Economic evaluations with agent-based modelling: an introduction

Modelling Methods of Economic Evaluations of HIV Testing Strategies in Sub-Saharan Africa: A Systematic Review

BACKGROUND AND OBJECTIVE

Economic evaluations, a decision-support tool for policy makers, will be crucial in planning and tailoring HIV prevention and treatment strategies especially in the wake of stalled and decreasing funding for the global HIV response. As HIV testing and treatment coverage increase, case identification becomes increasingly difficult and costly. Determining which subset of the population these strategies should be targeted to becomes of vital importance as well. Generating quality economic evidence begins with the validity of the modelling approach and the model structure employed. This study synthesises and critiques the reporting around modelling methodology of economic models in the evaluation of HIV testing strategies in sub-Saharan Africa.

METHODS

The following databases were searched from January 2000 to September 2020: MEDLINE, Embase, Scopus, EconLit and Global Health. Any model-based economic evaluation of a unique HIV testing strategy conducted in sub-Saharan Africa presenting a cost-effectiveness measure published from 2013 onwards was eligible. Data were extracted around three components: general study characteristics; economic evaluation design; and quality of model reporting using a novel tool developed for the purposes of this study.

RESULTS

A total of 21 studies were included; 10 cost-effectiveness analyses, 11 cost-utility analyses. All but one study was conducted in Eastern and Southern Africa. Modelling approaches for HIV testing strategies can be broadly characterised as static aggregate models (3/21), static individual models (6/21), dynamic aggregate models (5/21) and dynamic individual models (7/21). Adequate reporting around data handling was the highest of the three categories assessed (74%), and model validation, the lowest (45%). Limitations to model structure, justification of chosen time horizon and cycle length, and description of external model validation process were all adequately reported in less than 40% of studies. The predominant limitation of this review relates to the potential implications of the narrow inclusion criteria.

CONCLUSIONS

This review is the first to synthesise economic evaluations of HIV testing strategies in sub-Saharan Africa. The majority of models exhibited dynamic, stochastic and individual properties. Model reporting against the 13 criteria in our novel tool was mixed. Future model-based economic evaluations of HIV testing strategies would benefit from transparency around the choice of modelling approach, model structure, data handling procedures and model validation techniques.

Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data

As agent-based models (ABMs) are increasingly used for modeling infectious diseases, model validation is becoming more crucial. In this study, we present an alternate approach to validating hospital ABMs that focuses on replicating hospital-specific conditions and proposes a new metric for validating the social-environmental network structure of ABMs. We adapted an established ABM representing Clostridioides difficile infection (CDI) spread in a generic hospital to a 426-bed Midwestern academic hospital. We incorporated hospital-specific layout, agent behaviors, and input parameters estimated from primary hospital data into the model, referred to as H-ABM. We compared the predicted CDI rate against the observed rate from 2013-2018. We used colonization pressure, a measure of nearby infectious agents, to validate the socio-environmental agent networks in the ABM. Finally, we conducted additional experiments to compare the performance of individual infection control interventions in the H-ABM and the generic model. We find that the H-ABM is able to replicate CDI trends during 2013-2018, including a roughly 46% drop during a period of greater infection control investment. High CDI burden in socio-environmental networks was associated with a significantly increased risk of C. difficile colonization or infection (Risk ratio: 1.37; 95% CI: [1.17, 1.59]). Finally, we found that several high-impact infection control interventions have diminished impact in the H-ABM. This study presents an alternate approach to validation of ABMs when large-scale calibration is not appropriate for specific settings and proposes a new metric for validating socio-environmental network structure of ABMs. Our findings also demonstrate the utility of hospital-specific modeling.

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.

Collaborative Supply Mechanism of Government-Subsidized Rental Housing from the Perspective of Tripartite Evolutionary Game in Metropolitan Cities of China

