing scabies control programmes. Results of our analysis suggest that single-dose ivermectin MDA is cost-effective in scabies control and can be initiated at a scabies prevalence >10%.

Cost-Effectiveness of Plasma Microbial Cell-Free DNA Sequencing When Added to Usual Care Diagnostic Testing for Immunocompromised Host Pneumonia

INTRODUCTION

Immunocompromised host pneumonia (ICHP) is an important cause of morbidity and mortality, yet usual care (UC) diagnostic tests often fail to identify an infectious etiology. A US-based, multicenter study (PICKUP) among ICHP patients with hematological malignancies, including hematological cell transplant recipients, showed that plasma microbial cell-free DNA (mcfDNA) sequencing provided significant additive diagnostic value.

AIM

The objective of this study was to perform a cost-effectiveness analysis (CEA) of adding mcfDNA sequencing to UC diagnostic testing for hospitalized ICHP patients.

METHODS

A semi-Markov model was utilized from the US third-party payer's perspective such that only direct costs were included, using a lifetime time horizon with discount rates of 3% for costs and benefits. Three comparators were considered: (1) All UC, which included non-invasive (NI) and invasive testing and early bronchoscopy; (2) All UC & mcfDNA; and (3) NI UC & mcfDNA & conditional UC Bronch (later bronchoscopy if the initial tests are negative). The model considered whether a probable causative infectious etiology was identified and if the patient received appropriate antimicrobial treatment through expert adjudication, and if the patient died in-hospital. The primary endpoints were total costs, life-years (LYs), equal value life-years (evLYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio per QALY. Extensive scenario and probabilistic sensitivity analyses (PSA) were conducted.

RESULTS

At a price of $2000 (2023 USD) for the plasma mcfDNA, All UC & mcfDNA was more costly ($165,247 vs $153,642) but more effective (13.39 vs 12.47 LYs gained; 10.20 vs 9.42 evLYs gained; 10.11 vs 9.42 QALYs gained) compared to All UC alone, giving a cost/QALY of $16,761. NI UC & mcfDNA & conditional UC Bronch was also more costly ($162,655 vs $153,642) and more effective (13.19 vs 12.47 LYs gained; 9.96 vs 9.42 evLYs gained; 9.96 vs 9.42 QALYs gained) compared to All UC alone, with a cost/QALY of $16,729. The PSA showed that above a willingness-to-pay threshold of $50,000/QALY, All UC & mcfDNA was the preferred scenario on cost-effectiveness grounds (as it provides the most QALYs gained). Further scenario analyses found that All UC & mcfDNA always improved patient outcomes but was not cost saving, even when the price of mcfDNA was set to $0.

CONCLUSIONS

Based on the evidence available at the time of this analysis, this CEA suggests that mcfDNA may be cost-effective when added to All UC, as well as in a scenario using conditional bronchoscopy when NI testing fails to identify a probable infectious etiology for ICHP. Adding mcfDNA testing to UC diagnostic testing should allow more patients to receive appropriate therapy earlier and improve patient outcomes.

Validating Health Economic Models With the Probabilistic Analysis Check dashBOARD

OBJECTIVES

Health economic (HE) models are often considered as "black boxes" because they are not publicly available and lack transparency, which prevents independent scrutiny of HE models. Additionally, validation efforts and validation status of HE models are not systematically reported. Methods to validate HE models in absence of their full underlying code are therefore urgently needed to improve health policy making. This study aimed to develop and test a generic dashboard to systematically explore the workings of HE models and validate their model parameters and outcomes.

METHODS

The Probabilistic Analysis Check dashBOARD (PACBOARD) was developed using insights from literature, health economists, and a data scientist. Functionalities of PACBOARD are (1) exploring and validating model parameters and outcomes using standardized validation tests and interactive plots, (2) visualizing and investigating the relationship between model parameters and outcomes using metamodeling, and (3) predicting HE outcomes using the fitted metamodel. To test PACBOARD, 2 mock HE models were developed, and errors were introduced in these models, eg, negative costs inputs, utility values exceeding 1. PACBOARD metamodeling predictions of incremental net monetary benefit were validated against the original model's outcomes.

RESULTS

PACBOARD automatically identified all errors introduced in the erroneous HE models. Metamodel predictions were accurate compared with the original model outcomes.

CONCLUSIONS

PACBOARD is a unique dashboard aiming at improving the feasibility and transparency of validation efforts of HE models. PACBOARD allows users to explore the working of HE models using metamodeling based on HE models' parameters and outcomes.

Cost-effectiveness of applying high-sensitivity troponin I to a score for cardiovascular risk prediction in asymptomatic population

INTRODUCTION

Risk stratification scores such as the European Systematic COronary Risk Evaluation (SCORE) are used to guide individuals on cardiovascular disease (CVD) prevention. Adding high-sensitivity troponin I (hsTnI) to such risk scores has the potential to improve accuracy of CVD prediction. We investigated how applying hsTnI in addition to SCORE may impact management, outcome, and cost-effectiveness.

METHODS

Characteristics of 72,190 apparently healthy individuals from the Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project were included into a discrete-event simulation comparing two strategies for assessing CVD risk. The standard strategy reflecting current practice employed SCORE (SCORE); the alternative strategy involved adding hsTnI information for further stratifying SCORE risk categories (S-SCORE). Individuals were followed over ten years from baseline examination to CVD event, death or end of follow-up. The model tracked the occurrence of events and calculated direct costs of screening, prevention, and treatment from a European health system perspective. Cost-effectiveness was expressed as incremental cost-effectiveness ratio (ICER) in € per quality-adjusted life year (QALYs) gained during 10 years of follow-up. Outputs were validated against observed rates, and results were tested in deterministic and probabilistic sensitivity analyses.

RESULTS

S-SCORE yielded a change in management for 10.0% of individuals, and a reduction in CVD events (4.85% vs. 5.38%, p<0.001) and mortality (6.80% vs. 7.04%, p<0.001). S-SCORE led to 23 (95%CI: 20-26) additional event-free years and 7 (95%CI: 5-9) additional QALYs per 1,000 subjects screened, and resulted in a relative risk reduction for CVD of 9.9% (95%CI: 7.3-13.5%) with a number needed to screen to prevent one event of 183 (95%CI: 172 to 203). S-SCORE increased costs per subject by 187€ (95%CI: 177 € to 196 €), leading to an ICER of 27,440€/QALY gained. Sensitivity analysis was performed with eligibility for treatment being the most sensitive.

CONCLUSION

Adding a person's hsTnI value to SCORE can impact clinical decision making and eventually improves QALYs and is cost-effective compared to CVD prevention strategies using SCORE alone. Stratifying SCORE risk classes for hsTnI would likely offer cost-effective alternatives, particularly when targeting higher risk groups.

The Spillover Effects of Extending Liver Transplantation to Patients with Colorectal Liver Metastases: A Discrete Event Simulation Analysis

BACKGROUND

Liver transplantation is an alternative treatment for patients with nonresectable colorectal cancer liver-only metastases (CRLM); however, the potential effects on wait-list time and life expectancy to other patients on the transplant waiting list have not been considered. We explored the potential effects of expanding liver transplantation eligibility to include patients with CRLM on wait-list time and life expectancy in Norway.

