Markov modeling and discrete event simulation in health care: a systematic comparison

Evaluating Semi-Markov Processes and Other Epidemiological Time-to-Event Models by Computing Disease Sojourn Density as Partial Differential Equations

IntroductionEpidemiological models benefit from incorporating detailed time-to-event data to understand how disease risk evolves. For example, decompensation risk in liver cirrhosis depends on sojourn time spent with cirrhosis. Semi-Markov and related models capture these details by modeling time-to-event distributions based on published survival data. However, implementations of semi-Markov processes rely on Monte Carlo sampling methods, which increase computational requirements and introduce stochastic variability. Explicitly calculating the evolving transition likelihood can avoid these issues and provide fast, reliable estimates.MethodsWe present the sojourn time density framework for computing semi-Markov and related models by calculating the evolving sojourn time probability density as a system of partial differential equations. The framework is parametrized by commonly used hazard and models the distribution of current disease state and sojourn time. We describe the mathematical background, a numerical method for computation, and an example model of liver disease.ResultsModels developed with the sojourn time density framework can directly incorporate time-to-event data and serial events in a deterministic system. This increases the level of potential model detail over Markov-type models, improves parameter identifiability, and reduces computational burden and stochastic uncertainty compared with Monte Carlo methods. The example model of liver disease was able to accurately reproduce targets without extensive calibration or fitting and required minimal computational burden.ConclusionsExplicitly modeling sojourn time distribution allows us to represent semi-Markov systems using detailed survival data from epidemiological studies without requiring sampling, avoiding the need for calibration, reducing computational time, and allowing for more robust probabilistic sensitivity analyses.HighlightsTime-inhomogeneous semi-Markov models and other time-to-event-based modeling approaches can capture risks that evolve over time spent with a disease.We describe an approach to computing these models that represents them as partial differential equations representing the evolution of the sojourn time probability density.This sojourn time density framework incorporates complex data sources on competing risks and serial events while minimizing computational complexity.

Cost-effectiveness analysis of aducanumab versus placebo for patients with mild cognitive impairment and mild Alzheimer's disease

OBJECTIVES

To assess the cost-effectiveness of aducanumab at its updated price for treating patients with mild cognitive impairment (MCI) and mild Alzheimer's disease (AD).

DESIGN

Cost-effectiveness analysis.

SETTINGS

A five-state Markov model was constructed using 10 000 virtual patients to assess the cost-effectiveness of aducanumab from the perspective of the US healthcare system. The model employed a one-year cycle time and a lifetime time horizon. Transition probabilities and mortality rates were derived from a literature review. To address uncertainty and generalise the base case results, both one-way and probabilistic sensitivity analyses were conducted.

PARTICIPANTS

10 000 virtual patients with MCI and mild AD.

INTERVENTIONS

The study group consisted of patients using aducanumab, while the control group consisted of those using a placebo.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcomes included costs and quality-adjusted life years (QALYs). In line with the healthcare system perspective, only direct medical costs were included. Drug costs were obtained from official records, while other medical costs were derived from literature reviews. Utilities used to calculate QALYs were also obtained from the literature. Incremental analysis was conducted to assess cost-effectiveness in the base case analysis by comparing the incremental cost-effectiveness ratio (ICER) against the willingness-to-pay (WTP) threshold. A discount rate of 3% was applied to both costs and effectiveness.

RESULTS

From the perspective of the US healthcare system, compared with the control group, the study group had an incremental cost of US$143 821.1 and an incremental QALY of 0.10. The ICER of patients using aducanumab compared with those using placebo was US$1 012 219.0 per QALY gained, which was much greater than the WTP threshold of US$50 000 to US$150 000, indicating that using aducanumab was not cost-effective. One-way sensitivity analysis showed the five most sensitive parameters were relative risk of progressing from MCI to mild AD, the utility of MCI, initial age, discount rate and the price of aducanumab. In the probabilistic sensitivity analysis, when the WTP was the WTP threshold of US$150 000, the probability of aducanumab being cost-effective was 0%. In addition, when the probability of aducanumab being cost-effective was 50%, the WTP was US$1 180 000, and when the probability of aducanumab being cost-effective was 95%, the WTP was US$1 906 000.

CONCLUSIONS

Even with the updated price being half of the original, aducanumab is still not cost-effective, underscoring the need for affordable, evidence-based AD treatments.

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.

Cost-effectiveness of endovascular thrombectomy for acute ischemic stroke with established large infarct in Germany: a decision tree and Markov model

BACKGROUND

Recent studies, including the TENSION trial, support the use of endovascular thrombectomy (EVT) in acute ischemic stroke with large infarct (Alberta Stroke Program Early Computed Tomography Score (ASPECTS) 3-5).

OBJECTIVE

To evaluate the cost-effectiveness of EVT compared with best medical care (BMC) alone in this population from a German healthcare payer perspective.

METHODS

A short-term decision tree and a long-term Markov model (lifetime horizon) were used to compare healthcare costs and quality-adjusted life years (QALYs) between EVT and BMC. The effectiveness of EVT was reflected by the 90-day modified Rankin Scale (mRS) outcome from the TENSION trial. QALYs were based on published mRS-specific health utilities (EQ-5D-3L indices). Long-term healthcare costs were calculated based on insurance data. Costs (reported in 2022 euros) and QALYs were discounted by 3% annually. Cost-effectiveness was assessed using incremental cost-effectiveness ratios (ICERs). Deterministic and probabilistic sensitivity analyses were performed to account for parameter uncertainties.

RESULTS

Compared with BMC, EVT yielded higher lifetime incremental costs (€24 257) and effects (1.41 QALYs), resulting in an ICER of €17 158/QALY. The results were robust to parameter variation in sensitivity analyses (eg, 95% probability of cost-effectiveness was achieved at a willingness to pay of >€22 000/QALY). Subgroup analyses indicated that EVT was cost-effective for all ASPECTS subgroups.

CONCLUSIONS

EVT for acute ischemic stroke with established large infarct is likely to be cost-effective compared with BMC, assuming that an additional investment of €17 158/QALY is deemed acceptable by the healthcare payer.

In Silico Evaluation of Algorithm-Based Clinical Decision Support Systems: Protocol for a Scoping Review

BACKGROUND

Integrating algorithm-based clinical decision support (CDS) systems poses significant challenges in evaluating their actual clinical value. Such CDS systems are traditionally assessed via controlled but resource-intensive clinical trials.

OBJECTIVE

This paper presents a review protocol for preimplementation in silico evaluation methods to enable broadened impact analysis under simulated environments before clinical trials.

METHODS

We propose a scoping review protocol that follows an enhanced Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to investigate the scope and research gaps in the in silico evaluation of algorithm-based CDS models-specifically CDS decision-making end points and objectives, evaluation metrics used, and simulation paradigms used to assess potential impacts. The databases searched are PubMed, Embase, CINAHL, PsycINFO, Cochrane, IEEEXplore, Web of Science, and arXiv. A 2-stage screening process identified pertinent articles. The information extracted from articles was iteratively refined. The review will use thematic, trend, and descriptive analyses to meet scoping aims.

