Health and economic outcomes modeling practices: a suggested framework

Cost-Effectiveness of Combination of Baricitinib and Remdesivir in Hospitalized Patients with COVID-19 in the United States: A Modelling Study

INTRODUCTION

Baricitinib-remdesivir (BARI-REM) combination is superior to remdesivir (REM) in reducing recovery time and accelerating clinical improvement among hospitalized patients with coronavirus disease 2019 (COVID-19), specifically those receiving high-flow oxygen/noninvasive ventilation. Here we assessed the cost-effectiveness of BARI-REM versus REM in hospitalized patients with COVID-19 in the USA.

METHODS

A three-state model was developed addressing costs and patient utility associated with COVID-19 hospitalization, immediate post hospital care, and subsequent lifetime medical care. Analysis was performed from the perspective of a payer and a hospital. Both perspectives evaluated two subgroups: all patients and patients who required oxygen. The primary measures of benefit in the model were patient quality-adjusted life years (QALYs) accrued during and after hospitalization, cost per life years gained, cost per death avoided, and cost per use of mechanical ventilation avoided.

RESULTS

In the base-case payer perspective with a lifetime horizon, treatment with BARI-REM versus REM resulted in an incremental total cost of $7962, a gain of 0.446 life years and gain of 0.3565 QALYs over REM. The incremental cost-effectiveness ratios of using BARI-REM were estimated as $22,334 per QALY and $17,858 per life year. The base-case and sensitivity analyses showed that the total incremental cost per QALY falls within the reduced willingness-to-pay threshold of $50,000/QALY applied under health emergencies. In all hospitalized patients, treatment with BARI-REM versus REM reduced total hospital expenditures per patient by $1778 and total reimbursement payments by $1526, resulting in a $252 reduction in net costs per patient; it also resulted in a net gain of 0.0018 QALYs and increased survival of COVID-19 hospitalizations by 2.7%.

CONCLUSION

Our study showed that BARI-REM is cost-effective compared to using REM for hospitalized patients with COVID-19. The base-case results of this cost-effectiveness model were most sensitive to average annual medical costs for recovered patients.

Cost-Effectiveness of Baricitinib Compared With Standard of Care: A Modeling Study in Hospitalized Patients With COVID-19 in the United States

Purpose

In the Phase III COV-BARRIER (Efficacy and Safety of Baricitinib for the Treatment of Hospitalised Adults With COVID-19) trial, treatment with baricitinib, an oral selective Janus kinase 1/2 inhibitor, in addition to standard of care (SOC), was associated with significantly reduced mortality over 28 days in hospitalized patients with coronavirus disease–2019 (COVID-19), with a safety profile similar to that of SOC alone. This study assessed the cost-effectiveness of baricitinib + SOC versus SOC alone (which included systemic corticosteroids and remdesivir) in hospitalized patients with COVID-19 in the United States.

Methods

An economic model was developed to simulate inpatients' stay, discharge to postacute care, and recovery. Costs modeled included payor costs, hospital costs, and indirect costs. Benefits modeled included life-years (LYs) gained, quality-adjusted life-years (QALYs) gained, deaths avoided, and use of mechanical ventilation avoided. The primary analysis was performed from a payor perspective over a lifetime horizon; a secondary analysis was performed from a hospital perspective. The base-case analysis modeled the numeric differences in treatment effectiveness observed in the COV-BARRIER trial. Scenario analyses were also performed in which the clinical benefit of baricitinib was limited to the statistically significant reduction in mortality demonstrated in the trial.

Findings

In the base-case payor perspective model, an incremental total cost of 17,276 US dollars (USD), total QALYs gained of 0.6703, and total LYs gained of 0.837 were found with baricitinib + SOC compared with SOC alone. With the addition of baricitinib, survival was increased by 5.1% and the use of mechanical ventilation was reduced by 1.6%. The base-case incremental cost-effectiveness ratios were 25,774 USD/QALY gained and 20,638 USD/LY gained; a “mortality-only” scenario analysis yielded similar results of 26,862 USD/QALY gained and 21,433 USD/LY gained. From the hospital perspective, combination treatment with baricitinib + SOC was more effective and less costly than was SOC alone in the base case, with an incremental cost of 38,964 USD per death avoided in the mortality-only scenario.

Implications

In hospitalized patients with COVID-19 in the United States, the addition of baricitinib to SOC was cost-effective. Cost-effectiveness was demonstrated from both the payor and the hospital perspectives. These findings were robust to sensitivity analysis and to conservative assumptions limiting the clinical benefits of baricitinib to the statistically significant reduction in mortality demonstrated in the COV-BARRIER trial.

Cost-effectiveness analysis of text messaging to support health advice for smoking cessation

BACKGROUND

Smoking in one of the most serious public health problems. It is well known that it constitutes a major risk factor for chronic diseases and the leading cause of preventable death worldwide. Due to high prevalence of smokers, new cost-effective strategies seeking to increase smoking cessation rates are needed.

