Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4

Fish oil lipid emulsion compared with soybean oil lipid emulsion in pediatric patients with parenteral nutrition-associated cholestasis: A cost-effectiveness study

OBJECTIVES

Evidence indicates that, in pediatric patients with parenteral nutrition-associated cholestasis (PNAC), the use of a 100% fish oil lipid emulsion (FOLE) increased the likelihood of PNAC resolution and reduced the likelihood of liver transplantation compared with a 100% soybean oil lipid emulsion (SOLE). To evaluate the potential economic benefit, we conducted a cost-effectiveness analysis comparing FOLE with SOLE.

STUDY DESIGN

A discrete event simulation model evaluated cost-effectiveness by simulating clinical outcomes and estimating associated healthcare costs in pediatric patients with PNAC receiving parenteral nutrition (PN) with FOLE (1 g/kg) or SOLE (1.9 g/kg) over a time horizon of 6 years. Model inputs for clinical outcomes were derived from the integrated analysis of two US Phase 3 trials (NCT00910104 and NCT00738101). Cost estimates were estimated from the perspective of the US payer including the cost of PN, transplantation, and adverse events.

RESULTS

The total cost associated with FOLE was $69,847 USD vs $141,605 USD for SOLE. The cost reduction of $71,757 USD was attributable to the avoidance of liver transplantation (-15.7%) and reduction in adverse events (-4.8%). Life-years and the quality-adjusted life-years were increased with FOLE compared with SOLE (by 0.248 and 0.295, respectively).

CONCLUSION

By reducing the need for liver transplant and providing time to transition to full enteral nutrition, FOLE leads to cost-savings, compared with SOLE, in pediatric patients with PNAC in the perspective of the US payer. These findings support the use of FOLE in pediatric patients with PNAC who require PN.

A Fast Nonparametric Sampling Method for Time to Event in Individual-Level Simulation Models

HIGHLIGHTS

The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.We provide a multivariate categorical sampling function for R and Python programming languages to sample times to events from processes with different hazards simultaneously.

Good Practices for Health Technology Assessment Guideline Development: A Report of the Health Technology Assessment International, HTAsiaLink, and ISPOR Special Task Force

OBJECTIVES

Health technology assessment (HTA) guidelines are intended to support the successful implementation of HTA by enhancing consistency and transparency in concepts, methods, processes, and use, thereby enhancing the legitimacy of the decision-making process. This report lays out good practices and practical recommendations for developing or updating HTA guidelines to ensure successful implementation.

METHODS

The task force was established in 2022 and comprised experts and academics from various geographical regions, each with substantial experience in developing HTA guidelines for national health policy making. Literature reviews and key informant interviews were conducted to inform these good practices. Stakeholder consultations, open peer reviews, and expert opinions validated the recommendations. A series of teleconferences among task force members was held to iteratively refine the report.

RESULTS

The recommendations cover six key aspects throughout the guideline development cycle: (1) setting objectives, scope, and principles of the guideline, (2) building a team for a quality guideline, (3) defining a stakeholder engagement plan, (4) developing content and utilizing available resources, (5) putting in place appropriate institutional arrangements, and (6) monitoring and evaluating guideline success.

CONCLUSION

This report presents a set of resources and context-appropriate practices for developing or updating HTA guidelines. Across all contexts, the recommendations emphasize transparency, building trust among stakeholders, and fostering a culture of ongoing learning and improvement. The report recommends timing development and revision of guidelines according to the HTA landscape and pace of HTA institutionalization. Because HTA is increasingly used to inform different kinds of decision making in a variety of country contexts, it will be important to continue to monitor lessons learned to ensure the recommendations remain relevant and effective.

Cost-effectiveness of ten commonly used antipsychotics in first-episode schizophrenia in the UK: economic evaluation based on a de novo discrete event simulation model

BACKGROUND

Previous economic evidence about interventions for schizophrenia is outdated, non-transparent and/or limited to a specific clinical context.

AIMS

We developed a de novo discrete event simulation (DES) model for estimating the cost-effectiveness of interventions in schizophrenia in the UK.

METHOD

The DES model was developed based on the structure of previous models, populated with demographic, clinical and cost data from the UK, and antipsychotics' effects from recent network meta-analyses. We simulated treatment pathways for patients with first-episode schizophrenia including events such as relapse, remission, treatment discontinuation, cardiovascular disease and death and estimated costs (2020£) taking the National Health Service perspective and quality-adjusted life years (QALYs) over ten years. Using the model, we ranked ten first-line antipsychotics based on their QALYs and cost-effectiveness.

RESULTS

Amisulpride was associated with the highest QALYs, followed by risperidone long-acting injection (LAI), aripiprazole-LAI (6.121, 6.084, 6.070, respectively) and others (5.947-6.058). The most cost-effective antipsychotics were amisulpride, olanzapine and risperidone-LAI, with total probability of rankings of 1, ≤2, ≤3, that is, 95%, 89%, 80%, respectively; meanwhile, the least cost-effective were cariprazine, lurasidone and quetiapine, with total probability of rankings of 10, ≥9, ≥8, that is, 96%, 92%, 81%, respectively. Results were robust across sensitivity analyses and influenced primarily by relapse relevant parameters.

CONCLUSIONS

Our findings suggest amisulpride (or risperidone-LAI where oral treatment is inappropriate) as the best overall first-line option based on QALYs and cost-effectiveness. Our ranking may be used to guide decision-making between antipsychotics. Our model is open source and could be applied to the other settings.

Incorporating healthcare access and equity in economic evaluations: a scoping review of guidelines

BACKGROUND

International development agendas increasingly push for access to healthcare for all through universal healthcare coverage. Health economic evaluations and health technology assessment (HTA) could provide evidence to support this but do not routinely incorporate consideration of equitable access.

METHODS

We undertook an international scoping review of health economic evaluation and HTA guidelines to examine how well issues of healthcare access and equity are represented, evidence recommendations, and gaps in current guidance to support evidence generation in this area. Guidelines were sourced from guideline repositories and websites of international agencies and organizations providing best practice methods guidance. Articles providing methods guidance for the conduct of HTA, or health economic evaluation, were included, except where they were not available in English and a suitable translation could not be obtained.

RESULTS

The search yielded forty-seven national, four international, and nine independent guidelines, along with eighty-six articles providing specific methods guidance. The inclusion of equity and access considerations in current guidance is extremely limited. Where they do feature, detail on specific methods for providing evidence on these issues is sparse.

DISCUSSION

Economic evaluation could be a valuable tool to provide evidence for the best healthcare strategies that not only maximize health but also ensure equitable access to care for all. Such evidence would be invaluable in supporting progress towards universal healthcare coverage. Clear guidance is required to ensure evaluations provide evidence on the best strategies to support equitable access to healthcare, but such guidance rarely exists in current best practice and guidance documents.

A Fast Nonparametric Sampling (NPS) Method for Time-to-Event in Individual-Level Simulation Models

Purpose

Individual-level simulation models often require sampling times to events, however efficient parametric distributions for many processes may often not exist. For example, time to death from life tables cannot be accurately sampled from existing parametric distributions. We propose an efficient nonparametric method to sample times to events that does not require any parametric assumption on the hazards.