RESULTS

We conducted an automated search of the databases above in May 2023, with most title and abstract screenings completed by November 2023 and full-text screening extended from December 2023 to May 2024. Concurrent charting and full-text analysis were carried out, with the final analysis and manuscript preparation set for completion in July 2024. Publication of the review results is targeted from July 2024 to February 2025. As of April 2024, a total of 21 articles have been selected following a 2-stage screening process; these will proceed to data extraction and analysis.

CONCLUSIONS

We refined our data extraction strategy through a collaborative, multidisciplinary approach, planning to analyze results using thematic analyses to identify approaches to in silico evaluation. Anticipated findings aim to contribute to developing a unified in silico evaluation framework adaptable to various clinical workflows, detailing clinical decision-making characteristics, impact measures, and reusability of methods. The study's findings will be published and presented in forums combining artificial intelligence and machine learning, clinical decision-making, and health technology impact analysis. Ultimately, we aim to bridge the development-deployment gap through in silico evaluation-based potential impact assessments.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/63875.

Timing of parathyroidectomy after kidney transplantation: A cost-effectiveness analysis

INTRODUCTION

Parathyroidectomy is the definitive treatment for tertiary hyperparathyroidism post-kidney transplantation. However, cinacalcet-based medical management is increasingly used as an alternative. The financial consequences of each treatment remain unclear. We aimed to identify the most cost-effective strategy for managing hyperparathyroidism from the kidney transplantation recipient's perspective.

METHODS

We constructed a patient-level discrete event simulation model to compare parathyroidectomy and cinacalcet-based medical management. The effects of hyperparathyroidism on allograft survival and all-cause mortality were considered in the discrete event simulation model with a time horizon of 15 years. Our base case was a 55-year-old kidney transplantation recipient with persistent hyperparathyroidism and hypercalcemia. The primary outcome was the cost-effectiveness measured by cost per quality-adjusted life years.

RESULTS

The monthly out-of-pocket cost of cinacalcet ranged from $12 to $288, depending on insurance coverage, with a base case cost of $150. Our base case analysis showed that parathyroidectomy was the dominant treatment with lesser cost ($1,315 vs $7,147) and greater effectiveness (3.17 quality-adjusted life years and 2.92 quality-adjusted life years) than cinacalcet. One-way sensitivity analysis on the cinacalcet treatment duration showed that parathyroidectomy became more cost-effective at 9 months. Two-way sensitivity analysis on the cost of cinacalcet and the duration of treatment with cinacalcet showed that as the monthly cost of cinacalcet increases, the expense of cinacalcet-based medical management quickly exceeds the cost of parathyroidectomy.

CONCLUSION

Parathyroidectomy becomes more cost-effective for kidney transplantation recipients with tertiary hyperparathyroidism when they require cinacalcet-based medical management for more than 9 months. As part of shared decision-making, it is important to discuss the financial costs involved in treating tertiary hyperparathyroidism.

Hospital to community in Wales: What is the value of optometrists playing a greater role in managing neovascular AMD and glaucoma in primary care?

PURPOSE

To evaluate the value of enhanced optometric services for managing neovascular age-related macular degeneration (nAMD) and glaucoma in primary care optometry services, instead of hospital eye services (HES).

METHODS

Seven enhanced optometric service pathways in primary care in Wales were assessed with a mixed-methods approach: three for nAMD and four for glaucoma. The methods were a patient-related experience measure (PREM), a Realist Review and Evaluation involving both patients and staff, a discrete event simulation model estimating the economic impact of the pathways and a workforce survey of optometrists to gauge capability and capacity.

RESULTS

Patient-related experience measure responses (802) indicated that primary care experience was comparable to that of HES. Utilising enhanced optometric services in primary care resulted in reduced wait times compared with HES, with suspected nAMD shortened to 4-5 days and glaucoma monitoring to 5 days. Waiting lists were dramatically reduced with primary care-based services to just three people waiting for nAMD and five for glaucoma, compared with 216 and 5691 people, respectively, in HES. Consultant ophthalmologist time was reduced from 57% to 15%-16% for nAMD services and from 48% to 22%-23% for glaucoma services. Integrating enhanced optometric services into primary care incurred a similar cost. The workforce survey confirms that optometrists possess the skills and qualifications and are willing to deliver these enhanced optometric services. The Realist Review and Evaluation revealed that clear patient communication, effective coordination and strong interprofessional communication between optometrists and ophthalmologists along with a shared electronic record are crucial to the success of this change.

CONCLUSION

Providing enhanced optometric services in primary care for nAMD and glaucoma brings substantial benefits for the UK National Health Service and patients, including reduced waiting times, waiting lists and released HES capacity. The success of this transition hinges on clear patient communication, administrative co-ordination and effective interprofessional communication.

Different Models, Same Results: Considerations When Choosing Between Approaches to Model Cost Effectiveness of Chimeric-Antigen Receptor T-Cell Therapy Versus Standard of Care

OBJECTIVE

Chimeric antigen-receptor T-cell therapy (CAR-T) is characterised by early phase data at the time of registration, high upfront cost and a complex manufacturing and administration process compared with standard therapies. Our objective was to compare the performance of different models to assess the cost effectiveness of CAR-T using a state-transition model (STM), partitioned survival model (PSM) and discrete event simulation (DES).

METHODS

Individual data for tisagenlecleucel for the treatment of young patients with acute lymphoblastic leukaemia (ALL) were used to populate the models. Costs and benefits were measured over a lifetime to generate a cost per quality-adjusted life-year (QALY). Model performance was compared quantitatively on the outcomes generated and a checklist developed summarising the components captured by each model type relevant to assessing cost effectiveness of CAR-T.

RESULTS

Models generated similar results with base-case analyses ranging from an incremental cost per QALY of $96,074-$99,625. DES was the only model to specifically capture CAR-T wait time, demonstrating a substantial loss of benefit of CAR-T with increased wait time.

CONCLUSION

Although model type did not meaningfully impact base-case results, the ability to incorporate an outcome-based payment arrangement (OBA) and wait time are important elements to consider when selecting a model for CAR-T. DES provided greater flexibility compared with STM and PSM approaches to deal with the complex manufacturing and administration process that can lead to extended wait times and substantially reduce the benefit of CAR-T. This is an important consideration when selecting a model type for CAR-T, so major drivers of uncertainty are considered in funding decisions.

Reflecting Parameter Uncertainty in Addition to Variability in Constrained Healthcare Resource Discrete Event Simulations: Worth Going the Extra Mile or a Road to Nowhere?

OBJECTIVES

Probabilistic sensitivity analysis (PSA) has been shown to reduce bias in outcomes of health economic models. However, only 1 existing study has been identified that incorporates PSA within a resource-constrained discrete event simulation (DES) model. This article aims to assess whether it is feasible and appropriate to use PSA to characterize parameter uncertainty in DES models that are primarily constructed to explore the impact of constrained resources.

