The development of a simulation model of the treatment of coronary heart disease

A protocol for an economic evaluation of a polypill in patients with established or at high risk of cardiovascular disease in a UK NHS setting: RUPEE (NHS) study

INTRODUCTION

The 'Use of a Multi-drug Pill in Reducing cardiovascular Events' (UMPIRE) trial was a randomised controlled clinical trial evaluating the impact of a polypill strategy on adherence to indicated medication in a population with established cardiovascular disease (CVD) of or at high risk thereof. The aim of Researching the UMPIRE Processes for Economic Evaluation in the National Health Service (RUPEE NHS) is to estimate the potential health economic impact of a polypill strategy for CVD prevention within the NHS using UMPIRE trial and other relevant data. This paper describes the design of a modelled economic evaluation of the impact of increased adherence to the polypill versus usual care among the UK UMPIRE participants.

METHODS AND ANALYSIS

As recommended by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making modelling guidelines, a review of published CVD models was undertaken to identify the most appropriate modelling approach and structure. The review was carried out in the electronic databases, MEDLINE and EMBASE. 40 CVD models were identified from 57 studies, the majority of economic models were health state transition cohort models and individual-level simulation models. The findings were discussed with clinical experts to confirm the approach and structure. An individual simulation approach was identified as the most suitable method to capture the heterogeneity in the population at CVD risk. RUPEE-NHS will use UMPIRE trial data on adherence to estimate the long-term cost-effectiveness of the polypill strategy.

DISSEMINATION

The evaluation findings will be presented in open-access scientific and healthcare policy journals and at national and international conferences. We will also present findings to NHS policy makers and pharmaceutical companies.

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.

A model for undertaking effectiveness and cost-effectiveness analyses of primary preventive strategies in cardiovascular disease

BACKGROUND

Clinical trials generally provide strong evidence of the efficacy of cardiovascular preventive strategies, but poor evidence of their 'real-life' utility, in terms of effectiveness and cost-effectiveness.

DESIGN AND METHODS

The Cardiovascular Disease Prevention Model is presented, which represents a means of extrapolating the results of clinical trials to a broader, more relevant context. The model is configured as a decision-analysis tree, and underpinned by life-course analysis and Markov processes. Uncertainty and sensitivity analyses are undertaken by Monte Carlo simulation.

RESULTS

The results of effectiveness and cost-effectiveness analyses of a hypothetical preventive intervention are presented to demonstrate the outputs of the model. The potential impact and efficiency of the intervention are made obvious.

CONCLUSIONS

The Cardiovascular Disease Prevention Model offers a means to translate the results of trials of cardiovascular preventive interventions, in order to inform clinical and public health practice, as well as health policy.

The Economic Impact of Lower Protein Infant Formula for the Children of Overweight and Obese Mothers

The global prevalence of obesity is rising rapidly, highlighting the importance of understanding risk factors related to the condition. Childhood obesity, which has itself become increasingly prevalent, is an important predictor of adulthood obesity. Studies suggest that the protein content consumed in infanthood is an important predictor of weight gain in childhood, which may contribute to higher body mass index (BMI). For instance, there is evidence that a lower protein infant formula (lpIF) for infants of overweight or obese mothers can offer advantages over currently-used infant formulas with regard to preventing excessive weight gain. The current study used health economic modelling to predict the long-term clinical and economic outcomes in Mexico associated with lpIF compared to a currently-used formula. A discrete event simulation was constructed to extrapolate the outcomes of trials on the use of formula in infanthood to changes in lifetime BMI, the health outcomes due to the changes in BMI and the healthcare system costs, productivity and quality of life impact associated with these outcomes. The model predicts that individuals who receive lpIF in infancy go on to have lower BMI levels throughout their lives, are less likely to be obese or develop obesity-related disease, live longer, incur fewer health system costs and have improved productivity. Simulation-based economic modelling suggests that the benefits seen in the short term, with the use of lpIF over a currently-used formula, could translate into considerable health and economic benefits in the long term. Modelling over such long timeframes is inevitably subject to uncertainty. Further research should be undertaken to improve the certainty of the model.