Methods

We developed a nonparametric sampling (NPS) approach that simultaneously draws multiple time-to-event samples from a categorical distribution. This approach can be applied to univariate and multivariate processes. We discretize the entire period into equal-length time intervals and then derived the interval-specific probabilities. The times to events can then be used directly in individual-level simulation models. We compared the accuracy of our approach in sampling time-to-events from common parametric distributions, including exponential, gamma, and Gompertz. In addition, we evaluated the method's performance in sampling age to death from US life tables and sampling times to events from parametric baseline hazards with time-dependent covariates.

Results

The NPS method estimated similar expected times to events from 1 million draws for the three parametric distributions, 100,000 draws for the homogenous cohort, 200,000 draws from the heterogeneous cohort, and 1 million draws for the parametric distributions with time-varying covariates, all in less than a second.

Conclusion

Our method produces accurate and computationally efficient samples for time-to-events from hazards without requiring parametric assumptions.

A Systematic Review of Modeling Approaches to Evaluate Treatments for Relapsed Refractory Multiple Myeloma: Critical Review and Considerations for Future Health Economic Models

BACKGROUND AND OBJECTIVE

Multiple myeloma is a rare incurable hematological cancer in which most patients relapse or become refractory to treatment. This systematic literature review aimed to critically review the existing economic models used in economic evaluations of systemic treatments for relapsed/refractory multiple myeloma and to summarize how the models addressed differences in the line of therapy and exposure to prior treatment.

METHODS

Following a pre-approved protocol, literature searches were conducted on 17 February, 2023, in relevant databases for models published since 2014. Additionally, key health technology assessment agency websites were manually searched for models published as part of submission dossiers since 2018. Reported information related to model conceptualization, structure, uncertainty, validation, and transparency were extracted into a pre-defined extraction sheet.

RESULTS

In total, 49 models assessing a wide range of interventions across multiple lines of therapy were included. Only five models specific to heavily pre-treated patients and/or those who were refractory to multiple treatment classes were identified. Most models followed a conventional simple methodology, such as partitioned survival (n = 28) or Markov models (n = 9). All included models evaluated specific interventions rather than the whole treatment sequence. Where subsequent therapies were included in the model, these were generally only considered from a cost and resource use perspective. The models generally used overall and progression-free survival as model inputs, although data were often immature. Sensitivity analyses were frequently reported (n = 41) whereas validation was only considered in less than half (n = 19) of the models.

CONCLUSIONS

Published economic models in relapsed/refractory multiple myeloma rarely followed an individual patient approach, mainly owing to the higher need for complex data assumptions compared with simpler modeling approaches. As many patients experience disease progression on multiple treatment lines, there is a growing need for modeling complex treatment strategies, leading to more sophisticated approaches in the future. Maintaining transparency, high reporting standards, and thorough analyses of uncertainty are crucial to support these advancements.

Deep Inspiration Breath Hold in Left-Sided Breast Radiotherapy: A Balance Between Side Effects and Costs

Background

Deep inspiration breath hold (DIBH) is an effective technique for reducing heart exposure during radiotherapy for left-sided breast cancer. Despite its benefits, cost considerations and its impact on workflow remain significant barriers to widespread adoption.

Objectives

This study aimed to assess the cost-effectiveness of DIBH and compare its operational, financial, and clinical outcomes with free breathing (FB) in breast cancer treatment.

Methods

Treatment plans for 100 patients with left-sided breast cancer were generated using both DIBH and FB techniques. Dosimetric data, including the average mean heart dose, were calculated for each technique and used to estimate the cardiotoxicity of radiotherapy. A state-transition microsimulation model based on SCORE2 (Systematic Coronary Risk Evaluation) algorithms projected the effects of DIBH on cardiovascular outcomes and quality-adjusted life-years (QALYs). Costs were calculated from a provider perspective using time-driven activity-based costing, applying a willingness-to-pay threshold of €40,000 for cost-effectiveness assessment. A discrete event simulation model assessed the impacts of DIBH vs FB on throughput and waiting times in the radiotherapy workflow.

Results

In the base case scenario, DIBH was associated with an absolute risk reduction of 1.72% (95% CI: 1.67%-1.76%) in total cardiovascular events and 0.69% (95% CI: 0.67%-0.72%) in fatal cardiovascular events over 20 years. Additionally, DIBH was estimated to provide an incremental 0.04 QALYs (95% CI: 0.05-0.05) per left-sided breast cancer patient over the same time period. However, DIBH increased treatment times, reducing maximum achievable throughput by 12.48% (95% CI: 12.36%-12.75%) and increasing costs by €617 per left-sided breast cancer patient (95% CI: €615-€619). The incremental cost-effectiveness ratio was €14,023 per QALY.

Conclusions

Despite time investments, DIBH is cost-effective in the Belgian population. The growing adoption of DIBH may benefit long-term cardiovascular health in breast cancer survivors.

Evaluating Reception Center Models for Radiation Response Screening Capacity and Throughput Predictions

ABSTRACT

Introduction: The current fleet of nuclear reactors in the United States is mandated to provide evidence that surrounding jurisdictions can screen their populations should an incident occur. Capacity can be measured as throughput in reception centers used for screening. Due to the significant staffing and resources required to exercise screening capacity, most jurisdictions typically perform smaller exercises and use models to estimate their overall throughput. Objective: To evaluate the applicability and realism of current throughput models and practices. Methods: Throughput capacity for radiation screening is estimated with a mathematical model derived by the Federal Emergency Management Agency (FEMA). The Centers for Disease Control and Prevention developed a discrete event simulation model as a tool, SimPLER, to evaluate capacity and make throughput predictions. Model estimates will be compared and evaluated using timing data collected at a large-scale exercise. Results: The FEMA model estimated a throughput 41.2% higher than the actual radiation screening throughput, while the SimPLER model provided identical values. The FEMA and SimPLER models' predicted throughputs were 50% and 3.8%, respectively, higher than total exercise throughput. Applying each model to the throughput projections for a 12-hour shift, the FEMA model estimates ranged from 665 to 6,646 people and the SimPLER model yielded an estimated throughput of 1,809 people with a standard deviation of 74.6. Conclusion: Discrete event simulation models, such as SimPLER, may provide more realistic and accurate predictions of radiation screening and throughput capacity of reception centers than mathematical models such as the FEMA model.

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models

PURPOSE

To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data.

METHODS

Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure.

RESULTS

The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit.

CONCLUSIONS

Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data.

HIGHLIGHTS

We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual dataWe used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty.Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.

Intended and unintended consequences of strategies to meet performance benchmarks for rheumatologist referrals in a centralized intake system

RATIONALE

Timely assessment of a chronic condition is critical to prevent long-term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge.

METHODS

We applied a system simulation approach to model a high-volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change.

RESULTS

The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178-269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178-43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit.

CONCLUSION

An MWTG policy can result in intended and unintended consequences-ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.

Introduction of ultra-hypofractionation in breast cancer: Implications for costs and resource use

PURPOSE

A shift towards (ultra-)hypofractionated breast irradiation can have important implications for the practice of contemporary radiation oncology. This paper presents a systematic analysis of the impact of different fractionation schedules on multiple key performance indicators, namely resource use, costs, work times, throughput and waiting times.

MATERIALS AND METHODS

Time-driven activity-based costing (TD-ABC) is applied to calculate the costs and resources consumed where the perspective of the radiotherapy department in adopted. Three fractionation regimens are considered: ultra-hypofractionation (5 x 5.2 Gy, UHF), moderate hypofractionation (15 x 2.67 Gy, HF) and conventional fractionation (25 x 2 Gy, CF). Subsequently, a discrete event simulation (DES) model of the radiotherapy care pathway is developed and scenarios are compared in which the following factors are varied: distribution of fractionation regimens, patient volume and operating hours.

