Planning cost-effective operational forest inventories.

We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables better scheduling decisions but also implies higher costs. We focus on the outer stage, which we formulate as a maximization of the posterior value of the inventory decision under a budget constraint. The posterior value depends on the solution to the inner stage problem and its computation is analytically intractable, featuring an NP-hard binary optimization problem within a high-dimensional integral. In particular, the binary optimization problem is a special case of a generalized quadratic assignment problem. We present a practical method that solves the outer stage problem with an approximation which combines Monte Carlo sampling with a greedy, randomized method for the binary optimization problem. We derive inventory decisions for a dataset of 100 Swedish forest tracts across a range of inventory budgets and estimate the value of the information to be obtained.

A robust decision-making approach for designing coastal groundwater quality monitoring networks.

This paper presents a new approach to the spatiotemporal design of groundwater quality monitoring networks for coastal aquifers. A fusion model combines the outputs of several developed simulation models to make estimates more accurate. A modified GALDIT method is used to incorporate the aquifer vulnerability to saltwater intrusion. The value of information (VOI) theory is applied to determine sufficient monitoring wells. The groundwater quality monitoring network is designed by employing a robust decision-making (RDM) approach under different management strategies and economic considerations. This approach incorporates the deep uncertainties of some critical variables, including water level and total dissolved solids (TDS) concentration at the coastline and pumping flow rates of agricultural wells. The new methodology is implemented in the coastal Qom-Kahak aquifer, Iran. The results illustrate that the combination model has significantly improved evaluation criteria compared to individual prediction models. The fusion model results indicate that thirty monitoring wells would be ideal. The RDM-based analyses in the Qom-Kahak aquifer showed that an optimal network with 30 monitoring wells outperforms the current network regarding various criteria, such as VOI and variance of estimation error. The new well configuration also demonstrates a suitable spatial distribution. Given that the current sampling frequencies are unsuitable for areas with varying vulnerabilities, we recommend sampling every 3 months in areas with moderate vulnerabilities and once every three seasons in areas with low vulnerabilities, based on the information transfer index. Finally, a management strategy in which the pumping rate should be less than 60% of the current rate is suggested to prevent saltwater intrusion into the aquifer.

Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.

HIGHLIGHTS: The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.

Hyperthermic intraoperative peritoneal chemotherapy and cytoreductive surgery for people with peritoneal metastases: a systematic review and cost-effectiveness analysis.

Background: We compared the relative benefits, harms and cost-effectiveness of hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery ± systemic chemotherapy versus cytoreductive surgery ± systemic chemotherapy or systemic chemotherapy alone in people with peritoneal metastases from colorectal, gastric or ovarian cancers by a systematic review, meta-analysis and model-based cost-utility analysis. Methods: We searched MEDLINE, EMBASE, Cochrane Library and the Science Citation Index, ClinicalTrials.gov and WHO ICTRP trial registers until 14 April 2022. We included only randomised controlled trials addressing the research objectives. We used the Cochrane risk of bias tool version 2 to assess the risk of bias in randomised controlled trials. We used the random-effects model for data synthesis when applicable. For the cost-effectiveness analysis, we performed a model-based cost-utility analysis using methods recommended by The National Institute for Health and Care Excellence. Results: The systematic review included a total of eight randomised controlled trials (seven randomised controlled trials, 955 participants included in the quantitative analysis). All comparisons other than those for stage III or greater epithelial ovarian cancer contained only one trial, indicating the paucity of randomised controlled trials that provided data. For colorectal cancer, hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy probably results in little to no difference in all-cause mortality (60.6% vs. 60.6%; hazard ratio 1.00, 95% confidence interval 0.63 to 1.58) and may increase the serious adverse event proportions compared to cytoreductive surgery ± systemic chemotherapy (25.6% vs. 15.2%; risk ratio 1.69, 95% confidence interval 1.03 to 2.77). Hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy probably decreases all-cause mortality compared to fluorouracil-based systemic chemotherapy alone (40.8% vs. 60.8%; hazard ratio 0.55, 95% confidence interval 0.32 to 0.95). For gastric cancer, there is high uncertainty about the effects of hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy versus cytoreductive surgery + systemic chemotherapy or systemic chemotherapy alone on all-cause mortality. For stage III or greater epithelial ovarian cancer undergoing interval cytoreductive surgery, hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy probably decreases all-cause mortality compared to cytoreductive surgery + systemic chemotherapy (46.3% vs. 57.4%; hazard ratio 0.73, 95% confidence interval 0.57 to 0.93). Hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy may not be cost-effective versus cytoreductive surgery + systemic chemotherapy for colorectal cancer but may be cost-effective for the remaining comparisons. Limitations: We were unable to obtain individual participant data as planned. The limited number of randomised controlled trials for each comparison and the paucity of data on health-related quality of life mean that the recommendations may change as new evidence (from trials with a low risk of bias) emerges. Conclusions: In people with peritoneal metastases from colorectal cancer with limited peritoneal metastases and who are likely to withstand major surgery, hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy should not be used in routine clinical practice (strong recommendation). There is considerable uncertainty as to whether hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy or cytoreductive surgery + systemic chemotherapy should be offered to patients with gastric cancer and peritoneal metastases (no recommendation). Hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy should be offered routinely to women with stage III or greater epithelial ovarian cancer and metastases confined to the abdomen requiring and likely to withstand interval cytoreductive surgery after chemotherapy (strong recommendation). Future work: More randomised controlled trials are necessary. Study registration: This study is registered as PROSPERO CRD42019130504. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/135/02) and is published in full in Health Technology Assessment; Vol. 28, No. 51. See the NIHR Funding and Awards website for further award information.

