Value Of Information Analysis Research Articles

Rationalising data collection for supporting decision making in building energy systems using value of information analysis

Rationalising data collection for supporting decision making in building energy systems using value of information analysis

Cost-effectiveness analysis of Chagas disease screening in pregnant women and their infants in Italy

Abstract Background Currently, Chagas disease (CD) is widespread in European nations that host notable populations from Latin America. Vertical transmission is a pathway for the spread of the disease in countries where it is not endemic. Due to socio-economic disparities, CD is a public health concern in non-endemic regions, raised by migration phenomena in vulnerable population groups who face similar hindrances as those most at risk in endemic areas. The aim is to evaluate the cost-effectiveness of CD screening versus no screening in pregnant women and their infants in Italy. Methods A Bayesian Markov model was employed to compare the CD screening versus the no screening alternative. Model parameters were sourced from pertinent scientific literature. A lifetime perspective was adopted, aligning with the perspective of the Italian National Health Service. The Eurozone threshold was applied. Costs and benefits were discounted at a rate of 3%. Gibbs sampling served as the algorithm for Bayesian inference. Uncertainty was addressed through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI), depicted via the Cost-Effectiveness Acceptability Curve (CEAC) and Expected Value of Perfect Information (EVPI). Results were presented as the Incremental Cost-Effectiveness Ratio (ICER). Results The base case results showed that CD screening was cost-effective, with an ICER of €5,112 [€1,850 - €10,043]. Furthermore, according to the CEAC, the screening option exhibited the greatest probability of being cost-effective across all thresholds. Additionally, the EVPI per mother was €818 based on a threshold of €50,000/diagnosis. Conclusions Implementing a structured CD screening program also in non-endemic countries has the potential for early detection and treatment. This contributes to SDG 3 by improving access to healthcare, preventing transmission, and reducing the disease burden as well as informing public health policies for marginalised populations. Key messages • This study proves that screening for CD in pregnant women and their infants in a non-endemic area is a cost-effective strategy and possibly cost-saving in the long term. • Study findings could support a wider implementation of CD screening in Italy by informing health policy-makers in their decision-making process.

Calculating carbon: The value of information in precision for blue carbon restoration projects

Coastal wetland restoration projects can receive payments for ecosystem services but often occur in regions with limited data, and additional data collection can be financially prohibitive. Value of Information analysis can quantify the difference between the expected value of an action before and after new information has been collected, aiming to understand how much data is required to make decisions that balance the costs of implementation versus the benefits of the project. The Australian carbon market provides a method that uses reintroduction of tidal flows to restore coastal wetland ecosystems for their carbon sequestration functions. The method requires a hydrological assessment of prospective sites, which is employed to estimate carbon sequestration potential. This research investigates how different amounts of data collection and different levels of complexity in the hydrological assessment influence the carbon abatement emissions estimated using the method. The results indicate that tidal restoration for blue carbon credits on grazing land may not be financially viable. We found that tidal data collected onsite were important for decision-making while complex hydrological models have low value compared to more simplistic approaches. While investing in data collection provides more value than increasing the complexity of modelling approaches, the value of information was still low. Additionally, restoration of coastal wetlands is unlikely to be financially attractive at current carbon prices, and the land would have to be unsuitable for cattle to become profitable for restoration. This work provides a framework for evaluating the financial benefit of collecting on-site data and using robust methods for estimating inundation, that can be used to guide decision-making to achieve optimal income.