RESULTS

The application of (U)HF can permit radiotherapy departments to reduce the use of scarce resources, realise work time and cost savings, increase throughput and reduce waiting times. The financial advantages of (U)HF are, however, reduced in cases of excess capacity and cost savings may therefore be limited in the short-term. Moreover, although an extension of operating hours has favourable effects on throughput and waiting times, it may also reduce cost differences between fractionation schedules by increasing the capacity of resources.

CONCLUSION

By providing an in-depth analysis of the consequences associated with a shift towards (U)HF in breast cancer, the present study demonstrates how a DES model based on TD-ABC costing can assist radiotherapy professionals in making data-driven decisions.

System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual model

INTRODUCTION

Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment.

AREAS COVERED

We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation.

EXPERT OPINION

The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.

Mental health systems modelling for evidence-informed service reform in Australia

Australia's Fifth National Mental Health Plan required governments to report, not only on the progress of changes to mental health service delivery, but to also plan for services that should be provided. Future population demand for treatment and care is challenging to predict and one solution involves modelling the uncertain demands on the system. Modelling can help decision-makers understand likely future changes in mental health service demand and more intelligently choose appropriate responses. It can also support greater scrutiny, accountability and transparency of these processes. Australia has an emerging national capacity for systems modelling in mental health which can enhance the next phase of mental health reform. This paper introduces concepts useful for understanding mental health modelling and identifies where modelling approaches can support health service planners to make evidence-informed decisions regarding planning and investment for the Australian population.

Implementing competing risks in discrete event simulation: the event-specific probabilities and distributions approach

Background: Although several strategies for modelling competing events in discrete event simulation (DES) exist, a methodological gap for the event-specific probabilities and distributions (ESPD) approach when dealing with censored data remains. This study defines and illustrates the ESPD strategy for censored data. Methods: The ESPD approach assumes that events are generated through a two-step process. First, the type of event is selected according to some (unknown) mixture proportions. Next, the times of occurrence of the events are sampled from a corresponding survival distribution. Both of these steps can be modelled based on covariates. Performance was evaluated through a simulation study, considering sample size and levels of censoring. Additionally, an oncology-related case study was conducted to assess the ability to produce realistic results, and to demonstrate its implementation using both frequentist and Bayesian frameworks in R. Results: The simulation study showed good performance of the ESPD approach, with accuracy decreasing as sample sizes decreased and censoring levels increased. The average relative absolute error of the event probability (95%-confidence interval) ranged from 0.04 (0.00; 0.10) to 0.23 (0.01; 0.66) for 60% censoring and sample size 50, showing that increased censoring and decreased sample size resulted in lower accuracy. The approach yielded realistic results in the case study. Discussion: The ESPD approach can be used to model competing events in DES based on censored data. Further research is warranted to compare the approach to other modelling approaches for DES, and to evaluate its usefulness in estimating cumulative event incidences in a broader context.

Modelling NHS England 111 demand for primary care services: a discrete event simulation

OBJECTIVES

This feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service contact.

DESIGN

Discrete event simulation.

SETTING

Single English NHS 111 call centre in Yorkshire.

PARTICIPANTS

Callers registered with a Bradford general practitioner who contacted the NHS 111 service in 2021 and were triaged to a primary care disposition.

PRIMARY AND SECONDARY OUTCOME MEASURES

Face validity of conceptual model. Comparison between real and simulated data for quarterly counts (and 95% CIs) for patient contact with emergency ambulance (999), 111, and primary and secondary care services. Mean difference and 95% CIs in healthcare system usage between simulations and difference in mean proportion of avoidable admissions for callers who presented to an emergency department (ED).

RESULTS

The simulation of the current system estimated that there would be 39 283 (95% CI 39 237 to 39 328) primary care contacts, 2042 (95% CI 2032 to 2051) 999 calls and 1120 (95% CI 1114 to 1127) avoidable ED attendances. Modifying the model to ensure a timely primary care response resulted in a mean percentage increase of 196.1% (95% CI 192.2% to 199.9%) in primary care contacts, and a mean percentage decrease of 78.0% (95% CI 69.8% to 86.2%) in 999 calls and 88.1% (95% CI 81.7% to 94.5%) in ED attendances. Avoidable ED attendances reduced by a mean of -26 (95% CI -35 to -17).

CONCLUSION

In this simulated study, ensuring timely contact with a primary care service would lead to a significant reduction in 999 and 111 calls, and ED attendances (although not avoidable ED attendance). However, this is likely to be impractical given the need to almost double current primary care service provision. Further economic and qualitative research is needed to determine whether this intervention would be cost-effective and acceptable to both patients and primary care clinicians.

The cost-effectiveness of using a prognosis-tailored strategy model to triage couples with idiopathic infertility for assisted reproduction technology

OBJECTIVES

To evaluate whether a prognosis-tailored triage of ART for couples with idiopathic infertility by using the Hunault prognostic model can decrease the cost of treatment without compromising the chance of live birth.

STUDY DESIGN

This is a retrospective study conducted in an Australian fertility clinic. Couples seeking infertility consultation who were subsequently found to have idiopathic infertility after evaluation were included. We compared the costs per conception leading to live birth of the prognosis-tailored strategy with the immediate ART strategy, which generally reflects the current practice in Australian fertility clinics, over a 24-month period. In the prognosis-tailored strategy, for each couple, the prognosis for natural conception was assessed using the well-established Hunault model. Total cost of treatments were calculated as the sum of typical out-of-pocket and Australian Medicare cost (Australian national insurance scheme).

RESULTS

We studied 261 couples. In the prognosis-tailored strategy, the total cost was $2,766,781 and the live birth rate was 63.9%. In contrast, the immediate ART strategy yielded a live birth rate of 64.4% with a total cost of $3,176,845. Implementing the prognosis-tailored strategy using the Hunault model saved $410,064 in total and $1,571 per couple. The incremental cost-effectiveness ratio (ICER) was $341,720 per live birth.

CONCLUSION

In couples with idiopathic infertility, assessment of prognosis for natural conception using the Hunault model and delaying ART for 12 months in couples with favourable prognoses can considerably reduce costs without significantly compromising live birth rates.

Cost-utility analysis of the UPRIGHT intervention promoting resilience in adolescents

BACKGROUND

As mental health in adulthood is related to mental status during adolescence, school-based interventions have been proposed to improve resilience. The objective of this study was to build a simulation model representing the natural history of mental disorders in childhood, adolescence and youth to estimate the cost-effectiveness of the UPRIGHT school-based intervention in promoting resilience and mental health in adolescence.

METHODS

We built a discrete event simulation model fed with real-world data (cumulative incidence disaggregated into eight clusters) from the Basque Health Service database (609,381 individuals) to calculate utilities (quality-adjusted life years [QALYs]) and costs for the general population in two scenarios (base case and intervention). The model translated changes in the wellbeing of adolescents into different risks of mental illnesses for a time horizon of 30 years.