METHODS

We performed a Markov model-based cost-effectiveness analysis comparing two treatments: health advice provided by general practitioners and nurses in primary care, and health advice reinforced by sending motivational text messages to smokers' mobile phones. A Markov model was used in which smokers transitioned between three mutually exclusive health states (smoker, former smoker and dead) after 6-month cycles. We calculated the cost-effectiveness ratio associated with the sending of motivational messages. Health care and society perspectives (separately) was adopted. Costs taken into account were direct health care costs and direct health care cost and costs for lost productivity, respectively. Additionally, deterministic sensitivity analysis was performed modifying the probability of smoking cessation with each option.

RESULTS

Sending of text messages as a tool to support health advice was found to be cost-effective as it was associated with increases in costs of €7.4 and €1,327 per QALY gained (ICUR) for men and women respectively from a healthcare perspective, significantly far from the published cost-effectiveness threshold. From a societal perspective, the combined programmed was dominant.

CONCLUSIONS

Sending text messages is a cost-effective approach. These findings support the implantation of the combined program across primary care health centres.

"You can't always get what you want…": economic thinking, constrained optimization and health professions education

Every choice we make in health professions education has a cost, whether it be financial or otherwise; by choosing one action (e.g., integrating more simulation, studying more for a summative examination) we lose the opportunity to take an alternative action (e.g., freeing up time for other teaching, leisure time). Economics significantly shapes the way we behave and think as educators and learners and so there is increasing interest in using economic ways of thinking and approaches to examine and understand how choices are made, the influence of constraints and boundaries in educational decision making, and how costs are felt. Thus, in this article, we provide a brief historical overview of modern economics, to illustrate how the core concepts of economics-scarcity (and desirability), rationality, and optimization-developed over time. We explain the important concept of bounded rationality, which explains how individual, meso-factors and contextual factors influence decision making. We then consider the opportunities that these concepts afford for health professions education and research. We conclude by proposing that embracing economic thinking opens up new questions and new ways of approaching old questions which can add knowledge about how choice is enacted in contemporary health professions education.

Risk Prediction Scores for Mortality, Cerebrovascular, and Heart Disease Among Chinese People With Type 2 Diabetes

CONTEXT

Risk scores for cardiovascular and mortality outcomes have not been commonly applied in Chinese populations.

OBJECTIVE

To develop and externally validate a set of parsimonious risk scores [University of Hong Kong-Singapore (HKU-SG)] to predict the risk of mortality, cerebrovascular disease, and ischemic heart disease among Chinese people with type 2 diabetes and compare HKU-SG risk scores to other existing ones.

DESIGN

Retrospective population-based cohorts drawn from Hong Kong Hospital Authority health records from 2006 to 2014 for development and Singapore Ministry of Health records from 2008 to 2016 for validation. Separate five-year risk scores were derived using Cox proportional hazards models for each outcome.

SETTING

Study participants were adults with type 2 diabetes aged 20 years or over, consisting of 678,750 participants from Hong Kong and 386,425 participants from Singapore.

MAIN OUTCOME MEASURES

Performance was evaluated by discrimination (Harrell C-index), and calibration plots comparing predicted against observed risks.

RESULTS

All models had fair external discrimination. Among the risk scores for the diabetes population, ethnic-specific risk scores (HKU-SG and Joint Asia Diabetes Evaluation) performed better than UK Prospective Diabetes Study and Risk Equations for Complications Of type 2 Diabetes models. External validation of the HKU-SG risk scores for mortality, cerebrovascular disease, and ischemic heart disease had corresponding C-indices of 0.778, 0.695, and 0.644. The HKU-SG models appeared well calibrated on visual plots, with predicted risks closely matching observed risks.

CONCLUSIONS

The HKU-SG risk scores were developed and externally validated in two large Chinese population-based cohorts. The parsimonious use of clinical predictors compared with previous risk scores could allow wider implementation of risk estimation in diverse Chinese settings.

Cost-effectiveness of option B+ in prevention of mother-to-child transmission of HIV in Yunnan Province, China

BACKGROUND

Although Option B+ may be more costly than Options B, it may provide additional health benefits that are currently unclear in Yunnan province. We created deterministic models to estimate the cost-effectiveness of Option B+.

METHODS

Data were used in two deterministic models simulating a cohort of 2000 HIV+ pregnant women. A decision tree model simulated the number of averted infants infections and QALY acquired for infants in the PMTCT period for Options B and B+. The minimum cost was calculated. A Markov decision model simulated the number of maternal life year gained and serodiscordant partner infections averted in the ten years after PMTCT for Option B or B+. ICER per life year gained was calculated. Deterministic sensitivity analyses were conducted.

RESULTS

If fully implemented, Option B and Option B+ averted 1016.85 infections and acquired 588,01.02 QALYs.The cost of Option B was US$1,229,338.47, the cost of Option B+ was 1,176,128.63. However, when Options B and B+ were compared over ten years, Option B+ not only improved mothers'ten-year survival from 69.7 to 89.2%, saving more than 3890 life-years, but also averted 3068 HIV infections between serodiscordant partners. Option B+ yielded a favourable ICER of $32.99per QALY acquired in infants and $5149per life year gained in mothers. A 1% MTCT rate, a 90% coverage rate and a 20-year horizon could decrease the ICER per QALY acquired in children and LY gained in mothers.