METHODS

PSA is incorporated into a new case study of an Emergency Department DES. Structured expert elicitation is used to derive the variability and uncertainty input distributions associated with length of time taken to complete key activities within the Emergency Department. Potential challenges of implementation and analysis are explored.

RESULTS

The results of a trial of the model, which used the best estimates of the elicited means and variability around the time taken to complete activities, provided a reasonable fit to the data for length of time within the Emergency Department. However, there was substantial and skewed uncertainty around the activity times estimated from the elicitation exercise. This led to patients taking almost 3 weeks to leave the Emergency Department in some PSA runs, which would not occur in practice.

CONCLUSIONS

Structured expert elicitation can be used to derive plausible estimates of activity times and their variability, but experts' uncertainty can be substantial. For parameters that have an impact on interactions within a resource-constrained simulation model, PSA can lead to implausible model outputs; hence, other methods may be needed.

Prioritizing Quality Measures in Acute Stroke Care : A Cost-Effectiveness Analysis

BACKGROUND

The American Heart Association and American Stroke Association (AHA/ASA) endorsed 15 process measures for acute ischemic stroke (AIS) to improve the quality of care. Identifying the highest-value measures could reduce the administrative burden of quality measure adoption while retaining much of the value of quality improvement.

OBJECTIVE

To prioritize AHA/ASA-endorsed quality measures for AIS on the basis of health impact and cost-effectiveness.

DESIGN

Individual-based stroke simulation model.

DATA SOURCES

Published literature.

TARGET POPULATION

U.S. patients with incident AIS.

TIME HORIZON

Lifetime.

PERSPECTIVE

Health care sector.

INTERVENTION

Current versus complete (100%) implementation at the population level of quality measures endorsed by the AHA/ASA with sufficient clinical evidence (10 of 15).

OUTCOME MEASURES

Life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios, and incremental net health benefits.

RESULTS OF BASE-CASE ANALYSIS

Discounted life-years gained from complete implementation would range from 472 (tobacco use counseling) to 34 688 (early carotid imaging) for an annual AIS patient cohort. All AIS quality measures were cost-saving or highly cost-effective by AHA standards (<$50 000 per QALY for high-value care). Early carotid imaging and intravenous tissue plasminogen activator contributed the largest fraction of the total potential value of quality improvement (measured as incremental net health benefit), accounting for 72% of the total value. The top 5 quality measures accounted for 92% of the total potential value.

RESULTS OF SENSITIVITY ANALYSIS

A web-based user interface allows for context-specific sensitivity and scenario analyses.

LIMITATION

Correlations between quality measures were not incorporated.

CONCLUSION

Substantial variation exists in the potential net benefit of quality improvement across AIS quality measures. Benefits were highly concentrated among 5 of 10 measures assessed. Our results can help providers and payers set priorities for quality improvement efforts and value-based payments in AIS care.

PRIMARY FUNDING SOURCE

National Institute of Neurological Disorders and Stroke.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Systematic Literature Review of Economic Evaluations of Treatment Alternatives in Chronic Lymphocytic Leukemia

BACKGROUND

Economic evaluations are widely used to predict the economic impact of new treatment alternatives. Comprehensive economic reviews in the field of chronic lymphocytic leukemia (CLL) are warranted to supplement the existing analyses focused on specific therapeutic areas.

METHODS

A systematic literature review was conducted based on literature searches in Medline and EMBASE to summarize the published health economics models related to all types of CLL therapies. Narrative synthesis of relevant studies was performed focusing on compared treatments, patient populations, modelling approaches and key findings.

RESULTS

We included 29 studies, the majority of which were published between 2016 and 2018, when data from large clinical trials in CLL became available. Treatment regimens were compared in 25 cases, while the remaining four studies considered treatment strategies with more complex patient pathways. Based on the review results, Markov modelling with a simple structure of three health states (progression-free, progressed, death) can be considered as the traditional basis to simulate cost effectiveness. However, more recent studies added further complexity, including additional health states for different therapies (e.g. best supportive care or stem cell transplantation), for progression-free state (e.g. by differentiating between with or without treatment), or for response status (i.e. partial response and complete response).

CONCLUSIONS

As personalized medicine is increasingly gaining recognition, we expect that future economic evaluations will also incorporate new solutions, which are necessary to capture a larger number of genetic and molecular markers and more complex patient pathways with individual patient-level allocation of treatment options and thus economic assessments.

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.

Planning for resilience in screening operations using discrete event simulation modeling: example of HPV testing in Peru

Background

The World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru.

Methods

Using data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with “what-if” scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system.

Results

Our simulations show how much the screening system’s capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general “rules” for resource and operational planning.

Conclusions

Multilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions—how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.

A systematic review of the prediction of hospital length of stay: Towards a unified framework

Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the approaches employed for the prediction of LoS in terms of their merits and shortcomings. In order to address some of these problems, a unified framework is proposed to better generalise the approaches that are being used to predict length of stay. This includes the investigation of the types of routinely collected data used in the problem as well as recommendations to ensure robust and meaningful knowledge modelling. This unified common framework enables the direct comparison of results between length of stay prediction approaches and will ensure that such approaches can be used across several hospital environments. A literature search was conducted in PubMed, Google Scholar and Web of Science from 1970 until 2019 to identify LoS surveys which review the literature. 32 Surveys were identified, from these 32 surveys, 220 papers were manually identified to be relevant to LoS prediction. After removing duplicates, and exploring the reference list of studies included for review, 93 studies remained. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS as a unified framework enables the direct comparison of length of stay methods. Additional research is also required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability.

Real-World Cost-Effectiveness of Late Time Window Thrombectomy for Patients With Ischemic Stroke

Background: To compare the cost-effectiveness of providing endovascular thrombectomy (EVT) for patients with ischemic stroke in the >4.5 h time window between patient groups who met and did not meet the perfusion imaging trial criteria.

Methods: A discrete event simulation (DES) model was developed to simulate the long-term outcome post EVT in patients meeting or not meeting the extended time window clinical trial perfusion imaging criteria at presentation, vs. medical treatment alone (including intravenous thrombolysis). The effectiveness of thrombectomy in patients meeting the landmark trial criteria (DEFUSE 3 and DAWN) was derived from a prospective cohort study of Australian patients who received EVT for ischemic stroke, between 2015 and 2019, in the extended time window (>4.5 h).

Results: Endovascular thrombectomy was shown to be a cost-effective treatment for patients satisfying the clinical trial criteria in our prospective cohort [incremental cost-effectiveness ratio (ICER) of $11,608/quality-adjusted life year (QALY) for DEFUSE 3-postive or $34,416/QALY for DAWN-positive]. However, offering EVT to patients outside of clinical trial criteria was associated with reduced benefit (−1.02 QALY for DEFUSE 3; −1.43 QALY for DAWN) and higher long-term patient costs ($8,955 for DEFUSE 3; $9,271 for DAWN), thereby making it unlikely to be cost-effective in Australia.

Conclusions: Treating patients not meeting the DAWN or DEFUSE 3 clinical trial criteria in the extended time window for EVT was associated with less gain in QALYs and higher cost. Caution should be exercised when considering this procedure for patients not satisfying the trial perfusion imaging criteria for EVT.