METHODS

We developed a discrete event simulation model to reflect the Norwegian liver transplantation waiting list process and included 2 groups: 1) patients currently eligible for liver transplantation and 2) CRLM patients. Under 2 alternative CRLM-patient transplant eligibility criteria, we simulated 2 strategies: 1) inclusion of only currently eligible patients (CRLM patients received standard-of-care palliative chemotherapy) and 2) expanding waiting list eligibility to include CRLM patients under 2 eligibility criteria. Model outcomes included median waiting list time, life expectancy, and total life-years.

RESULTS

For every additional CRLM patient listed per year, the overall median wait-list time, initially 52 d, increased by 8% to 11%. Adding 2 additional CRLM patients under the most restrictive eligibility criteria increased the CRLM patients' average life expectancy by 10.64 y and decreased the average life expectancy for currently eligible patients by 0.05 y. Under these assumptions, there was a net gain of 149.61 life-years over a 10-y programmatic period, which continued to increase under scenarios of adding 10 CRLM patients to the wait-list. Health gains were lower under less restrictive CRLM eligibility criteria. For example, adding 4 additional CRLM patients under the less restrictive eligibility criteria increased the CRLM patients' average life expectancy by 5.64 y and decreased the average life expectancy for currently eligible patients by 0.12 y. Under these assumptions, there was a net gain of 96.36 life-years over a 10-y programmatic period, which continued to increase up to 7 CRLM patients.

CONCLUSIONS

Our model-based analysis enabled the consideration of the potential effects of enlisting Norwegian CRLM patients for liver transplantation on wait-list time and life expectancy. Enlisting CRLM patients is expected to increase the total health effects, which supports the implementation of liver transplantation for CRLM patients in Norway.

HIGHLIGHTS

Given the Norwegian donor liver availability, adding patients with nonresectable colorectal cancer liver-only metastases (CRLM) to the liver transplantation waiting list had an overall modest, but varying, impact on total waiting list time.Survival gains for selected CRLM patients treated with liver transplantation would likely outweigh the losses incurred to patients listed currently.To improve the total life-years gained in the population, Norway should consider expanding the treatment options for CRLM patients to include liver transplantation.Other countries may also have an opportunity to gain total life-years by extending the waiting list eligibility criteria; however, country-specific analyses are required.

Model-Based Economic Evaluations of Interventions for Dementia: An Updated Systematic Review and Quality Assessment

BACKGROUND

There has been an increase in model-based economic evaluations of interventions for dementia. The most recent systematic review of economic evaluations for dementia highlighted weaknesses in studies, including lack of justification for model assumptions and data inputs.

OBJECTIVE

This study aimed to update the last published systematic review of model-based economic evaluations of interventions for dementia, including Alzheimer's disease, with a focus on any methodological improvements and quality assessment of the studies.

METHODS

Systematic searches in eight databases, including PubMed, Cochrane, Embase, CINAHL, PsycINFO, EconLit, international HTA database, and the Tufts Cost-Effectiveness Analysis Registry were undertaken from February 2018 until August 2022. The quality of the included studies was assessed using the Philips checklist and the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklist. The findings were summarized through narrative analysis.

RESULTS

This review included 23 studies, comprising cost-utility analyses (87%), cost-benefit analyses (9%) and cost-effectiveness analyses (4%). The studies covered various interventions, including pharmacological (n = 10, 43%), non-pharmacological (n = 4, 17%), prevention (n = 4, 17%), diagnostic (n = 4, 17%) and integrated (n = 1, 4%) [diagnostics-pharmacologic] strategies. Markov transition models were commonly employed (65%), followed by decision trees (13%) and discrete-event simulation (9%). Several interventions from all categories were reported as being cost effective. The quality of reporting was suboptimal for the Methods and Results sections in almost all studies, although the majority of studies adequately addressed the decision problem, scope, and model-type selection in their economic evaluations. Regarding the quality of methodology, only a minority of studies addressed competing theories or clearly explained the rationale for model structure. Furthermore, few studies systematically identified key parameters or assessed data quality, and uncertainty was mostly addressed partially.

CONCLUSIONS

This review informs future research and resource allocation by providing insights into model-based economic evaluations for dementia interventions and highlighting areas for improvement.

Cost-Effectiveness of Lovotibeglogene Autotemcel (Lovo-Cel) Gene Therapy for Patients with Sickle Cell Disease and Recurrent Vaso-Occlusive Events in the United States

BACKGROUND AND OBJECTIVE

Gene therapies for sickle cell disease (SCD) may offer meaningful benefits for patients and society. This study evaluated the cost-effectiveness of lovotibeglogene autotemcel (lovo-cel), a one-time gene therapy administered via autologous hematopoietic stem cell transplantation, compared with common care for patients in the United States (US) with SCD aged ≥ 12 years with ≥ 4 vaso-occlusive events (VOEs) in the past 24 months.

METHODS

We developed a patient-level simulation model accounting for lovo-cel and SCD-related events, complications, and mortality over a lifetime time horizon. The pivotal phase 1/2 HGB-206 clinical trial (NCT02140554) served as the basis for lovo-cel efficacy and safety. Cost, quality-of-life, and other clinical data were sourced from HGB-206 data and the literature. Analyses were conducted from US societal and third-party payer perspectives. Uncertainty was assessed through probabilistic sensitivity analysis and extensive scenario analyses.

RESULTS

Patients treated with lovo-cel were predicted to survive 23.84 years longer on average (standard deviation [SD], 12.80) versus common care (life expectancy, 62.24 versus 38.40 years), with associated discounted patient quality-adjusted life-year (QALY) gains of 10.20 (SD, 4.10) and direct costs avoided of $1,329,201 (SD, $1,346,446) per patient. Predicted societal benefits included discounted caregiver QALY losses avoided of 1.19 (SD, 1.38) and indirect costs avoided of $540,416 (SD, $262,353) per patient. Including lovo-cel costs ($3,282,009 [SD, $29,690] per patient) resulted in incremental cost-effectiveness ratios of $191,519 and $124,051 per QALY gained from third-party payer and societal perspectives, respectively. In scenario analyses, the predicted cost-effectiveness of lovo-cel also was sensitive to baseline age and VOE frequency and to the proportion of patients achieving and maintaining complete resolution of VOEs.

CONCLUSIONS

Our analysis of lovo-cel gene therapy compared with common care for patients in the US with SCD with recurrent VOEs estimated meaningful improvements in survival, quality of life, and other clinical outcomes accompanied by increased overall costs for the health care system and for broader society. The predicted economic value of lovo-cel gene therapy was influenced by uncertainty in long-term clinical effects and by positive spillover effects on patient productivity and caregiver burden.

Critical Examination of Modeling Approaches Used in Economic Evaluations of First-Line Treatments for Locally Advanced or Metastatic Non-Small Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations: A Systematic Literature Review

BACKGROUND

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with up to 32% of patients with NSCLC harboring an epidermal growth factor receptor (EGFR) mutation. NSCLC harboring an EGFR mutation has a dedicated treatment pathway, with EGFR tyrosine kinase inhibitors and platinum-based chemotherapy often being the therapy of choice.

OBJECTIVE

The aim of this study was to systemically review and summarize economic models of first-line treatments used for locally advanced or metastatic NSCLC harboring EGFR mutations, as well as to identify areas for improvement for future models.

METHODS

Literature searches were conducted via Ovid in PubMed, MEDLINE, MEDLINE In-Process, Embase, Evidence-Based Medicine Reviews: Health Technology Assessment, Evidence-Based Medicine Reviews: National Health Service Economic Evaluation Database, and EconLit. An initial search was conducted on 19 December 2022 and updated on 11 April 2023. Studies were selected according to predefined criteria using the Population, Intervention, Comparator, Outcome and Study design (PICOS) framework.