CONCLUSIONS

Option B+ is a cost-effective treatment for comprehensive HIV prevention for infants and serodiscordant partners and life-long treatment for mothers in Yunnan province, China. Option B+ could be implemented in Yunnan province, especially as the goals of elimination mother-to-child transmission of HIV and "90-90-90" achieved, Option B+ would be more attractive.

A scoping review of simulation modeling in built environment and physical activity research: Current status, gaps, and future directions for improving translation

Existing reviews have suggested that simulation studies of physical activity and environments are an emerging area, but none have explored findings in this area systematically. We used a scoping review framework to assess the use of simulation modeling to inform decision-making about built environment influences on physical activity. A systematic literature search was conducted in multiple databases in January 2018. Sixteen articles met the inclusion criteria. The studies evaluated interventions and features that were related to neighborhood safety (crime or traffic), active transportation, land use design, and walking and biking infrastructure. All of the studies focused on urban areas and most considered heterogeneity of outcomes based on local context. The majority of studies (70%) did not appear to have engaged or been used by practitioners or policy-makers to inform real-world decisions. There has been a growth of simulation modeling studies, but there remain gaps. The studies evaluated built environment interventions that have been recommended by expert panels, but more were of interventions related to active transportation; few considered recommended interventions to support recreational activity. Furthermore, studies have all focused on urban settings and there is a need to consider non-urban settings and how heterogeneity could reduce or exacerbate health disparities. More work to involve and evaluate practices for engaging stakeholders in model development and interpretation is also needed to overcome the translation of simulation research to practice gap, and realize its potential impact on the built environment and physical activity.

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies.

What do we know about managing Dupuytren's disease cost-effectively?

BACKGROUND

Dupuytren's disease (DD) is a common and progressive, fibroproliferative disorder of the palmar and digital fascia of the hand. Various treatments have been recommended for advanced disease or to retard progression of early disease and to prevent deterioration of the finger contracture and quality of life. Recent studies have tried to evaluate the clinical and cost-effectiveness of therapies for DD, but there is currently no systematic assessment and appraisal of the economic evaluations.

METHODS

A systematic literature review was conducted, following PRISMA guidelines, to identify studies reporting economic evaluations of interventions for managing DD. Databases searched included the Ovid MEDLINE/Embase (without time restriction), National Health Service (NHS) Economic Evaluation Database (all years) and the National Institute for Health Research (NIHR) Journals Library) Health Technology Assessment (HTA). Cost-effectiveness analyses of treating DD were identified and their quality was assessed using the CHEERS assessment tool for quality of reporting and Phillips checklist for model evaluation.

RESULTS

A total of 103 studies were screened, of which 4 met the study inclusion criteria. Two studies were from the US, one from the UK and one from Canada. They all assessed the same interventions for advanced DD, namely collagenase Clostridium histolyticum injection, percutaneous needle fasciotomy and partial fasciectomy. All studies conducting a cost-utility analysis, two implemented a decision analytic model and two a Markov model approach. None of them were based on a single randomised controlled trial, but rather synthesised evidence from various sources. Studies varied in their time horizon, sources of utility estimates and perspective of analysis. The overall quality of study reporting was good based on the CHEERS checklist. The quality of the model reporting in terms of model structure, data synthesis and model consistency varied across the included studies.

CONCLUSION

Cost-effectiveness analyses for patients with advanced DD are limited and have applied different approaches with respect to modelling. Future studies should improve the way they are conducted and report their findings according to established guidance for conducting economic modelling of health care technologies.

TRIAL REGISTRATION

The protocol was registered ( CRD42016032989 ; date 08/01/2016) with the PROSPERO international prospective register of systematic reviews.

Methods to construct a step-by-step beginner's guide to decision analytic cost-effectiveness modeling

BACKGROUND

Although guidance on good research practice in health economic modeling is widely available, there is still a need for a simpler instructive resource which could guide a beginner modeler alongside modeling for the first time.

AIM

To develop a beginner's guide to be used as a handheld guide contemporaneous to the model development process.

METHODS

A systematic review of best practice guidelines was used to construct a framework of steps undertaken during the model development process. Focused methods review supplemented this framework. Consensus was obtained among a group of model developers to review and finalize the content of the preliminary beginner's guide. The final beginner's guide was used to develop cost-effectiveness models.

RESULTS

Thirty-two best practice guidelines were data extracted, synthesized, and critically evaluated to identify steps for model development, which formed a framework for the beginner's guide. Within five phases of model development, eight broad submethods were identified and 19 methodological reviews were conducted to develop the content of the draft beginner's guide. Two rounds of consensus agreement were undertaken to reach agreement on the final beginner's guide. To assess fitness for purpose (ease of use and completeness), models were developed independently and by the researcher using the beginner's guide.

