The path to scientifically sound biodiversity valuation in the context of the Global Biodiversity Framework.

Successful implementation of the Kunming-Montreal Global Biodiversity Framework requires identifying a process for measuring and valuing changes in biodiversity that build on the recognition that economics and valuation must play a key role in "halting and reversing" biodiversity loss. Here, we discuss considerations for a practical path to valuing changes in biodiversity. Framing changes in the value of biodiversity as a summary of changes in certain natural assets enables leveraging existing approaches and international standards associated with environmental-economic accounting. We discuss why an approach that builds from individual species, evolutionary groups, or functional groups into a practical, hierarchical statistical classification system is better than the development of any one biodiversity index. We merge techniques from ecology and other natural sciences, national and environmental-economic accounting, and economics, which are all on the cusp of making measurement of the change in the value of biodiversity possible. The focus should be on scaling and integrating these approaches. The path forward appears to begin with imperfect but useful measures, grounded in robust concepts, while establishing ambition to further scale-up measurements-just like the past evolution of many other official statistical series.

The visual effect of wind turbines on property values is small and diminishing in space and time.

Renewable power generation is the key to decarbonizing the electricity system. Wind power is the fastest-growing renewable source of electricity in the United States. However, expanding wind capacity often faces local opposition, partly due to a perceived visual disamenity from large wind turbines. Here, we provide a US-wide assessment of the externality costs of wind power generation through the visibility impact on property values. To this end, we create a database on wind turbine visibility, combining information on the site and height of each utility-scale turbine having fed power into the U.S. grid, with a high-resolution elevation map to account for the underlying topography of the landscape. Building on hedonic valuation theory, we statistically estimate the impact of wind turbine visibility on home values, informed by data from the majority of home sales in the United States since 1997. We find that on average, wind turbine visibility negatively affects home values in an economically and statistically significant way in close proximity ([Formula: see text]5 miles/8 km). However, the effect diminishes over time and in distance and is indistinguishable from zero for larger distances and toward the end of our sample.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

Perspectives on valuing water quality improvements using stated preference methods.

Improvements to the quality of freshwater rivers and lakes can generate a wide array of benefits, from "use values" such as recreational boating, fishing, and swimming to "nonuse values" such as improved outcomes for aquatic biodiversity. Bringing these nonmarket values into decision-making is crucial to determining appropriate levels of investment in water quality improvements. However, progress in the economic valuation of water quality benefits has lagged similar efforts to value air quality benefits, with implications for water policy. New data sources, modeling techniques, and innovation in stated preference survey methods offer notable improvements to estimates of use and nonuse benefits of improved water quality. Here, we provide a perspective on how recent applications of stated preference techniques to the valuation of the nonmarket benefits of water quality improvements have advanced the field of environmental valuation. This overview is structured around four key questions: i) What is it about water quality that we seek to value? ii) How should we design and implement the surveys which elicit individuals' stated preferences? iii) How do we assess the validity of the findings provided by such studies? and iv) What are the contributions of these valuation exercises to public policy? In answering these questions, we make reference to the contributions provided by the papers in this Symposium.

Estimating the benefits of stream water quality improvements in urbanizing watersheds: An ecological production function approach.

Streams in urbanizing watersheds are threatened by economic development that can lead to excessive sediment erosion and surface runoff. These anthropogenic stressors diminish valuable ecosystem services and result in pervasive degradation commonly referred to as "urban stream syndrome." Understanding how the public perceives and values improvements in stream conditions is necessary to support efforts to quantify the economic benefits of water quality improvements. We develop an ecological production framework that translates measurable indicators of stream water quality into ecological endpoints. Our interdisciplinary approach integrates a predictive hierarchical water quality model that is well suited for sparse data environments, an expert elicitation that translates measurable water quality indicators into ecological endpoints that focus group participants identified as most relevant, and a stated preference survey that elicits the public's willingness to pay for changes in these endpoints. To illustrate our methods, we develop an application to the Upper Neuse River Watershed located in the rapidly developing Triangle region of North Carolina (the United States). Our results suggest, for example, that residents are willing to pay roughly $127 per household and $54 million per year in aggregate (2021 US$) for water quality improvements resulting from a stylized intervention that increases stream bank canopy cover by 25% and decreases runoff from impervious surfaces, leading to improvements in water quality and ecological endpoints for local streams. Although the three components of our analysis are conducted with data from North Carolina, we discuss how our findings are generalizable to urban and urbanizing areas across the larger Piedmont ecoregion of the Eastern United States.