Designing and testing of a health-economic Markov model to assess the cost-effectiveness of treatments for Bipolar disorder: TiBipoMod

BACKGROUND

Bipolar disorder is an often recurrent mood disorder that is associated with a significant economic and health-related burden. Increasing the availability of health-economic evidence may aid in reducing this burden. The aim of this study is to describe the design of an open-source health-economic Markov model for assessing the cost-effectiveness of interventions in the treatment of Bipolar Disorders type I and II, TiBipoMod.

METHODS

TiBipoMod is a decision-analytic Markov model that allows for user-defined incorporation of both pharmacological and non-pharmacological interventions for the treatment of BD. TiBipoMod includes the health states remission, depression, (hypo)mania and death. Costs and effects are modeled over a lifetime horizon from a societal and healthcare perspective, and results are presented as the total costs, Quality-Adjusted Life Years (QALY), Life Years (LY), and incremental costs per QALYs and LYs gained.

RESULTS

Functionalities of TiBipoMod are demonstrated by performing a cost-utility analysis of mindfulness-based cognitive therapy (MBCT) compared to the standard of care. Treatment with MBCT resulted in an increase of 0.18 QALYs per patient, and a dominant incremental cost-effectiveness ratio per QALY gained for MBCT at a probability of being cost-effective of 71% when assuming a €50,000 willingness-to-pay threshold.

CONCLUSION

TiBipoMod can easily be adapted and used to determine the cost-effectiveness of interventions in the treatment in Bipolar Disorder type I and II, and is freely available for academic purposes upon request at the authors.

Comparing Modeling Approaches for Discrete Event Simulations With Competing Risks Based on Censored Individual Patient Data: A Simulation Study and Illustration in Colorectal Cancer

OBJECTIVES

This study aimed to provide detailed guidance on modeling approaches for implementing competing events in discrete event simulations based on censored individual patient data (IPD).

METHODS

The event-specific distributions (ESDs) approach sampled times from event-specific time-to-event distributions and simulated the first event to occur. The unimodal distribution and regression approach sampled a time from a combined unimodal time-to-event distribution, representing all events, and used a (multinomial) logistic regression model to select the event to be simulated. A simulation study assessed performance in terms of relative absolute event incidence difference and relative entropy of time-to-event distributions for different types and levels of right censoring, numbers of events, distribution overlap, and sample sizes. Differences in cost-effectiveness estimates were illustrated in a colorectal cancer case study.

RESULTS

Increased levels of censoring negatively affected the modeling approaches' performance. A lower number of competing events and higher overlap of distributions improved performance. When IPD were censored at random times, ESD performed best. When censoring occurred owing to a maximum follow-up time for 2 events, ESD performed better for a low level of censoring (ie, 10%). For 3 or 4 competing events, ESD better represented the probabilities of events, whereas unimodal distribution and regression better represented the time to events. Differences in cost-effectiveness estimates, both compared with no censoring and between approaches, increased with increasing censoring levels.

CONCLUSIONS

Modelers should be aware of the different modeling approaches available and that selection between approaches may be informed by data characteristics. Performing and reporting extensive validation efforts remains essential to ensure IPD are appropriately represented.

A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries

INTRODUCTION

The burden of type 2 diabetes is steadily increasing in low-and-middle-income countries, thereby posing a major threat from both a treatment, and funding standpoint. Although simulation modelling is generally relied upon for evaluating long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the characteristics and capabilities of available models used in low-and-middle-income countries. We review the use of computer simulation modelling for the management of type 2 diabetes in low-and-middle-income countries.

METHODS

A search for studies reporting computer simulation models of the natural history of individuals with type 2 diabetes and/or decision models to evaluate the impact of treatment strategies on these populations was conducted in PubMed. Data were extracted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and assessed using modelling checklists. Publications before the year 2000, from high-income countries, studies involving animals and analyses that did not use mathematical simulations were excluded. The full text of eligible articles was sourced and information about the intervention and population being modelled, type of modelling approach and the model structure was extracted.

RESULTS

Of the 79 articles suitable for full text review, 44 studies met the inclusion criteria. All were cost-effectiveness/utility studies with the majority being from the East Asia and Pacific region (n = 29). Of the included studies, 34 (77.3%) evaluated the cost-effectiveness of pharmacological interventions and approximately 75% of all included studies used HbA1c as one of the treatment effects of the intervention. 32 (73%) of the publications were microsimulation models, and 29 (66%) were state-transition models. Most of the studies utilised annual cycles (n = 29, 71%), and accounted for costs and outcomes over 20 years or more (n = 38, 86.4%).

CONCLUSIONS

While the use of simulation modelling in the management of type 2 diabetes has been steadily increasing in low-and-middle-income countries, there is an urgent need to invest in evaluating therapeutic and policy interventions related to type 2 diabetes in low-and-middle-income countries through simulation modelling, especially with local research data. Moreover, it is important to improve transparency and credibility in the reporting of input data underlying model-based economic analyses, and studies.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Simulation modeling to assess performance of integrated healthcare systems: Literature review to characterize the field and visual aid to guide model selection

BACKGROUND

The guiding principle of many health care reforms is to overcome fragmentation of service delivery and work towards integrated healthcare systems. Even though the value of integration is well recognized, capturing its drivers and its impact as part of health system performance assessment is challenging. The main reason is that current assessment tools only insufficiently capture the complexity of integrated systems, resulting in poor impact estimations of the actions taken towards the 'Triple Aim'. We describe the unique nature of simulation modeling to consider key health reform aspects: system complexity, optimization of actions, and long-term assessments.

RESEARCH QUESTION

How can the use and uptake of simulation models be characterized in the field of performance assessment of integrated healthcare systems?

METHODS

A systematic search was conducted between 2000 and 2018, in 5 academic databases (ACM D. Library, CINAHL, IEEE Xplore, PubMed, Web of Science) complemented with grey literature from Google Scholar. Studies using simulation models with system thinking to assess system performance in topics relevant to integrated healthcare were selected for revision.

RESULTS

After screening 2274 articles, 30 were selected for analysis. Five modeling techniques were characterized, across four application areas in healthcare. Complexity was defined in nine aspects, embedded distinctively in each modeling technique. 'What if?' & 'How to?' scenarios were identified as methods for system optimization. The mean time frame for performance assessments was 18 years.

CONCLUSIONS

Simulation models can evaluate system performance emphasizing the complex relations between components, understanding the system's adaptability to change in short or long-term assessments. These advantages position them as a useful tool for complementing performance assessment of integrated healthcare systems in their pursuit of the 'Triple Aim'. Besides literacy in modeling techniques, accurate model selection is facilitated after identification and prioritization of the complexities that rule system performance. For this purpose, a tool for selecting the most appropriate simulation modeling techniques was developed.

Time-to-event analysis in economic evaluations: a comparison of modelling methods to assess the cost-effectiveness of transplanting a marginal quality kidney

BACKGROUND

Economic-evaluations using decision analytic models such as Markov-models (MM), and discrete-event-simulations (DES) are high value adds in allocating resources. The choice of modelling method is critical because an inappropriate model yields results that could lead to flawed decision making. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney.