Discrete event simulation model of sudden cardiac death predicts high impact of preventive interventions

Sudden Cardiac Death (SCD) is responsible for at least 180,000 deaths a year and incurs an average cost of $286 billion annually in the United States alone. Herein, we present a novel discrete event simulation model of SCD, which quantifies the chains of events associated with the formation, growth, and rupture of atheroma plaques, and the subsequent formation of clots, thrombosis and on-set of arrhythmias within a population. The predictions generated by the model are in good agreement both with results obtained from pathological examinations on the frequencies of three major types of atheroma, and with epidemiological data on the prevalence and risk of SCD. These model predictions allow for identification of interventions and importantly for the optimal time of intervention leading to high potential impact on SCD risk reduction (up to 8-fold reduction in the number of SCDs in the population) as well as the increase in life expectancy.

Accounting for costs, QALYs, and capacity constraints: using discrete-event simulation to evaluate alternative service delivery and organizational scenarios for hospital-based glaucoma services

BACKGROUND

Decision-analytic models are routinely used as a framework for cost-effectiveness analyses of health care services and technologies; however, these models mostly ignore resource constraints. In this study, we use a discrete-event simulation model to inform a cost-effectiveness analysis of alternative options for the organization and delivery of clinical services in the ophthalmology department of a public hospital. The model is novel, given that it represents both disease outcomes and resource constraints in a routine clinical setting.

METHODS

A 5-year discrete-event simulation model representing glaucoma patient services at the Royal Adelaide Hospital (RAH) was implemented and calibrated to patient-level data. The data were sourced from routinely collected waiting and appointment lists, patient record data, and the published literature. Patient-level costs and quality-adjusted life years were estimated for a range of alternative scenarios, including combinations of alternate follow-up times, booking cycles, and treatment pathways.

RESULTS

The model shows that a) extending booking cycle length from 4 to 6 months, b) extending follow-up visit times by 2 to 3 months, and c) using laser in preference to medication are more cost-effective than current practice at the RAH eye clinic.

CONCLUSIONS

The current simulation model provides a useful tool for informing improvements in the organization and delivery of glaucoma services at a local level (e.g., within a hospital), on the basis of expected effects on costs and health outcomes while accounting for current capacity constraints. Our model may be adapted to represent glaucoma services at other hospitals, whereas the general modeling approach could be applied to many other clinical service areas.

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

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article is to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as the wider modeling task force.

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

Discrete event simulation (DES) is a form of computer-based modeling that provides an intuitive and flexible approach to representing complex systems. It has been used in a wide range of health care applications. Most early applications involved analyses of systems with constrained resources, where the general aim was to improve the organization of delivered services. More recently, DES has increasingly been applied to evaluate specific technologies in the context of health technology assessment. The aim of this article was to provide consensus-based guidelines on the application of DES in a health care setting, covering the range of issues to which DES can be applied. The article works through the different stages of the modeling process: structural development, parameter estimation, model implementation, model analysis, and representation and reporting. For each stage, a brief description is provided, followed by consideration of issues that are of particular relevance to the application of DES in a health care setting. Each section contains a number of best practice recommendations that were iterated among the authors, as well as among the wider modeling task force.

Cost estimation of cardiovascular disease events in the US

BACKGROUND

In this study, we developed cost prediction equations that facilitate estimation of the costs of various cardiovascular events for patients of specific demographic and clinical characteristics over varying time horizons.

METHODS

We used administrative claims data and generalized linear models to develop cost prediction equations for selected cardiovascular events, including myocardial infarction (MI), angina, strokes and revascularization procedures. Separate equations were estimated for patients with events and for their propensity score-matched controls. Attributable costs were estimated on a monthly basis for the first 36 months after each event and annually thereafter, with differences in survival between cases and controls factored into the longitudinal cost calculations. The regression models were used to estimate event costs ($US, year 2007 values) for the 'average' patient in each event group, over various time periods ranging from 1 month to lifetime.