Distribution of capitalized benefits from land conservation.

Land conservation efforts throughout the United States sustain ecological benefits while generating wealth in the housing market through capitalization of amenities. This paper estimates the benefits of conservation that are capitalized into proximate home values and quantifies how those benefits are distributed across demographic groups. Using detailed property and household-level data from Massachusetts, we estimate that new land conservation led to $62 million in new housing wealth equity. However, houses owned by low-income or Black or Hispanic households are less likely to be located near protected areas, and hence, these populations are less likely to benefit financially. Direct study of the distribution of this new wealth from capitalized conservation is highly unequal, with the richest quartile of households receiving 43%, White households receiving 91%, and the richest White households receiving 40%, which is nearly 140% more than would be expected under equal distribution. We extend our analysis using census data for the entire United States and observe parallel patterns. We estimate that recent land conservation generated $9.8 billion in wealth through the housing market and that wealthier and White households benefited disproportionately. These findings suggest regressive and racially disparate incidence of the wealth benefits of land conservation policy.

Interacting pest control and pollination services in coffee systems.

Biodiversity-mediated ecosystem services (ES) support human well-being, but their values are typically estimated individually. Although ES are part of complex socioecological systems, we know surprisingly little about how multiple ES interact ecologically and economically. Interactions could be positive (synergy), negative (trade-offs), or absent (additive effects), with strong implications for management and valuation. Here, we evaluate the interactions of two ES, pollination and pest control, via a factorial field experiment in 30 Costa Rican coffee farms. We found synergistic interactions between these two critical ES to crop production. The combined positive effects of birds and bees on fruit set, fruit weight, and fruit weight uniformity were greater than their individual effects. This represents experimental evidence at realistic farm scales of positive interactions among ES in agricultural systems. These synergies suggest that assessments of individual ES may underestimate the benefits biodiversity provides to agriculture and human well-being. Using our experimental results, we demonstrate that bird pest control and bee pollination services translate directly into monetary benefits to coffee farmers. Excluding both birds and bees resulted in an average yield reduction of 24.7% (equivalent to losing US$1,066.00/ha). These findings highlight that habitat enhancements to support native biodiversity can have multiple benefits for coffee, a valuable crop that supports rural livelihoods worldwide. Accounting for potential interactions among ES is essential to quantifying their combined ecological and economic value.

The fatigue-inducing effects of cancer and its therapy are characterized by decreased physical activity in the absence of any motivational deficit.

Although cancer and its therapy are well known to be associated with fatigue, the exact nature of cancer-related fatigue remains ill-defined. We previously reported that fatigue-like behavior induced independently by tumor growth and by the chemotherapeutic agent cisplatin is characterized by reduced voluntary wheel running and an intact motivation to expand effort for food rewards. The present set of experiments was initiated to characterize the functional consequences of fatigue induced by chemoradiotherapy in tumor-bearing mice and relate them to changes in the expression of genes coding for inflammation, mitochondria dynamics and metabolism. Two syngeneic murine models of cancer were selected for this purpose, a model of human papilloma virus-related head and neck cancer and a model of lung cancer. In both models, tumor-bearing mice were submitted to chemoradiotherapy to limit tumor progression. Two dimensions of fatigue were assessed, the physical dimension by changes in physical activity in mice trained to run in wheels and the motivational dimension by changes in the performance of mice trained to nose poke to obtain a food reward in a progressive ratio schedule of food reinforcement. Chemoradiotherapy reliably decreased wheel running activity but had no effect on performance in the progressive ratio in both murine models of cancer. These effects were the same for the two murine models of cancer and did not differ according to sex. Livers and brains were collected at the end of the experiments for qRT-PCR analysis of expression of genes coding for inflammation, mitochondria dynamics, and metabolism. The observed changes were mainly apparent in the liver and typical of activation of type I interferon and NF-κB-dependent signaling, with alterations in mitochondrial dynamics and a shift toward glycolysis. Although the importance of these alterations for the pathophysiology of cancer-related fatigue remains to be explored, the present findings indicate that fatigue brought on by cancer therapy in tumor-bearing mice is more physical than motivational.