RESULTS

Sixty-seven articles were included in the review, representing 59 unique studies. The majority of included models were cost-utility analyses (n = 52), with the remaining studies being cost-effectiveness analyses (n = 4) and a cost-minimization analysis (n = 1). Two studies incorporated both a cost-utility and cost-minimization analysis. Although the model structure across studies was consistently reported, justification for this choice was often lacking.

CONCLUSIONS

Although the reporting of economic models in NSCLC harboring EGFR mutations is generally good, many of these studies lacked sufficient reporting of justification for structural choices, performing extensive sensitivity analyses and validation in economic evaluations. In resolving such gaps, the validity of future models can be increased to guide healthcare decision making in rare indications.

Healthcare utilisation and economic burden of migraines among bank employees in China: a probabilistic modelling study

BACKGROUND

Despite the recognised high prevalence of migraines among bank employees, yet their healthcare utilisation patterns and the economic burden of migraines remain underexplored.

AIM

To examine migraine-related healthcare utilisation among bank employees in China, and to estimate the economic burden of migraines.

METHODS

A cross-sectional survey was conducted in Guizhou province, China between May and October 2022. The HARDSHIP questionnaire was used to identify migraine-positive individuals and enquire about their healthcare utilisation and productivity losses. A probabilistic decision-analytic model with a micro-costing approach was used to estimate the economic burden from the perspectives of the healthcare system, employers, and society. All costs were expressed in 2022 United States dollars. One-way and probabilistic sensitivity analyses were performed.

RESULTS

Nearly half of individuals with migraines reported not seeking medical care. Only 21.8% reported seeking outpatient consultations, 52.5% reported taking medicines, and 27.1% reported using complementary therapies. Chronic migraine patients had significantly higher healthcare utilisation than episodic migraine patients. Among individuals with a monthly migraine frequency of 15 days or more, 63.6% took inappropriate treatments by excessively using acute medications. Migraines in the banking sector in Guizhou cost the healthcare system a median of $7,578.0 thousand (25th to 75th percentile $4,509.2-$16,434.9 thousand) per year, employers $89,750.3 thousand (25th to 75th percentile $53,211.6-$151,162.2 thousand), and society $108,850.3 thousand (25th to 75th percentile $67,370.1-$181,048.6 thousand). The median societal cost per patient-year is $3,078.1. Migraine prevalence and productivity losses were identified as key cost drivers.

CONCLUSIONS

The study points to the need to raise awareness of migraines across all stakeholders and to improve the organisation of the migraine care system. A substantial economic burden of migraines on the healthcare system, employers, and society at large was highlighted. These cost estimates offer evidence-based benchmarks for assessing economic savings from improved migraine management, and can also draw the attention of Chinese policymakers to prioritise migraine policies within the banking and other office-based occupations.

A Framework for Assessing the Impact of Outbreak Response Immunization Programs

The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.

Cross-model validation of public health microsimulation models; comparing two models on estimated effects of a weight management intervention

BACKGROUND

Health economic modelling indicates that referral to a behavioural weight management programme is cost saving and generates QALY gains compared with a brief intervention. The aim of this study was to conduct a cross-model validation comparing outcomes from this cost-effectiveness analysis to those of a comparator model, to understand how differences in model structure contribute to outcomes.

METHODS

The outcomes produced by two models, the School for Public Health Research diabetes prevention (SPHR) and Health Checks (HC) models, were compared for three weight-management programme strategies; Weight Watchers (WW) for 12 weeks, WW for 52 weeks, and a brief intervention, and a simulated no intervention scenario. Model inputs were standardised, and iterative adjustments were made to each model to identify drivers of differences in key outcomes.

RESULTS

The total QALYs estimated by the HC model were higher in all treatment groups than those estimated by the SPHR model, and there was a large difference in incremental QALYs between the models. SPHR simulated greater QALY gains for 12-week WW and 52-week WW relative to the Brief Intervention. Comparisons across socioeconomic groups found a stronger socioeconomic gradient in the SPHR model. Removing the impact of treatment on HbA1c from the SPHR model, running both models only with the conditions that the models have in common and, to a lesser extent, changing the data used to estimate risk factor trajectories, resulted in more consistent model outcomes.

CONCLUSIONS

The key driver of difference between the models was the inclusion of extra evidence-based detail in SPHR on the impacts of treatments on HbA1c. The conclusions were less sensitive to the dataset used to inform the risk factor trajectories. These findings strengthen the original cost-effectiveness analyses of the weight management interventions and provide an increased understanding of what is structurally important in the models.

Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models

BACKGROUND

Current generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses.

METHODS

The two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We developed prompts which instructed GPT-4 to programme the NSCLC and RCC models in R, and which provided descriptions of each model's methods, assumptions and parameter values. The results of the generated scripts were compared to the published values from the original, human-programmed models. The models were replicated 15 times to capture variability in GPT-4's output.

RESULTS

GPT-4 fully replicated the NSCLC model with high accuracy: 100% (15/15) of the artificial intelligence (AI)-generated NSCLC models were error-free or contained a single minor error, and 93% (14/15) were completely error-free. GPT-4 closely replicated the RCC model, although human intervention was required to simplify an element of the model design (one of the model's fifteen input calculations) because it used too many sequential steps to be implemented in a single prompt. With this simplification, 87% (13/15) of the AI-generated RCC models were error-free or contained a single minor error, and 60% (9/15) were completely error-free. Error-free model scripts replicated the published incremental cost-effectiveness ratios to within 1%.

CONCLUSION

This study provides a promising indication that GPT-4 can have practical applications in the automation of health economic model construction. Potential benefits include accelerated model development timelines and reduced costs of development. Further research is necessary to explore the generalisability of LLM-based automation across a larger sample of models.

Cost-effectiveness of first-line versus second-line use of brigatinib followed by lorlatinib in patients with ALK-positive non-small cell lung cancer

Background

The ALTA-1 L trial and EXP-3B arm of NCT01970865 trial found that both brigatinib and lorlatinib showed durable and robust responses in treating ALK-positive non-small cell lung cancer (NSCLC) patients. However, brigatinib and lorlatinib treatments are costly and need indefinite administration until the disease progression. Thus, it remains uncertain whether using brigatinib followed by lorlatinib before chemotherapy is cost-effective compared to reserving these two drugs until progression after chemotherapy.

Methods

We used a Markov model to assess clinical outcomes and healthcare costs of treating ALK-positive NSCLC individuals with brigatinib followed by lorlatinib before chemotherapy versus a strategy of reserving these drugs until progression after chemotherapy. Transition probabilities were estimated using parametric survival modeling based on multiple clinical trials. The drug acquisition costs, adverse events costs, administration costs were extracted from published studies before and publicly available data. We calculated lifetime direct healthcare costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios from the perspective of a United States payer.

Results

Our base-case analysis indicated that the incremental cost-effectiveness ratios of using first-line brigatinib followed by lorlatinib compared with second-line brigatinib followed by lorlatinib is $-400,722.09/QALY which meant that second-line brigatinib followed by lorlatinib had less costs and better outcomes. Univariate sensitivity analysis indicated the results were most sensitive to the cost of brigatinib. Probability sensitivity analysis revealed that using brigatinib followed by lorlatinib before chemotherapy had a 0% probability of cost-effectiveness versus delaying these two drugs until progression after chemotherapy at a willingness-to-pay threshold of $150,000 per QALY. Sensitivity analyses conducted revealed the robustness of this result, as incremental cost-effectiveness ratios never exceeded the willingness-to-pay threshold.

