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Introduction: The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR). Methods: We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature. Results: Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care. Discussion/Conclusion: The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting. Highlights: We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England. We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG. This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times. [ABSTRACT FROM AUTHOR]

Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors, and countries. Moreover, the rules are often complex and change regularly. Therefore, payroll management systems must be flexible in design. In this paper, we suggest an approach based on a flexible answer set programming (ASP) model and an easy-to-read tabular representation based on the decision model and notation standard. It allows HR consultants to represent complex rules without the need for a software engineer and to ultimately design payroll systems for a variety of different scenarios. We show how the multi-shot solving capabilities of the clingo ASP system can be used to reach the performance that is necessary to handle real-world instances. [ABSTRACT FROM AUTHOR]

Background In settings with large case detection gaps, active case-finding (ACF) may play a critical role in the uberculosis (TB) response. However, ACF is resource intensive, and its effectiveness depends on whether people detected with TB through ACF might otherwise spontaneously resolve or be diagnosed through routine care. We analysed the potential effectiveness of ACF for TB relative to the counterfactual scenario of routine care alone. Methods We constructed a Markov simulation model of TB natural history, diagnosis, symptoms, ACF and treatment, using a hypothetical reference setting using data from South East Asian countries. We calibrated the model to empirical data using Bayesian methods, and simulated potential 5-year outcomes with an 'aspirational' ACF intervention (reflecting maximum possible effectiveness) compared with the standard-of-care outcomes. Results Under the standard of care, 51% (95% credible interval, CrI: 31%, 75%) of people with prevalent TB at baseline were estimated to be diagnosed and linked to care over 5 years. With aspirational ACF, this increased to 88% (95% CrI: 84%, 94%). Most of this difference represented people who were diagnosed and treated through ACF but experienced spontaneous resolution under standard-of-care. Aspirational ACF was projected to reduce the average duration of TB disease by 12 months (95% CrI: 6%, 18%) and TB-associated disability-adjusted life-years by 71% (95% CrI: 67%, 76%). Conclusion These data illustrate the importance of considering outcomes in a counterfactual standard of care scenario, as well as trade-offs between overdiagnosis and averted morbidity through earlier diagnosis—not just for TB, but for any disease in which population-based screening is recommended. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide Treatment decisions in healthcare often carry lifelong consequences that can be challenging to foresee. As such, tools that visualize and estimate outcome after different lifetime treatment strategies are lacking and urgently needed to support clinical decision-making in the setting of rapidly evolving healthcare systems, with increasingly numerous potential treatments. In this regard, microsimulation models may prove to be valuable additions to current risk-prediction models. Notable advantages of microsimulation encompass input from multiple data sources, the ability to move beyond time-to-first-event analysis, accounting for multiple types of events and generating projections of lifelong outcomes. This review aims to clarify the concept of microsimulation, also known as individualized state-transition models, and help clinicians better understand its potential in clinical decision-making. A practical example of a patient with heart valve disease is used to illustrate key components of microsimulation models, such as health states, transition probabilities, input parameters (e.g. evidence-based risks of events) and various aspects of mortality. Finally, this review focuses on future efforts needed in microsimulation to allow for increasing patient-tailoring of the models by extending the general structure with patient-specific prediction models and translating them to meaningful, user-friendly tools that may be used by both clinician and patient to support clinical decision-making. [ABSTRACT FROM AUTHOR]

Introduction: Translating narrative clinical guidelines to computable knowledge is a long‐standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed. Objectives: The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content. Methods: Following an initial 'collaborathon' in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon‐scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete. Results: While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology‐agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision‐support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems. Conclusions: The project has shown that the WHO DAK, with some modification, is a promising approach to build technology‐neutral logical specifications of NICE recommendations. Implementation of concurrent computable modelling by multidisciplinary teams during guideline development poses methodological and cultural questions that are complex but tractable given suitable will and leadership. [ABSTRACT FROM AUTHOR]

Abstract Introduction Translating narrative clinical guidelines to computable knowledge is a long‐standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed. Objectives The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content. Methods Following an initial ‘collaborathon’ in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon‐scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete. Results While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology‐agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision‐support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems. Conclusions The project has shown that the WHO DAK, with some modification, is a promising approach to build technology‐neutral logical specifications of NICE recommendations. Implementation of concurrent computable modelling by multidisciplinary teams during guideline development poses methodological and cultural questions that are complex but tractable given suitable will and leadership.