With the advancement of urbanisation, the inflow of population in China's large cities has been increasing and the demand for rental housing of “new citizens” with insufficient housing affordability has become increasingly strong. Therefore, the Chinese government proposes to provide government-subsidized rental housing (GSRH) different from public rental housing. At present, the supply mode of public rental housing in China is mainly government construction and operation, which has the problems of low supply efficiency and low service level. It is critical to explore an efficient supply model in the construction of the GSRH system. Therefore, this study, starting from the three supply subjects of government, market, and society, constructs an evolutionary game model and uses agent-based modelling simulation to explore how multisubjects achieve optimal collaboration in the supply process of GSRH. The results are as follows: First, the development of a collaborative supply system includes four stages: noncooperative behaviour, collaborative exploration, collaborative game, and three-subject collaborative supply. Second, the government is the core of realising multisubject coordination. Increasing government supervision will boost market participation, while increasing government subsidies can fully mobilise the enthusiasm of social subjects but cannot continuously improve the market's enthusiasm. Third, increasing the participation ratio of social subjects will help mobilise the enthusiasm of other subjects to participate, while the excessive participation ratio of market subjects may cause an imbalance in the collaborative supply system. This study provides theoretical support for the efficient supply of GSRH.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

Representing Tuberculosis Transmission with Complex Contagion: An Agent-Based Simulation Modeling Approach

OBJECTIVE

A recent study reported a tuberculosis (TB) outbreak in which, among newly infected individuals, exposure to additional active infections was associated with a higher probability of developing active disease. Referred to as complex contagion, multiple reexposures to TB within a short period after initial infection is hypothesized to confer a greater likelihood of developing active infection in 1 y. The purpose of this article is to develop and validate an agent-based simulation model (ABM) to study the effect of complex contagion on population-level TB transmission dynamics.

METHODS

We built an ABM of a TB epidemic using data from a series of outbreaks recorded in the 20th century in Saskatchewan, Canada. We fit 3 dynamical schemes: base, with no complex contagion; additive, in which each reexposure confers an independent risk of activated infection; and threshold, in which a small number of reexposures confers a low risk and a high number of reexposures confers a high risk of activation.

RESULTS

We find that the base model fits the mortality and incidence output targets best, followed by the threshold and then the additive models. The threshold model fits the incidence better than the base model does but overestimates mortality. All 3 models produce qualitatively realistic epidemic curves.

CONCLUSION

We find that complex contagion qualitatively changes the trajectory of a TB epidemic, although data from a high-incidence setting are reproduced better with the base model. Results from this model demonstrate the feasibility of using ABM to capture nuances in TB transmission.

Overview and Use of Tools for Selecting Modelling Techniques in Health Economic Studies

The availability and use of tools to guide the choice of modelling technique are not well understood. Our study aims to review existing tools and explore the use of those tools in health economic models. Two reviews and one case study were conducted. Review 1 aimed to identify tools based on expert opinion and citation searching and explore the value of the tools for health economic models. Review 2, based on citation searching, aimed to describe how those tools have been used in health economic models. Both reviews were conducted using Web of Science and Scopus. Two independent reviewers selected studies for inclusion. A case study, focused on economic evaluations of antipsychotic medication in schizophrenia, was conducted to compare the modelling techniques used by existing models with modelling techniques recommended by identified tools. Seven tools were identified, of which the revised Brennan's toolkit, was assessed to be the most appropriate for health economic models. The seven tools were cited 126 times in publications reporting health economic models. Only 17 of these (13.5%) reported that they used the tool(s) to guide the choice of modelling technique. Application of these tools suggested discrete event simulation is most appropriate for modelling antipsychotic medication in schizophrenia, but discrete event simulation was only used by 17% of existing models. There is considerable inconsistency between the modelling techniques used by existing models and modelling techniques recommended by tools. It is recommended that for future modelling studies the choice of modelling technique should be justified, this can be achieved by the application of model selection tools, such as the revised Brennan's toolkit. Future research is required to explore the barriers to using model selection tools in health economic models and to update existing tools and make them easier to use.

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.

Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling

OBJECTIVES

The objective of this article is to describe the unique challenges and present potential solutions and approaches for economic evaluations of precision medicine (PM) interventions using simulation modeling methods.

METHODS

Given the large and growing number of PM interventions and applications, methods are needed for economic evaluation of PM that can handle the complexity of cascading decisions and patient-specific heterogeneity reflected in the myriad testing and treatment pathways. Traditional approaches (eg, Markov models) have limitations, and other modeling techniques may be required to overcome these challenges. Dynamic simulation models, such as discrete event simulation and agent-based models, are used to design and develop mathematical representations of complex systems and intervention scenarios to evaluate the consequence of interventions over time from a systems perspective.

RESULTS

Some of the methodological challenges of modeling PM can be addressed using dynamic simulation models. For example, issues regarding companion diagnostics, combining and sequencing of tests, and diagnostic performance of tests can be addressed by capturing patient-specific pathways in the context of care delivery. Issues regarding patient heterogeneity can be addressed by using patient-level simulation models.