Cancers of the bowel, ovary or stomach can spread to the lining of the abdomen ('peritoneal metastases'). Chemotherapy (the use of drugs that aim to kill cancer cells) given by injection or tablets ('systemic chemotherapy') is one of the main treatment options. There is uncertainty about whether adding cytoreductive surgery (cytoreductive surgery; an operation to remove the cancer) and 'hyperthermic intraoperative peritoneal chemotherapy' (warm chemotherapy delivered into the lining of the abdomen during cytoreductive surgery) are beneficial. We reviewed all the information from medical literature published until 14 April 2022, to answer the above uncertainty. We found the following from eight trials, including about 1000 participants. In people with peritoneal metastases from bowel cancer, hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy probably does not provide any benefits and increases harm compared to cytoreductive surgery + systemic chemotherapy, while cytoreductive surgery + systemic chemotherapy appears to increase survival compared to systemic chemotherapy alone. There is uncertainty about the best treatment for people with peritoneal metastases from stomach cancer. In women with peritoneal metastases from ovarian cancer who require systemic chemotherapy before cytoreductive surgery to shrink the cancer to allow surgery ('advanced ovarian cancer'), hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy probably increases survival compared to cytoreductive surgery + systemic chemotherapy. In people who can withstand a major operation and in whom cancer can be removed, cytoreductive surgery + systemic chemotherapy should be offered to people with peritoneal metastases from bowel cancer, while hyperthermic intraoperative peritoneal chemotherapy + cytoreductive surgery + systemic chemotherapy should be offered to women with peritoneal metastases from 'advanced ovarian cancer'. Uncertainty in treatment continues for gastric cancer. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/135/02) and is published in full in Health Technology Assessment; Vol. 28, No. 51. See the NIHR Funding and Awards website for further award information.

Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.

BACKGROUND: The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models. METHODS: Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation. RESULTS: The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods. CONCLUSIONS: The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI. HIGHLIGHTS: The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.

Methods to Quantify the Importance of Parameters for Model Updating and Distributional Adaptation.

PURPOSE: Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA). METHODS: Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study. RESULTS: In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these. CONCLUSIONS: These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters. HIGHLIGHTS: It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.

Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty.