METHODS

Cost-effectiveness was assessed using MM and DES. We used parametric survival models to estimate the time-dependent transition probabilities of MM and distribution of time-to-event in DES. MMs were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizon.

RESULTS

DES model output had a close fit to the actual data. Irrespective of the modelling method, the cycle length of MM or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy. However, there were discrepancies in costs, effectiveness and net monetary benefit (NMB) among different modelling methods. The incremental NMB of the MM in the 6-months cycle lengths was a closer fit to the incremental NMB of the DES. The gap in the fit of the two cycle lengths to DES output reduced as the time horizon increased.

CONCLUSION

Different modelling methods were unlikely to influence the decision to accept a lower quality kidney transplant or remain waitlisted on dialysis. Both models produced similar results when time-dependant transition probabilities are used, most notable with shorter cycle lengths and longer time-horizons.

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.

Cost-effectiveness of fingolimod versus interferon-β1a for the treatment of pediatric-onset multiple sclerosis in Canada

AIMS

To evaluate the cost-effectiveness of fingolimod versus interferon (IFN)-β1a at a dose of 30 μg per week for the treatment of relapsing pediatric-onset multiple sclerosis (POMS) in Canada.

MATERIAL AND METHODS

A discrete-time Markov model was developed to compare fingolimod with IFN β-1a over a time horizon of two years representing patients followed up to mean age of 18 years from a Canadian health care system perspective. Twenty-one health states based on the Expanded Disability Status Scale (EDSS) were considered: EDSS 0‒9 for relapsing multiple sclerosis (MS), EDSS 0‒9 for secondary progressive MS, and "Death." Relative treatment efficacy for fingolimod versus IFN-β1a was estimated from the PARADIGMS study. Costs and resource use were obtained from published literature and Canadian sources. Utilities were estimated by mapping the Pediatric Quality of Life inventory data onto the Child Health Utility Index-9 Dimension using a published mapping algorithm. Future costs and benefits were discounted at 1.5% per annum.

RESULTS

Compared with IFN β-1a, fingolimod led to an increase in quality-adjusted life-years (QALYs) (0.125) with incremental costs (Canadian dollars [CAD] 2,977) and to an incremental cost-effectiveness ratio (ICER) of CAD 23,886/QALY over a time horizon of two years representing patients followed up to mean age of 18 years. The monetary benefits of fingolimod treatment versus IFN β-1a at a willingness-to-pay (WTP) threshold of CAD 50,000 per QALY gained were higher than the costs. One-way sensitivity analysis and probabilistic sensitivity analysis (PSA) both confirmed the robustness of the results.

LIMITATIONS

The main limitations of this analysis primarily stem from the limited data availability in POMS.

CONCLUSIONS

Fingolimod is cost effective compared with IFN β-1a for the treatment of POMS over a time horizon of two years representing patients followed up to a mean age of 18 years in Canada.

The Diffusion of Discrete Event Simulation Approaches in Health Care Management in the Past Four Decades: A Comprehensive Review

This study systematically examines the diffusion of the discrete event simulation (DES) approach in health services and health care management by examining relevant factors such as research areas, channels with the objective of promoting the application of DES in the health field. We examined 483 journal papers referencing this approach that were published in 230 journals during 1981 to 2014. The application of DES has extended from health service operational research evaluation to the assessment of interventions in diverse health arenas. The increase in the number of adopters (paper authors) of DES and the increase in number of related channels (journals publishing DES-related articles) are highly correlated, which suggests an increase of DES-related publications in health research. The same conclusion is reached, that is, an increased diffusion of DES in health research, when we focus on the temporal trends of the channels and adopters. The applications of DES in health research cover 22 major areas based on our categorization. The expansion in the health areas also suggests to a certain extent the rapid diffusion of DES in health research.

Simulation modeling validity and utility in colorectal cancer screening delivery: A systematic review

OBJECTIVE

This study sought to assess the impact and validity of simulation modeling in informing decision making in a complex area of healthcare delivery: colorectal cancer (CRC) screening.

MATERIALS AND METHODS

We searched 10 electronic databases for English-language articles published between January 1, 2008, and March 1, 2019, that described the development of a simulation model with a focus on average-risk CRC screening delivery. Included articles were reviewed for evidence that the model was validated, and provided real or potential contribution to informed decision making using the GRADE EtD (Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision) framework.

RESULTS

A total of 43 studies met criteria. The majority used Markov modeling (n = 31 [72%]) and sought to determine cost-effectiveness, compare screening modalities, or assess effectiveness of screening. No study reported full model validation and only (58%) reported conducting any validation. Majority of models were developed to address a specific health systems or policy question; few articles report the model's impact on this decision (n = 39 [91%] vs. n = 5 [12%]). Overall, models provided evidence relevant to every element important to decision makers as outlined in the GRADE EtD framework.

DISCUSSION AND CONCLUSION

Simulation modeling contributes evidence that is considered valuable to decision making in CRC screening delivery, particularly in assessing cost-effectiveness and comparing screening modalities. However, the actual impact on decisions and validity of models is lacking in the literature. Greater validity testing, impact assessment, and standardized reporting of both is needed to understand and demonstrate the reliability and utility of simulation modeling.

Reporting Quality of Discrete Event Simulations in Healthcare-Results From a Generic Reporting Checklist

OBJECTIVES

The aims of this study were to formulate a generic reporting checklist for healthcare-related discrete event simulation (DES) studies and to critically appraise the existing studies.

METHODS

Based on the principles of accessibility and generality, assessment items were derived from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR)-Society for Medical Decision Making (SMDM) Task Force reports. The resulting checklist was applied to all 211 DES studies identified in a previous review. The proportion of fulfilled checklist items served as an indicator of reporting quality. A logistic regression was conducted to investigate whether study characteristics (eg, publication before or after the publication of the ISPOR-SMDM reports) increased the likelihood of fulfilling more than the mean number of items fulfilled by the appraised DES studies.

RESULTS

An 18-item checklist was formulated covering model conceptualization, parameterization and uncertainty assessment, validation, generalizability, and stakeholder involvement. The reporting quality of the DES models fluctuated around the mean of 63.7% (SD 11.0%) over the period studied. A modest nonsignificant improvement in reporting quality was found after the publication of the ISPOR-SMDM reports (64.5% vs 62.9%). Items with the lowest performance were related to predictive validation (2.8% of studies), cross validation (8.5%), face validity assessment (26.5%), and stakeholder involvement (27.5%). Models applied to health economic evaluation (HEE), country under study, and industry sponsorship were significantly associated with the odds of achieving above-average reporting quality.

CONCLUSIONS

The checklist is applicable across various model-based analyses beyond HEEs. Adherence to the ISPOR-SMDM guidelines should be improved, particularly regarding model validation.