CONCLUSION

The economic evaluation of PM interventions poses unique methodological challenges that might require new solutions. Simulation models are well suited for economic evaluation in PM because they enable patient-level analyses and can capture the dynamics of interventions in complex systems specific to the context of healthcare service delivery.

Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions

For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity.

Estimation of Hepatitis C Disease Burden and Budget Impact of Treatment Using Health Economic Modeling

Oral direct-acting antiviral agents have revolutionized treatment of hepatitis C virus (HCV) infection. Nonetheless, barriers exist to elimination of HCV as a public health threat including low uptake of treatment, limited budget allocations for HCV treatment, and low awareness rates of HCV status among infected people. Mathematical modeling provides a systematic framework to analyze and compare potential solutions and elimination strategies by simulating the HCV epidemic under different conditions. Such models evaluate impact of interventions in advance of implementation. This article describes key components of developing an HCV burden model and illustrates its use by simulating the HCV epidemic in the United States.

Dynamic transmission models for economic analysis applied to health care-associated infections: A review of the literature

BACKGROUND

Cost-effectiveness analyses are an important methodology in assessing whether a health care technology is suitable for widespread adoption. Common models used by economists, such as decision trees and Markov models, are appropriate for noninfectious diseases where treatment and exposure are independent. Diseases whose treatment and exposure are dependent require dynamic models to incorporate the nonlinear transmission effect. Two different types of models are often used for dynamic cost-effectiveness analyses: compartmental models and individual models. In this methodology-focused literature review, we describe each model type and summarize the literature associated with each using the example of health care-associated infections (HAIs).

METHODS

We conducted a review of the literature to identify dynamic cost-effectiveness analyses that examined interventions to prevent or treat HAIs. To be included in the review, studies needed to have each of 3 necessary components: involve economics, such as cost-effectiveness analysis and evidence of economic theory, use a dynamic transmission model, and examine HAIs.

RESULTS

Of the 9 articles published between 2005 and 2016 that met criteria to be included in our study, 3 used compartmental models and 6 used individual models.

CONCLUSIONS

Very few published studies exist that use dynamic transmission models to conduct economic analyses related to HAIs and even fewer studies have used these models to perform cost-effectiveness analyses.

Systematic review: cost-effectiveness of direct-acting antivirals for treatment of hepatitis C genotypes 2-6

BACKGROUND

The availability of direct-acting antivirals (DAAs) has dramatically changed the landscape of hepatitis C virus (HCV) therapy; however, the cost and budget requirements for DAA treatment have been widely debated.

AIMS

To systematically review published studies evaluating the cost-effectiveness of DAAs for HCV genotype 2-6 infections, and synthesise and re-evaluate results with updated drug prices.

METHODS

We conducted a systematic search of various electronic databases, including Medline, EMBASE, Cochrane library and EconLit for cost-effectiveness studies published from 2011 to 2016. Studies evaluating DAAs for genotypes 2-6 were included. Reported costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs) were abstracted. We re-estimated ICERs by varying the price of DAAs from $20 000 to $100 000, and estimated the threshold price at which DAA regimens would be deemed cost-effective (ICER≤$100 000/QALY).

RESULTS

A total of 92 ICERs for 7 different DAA regimens from 10 published articles were included. Among the abstracted 92 ICERs, 20 were for genotype 2, 40 for genotype 3, 30 for genotype 4, 2 for genotype 5 and none for genotype 6; therefore, only genotypes 2-5 were analysed. At the discounted price of $40 000, 87.0% analyses found DAA regiments to be cost-effective, and 7.6% found to be cost-saving. The median threshold price below which DAAs would be deemed cost-effective was between $144 400 and $225 000, and cost-saving between $17 300 and $25 400.

CONCLUSIONS

HCV treatment with DAAs is highly cost-effective in patients with HCV genotypes 2-5 at a $100 000/QALY threshold. Timely HCV treatment would be an optimal strategy from both a public health and economic perspective.

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.

Deriving effective vaccine allocation strategies for pandemic influenza: Comparison of an agent-based simulation and a compartmental model

Individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by using the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about potential differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models.