Conclusion

Using brigatinib as first-line treatment followed by lorlatinib for ALK-positive NSCLC may not be cost-effective given current pricing from the perspective of a United States payer. Delaying brigatinib followed by lorlatinib until subsequent lines of treatment may be a reasonable strategy that could limit healthcare costs without affecting clinical outcomes. More mature data are needed to better estimate cost-effectiveness in this setting.

Cost-Effectiveness of Population-Based Multigene Testing for Breast and Ovarian Cancer Prevention

Importance

The current method of BRCA testing for breast and ovarian cancer prevention, which is based on family history, often fails to identify many carriers of pathogenic variants. Population-based genetic testing offers a transformative approach in cancer prevention by allowing for proactive identification of any high-risk individuals and enabling early interventions.

Objective

To assess the lifetime incremental effectiveness, costs, and cost-effectiveness of population-based multigene testing vs family history-based testing.

Design, Setting, and Participants

This economic evaluation used a microsimulation model to assess the cost-effectiveness of multigene testing (BRCA1, BRCA2, and PALB2) for all women aged 30 to 35 years compared with the current standard of care that is family history based. Carriers of pathogenic variants were offered interventions, such as magnetic resonance imaging with or without mammography, chemoprevention, or risk-reducing mastectomy and salpingo-oophorectomy, to reduce cancer risk. A total of 2000 simulations were run on 1 000 000 women, using a lifetime time horizon and payer perspective, and costs were adjusted to 2022 US dollars. This study was conducted from September 1, 2020, to December 15, 2023.

Main Outcomes and Measures

The main outcome measure was the incremental cost-effectiveness ratio (ICER), quantified as cost per quality-adjusted life-year (QALY) gained. Secondary outcomes included incremental cost, additional breast and ovarian cancer cases prevented, and excess deaths due to coronary heart disease (CHD).

Results

The study assessed 1 000 000 simulated women aged 30 to 35 years in the US. In the base case, population-based multigene testing was more cost-effective compared with family history-based testing, with an ICER of $55 548 per QALY (95% CI, $47 288-$65 850 per QALY). Population-based multigene testing would be able to prevent an additional 1338 cases of breast cancer and 663 cases of ovarian cancer, but it would also result in 69 cases of excess CHD and 10 excess CHD deaths per million women. The probabilistic sensitivity analyses show that the probability that population-based multigene testing is cost-effective was 100%. When the cost of the multigene test exceeded $825, population-based testing was no longer cost-effective (ICER, $100 005 per QALY; 95% CI, $87 601-$11 6323).

Conclusions and Relevance

In this economic analysis of population-based multigene testing, population-based testing was a more cost-effective strategy for the prevention of breast cancer and ovarian cancer when compared with the current family history-based testing strategy at the $100 000 per QALY willingness-to-pay threshold. These findings support the need for more comprehensive genetic testing strategies to identify pathogenic variant carriers and enable informed decision-making for personalized risk management.

Microsimulation Models on Mental Health: A Critical Review of the Literature

OBJECTIVES

To retrieve and synthesize the literature on existing mental health-specific microsimulation models or generic microsimulation models used to examine mental health, and to critically appraise them.

METHODS

All studies on microsimulation and mental health published in English in MEDLINE, Embase, PsycINFO, and EconLit between January 1, 2010, and September 30, 2022, were considered. Snowballing, Google searches, and searches on specific journal websites were also undertaken. Data extraction was done on all studies retrieved and the reporting quality of each model was assessed using the Quality Assessment Reporting for Microsimulation Models checklist, a checklist developed by the research team. A narrative synthesis approach was used to synthesize the evidence.

RESULTS

Among 227 potential hits, 19 studies were found to be relevant. Some studies covered existing economic-demographic models, which included a component on mental health and were used to answer mental-health-related research questions. Other studies were focused solely on mental health and included models that were developed to examine the impact of specific policies or interventions on specific mental disorders or both. Most models examined were of medium quality. The main limitations included the use of model inputs based on self-reported and/or cross-sectional data, small and/or nonrepresentative samples and simplifying assumptions, and lack of model validation.

CONCLUSIONS

This review found few high-quality microsimulation models on mental health. Microsimulation models developed specifically to examine mental health are important to guide healthcare delivery and service planning. Future research should focus on developing high-quality mental health-specific microsimulation models with wide applicability and multiple functionalities.

A Scoping Review and Taxonomy of Epidemiological-Macroeconomic Models of COVID-19

OBJECTIVES

The COVID-19 pandemic placed significant strain on many health systems and economies. Mitigation policies decreased health impacts but had major macroeconomic impact. This article reviews models combining epidemiological and macroeconomic projections to enable policy makers to consider both macroeconomic and health objectives.

METHODS

A scoping review of epidemiological-macroeconomic models of COVID-19 was conducted, covering preprints, working articles, and journal publications. We assessed model methodologies, scope, and application to empirical data.

RESULTS

We found 80 articles modeling both the epidemiological and macroeconomic outcomes of COVID-19. Model scope is often limited to the impact of lockdown on health and total gross domestic product or aggregate consumption and to high-income countries. Just 14% of models assess disparities or poverty. Most models fall under 4 categories: compartmental-utility-maximization models, epidemiological models with stylized macroeconomic projections, epidemiological models linked to computable general equilibrium or input-output models, and epidemiological-economic agent-based models. We propose a taxonomy comparing these approaches to guide future model development.

CONCLUSIONS

The epidemiological-macroeconomic models of COVID-19 identified have varying complexity and meet different modeling needs. Priorities for future modeling include increasing developing country applications, assessing disparities and poverty, and estimating of long-run impacts. This may require better integration between epidemiologists and economists.

The potential impact of policies and structural interventions in reducing cardiovascular disease and mortality: a systematic review of simulation-based studies

Background

The aim of the study was to investigate the potential effect of different structural interventions for preventing cardiovascular disease.

Methods

Medline and EMBASE were searched for peer-reviewed simulation-based studies of structural interventions for prevention of cardiovascular disease. We performed a systematic narrative synthesis.

Results

A total of 54 studies met the inclusion criteria. Diet, nutrition, tobacco and alcohol control and other programmes are among the policy simulation models explored. Food tax and subsidies, healthy food and lifestyles policies, palm oil tax, processed meat tax, reduction in ultra-processed foods, supplementary nutrition assistance programmes, stricter food policy and subsidised community-supported agriculture were among the diet and nutrition initiatives. Initiatives to reduce tobacco and alcohol use included a smoking ban, a national tobacco control initiative and a tax on alcohol. Others included the NHS Health Check, WHO 25 × 25 and air quality management policy.

Future work and limitations

There is significant heterogeneity in simulation models, making comparisons of output data impossible. While policy interventions typically include a variety of strategies, none of the models considered possible interrelationships between multiple policies or potential interactions. Research that investigates dose-response interactions between numerous modifications as well as longer-term clinical outcomes can help us better understand the potential impact of policy-level interventions.

Conclusions

The reviewed studies underscore the potential of structural interventions in addressing cardiovascular diseases. Notably, interventions in areas such as diet, tobacco, and alcohol control demonstrate a prospective decrease in cardiovascular incidents. However, to realize the full potential of such interventions, there is a pressing need for models that consider the interplay and cumulative impacts of multiple policies. Rigorous research into holistic and interconnected interventions will pave the way for more effective policy strategies in the future.