RESULTS

The number of cases of anxiety was estimated to fall by 5,125 or 9,592 and those of depression by 1,269 and 2,165 if the effect of the intervention lasted 2 or 5 years respectively. From a healthcare system perspective, the intervention was cost-effective for all cases considered with incremental cost-utility ratios always lower than €10,000/QALY and dominant for some subgroups. The intervention was always dominant when including indirect and non-medical costs (societal perspective).

CONCLUSIONS

Although the primary analysis of the trial did not did not detect significant differences, the UPRIGHT intervention promoting positive mental health was dominant in the economic evaluation from the societal perspective. Promoting resilience was more cost-effective in the most deprived group. Despite a lack of information about the spillover effect in some sectors, the economic evaluation framework developed principally for pharmacoeconomics can be applied to interventions to promote resilience in adolescents. As prevention of mental health disorders is even more necessary in the post-coronavirus disease-19 era, such evaluation is essential to assess whether investment in mental health promotion would be good value for money by avoiding costs for healthcare providers and other stakeholders.

Systematic review of applied mathematical models for the control of Schistosoma japonicum

BACKGROUND

Schistosoma japonicum remains endemic in China and the Philippines. Substantial progress has been made in the control of Japonicum in both China and the Philippines. China is reaching elimination thanks to a concerted effort of control strategies. Mathematical modelling has been a key tool in the design of control strategies, in place of expensive randomised-controlled trials. We conducted a systematic review to investigate mathematical models of Japonicum control strategies in China and the Philippines.

METHODS

We conducted a systematic review on July 5, 2020, in four electronic bibliographic databases - PubMed, Web of Science, SCOPUS and Embase. Articles were screened for relevance and for meeting the inclusion criteria. Data extracted included authors, year of publication, year of data collection, setting and ecological context, objectives, control strategies, main findings, the form and content of the model including its background, type, representation of population dynamics, heterogeneity of hosts, simulation period, source of parameters, model validation and sensitivity analysis. Results After screening, 19 eligible papers were included in the systematic review. Seventeen considered control strategies in China and two in the Philippines. Two frameworks were identified; the mean-worm burden framework and the prevalence-based framework, the latter of which increasingly common. Most models considered human and bovine definitive hosts. There were mixed additional elements included in the models, such as alternative definitive hosts and the role of seasonality and weather. Models generally agreed upon the need for an integrated control strategy rather than reliance on mass drug administration alone to sustain reductions in prevalence.

CONCLUSIONS

Mathematical modelling of Japonicum has converged from multiple approaches to modelling using the prevalence-based framework with human and bovine definitive hosts and find integrated control strategies to be most effective. Further research could investigate the role of other definitive hosts and model the effect of seasonal fluctuations in transmission.

A population-based study on the risk of prescription opioid abuse in patients with chronic opioid use and cost-effectiveness of prescription drug monitoring program using a patient simulation model in South Korea

BACKGROUND

Concerns regarding the burden of inappropriate opioid use are growing. We examined the association between prescription opioid abuse and patient characteristics and estimated the cost-effectiveness of the prescription drug monitoring program (PDMP) implemented in South Korea, considering patient-level information.

METHODS

A retrospective cohort study was conducted to explore the association between opioid abuse and patient characteristics using the National Health Insurance Service-National Sample Cohort (NHIS-NSC) database. We selected non-cancer patients with chronic opioid use and investigated the incidence of opioid abuse between 2010 and 2015. The association between opioid abuse and patient characteristics was assessed using the Cox proportional hazards model. The cost-effectiveness of the PDMP was assessed using discrete event simulation (DES) with a time horizon of 30 years from a societal perspective. Time-to-event data and event costs were obtained from the NHIS-NSC database. The abuse rate was adjusted for each patient based on the baseline characteristics and history of abuse experienced in the model. Program effectiveness, program costs, and health-state utilities were obtained from the published literature. The incremental cost-utility ratio (ICUR) was estimated at a discount rate of 5% for both costs and quality-adjusted life-years (QALYs).

RESULTS

We identified 22,524 patients with chronic opioid use in the NHIS-NSC database. Every one-year increase in age (hazard ratio: 1.002 [95% CI: 1.000-1.003]), medical aid program (1.130 [95% CI: 1.072-1.191]), high Charlson Comorbidity Index (1.054 [95% CI: 1.044-1.065]), and history of opioid abuse (1.501 [95% CI: 1.391-1.620] and 3.005 [95% CI: 2.387-3.783] for 1-2 and ≥3 abuse events, respectively) significantly increased the risk of opioid abuse. In the DES, the PDMP was cost-effective, with an estimated ICUR of $2,227/QALY, which was most affected by the program's effectiveness.

CONCLUSION

Patient characteristics and history of opioid abuse affected the risk of opioid abuse. Considering patient-level information, the PDMP implemented in South Korea is likely to be cost-effective.

Enhancement of Intra-hospital patient transfer in medical center hospital using discrete event system simulation

The intra-hospital transfer of critically ill patients are associated with complications at up to 70%. Numerous issues can be avoided with optimal pre-transport planning and communication. Simulation models have been demonstrated to be an effective method for modeling processes and enhancing on-time service and queue management. Discrete-event simulation (DES) models are acceptable for general hospital systems with increased variability. Herein, they are used to improve service effectiveness. A prospective observational study was conducted on 13 official day patient transfers, resulting in a total of 827 active patient transfers. Patient flow was simulated using discrete-event simulation (DES) to accurately and precisely represent real-world systems and act accordingly. Several patient transfer criteria were examined to create a more realistic simulation of patient flow. Waiting times were also measured to assess the efficiency of the patient transfer process. A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. The most effective approach for decreasing waiting times involved prioritizing patients with the most severe symptoms. After a transfer process was completed, staff attended to the next transfer process without returning to base. Results show that the average waiting time was reduced by 21.78% which is significantly important for emergency cases. A significant difference was recorded between typical and recommended patient transfer processes when the number of requests increased. To decrease waiting times, the patient transfer procedure should be modified according to our proposed DES model, which can be used to analyze and design queue management systems that achieve optimal waiting times.

A Simulation Modelling Study of Referral Distribution Policies in a Centralized Intake System for Surgical Consultation

Delays beyond recommended wait times, especially for specialist services, are associated with adverse health outcomes. The Alberta Surgical Initiative aims to improve the referral wait time-the time between a referral is received at the central intake to the time a specialist sees the patient. Using the discrete event simulation modelling approach, we evaluated and compared the impact of four referral distribution policies in a central intake system on three system performance measures (number of consultations, referral wait time and surgeon utilization). The model was co-designed with clinicians and clinic staff to represent the flow of patients through the system. We used data from the Facilitated Access to Surgical Treatment (FAST) centralized intake referral program for General Surgery to parameterize the model. Four distribution policies were evaluated - next-available-surgeon, sequential, "blackjack," and "kanban." A sequential distribution of referrals for surgical consultation among the surgeons resulted in the worst performance in terms of the number of consultations, referral wait time and surgeon utilization. The three other distribution policies are comparable in performance. The "next available surgeon" model provided the most efficient and robust model, with approximately 1,000 more consultations, 100 days shorter referral time and a 14% increase in surgeon utilization. Discrete event simulation (DES) modelling can be an effective tool to illustrate and communicate the impact of the referral distribution policy on system performance in terms of the number of consultations, referral wait time and surgeon utilization.

Decision-analytic modeling for early health technology assessment of medical devices - a scoping review

Objective

The goal of this review was to identify decision-analytic modeling studies in early health technology assessments (HTA) of high-risk medical devices, published over the last three years, and to provide a systematic overview of model purposes and characteristics. Additionally, the aim was to describe recent developments in modeling techniques.