Why shouldn't I collect more data? Reconciling disagreements between intuition and value of information analyses

Abstract Value of information (VoI) analysis is a method for quantifying how additional information may improve management decisions, with applications ranging from conservation to fisheries. However, VoI studies frequently suggest that collecting more data will not substantially improve management outcomes. This often contradicts the intuition of ecologists and managers who usually believe new information is critical for management. This inconsistency is exacerbated by the perception that VoI is a black‐box method. A lack of understanding as to why VoI is usually lower than ecologists expect is hampering on‐ground uptake. There is an urgent need to identify the factors that drive VoI methodology to produce low values. Here, we use a rigorous approach to provide insights into why VoI values are often low. We first derive analytic solutions and upper bounds for a VoI problem with two uncertain states, two actions, and four management outcomes. We show how VoI changes with respect to the benefit (i.e. utility) of implementing actions in each state, and the probability the system is in each state. We apply our formulation to a published frog population management case study and extend the results numerically to 10 million randomly generated larger‐sized problems. Zero VoI occurred half of the time in our two‐action two‐state simulations, corresponding to when one action is best, or equal best, across all states. Even when VoI values were positive, they were typically low. However, on average, VoI tended to increase with the number of states and actions. Our analytic expression for VoI, in the case where VoI is positive, demonstrates that VoI is characterized by the state probabilities and, the utility gaps, that is the difference in utility of deploying each action in each state. Our derived bounds reveal that, in all two‐action two‐state systems, VoI cannot be larger than half the largest utility gap. Our simple, yet powerful, analysis provides precious insight into the important factors that drive VoI analysis. Our work provides an essential stepping stone towards increasing the interpretability of VoI analysis in more complex settings, ultimately empowering managers to use VoI to help inform their decisions.

Cost-benefit analysis for SHM systems based on minimum detectable parameter change

Value of information (VoI) analysis can be used to assess the benefit of monitoring the health of infrastructure such as bridges and waterway locks. Such information supports decision-making processes, guiding operators towards the best actions for the infrastructure. In recent studies, VoI analysis is integrated with the modeling of the temporal evolution of structural conditions, so that the value of Structural Health Monitoring (VoSHM) can be assessed over the life cycle of the infrastructure. However, none of these studies considers the ability of the measurement system to detect small changes and its impact on the assessment of the VoI. This paper presents an approach to VoI assessment based on the concept of minimum change detectability and Bayesian linear filters. By exploiting a feature of the Kalman filter whereby posterior covariance is independent of observations, it is possible to predict the minimum shift in the mean value of a structural parameter for which its posterior density function would exceed a user-defined safety threshold. By constructing the decision tree and considering the cost of monitoring and maintenance, the value of detecting smaller changes can be estimated, which is the value of early change detection and performing preventive maintenance. As a proof of concept, this approach is applied to assess the VoSHM for a navigation lock built with precast reinforced concrete elements, where the steel bars in the chamber are damaged by fatigue and corrosion. The VoSHM is compared with the VoI associated to other Non Destructive Tests. The paper then discusses the implications of applying Bayesian linear filters in SHM for parameter change detection, the main one being the development of refined scales for structural condition assessment before any data from the changed state is available. Ultimately, the VoSHM is the benefit brought by adopting these refined scales in maintenance management.

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.

The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses. We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation. Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion). Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses. This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

On information value and decision analyses

This paper addresses value of information, structural health information, and decision analyses in built environment engineering. Decision analyses are used for the joint optimization of information collection and the identification of physical measures for the management and enhancement of the expected utility of built environment systems. To these ends, this paper contains (1) principal descriptions of decision scenarios and the model basis in conjunction with the objective functions, embedded in (a) a summary of the historical background and (b) the origins of the methods of decision and value of information analyses, (2) an overview of methods for decision analyses highlighting solution strategies to handle comprehensiveness and computational challenges, (3) principal application areas and case studies, and (4) insights into recent advances. The main insights and an outreach to potential future research perspectives are summarised at the end of this paper.

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature.

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

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.