Study registration

The study is registered as PROSPERO CRD42019154836.

Funding

This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 17/148/05.

Second-Line Chimeric Antigen Receptor T-Cell Therapy in Diffuse Large B-Cell Lymphoma : A Cost-Effectiveness Analysis

BACKGROUND

First-line treatment of diffuse large B-cell lymphoma (DLBCL) achieves durable remission in approximately 60% of patients. In relapsed or refractory disease, only about 20% achieve durable remission with salvage chemoimmunotherapy and consolidative autologous stem cell transplantation (ASCT). The ZUMA-7 (axicabtagene ciloleucel [axi-cel]) and TRANSFORM (lisocabtagene maraleucel [liso-cel]) trials demonstrated superior event-free survival (and, in ZUMA-7, overall survival) in primary-refractory or early-relapsed (high-risk) DLBCL with chimeric antigen receptor T-cell therapy (CAR-T) compared with salvage chemoimmunotherapy and consolidative ASCT; however, list prices for CAR-T exceed $400 000 per infusion.

OBJECTIVE

To determine the cost-effectiveness of second-line CAR-T versus salvage chemoimmunotherapy and consolidative ASCT.

DESIGN

State-transition microsimulation model.

DATA SOURCES

ZUMA-7, TRANSFORM, other trials, and observational data.

TARGET POPULATION

"High-risk" patients with DLBCL.

TIME HORIZON

Lifetime.

PERSPECTIVE

Health care sector.

INTERVENTION

Axi-cel or liso-cel versus ASCT.

OUTCOME MEASURES

Incremental cost-effectiveness ratio (ICER) and incremental net monetary benefit (iNMB) in 2022 U.S. dollars per quality-adjusted life-year (QALY) for a willingness-to-pay (WTP) threshold of $200 000 per QALY.

RESULTS OF BASE-CASE ANALYSIS

The increase in median overall survival was 4 months for axi-cel and 1 month for liso-cel. For axi-cel, the ICER was $684 225 per QALY and the iNMB was -$107 642. For liso-cel, the ICER was $1 171 909 per QALY and the iNMB was -$102 477.

RESULTS OF SENSITIVITY ANALYSIS

To be cost-effective with a WTP of $200 000, the cost of CAR-T would have to be reduced to $321 123 for axi-cel and $313 730 for liso-cel. Implementation in high-risk patients would increase U.S. health care spending by approximately $6.8 billion over a 5-year period.

LIMITATION

Differences in preinfusion bridging therapies precluded cross-trial comparisons.

CONCLUSION

Neither second-line axi-cel nor liso-cel was cost-effective at a WTP of $200 000 per QALY. Clinical outcomes improved incrementally, but costs of CAR-T must be lowered substantially to enable cost-effectiveness.

PRIMARY FUNDING SOURCE

No research-specific funding.

Pharmacoeconomic evaluation of anti-obesity drugs for chronic weight management: a systematic review of literature

Introduction

Pharmacological therapy is recommended as a second-line alternative to reverse obesity. Currently, five anti-obesity drugs (AODs) have been approved by the U.S. Food and Drug Administration (FDA) for chronic weight management. The aim of this paper is to investigate the pharmacoeconomic evaluation of AODs through a systematic review with a special focus on methodological considerations.

Methods

We searched the general and specific databases to identify the primary pharmacoeconomic evaluation of AODs.

Results

A total of 18 full-text articles and three conference abstracts were included in this review. Most of the economic assessments were still about Orlistat. And the observations we could make were consistent with the previous systematic review. A few studies were on the combined therapies (i.e. PHEN/TPM ER and NB ER) compared to different comparators, which could hardly lead to a generalized summary of the cost-effectiveness. Most recently, pharmacoeconomic evidence on the newest GLP 1 RA approved for the indication of obesity or obesity with at least one comorbidity emerged gradually. Modelling-based cost-utility analysis is the major type of assessment method. In the modelling studies, a manageable number of the key health states and the state transitions were structured to capture the disease progression. In particular, the principal structure of the decision model adopted in the three studies on the newly approved drug was nearly the same, which enables more in-depth comparisons and generalizations of the findings.

Conclusion

This study provided an up-to-date overview of the strengths and areas for improvement in the methodological design of the pharmacoeconomic evaluation of the licensed drugs for chronic weight management. Future modelling evaluations would benefit from a better understanding of the long-term weight loss effects of the current therapeutic options and the weight rebound process after the discontinuation of treatment.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022302648, identifier CRD42022302648.

Comparative analysis of disease modelling for health economic evaluations of systemic therapies in advanced hepatocellular carcinoma

BACKGROUND

The objective of this study was to systematically analyse methodological and structural assumptions utilised in model-based health economic evaluations of systemic advanced hepatocellular carcinoma (HCC) therapies, discuss the existing challenges, and develop methodological recommendations for future models in advanced HCC.

METHODS

We performed literature searches using five databases (Embase, PubMed, Web of Science, Econlit, and CNKI) up to December 4, 2022. Technology appraisals from Canada, England, Australia, and the United States were also considered. Model-based full economic evaluations of systemic advanced HCC therapies in English or Chinese met the eligibility criteria. The reporting quality was assessed by using the Consolidated Health Economic Evaluation Reporting Standards 2022 checklist.

RESULTS

Of 12,863 records retrieved, 55 were eligible for inclusion. Markov model (n = 29, 53%) and partitioned survival model (n = 27, 49%) were the most commonly used modelling techniques. Most studies were based on health-state-driven structure (n = 51, 93%), followed by treatment-line-driven structure (n = 2, 4%) and combination structure (n = 1, 2%). Only three studies (5%) adopted external real-world data to extrapolate the overall survival or calibrate the extrapolation. Few studies reported the assumptions of transition probabilities. Utility modelling approaches were state-based (n = 51, 93%) and time-to-death (n = 1, 2%). Only 13 studies (24%) reported five types of model validation. Economic evaluation results of specific treatment strategies varied among studies.

CONCLUSIONS

Disease modelling for health economic evaluations of systemic therapies in advanced HCC has adopted various modelling approaches and assumptions, leading to marked uncertainties in results. By proposing methodological recommendations, we suggest that future model-based studies for health economic evaluation of HCC therapies should follow good modelling practice guidelines and improve modelling methods to generate reliable health and economic evidence.

Economic Evaluations of Guideline-Directed Medical Therapies for Heart Failure With Reduced Ejection Fraction: A Systematic Review

OBJECTIVES

Decision-analytic models (DAMs) with varying structures and assumptions have been applied in economic evaluations (EEs) to assist decision making for heart failure with reduced ejection fraction (HFrEF) therapeutics. This systematic review aimed to summarize and critically appraise the EEs of guideline-directed medical therapies (GDMTs) for HFrEF.

METHODS

A systematic search of English articles and gray literature, published from January 2010, was performed on databases including MEDLINE, Embase, Scopus, NHSEED, health technology assessment, Cochrane Library, etc. The included studies were EEs with DAMs that compared the costs and outcomes of angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, angiotensin-receptor neprilysin inhibitors, beta-blockers, mineralocorticoid-receptor agonists, and sodium-glucose cotransporter-2 inhibitors. The study quality was evaluated using the Bias in Economic Evaluation (ECOBIAS) 2015 checklist and Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 checklists.