CONCLUSION

A combination of systematic review, methods reviews, consensus agreement, and validation was used to construct a step-by-step beginner's guide for developing decision analytical cost-effectiveness models. The final beginner's guide is a step-by-step resource to accompany the model development process from understanding the problem to be modeled, model conceptualization, model implementation, and model checking through to reporting of the model results.

AdViSHE: A Validation-Assessment Tool of Health-Economic Models for Decision Makers and Model Users

BACKGROUND

A trade-off exists between building confidence in health-economic (HE) decision models and the use of scarce resources. We aimed to create a practical tool providing model users with a structured view into the validation status of HE decision models, to address this trade-off.

METHODS

A Delphi panel was organized, and was completed by a workshop during an international conference. The proposed tool was constructed iteratively based on comments from, and the discussion amongst, panellists. During the Delphi process, comments were solicited on the importance and feasibility of possible validation techniques for modellers, their relevance for decision makers, and the overall structure and formulation in the tool.

RESULTS

The panel consisted of 47 experts in HE modelling and HE decision making from various professional and international backgrounds. In addition, 50 discussants actively engaged in the discussion at the conference workshop and returned 19 questionnaires with additional comments. The final version consists of 13 items covering all relevant aspects of HE decision models: the conceptual model, the input data, the implemented software program, and the model outcomes.

CONCLUSIONS

Assessment of the Validation Status of Health-Economic decision models (AdViSHE) is a validation-assessment tool in which model developers report in a systematic way both on validation efforts performed and on their outcomes. Subsequently, model users can establish whether confidence in the model is justified or whether additional validation efforts should be undertaken. In this way, AdViSHE enhances transparency of the validation status of HE models and supports efficient model validation.

Febuxostat in the management of gout: a cost-effectiveness analysis

OBJECTIVE

To determine the cost-effectiveness of febuxostat vs allopurinol for the management of gout.

METHODS

A stochastic microsimulation cost-effectiveness model with a US private-payer perspective and 5-year time horizon was developed. Model flow based on guideline and real-world treatment paradigms incorporated gout flare, serum uric acid (sUA) testing, treatment titration, discontinuation, and adverse events, chronic kidney disease (CKD) incidence and progression, and type 2 diabetes mellitus (T2DM) incidence. Outcomes were estimated for the general gout population and for gout patients with CKD stages 3/4. Modeled treatment interventions were daily oral febuxostat 40-80 mg and allopurinol 100-300 mg. Baseline patient characteristics were taken from epidemiologic studies, efficacy data from randomized controlled trials, adverse event rates from package inserts, and costs from the literature, government sources, and expert opinion. Eight clinically-relevant incremental cost-effectiveness ratios were estimated: per patient reaching target sUA, per flare avoided, per CKD incidence, progression, stages 3/4 progression, and stage 5 progression avoided, per incident T2DM avoided, and per death avoided.

RESULTS

Five-year incremental cost-effectiveness ratios for the general gout population were $5377 per patient reaching target sUA, $1773 per flare avoided, $221,795 per incident CKD avoided, $29,063 per CKD progression avoided, $36,018 per progression to CKD 3/4 avoided, $71,426 per progression to CKD 5 avoided, $214,277 per incident T2DM avoided, and $217,971 per death avoided. In patients with CKD 3/4, febuxostat dominated allopurinol for all cost-effectiveness outcome measures.

CONCLUSIONS

Febuxostat may be a cost-effective alternative to allopurinol, especially for patients with CKD stages 3 or 4.

Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations

Background

In constructing or appraising a health economic model, an early consideration is whether the modelling approach selected is appropriate for the given decision problem. Frameworks and taxonomies that distinguish between modelling approaches can help make this decision more systematic and this study aims to identify and compare the decision frameworks proposed to date on this topic area.

Methods

A systematic review was conducted to identify frameworks from peer-reviewed and grey literature sources. The following databases were searched: OVID Medline and EMBASE; Wiley’s Cochrane Library and Health Economic Evaluation Database; PubMed; and ProQuest.

Results

Eight decision frameworks were identified, each focused on a different set of modelling approaches and employing a different collection of selection criterion. The selection criteria can be categorized as either: (i) structural features (i.e. technical elements that are factual in nature) or (ii) practical considerations (i.e. context-dependent attributes). The most commonly mentioned structural features were population resolution (i.e. aggregate vs. individual) and interactivity (i.e. static vs. dynamic). Furthermore, understanding the needs of the end-users and stakeholders was frequently incorporated as a criterion within these frameworks.

Conclusions

There is presently no universally-accepted framework for selecting an economic modelling approach. Rather, each highlights different criteria that may be of importance when determining whether a modelling approach is appropriate. Further discussion is thus necessary as the modelling approach selected will impact the validity of the underlying economic model and have downstream implications on its efficiency, transparency and relevance to decision-makers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1202-0) contains supplementary material, which is available to authorized users.

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.