BACKGROUND: Methods to present the result of cost-effectiveness analyses under parameter uncertainty include cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), expected loss curves (ELCs), and net monetary benefit (NMB) lines. We describe how NMB lines can be augmented to present NMB values that could be achieved by reducing or resolving parameter uncertainty. We evaluated the ability of these methods to correctly 1) identify the alternative with the highest expected NMB and 2) communicate the magnitude of parameter and decision uncertainty. METHODS: We considered 4 hypothetical decision problems representing scenarios with high variance or correlated cost and effect estimates and alternatives with similar cost-effectiveness ratios. We used these decision problems to demonstrate the limitations of existing methods and the potential of augmented NMB lines to resolve these issues. RESULTS: CEPs and CEACs/CEAF could falsely imply the lack of sufficient evidence to identify the optimal option if cost and effect estimates have high variance, are correlated across alternatives, or when alternatives have similar cost-effectiveness ratios. The augmented NMB lines and ELCs can correctly identify the option with the highest expected NMB and communicate the potential benefit of resolving uncertainties. Like ELCs, the augmented NMB lines provide information about the value of resolving parameter uncertainties, but augmented NMB lines may be easier to interpret for decision makers. CONCLUSIONS: Our analysis supports recommending the augment NMB lines as an important method to present the results of economic evaluation studies under parameter uncertainty. HIGHLIGHTS: The results of cost-effectiveness analyses (CEAs) when the cost and effect estimates of alternatives are uncertain are commonly presented using cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), and expected loss curves (ELCs).Although currently not often used, net monetary benefit (NMB) lines could present the results of cost-effectiveness to identify the alternative with the highest expected NMB values given the current level of uncertainty. Furthermore, NMB lines can be augmented to 1) show metrics of value of information, which measure the value of additional research to reduce or eliminate the decision uncertainty, and 2) display the confidence intervals along the NMB lines to ensure that NMB values are estimated accurately using a sufficiently large number of parameter samples.Using several decision problems, we demonstrate the limitation of existing methods to present the results of CEAs under parameter uncertainty and how augmented NMB lines could resolve these issues.Our analysis supports recommending augmented NMB lines as an important method to present the results of CEA under uncertainty since they 1) correctly identify the alternative with the highest expected NMB value given the current evidence, 2) provide information about the potential value of additional research to improve the decision by reducing or resolving uncertainty in model parameters, 3) assist the analysis to visually ensure that enough parameter samples are used to estimate the expected NMB of alternatives, and 4) are easier to interpret for decision makers compared with other methods.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Neural mechanisms of information seeking.

We ubiquitously seek information to make better decisions. Particularly in the modern age, when more information is available at our fingertips than ever, the information we choose to collect determines the quality of our decisions. Decision neuroscience has long adopted empirical approaches where the information available to decision-makers is fully controlled by the researchers, leaving neural mechanisms of information seeking less understood. Although information seeking has long been studied in the context of the exploration-exploitation trade-off, recent studies have widened the scope to investigate more overt information seeking in a way distinct from other decision processes. Insights gained from these studies, accumulated over the last few years, raise the possibility that information seeking is driven by the reward system signaling the subjective value of information. In this piece, we review findings from the recent studies, highlighting the conceptual and empirical relationships between distinct literatures, and discuss future research directions necessary to establish a more comprehensive understanding of how individuals seek information as a part of value-based decision-making.

Cytoreductive surgery plus hyperthermic intraoperative peritoneal chemotherapy for people with peritoneal metastases from colorectal, ovarian or gastric origin: A systematic review of randomized controlled trials.

BACKGROUND: There is uncertainty in the relative benefits and harms of hyperthermic intraoperative peritoneal chemotherapy (HIPEC) when added to cytoreductive surgery (CRS) +/- systemic chemotherapy or systemic chemotherapy alone in people with peritoneal metastases from colorectal, gastric, or ovarian cancers. METHODS: We searched randomized controlled trials (RCTs) in the medical literature until April 14, 2022 and applied methods used for high-quality systematic reviews. FINDINGS: We included a total of eight RCTs (seven RCTs included in quantitative analysis as one RCT did not provide data in an analyzable format). All comparisons other than ovarian cancer contained only one trial. For gastric cancer, there is high uncertainty about the effect of CRS + HIPEC + systemic chemotherapy. For stage III or greater epithelial ovarian cancer undergoing interval cytoreductive surgery, CRS + HIPEC + systemic chemotherapy probably decreases all-cause mortality compared to CRS + systemic chemotherapy. For colorectal cancer, CRS + HIPEC + systemic chemotherapy probably results in little to no difference in all-cause mortality and may increase the serious adverse events proportions compared to CRS +/- systemic chemotherapy, but probably decreases all-cause mortality compared to fluorouracil-based systemic chemotherapy alone. INTERPRETATION: The role of CRS + HIPEC in gastric peritoneal metastases is uncertain. CRS + HIPEC should be standard of care in women with stage III or greater epithelial ovarian cancer undergoing interval CRS. CRS + systemic chemotherapy should be standard of care for people with colorectal peritoneal metastases, with HIPEC given only as part of a RCT focusing on subgroups and regimes. PROSPERO REGISTRATION: CRD42019130504.

Utilizing direct and indirect information to improve the COVID-19 vaccination booster scheduling.