Comparing Strategies for Modeling Competing Risks in Discrete-Event Simulations: A Simulation Study and Illustration in Colorectal Cancer

BACKGROUND

Different strategies toward implementing competing risks in discrete-event simulation (DES) models are available. This study aims to provide recommendations regarding modeling approaches that can be defined based on these strategies by performing a quantitative comparison of alternative modeling approaches.

METHODS

Four modeling approaches were defined: 1) event-specific distribution (ESD), 2) event-specific probability and distribution (ESPD), 3) unimodal joint distribution and regression model (UDR), and 4) multimodal joint distribution and regression model (MDR). Each modeling approach was applied to uncensored individual patient data in a simulation study and a case study in colorectal cancer. Their performance was assessed in terms of relative event incidence difference, relative absolute event incidence difference, and relative entropy of time-to-event distributions. Differences in health economic outcomes were also illustrated for the case study.

RESULTS

In the simulation study, the ESPD and MDR approaches outperformed the ESD and UDR approaches, in terms of both event incidence differences and relative entropy. Disease pathway and data characteristics, such as the number of competing risks and overlap between competing time-to-event distributions, substantially affected the approaches' performance. Although no considerable differences in health economic outcomes were observed, the case study showed that the ESPD approach was most sensitive to low event rates, which negatively affected performance.

CONCLUSIONS

Based on overall performance, the recommended modeling approach for implementing competing risks in DES models is the MDR approach, which is defined according to the general strategy of selecting the time-to-event first and the corresponding event second. The ESPD approach is a less complex and equally performing alternative if sufficient observations are available for each competing event (i.e., the internal validity shows appropriate data representation).

A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping

We discuss a decision-theoretic approach to building a panel-based, preemptive genotyping program. The method is based on findings that a large percentage of patients are prescribed medications that are known to have pharmacogenetic associations, and over time, a substantial proportion are prescribed additional such medication. Preemptive genotyping facilitates genotype-guided therapy at the time medications are prescribed; panel-based testing allows providers to reuse previously collected genetic data when a new indication arises. Because it is cost-prohibitive to conduct panel-based genotyping on all patients, we describe a three-step approach to identify patients with the highest anticipated benefit. First, we construct prediction models to estimate the risk of being prescribed one of the target medications using readily available clinical data. Second, we use literature-based estimates of adverse event rates, variant allele frequencies, secular death rates, and costs to construct a discrete event simulation that estimates the expected benefit of having an individual’s genetic data in the electronic health record after an indication has occurred. Finally, we combine medication prescription risk with expected benefit of genotyping once a medication is indicated to calculate the expected benefit of preemptive genotyping. For each patient-clinic visit, we calculate this expected benefit across a range of medications and select patients with the highest expected benefit overall. We build a proof of concept implementation using a cohort of patients from a single academic medical center observed from July 2010 through December 2012. We then apply the results of our modeling strategy to show the extent to which we can improve clinical and economic outcomes in a cohort observed from January 2013 through December 2015.

Optimizing Resources for Endovascular Clot Retrieval for Acute Ischemic Stroke, a Discrete Event Simulation

Objective: Endovascular clot retrieval (ECR) is the standard of care for acute ischemic stroke due to large vessel occlusion. Performing ECR is a time critical and complex process involving many specialized care providers and resources. Maximizing patient benefit while minimizing service cost requires optimization of human and physical assets. The aim of this study is to develop a general computational model of an ECR service, which can be used to optimize resource allocation.

Methods: Using a discrete event simulation approach, we examined ECR performance under a range of possible scenarios and resource use configurations.

Results: The model demonstrated the impact of competing emergency interventional cases upon ECR treatment times and time impact of allocating more physical (more angiographic suites) or staff resources (extending work hours).

Conclusion: Our DES model can be used to optimize resources for interventional treatment of acute ischemic stroke and large vessel occlusion. This proof-of-concept study of computational simulation of resource allocation for ECR can be easily extended. For example, center-specific cost data may be incorporated to optimize resource allocation and overall health care value.

A Discrete Event Simulation Model to Assess the Economic Value of a Hypothetical Pharmacogenomics Test for Statin-Induced Myopathy in Patients Initiating a Statin in Secondary Cardiovascular Prevention

BACKGROUND

Statin (HMG-CoA reductase inhibitor) therapy is the mainstay dyslipidemia treatment and reduces the risk of a cardiovascular (CV) event (CVE) by up to 35%. However, adherence to statin therapy is poor. One reason patients discontinue statin therapy is musculoskeletal pain and the associated risk of rhabdomyolysis. Research is ongoing to develop a pharmacogenomics (PGx) test for statin-induced myopathy as an alternative to the current diagnosis method, which relies on creatine kinase levels. The potential economic value of a PGx test for statin-induced myopathy is unknown.

METHODS

We developed a lifetime discrete event simulation (DES) model for patients 65 years of age initiating a statin after a first CVE consisting of either an acute myocardial infarction (AMI) or a stroke. The model evaluates the potential economic value of a hypothetical PGx test for diagnosing statin-induced myopathy. We have assessed the model over the spectrum of test sensitivity and specificity parameters.

RESULTS

Our model showed that a strategy with a perfect PGx test had an incremental cost-utility ratio of 4273 Canadian dollars ($Can) per quality-adjusted life year (QALY). The probabilistic sensitivity analysis shows that when the payer willingness-to-pay per QALY reaches $Can12,000, the PGx strategy is favored in 90% of the model simulations.

CONCLUSION

We found that a strategy favoring patients staying on statin therapy is cost effective even if patients maintained on statin are at risk of rhabdomyolysis. Our results are explained by the fact that statins are highly effective in reducing the CV risk in patients at high CV risk, and this benefit largely outweighs the risk of rhabdomyolysis.

Smoking Cessation: A Comparison of Two Model Structures

BACKGROUND

Most economic evaluations of smoking cessation interventions have used cohort state-transition models. Discrete event simulations (DESs) have been proposed as a superior approach.

OBJECTIVE

We developed a state-transition model and a DES using the discretely integrated condition event (DICE) framework and compared the cost-effectiveness results. We performed scenario analysis using the DES to explore the impact of alternative assumptions.

METHODS

The models estimated the costs and quality-adjusted life years (QALYs) for the intervention and comparator from the perspective of the UK National Health Service and Personal Social Services over a lifetime horizon. The models considered five comorbidities: chronic obstructive pulmonary disease, myocardial infarction, coronary heart disease, stroke and lung cancer. The state-transition model used prevalence data, and the DES used incidence. The costs and utility inputs were the same between two models and consistent with those used in previous analyses for the National Institute for Health and Care Excellence.

RESULTS

In the state-transition model, the intervention produced an additional 0.16 QALYs at a cost of £540, leading to an incremental cost-effectiveness ratio (ICER) of £3438. The comparable DES scenario produced an ICER of £5577. The ICER for the DES increased to £18,354 when long-term relapse was included.

CONCLUSIONS

The model structures themselves did not influence smoking cessation cost-effectiveness results, but long-term assumptions did. When there is variation in long-term predictions between interventions, economic models need a structure that can reflect this.