While the One Health perspective provides a useful approach by which to influence the complex interactions that pose a risk to the individual and collective health of humans, animals, and the environment, approaches to its operationalization can be enhanced. This study examines models as contributors toward operationalization of One Health. It describes how modelling approaches have been applied to resolve the complexity of One Health problems. A scoping literature review is based on the PRISMA-Scr approach. The literature is mapped from a One Health (area of application) as well as from a modelling (model type, modelling trends, unit of analysis, level of abstraction) perspective. 24 articles were identified that address a spectrum from strategic to tactical and operational considerations in One Health, with the bulk of the work slanted towards the former two aspects. Models focus on supporting high-level decision-makers on disease management strategies, investments, and resource allocation, and were developed with involvement of a broad set of stakeholders in One Health. Evidence could be found of the use of the major categories of simulation models, namely, systems dynamic models, agent-based models, and discrete-event models. The identified models are used to address different levels of complexity and decision-making, as such pointing towards the potential for integrating different modelling approaches in informing One Health decision-making in practical contexts. • One Health provides a lens on the complexity of integrated health problems. • Modelling approaches provide a means of structuring One Health complexities. • A scoping review of modelling approaches in the One Health domain was undertaken. • Current approaches mostly support strategic and tactical decision-making. • Scope exists for integrated modelling frameworks to support One Health operationalisation. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: The aim of this study was to determine the long-term cost-effectiveness and return on investment of implementing a structured lifestyle intervention to reduce excessive gestational weight gain and associated incidence of gestational diabetes mellitus (GDM) and type 2 diabetes mellitus. Methods: A decision-analytic Markov model was used to compare the health and cost-effectiveness outcomes for (1) a structured lifestyle intervention during pregnancy to prevent GDM and subsequent type 2 diabetes; and (2) current usual antenatal care. Life table modelling was used to capture type 2 diabetes morbidity, mortality and quality-adjusted life years over a lifetime horizon for all women giving birth in Australia. Costs incorporated both healthcare and societal perspectives. The intervention effect was derived from published meta-analyses. Deterministic and probabilistic sensitivity analyses were used to capture the impact of uncertainty in the model. Results: The model projected a 10% reduction in the number of women subsequently diagnosed with type 2 diabetes through implementation of the lifestyle intervention compared with current usual care. The total net incremental cost of intervention was approximately AU$70 million, and the cost savings from the reduction in costs of antenatal care for GDM, birth complications and type 2 diabetes management were approximately AU$85 million. The intervention was dominant (cost-saving) compared with usual care from a healthcare perspective, and returned AU$1.22 (95% CI 0.53, 2.13) per dollar invested. The results were robust to sensitivity analysis, and remained cost-saving or highly cost-effective in each of the scenarios explored. Conclusions/interpretation: This study demonstrates significant cost savings from implementation of a structured lifestyle intervention during pregnancy, due to a reduction in adverse health outcomes for women during both the perinatal period and over their lifetime. [ABSTRACT FROM AUTHOR]

Abstract Background The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures. Methods In this study, we used the previously published three-state cohort state-transition model, to evaluate the health effects of surgery postponement for 28 surgical procedures commonly performed in nonacademic hospitals. Scientific literature and national registries yielded nearly all input parameters, except for the quality of life (QoL) estimates which were obtained from experts using the Delphi method. Two expert panels, one from a single nonacademic hospital and one from different nonacademic hospitals in the Netherlands, were invited to estimate QoL weights. We compared estimated model results (disability adjusted life years (DALY)/month of surgical delay) based on the QoL estimates from the two panels by calculating the mean difference and the correlation between the ranks of the different surgical procedures. The eventual model was based on the combined QoL estimates from both panels. Results Pacemaker implantation was associated with the most DALY/month of surgical delay (0.054 DALY/month, 95% CI: 0.025–0.103) and hemithyreoidectomy with the least DALY/month (0.006 DALY/month, 95% CI: 0.002–0.009). The overall mean difference of QoL estimates between the two panels was 0.005 (95% CI -0.014–0.004). The correlation between ranks was 0.983 (p