Does Willingness to Pay Differ for Mental and Physical Health?

OBJECTIVES: The urban environment can have a significant impact on mental and physical health. Health impact appraisal of new developments should address these issues. However, transferable economic valuation evidence for urban planners in the United Kingdom is thin, especially around mental health, making it harder to estimate the cost-efficiency of public health interventions to address these conditions. A further complication is that mental health may be perceived differently from physical health. This study examines willingness to pay (WTP) to avoid depression and lower back pain. METHODS: WTP estimates were obtained by applying contingent valuation tasks in an online survey with a representative sample in the United Kingdom (N = 1553). Interval regression models were used to estimate the effects of disease severity, payment frequency, and respondent characteristics on WTP. RESULTS: Respondents' WTP to avoid both conditions was relatively high (around 5%-6% of stated income to return to current health state). Depression was rated as being twice as burdensome on quality of life than pain, and bids to avoid depression were 20% to 30% more than pain. Analysis of motivation responses suggests mental health treatment is perceived as less easy to access and less effective than the equivalent for pain, and respondents expect a larger burden on their family and relationships as they try to manage their condition themselves. CONCLUSIONS: Results suggest that depression bids may be affected by uncertainty around access to effective treatment in the healthcare system. This has implications for how mental illness may be prioritized in resource allocation toward public health interventions.

A New and Improved Experimental Design for the Discrete Choice Experiment Module of the EuroQol Valuation Technology Protocol.

OBJECTIVES: The EuroQol Valuation Technology (EQ-VT) protocol, which is used to value the EQ-5D-5L instrument, comprises a composite time trade-off and a discrete choice experiment (DCE) module. Despite significant limitations, the DCE module has not been updated since its inception in 2012. This study aimed to update the EQ-VT DCE design using state-of-the-art methods. METHODS: DCE data from 19 EQ-5D-5L valuation studies were summarized using a Bayesian hierarchical meta-analysis model, which created the priors for our Bayesian efficient DCE design. This design comprised 20 subdesigns, each with 12 choice tasks, and included 2 levels that overlapped to reduce the complexity of the choice tasks. The relative efficiency and robustness of the new design were established by comparing the D-errors and minimal sample size requirements for the 19 within-sample and 7 out-of-sample countries with the previous DCE design. RESULTS: The updated DCE design shows large reductions in the D-error: by 20% and 22% for the 19 within-sample and 7 out-of-sample countries, respectively. Sample size requirements were also reduced, resulting in an average reduction of 45% for both the within and out-of-sample countries. CONCLUSIONS: The updated DCE design outperforms the current EQ-VT design. Given its enhanced performance and reduced complexity, it is set to replace the existing DCE design in future EQ-5D-5L valuation studies using the EQ-VT protocol.

Putting a Dollar Value on Informal Care Time Provided to People Living With Dementia: A Discrete Choice Experiment.