Requirements for benefit assessment in Germany and England - overview and comparison

BACKGROUND

This study compared the methodological requirements for early health technology appraisal (HTA) by the Federal Joint Committee/Institute for Quality and Efficiency in Health Care (G-BA/IQWiG; Germany) and the National Institute for Health and Care Excellence (NICE; England).

METHODS

The following aspects were examined: guidance texts on methodology and information sources for the assessment; clinical study design and methodology; statistical analysis, quality of evidence base, extrapolation of results (modeling), and generalisability of study results; and categorisation of outcome.

RESULTS

There is some degree of similarity regarding basic methodological elements such as selection of information sources (e.g. preference of randomised controlled studies, RCTs) and quality assessment of the available evidence. Generally, the approach taken by NICE seems to be more open and less restrictive as compared with G-BA/IQWiG. Any kind of potentially relevant evidence is requested, including data from non-RCTs. Surrogate endpoints are also accepted more readily, if they are reasonably likely to predict clinical benefit. Modeling is expected to be performed wherever possible and appropriate, e.g. for study duration, patient population, choice of comparator, and type of outcomes. The resulting uncertainty is quantified through sensitivity analyses before making a recommendation regarding reimbursement. By contrast, G-BA/IQWiG bases its assessment and quantification of the additional benefit largely, if not exclusively, on evidence of the highest level and quality and on measurements of "hard" clinical endpoints. This more conservative approach rather firmly dismisses evidence from non-RCTs and measurements of surrogate endpoints that have not or only partly been validated. Moreover, neither qualitative extrapolation nor quantitative modeling of data is done.

CONCLUSIONS

Methodological requirements differed mainly in the acceptance of low-level evidence, surrogate endpoints, and data modeling. Some of the discrepancies may be explained, at least in part, by differences in the health care system and procedural aspects (e.g. timing of assessment).

Attempt to increase the transparency of fourth hurdle implementation in Central-Eastern European middle income countries: publication of the critical appraisal methodology

BACKGROUND

In middle income countries the number of trained health technology assessment specialists is limited and the public budget for health technology assessment is considerably lower compared to developed countries. These countries therefore must develop their own solutions to improve the quality and efficiency of health technology assessment implementation in reimbursement decisions. Our study aimed to develop a scientifically rigorous and detailed appraisal checklist for economic evaluations of pharmaceuticals in the single health technology assessment process.

METHODS

The research design entailed a review of economic evaluations, submitted for reimbursement of pharmaceuticals, by two independent academic reviewers to identify the most common methodological problems. Fifty economic evaluations submitted in 2007-2008, randomly selected by the Health Technology Assessment Office served as data sources. The new checklist was developed by an iterative working process: first by assessing ten economic evaluations, then improving the checklist by generating new question items, then employing the improved checklist to assess the next ten economic evaluations. After appraising 25 documents, the reviewers reconciled their opinions and improved the checklist with the researchers of the Health Technology Assessment Office during an expert panel discussion. The reviewers scrutinized the second 25 economic evaluations, after which the expert panel finalized the checklist with consensus.

RESULTS

The final checklist consists of 91 yes or no questions in 11 main topics concerning comparator selection, efficacy, effectiveness, costs, sensitivity analysis, methodological approach, transparency, and interpretation of results. The new checklist is based on current Hungarian evaluation practice. As the published checklist will be part of the official single health technology assessment process of pharmaceuticals, submitters will be able to assure the quality of their economic evaluation.

CONCLUSIONS

The transparent critical appraisal method should improve the consistency of pharmaceutical reimbursement decisions and facilitate the utilization of economic evaluations in other fields of health care decision-making in other Central-Eastern European countries.

A proposed model for economic evaluations of major depressive disorder

In countries like UK and Australia, the comparability of model-based analyses is an essential aspect of reimbursement decisions for new pharmaceuticals, medical services and technologies. Within disease areas, the use of models with alternative structures, type of modelling techniques and/or data sources for common parameters reduces the comparability of evaluations of alternative technologies for the same condition. The aim of this paper is to propose a decision analytic model to evaluate long-term costs and benefits of alternative management options in patients with depression. The structure of the proposed model is based on the natural history of depression and includes clinical events that are important from both clinical and economic perspectives. Considering its greater flexibility with respect to handling time, discrete event simulation (DES) is an appropriate simulation platform for modelling studies of depression. We argue that the proposed model can be used as a reference model in model-based studies of depression improving the quality and comparability of studies.