• Postpartum haemorrhage (PPH) is a leading cause of morbidity and mortality worldwide. • Preventing blood loss where possible is an important public health concern. • There is a high cost of the PPH treatment pathway. • PPH Butterfly is highly likely to offer value for money in the UK NHS setting. • Extrapolation on an international scale to lower and income countries could prevent maternal mortality. To explore the cost-effectiveness of a novel PPH device as compared with usual care. A decision analytical model was used to explore the cost-effectiveness of the PPH Butterfly device compared with usual care. This was part of a United Kingdom, UK, clinical trial ISRCTN15452399 using a matched historical cohort who had standard PPH management without the use of the PPH Butterfly device. The economic evaluation was conducted from a UK National Health Service (NHS) perspective. Liverpool Women's Hospital, UK. 57 women with 113 matched controls. The PPH Butterfly is a novel device that has been invented and developed in the UK to facilitate bimanual compression of the uterus in the treatment of PPH. Main outcome measures included healthcare costs, blood loss, and maternal morbidity events. Mean treatment costs in the Butterfly cohort were £3,459.66 as compared with standard care £3,223.93. Treatment with the Butterfly device resulted in decreased total blood loss in comparison with standard care. The Butterfly device had an incremental cost-effectiveness ratio of £3,795.78 per PPH progression avoided (defined as ≤ 1000 ml additional blood loss from device insertion point). If the NHS is prepared to pay £8,500 per PPH progression avoided, then the Butterfly device is cost-effective with a probability of 87 percent. In the PPH Butterfly treatment arm there were 9% fewer cases of massive obstetric haemorrhage (severe PPH of more than 2000mls or more than 4 units of blood transfusion required) recorded as compared with the standard care historical cohort. As a low-cost device, the PPH Butterfly device is cost-effective but can be cost-saving to the NHS. The PPH pathway can result in high-cost resource use such as blood transfusion or high dependence unit hospital stays. The Butterfly device is a relative low-cost device in a UK NHS setting with a high probability of being cost-effective. The National Institute for Health and Care Excellence (NICE) can use this evidence in considering the adoption of innovative technologies such as the Butterfly device in the NHS. Extrapolation on an international scale to lower and middle-income countries could prevent mortality associated with PPH. [ABSTRACT FROM AUTHOR]

This study focuses on the choice of rural residents to provide ecotourism services and the effects on their household income as a path to alleviate urban-rural income discrepancy using survey data collected in villages located in Beijing Province in China. The empirical modelling of the decision to offer ecotourism services originated from the framework of the random utility model (RUM). The decision equation and income equation are estimated using the logit and ordered logit techniques, respectively. The calculated probability changes based on the estimated regression coefficients provide measures of the likelihood of choosing to provide services or factors related to the change in rural household income. Older residents, those with children in a household, and those having relatively more income are less likely to decide to provide ecotourism services. Factors affecting income are identified in another equation. Income increases if a rural resident completed high school, is married, or male, but decreases as resident age increases. The probability changes are largest for households with a relatively lower income (excluding any income from ecotourism services), although the specific probability changes vary by household income level. Improvement in income is possible for households engaged in ecoservice provision and is facilitated by enhancing education of residents. However, low income households are unlikely to engage in tourism services and appear to be run by older, less educated residents.