Sampling design methods for making improved lake management decisions

AbstractThe ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run‐off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and restoration costs, the value of information analysis can be compared with the survey's costs. Data are worth gathering if the expected value from the data exceeds the costs. From existing data, we specify a hierarchical Bayesian spatial logistic regression model for the ecological status of lakes. We then rely on functional approximations and Laplace approximations to get closed‐form expressions for the value of information of a sampling design. The case study contains thousands of lakes. The combinatorially difficult design problem is to wisely pick the right subset of lakes for data gathering. To solve this optimization problem, we study the performance of various heuristics: greedy forward algorithms, exchange algorithms and Bayesian optimization approaches. The value of information increases quickly when adding lakes to a small design but then flattens out. Good designs are usually composed of lakes that are difficult to manage, while also balancing a variety of covariates and geographic coverage. The designs achieved by forward selection are reasonably good, but we can outperform them with the more nuanced search algorithms. Statistical designs clearly outperform other designs selected according to simpler criteria.

Cost-Effectiveness of Whole-Genome vs Whole-Exome Sequencing Among Children With Suspected Genetic Disorders

The diagnosis of rare diseases and other genetic conditions can be daunting due to vague or poorly defined clinical features that are not recognized even by experienced clinicians. Next-generation sequencing technologies, such as whole-genome sequencing (WGS) and whole-exome sequencing (WES), have greatly enhanced the diagnosis of genetic diseases by expanding the ability to sequence a large part of the genome, rendering a cost-effectiveness comparison between them necessary. To assess the cost-effectiveness of WGS compared with WES and conventional testing in children with suspected genetic disorders. In this economic evaluation, a bayesian Markov model was implemented from January 1 to June 30, 2023. The model was developed using data from a cohort of 870 pediatric patients with suspected genetic disorders who were enrolled and underwent testing in the Ospedale Pediatrico Bambino Gesù, Rome, Italy, from January 1, 2015, to December 31, 2022. The robustness of the model was assessed through probabilistic sensitivity analysis and value of information analysis. Overall costs, number of definitive diagnoses, and incremental cost-effectiveness ratios per diagnosis were measured. The cost-effectiveness analyses involved 4 comparisons: first-tier WGS with standard of care; first-tier WGS with first-tier WES; first-tier WGS with second-tier WES; and first-tier WGS with second-tier WGS. The ages of the 870 participants ranged from 0 to 18 years (539 [62%] girls). The results of the analysis suggested that adopting WGS as a first-tier strategy would be cost-effective compared with all other explored options. For all threshold levels above €29 800 (US $32 408) per diagnosis that were tested up to €50 000 (US $54 375) per diagnosis, first-line WGS vs second-line WES strategy (ie, 54.6%) had the highest probability of being cost-effective, followed by first-line vs second-line WGS (ie, 54.3%), first-line WGS vs the standard of care alternative (ie, 53.2%), and first-line WGS vs first-line WES (ie, 51.1%). Based on sensitivity analyses, these estimates remained robust to assumptions and parameter uncertainty. The findings of this economic evaluation encourage the development of policy changes at various levels (ie, macro, meso, and micro) of international health systems to ensure an efficient adoption of WGS in clinical practice and its equitable access.

Cost-effectiveness of linezolid to ventilator-associated pneumonia in Colombia

IntroductionVentilator-associated pneumonia (VAP) is a prominent cause of morbidity and mortality in intensive care unit (ICU) patients. Due to the increase in Methicillin resistant Staphylococcus aureus infection, it is important to consider other more effective and safer alternatives compared to vancomycin. This motivates evaluating whether the use of an apparently more expensive drug such as linezolid can be cost-effective in Colombia.MethodsA decision tree was used to simulate the results in terms of the cost and proportion of cured patients. In the simulation, patients can receive antibiotic treatment with linezolid (LZD 600 mg IV/12 h) or vancomycin (VCM 15 mg/kg iv/12 h) for 7 days, patients they can experience events adverse (renal failure and thrombocytopenia). The model was analyzed probabilistically, and a value of information analysis was conducted to inform the value of conducting further research to reduce current uncertainties in the evidence base. Cost-effectiveness was evaluated at a willingness-to-pay (WTP) value of US$5180.ResultsThe mean incremental cost of LZD versus VCM is US$-517. This suggests that LZD is less costly. The proportion of patients cured when treated with LZD compared with VCM is 53 vs. 43%, respectively. The mean incremental benefit of LZD versus VCM is 10 This position of absolute dominance (LZD has lower costs and higher proportion of clinical cure than no supplementation) is unnecessary to estimate the incremental cost-effectiveness ratio. There is uncertainty with a 0.999 probability that LZD is more cost-effective than VCM. Our base‐case results were robust to variations in all assumptions and parameters.ConclusionLNZ is a cost-effective strategy for patients, ≥ 18 years of age, with VAP in Colombia- Our study provides evidence that can be used by decision-makers to improve clinical practice guidelines.