A critical review of model-based economic studies of depression: modelling techniques, model structure and data sources

Depression is the most common mental health disorder and is recognized as a chronic disease characterized by multiple acute episodes/relapses. Although modelling techniques play an increasingly important role in the economic evaluation of depression interventions, comparatively little attention has been paid to issues around modelling studies with a focus on potential biases. This, however, is important as different modelling approaches, variations in model structure and input parameters may produce different results, and hence different policy decisions. This paper presents a critical review of literature on recently published model-based cost-utility studies of depression. Taking depression as an illustrative example, through this review, we discuss a number of specific issues in relation to the use of decision-analytic models including the type of modelling techniques, structure of models and data sources. The potential benefits and limitations of each modelling technique are discussed and factors influencing the choice of modelling techniques are addressed. This review found that model-based studies of depression used various simulation techniques. We note that a discrete-event simulation may be the preferred technique for the economic evaluation of depression due to the greater flexibility with respect to handling time compared with other individual-based modelling techniques. Considering prognosis and management of depression, the structure of the reviewed models are discussed. We argue that a few reviewed models did not include some important structural aspects such as the possibility of relapse or the increased risk of suicide in patients with depression. Finally, the appropriateness of data sources used to estimate input parameters with a focus on transition probabilities is addressed. We argue that the above issues can potentially bias results and reduce the comparability of economic evaluations.

General population versus disease-specific event rate and cost estimates: potential bias for economic appraisals

Economic appraisals are increasingly being used for reimbursement decision making. Differences exist in the population data sources used in different studies and these differences may result in errors or biased estimates. A review of the literature suggests that very little has been written on this topic and guidelines and good practice documents are silent on the issue. Using illustrative examples, it was found that the population chosen for event/complication costing did not have a large impact on a cost-effectiveness analysis; however, the choice of population did have a large impact for cost-of-illness (COI) estimation. It was found that not controlling for event/complication rates in a nondiseased population resulted in a 15% inflated COI estimate and using event costs from the general population underestimated COI by 20-32%. Our analysis suggests that using event costs from the general population instead of a diseased population may not have a significant impact on cost-effectiveness estimates; however, COI studies should only use excess event/complication rates and should also only use event costs from populations with the disease.

Exploring Model Uncertainty in Economic Evaluation of Health Interventions: The Example of Rotavirus Vaccination in Vietnam

Objective . Motivated by observed discrepancies between 2 published studies on the cost-effectiveness of rotavirus vaccination in Vietnam, the authors’ objectives were to illustrate a specific, systematic approach to assessing model (structure and process) uncertainty and to quantify explicitly the contributions of different sources of variation in the outputs of different studies that share the same research question. Methods . On the basis of a series of working definitions of key model elements, the authors developed 5 alternative computer simulation (state-transition) models of rotavirus disease. They examined how epidemiological outcomes and cost-effectiveness ratios associated with rotavirus vaccination would change as elements of model structure and modeling process were progressively modified. They also explicitly decomposed the relative contributions of different modeling elements to differences in the cost-effectiveness results between the 2 previous analyses motivating the present study. Results . The findings suggest that within the category of a static, deterministic, aggregate-level model, different choices in model structure and process lead to relatively modest differences in the estimated cost-effectiveness of rotavirus vaccination, but that intermediate epidemiologic outcomes vary more substantially depending on the choice of model structure. Conclusions . The authors caution against generalizing the quantitative results in this study beyond the present example but suggest that the approach presented here may serve as a template for other examinations of model uncertainty. As new research questions arise after the introduction of rotavirus vaccination programs, a reevaluation of model uncertainty is likely to be needed.

A systematic review comparing the relative effectiveness of antimicrobial-coated catheters in intensive care units

BACKGROUND

Bloodstream infection related to a central venous catheter is a substantial clinical and economic problem. To develop policy for managing the risks of these infections, all available evidence for prevention strategies should be synthesized and understood.

METHODS

We evaluate evidence (1985-2006) for short-term antimicrobial-coated central venous catheters in lowering rates of catheter-related bloodstream infection (CRBSI) in the adult intensive care unit. Evidence was appraised for inclusion against predefined criteria. Data extraction was by 2 independent reviewers. Thirty-four studies were included in the review. Antiseptic, antibiotic, and heparin-coated catheters were compared with uncoated catheters and one another. Metaanalysis was used to generate summary relative risks for CRBSI and catheter colonization by antimicrobial coating.

RESULTS

Externally impregnated chlorhexidine/silver sulfadiazine catheters reduce risk of CRBSI relative to uncoated catheters (RR, 0.66; 95% CI: 0.47-0.93). Minocycline and rifampicin-coated catheters are significantly more effective relative to CHG/SSD catheters (RR, 0.12; 95% CI: 0.02-0.67). The new generation chlorhexidine/silver sulfadiazine catheters and silver, platinum, and carbon-coated catheters showed nonsignificant reductions in risk of CRBSI compared with uncoated catheters.

CONCLUSION

Two decades of evidence describe the effectiveness of antimicrobial catheters in preventing CRBSI and provide useful information about which catheters are most effective. Questions surrounding their routine use will require supplementation of this trial evidence with information from more diverse sources.

Informing the efficient use of health care and health care research resources - the case of screening for abdominal aortic aneurysm in Sweden

BACKGROUND

An analytical framework using Bayesian decision theory and value-of-information analysis has recently been advocated for the economic evaluation of health technologies. The purpose of this study was to apply this framework to screening for abdominal aortic aneurysm (AAA) in Sweden and to compare the conclusions from this study with the conclusions presented in an assessment performed by the Swedish Council of Technology Assessment (SBU).