Abstract Background Falls impose significant health and economic burdens on community-dwelling older persons. Decision modelling can inform commissioning of alternative falls prevention strategies. Several methodological challenges arise when modelling public health interventions including community-based falls prevention. This study aims to conduct a systematic review (SR) to: systematically identify community-based falls prevention economic models; synthesise and critically appraise how the models handled key methodological challenges associated with public health modelling; and suggest areas for further methodological research. Methods The SR followed the 2021 PRISMA reporting guideline and covered the period 2003–2020 and 12 academic databases and grey literature. The extracted methodological features of included models were synthesised by their relevance to the following challenges: (1) capturing non-health outcomes and societal intervention costs; (2) considering heterogeneity and dynamic complexity; (3) considering theories of human behaviour and implementation; and (4) considering equity issues. The critical appraisal assessed the prevalence of each feature across models, then appraised the methods used to incorporate the feature. The methodological strengths and limitations stated by the modellers were used as indicators of desirable modelling practice and scope for improvement, respectively. The methods were also compared against those suggested in the broader empirical and methodological literature. Areas of further methodological research were suggested based on appraisal results. Results 46 models were identified. Comprehensive incorporation of non-health outcomes and societal intervention costs was infrequent. The assessments of heterogeneity and dynamic complexity were limited; subgroup delineation was confined primarily to demographics and binary disease/physical status. Few models incorporated heterogeneity in intervention implementation level, efficacy and cost. Few dynamic variables other than age and falls history were incorporated to characterise the trajectories of falls risk and general health/frailty. Intervention sustainability was frequently based on assumptions; few models estimated the economic/health returns from improved implementation. Seven models incorporated ethnicity- and severity-based subgroups but did not estimate the equity-efficiency trade-offs. Sixteen methodological research suggestions were made. Conclusion Existing community-based falls prevention models contain methodological limitations spanning four challenge areas relevant for public health modelling. There is scope for further methodological research to inform the development of falls prevention and other public health models.

The aims of this study were to: i) identify performance indicators associated with match outcomes in the United Rugby Championship; ii) compare the efficacy of isolated and relative datasets to predict match outcome; and iii) investigate whether reduced statistical models can reproduce predictive accuracy. Retrospective analysis of key performance indicators in the United Rugby Championship. Twenty-seven performance indicators were selected from 96 matches (2020–21 United Rugby Championship). Random forest classification was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy performance indicator selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021–22 season to ascertain prediction accuracy. Within the 2020–21 datasets, the full models correctly classified 83% of match performances for the relative dataset and 64% for isolated data, the equivalent reduced models classified 85% and 66% respectively. The reduced relative model successfully predicted 90% of match performances in the 21–22 season, highlighting that five performance indicators were significant: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties. Relative performance indicators were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship. [ABSTRACT FROM AUTHOR]

Abstract Introduction Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up. Methods Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30–500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error). Results The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples. Conclusions Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model’s extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

Background: Falls impose significant health and economic burdens on community-dwelling older persons. Decision modelling can inform commissioning of alternative falls prevention strategies. Several methodological challenges arise when modelling public health interventions including community-based falls prevention. This study aims to conduct a systematic review (SR) to: systematically identify community-based falls prevention economic models; synthesise and critically appraise how the models handled key methodological challenges associated with public health modelling; and suggest areas for further methodological research. Methods: The SR followed the 2021 PRISMA reporting guideline and covered the period 2003–2020 and 12 academic databases and grey literature. The extracted methodological features of included models were synthesised by their relevance to the following challenges: (1) capturing non-health outcomes and societal intervention costs; (2) considering heterogeneity and dynamic complexity; (3) considering theories of human behaviour and implementation; and (4) considering equity issues. The critical appraisal assessed the prevalence of each feature across models, then appraised the methods used to incorporate the feature. The methodological strengths and limitations stated by the modellers were used as indicators of desirable modelling practice and scope for improvement, respectively. The methods were also compared against those suggested in the broader empirical and methodological literature. Areas of further methodological research were suggested based on appraisal results. Results: 46 models were identified. Comprehensive incorporation of non-health outcomes and societal intervention costs was infrequent. The assessments of heterogeneity and dynamic complexity were limited; subgroup delineation was confined primarily to demographics and binary disease/physical status. Few models incorporated heterogeneity in intervention implementation level, efficacy and cost. Few dynamic variables other than age and falls history were incorporated to characterise the trajectories of falls risk and general health/frailty. Intervention sustainability was frequently based on assumptions; few models estimated the economic/health returns from improved implementation. Seven models incorporated ethnicity- and severity-based subgroups but did not estimate the equity-efficiency trade-offs. Sixteen methodological research suggestions were made. Conclusion: Existing community-based falls prevention models contain methodological limitations spanning four challenge areas relevant for public health modelling. There is scope for further methodological research to inform the development of falls prevention and other public health models. [ABSTRACT FROM AUTHOR]