Estimating the value of future research into thromboprophylaxis for women during pregnancy and after delivery: a value of information analysis

BackgroundRisk assessment models (RAMs) are used to select women at increased risk of venous thromboembolism (VTE) during pregnancy and the puerperium for thromboprophylaxis. ObjectivesTo estimate the value of potential future studies that would reduce the decision uncertainty associated with offering thromboprophylaxis according to available RAMs in the following groups: high-risk antepartum women (eg, prior VTE), unselected postpartum women, and postpartum women with risk factors (obesity or cesarean delivery). MethodsA decision-analytic model was developed to simulate clinical outcomes, lifetime costs, and quality-adjusted life-years for different thromboprophylaxis strategies, including thromboprophylaxis for all, thromboprophylaxis for none, and RAM-based thromboprophylaxis. The expected value of perfect information analysis was used to determine which factors are associated with high decision uncertainty. The value of future research studies was estimated using expected value of sample information analysis. Costs were assessed from a health and social services perspective. ResultsThe expected value of perfect information analysis identified high decision uncertainty for high-risk antepartum women (£21.8 million) and obese postpartum women (£13.4 million), which was largely attributable to uncertainty regarding the effectiveness of thromboprophylaxis in reducing VTE. A randomized controlled trial of thromboprophylaxis compared with none in obese postpartum women is likely to have substantial value (£2.8 million; 300 participants per arm). A trial in women with previous VTE would have higher value but would be less acceptable. ConclusionFuture research should focus on estimating the effectiveness of thromboprophylaxis in obese postpartum women with additional risk factors who have not had a previous VTE.

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

ObjectivesCeliac 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. MethodsA 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. ResultsUsing 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. ConclusionsFor 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.

OP47 The Risk-Based Price: Incorporating Uncertainty And Risk Attitudes In Health Technology Pricing

IntroductionDecision makers often use value-based decision rules to determine whether technologies offer good value for money and should therefore be adopted, comparing cost-effectiveness analysis results with a threshold value. This assumes that decision makers are indifferent to interventions with the same expected value but different levels of underlying uncertainty. Such indifference is unlikely to hold in practice. We propose a risk-based price and accompanying decision rules to address this limitation.MethodsWe characterized risk using the per-patient expected value of perfect independent information (EVPII), a modification of a standard output from value of information analysis. The EVPII estimates the expected value of net benefit losses caused by uncertainty related to a technology, independent of the uncertainty related to alternative treatments. ‘Payer risk tolerance’ is then defined as the maximum per-patient risk of making wrong decisions that payers are willing to accept, expressed in monetary terms. The risk-based price is the price at which the EVPII is equal to the payer risk tolerance.ResultsThe risk-based pricing decision rules are as follows: (i) a technology is acceptable for adoption at the submitted price if the incremental net benefit of the technology is greater than or equal to zero and the EVPII is less than or equal to the payer risk tolerance; and (ii) the optimal technology has the greatest expected net benefit at the lowest of the sponsor submitted, value-based, or risk-based price at a given cost-effectiveness threshold value.ConclusionsThe risk-based price incorporates uncertainty and risk attitudes into decision-making. We demonstrate that both risk-averse and risk-neutral payers prefer the outcomes of risk-based pricing. Risk-based decision rules incentivize sponsors to develop evidence. Implementation of the risk-based price improves outcomes for patients by increasing health system net benefits under constrained resources, with better alignment to decision maker risk attitudes.