Methods

For this scoping review, we performed a systematic literature search in PubMed and Embase including studies published in English or German. The search code consisted of terms describing early health technology assessment and terms for decision-analytic models. In abstract and full-text screening, studies were excluded that were not modeling studies for a high-risk medical device or an in-vitro diagnostic test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to report on the search and exclusion of studies. For all included studies, study purpose, framework and model characteristics were extracted and reported in systematic evidence tables and a narrative summary.

Results

Out of 206 identified studies, 19 studies were included in the review. Studies were either conducted for hypothetical devices or for existing devices after they were already available on the market. No study extrapolated technical data from early development stages to estimate potential value of devices in development. All studies except one included cost as an outcome. Two studies were budget impact analyses. Most studies aimed at adoption and reimbursement decisions. The majority of studies were on in-vitro diagnostic tests for personalized and targeted medicine. A timed automata model, to our knowledge a model type new to HTA, was tested by one study. It describes the agents in a clinical pathway in separate models and, by allowing for interaction between the models, can reflect complex individual clinical pathways and dynamic system interactions. Not all sources of uncertainty for in-vitro tests were explicitly modeled. Elicitation of expert knowledge and judgement was used for substitution of missing empirical data. Analysis of uncertainty was the most valuable strength of decision-analytic models in early HTA, but no model applied sensitivity analysis to optimize the test positivity cutoff with regard to the benefit-harm balance or cost-effectiveness. Value-of-information analysis was rarely performed. No information was found on the use of causal inference methods for estimation of effect parameters from observational data.

Conclusion

Our review provides an overview of the purposes and model characteristics of nineteen recent early evaluation studies on medical devices. The review shows the growing importance of personalized interventions and confirms previously published recommendations for careful modeling of uncertainties surrounding diagnostic devices and for increased use of value-of-information analysis. Timed automata may be a model type worth exploring further in HTA. In addition, we recommend to extend the application of sensitivity analysis to optimize positivity criteria for in-vitro tests with regard to benefit-harm or cost-effectiveness. We emphasize the importance of causal inference methods when estimating effect parameters from observational data.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Objectives

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.

Methods

To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.

Results

Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.

Conclusions

Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Evaluation of direct-to-angiography suite (DTAS) and conventional clinical pathways in stroke care: a simulation study

BACKGROUND

Rapid time to reperfusion is essential to minimize morbidity and mortality in acute ischemic stroke due to large vessel occlusion (LVO). We aimed to evaluate the workflow times when utilizing a direct-to-angiography suite (DTAS) pathway for patients with suspected stroke presenting at a comprehensive stroke center compared with a conventional CT pathway.

METHODS

We developed a discrete-event simulation (DES) model to evaluate DTAS workflow timelines compared with a conventional CT pathway, varying the admission NIHSS score treatment eligibility criteria. Model parameters were estimated based on 2 year observational data from our institution. Sensitivity analyses of simulation parameters were performed to assess the impact of patient volume and baseline utilization of angiography suites on workflow times utilizing DTAS.

RESULTS

Simulation modeling of stroke patients (SimStroke) demonstrated door-to-reperfusion time savings of 0.2-3.5 min (p=0.05) for a range of DTAS eligibility criteria (ie, last known well to arrival <6 hours and National Institutes of Health Stroke Scale ≥6-11), when compared with the conventional stroke care pathway. Sensitivity analyses revealed that DTAS time savings is highly dependent on baseline utilization of angiography suites.

CONCLUSIONS

The results of the SimStroke model showed comparable time intervals for door-to-reperfusion for DTAS compared with a conventional stroke care pathway. However, the DTAS pathway was very sensitive to baseline angiography suite utilization, with even a 10% increase eliminating the advantages of DTAS compared with the conventional pathway. Given the minimal time savings modeled here, further investigation of implementing the DTAS pathway in clinical care is warranted.

Budget impact analysis of the use of Souvenaid in patients with prodromal Alzheimer’s Disease in Spain

Introduction

The effectiveness, safety, and cost-effectiveness of the use of Souvenaid for Alzheimer’s disease (AD) have been previously evidenced. To complete the economic analysis, there is a need to assess whether society can afford it. The objective of this study was to carry out a budget impact analysis of the use of Souvenaid in Spain under the conditions of the LipiDidiet clinical trial from a societal perspective.

Methods

We built a population model that took into account all the cohorts of individuals with AD, their individual progression, and the potential impact of Souvenaid treatment on their trajectories. Patient progression data were obtained from mixed models. The target population was estimated based on the population forecast for 2020–2035 and the incidence of dementia. Individual progression to dementia measured by the Clinical Dementia Rating-Sum of Boxes was reproduced using mixed models. Besides the costs of treatment and diagnosis, direct costs of medical and non-medical care and indirect costs were included.

Results

The epidemiological indicators and the distribution of life expectancy by stages validated the model. From the third year (2022), the differences in the cost of dementia offset the incremental cost of diagnosis and treatment. The costs of dependency reached €500 million/year while those of the intervention were limited to €40 million.

Conclusions

Souvenaid, with modest effectiveness in delaying dementia associated with AD, achieved a positive economic balance between costs and savings. Its use in the treatment of prodromal AD would imply an initial cost that would be ongoing, but this would be offset by savings in the care system for dependency associated with dementia from the third year. These results were based on adopting a societal perspective taking into account the effect of treatment on the use of health, social, and family resources.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01111-7.

Optimal Planning of Health Services through Genetic Algorithm and Discrete Event Simulation: A Proposed Model and Its Application to Stroke Rehabilitation Care

Background. Increasing demand for provision of care to stroke survivors creates challenges for health care planners. A key concern is the optimal alignment of health care resources between provision of acute care, rehabilitation, and among different segments of rehabilitation, including inpatient rehabilitation, early supported discharge (ESD), and outpatient rehabilitation (OPR). We propose a novel application of discrete event simulation (DES) combined with a genetic algorithm (GA) to identify the optimal configuration of rehabilitation that maximizes patient benefits subject to finite health care resources. Design. Our stroke rehabilitation optimal model (sROM) combines DES and GA to identify an optimal solution that minimizes wait time for each segment of rehabilitation by changing care capacity across different segments. sROM is initiated by generating parameters for DES. GA is used to evaluate wait time from DES. If wait time meets specified stopping criteria, the search process stops at a point at which optimal capacity is reached. If not, capacity estimates are updated, and an additional iteration of the DES is run. To parameterize the model, we standardized real-world data from medical records by fitting them into probability distributions. A meta-analysis was conducted to determine the likelihood of stroke survivors flowing across rehabilitation segments. Results. We predict that rehabilitation planners in Alberta, Canada, have the potential to improve services by increasing capacity from 75 to 113 patients per day for ESD and from 101 to 143 patients per day for OPR. Compared with the status quo, optimal capacity would provide ESD to 138 (s = 29.5) more survivors and OPR to 262 (s = 45.5) more annually while having an estimated net annual cost savings of $25.45 (s = 15.02) million. Conclusions. The combination of DES and GA can be used to estimate optimal service capacity.