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Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility, and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group provides a suitable solution for externalising decisions from processes and automating decision enactments for processes. This article introduces a systematic way of tackling the separation of the decision modelling concern from process modelling by providing a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA) which approaches decisions as automated and externalised services that processes need to invoke on demand to obtain the decision outcome. The DaaS mechanism is elucidated by a formalisation of DMN constructs and the relevant layer elements. Furthermore, DaaS is evaluated against the fundamental characteristics of the SOA paradigm, proving its contribution in terms of abstraction, reusability, loose coupling, and other pertinent SOA principles. Additionally, the benefits of the DaaS design on process-decision modelling and mining are discussed. Finally, the DaaS design is illustrated on a real-life event log of a bank loan application and approval process, and the SOA maturity of DaaS is assessed. [ABSTRACT FROM AUTHOR]

Introduction: Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up.Methods: Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30-500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error).Results: The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples.Conclusions: Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model's extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence. [ABSTRACT FROM AUTHOR]

Early economic modelling has long been recommended to aid research and development (R&D) decisions in medical innovation, although they are less frequently published and critically appraised. A review of 30 innovations by Grutters et al provides an opportunity to evaluate how early models are used in practice. The evidence of early models can be used to inform two types of decision: to continue development (“stop or go”) or to alter future R&D activities. I argue that early models have limited use in stop or go decisions, as less resource and data undermine the reliability of the models’ indicative estimates of cost-effectiveness. Whilst they are far more useful for informing future R&D directions, the best techniques available from statistical decision science, such as value of information analysis, are not regularly used. It is highly recommended that early models adopt these methods to best deal with uncertainty, quantify the potential value of further research, identify areas of study with the greatest potential benefit and generate recommendations on study design and sample size.

Abstract Objectives The main objective of this study is to conduct a systematic review to identify and discuss methodological issues surrounding decision modelling for economic evaluation of non-pharmacological interventions (NPIs) in dementia. Methods A systematic search was conducted for publications using decision modelling to investigate the cost-effectiveness of NPIs for individuals with dementia. Search was limited to studies in English. Studies were excluded if they evaluated interventions aimed only at caregivers of patients with dementia, or if they only included economic evaluation alongside an RCT without additional modelling. Results Two primary, five secondary and three tertiary prevention intervention studies were identified and reviewed. Five studies utilised Markov models, with others using discrete event, regression-based simulation, and decision tree approaches. A number of challenging methodological issues were identified, including the use of MMSE-score as the main outcome measure, limited number of strategies compared, restricted time horizons, and limited or dated data on dementia onset, progression and mortality. Only one of the three tertiary prevention studies explicitly considered the effectiveness of pharmacological therapies alongside their intervention. Conclusions Economic evaluations of NPIs in dementia should utilise purposefully-developed decision models, and avoid models for evaluation of pharmaceuticals. Broader outcome measures could be a way to capture the wide impact of NPIs for dementia in future decision models. It is also important to account for the effects of pharmacological therapies alongside the NPIs in economic evaluations. Access to more localised and up-to-date data on dementia onset, progression and mortality is a priority for accurate prediction.

Since the introduction of the Decision model and notation (DMN), the standard has successfully been adopted in both industry and academia. However, no clear modelling guidelines can be found regarding the development of DMN decision models. For approaching this gap, this paper discusses modelling methodologies for the DMN standard, both at the decision requirements level as well as at the decision logic level. For that purpose, we capitalise on existing guidelines in related fields, such as decision table modelling, information systems engineering, and software engineering. We adapt, expand, and restructure the guidelines to conform to DMN model development. Additionally, we provide a real-life case that was modelled using the suggested modelling strategies and consequently, we deploy the model as a government e-service for tax management. The real-life case is concerned with clarifying tax regulations for visiting performing artists in Belgium, and it was carried out in cooperation with oKo, a Belgian industry federation for the arts, and the Ministry of Culture. The decision model was built in the Avola tool and implemented as an e-service in order to demonstrate the suitability of the DMN standard for the deployment of expert system e-government services. [ABSTRACT FROM AUTHOR]