OBJECTIVES: Informal care represents a significant cost driver in dementia but monetizing informal care hours to inform cost-of-illness or economic evaluation studies remains a challenge. This study aimed to use a discrete choice experiment to estimate the value of informal care time provided to people with dementia in Australia accounting for positive and negative impacts of caregiving. METHODS: Attributes and levels were derived from a literature review, interviews with carers, and advice received from an advisory group. Attributes included 4 positive and negative caregiving experiences, in addition to "hours of care provided" and the "monetary compensation from the government." A D-efficient design was constructed with 2 generic alternatives that represented hypothetical informal caregiving situations. The discrete choice experiment survey was administered online to a representative sample of the Australian general population and a group of informal carers of people with dementia. The willingness to accept estimates were calculated for the 2 samples separately using the mixed logit model in the willingness to pay space. RESULTS: Based on 700 respondents included in the analysis (n = 488 general public, n = 212 informal carers), the mean willingness to accept for an additional hour of informal care, corrected for the positive and negative impacts of informal care, was $21 (95% CI 18-23) for the general public and $20 (95% CI 16-25) for the informal carers sample. CONCLUSION: The estimates generated in this study can be used to inform future cost-of-illness studies and economic evaluations, ensuring that informal care time is considered in future policy and funding decisions.

Land-Use Decisions Have Substantial Air Quality Health Effects.

Understanding how best to use limited land without compromising food security, health, and beneficial ecosystem functions is a critical challenge of our time. Ecosystem service assessments increasingly inform land-use decisions but seldom include the effects of land use on air quality, the largest environmental health risk. Here, we estimate and value the air quality health effects of potential land-use policies and projected trends in the United States, alongside carbon sequestration and economic returns to land, until 2051. We show that air quality health effects are of first-order importance in land-use decisions, often larger in value than carbon sequestration and economic returns combined. When air quality is properly accounted for, policies that appeared beneficial are shown to be detrimental and vice versa. Land-use-driven air quality impacts are largely from agricultural emissions and biogenic forest emissions, although incentives for reduced deforestation remain beneficial overall. Without evaluating air quality, we are unable to determine whether land-use decisions make us better or worse off.

Cleaning up while Changing Gears: The Role of Battery Design, Fossil Fuel Power Plants, and Vehicle Policy for Reducing Emissions in the Transition to Electric Vehicles.

Plug-in electric vehicles (PEVs) can reduce air emissions when charged with clean power, but prior work estimated that in 2010, PEVs produced 2 to 3 times the consequential air emission externalities of gasoline vehicles in PJM (the largest US regional transmission operator, serving 65 million people) due largely to increased generation from coal-fired power plants to charge the vehicles. We investigate how this situation has changed since 2010, where we are now, and what the largest levers are for reducing PEV consequential life cycle emission externalities in the near future. We estimate that PEV emission externalities have dropped by 17% to 18% in PJM as natural gas replaced coal, but they will remain comparable to gasoline vehicle externalities in base case trajectories through at least 2035. Increased wind and solar power capacity is critical to achieving deep decarbonization in the long run, but through 2035 we estimate that it will primarily shift which fossil generators operate on the margin at times when PEVs charge and can even increase consequential PEV charging emissions in the near term. We find that the largest levers for reducing PEV emissions over the next decade are (1) shifting away from nickel-based batteries to lithium iron phosphate, (2) reducing emissions from fossil generators, and (3) revising vehicle fleet emission standards. While our numerical estimates are regionally specific, key findings apply to most power systems today, in which renewable generators typically produce as much output as possible, regardless of the load, while dispatchable fossil fuel generators respond to the changes in load.

Willingness to pay for cataract surgery and its associated factors in selected medical centers in Urmia, Iran.

INTRODUCTION: Cataracts represent a significant public health issue and are the most prevalent vision disorder following refractive errors. They can restrict the economic, social, and psychological activities of those affected, thereby diminishing their quality of life. This study was undertaken to examine the willingness to pay for cataract surgery and the factors influencing this decision in selected medical centers in Urmia. The two-stage Heckman model was utilized to aid decision-making in financing this service. METHODS: This research was a descriptive-analytical study evaluating the economic aspects of health interventions, conducted from the patient's perspective. In this cross-sectional study, a sample size of 217 patients with cataract issues was selected using Michel Carson's table. These patients were from Imam Khomeini Hospital and Aftab Urmia Surgery Center in 2023. Data was collected using a conditional valuation approach to determine the maximum amount patients were willing to pay, as gathered through a questionnaire. The factors influencing the willingness to pay for cataract surgery were estimated using the two-stage Heckman regression model. The final effect value of each variable was calculated using Stata version 14 software. RESULTS: In this study, 81.11% of participants (176 individuals) expressed a willingness to pay for cataract surgery, with the average amount they were willing to pay being $206.3. However, out of the 217 respondents, 41 rejected the proposed amounts and were not willing to pay anything. The results of the Hemken model indicated that the income of patients and the size of their households significantly influenced their willingness to pay for cataract surgery. However, other variables investigated did not have a significant impact. CONCLUSION: The study results revealed a significant influence of both income and household size on the likelihood of willingness to pay, as well as the extent of willingness to pay for cataract surgery. Consequently, it is crucial to formulate policies and provisions that guarantee access to cataract services, particularly for individuals from large households with low income and high costs.