RESULTS

When the equations are run for the average patient in each event group, attributable costs of each event in the acute phase (i.e. first 3 years) are substantial (e.g. MI $US 73 300; hospitalization for angina $US 36 000; non-fatal haemorrhagic stroke $US 71 600). Furthermore, for most events, cumulative costs remain substantially higher among cases than among controls over the remaining lifetime of the patients.

CONCLUSIONS

This study provides updated estimates of medical care costs of cardiovascular events among a managed care population over various time horizons. Results suggest that the economic burden of cardiovascular disease is substantial, both in the acute phase as well as over the longer term.

A discrete-event simulation of smoking-cessation strategies based on varenicline pivotal trial data

BACKGROUND

Smoking is the leading cause of preventable death in the US. While one in five individuals smoke, and 70% of these indicate a desire to quit, <5% of unaided quit attempts succeed. Cessation aids can double or triple the odds of successfully quitting. Models of smoking-cessation behaviour can elucidate the implications of individual abstinence patterns to allow better tailoring of quit attempts to an individual's characteristics.

OBJECTIVE

The objectives of this study were to develop and validate a discrete-event simulation (DES) to evaluate the benefits of smoking abstinence using data from the pooled pivotal clinical trials of varenicline versus bupropion or placebo for smoking cessation and to provide a foundation for the development of a lifetime smoking-cessation model.

METHODS

The DES model simulated the outcome of a single smoking-cessation attempt over 1 year, in accordance with the clinical trial timeframes. Pharmaceutical costs were assessed from the perspective of a healthcare payer. The model randomly sampled patient profiles from the pooled varenicline clinical trials. All patients were physically and mentally healthy adult smokers who were motivated to quit abruptly. The model allowed for comparisons of up to five distinct treatment approaches for smoking cessation. In the current analyses, three interventions corresponding to the clinical trials were evaluated, which included brief counselling plus varenicline 1.0 mg twice daily (bid) or bupropion SR 150 mg bid versus placebo (i.e. brief counselling only). The treatment periods in the clinical trials were 12 weeks (target quit date: day 8), with a 40-week non-treatment follow-up, and counselling continuing over the entire 52-week period in all treatment groups. The main outcome modelled was the continuous abstinence rate (CAR; defined as complete abstinence from smoking and confirmed by exhaled carbon monoxide ≤ 10 ppm) at end of treatment (weeks 9-12) and long-term follow-up (weeks 9-52), and total time abstinent from smoking over the course of 52 weeks. The model also evaluated costs and cost-effectiveness outcomes.

RESULTS

For the varenicline, bupropion and placebo cohorts, respectively, the model predicted CARs for weeks 9-12 of 44.3%, 30.4% and 18.6% compared with observed rates of 44.0%, 29.7% and 17.7%; over weeks 9-52, predicted CARs in the model compared with observed rates in the pooled clinical studies were 22.9%, 16.4% and 9.4% versus 22.4%, 15.4% and 9.3%, respectively. Total mean abstinence times accrued in the model varenicline, bupropion and placebo groups, respectively, were 3.6, 2.6 and 1.5 months and total pharmaceutical treatment costs were $US261, $US442 and $US0 (year 2008 values) over the 1-year model period. Using cost per abstinent-month achieved as a measure of cost effectiveness, varenicline dominated bupropion and yielded an incremental cost-effectiveness ratio of $US124 compared with placebo.

CONCLUSION

The model accurately replicated abstinence patterns observed in the clinical trial data using individualized predictions and indicated that varenicline was more effective and may be less costly than bupropion. This simulation incorporated individual predictions of abstinence and relapse, and provides a framework for lifetime modelling that considers multiple quit attempts over time in diverse patient populations using a variety of quit attempt strategies.

The CHD challenge: comparing four cost-effectiveness models

OBJECTIVES

To compare four UK models evaluating the cost-effectiveness of interventions in coronary heart disease (CHD), exploring the relative importance of structure and inputs in accounting for differences, and the scope for consensus on structure and data.