Bayesian cost-effectiveness analysis of Whole genome sequencing versus Whole exome sequencing in a pediatric population with suspected genetic disorders

Genetic diseases are medical conditions caused by sequence or structural changes in an individual’s genome. Whole exome sequencing (WES) and whole genome sequencing (WGS) are increasingly used for diagnosing suspected genetic conditions in children to reduce the diagnostic delay and accelerating the implementation of appropriate treatments. While more information is becoming available on clinical efficacy and economic sustainability of WES, the broad implementation of WGS is still hindered by higher complexity and economic issues. The aim of this study is to estimate the cost-effectiveness of WGS versus WES and standard testing for pediatric patients with suspected genetic disorders. A Bayesian decision tree model was set up. Model parameters were retrieved both from hospital administrative datasets and scientific literature. The analysis considered a lifetime time frame and adopted the perspective of the Italian National Health Service (NHS). Bayesian inference was performed using the Markov Chain Monte Carlo simulation method. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI). The present analysis showed that implementing first-line WGS would be a cost-effective strategy, against the majority of the other tested alternatives at a threshold of €30,000–50,000, for diagnosing outpatient pediatric patients with suspected genetic disorders. According to the sensitivity analyses, the findings were robust to most assumption and parameter uncertainty. Lessons learnt from this modeling study reinforces the adoption of first-line WGS, as a cost-effective strategy, depending on actual difficulties for the NHS to properly allocate limited resources.

Cost-utility of cardiac contractility modulation in patients with heart failure with reduced ejection fraction in Italy.

Cardiac contractility modulation (CCM) is a device therapy for heart failure, based on the delivery of high-voltage biphasic impulses to the right ventricular septum during the myocardial absolute refractory period. This study evaluated the cost-effectiveness of CCM therapy plus optimal medical therapy (OMT) vs. OMT alone in patients with heart failure with reduced ejection fraction. A Markov model with a lifespan time horizon was developed to assess the cost-utility using the FIX trials as main data sources. A deterministic sensitivity analysis and a probabilistic sensitivity analysis were run to analyse the decision uncertainty in the model through cost-effectiveness acceptability curve (CEAC) and cost-effectiveness acceptability frontier (CEAF). Value of information analysis was also conducted computing the expected value of perfect information (EVPI) and the expected value of partial perfect information. The base case results showed that the CCM plus OMT option was highly cost-effective compared with OMT alone with an incremental cost-utility ratio of €7034/quality-adjusted life year (QALY). The CEAC and CEAF illustrated that for all willingness to pay levels above €5600/QALY, tested up to €50000/QALY, CCM plus OMT alternative had the highest probability of being cost-effective. The EVPI per patient was estimated to be €124412 on a willingness to pay threshold of €30000/QALY. For patients with heart failure with reduced ejection fraction, CCM therapy could be cost-effective when taking a lifetime horizon. Further long-term, post-approval clinical studies are needed to verify these results in a real-world context, particularly concerning the effect of CCM therapy on mortality.

Optimal vibration sensor placement for jacket support structures of offshore wind turbines based on value of information analysis

Information on the condition and reliability of an offshore jacket structure provided by a vibration-based structural health monitoring system can guide decisions on inspection and maintenance. When selecting the sensor setup, the designer of the monitoring system must assess its overall benefit compared to its costs before installation. The potential benefit of continuously monitoring the dynamic response of a jacket structure can be formally quantified through a value of information analysis from Bayesian decision theory. In this contribution, we present a framework for optimizing the placement of vibration sensors on offshore jacket structures by maximizing the value of information of the monitoring system. To solve the resulting discrete optimization problem, we adapt a genetic algorithm. The framework is demonstrated in a numerical example considering a redundant jacket-type steel frame. The numerical study shows that monitoring the vibration response of the frame is beneficial. Good sensor setups consist of relatively few sensors located towards the upper part of the frame. The adapted genetic algorithm performs similarly well as established sequential sensor placement algorithms and holds substantial promise for application to real jacket structures.