HIGHLIGHTS

We created a hybrid model combining a genetic algorithm and discrete event simulation to search for the optimal configuration of health care service capacity that maximizes patient outcomes subject to finite health system resources.We applied a probability distribution fitting process to standardize real-world data to probability distributions. The process consists of choosing the distribution type and estimating the parameters of that distribution that best reflects the data. Standardizing real-word data to a best-fitted distribution can increase model generalizability.In an illustrative study of stroke rehabilitation care, resource allocation to stroke rehabilitation services under an optimal configuration allows provision of care to more stroke survivors who need services while reducing wait time.Resources needed to expand rehabilitation services could be reallocated from the savings due to reduced wait time in acute care units. In general, the predicted optimal configuration of stroke rehabilitation services is associated with a net cost savings to the health care system.

Long-Term Cost-Effectiveness Comparison of Catheter Ablation and Antiarrhythmic Drugs in Atrial Fibrillation Treatment Using Discrete Event Simulation

OBJECTIVES

To evaluate the lifetime cost-effectiveness of 3 widely used atrial fibrillation (AF) treatments from the perspectives of Chinese healthcare system: antiarrhythmic drugs (AADs), ThermoCool SmartTouch guided by ablation index (STAI), and second-generation cryoballoon (CB2).

METHODS

A discrete event simulation (DES) model was implemented to compare the lifetime cost-effectiveness of AADs, STAI, and CB2. AF disease progression was explicitly modeled based on the Atrial Fibrillation Progression Trial clinical study results. The base-case analysis assumed that patients with paroxysmal AF (PAF) entered the model at the age of 55 years and had a CHA2DS2-VASc (Congestive heart failure, Hypertension, Age ( > 65 = 1 point, > 75 = 2 points), Diabetes, previous Stroke/transient ischemic attack (2 points)-Vascular disease (peripheral arterial disease, previous myocardial infarction, aortic atheroma), Age 65 to 74 years, and Sex category) score of 2 for males and 3 for females. Model parameter uncertainties were incorporated throughout the DES simulation with full probabilistic model parameterization.

RESULTS

The lifetime cost-effectiveness evaluations showed that patients treated with AADs gained an average of 4.98 quality-adjusted life-years (QALYs) and 9.63 life-years (LYs) at an average cost of US dollar (USD) 15 374. Patients treated with CB2 gained 5.92 QALYs and 10.74 LYs at an average cost of USD 26 811. The STAI group gained an average of 6.55 QALYs and 11.57 LYs at an average cost of USD 24 722. The incremental cost-effectiveness ratios was USD 5927 and USD 12 167 per QALY for STAI versus AADs and CB2 versus AADs, respectively. Assuming the willingness-to-pay threshold for China is USD 30 390 per QALY, both ablation treatments will be cost-effective compared with AADs for patients with PAF.

CONCLUSIONS

The DES model demonstrated that catheter ablations are more cost-effective than AADs for patients with PAF under the healthcare system in China. Among catheter ablation technologies, STAI provides better outcomes at lower costs.

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary

There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.

Identifying and managing psoriasis-associated comorbidities: the IMPACT research programme

Background

Psoriasis is a common, lifelong inflammatory skin disease, the severity of which can range from limited disease involving a small body surface area to extensive skin involvement. It is associated with high levels of physical and psychosocial disability and a range of comorbidities, including cardiovascular disease, and it is currently incurable.

Objectives

To (1) confirm which patients with psoriasis are at highest risk of developing additional long-term conditions and identify service use and costs to patient, (2) apply knowledge about risk of comorbid disease to the development of targeted screening services to reduce risk of further disease, (3) learn how patients with psoriasis cope with their condition and about their views of service provision, (4) identify the barriers to provision of best care for patients with psoriasis and (5) develop patient self-management resources and staff training packages to improve the lives of people with psoriasis.

Design

Mixed methods including two systematic reviews, one population cohort study, one primary care screening study, one discrete choice study, four qualitative studies and three mixed-methodology studies.

Setting

Primary care, secondary care and online surveys.

Participants

People with psoriasis and health-care professionals who manage patients with psoriasis.

Results

Prevalence rates for psoriasis vary by geographical location. Incidence in the UK was estimated to be between 1.30% and 2.60%. Knowledge about the cost-effectiveness of therapies is limited because high-quality clinical comparisons of interventions have not been done or involve short-term follow-up. After adjusting for known cardiovascular risk factors, psoriasis (including severe forms) was not found to be an independent risk factor for major cardiovascular events; however, co-occurrence of inflammatory arthritis was a risk factor. Traditional risk factors were high in patients with psoriasis. Large numbers of patients with suboptimal management of known risk factors were found by screening patients in primary care. Risk information was seldom discussed with patients as part of screening consultations, meaning that a traditional screening approach may not be effective in reducing comorbidities associated with psoriasis. Gaps in training of health-care practitioners to manage psoriasis effectively were identified, including knowledge about risk factors for comorbidities and methods of facilitating behavioural change. Theory-based, high-design-quality patient materials broadened patient understanding of psoriasis and self-management. A 1-day training course based on motivational interviewing principles was effective in increasing practitioner knowledge and changing consultation styles. The primary economic analysis indicated a high level of uncertainty. Sensitivity analysis indicated some situations when the interventions may be cost-effective. The interventions need to be assessed for long-term (cost-)effectiveness.

Limitations

The duration of patient follow-up in the study of cardiovascular disease was relatively short; as a result, future studies with longer follow-up are recommended.

Conclusions

Recognition of the nature of the psoriasis and its impact, knowledge of best practice and guideline use are all limited in those most likely to provide care for the majority of patients. Patients and practitioners are likely to benefit from the provision of appropriate support and/or training that broadens understanding of psoriasis as a complex condition and incorporates support for appropriate health behaviour change. Both interventions were feasible and acceptable to patients and practitioners. Cost-effectiveness remains to be explored.

Future work

Patient support materials have been created for patients and NHS providers. A 1-day training programme with training materials for dermatologists, specialist nurses and primary care practitioners has been designed. Spin-off research projects include a national study of responses to psoriasis therapy and a global study of the prevalence and incidence of psoriasis. A new clinical service is being developed locally based on the key findings of the Identification and Management of Psoriasis Associated ComorbidiTy (IMPACT) programme.

Funding

This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 10, No. 3. See the NIHR Journals Library website for further project information.

Understanding innovation of health technology assessment methods: the IHTAM framework

Adequate methods are urgently needed to guarantee the good practice of health technology assessment (HTA) for technologies with novel properties. The aim of the study was to construct a conceptual framework to help understand the innovation of HTA methods (IHTAM). The construction of the IHTAM framework was based on two scoping reviews, one on the current practice of innovating methods, that is existing HTA frameworks, and one on theoretical foundations for innovating methods outside the HTA discipline. Both aimed to identify and synthesize concepts of innovation (i.e., innovation processes and roles of stakeholders in innovation). Using these concepts, the framework was developed in iterative brainstorming sessions and subsequent discussions with representatives from various stakeholder groups. The framework was constructed based on twenty documents on innovating HTA frameworks and fourteen guidelines from three scientific disciplines. It includes a generic innovation process consisting of three phases (“Identification,” “Development,” and “Implementation”) and nine subphases. In the framework, three roles that HTA stakeholders can play in innovation (“Developers,” “Practitioners,” and “Beneficiaries”) are defined, and a process on how the stakeholders innovate HTA methods is included. The IHTAM framework visualizes systematically which elements and stakeholders are important to the development and implementation of novel HTA methods. The framework could be used by all stakeholders involved in HTA innovation to learn how to engage dynamically and collaborate effectively throughout the innovation process. HTA stakeholders in practice have welcomed the framework, though additional testing of its applicability and acceptance is essential.

Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System

Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital’s information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study’s dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10–21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs.

Health economic modelling of diabetic kidney disease in patients with type 2 diabetes treated with Canagliflozin in Belgium

OBJECTIVES

The Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) trial showed reduced renal and cardiovascular (CV) events in patients with type 2 diabetes (T2D) and diabetic kidney disease (DKD) treated with canagliflozin 100 mg added to Standard of Care (SoC) versus SoC alone. This led to an extension of the canagliflozin 100 mg European marketing authorisation, making canagliflozin the first pharmacological therapy to receive authorisation for the treatment of DKD since the RENAAL and IDNT trials more than 20 years ago. Given the importance of cost-effectiveness analyses in health care, this study aimed to leverage the CREDENCE trial outcomes to estimate the cost-effectiveness of canagliflozin 100 mg from the perspective of the Belgian healthcare system.

METHODS

A microsimulation model (CREDENCE Economic Model of DKD), developed using patient-level CREDENCE trial data, was leveraged to model the progression of DKD and CV outcomes, associated costs, and life quality. Unit costs and quality-adjusted life years (QALYs) were sourced from the literature. The time horizon was 10 years and sensitivity analyses were performed.

RESULTS

Canagliflozin was associated with sizable gains in life-years and QALYs over 10 years, and the incremental cost-effectiveness ratio cost offsets associated with reductions in CV and renal complications resulted in overall net cost savings from the perspective of the Belgian healthcare system.

CONCLUSION

Model-based results suggest that adding canagliflozin 100 mg to SoC can improve outcomes for patients with DKD while reducing overall net costs for the Belgian healthcare system.

How Can Hybrid Simulation Support Organizations in Assessing COVID-19 Containment Measures?

Simulation models have always been an aid in epidemiology for understanding the spread of epidemics and evaluating their containment policies. This paper illustrates how hybrid simulation can support companies in assessing COVID-19 containment measures in indoor environments. In particular, a Hybrid Simulation (HS) is presented. The HS model consists of an Agent-Based Simulation (ABS) to simulate the virus contagion model and a Discrete Event Simulation (DES) model to simulate the interactions between flows of people in an indoor environment. Compared with previous works in the field of simulation and COVID-19, this study provides the possibility to model the specific behaviors of individuals moving in time and space and the proposed HS model could be adapted to several epidemiological conditions (just setting different parameters in the agent-based model) and different kinds of facilities. The HS approach has been developed and then successfully tested with a real case study related to a university campus in northern Italy. The case study highlights the potentials of hybrid simulation in assessing the effectiveness of the containment measures adopted during the period under examination in the pandemic context. From a managerial perspective, this study, exploiting the complementarity of the ABM and DES approaches in a HS model, provides a complete and usable tool to support decision-makers in evaluating different contagion containment measures.

Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation

Drive-through clinics have previously been utilized in vaccination efforts and are now being more widely adopted for COVID-19 vaccination in different parts of the world by offering many advantages including utilizing existing infrastructure, large daily throughput and enforcing social distancing by default. Successful, effective, and efficient drive-through facilities require a suitable site and keen focus on layout and process design. To demonstrate the role that high fidelity computer simulation can play in planning and design of drive-through mass vaccination clinics, we used multiple integrated discrete event simulation (DES) and agent-based modelling methods. This method using AnyLogic simulation software to aid in planning, design, and implementation of one of the largest and most successful early COVID-19 mass vaccination clinics operated by UCHealth in Denver, Colorado. Simulations proved to be helpful in aiding the optimization of UCHealth drive through mass vaccination clinic design and operations by exposing potential bottlenecks, overflows, and queueing, and clarifying the necessary number of supporting staff. Simulation results informed the target number of vaccinations and necessary processing times for different drive through station set ups and clinic formats. We found that modern simulation tools with advanced visual and analytical capabilities to be very useful for effective planning, design, and operations management of mass vaccination facilities.

Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases

(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of the T2D Holistic Care model via estimating the probability of diabetes-related complications and the time-to-occurrence from a population-based database. (2) Methods: The model was based on the database of a Taiwan pay-for-performance reimbursement scheme for T2D between November 2002 and July 2017. A nonhomogeneous Markov model was applied to simulate multistate (7 main complications and death) transition probability after considering the sequential and repeated difficulties. (3) Results: The Markov model was constructed based on clinical care information from 163,452 patients with T2D, with a mean follow-up time of 5.5 years. After simulating a cohort of 100,000 hypothetical patients over a 10-year time horizon based on selected patient characteristics at baseline, a good predicted complication and mortality rates with a small range of absolute error (0.3–3.2%) were validated in the original cohort. Better and optimal predictabilities were further confirmed compared to the UKPDS Outcomes model and applied the model to other Asian populations, respectively. (4) Contribution: The study provides well-elucidated evidence to apply real-world data to the estimation of the occurrence and time point of major diabetes-related complications over a patient’s lifetime. Further applications in health decision science are encouraged.

Systematic review of reporting quality of economic evaluations in plastic surgery based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement

BACKGROUND

Economic evaluations in healthcare are designed to inform decisions by the estimation of cost and effect trade-off of two or more interventions. This review identified and appraised the quality of reporting of economic evaluations in plastic surgery based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement.

METHODS

Electronic databases were searched: MEDLINE, EMBASE, The Cochrane Library, Ovid Health Star, and Business Source Complete from January 1, 2012 to November 30, 2019. Data extracted included: the type of economic evaluation (i.e., cost-utility analysis (CUA), cost-effectiveness analysis (CEA), cost-benefit analysis (CBA), cost-minimization analysis (CMA)), domain of plastic surgery, journal, year, and country of publication. The CHEERS checklist (with 24 items) was used to appraise the quality of reporting.

RESULTS

Ninety-two economic evaluations were identified; CUA (10%), CEA (31%), CBA (4%), and CMA (50%). Breast surgery was the top domain (48%). Most were conducted in the USA (61%) and published in Plastic and Reconstructive Surgery journal (28%). One-third were published in the last two years. The average CHEERS checklist compliance score was 15 (63%). The average CHEERS checklist compliance score per type of evaluation was 19 (77%) for CUA, 17 (70%) for CEA, 13 (52%) for CBA, and 14 (57%) for CMA. The least reported CHEERS checklist items included: time horizon (15%), discount rate (18%), and assessment of heterogeneity (15%). Thirty-two percent of studies were inappropriately titled (i.e., methodologically incorrect).

CONCLUSION

Quality of reporting of economic evaluations is suboptimal. The CHEERS checklist should be consulted when performing and reporting economic evaluations in plastic surgery.

Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of "jumpover" states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

Burden of cardiovascular diseases associated with fine particulate matter in Beijing, China: an economic modelling study

Objective

To evaluate the economic and humanistic burden associated with cardiovascular diseases that were attributable to fine particulate matter (≤ 2.5 μg/m³ in aerodynamic diameter; PM_2.5) in Beijing.