METHODS

We compared published cost-effectiveness results (incremental cost, quality-adjusted life year, and cost-effectiveness ratio) of three models conforming to the National Institute for Health and Clinical Excellence guidelines dealing with three interventions (statins, percutaneous coronary intervention, and clopidogrel) with a model developed in Southampton. Comparisons were made using three separate stages: 1) comparison of published results; 2) comparison of the results using the same data inputs wherever possible; and 3) an in-depth exploration of reasons for differences and the potential for consensus.

RESULTS

Although published results differed by up to 73% (for statins), standardization of inputs (stage 2) narrowed these gaps. Greater understanding of the reasons for differences was achieved, but a consensus on preferred values for all data inputs was not reached.

CONCLUSIONS

We found that published guidance on methods was important to reduce variation in important model inputs. Although the comparison of models did not lead to consensus for all model inputs, it provided a better understanding of the reasons for these differences, and enhanced the transparency and credibility of all models. Similar comparisons would be aided by fuller publication of models, perhaps through detailed web appendices.

Explaining the decrease in coronary heart disease mortality in Italy between 1980 and 2000

OBJECTIVES

We examined the extent to which the decrease in coronary heart disease (CHD) mortality rates in Italy could be explained by changes in cardiovascular risk factors versus the use of medical and surgical treatments.

METHODS

We used a validated model to combine data on changes in risk factors and uptake and effectiveness of cardiac treatments among adult men and women in Italy between 1980 and 2000. Data sources included results of published trials, meta-analyses, official statistics, longitudinal studies, and national surveys. The difference between observed and expected CHD deaths in 2000 was partitioned among treatments and risk factors.

RESULTS

From 1980 to 2000, the age-adjusted CHD mortality rate in Italy fell among persons aged 25 to 84 years, resulting in 42 930 fewer CHD deaths in 2000. Approximately 40% of this decrease was attributed to treatments and 55% to changes in risk factors.

CONCLUSIONS

Over half of the CHD mortality fall in Italy between 1980 and 2000 was attributable to reductions in major risk factors, mainly cholesterol and blood pressure, and less than half to evidence-based medical therapies. These results are becoming increasingly important, both for understanding past trends and for planning future prevention and treatment strategies.

Systematic review of the use of computer simulation modeling of patient flow in surgical care

Computer simulation has been employed to evaluate proposed changes in the delivery of health care. However, little is known about the utility of simulation approaches for analysis of changes in the delivery of surgical care. We searched eight bibliographic databases for this comprehensive review of the literature published over the past five decades, and found 34 publications that reported on simulation models for the flow of surgical patients. The majority of these publications presented a description of the simulation approach: 91% outlined the underlying assumptions for modeling, 88% presented the system requirements, and 91% described the input and output data. However, only half of the publications reported that models were constructed to address the needs of policy-makers, and only 26% reported some involvement of health system managers and policy-makers in the simulation study. In addition, we found a wide variation in the presentation of assumptions, system requirements, input and output data, and results of simulation-based policy analysis.

Treating relapsing multiple sclerosis with subcutaneous versus intramuscular interferon-beta-1a: modelling the clinical and economic implications

