Accounting for planetary boundaries in health economic evaluation.

INTRODUCTION: Health economic evaluation (HEE) provides guidance for decision-making in the face of scarcity but ignores ecological scarcities as long as they involve external costs only. Following the imperative to account for planetary health, this study explores how this blind spot can be addressed. AREAS COVERED: The study is based on a critical review of relevant work, particularly in the fields of HEE and life cycle assessment (LCA). LCA can provide information on a technology's environmental impacts which can be accounted for on both the effect and cost sides of HEE. Cost-benefit analyses can incorporate environmental impacts in case vignettes used for eliciting consumers' willingness to pay. Existing LCA impact models can be used to estimate human health risks associated with environmental impacts and add them to the health benefits in cost-utility analyses. Many jurisdictions offer lists of shadow prices that can be used to incorporate environmental impacts on the cost side of HEE. Also, environmental impacts can be reported in a disaggregated manner. EXPERT OPINION: Accounting for planetary boundaries is likely to become a key field of methodological innovation in HEE. Decision relevance is likely to be highest for technologies with similar cost-effectiveness but different ecological impacts.

One Health Economics: why and how economics should take on the interdisciplinary challenges of a promising public health paradigm.

In this perspective paper, we argue that Economics could and should contribute to the development and implementation of the One Health approach currently emerging as a relevant interdisciplinary framework to address present and future infectious diseases. We show how proven tools from Health and Environmental Economics, such as burden evaluation, can be extended to fit the One Health multisectoral perspective. This global health framework could also benefit significantly from Economics to design efficient schemes for prevention and disease control. In return, adapting Economics to the challenges of One Health issues could pave the way for exciting developments in the Economics discipline itself, across many subfields.

Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research.

OBJECTIVE: Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening. METHODS: This study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms. RESULTS AND CONCLUSIONS: The HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.

Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).

OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

Temporal and geographical variations in diagnostic imaging in Norway.

BACKGROUND: Unwarranted temporal and geographical variations are acknowledged as a profound problem for equal access and justice in the provision of health services. Even more, they challenge the quality, safety, and efficiency of such services. This is highly relevant for imaging services. OBJECTIVE: To analyse the temporal and geographical variation in the number of diagnostic images in Norway from 2013 to 2021. METHODS: Data on outpatient imaging provided by the Norwegian Health Economics Administration (HELFO) and inpatient data afforded by fourteen hospital trusts and hospitals in Norway. Data include the total number of imaging examinations according to the Norwegian Classification of Radiological Procedures (NCRP). Analyses were performed with descriptive statistics. RESULTS: More than 37 million examinations were performed in Norway during 2013-2021 giving an average of 4.2 million examinations per year. In 2021 there was performed and average of 0.8 examinations per person and 2.2 examinations per person for the age group > 80. There was a 9% increase in the total number of examinations from 2013 to 2015 and a small and stable decrease of 0.5% per year from 2015 to 2021 (with the exception of 2020 due to the pandemic). On average 71% of all examinations were outpatient examinations and 32% were conducted at private imaging centres. There were substantial variations between the health regions, with Region South-East having 53.1% more examinations per inhabitant than Region West. The geographical variation was even more outspoken when comparing catchment areas, where Oslo University Hospital Trust had twice as many examinations per inhabitant than Finnmark Hospital Trust. CONCLUSION: As the population in Norway is homogeneous it is difficult to attribute the variations to socio-economic or demographic factors. Unwarranted and supply-sensitive variations are challenging for healthcare systems where equal access and justice traditionally are core values.

Health Economics in a World of Uneconomic Growth.

Multiple, accelerating and interacting ecological crises are increasingly understood as constituting a major threat to human health and well-being. Unconstrained economic growth is strongly implicated in these growing crises, and it has been argued that this growth has now become "uneconomic growth", which is a situation where the size of the economy is still expanding, but this expansion is causing more harm than benefit. This article summarises the multiple pathways by which uneconomic growth can be expected to harm human health. It describes how health care systems-especially through overuse, low value and poor quality care-can themselves drive uneconomic growth. Health economists need to understand not only the consequences of environmental impacts on health care, but also the significance of uneconomic growth, and pay closer attention to the growing body of work by heterodox economists, especially in the fields of ecological and feminist economics. This will involve paying closer heed to the existence and consequences of diminishing marginal returns to health care consumption at high levels; the central importance of inequalities and injustice in health; and the need to remedy health economists' currently limited ability to deal effectively with low value care, overdiagnosis and overtreatment.

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

BACKGROUND: The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. METHODS: We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. RESULTS: In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). CONCLUSIONS: MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.

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.

Industry Payments to US Physicians by Specialty and Product Type.

This study examines the distribution of payments within and across specialties and the medical products associated with the largest total payments.

#SharingHEOR: Developing Modern Media for Communication and Dissemination of Health Economics and Outcomes Research.