Current global COVID-19 booster scheduling strategies mainly focus on vaccinating high-risk populations at predetermined intervals. However, these strategies overlook key data: the direct insights into individual immunity levels from active serological testing and the indirect information available either through sample-based sero-surveillance, or vital demographic, location, and epidemiological factors. Our research, employing an age-, risk-, and region-structured mathematical model of disease transmission-based on COVID-19 incidence and vaccination data from Israel between 15 May 2020 and 25 October 2021-reveals that a more comprehensive strategy integrating these elements can significantly reduce COVID-19 hospitalizations without increasing existing booster coverage. Notably, the effective use of indirect information alone can considerably decrease COVID-19 cases and hospitalizations, without the need for additional vaccine doses. This approach may also be applicable in optimizing vaccination strategies for other infectious diseases, including influenza.

It Takes 2 to Tango. Setting Out the Conditions in Which Performance-Based Risk-Sharing Arrangements Work for Both Parties.

OBJECTIVES: Faster regulatory approval processes often fail to achieve faster patient access. We seek an approach, using performance-based risk-sharing arrangements, to address uncertainty for payers regarding the relative effectiveness and value for money of products launched through accelerated approval schemes. One important reason for risk sharing is to resolve differences of opinion between innovators and payers about a technology's underlying value. To date, there has been no formal attempt to set out the circumstances in which risk sharing can address these differences. METHODS: We use a value of information framework to understand what a performance-based risk-sharing arrangements can, in principle, add to a reimbursement scheme, separating payer perspectives on cost-effectiveness and the value of research from those of the innovator. We find 16 scenarios, developing 5 rules to analyze these 16 scenarios, identifying cases in which risk sharing adds value for both parties. RESULTS: We find that risk sharing provides an improved solution in 9 out of 16 combinations of payer and innovator expectations about treatment outcome and the value of further research. Among our assumptions, who pays for research and scheme administration costs are key. CONCLUSIONS: Steps should be undertaken to make risk sharing more practical, ensuring that payers consider it an option. This requires additional costs to the health system falling on the innovator in an efficient way that aligns incentives for product development for global markets. Health systems benefits are earlier patient access to cost-effective treatments and payers with higher confidence of not wasting money. Innovators get greater returns while conducting research.

Potential Cost-Effectiveness of Maternal Influenza Immunisation in Low-Income Countries: An Explorative Modelling Study and Value of Information Analysis to Guide Future Clinical Research.

Maternal influenza immunisation (MII) is recommended for protecting pregnant women and infants under six months of age from severe disease related to influenza. However, few low-income countries have introduced this vaccine. Existing cost-effectiveness studies do not consider potential vaccine non-specific effects (NSE) observed in some settings, such as reductions in preterm birth. A decision tree model was built to examine the potential cost-effectiveness of MII in a hypothetical low-income country compared to no vaccination, considering possible values for NSE on preterm birth in addition to vaccine-specific effects on influenza. We synthesized epidemiological and cost data from low-income countries. All costs were adjusted to 2021 United States dollars (USD). We considered cost-effectiveness thresholds that reflect opportunity costs (USD 188 per disability-adjusted life year averted; range: USD 28-538). Results suggest that even a small (5%) NSE on preterm birth may make MII a cost-effective strategy in these settings. A value of information analysis indicated that acquiring more information on the presence and possible size of NSE of MII could greatly reduce the uncertainty in decision-making on MII. Further clinical research investigating NSE in low-income countries may be of high value to optimise immunisation policy.

Disease X epidemic control using a stochastic model and a deterministic approximation: Performance comparison with and without parameter uncertainties.

BACKGROUND: The spread of infectious diseases can be modeled using deterministic or stochastic models. A deterministic approximation of a stochastic model can be appropriate under some conditions, but is unable to capture the discrete nature of populations. We look into the choice of a model from the perspective of decision making. METHOD: We consider an emerging disease (Disease X) in a closed population modeled by a stochastic SIR model or its deterministic approximation. The objective of the decision maker is to minimize the cumulative number of symptomatic infected-days over the course of the epidemic by picking a vaccination policy. We consider four decision making scenarios: based on the stochastic model or the deterministic model, and with or without parameter uncertainty. We also consider different sample sizes for uncertain parameter draws and stochastic model runs. We estimate the average performance of decision making in each scenario and for each sample size. RESULTS: The model used for decision making has an influence on the picked policies. The best achievable performance is obtained with the stochastic model, knowing parameter values, and for a large sample size. For small sample sizes, the deterministic model can outperform the stochastic model due to stochastic effects. Resolving uncertainties may bring more benefit than switching to the stochastic model in our example. CONCLUSION: This article illustrates the interplay between the choice of a type of model, parameter uncertainties, and sample sizes. It points to issues to be considered when optimizing a stochastic model.