The EVIDENCE trial concluded that administering high-dose/high-frequency subcutaneous (SC) interferon-beta-1a (IFNb1a) was more effective in preventing relapses among patients with relapsing multiple sclerosis (MS) than low-dose weekly intramuscular (IM) IFNb1a after 64 weeks. This analysis utilized discrete-event simulation (DES) to model the potential longer-term clinical and economic implications of this trial. A DES predicting the course of relapsing MS and incorporating the effect of IFNb1a therapy was developed. The model began by randomly reading in actual patient data from the trial to create 1000 patients. Each simulated patient was replicated - one was assigned to receive SC IFNb1a three times a week and the other to receive IM IFNb1a once a week. During the simulation, patients may (i) experience relapses, with associated short- and long-term impacts on costs and disability; (ii) develop new T2 lesions detected by a magnetic resonance imaging scan; (iii) discontinue treatment because of adverse events or lack of response; (iv) advance to secondary progressive MS; or (v) die. Model inputs were mainly obtained from the EVIDENCE trial, but were taken from published literature if they could not be obtained from the trial. Direct medical costs ($US, year 2006 values) to the US payers were primarily obtained by updating a published cost analysis. Costs and benefits were discounted at 3% per annum. Extensive sensitivity analyses were conducted to test the robustness of the model results. Based on 100 replications of 1000 patient pairs over 4 years, SC IFNb1a was predicted to enable more patients to avoid relapse (216 vs 147). Total mean costs per patient (discounted) were $US79 890 with SC IFNb1a versus $US74 485 with IM administration, a net increase of $US5405 per patient. However, SC IFNb1a was estimated to prevent 0.50 relapses and save 23 relapse-free days per patient, yielding incremental cost-effectiveness ratios of $US10 755 per relapse prevented and $US232 per relapse-free day gained. Sensitivity analyses revealed that the result was most sensitive to the treatment efficacy, model time horizon and cost of IFNb1a treatment. Based on the results observed in the EVIDENCE trial, the model predicted that SC IFNb1a would yield greater health benefits over 4 years than IM IFNb1a, at a cost that would seem to be a reasonable trade-off.

Using the Statecharts paradigm for simulation of patient flow in surgical care

Computer simulation of patient flow has been used extensively to assess the impacts of changes in the management of surgical care. However, little research is available on the utility of existing modeling techniques. The purpose of this paper is to examine the capacity of Statecharts, a system of graphical specification, for constructing a discrete-event simulation model of the perioperative process. The Statecharts specification paradigm was originally developed for representing reactive systems by extending the formalism of finite-state machines through notions of hierarchy, parallelism, and event broadcasting. Hierarchy permits subordination between states so that one state may contain other states. Parallelism permits more than one state to be active at any given time. Broadcasting of events allows one state to detect changes in another state. In the context of the peri-operative process, hierarchy provides the means to describe steps within activities and to cluster related activities, parallelism provides the means to specify concurrent activities, and event broadcasting provides the means to trigger a series of actions in one activity according to transitions that occur in another activity. Combined with hierarchy and parallelism, event broadcasting offers a convenient way to describe the interaction of concurrent activities. We applied the Statecharts formalism to describe the progress of individual patients through surgical care as a series of asynchronous updates in patient records generated in reaction to events produced by parallel finite-state machines representing concurrent clinical and managerial activities. We conclude that Statecharts capture successfully the behavioral aspects of surgical care delivery by specifying permissible chronology of events, conditions, and actions.

Modeling the economic and health consequences of managing chronic osteoarthritis pain with opioids in Germany: comparison of extended-release oxycodone and OROS hydromorphone

OBJECTIVE

The Osmotic controlled-Release Oral delivery System (OROS) hydromorphone ensures continuous release of hydromorphone over 24 hours. It is anticipated that this will facilitate optimal pain relief, improve quality of sleep and compliance. This simulation compared managing chronic osteoarthritis pain with once-daily OROS hydromorphone with an equianalgesic dose of extended-release (ER) oxycodone administered two or three times a day.

METHODS

This discrete event simulation follows patients for a year after initiating opioid treatment. Pairs of identical patients are created; one receives OROS hydromorphone the other ER oxycodone; undergo dose adjustments and after titration can be dissatisfied or satisfied, suffer adverse events, pain recurrence, or discontinue the opioid. Each is assigned an initial sleep problems score, and an improved score from a treatment dependent distribution at the end of titration; these are translated to a utility value. Utilities are assigned pre-treatment, updated until the patient reaches the optimal dose or is non-compliant or dissatisfied. The OROS hydromorphone and ER oxycodone doses are converted to equianalgesic morphine doses using the following ratios: hydromorphone to morphine ratio; 1:5, oxycodone to morphine ratio; 1:2. Sensitivity analyses explored uncertainty in the conversion ratios and other key parameters. Direct medical costs are in 2005 euros.