Scale and rate heterogeneity in the EQ-5D-5L valuation.

OBJECTIVES: To estimate values on a quality-adjusted life year (QALY) scale using individual preference evidence, choice analyses typically include ancillary parameters, such as scale factors and discount rates. These parameters potentially differ among respondents. In this study, we investigated how allowing heterogeneity in scale and rate affects the estimation of EQ-5D-5L values. METHODS: Using the first wave of the 2016 EQ-5D-5L valuation study (N = 1017), we estimated a conditional logit (CL) model and three mixed logit models: random scale, random rate, and bivariate. Prior to the exploratory study, we hypothesized that scale and rate are correlated and that allowing heterogeneity in both parameters decreases the number of insignificant incremental effects. We confirmed the exploratory findings by re-estimating these models using paired comparison responses from a second wave (N = 1229). RESULTS: Scale and rate exhibited significant heterogeneity and were positively correlated. As hypothesized, allowing this heterogeneity improved the face validity of the EQ-5D-5L value set by reducing the number of insignificant incremental effects (from 6 to 2 p-values > 0.05; out of 20). Nevertheless, the CL and bivariate mixed logit estimates are highly correlated and concordant (Pearson correlation coefficient of 0.897, Spearman correlation coefficient of 0.888, Lin's concordance coefficient of 0.763). CONCLUSIONS: Allowing this heterogeneity adds three parameters to the estimation (two variances and a correlation) and improves the face validity of the EQ-5D-5L values. This finding may influence experimental design and choice analysis in health valuation more generally.

The dilemma of valuing geodiversity: geoconservation versus geotourism.

Geodiversity and geosystem services are confronting global threats. However, the majority of conservation strategies tend to overlook the geological component within ecosystems. The existing literature centres on biodiversity, ecosystem services and their economic valuation. In this paper, we conduct a systematic literature review to identify the gap in the assessment of geological diversity, pinpointing areas where scientific contributions are needed to safeguard geological resources. Our findings reveal a concentration of studies assessing geodiversity in European and Asian countries. While the majority of the reviewed papers emphasizes the recreational features and associated values of geological resources, promoting geotourism and recognizing its potential for economic growth, there is a significant oversight concerning the impact of tourism on geological resources. Existing assessments predominantly focus on visitors' perceptions and preferences, sidelining the inhabitants' perspective and their crucial roles in the conservation of geodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Exploring health preference heterogeneity in the UK: Using the online elicitation of personal utility functions approach to construct EQ-5D-5L value functions on societal, group and individual level.

A new method has recently been developed for valuing health states, called 'Online elicitation of Personal Utility Functions' (OPUF). In contrast to established methods, such as time trade-off or discrete choice experiments, OPUF does not require hundreds of respondents, but allows estimating utility functions for small groups and even at the individual level. In this study, we used OPUF to elicit EQ-5D-5L health state preferences from a (not representative) sample of the UK general population, and then compared utility functions on the societal-, group-, and individual level. A demo version of the survey is available at: https://eq5d5l.me. Data from 874 respondents were included in the analysis. For each respondent, we constructed a personal EQ-5D-5L value set. These personal value sets predicted respondents' choices in three hold-out discrete choice tasks with an accuracy of 78%. Overall, preferences varied greatly between individuals. However, PERMANOVA analysis showed that demographic characteristics explained only a small proportion of the variability between subgroups. While OPUF is still under development, it has important strengths: it can be used to construct value sets for patient reported outcome instruments such as EQ-5D-5L, while also allowing examination of underlying preferences in an unprecedented level of detail. In the future, OPUF could be used to complement existing methods, allowing valuation studies in smaller samples, and providing more detailed insights into the heterogeneity of preferences across subgroups.