Identifying opportunities for high resolution pesticide usage data to improve the efficiency of endangered species pesticide risk assessment.

The US pesticide registration and review process requires regular re-assessment of the risk of pesticide use to species listed under the Endangered Species Act (ESA), yet current assessment methods are inefficient when applied to hundreds of pesticides potentially impacting multiple species across a continent. Thus, many pesticides remain on the market without complete review. We assessed the value of using high resolution pesticide usage data in the risk assessment process to rapidly improve process efficiency. By using data available only in California, we found that high resolution data increased the number of species deemed not likely to be adversely affected by pesticides from <5 % to nearly 50 %. Across the contiguous US, we predicted that 48 % of species would be deemed not likely to be adversely affected using high resolution data, compared to 20 % without. However, if such data were available in just 11 states, 68 % of the available gains in efficiency could be obtained. Overall, using existing high-resolution data in California and a focused collection of such information from 11 other states could reduce risk assessment burden across the contiguous U.S. by one-quarter.

Value of information dynamics in Disease X vaccine clinical trials.

BACKGROUND: Solutions have been proposed to accelerate the development and rollout of vaccines against a hypothetical disease with epidemic or pandemic potential called Disease X. This may involve resolving uncertainties regarding the disease and the new vaccine. However the value for public health of collecting this information will depend on the time needed to perform research, but also on the time needed to produce vaccine doses. We explore this interplay, and its effect on the decision on whether or not to perform research. METHOD: We simulate numerically the emergence and transmission of a disease in a population using a susceptible-infected-recovered (SIR) compartmental model with vaccination. Uncertainties regarding the disease and the vaccine are represented by parameter prior distributions. We vary the date at which vaccine doses are available, and the date at which information about parameters becomes available. We use the expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) to measure the value of information. RESULTS: As expected, information has less or no value if it comes too late, or (equivalently) if it can only be used too late. However we also find non trivial dynamics for shorter durations of vaccine development. In this parameter area, it can be optimal to implement vaccination without waiting for information depending on the respective durations of dose production and of clinical research. CONCLUSION: We illustrate the value of information dynamics in a Disease X outbreak scenario, and present a general approach to properly take into account uncertainties and transmission dynamics when planning clinical research in this scenario. Our method is based on numerical simulation and allows us to highlight non trivial effects that cannot otherwise be investigated.

Value of Information Analysis of a Web-Based Self-Management Intervention for Chronic Obstructive Pulmonary Disease.

Objective: Technology-based programs can be cost-effective in the management of chronic obstructive pulmonary disease (COPD). However, cost-effectiveness estimates always contain some uncertainty, and decisions based upon them carry some risk. We conducted a value of information (VOI) analysis to estimate the value of additional research of a web-based self-management intervention for COPD to reduce the costs associated with uncertainty. Methods: We used a 10,000-iteration cost-effectiveness model from the health care payer perspective to calculate the expected value of perfect information (EVPI) at the patient- and population-level. An opportunity loss was incurred when the web-based intervention did not produce a greater net monetary benefit than usual care in an iteration. We calculated the probability of opportunity loss and magnitude of opportunity costs as a function of baseline health utility. We aggregated opportunity costs over the projected incident population of inpatient COPD patients over 10 years and estimated it as a function of the willingness-to-pay (WTP) threshold. Costs are in 2022 U.S. Dollars. Results: Opportunity losses were found in 22.7% of the iterations. The EVPIpatient was $78 per patient (95% confidence interval: $75-$82). The probability that the intervention was the optimal strategy varied across baseline health utilities. The EVPIpopulation was $506,666,882 over 10 years for a WTP of $50,000. Conclusions: Research estimated to cost up to $500 million would be warranted to reduce uncertainty. Future research could focus on identifying the impact of baseline health utilities to maximize the cost savings of the intervention. Other considerations for future research priorities include implementation efforts for technology-based interventions.

Identifying the Optimum Strategy for Identifying Adults and Children With Celiac Disease: A Cost-Effectiveness and Value of Information Analysis.