RESULTS

Over 1 year on a mean daily morphine-equivalent dose of 90 mg, 14% were estimated to be dissatisfied with each opioid. OROS hydromorphone was predicted to yield 0.017 additional quality-adjusted life years (QALYs)/patient for a small additional annual cost (E141/patient), yielding an incremental cost-effectiveness ratio (ICER) of E8343/QALY gained. Changing the assumed conversion ratio for oxycodone:morphine to 1:1.5 led to lower net costs of E68 per patient, E3979/QALY, and for hydromorphone to 1:7.5 to savings.

CONCLUSION

Based on these analyses, OROS hydromorphone is expected to yield health benefits at reasonable cost in Germany.

Explaining the decrease in U.S. deaths from coronary disease, 1980-2000

BACKGROUND

Mortality from coronary heart disease in the United States has decreased substantially in recent decades. We conducted a study to determine how much of this decrease could be explained by the use of medical and surgical treatments as opposed to changes in cardiovascular risk factors.

METHODS

We applied a previously validated statistical model, IMPACT, to data on the use and effectiveness of specific cardiac treatments and on changes in risk factors between 1980 and 2000 among U.S. adults 25 to 84 years old. The difference between the observed and expected number of deaths from coronary heart disease in 2000 was distributed among the treatments and risk factors included in the analyses.

RESULTS

From 1980 through 2000, the age-adjusted death rate for coronary heart disease fell from 542.9 to 266.8 deaths per 100,000 population among men and from 263.3 to 134.4 deaths per 100,000 population among women, resulting in 341,745 fewer deaths from coronary heart disease in 2000. Approximately 47% of this decrease was attributed to treatments, including secondary preventive therapies after myocardial infarction or revascularization (11%), initial treatments for acute myocardial infarction or unstable angina (10%), treatments for heart failure (9%), revascularization for chronic angina (5%), and other therapies (12%). Approximately 44% was attributed to changes in risk factors, including reductions in total cholesterol (24%), systolic blood pressure (20%), smoking prevalence (12%), and physical inactivity (5%), although these reductions were partially offset by increases in the body-mass index and the prevalence of diabetes, which accounted for an increased number of deaths (8% and 10%, respectively).

CONCLUSIONS

Approximately half the decline in U.S. deaths from coronary heart disease from 1980 through 2000 may be attributable to reductions in major risk factors and approximately half to evidence-based medical therapies.

A taxonomy of model structures for economic evaluation of health technologies

Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good modelling practice, but few describe how to choose from the many types of available models. This paper develops a new taxonomy of model structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate models, including decision trees and Markov models require large population numbers, homogeneous sub-groups and linear interactions. Individual models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov models, and discrete event simulation are fairly uncommon in the health economics but are necessary for modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a model, based on key requirements, including output requirements, the population size, and system complexity.

A review of health care models for coronary heart disease interventions

This article reviews models for the treatment of coronary heart disease (CHD). Whereas most of the models described were developed to assess the cost effectiveness of different treatment strategies, other models have also been used to extrapolate clinical trials, for capacity and resource planning, or to predict the future population with heart disease. In this paper we investigate the use of modelling techniques in relation to different types of health intervention, and we discuss the assumptions and limitations of these approaches. Many of the models reviewed in this paper use decision tree models for acute or short term interventions, and Markov or state transition models for chronic or long term interventions. Discrete event simulation has, however, been used for more complex whole system models, and for modelling resource-constrained interventions and operational planning. Nearly all of the studies in our review used cohort-based models rather than population based models, and therefore few models could estimate the likely total costs and benefits for a population group. Most studies used de novo purpose built models consisting of only a small number of health states. Models of the whole disease system were less common. The model descriptions were often incomplete. We recommend that the reporting of model structure, assumptions and input parameters is more explicit, to reduce the risk of biased reporting and ensure greater confidence in the model results.

Coronary heart disease policy models: a systematic review

BACKGROUND

The prevention and treatment of coronary heart disease (CHD) is complex. A variety of models have therefore been developed to try and explain past trends and predict future possibilities. The aim of this systematic review was to evaluate the strengths and limitations of existing CHD policy models.