Using Cerebrospinal Fluid Biomarker Testing to Target Treatment to Patients with Mild Cognitive Impairment: A Cost-Effectiveness Analysis

OBJECTIVE

Cerebrospinal fluid (CSF) biomarkers are shown to facilitate a risk identification of patients with mild cognitive impairment (MCI) into different risk levels of progression to Alzheimer's disease (AD). Knowing a patient's risk level provides an opportunity for earlier interventions, which could result in potential greater benefits. We assessed the cost effectiveness of the use of CSF biomarkers in MCI patients where the treatment decision was based on patients' risk level.

METHODS

We developed a state-transition model to project lifetime quality-adjusted life-years (QALYs) and costs for a cohort of 65-year-old MCI patients from a US societal perspective. We compared four test-and-treat strategies where the decision to treat was based on a patient's risk level (low, intermediate, high) of progressing to AD with two strategies without testing, one where no patients were treated during the MCI phase and in the other all patients were treated. We performed deterministic and probabilistic sensitivity analyses to evaluate parameter uncertainty.

RESULTS

Testing and treating low-risk MCI patients was the most cost-effective strategy with an incremental cost-effectiveness ratio (ICER) of US$37,700 per QALY. Our results were most sensitive to the level of treatment effectiveness for patients with mild AD and for MCI patients. Moreover, the ICERs for this strategy at the 2.5th and 97.5th percentiles were US$18,900 and US$50,100 per QALY, respectively.

CONCLUSION

Based on the best available evidence regarding the treatment effectiveness for MCI, this study suggests the potential value of performing CSF biomarker testing for early targeted treatments among MCI patients with a narrow range for the ICER.

Mathematical Modelling for Patient Selection in Proton Therapy

Proton beam therapy (PBT) is still relatively new in cancer treatment and the clinical evidence base is relatively sparse. Mathematical modelling offers assistance when selecting patients for PBT and predicting the demand for service. Discrete event simulation, normal tissue complication probability, quality-adjusted life-years and Markov Chain models are all mathematical and statistical modelling techniques currently used but none is dominant. As new evidence and outcome data become available from PBT, comprehensive models will emerge that are less dependent on the specific technologies of radiotherapy planning and delivery.

Cost effectiveness and return on investment of a scalable community weight loss intervention

This study assessed the lifetime health and economic consequences of an efficacious scalable community weight loss program for overweight and obese adults. We applied a state-transition Markov model to project lifetime economic outcome (US dollar) and the degree of disease averted as a result of a weight loss intervention, compared with no intervention, from a payer perspective. Effect sizes of the intervention on weight loss, by sex, race and ethnicity, and body mass index (BMI) of participants, were derived from a 12-month community program. Relative risk of diseases across BMI levels and other parameters were informed by the literature. A return on investment (ROI) analysis was conducted to present the overall cost-benefit of the program. Simulation results showed that among 33,656 participants and at a cost of $2.88 million, the program was predicted to avert (with a corresponding estimated medical costs saved of) 78 cases of coronary heart disease ($28 million), 9 cases of strokes ($971,832), 92 cases of type 2 diabetes ($24 million), 1 case of colorectal cancer ($357,022), and 3 cases of breast cancer ($483,259) over the participant lifetime. The estimated medical costs saved per participant was $1403 ($1077 of African American men and $1532 of Hispanic men), and the ROI was $16.7 ($12.8 for African American men and $18.3 for Hispanic men) for every $1 invested. We concluded that a scalable efficacious community weight loss program provides a cost-effective approach with significant ROI, which will assist informed decisions for future adoption and dissemination.

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

OBJECTIVE

The objective of this article was to conduct a systematic review of published research on the use of discrete event simulation (DES) for resource modelling (RM) in health technology assessment (HTA). RM is broadly defined as incorporating and measuring effects of constraints on physical resources (e.g. beds, doctors, nurses) in HTA models.

METHODS

Systematic literature searches were conducted in academic databases (JSTOR, SAGE, SPRINGER, SCOPUS, IEEE, Science Direct, PubMed, EMBASE) and grey literature (Google Scholar, NHS journal library), enhanced by manual searchers (i.e. reference list checking, citation searching and hand-searching techniques).

RESULTS

The search strategy yielded 4117 potentially relevant citations. Following the screening and manual searches, ten articles were included. Reviewing these articles provided insights into the applications of RM: firstly, different types of economic analyses, model settings, RM and cost-effectiveness analysis (CEA) outcomes were identified. Secondly, variation in the characteristics of the constraints such as types and nature of constraints and sources of data for the constraints were identified. Thirdly, it was found that including the effects of constraints caused the CEA results to change in these articles.

CONCLUSION

The review found that DES proved to be an effective technique for RM but there were only a small number of studies applied in HTA. However, these studies showed the important consequences of modelling physical constraints and point to the need for a framework to be developed to guide future applications of this approach.

Model-Based Economic Evaluation of Treatments for Depression: A Systematic Literature Review

BACKGROUND

An increasing number of model-based studies that evaluate the cost effectiveness of treatments for depression are being published. These studies have different characteristics and use different simulation methods.

OBJECTIVE

We aimed to systematically review model-based studies evaluating the cost effectiveness of treatments for depression and examine which modelling technique is most appropriate for simulating the natural course of depression.

METHODS

The literature search was conducted in the databases PubMed, EMBASE and PsycInfo between 1 January 2002 and 1 October 2016. Studies were eligible if they used a health economic model with quality-adjusted life-years or disability-adjusted life-years as an outcome measure. Data related to various methodological characteristics were extracted from the included studies. The available modelling techniques were evaluated based on 11 predefined criteria.

RESULTS

This methodological review included 41 model-based studies, of which 21 used decision trees (DTs), 15 used cohort-based state-transition Markov models (CMMs), two used individual-based state-transition models (ISMs), and three used discrete-event simulation (DES) models. Just over half of the studies (54%) evaluated antidepressants compared with a control condition. The data sources, time horizons, cycle lengths, perspectives adopted and number of health states/events all varied widely between the included studies. DTs scored positively in four of the 11 criteria, CMMs in five, ISMs in six, and DES models in seven.

CONCLUSION

There were substantial methodological differences between the studies. Since the individual history of each patient is important for the prognosis of depression, DES and ISM simulation methods may be more appropriate than the others for a pragmatic representation of the course of depression. However, direct comparisons between the available modelling techniques are necessary to yield firm conclusions.

Intestinal rehabilitation for children with intestinal failure is cost-effective: a simulation study

BACKGROUND

Children with intestinal failure (IF) depend on parenteral nutrition (PN). The goal in the treatment of IF is to wean children off PN through intestinal rehabilitation (IR). Although the healthcare burden of IF is enormous, to our knowledge there has been no previous cost-effectiveness analysis in pediatric IF including IR.

OBJECTIVE

We sought to determine the cost-effectiveness of IR in terms of costs and life-years.