Cost-effectiveness analysis of Next generation sequencing tests in critically ill pediatric patients

Abstract Rare genetic diseases in the pediatric population constitute an urgent global public health issue; overall, more than 300 million people are affected worldwide. Next Generation Sequencing techniques, as Whole genome sequencing (WGS) and Whole exome sequencing (WES), have proven to be significantly supportive in diagnosing these complex conditions. The aim is to evaluate the cost-effectiveness of WGS versus WES in pediatric patients with suspected genetic disorders. A Bayesian Markov model was calibrated among this target population, comparing WGS to WES. Model parameters were retrieved from the scientific literature. Costs and benefits were discounted at a rate of 3%. A lifetime time horizon and the National Health Service perspective was chosen. The Eurozone threshold, ranging from €30,000 to €50,000, was adopted. Markov Chain Monte Carlo was used as the simulation method for Bayesian inference. Uncertainty was explored through a probabilistic sensitivity analysis (PSA) and a value of information analysis (VOI), illustrated through Cost-Effectiveness Acceptability Curve (CEAC) and Expected Value of Perfect Information (EVPI). Results were reported as Incremental Cost-Effectiveness Ratio (ICER), expressed as euros per additional diagnosis. The base case findings highlighted that WGS was cost-effective with an ICER of €31,973. The CEAC showed that for all thresholds over the ICER, WGS had the highest probability of being cost-effective. The EVPI per patient was estimated to be €6,535 on a threshold of €50,000/diagnosis. In addition to being cost-effective, WGS could allow early genetic diagnosis shortening the diagnostic odyssey. The use of WGS in the diagnostic workup has the potential to revolutionise personalised medicine and to play a significant role in achieving SDG 3 by providing personalised healthcare, identifying genetic risk factors for diseases, and informing public health policies for a target population that represents the human capital of the future. Key messages • This analysis informs that WGS is cost-effective and possibly cost-saving in the long term. • WGS could be implemented in the diagnostic workup of children with suspected rare disorders.

Home Gardening of Yellow Cassava and Orange Maize for the Prevention of Nutritional Blindness in Children: An Economic Evaluation and Value of Information Analysis

ObjectivesVitamin A deficiency is the leading cause of childhood blindness worldwide, affecting mostly Sub-Saharan Africa. We aimed to predict the cost-effectiveness of home gardening (HG) of yellow cassava and orange maize to prevent nutritional blindness in children below 5 years and to assess the likely value of obtaining additional information in reducing uncertainty surrounding its cost-effectiveness. MethodsWe developed a Markov model and carried out probabilistic sensitivity analysis with a value of information analysis. We costed resources from a societal perspective and outcomes were measured in disability-adjusted life years (DALYs). ResultsHG was estimated to cost an additional Intl$395.00 per DALY averted, with a 72.27% likelihood of being cost-effective at a threshold of Intl$2800 per DALY. The expected value of information was estimated to be Intl$29 843.50 for 1 child or Intl$925 billion for 31 million Nigerian children affected by the decision. Further research is only worthwhile for 1 parameter (relative risk of low serum retinol; expected value of perfect parameter information Intl$29 854.53 per child and Intl$925 billion for 31 million children). ConclusionHG of yellow cassava and orange maize is expected to be highly cost-effective in preventing nutritional blindness in Nigerian children. Worthwhile further research includes a cost analysis of the intervention and a high-quality randomized trial to assess the effectiveness of HG on serum retinol levels in young children.