METHODS

A search strategy was developed, piloted and run in MEDLINE and EMBASE electronic databases, supplemented by manually searching reference lists of relevant articles and reviews. Two reviewers independently checked the papers for inclusion and appraisal. All CHD modelling studies were included which addressed a defined population and reported on one or more key outcomes (deaths prevented, life years gained, mortality, incidence, prevalence, disability or cost of treatment).

RESULTS

In total, 75 articles describing 42 models were included; 12 (29%) of the 42 models were micro-simulation, 8 (19%) cell-based, and 8 (19%) life table analyses, while 14 (33%) used other modelling methods. Outcomes most commonly reported were cost-effectiveness (36%), numbers of deaths prevented (33%), life-years gained (23%) or CHD incidence (23%). Among the 42 models, 29 (69%) included one or more risk factors for primary prevention, while 8 (19%) just considered CHD treatments. Only 5 (12%) were comprehensive, considering both risk factors and treatments. The six best-developed models are summarised in this paper, all are considered in detail in the appendices.

CONCLUSION

Existing CHD policy models vary widely in their depth, breadth, quality, utility and versatility. Few models have been calibrated against observed data, replicated in different settings or adequately validated. Before being accepted as a policy aid, any CHD model should provide an explicit statement of its aims, assumptions, outputs, strengths and limitations.

Modelling the economic and health consequences of cardiac resynchronization therapy in the UK

OBJECTIVE

Clinical evidence supports the use of cardiac resynchronization therapy (CRT) in advanced heart failure, but its cost-effectiveness is still unclear. This analysis assessed the economic and health consequences in the UK of implanting a CRT in patients with NYHA class III-IV heart failure.

METHODS

A discrete event simulation of heart failure was used to compare the course over 5 years of 1000 identical pairs of patients -- one receiving both CRT and optimum pharmacologic treatment (OPT), the other OPT alone. All inputs were obtained from the data collected in the CArdiac REsynchronization in Heart Failure (CARE-HF) trial and a hospital in the UK. Direct medical costs (in 2004 pound) from the perspective of the National Health Service were considered. Both costs and benefits were discounted at 3.5%. Sensitivity analyses addressed all model inputs and multivariate analyses were performed by varying key parameters simultaneously.

RESULTS

The model predicted 471 deaths and 2263 hospitalizations over 5 years with OPT alone and 348 deaths and 1764 hospitalizations with CRT, equivalent to a 26% reduction in mortality and 22% in hospitalizations, at a discounted cost of pound 11,423 per patient with CRT vs. pound 4,900 with OPT alone. CRT was predicted to increase quality-adjusted survival by 0.43 QALYs per patient, resulting in an incremental cost-effectiveness ratio of pound 15,247 per QALY gained (range: pound 12,531- pound 23,184). Sensitivity analyses revealed that this outcome was most sensitive to time horizon and cost of implantation.

CONCLUSION

Based on these 5-year analyses, CRT is expected to yield substantial health benefits at a reasonable cost.

Using simulation to estimate the cost effectiveness of improving ambulance and thrombolysis response times after myocardial infarction

OBJECTIVES

To quantify the health gains and costs associated with improving ambulance and thrombolysis response times for acute myocardial infarction.

DESIGN

A computer simulation model. PATIENTS/SETTINGS: Patients experiencing acute myocardial infarction in England.

INTERVENTIONS

Improving the ambulance response time to 75% of calls reached within 8 minutes and the hospital arrival to thrombolysis time interval (door-to-needle time) to 75% receiving it within 30 minutes and 20 minutes, compared to best estimates of response times in the mid-1990s.

MAIN OUTCOME MEASURES

Deaths prevented, life years saved, and discounted cost per life year saved.