Objectives: To determine the probability of controlling the outbreak of COVID-19 in Peru, in a pre- and post-quarantine scenario using mathematical simulation models. Materials and methods: Outbreak simulations for the COVID-19 pandemic are performed, using stochastic equations under the following assumptions: a pre-quarantine population R0 of 2.7 or 3.5, a post-quarantine R0 of 1.5, 2 or 2.7, 18% or 40%, of asymptomatic positives and a maximum response capacity of 50 or 150 patients in the intensive care units. The success of isolation and contact tracing is evaluated, no other mitigation measures are included. Results: In the pre-quarantine stage, success in controlling more than 80% of the simulations occurred only if the isolation of positive cases was implemented from the first case, after which there was less than 40% probability of success. In post-quarantine, with 60 positive cases it is necessary to isolate them early, track all of their contacts and decrease the R0 to 1.5 for outbreak control to be successful in more than 80% of cases. Other scenarios have a low probability of success. Conclusions: The control of the outbreak in Peru during pre-quarantine stage demanded requirements that were difficult to comply with, therefore quarantine was necessary; to successfully suspend it would require a significant reduction in the spread of the disease, early isolation of positives and follow-up of all contacts of positive patients. [ABSTRACT FROM AUTHOR]

Functional neurological disorder is a common and phenomenologically diverse condition. Resultant disability is caused by both the dominant clinical presentation, e.g. paralysis or tremor and additional symptomatology such as cognitive symptoms. Recently the similarity of neuropsychiatric profiles across a range of functional syndromes has been highlighted. This is suggestive of a common underlying mechanism with a theoretical deficit of information processing proposed. Identification of an experimental biomarker for such deficits could offer novel assessment and therapeutic strategies. In this study, we took the temporal discrimination threshold as a paradigm that can be used to model sensory processing in functional movement disorders. Our hypothesis was that we would be able to delineate markers of slowed information processing in this paradigm removed from the phenomenological presentation with a movement disorder. We recorded both response accuracy and reaction time in a two-choice temporal resolution/discrimination task in 36 patients with functional movement disorders and 36 control subjects. A psychometric function was fitted to accuracy data for each individual revealing both abnormally high threshold values (P = 0.0053) and shallow psychometric slopes in patients (P = 0.0015). Patients with functional movement disorders also had significantly slower response times (P = 0.0065). We then used a well-established model for decision-making (the drift diffusion model) that uses both response accuracy and reaction time data to estimate mechanistic physiological dimensions of decision-making and sensory processing. This revealed pathologically reduced drift rate in the patient group, a parameter that quantifies the quality and rate of information accumulation within this sensory task (P = 0.002). We discuss how the deficits we observed in patients with functional movement disorders are likely to stem from abnormal allocation of attention that impairs the quality of sensory information available. Within a predictive coding framework sensory information could be down-weighted in favour of predictions encoded by the prior. Our results therefore offer a parsimonious account for a range of experimental and clinical findings. Reduced drift rate is a potential experimental marker for a generalized deficit in information processing across functional disorders that allows diverse symptomatology to be quantified under a common disease framework. [ABSTRACT FROM AUTHOR]

A resource constrained early-stage firm commercialising assembled products faces supply chain (SC) problems that could ultimately cause the company to fail. Delays, complexity, and associated cost overruns throughout the SC can quickly drain cash flow, putting a new firm in a disadvantaged position. This is exacerbated by the increased use of outsourced global suppliers, demands of resellers, and pressure for rapid growth. A decision-making tableau over a conceptual delay-based SC model is proposed, which identifies qualitative behaviour of inventories with respect to delays inherently present in the SC. The method can foster improved strategic decisions in new venture SC set-up and management that can reduce costly variations. A real-world case scenario is given illustrating a current state and substantially improved future state with the help of the proposed approach. The paper concludes with a discussion on the results, limitations, and direction of future research. [ABSTRACT FROM AUTHOR]