RESULTS

A total of 59 EEs were included. Markov model, with a lifetime horizon and a monthly cycle length, was most commonly used in evaluating GDMTs for HFrEF. Most EEs conducted in the high-income countries demonstrated that novel GDMTs for HFrEF were cost-effective compared with the standard of care, with the standardized median incremental cost-effectiveness ratio (ICER) of $21 361/quality-adjusted life-year. The key factors influencing ICERs and study conclusions included model structures, input parameters, clinical heterogeneity, and country-specific willingness-to-pay threshold.

CONCLUSIONS

Novel GDMTs were cost-effective compared with the standard of care. Given the heterogeneity of the DAMs and ICERs, alongside variations in willingness-to-pay thresholds across countries, there is a need to conduct country-specific EEs, particularly in low- and middle-income countries, using model structures that are coherent with the local decision context.

The value of correctly diagnosing axial spondyloarthritis for patients and society

OBJECTIVE

To demonstrate the value of diagnosing axSpA, by comparing health and costs associated with available diagnostic algorithms and perfect diagnosis.

METHODS

Using data from SPACE and other cohorts, a model was developed to estimate health (quality-adjusted life-years, QALYs) and costs (healthcare consumption and work productivity losses) of different diagnostic algorithms for axSpA amongst patients with low back pain referred to a rheumatologist, over a 60-year horizon. The model combined a decision-tree (diagnosis) with a state-transition model (treatment). The three algorithms (Berlin [BER, highest specificity], Modification 1 [M1; less strict inflammatory back pain (IBP) criterion] and Modification 2 [M2; IBP not mandatory as entry criterion, highest sensitivity]) were compared. Changes in sensitivity/specificity were explored and the value of perfect diagnosis was investigated.

RESULTS

For each correctly diagnosed axSpA patient, up to 4.7 QALYs and €60,000 could be gained/saved, considering a societal perspective. Algorithm M2 resulted in more health and lower costs per patient (24.23 QALYs; €157,469), compared to BER (23.96 QALYs; €159,423) and M1 (24.15 QALYs; €158,417). Hypothetical improvements in M2 sensitivity resulted in slightly more value compared to improvements in specificity. Perfect diagnosis can cost €7,500 per patient and still provide enough value.

CONCLUSION

Correct diagnosis of axSpA results in substantial health and cost benefits for patients and society. Not requiring IBP as mandatory for diagnosis of axSpA (algorithm M2) provides more value and would be preferable. A considerably more expensive diagnostic algorithm with better accuracy than M2 would still be considered good value for money.

Professionals' views on the justification for esophageal adenocarcinoma screening: A systematic literature search and qualitative analysis

Screening for early esophageal adenocarcinoma (EAC), including screening for its precursor Barrett's esophagus (BE), has the potential to reduce EAC-related mortality and morbidity. This literature review aimed to explore professionals' views on the justification for EAC screening. A systematic search of Ovid Medline, EMBASE, and PsycInfo, from January 1, 2000 to September 22, 2022, identified 5 original studies and 63 expert opinion articles reporting professionals' perspectives on EAC screening. Included articles were qualitatively analyzed using the framework method, which was deductively led by modernized screening principles. The analyses showed that many professionals are optimistic about technological advancements in BE detection and treatment. However, views on whether the societal burden of EAC merits screening were contradictory. In addition, knowledge of the long-term benefits and risks of EAC screening is still considered insufficient. There is no consensus on who to screen, how often to screen, which screening test to use, and how to manage non-dysplastic BE. Professionals further point out the need to develop technology that facilitates automated test sample processing and public education strategies that avoid causing disproportionately high cancer worry and social stigma. In conclusion, modernized screening principles are currently insufficiently fulfilled to justify widespread screening for EAC. Results from future clinical screening trials and risk prediction modeling studies may shift professionals' thoughts regarding justification for EAC screening.

Patient and Public Involvement in Health Economics Modelling Raises the Need for Normative Guidance

Patient and public involvement in health economics research and health technology assessment has been increasing for some time; however, patient and public involvement in health economics modelling is a more recent development. One reason to advance this type of involvement is to help appropriately manage the social and ethical value judgements that are required throughout model development and interpretation. At the same time, patient and public involvement in health economics modelling raises numerous practical and philosophical issues that invite discussion and debate. Recently, we attended an engagement event which invited patients, members of the public, researchers and decision-makers to discuss some of these issues. One priority that emerged in the discussion was to develop normative guidance for patient and public involvement in health economics modelling. In this article, we reflect on this goal from our own perspective, focusing on why normative guidance is needed and what questions that guidance should answer.

Guidance on the use of complex systems models for economic evaluations of public health interventions

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations

Uncertainty assessment is a cornerstone in model-based health economic evaluations (HEEs) that inform reimbursement decisions. No comprehensive overview of available uncertainty assessment methods currently exists. We aimed to review methods for uncertainty assessment for use in model-based HEEs, by conducting a snowballing review. We categorised all methods according to their stage of use relating to uncertainty assessment (identification, analysis, communication). Additionally, we classified identification methods according to sources of uncertainty, and subdivided analysis and communication methods according to their purpose. The review identified a total of 80 uncertainty methods: 30 identification, 28 analysis, and 22 communication methods. Uncertainty identification methods exist to address uncertainty from different sources. Most identification methods were developed with the objective to assess related concepts such as validity, model quality, and relevance. Almost all uncertainty analysis and communication methods required uncertainty to be quantified and inclusion of uncertainties in probabilistic analysis. Our review can help analysts and decision makers in selecting uncertainty assessment methods according to their aim and purpose of the assessment. We noted a need for further clarification of terminology and guidance on the use of (combinations of) methods to identify uncertainty and related concepts such as validity and quality. A key finding is that uncertainty assessment relies heavily on quantification, which may necessitate increased use of expert elicitation and/or the development of methods to assess unquantified uncertainty.

Calibration and validation of modeled 5-year survival predictions among people with cystic fibrosis treated with the cystic fibrosis transmembrane conductance regulator modulator ivacaftor using United States registry data

OBJECTIVES

Cystic fibrosis (CF) is a rare genetic disease characterized by life-shortening lung function decline. Ivacaftor, a CF transmembrane conductance regulator modulator (CFTRm), was approved in 2012 for people with CF with specific gene mutations. We used real-world evidence of 5-year mortality impacts of ivacaftor in a US registry population to validate a CF disease-progression model that estimates the impact of ivacaftor on survival.

METHODS

The model projects the impact of ivacaftor vs. standard care in people with CF aged ≥6 years with CFTR gating mutations by combining parametric equations fitted to historical registry survival data, with mortality hazards adjusted for fixed and time-varying person-level characteristics. Disease progression with standard care was derived from published registry studies and the expected impact of ivacaftor on clinical characteristics was derived from clinical trials. Individual-level baseline characteristics of the registry ivacaftor-treated population were entered into the model; 5-year model-projected mortality with credible intervals (CrIs) was compared with registry mortality to evaluate the model's validity.

RESULTS

Post-calibration 5-year mortality projections closely approximated registry mortality in populations treated with standard care (6.4% modeled [95% CrI: 5.3% to 7.6%] vs. 6.0% observed) and ivacaftor (3.4% modeled [95% CrI: 2.7% to 4.4%] vs. 3.1% observed). The model accurately predicted 5-year relative risk of mortality (0.53 modeled [0.47 to 0.60] vs. 0.51 observed) in people treated with ivacaftor vs. standard care.