RESULTS

Improving the ambulance response to 75% of calls within 8 minutes resulted in an estimate of 5 deaths prevented or 57 life years saved per million population per year, with a discounted incremental cost per life year saved of 8540 pounds sterling over 20 years. The corresponding benefit of improving the door-to-needle time to 75% of myocardial infarction patients within 30 minutes was an estimated 2 deaths prevented and 15 life years saved per million population per year, with a discounted incremental cost per life year saved of between 10,150 pounds sterling to 54,230 pounds sterling over 20 years. Little further gain was associated with reaching the 20 minute target. Combining ambulance and thrombolysis targets resulted in 70 life years saved per million population per year.

CONCLUSIONS

Improving ambulance response times appears to be cost effective. Reducing door-to-needle time will have a smaller effect at an uncertain cost. Further benefits may be gained from reducing the time from onset of symptoms to starting thrombolysis.

Explaining the decline in coronary heart disease mortality in Finland between 1982 and 1997

In Finland since the 1980s, coronary heart disease mortality has declined more than might be predicted by risk factor reductions alone. The aim of this study was to assess how much of the decline could be attributed to improved treatments and risk factor reductions. The authors used the cell-based IMPACT mortality model to synthesize effectiveness of treatments and risk factor reductions with data on treatments administered to patients and trends in cardiovascular risk factors in the population. Cardiovascular risk factors were measured in random samples of patients in 1982 (n=8,501) and 1997 (n=4,500). Mortality and treatment data were obtained from the National Causes of Death Register, Hospital Discharge Register, social insurance data, and medical records. Estimated and observed changes in coronary heart disease mortality were used as main outcome measures. Between 1982 and 1997, coronary heart disease mortality rates declined by 63%, with 373 fewer deaths in 1997 than expected from baseline mortality rates in 1982. Improved treatments explained approximately 23% of the mortality reduction, and risk factors explained some 53-72% of the reduction. These findings highlight the value of a comprehensive strategy that promotes primary prevention programs and actively supports secondary prevention. It also emphasizes the importance of maximizing population coverage of effective treatments.

Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000

BACKGROUND

Coronary heart disease mortality rates have been decreasing in the United Kingdom since the 1970s. Our study aimed to examine how much of the decrease in England and Wales between 1981 and 2000 could be attributed to medical and surgical treatments and how much to changes in cardiovascular risk factors.

METHODS AND RESULTS

The IMPACT mortality model was used to combine and analyze data on uptake and effectiveness of cardiological treatments and risk factor trends in England and Wales. The main data sources were published trials and meta-analyses, official statistics, clinical audits, and national surveys. Between 1981 and 2000, coronary heart disease mortality rates in England and Wales decreased by 62% in men and 45% in women 25 to 84 years old. This resulted in 68 230 fewer deaths in 2000. Some 42% of this decrease was attributed to treatments in individuals (including 11% to secondary prevention, 13% to heart failure treatments, 8% to initial treatments of acute myocardial infarction, and 3% to hypertension treatments) and 58% to population risk factor reductions (principally smoking, 48%; blood pressure, 9.5%; and cholesterol, 9.5%). Adverse trends were seen for physical activity, obesity and diabetes.

CONCLUSIONS

More than half the coronary heart disease mortality decrease in Britain between 1981 and 2000 was attributable to reductions in major risk factors, principally smoking. This emphasizes the importance of a comprehensive strategy that promotes primary prevention, particularly for tobacco and diet, and that maximizes population coverage of effective treatments, especially for secondary prevention and heart failure. These findings may be cautiously generalizable to the United States and other developed countries.

The development of a simulation model of primary prevention strategies for coronary heart disease

This paper describes the present state of development of a discrete-event micro-simulation model for coronary heart disease prevention. The model is intended to support health policy makers in assessing the impacts on health care resources of different primary prevention strategies. For each person, a set of times to disease events, conditional on the individual's risk factor profile, is sampled from a set of probability distributions that are derived from a new analysis of the Framingham cohort study on coronary heart disease. Methods used to model changes in behavioural and physiological risk factors are discussed and a description of the simulation logic is given. The model incorporates POST (Patient Oriented Simulation Technique) simulation routines.