Systematic Review of Modelling Approaches for the Cost Effectiveness of Hepatitis C Treatment with Direct-Acting Antivirals

BACKGROUND

New direct-acting antivirals (DAAs) are highly effective for hepatitis C virus (HCV) treatment. However, their prices have been widely debated. Decision-analytic models can project the long-term value of HCV treatment. Therefore, understanding of the methods used in these models and how they could influence results is important.

OBJECTIVE

Our objective was to describe and systematically review the methodological approaches in published cost-effectiveness models of chronic HCV treatment with DAAs.

DATA SOURCES

We searched several electronic databases, including Medline, Embase and EconLit, from 2011 to 2015.

STUDY ELIGIBILITY

Study selection was performed by two reviewers independently. We included any cost-effectiveness analysis comparing DAAs with the old standard of care for HCV treatment. We excluded non-English-language studies and studies not reporting quality-adjusted life-years.

STUDY APPRAISAL AND SYNTHESIS METHOD

One reviewer collected data and assessed the quality of reporting, using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Another reviewer crosschecked the abstracted information. The development methods of the included studies were synthetized on the basis of good modelling practice recommendations.

RESULTS

Review of 304 citations revealed 36 cost-effectiveness analyses. The reporting quality scores of most articles were rated as acceptable, between 67 and 100 %. The majority of the studies were conducted in Europe (50 %), followed by the USA (44 %). Fifty-six percent of the 36 studies evaluated the cost effectiveness of HCV treatment in both treatment-naive and treatment-experienced patients, 97 % included genotype 1 patients and 53 % evaluated the cost effectiveness of second-generation or oral DAAs in comparison with the previous standard of care or other DAAs. Twenty-one models defined health states in terms of METAVIR fibrosis scores. Only one study used a discrete-event simulation approach, and the remainder used state-transition models. The time horizons varied; however, 89 % of studies used a lifetime horizon. One study was conducted from a societal perspective. Thirty-three percent of studies did not conduct any model validation. We also noted that none of the studies modelled HCV treatment as a prevention strategy, 86 % of models did not consider the possibility of re-infection with HCV after successful treatment, 97 % of studies did not consider indirect economic benefits resulting from HCV treatment and none of the studies evaluating oral DAAs used real-world data.

LIMITATIONS

The search was limited by date (from 1 January 2011 to 8 September 2015) and was also limited to English-language and published reports.

CONCLUSIONS

Most modelling studies used a similar modelling structure and could have underestimated the value of HCV treatment. Future modelling efforts should consider the benefits of HCV treatment in preventing transmission, extra-hepatic and indirect economic benefits of HCV treatment, real-world cost-effectiveness analysis and cost effectiveness of HCV treatment in low- and middle-income countries.

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of 'big data'. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital's EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com ) suitable for visualization of both human-designed and data-mined processes which can then be used for 'what-if' analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively 'deep dive' into big data.

Prevention of Hepatitis C by Screening and Treatment in U.S. Prisons

BACKGROUND

The prevalence of hepatitis C virus (HCV) in U.S. prisoners is high; however, HCV testing and treatment are rare. Infected inmates released back into society contribute to the spread of HCV in the general population. Routine hepatitis screening of inmates followed by new therapies may reduce ongoing HCV transmission.

OBJECTIVE

To evaluate the health and economic effect of HCV screening and treatment in prisons on the HCV epidemic in society.

DESIGN

Agent-based microsimulation model of HCV transmission and progression of HCV disease.

DATA SOURCES

Published literature.

TARGET POPULATION

Population in U.S. prisons and general community.

TIME HORIZON

30 years.

PERSPECTIVE

Societal.

INTERVENTIONS

Risk-based and universal opt-out hepatitis C screening in prisons, followed by treatment in a portion of patients.

OUTCOME MEASURES

Prevention of HCV transmission and associated disease in prisons and society, costs, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratio (ICER), and total prison budget.

RESULTS OF BASE-CASE ANALYSIS

Implementing risk-based and opt-out screening could diagnose 41,900 to 122,700 new HCV cases in prisons in the next 30 years. Compared with no screening, these scenarios could prevent 5500 to 12,700 new HCV infections caused by released inmates, wherein about 90% of averted infections would have occurred outside of prisons. Screening could also prevent 4200 to 11,700 liver-related deaths. The ICERs of screening scenarios were $19,600 to $29,200 per QALY, and the respective first-year prison budget was $900 to $1150 million. Prisons would require an additional 12.4% of their current health care budget to implement such interventions.