• We are the first to provide a set of complexity metrics tailored towards the decision requirements level of DMN decision models. • This paper is the first to provide a set of complexity metrics tailored towards the decision logic level of DMN decision models. • Additionally, we are the first to empirically address DMN decision model complexity. • Furthermore, we provide a discussion towards integrated decision requirements and decision logic metrics together with an agenda for future research. Complexity impairs the maintainability and understandability of conceptual models. Complexity metrics have been used in software engineering and business process management (BPM) to capture the degree of complexity of conceptual models. The recent introduction of the Decision Model and Notation (DMN) standard provides opportunities to shift towards the Separation of Concerns paradigm when it comes to modelling processes and decisions. However, unlike for processes, no studies exist that address the representational complexity of DMN decision models. In this paper, we provide an initial set of complexity metrics for DMN models. We gather insights from the process modelling and software engineering fields to propose complexity metrics for DMN decision models. Additionally, we provide an empirical complexity assessment of DMN decision models. For the decision requirements level of the DMN standard 19 metrics were proposed, while 7 metrics were put forward for the decision logic level. For decision requirements, the model size-based metrics, the Durfee Square Metric (DSM) and the Perfect Square Metric (PSM) prove to be the most suitable. For the decision logic level of DMN the Hit Policy Usage (HPU) and the Total Number of Input Variables (TNIV) were evaluated as suitable for measuring DMN decision table complexity. [ABSTRACT FROM AUTHOR]

The sterilization of raw ready-to-eat aquatic products is a crucial method for ensuring their safety for consumption after production. However, achieving intelligent decision-making and higher sterilization efficiency remains a technical challenge. In this paper, an intelligent ultraviolet c radiation (UVC) pulse sterilization system for raw ready-to-eat aquatic products processing lines is presented. The objective of intelligently classifying and grading aquatic products in an automated production line and then sterilizing them is attained. On the basis of the thermal radiation perspective factor method, an irradiation prediction model for the columnar pulsed UVC field is developed, and an automated decision model for the UVC sterilization level is developed in conjunction with the hierarchical analysis method. Automatic control of the UVC lamp is achieved with precision. Using a combination of direct measurements and simulation calculations, the accuracy of the prediction structure is verified. In addition, the system's key model, the UVC intensity decision model, was evaluated, and the results indicated that the decision model had an error rate of 13.3% in the sterilization of raw ready-to-eat aquatic products and that the system's sterilization rate could reach approximately 95%. Additionally, the economics of implementation and overall performance of the sterilization system were evaluated and determined to be effective in reducing spoilage rates and human resource losses, thereby promoting sustainable and clean production in the raw food fish processing industry. • An intelligent UVC pulse sterilization system has been developed for raw ready-to-eat aquatic products processing lines. • An irradiation prediction model for UVC light and an automatic decision-making model for sterilization levels were developed. • The system was evaluated and validated in the processing of Spanish mackerel, Argentine sweet shrimp, and Pacific oysters. • Effectively reduce spoilage rates, thereby promoting sustainable and cleaner production in the raw fish processing industry. [ABSTRACT FROM AUTHOR]

Early economic modelling has long been recommended to aid research and development (R&D) decisions in medical innovation, although they are less frequently published and critically appraised. A review of 30 innovations by Grutters et al provides an opportunity to evaluate how early models are used in practice. The evidence of early models can be used to inform two types of decision: to continue development ("stop or go") or to alter future R&D activities. I argue that early models have limited use in stop or go decisions, as less resource and data undermine the reliability of the models' indicative estimates of cost-effectiveness. Whilst they are far more useful for informing future R&D directions, the best techniques available from statistical decision science, such as value of information analysis, are not regularly used. It is highly recommended that early models adopt these methods to best deal with uncertainty, quantify the potential value of further research, identify areas of study with the greatest potential benefit and generate recommendations on study design and sample size. [ABSTRACT FROM AUTHOR]

The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement. [ABSTRACT FROM AUTHOR]