Methods

This study used a health economic modelling approach to compare the actual annual average PM_2.5 concentration with the PM_2.5 concentration limit (35 µg/m³) as defined by the Chinese Ambient Air Quality Standard in terms of cardiovascular disease outcomes in Beijing adult population. The outcomes included medical costs, quality-adjusted life-years (QALYs) and net monetary loss (NML). Beijing annual average PM_2.5 concentration was around 105 µg/m³ during 2013–2015. Therefore, we estimated the differences in cardiovascular outcomes of Beijing adults between exposure to the PM_2.5 concentration of 105 µg/m³ and exposure to the concentration of 35 µg/m³. According to WHO estimates, the hazard ratios of coronary heart disease and stroke associated with the increase of PM_2.5 concentration from 35 to 105 µg/m³ were 1.15 and 1.29, respectively.

Results

The total 1-year excess medical costs of cardiovascular diseases associated with PM_2.5 pollution in Beijing was US$147.9 million and the total 1-year QALY loss was 92 574 in 2015, amounting to an NML of US$2281.8 million. The expected lifetime incremental costs for a male Beijing adult and a female Beijing adult were US$237 and US$163, the corresponding QALY loss was 0.14 and 0.12, and the corresponding NML was US$3514 and US$2935.

Conclusions

PM_2.5-related cardiovascular diseases imposed high economic and QALY burden on Beijing society. Continuous and intensive investment on reducing PM_2.5 concentration is warranted even when only cardiovascular benefits are considered.

Equity-Efficiency Trade-offs Associated With Alternative Approaches to Deceased Donor Kidney Allocation: A Patient-level Simulation

Supplemental Digital Content is available in the text.

Background.

The number of patients waiting to receive a kidney transplant outstrips the supply of donor organs. We sought to quantify trade-offs associated with different approaches to deceased donor kidney allocation in terms of quality-adjusted life years (QALYs), costs, and access to transplantation.

Methods.

An individual patient simulation model was developed to compare 5 different approaches to kidney allocation, including the 2006 UK National Kidney Allocation Scheme (NKAS) and a QALY maximization approach designed to maximize health gains from a limited supply of donor organs. We used various sources of patient-level data to develop multivariable regression models to predict survival, health state utilities, and costs. We simulated the allocation of kidneys from 2200 deceased donors to a waiting list of 5500 patients and produced estimates of total lifetime costs and QALYs for each allocation scheme.

Results.

Among patients who received a transplant, the QALY maximization approach generated 48 045 QALYs and cost £681 million, while the 2006 NKAS generated 44 040 QALYs and cost £625 million. When also taking into consideration outcomes for patients who were not prioritized to receive a transplant, the 2006 NKAS produced higher total QALYs and costs and an incremental cost-effectiveness ratio of £110 741/QALY compared with the QALY maximization approach.

Conclusions.

Compared with the 2006 NKAS, a QALY maximization approach makes more efficient use of deceased donor kidneys but reduces access to transplantation for older patients and results in greater inequity in the distribution of health gains between patients who receive a transplant and patients who remain on the waiting list.

Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs

Type 2 diabetes (T2D) is one of the most rapidly increasing non-communicable diseases worldwide. Lifestyle interventions are effective in preventing T2D but also resource intensive. This study evaluated with discrete event simulation (DES) the relative budget impacts of three hypothetical diabetes prevention programs (DPP), including group-based contact intervention, digital program with human coaching and fully automated program. The data for simulation were derived from research literature and national health and population statistics. The model was constructed using the iGrafx Process for Six Sigma software and simulations were carried out for 10 years. All simulated interventions produced cost savings compared to the situation without any intervention. However, this was a modeling study and future studies are needed to verify the results in real-life. Decision makers could benefit the predictive models regarding the long-term effects of diabetes prevention interventions, but more data is needed in particular on the usage, acceptability, effectiveness and costs of digital intervention tools.

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making

OBJECTIVES

The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs).

STUDY DESIGN AND SETTING

Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach.

RESULTS

Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either "off-the-shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines.

CONCLUSION

This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges

Supplemental Digital Content is available in the text.

Objectives:

As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios.

Design:

Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU.

Setting:

Combined data from two ICUs in United Kingdom.

Patients:

The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients.

Interventions:

None.

Measurements and Main Results:

During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization.

Conclusions:

Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased.

Impact of Transcatheter Aortic Valve Durability on Life Expectancy in Low-Risk Patients With Severe Aortic Stenosis

Background:

Recent clinical trial results showed that transcatheter aortic valve replacement (TAVR) is noninferior and may be superior to surgical aortic valve replacement (SAVR) for mortality, stroke, and rehospitalization. However, the impact of transcatheter valve durability remains uncertain.

Methods:

Discrete event simulation was used to model hypothetical scenarios of TAVR versus SAVR durability in which TAVR failure times were varied to determine the impact of TAVR valve durability on life expectancy in a cohort of low-risk patients similar to those in recent trials. Discrete event simulation modeling was used to estimate the tradeoff between a less invasive procedure with unknown valve durability (TAVR) and that of a more invasive procedure with known durability (SAVR). Standardized differences were calculated, and a difference >0.10 was considered clinically significant. In the base-case analysis, patients with structural valve deterioration requiring reoperation were assumed to undergo a valve-in-valve TAVR procedure. A sensitivity analysis was conducted to determine the impact of TAVR valve durability on life expectancy in younger age groups (40, 50, and 60 years).

Results:

Our cohort consisted of patients with aortic stenosis at low surgical risk with a mean age of 73.4±5.9 years. In the base-case scenario, the standardized difference in life expectancy was <0.10 between TAVR and SAVR until transcatheter valve prosthesis failure time was 70% shorter than that of surgical prostheses. At a transcatheter valve failure time <30% compared with surgical valves, SAVR was the preferred option. In younger patients, life expectancy was reduced when TAVR durability was 30%, 40%, and 50% shorter than that of surgical valves in 40-, 50-, and 60-year-old patients, respectively.

Conclusions:

According to our simulation models, the durability of TAVR valves must be 70% shorter than that of surgical valves to result in reduced life expectancy in patients with demographics similar to those of recent trials. However, in younger patients, this threshold for TAVR valve durability was substantially higher. These findings suggest that durability concerns should not influence the initial treatment decision concerning TAVR versus SAVR in older low-risk patients on the basis of current evidence supporting TAVR valve durability. However, in younger low-risk patients, valve durability must be weighed against other patient factors such as life expectancy.

Simulation models for transmission of health care-associated infection: A systematic review

BACKGROUND

Health care-associated infections (HAIs) are a global health burden because of their significant impact on patient health and health care systems. Mechanistic simulation modeling that captures the dynamics between patients, pathogens, and the environment is increasingly being used to improve understanding of epidemiological patterns of HAIs and to facilitate decisions on infection prevention and control (IPC). The purpose of this review is to present a systematic review to establish (1) how simulation models have been used to investigate HAIs and their mitigation and (2) how these models have evolved over time, as well as identify (3) gaps in their adoption and (4) useful directions for their future development.

METHODS

The review involved a systematic search and identification of studies using system dynamics, discrete event simulation, and agent-based model to study HAIs.

RESULTS

The complexity of simulation models developed for HAIs significantly increased but heavily concentrated on transmission dynamics of methicillin-resistant Staphylococcus aureus in the hospitals of high-income countries. Neither HAIs in other health care settings, the influence of contact networks within a health care facility, nor patient sharing and referring networks across health care settings were sufficiently understood.

CONCLUSIONS

This systematic review provides a broader overview of existing simulation models in HAIs to identify the gaps and to direct and facilitate further development of appropriate models in this emerging field.