CONCLUSIONS

Modeled 5-year survival projections for people with CF initiating ivacaftor vs. standard care align closely with real-world registry data. Findings support the validity of modeling CF to predict long-term survival and estimate clinical and economic outcomes of CFTRm.

Cost-Effectiveness Analysis of Frontline Polatuzumab-Rituximab, Cyclophosphamide, Doxorubicin, and Prednisone and/or Second-Line Chimeric Antigen Receptor T-Cell Therapy Versus Standard of Care for Treatment of Patients With Intermediate- to High-Risk Diffuse Large B-Cell Lymphoma

PURPOSE

Recent studies of polatuzumab vedotin and CD19 chimeric antigen receptor T-cell therapy (CAR-T) have shown significant improvements in progression-free survival over standard of care (SOC) for patients with diffuse large B-cell lymphoma. However, they are costly, and it is unclear whether these strategies, alone or combined, are cost-effective over SOC.

METHODS

A Markov model was constructed to compare four strategies for patients with newly diagnosed intermediate- to high-risk diffuse large B-cell lymphoma: strategy 1: polatuzumab-rituximab, cyclophosphamide, doxorubicin, and prednisone (R-CHP) plus second-line CAR-T for early relapse (< 12 months); strategy 2: polatuzumab-R-CHP plus second-line salvage therapy ± autologous stem-cell transplant; strategy 3: rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone plus second-line CAR-T for early relapse; strategy 4: SOC (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone plus second-line salvage therapy ± autologous stem-cell transplant). Transition probabilities were estimated from trial data. Lifetime costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated from US and Canadian payer perspectives. Willingness-to-pay (WTP) thresholds of $150,000 US dollars (USD) or Canadian dollars (CAD)/QALY were used.

RESULTS

In probabilistic analyses (10,000 simulations), each strategy was incrementally more effective than the previous strategy, but also more costly. Adding polatuzumab-R-CHP to the SOC had an ICER of $546,956 (338,797-1,199,923) USD/QALY and $245,381 (151,671-573,250) CAD/QALY. Adding second-line CAR-T to the SOC had an ICER of $309,813 (190,197-694,200) USD/QALY and $303,163 (221,300-1,063,864) CAD/QALY. Simultaneously adding both polatuzumab-R-CHP and second-line CAR-T to the SOC had an ICER of $488,284 (326,765-840,157) USD/QALY and $267,050 (182,832-520,922) CAD/QALY.

CONCLUSION

Given uncertain incremental benefits in long-term survival and high costs, neither polatuzumab-R-CHP frontline, CAR-T second-line, nor a combination are likely to be cost-effective in the United States or Canada at current pricing compared with the SOC.

The NCC mathematical modeling framework for decision-making of six major cancers

Objective

Mathematical modeling and simulation is a useful research method to inform decision-making. This article aims to describe the National Cancer Center (NCC) modeling framework and how well it reproduces observed empirical data for six major cancers.

Methods

We developed the NCC modeling framework for six major cancers in China (lung, liver, stomach, colorectal, esophageal, and breast), which simulates the life-histories represented by states among normal, precancerous lesion, stage-specific invasive cancer, and death for six cancers separately. Each NCC simulation model could be illustrated as an integrated framework of 3 modules: a demography module, natural history module, and screening module. Combined with costs and health utilities data, the models could have many detailed outputs for informing decisions, including the harm of screening (e.g., false positives, complications, and overdiagnosis), healthcare costs, and benefits (quality-adjusted life years gained, cancer incidence and mortality, and investment returns). We calibrated the models to Chinese population-based observations on cancer incidence, mortality, and stage distribution. All models are validated by comparing model simulated results to data observed from nationwide cancer registration and a large prospective cohort study.

Results

The simulated results from the calibrated models consistently match the epidemiological patterns in six major cancer incidence, mortality, and stage distributions in China. Model projected age-specific cancer incidence and mortality were close to the observed data in the national cancer registration. The NCC modeling framework reproduced the cumulative cancer cases and deaths observed in the prospective cohort study at 7.0 and 10.8 years of follow-up. Model estimated net survival rates also consistent with population-based statistics.

Conclusion

The NCC modeling framework's ability to reproduce the observed population-level cancer statistics and the cancer cases in a prospective cohort study suggests its results are reliable to inform decision-making related to six major cancers in China.

Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands

Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.

Generic Health-Related Quality of Life Utility Measure for Preschool Children (Health Utilities Preschool): Design, Development, and Properties

OBJECTIVES

Health Utilities Preschool (HuPS) was developed to fill the need for a generic preference-based measure (GPM) applicable in early childhood. A GPM has all the properties for higher-order summary measures, such as quality-adjusted life-years, required to inform important policy decisions regarding health and healthcare services.

METHODS

Development was in accordance with published standards for a GPM, statistical procedures, and modeling. HuPS incorporates key components of 2 existing measurement systems: Health Status Classification System for Preschool Children and Health Utilities Index Mark 3 (HUI3). The study included a series of 4 measurement surveys: definitional, adaptational, quantificational, and evaluational health-related quality of life (HRQL). HuPS measurements were evaluated for reliability, validity, interpretability, and acceptability.

RESULTS

Definitional measurements identified 8 Health Status Classification System for Preschool Children attributes in common with HUI3 (vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain and discomfort), making the HUI3 scoring equation commensurate with HuPS health states. Adaptational measurements informed the content of attribute-level descriptions (n = 35). Quantificational measurements determined level scoring coefficients. HRQL scoring inter-rater reliability (intraclass correlation coefficient = 0.79) was excellent. Continuity of HRQL scoring with HUI3 was reliable (intraclass correlation coefficient = 0.80, P < .001) and valid (mean absolute difference = 0.016, P = .396).

CONCLUSIONS

HuPS is an acceptable, reliable, and valid GPM. HRQL scoring is continuous with HUI3. Continuity expands the applicability of GPM (HUI3) scoring to include subjects as young as 2 years of age. Widespread applications of HuPS would inform important health policy and management decisions as HUI3 does for older subjects.

Modeling the Natural History and Screening Effects of Colorectal Cancer Using Both Adenoma and Serrated Neoplasia Pathways: The Development, Calibration, and Validation of a Discrete Event Simulation Model

Background. Existing colorectal cancer (CRC) screening models mostly focus on the adenoma pathway of CRC development, overlooking the serrated neoplasia pathway, which might result in overly optimistic screening predictions. In addition, Bayesian inference methods have not been widely used for model calibration. We aimed to develop a CRC screening model accounting for both pathways, calibrate it with approximate Bayesian computation (ABC) methods, and validate it with large CRC screening trials. Methods. A discrete event simulation (DES) of the CRC natural history (DECAS) was constructed using the adenoma and serrated pathways in R software. The model simulates CRC-related events in a specific birth cohort through various natural history states. Calibration took advantage of 74 prevalence data points from the German screening colonoscopy program of 5.2 million average-risk participants using an ABC method. CRC incidence outputs from DECAS were validated with the German national cancer registry data; screening effects were validated using 17-y data from the UK Flexible Sigmoidoscopy Screening sigmoidoscopy trial and a German screening colonoscopy cohort study. Results. The Bayesian calibration rendered 1,000 sets of posterior parameter samples. With the calibrated parameters, the observed age- and sex-specific CRC prevalences from the German registries were within the 95% DECAS-predicted intervals. Regarding screening effects, DECAS predicted a 41% (95% intervals 30%-51%) and 62% (95% intervals 55%-68%) reduction in 17-y cumulative CRC mortality for a single screening sigmoidoscopy and colonoscopy, respectively, falling within 95% confidence intervals reported in the 2 clinical studies used for validation. Conclusions. We presented DECAS, the first Bayesian-calibrated DES model for CRC natural history and screening, accounting for 2 CRC tumorigenesis pathways. The validated model can serve as a valid tool to evaluate the (cost-)effectiveness of CRC screening strategies.