Many attempts have been made to enhance irrigation decisions using Decision Support Systems (DSS). These have met with limited success for many reasons, one of which is well known: that DSS encode decision rules (waterbalances, financial models) narrower in scope than the criteria farmers really use to make decisions, thus their advice is of limited value or perhaps entirely irrelevant. To assist irrigation DSS designers build more flexible systems, we suggest they heed decision theory and decision modelling, separately from domain-specific DSS tasks. They may then find better ways of modelling real-world decisions which might allow for wider ranging sets of decision rules than previously. To facilitate this, we review three different decision modelling systems and with each model the seemingly straightforward irrigation decision “How much should I water today?”. In doing this we show how they can assist with wide-ranging rule integration. The systems we chose are: Decision Modelling Notation (DMN) from the business analysis community; the Decision Ontology (DO), a Semantic Web modelling system; and Decision Modelling Ontology (DMO) a formal ontology from Information Systems Engineering. We have determined that each of these modelling systems have useful aspects for irrigation DSS designers, which we list, but that they are not equally useful. Also, none of the systems provide designers with both the best modelling system and best technology & tools. We complete our work with a list of requirements for a future decision modelling system based on the intersection of the strengths of the systems investigated and our perceptions of irrigation DSS need. We believe a future system is possible to make and could serve irrigation DSS designers better than any current system. In future work, we indicate what steps might be taken with existing systems to evolve them in line with our future system requirements. Finally, we conclude with a summary of our findings. [ABSTRACT FROM AUTHOR]

Objectives: The main objective of this study is to conduct a systematic review to identify and discuss methodological issues surrounding decision modelling for economic evaluation of non-pharmacological interventions (NPIs) in dementia. Methods: A systematic search was conducted for publications using decision modelling to investigate the costeffectiveness of NPIs for individuals with dementia. Search was limited to studies in English. Studies were excluded if they evaluated interventions aimed only at caregivers of patients with dementia, or if they only included economic evaluation alongside an RCT without additional modelling. Results: Two primary, five secondary and three tertiary prevention intervention studies were identified and reviewed. Five studies utilised Markov models, with others using discrete event, regression-based simulation, and decision tree approaches. A number of challenging methodological issues were identified, including the use of MMSE-score as the main outcome measure, limited number of strategies compared, restricted time horizons, and limited or dated data on dementia onset, progression and mortality. Only one of the three tertiary prevention studies explicitly considered the effectiveness of pharmacological therapies alongside their intervention. Conclusions: Economic evaluations of NPIs in dementia should utilise purposefully-developed decision models, and avoid models for evaluation of pharmaceuticals. Broader outcome measures could be a way to capture the wide impact of NPIs for dementia in future decision models. It is also important to account for the effects of pharmacological therapies alongside the NPIs in economic evaluations. Access to more localised and up-to-date data on dementia onset, progression and mortality is a priority for accurate prediction. [ABSTRACT FROM AUTHOR]

In developed countries, the largest share of food waste is produced at household level. Most studies on consumers’ food waste use models that identify covariates as significant when in fact they may not be, particularly where these models use many variables. Here, using EU-level Eurobarometer data from 2013, we use alternative analytical methods that avoid these problems (Bayesian Networks) to identify the impact of household characteristics and other variables on self-assessed food waste. Our analysis confirmed that the country, the age of the respondent, the status (student/non-student), and a belief that the family wastes too much are related to the level of self-assessed food waste. But we found no evidence that waste behaviours differ between people living in urban and rural areas, and little support of a difference between genders. Households from lower-income EU countries (e.g. Portugal, Greece, Bulgaria, Cyprus and Latvia), as well as students and young adults tend to report higher levels of food waste. Hence, the adoption of an EU strategy based on the concept of subsidiarity, and of country-level policy measures targeting different age groups is suggested. Furthermore, our analysis shows that policy makers need to be wary of relying on analysis based on large datasets that do not control for false-positives, particularly when sample sizes are small. [ABSTRACT FROM AUTHOR]

Unimodal intertemporal decisions involve comparing options of the same type (e.g., apples now versus apples later), and cross-modal decisions involve comparing options of different types (e.g., a car now versus a vacation later). As we show, existing models of intertemporal choice do not allow time preference to depend on whether the comparisons to be made are unimodal or cross-modal. We test this restriction in an experiment using the delayed compensation method, a new extension of the standard method of eliciting intertemporal preferences that allows for assessment of time preference for nonmonetary and discrete outcomes, as well as for both cross-modal and unimodal comparisons. Participants were much more averse to delay for unimodal than cross-modal decisions. We provide two potential explanations for this effect: one drawing on multiattribute choice, the other drawing on construal-level theory. [ABSTRACT FROM AUTHOR]