OBJECTIVES: Celiac disease (CD) is thought to affect around 1% of people in the United Kingdom, but only approximately 30% are diagnosed. The aim of this work was to assess the cost-effectiveness of strategies for identifying adults and children with CD in terms of who to test and which tests to use. METHODS: A decision tree and Markov model were used to describe testing strategies and model long-term consequences of CD. The analysis compared a selection of pre-test probabilities of CD above which patients should be screened, as well as the use of different serological tests, with or without genetic testing. Value of information analysis was used to prioritize parameters for future research. RESULTS: Using serological testing alone in adults, immunoglobulin A (IgA) tissue transglutaminase (tTG) at a 1% pre-test probability (equivalent to population screening) was most cost-effective. If combining serological testing with genetic testing, human leukocyte antigen combined with IgA tTG at a 5% pre-test probability was most cost-effective. In children, the most cost-effective strategy was a 10% pre-test probability with human leukocyte antigen plus IgA tTG. Value of information analysis highlighted the probability of late diagnosis of CD and the accuracy of serological tests as important parameters. The analysis also suggested prioritizing research in adult women over adult men or children. CONCLUSIONS: For adults, these cost-effectiveness results suggest UK National Screening Committee Criteria for population-based screening for CD should be explored. Substantial uncertainty in the results indicate a high value in conducting further research.

Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, value of information, situational awareness, horizon scanning, and forecasting) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

The Value-of-Information and Value-of-Implementation from Clinical Trials of Diagnostic Tests for HIV-Associated Tuberculosis: A Modeling Analysis.

Objectives. Conventional value-of-information (VOI) analysis assumes complete uptake of an optimal decision. We employed an extended framework that includes value-of-implementation (VOM)-the benefit of encouraging adoption of an optimal strategy-and estimated how future trials of diagnostic tests for HIV-associated tuberculosis could improve public health decision making and clinical and economic outcomes. Methods. We evaluated the clinical outcomes and costs, given current information, of 3 tuberculosis screening strategies among hospitalized people with HIV in South Africa: sputum Xpert (Xpert), sputum Xpert plus urine AlereLAM (Xpert+AlereLAM), and sputum Xpert plus the newer, more sensitive, and costlier urine FujiLAM (Xpert+FujiLAM). We projected the incremental net monetary benefit (INMB) of decision making based on results of a trial comparing mortality with each strategy, rather than decision making based solely on current knowledge of FujiLAM's improved diagnostic performance. We used a validated microsimulation to estimate VOI (the INMB of reducing parameter uncertainty before decision making) and VOM (the INMB of encouraging adoption of an optimal strategy). Results. With current information, adopting Xpert+FujiLAM yields 0.4 additional life-years/person compared with current practices (assumed 50% Xpert and 50% Xpert+AlereLAM). While the decision to adopt this optimal strategy is unaffected by information from the clinical trial (VOI = $ 0 at $3,000/year-of-life saved willingness-to-pay threshold), there is value in scaling up implementation of Xpert+FujiLAM, which results in an INMB (representing VOM) of $650 million over 5 y. Conclusions. Conventional VOI methods account for the value of switching to a new optimal strategy based on trial data but fail to account for the persuasive value of trials in increasing uptake of the optimal strategy. Evaluation of trials should include a focus on their value in reducing barriers to implementation. Highlights: In conventional VOI analysis, it is assumed that the optimal decision will always be adopted even without a trial. This can potentially lead to an underestimation of the value of trials when adoption requires new clinical trial evidence. To capture the influence that a trial may have on decision makers' willingness to adopt the optimal decision, we also consider value-of-implementation (VOM), a metric quantifying the benefit of new study information in promoting wider adoption of the optimal strategy. The overall value-of-a-trial (VOT) includes both VOI and VOM.Our model-based analysis suggests that the information obtained from a trial of screening strategies for HIV-associated tuberculosis in South Africa would have no value, when measured using traditional methods of VOI assessment. A novel strategy, which includes the urine FujiLAM test, is optimal from a health economic standpoint but is underutilized. A trial would reduce uncertainties around downstream health outcomes but likely would not change the optimal decision. The high VOT (nearly $700 million over 5 y) lies solely in promoting uptake of FujiLAM, represented as VOM.Our results highlight the importance of employing a more comprehensive approach for evaluating prospective trials, as conventional VOI methods can vastly underestimate their value. Trialists and funders can and should assess the VOT metric instead when considering trial designs and costs. If VOI is low, the VOM and cost of a trial can be compared with the benefits and costs of other outreach programs to determine the most cost-effective way to improve uptake.