Questions regarding the effectiveness and safety of health interventions and allocation of health care resources are frequently discussed in mainstream and social media. Additionally, government and foundation funders are increasingly mandating that results be disseminated to the lay public and patients may benefit from being able to digest scientific research regarding their health conditions. Therefore, it is important to widely disseminate and clearly communication health economics and outcomes research (HEOR) findings to a range of interested parties. Digital media features such as graphical or visual abstracts, infographics and videos are informative and add value to research articles by improving reader engagement with articles, potentially increasing their impact, and allowing results to be more widely disseminated. However, use of novel digital media for research dissemination has been relatively limited to date. In this article, we discuss the rationale for developing novel media to communicate and disseminate research findings and offer practical advice for doing so. We conclude by outlining a future agenda for research regarding HEOR communication and dissemination.

Medical Economic Effect of Pharmaceutical Interventions by Board-Certified Pharmacists in Palliative Pharmacy for Patients with Cancer Using Medical Narcotics in Japan: A Multicenter, Retrospective Study.

BACKGROUND: The Japanese Society for Pharmaceutical Palliative Care and Sciences specializes in pharmacology in the field of palliative medicine. More than 700 board-certified pharmacists in palliative pharmacy (BCPPP) are actively involved in palliative pharmacotherapy at various hospitals and pharmacies. The purpose of this study was to determine the economic effect of pharmaceutical interventions by BCPPPs. METHODS: This multicenter retrospective study included 27 medical centers and analyzed the medical economic effect of interventions by BCPPPs (17 pharmacists) and non-BCPPPs (24 pharmacists) on patients using medical narcotics for cancer pain in September 2021. RESULTS: The percentage of patients who received a pharmaceutical intervention and whose drug costs were reduced by pharmacist intervention was significantly higher in the BCPPP group than in the non-BCPPP group. Although there was no significant difference between the two groups in drug cost reduction per patient per month (BCPPP group: $0.89 [-$64.91 to $106.76] vs. non-BCPPP group $0.00 [-$1,828.95 to $25.82]; P = 0.730), the medical economic benefit of pharmacist intervention in avoiding or reducing adverse drug reactions was higher in the BCPPP group ($103.18 [$0.00 to $628.03]) than in the non-BCPPP group ($0.00 [$0.00 to $628.03]) (P = 0.070). The total medical economic benefit-the sum of these-was significantly higher in the BCPPP group ($88.82 [-$14.62 to $705.37]) than in the non-BCPPP group ($0.66 [-$1,200.93 to $269.61]) (P = 0.006). CONCLUSION: Pharmacological intervention for patients with cancer using medical narcotics may have a greater medical economic benefit when managed by BCPPPs than by non-certified pharmacists in Japan.

Analysis of the health economics portfolio funded by the National Institutes of Health in response to published guidance.

Health services, economics, and outcomes research (referred to as health economics research hereinafter) is one of the interdisciplinary sciences that the National Institutes of Health (NIH) supports in order to pursue its overall mission to improve health. In 2015, NIH guidance was published to clarify the type of health economics research that NIH would continue to fund. This analysis aimed to determine if there were changes in the number of health economics applications received and funded by NIH after the release of the guidance. Health economics applications submitted to NIH both before and after publication of the guidance were identified using a machine learning approach with input from subject matter experts. Application and funding trends were examined by fiscal year, method of application (solicited vs. unsolicited), and activity code. This study found that application and funding rates of health economics research were decreasing prior to guidance. Following publication of this guidance, the application and funding rate of health economics applications increased.

Addressing the distributional consequences of spillovers in health economic evaluation: A prioritarian approach.

Health spillovers arise when an individual's serious illness affects those close to them emotionally, psychologically, and/or physically. As a result, healthcare interventions that improve the lives of patients may also confer wider health benefits. However, contrary to widespread calls for health spillovers to be included in health economic evaluation, others have argued this could have adverse distributional consequences and equity implications. This paper presents a novel approach to spillover inclusion in health economic evaluation using a 'prioritarian transformation' of health gains that allows these equity concerns to be addressed. Affording greater weight to the incremental change in patient outcomes when incorporating carer/family health spillovers into resource allocation decisions, the method provides a feasible means of moderating the distributional impact of spillover inclusion. It also introduces a normative, theoretical perspective to a largely empirical extant literature and, as such, its axiomatic basis is examined. Finally, an illustrative example of the approach is presented to demonstrate its application.

Examining how well economic evaluations capture the value of mental health.

Health economics evidence informs health-care decision making, but the field has historically paid insufficient attention to mental health. Economic evaluations in health should define an appropriate scope for benefits and costs and how to value them. This Health Policy provides an overview of these processes and considers to what extent they capture the value of mental health. We suggest that although current practices are both transparent and justifiable, they have distinct limitations from the perspective of mental health. Most social value judgements, such as the exclusion of interindividual outcomes and intersectoral costs, diminish the value of improving mental health, and this reduction in value might be disproportionate compared with other types of health. Economic analyses might have disadvantaged interventions that improve mental health compared with physical health, but research is required to test the size of such differential effects and any subsequent effect on decision-making systems such as health technology assessment systems. Collaboration between health economics and the mental health sciences is crucial for achieving mental-physical health parity in evaluative frameworks and, ultimately, improving population mental health.