Highlights

This article presents a new discrete event simulation model, DECAS, which models both adenoma-carcinoma and serrated neoplasia pathways for colorectal cancer (CRC) development and CRC screening effects.DECAS is calibrated based on a Bayesian inference method using the data from German screening colonoscopy program, which consists of more than 5 million first-time average-risk participants aged 55 years and older in 2003 to 2014.DECAS is flexible for evaluating various CRC screening strategies and can differentiate screening effects in different parts of the colon.DECAS is validated with large screening sigmoidoscopy and colonoscopy clinical study data and can be further used to evaluate the (cost-)effectiveness of German colorectal cancer screening strategies.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Objectives

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.

Methods

To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.

Results

Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.

Conclusions

Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Cost‐effectiveness models for Alzheimer's disease and related dementias: IPECAD modeling workshop cross‐comparison challenge

INTRODUCTION

The credibility of model-based economic evaluations of Alzheimer's disease (AD) interventions is central to appropriate decision-making in a policy context. We report on the International PharmacoEconomic Collaboration on Alzheimer's Disease (IPECAD) Modeling Workshop Challenge.

METHODS

Two common benchmark scenarios, for the hypothetical treatment of AD mild cognitive impairment (MCI) and mild dementia, were developed jointly by 29 participants. Model outcomes were summarized, and cross-comparisons were discussed during a structured workshop.

RESULTS

A broad concordance was established among participants. Mean 10-year restricted survival and time in MCI in the control group ranged across 10 MCI models from 6.7 to 9.5 years and 3.4 to 5.6 years, respectively; and across 4 mild dementia models from 5.4 to 7.9 years (survival) and 1.5 to 4.2 years (mild dementia).

DISCUSSION

The model comparison increased our understanding of methods, data used, and disease progression. We established a collaboration framework to assess cost-effectiveness outcomes, an important step toward transparent and credible AD models.

An ex-ante cost-utility analysis of the deemed consent legislation compared to expressed consent for kidney transplantations in Nova Scotia

Background

This study was an ex-ante cost-utility analysis of deemed consent legislation for deceased organ donation in Nova Scotia, a province in Canada. The legislation became effective in January 2021. The study's objective was to assess the conditions necessary for the legislation change’s cost-effectiveness compared to expressed consent, focusing on kidney transplantation (KT).

Method

We performed a cost-utility analysis using a Markov model with a lifetime horizon. The study was from a Canadian payer perspective. The target population was patients with end-stage kidney disease (ESKD) in Atlantic Canada waitlisted for KT. The intervention was the deemed consent and accompanying health system transformations. Expressed consent (before the change) was the comparator. We simulated the minimum required increase in deceased donor KT per year for the cost-effectiveness of the deemed consent. We also evaluated how changes in dialysis and maintenance immunosuppressant drug costs and living donor KT per year impacted cost-effectiveness in sensitivity analyses.

Results

The expected lifetime cost of an ESKD patient ranged from $177,663 to $553,897. In the deemed consent environment, the expected lifetime cost per patient depended on the percentage increases in the proportion of ESKD patients on the waitlist getting a KT in a year. The incremental cost-utility ratio (ICUR) increased with deceased donor KT per year. Cost-effectiveness of deemed consent compared to expressed consent required a minimum of a 1% increase in deceased donor KT per year. A 1% increase was associated with an ICUR of $32,629 per QALY (95% CI: − $64,279, $232,488) with a 81% probability of being cost-effective if the willingness-to-pay (WTP) was $61,466. Increases in dialysis and post-KT maintenance immunosuppressant drug costs above a threshold impacted value for money. The threshold for immunosuppressant drug costs also depended on the percent increases in deceased donor KT probability and the WTP threshold.

Conclusions

The deemed consent legislation in NS for deceased organ donation and the accompanying health system transformations are cost-effective to the extent that they are anticipated to contribute to more deceased donor KTs than before, and even a small increase in the proportion of waitlist patients receiving a deceased donor KT than before the change represents value for money.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12962-022-00390-z.

Simulation models of sugary drink policies: A scoping review

INTRODUCTION

Simulation modeling methods are an increasingly common tool for projecting the potential health effects of policies to decrease sugar-sweetened beverage (SSB) intake. However, it remains unknown which SSB policies are understudied and how simulation modeling methods could be improved. To inform next steps, we conducted a scoping review to characterize the (1) policies considered and (2) major characteristics of SSB simulation models.

METHODS

We systematically searched 7 electronic databases in 2020, updated in 2021. Two investigators independently screened articles to identify peer-reviewed research using simulation modeling to project the impact of SSB policies on health outcomes. One investigator extracted information about policies considered and key characteristics of models from the full text of included articles. Data were analyzed in 2021-22.

RESULTS

Sixty-one articles were included. Of these, 50 simulated at least one tax policy, most often an ad valorem tax (e.g., 20% tax, n = 25) or volumetric tax (e.g., 1 cent-per-fluid-ounce tax, n = 23). Non-tax policies examined included bans on SSB purchases (n = 5), mandatory reformulation (n = 3), warning labels (n = 2), and portion size policies (n = 2). Policies were typically modeled in populations accounting for age and gender or sex attributes. Most studies focused on weight-related outcomes (n = 54), used cohort, lifetable, or microsimulation modeling methods (n = 34), conducted sensitivity or uncertainty analyses (n = 56), and included supplementary materials (n = 54). Few studies included stakeholders at any point in their process (n = 9) or provided replication code/data (n = 8).

DISCUSSION

Most simulation modeling of SSB policies has focused on tax policies and has been limited in its exploration of heterogenous impacts across population groups. Future research would benefit from refined policy and implementation scenario specifications, thorough assessments of the equity impacts of policies using established methods, and standardized reporting to improve transparency and consistency.

Value-based evidence across health care sectors: a push for transparent real-world studies, data, and evidence dissemination

There is currently a heightened need for transparency in pharmaceutical sectors. The inclusion of real-world (RW) evidence, in addition to clinical trial evidence, in decision-making processes, was an important step forward toward a more inclusive established value proposition. This advance has introduced new transparency challenges. Increasing transparency is a critical step toward accelerating improvement in type, quality, and access to data, regardless of whether these originate from clinical trials or from RW studies. However, so far, advances in transparency have been relatively restricted to clinical trials, and there remains a lack of similar expectations or standards of transparency concerning the generation and reporting of RW data. This perspective paper aims to highlight the need for transparency concerning RW studies, data, and evidence across health care sectors, to identify areas for improvement, and provide concrete recommendations and practices for the future. Specific issues are discussed from different stakeholder perspectives, culminating in recommended actions, from individual stakeholder perspectives, for improved RW study, data, and evidence transparency. Furthermore, a list of potential guidelines for consideration by stakeholders is proposed. While recommendations from different stakeholder perspectives are made, true transparency in the processes involved in the generation, reporting, and use of RW evidence will require a concerted effort from all stakeholders across health care sectors.