DESIGN

We simulated the treatment of IF in children in a discrete-event model. Data for this model were derived from patient records, the Dutch Registry of Intestinal Failure and Transplantation, the Intestinal Transplant Registry, and the literature. The time horizon of the model was 40 y. Simulated patients were enrolled at a rate of 40 patients/mo for 10 y. Actual costs were calculated for hospital admissions, surgical interventions, endoscopies, PN, and immunosuppressive medication. We evaluated the cost-effectiveness of IR by comparing 1 scenario with IR with 1 scenario without IR. In the scenario with IR, a proportion of patients who represented those with the ability to wean off PN were assigned to IR. In the scenario without IR, all patients progressed to home PN (HPN). In both scenarios, a proportion of patients receiving HPN were eventually eligible for an intestinal transplantation.

RESULTS

IR prolonged survival; the mean number of life-years per patient was 19.4 in the scenario with IR compared with 18.2 in the scenario without IR. Average total costs per patient were €819,292 in the scenario with IR compared with €1,176,830 in the scenario without IR (equivalent to 1,129,230 US$ and 1,622,025 US$, respectively, in January 2014); costs mainly included hospital admissions and PN.

CONCLUSIONS

On the basis of our simulations, we concluded that IR improved the survival of children with IF and was associated with cost savings. Therefore, we consider IR to be a cost-effective treatment for children with IF.

An empirical comparison of Markov cohort modeling and discrete event simulation in a capacity-constrained health care setting

OBJECTIVES

To empirically compare Markov cohort modeling (MM) and discrete event simulation (DES) with and without dynamic queuing (DQ) for cost-effectiveness (CE) analysis of a novel method of health services delivery where capacity constraints predominate.

METHODS

A common data-set comparing usual orthopedic care (UC) to an orthopedic physiotherapy screening clinic and multidisciplinary treatment service (OPSC) was used to develop a MM and a DES without (DES-no-DQ) and with DQ (DES-DQ). Model results were then compared in detail.

RESULTS

The MM predicted an incremental CE ratio (ICER) of $495 per additional quality-adjusted life-year (QALY) for OPSC over UC. The DES-no-DQ showed OPSC dominating UC; the DES-DQ generated an ICER of $2342 per QALY.

CONCLUSIONS

The MM and DES-no-DQ ICER estimates differed due to the MM having implicit delays built into its structure as a result of having fixed cycle lengths, which are not a feature of DES. The non-DQ models assume that queues are at a steady state. Conversely, queues in the DES-DQ develop flexibly with supply and demand for resources, in this case, leading to different estimates of resource use and CE. The choice of MM or DES (with or without DQ) would not alter the reimbursement of OPSC as it was highly cost-effective compared to UC in all analyses. However, the modeling method may influence decisions where ICERs are closer to the CE acceptability threshold, or where capacity constraints and DQ are important features of the system. In these cases, DES-DQ would be the preferred modeling technique to avoid incorrect resource allocation decisions.

The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research

This paper addresses the rapid pace of change in the technologies that support digital interventions; the complexity of the health problems they aim to address; and the adaptation of scientific methods to accommodate the volume, velocity, and variety of data and interventions possible from these technologies. Information, communication, and computing technologies are now part of every societal domain and support essentially every facet of human activity. Ubiquitous computing, a vision articulated fewer than 30 years ago, has now arrived. Simultaneously, there is a global crisis in health through the combination of lifestyle and age-related chronic disease and multiple comorbidities. Computationally intensive health behavior interventions may be one of the most powerful methods to reduce the consequences of this crisis, but new methods are needed for health research and practice, and evidence is needed to support their widespread use. The challenges are many, including a reluctance to abandon timeworn theories and models of health behavior-and health interventions more broadly-that emerged in an era of self-reported data; medical models of prevention, diagnosis, and treatment; and scientific methods grounded in sparse and expensive data. There are also many challenges inherent in demonstrating that newer approaches are, indeed, effective. Potential solutions may be found in leveraging methods of research that have been shown to be successful in other domains, particularly engineering. A more "agile science" may be needed that streamlines the methods through which elements of health interventions are shown to work or not, and to more rapidly deploy and iteratively improve those that do. There is much to do to advance the issues discussed in this paper, and the papers in this theme issue. It remains an open question whether interventions based in these new models and methods are, in fact, equally if not more efficacious as what is available currently. Economic analyses of these new approaches are needed because assumptions of net worth compared to other approaches are just that, assumptions. Human-centered design research is needed to ensure that users ultimately benefit. Finally, a translational research agenda will be needed, as the status quo will likely be resistant to change.

The Efficiency of Increasing the Capacity of Physiotherapy Screening Clinics or Traditional Medical Services to Address Unmet Demand in Orthopaedic Outpatients: A Practical Application of Discrete Event Simulation with Dynamic Queuing

BACKGROUND

Hospital outpatient orthopaedic services traditionally rely on medical specialists to assess all new patients to determine appropriate care. This has resulted in significant delays in service provision. In response, Orthopaedic Physiotherapy Screening Clinics and Multidisciplinary Services (OPSC) have been introduced to assess and co-ordinate care for semi- and non-urgent patients.

OBJECTIVES

To compare the efficiency of delivering increased semi- and non-urgent orthopaedic outpatient services through: (1) additional OPSC services; (2) additional traditional orthopaedic medical services with added surgical resources (TOMS + Surg); or (3) additional TOMS without added surgical resources (TOMS - Surg).

METHODS

A cost-utility analysis using discrete event simulation (DES) with dynamic queuing (DQ) was used to predict the cost effectiveness, throughput, queuing times, and resource utilisation, associated with introducing additional OPSC or TOMS ± Surg versus usual care.

RESULTS

The introduction of additional OPSC or TOMS (±surgery) would be considered cost effective in Australia. However, OPSC was the most cost-effective option. Increasing the capacity of current OPSC services is an efficient way to improve patient throughput and waiting times without exceeding current surgical resources. An OPSC capacity increase of ~100 patients per month appears cost effective (A$8546 per quality-adjusted life-year) and results in a high level of OPSC utilisation (98 %).

CONCLUSION

Increasing OPSC capacity to manage semi- and non-urgent patients would be cost effective, improve throughput, and reduce waiting times without exceeding current surgical resources. Unlike Markov cohort modelling, microsimulation, or DES without DQ, employing DES-DQ in situations where capacity constraints predominate provides valuable additional information beyond cost effectiveness to guide resource allocation decisions.

Do different decision-analytic modeling approaches produce different results? A systematic review of cross-validation studies

When choosing a modeling approach for health economic evaluation, certain criteria are often considered (e.g., population resolution, interactivity, time advancement mechanism, resource constraints). However, whether these criteria and their associated modeling approach impacts results remain poorly understood. A systematic review was conducted to identify cross-validation studies (i.e., modeling a problem using different approaches with the same body of evidence) to offer insight on this topic. With respect to population resolution, reviewed studies suggested that both aggregate- and individual-level models will generate comparable results, although a practical trade-off exists between validity and feasibility. In terms of interactivity, infectious-disease models consistently showed that, depending on the assumptions regarding probability of disease exposure, dynamic and static models may produce dissimilar results with opposing policy recommendations. Empirical evidence on the remaining criteria is limited. Greater discussion will therefore be necessary to promote a deeper understanding of the benefits and limits to each modeling approach.