Is anchoring at 'dead' a theoretical requirement for health state valuation?

Values that accompany generic health measures are typically anchored at 1 = full health and 0 = dead. Some health states may then be considered 'worse than dead' (WTD) and assigned negative values, which causes fundamental measurement problems. In this paper, we challenge the assumption that anchoring values at 'dead = 0' is necessary for quality-adjusted life year (QALY) estimation. We summarise the role of 'dead' in health state valuation and consider three critical questions: (i) whether the measurement properties of health state values require 'dead'; (ii) whether 'dead' needs to be valued relative to health states; and (iii) whether values for states WTD are meaningful or useful. We conclude that anchoring 0 at dead is not a requirement of health status measurement or cost-effectiveness analysis. This results from reframing QALYs as the relevant unit of measurement and reframing values as being derived from QALYs rather than the reverse.

Valuing the SF-6Dv2 in the capital of Iran using a discrete choice experiment with duration.

BACKGROUND: The second version of the Short-Form 6-Dimension (SF-6Dv2) classification system has recently been developed. The objective of this study was to develop a value set for SF-6Dv2 based on the societal preferences of a general population in the capital of Iran. METHODS: A representative sample of the capital of Iran (n = 3061) was recruited using a stratified multistage quota sampling technique. Face-to-face interviews were conducted using binary choice sets from the international valuation protocol of the discrete choice experiment with duration. The conditional logit was used to estimate the final value set, and a latent class model was employed to assess heterogeneity of preferences. RESULTS: Coefficients generated from the models were logically consistent and significant. The best model was the one that included an additional interaction term for cases where one or more dimensions reached their most severe levels. It provides a value set with logical consistent coefficients and the lowest percentage of worse than death health states. Predicted values for the SF-6Dv2 were within the range of - 0.796-1. Pain dimension had the largest impact on utility decrement, whereas vitality had the least impact. The presence of preference heterogeneity was evident, and the Bayesian Information Criterion indicated the optimal fit for a latent class model with two classes. CONCLUSION: This study provided the SF-6Dv2 value set for application in the context of Iran. This value set will facilitate the use of the SF-6Dv2 instrument in health economic evaluations and clinical settings.

COVID-19-related health utility values and changes in COVID-19 patients and the general population: a scoping review.

PURPOSE: To summarise the diverse literature reporting the impact of COVID-19 on health utility in COVID-19 patients as well as in general populations being affected by COVID-19 control policies. METHODS: A literature search up to April 2023 was conducted to identify papers reporting health utility in COVID-19 patients or in COVID-19-affected general populations. We present a narrative synthesis of the health utility values/losses of the retained studies to show the mean health utility values/losses with 95% confidence intervals. Mean utility values/losses for categories defined by medical attendance and data collection time were calculated using random-effects models. RESULTS: In total, 98 studies-68 studies on COVID-19 patients and 30 studies on general populations-were retained for detailed review. Mean (95% CI) health utility values were 0.83 (0.81, 0.86), 0.78 (0.73, 0.83), 0.82 (0.78, 0.86) and 0.71 (0.65, 0.78) for general populations, non-hospitalised, hospitalised and ICU patients, respectively, irrespective of the data collection time. Mean utility losses in patients and general populations ranged from 0.03 to 0.34 and from 0.02 to 0.18, respectively. CONCLUSIONS: This scoping review provides a summary of the health utility impact of COVID-19 and COVID-19 control policies. COVID-19-affected populations were reported to have poor health utility, while a high degree of heterogeneity was observed across studies. Population- and/or country-specific health utility is recommended for use in future economic evaluation on COVID-19-related interventions.