RESULTS OF SENSITIVITY ANALYSIS

Results were sensitive to the time horizon, and ICERs otherwise remained less than $50,000 per QALY.

LIMITATION

Data on transmission network, reinfection rate, and opt-out HCV screening rate are lacking.

CONCLUSION

Universal opt-out HCV screening in prisons is highly cost-effective and would reduce HCV transmission and HCV-associated diseases primarily in the outside community. Investing in U.S. prisons to manage hepatitis C is a strategic approach to address the current epidemic.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Myths and Misconceptions of Within-Cycle Correction: A Guide for Modelers and Decision Makers

Commonly used decision-analytic models for cost-effectiveness analysis simulate time in discrete steps. Use of discrete-time steps can introduce errors when calculating cumulative outcomes such as costs and quality-adjusted life-years. There are a number of myths or misconceptions concerning how to correct these errors and the need to do so. This tutorial shows that, by neglecting to apply within-cycle (sometimes referred to as half-cycle or continuity) correction methods to the results of discrete-time models, the analyst may arrive at the wrong recommendation regarding the use of a technology. We show that the standard half-cycle correction method results in the same cumulative outcome as the trapezoidal rule and life-table method. However, the trapezoidal rule has the added advantage of applying the correction at each cycle, not just the initial and final cycle. We further show that the Simpson's 1/3 rule is more accurate than the trapezoidal rule. We recommend using the Simpson's 1/3 rule in the base-case analysis and, if needed, showing the results with other methods in the sensitivity analysis. We also demonstrate that both the trapezoidal and Simpson's rules can easily be implemented in commonly used software.

Complex systems approach to scientific publication and peer-review system: development of an agent-based model calibrated with empirical journal data

Scientific peer-review and publication systems incur a huge burden in terms of costs and time. Innovative alternatives have been proposed to improve the systems, but assessing their impact in experimental studies is not feasible at a systemic level. We developed an agent-based model by adopting a unified view of peer review and publication systems and calibrating it with empirical journal data in the biomedical and life sciences. We modeled researchers, research manuscripts and scientific journals as agents. Researchers were characterized by their scientific level and resources, manuscripts by their scientific value, and journals by their reputation and acceptance or rejection thresholds. These state variables were used in submodels for various processes such as production of articles, submissions to target journals, in-house and external peer review, and resubmissions. We collected data for a sample of biomedical and life sciences journals regarding acceptance rates, resubmission patterns and total number of published articles. We adjusted submodel parameters so that the agent-based model outputs fit these empirical data. We simulated 105 journals, 25,000 researchers and 410,000 manuscripts over 10 years. A mean of 33,600 articles were published per year; 19 % of submitted manuscripts remained unpublished. The mean acceptance rate was 21 % after external peer review and rejection rate 32 % after in-house review; 15 % publications resulted from the first submission, 47 % the second submission and 20 % the third submission. All decisions in the model were mainly driven by the scientific value, whereas journal targeting and persistence in resubmission defined whether a manuscript would be published or abandoned after one or many rejections. This agent-based model may help in better understanding the determinants of the scientific publication and peer-review systems. It may also help in assessing and identifying the most promising alternative systems of peer review.

Dealing with Time in Health Economic Evaluation: Methodological Issues and Recommendations for Practice

Time is an important aspect of health economic evaluation, as the timing and duration of clinical events, healthcare interventions and their consequences all affect estimated costs and effects. These issues should be reflected in the design of health economic models. This article considers three important aspects of time in modelling: (1) which cohorts to simulate and how far into the future to extend the analysis; (2) the simulation of time, including the difference between discrete-time and continuous-time models, cycle lengths, and converting rates and probabilities; and (3) discounting future costs and effects to their present values. We provide a methodological overview of these issues and make recommendations to help inform both the conduct of cost-effectiveness analyses and the interpretation of their results. For choosing which cohorts to simulate and how many, we suggest analysts carefully assess potential reasons for variation in cost effectiveness between cohorts and the feasibility of subgroup-specific recommendations. For the simulation of time, we recommend using short cycles or continuous-time models to avoid biases and the need for half-cycle corrections, and provide advice on the correct conversion of transition probabilities in state transition models. Finally, for discounting, analysts should not only follow current guidance and report how discounting was conducted, especially in the case of differential discounting, but also seek to develop an understanding of its rationale. Our overall recommendations are that analysts explicitly state and justify their modelling choices regarding time and consider how alternative choices may impact on results.