METHODS

A probabilistic decision-analytical model was developed to establish the cost-effectiveness of a screening programme for AAA relative to current clinical practice and to calculate the value-of-information.

RESULTS

The cost per quality-adjusted life-year for screening was 9700 euro. The expected value of perfect information for the assessment of overall cost-effectiveness was low, suggesting little benefit in conducting further research. Expected value of perfect partial information indicated that rupture probabilities were associated with the highest uncertainty. By contrast, the SBU report concluded there was limited evidence of cost-effectiveness and proposed further research.

CONCLUSION

The investigated screening programme for AAA is likely to be cost-effective and conducting another clinical trial is unlikely to add much valuable information to this decision problem. These recommendations contrast with the vaguer recommendations from SBU that more evidence is required of costs-effectiveness.

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.

Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment

The use of decision-analytic modelling for the purpose of health technology assessment (HTA) has increased dramatically in recent years. Several guidelines for best practice have emerged in the literature; however, there is no agreed standard for what constitutes a 'good model' or how models should be formally assessed. The objective of this paper is to identify, review and consolidate existing guidelines on the use of decision-analytic modelling for the purpose of HTA and to develop a consistent framework against which the quality of models may be assessed. The review and resultant framework are summarised under the three key themes of Structure, Data and Consistency. 'Structural' aspects relate to the scope and mathematical structure of the model including the strategies under evaluation. Issues covered under the general heading of 'Data' include data identification methods and how uncertainty should be addressed. 'Consistency' relates to the overall quality of the model. The review of existing guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, such as the need for transparency, they are contradictory in other areas. Particular areas of disagreement are how data should be incorporated into models and how uncertainty should be assessed. For the purpose of evaluation, the resultant framework is applied to a decision-analytic model developed as part of an appraisal for the National Institute for Health and Clinical Excellence (NICE) in the UK. As a further assessment, the review based on the framework is compared with an assessment provided by an independent experienced modeller not using the framework. It is hoped that the framework developed here may form part of the appraisals process for assessment bodies such as NICE and decision models submitted to peer review journals. However, given the speed with which decision-modelling methodology advances, there is a need for its continual update.

Populating decision-analytic models: The feasibility and efficiency of database searching for individual parameters

Objectives: The aim of the study was to investigate the feasibility and effectiveness of searching selected databases to identify information required to populate a decision-analytic model.

Methods: Methods of searching for information to populate a decision-analytic model were piloted using a case study of prophylactic antibiotics to prevent recurrent urinary tract infections in children. This study explored how the information requirements for a decision-analytic model could be developed into searchable questions and how search strategies could be derived to answer these questions. The study also assessed the usefulness of three published search filters and explored which resources might produce relevant information for the various model parameters.

Results: Based on the data requirements for this case study, 42 questions were developed for searching. These questions related to baseline event rates, health-related quality of life and outcomes, relative treatment effects, resource use and unit costs, and antibiotic resistance. A total of 1,237 records were assessed by the modeler, and of these, 48 were found to be relevant to the model. Search precision ranged from 0 percent to 38 percent, and no single database proved the most useful for all the questions.

Conclusions: The process of conducting specific searches to address each of the model questions provided information that was useful in populating the case study model. The most appropriate resources to search were dependent on the question, and multiple database searching using focused search strategies may prove more effective in finding relevant data than thorough searches of a single database.

A comprehensive natural history model of HPV infection and cervical cancer to estimate the clinical impact of a prophylactic HPV-16/18 vaccine

The object of our study is to project the impact of a prophylactic vaccine against persistent human papillomavirus (HPV)-16/18 infection on age-specific incidence of invasive cervical cancer. We developed a computer-based mathematical model of the natural history of cervical carcinogenesis to incorporate the underlying type-specific HPV distribution within precancerous lesions and invasive cancer. After defining plausible ranges for each parameter based on a comprehensive literature review, the model was calibrated to the best available population-based data. We projected the age-specific reduction in cervical cancer that would occur with a vaccine that reduced the probability of acquiring persistent infection with HPV 16/18, and explored the impact of alternative assumptions about vaccine efficacy and coverage, waning immunity and competing risks associated with non-16/18 HPV types in vaccinated women. The model predicted a peak age-specific cancer incidence of 90 per 100,000 in the 6th decade, a lifetime cancer risk of 3.7% and a reproducible representation of type-specific HPV within low and high-grade cervical precancerous lesions and cervical cancer. A vaccine that prevented 98% of persistent HPV 16/18 was associated with an approximate equivalent reduction in 16/18-associated cancer and a 51% reduction in total cervical cancer; the effect on total cancer was attenuated due to the competing risks associated with other oncogenic non-16/18 types. A vaccine that prevented 75% of persistent HPV 16/18 was associated with a 70% to 83% reduction in HPV-16/18 cancer cases. Similar effects were observed with high-grade squamous intraepithelial lesions (HSIL) although the impact of vaccination on the overall prevalence of HPV and low-grade squamous intraepithelial lesions (LSIL) was minimal. In conclusion, a prophylactic vaccine that prevents persistent HPV-16/18 infection can be expected to significantly reduce HPV-16/18-associated LSIL, HSIL and cervical cancer. The impact on overall prevalence of HPV or LSIL, however, may be minimal. Based on the relative importance of different parameters in the model, several priorities for future research were identified. These include a better understanding of the heterogeneity of vaccine response, the effect of type-specific vaccination on other HPV types and the degree to which vaccination effect persists over time.

Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation

Markov models have traditionally been used to evaluate the cost-effectiveness of competing health care technologies that require the description of patient pathways over extended time horizons. Discrete event simulation (DES) is a more flexible, but more complicated decision modelling technique, that can also be used to model extended time horizons. Through the application of a Markov process and a DES model to an economic evaluation comparing alternative adjuvant therapies for early breast cancer, this paper compares the respective processes and outputs of these alternative modelling techniques. DES displays increased flexibility in two broad areas, though the outputs from the two modelling techniques were similar. These results indicate that the use of DES may be beneficial only when the available data demonstrates particular characteristics.

Selecting methods for the prediction of future events in cost-effectiveness models: a decision-framework and example from the cardiovascular field

Evidence on the cost-effectiveness of healthcare interventions is increasingly required by decision-makers. Economic models can provide timely information on the long-term impact of new technologies. However, models have been criticised because of the implicit assumptions they make, in particular the methods used to extrapolate data are rarely documented. This paper presents a systematic process for choosing a method of predicting events in economic models. This process is illustrated using a model examining the cost-effectiveness of a new HMG-CoA reductase inhibitor (statin) for primary prevention of cardiovascular disease (CVD). The prediction of future CVD events is a central component of the model, and the choice of method for predicting events was an important issue in the model's development. A literature review identified 11 studies with the information required to predict CVD events. A set of criteria were developed to assess the different methods of risk estimation, covering issues like scientific validity and acceptability to decision-makers. Risk equations derived from the Framingham Heart Study were found to be most suitable for predicting future events in the economic model. The paper illustrates how the development of economic models can be made more transparent, and suggests that the process outlined may be applied to other disease areas where there are several event prediction methods to choose from. In disease areas where published methods for predicting events are not available, the process outlined can make the uncertainty this leads to explicit, and highlight where further research is required. Such transparency can help decision-makers understand the scientific basis underpinning models, and therefore make these models more acceptable and useful for health policy-making.

A potential error in evaluating cancer screening: a comparison of 2 approaches for modeling underlying disease progression

BACKGROUND

Evaluating cancer screening often requires modeling the underlying disease process and not observed disease, particularly in the absence of direct evidence linking screening to a survival benefit.

METHODS

To illustrate a potential error in modeling disease progression among healthy persons with a history of a precancerous lesion, we constructed 2 models with 4 basic health states (disease free, presence of a precancerous lesion, presence of cancer, dead), calibrated to predict the same 10-year cancer incidence. We assumed a homogeneous cohort enters each model free of disease, the probability of developing a precancerous lesion was greater for patients with a history of a prior lesion, and the screening test was perfect and riskless. In one model, we assigned a higher transition probability from a precancerous lesion to cancer in those with a history of a previously removed lesion; in the other, we assumed it was equal to those with no history.

RESULTS

Using the 1st model, life expectancy without screening was 2.4 months longer than with screening. This error did not occur using the 2nd model, in which the transition from precancerous lesions to cancer was not conditional on a history of a lesion. This modeling error's magnitude was examined under a variety of assumptions.

CONCLUSIONS

We have identified an important error to avoid when modeling the underlying disease process in evaluating screening programs for cancers associated with precancerous states.

Cost-effectiveness analysis of predischarge monitoring for apnea of prematurity

OBJECTIVE

It is standard practice to defer discharge of premature infants until they have achieved a set number of days without experiencing apnea. The duration of this period, however, is highly variable across institutions, and there is scant literature on its effectiveness or value-for-money. Our objective was to establish the economic impact of varying durations of predischarge observation for apnea of prematurity.

METHODS

Using computer simulation, we compared the alternatives of hospital monitoring for 1 to 10 days, after apparent cessation of apnea, with no monitoring and with the next longest period of monitoring. The daily probability of apnea requiring stimulation after a given number of apnea-free days was obtained from chart review of 216 infants, beginning on the day they attained both full feeds and temperature stability in an open crib. Baseline rates of survival or impairment, utilities for calculation of quality-adjusted life years (QALYs), outcomes for respiratory arrest at home, and long-run costs for neurodevelopmental impairment were derived from the literature. Hospital expenditures were obtained from itemized billing records for infants on each of the final 10 days of hospitalization and converted to costs using Medicare cost-to-charge ratios. Costs are reported in 2000 US dollars.

RESULTS

For infants born at 24 to 26 weeks' gestation, each additional day of monitoring cost from $41000 per QALY saved for the first day to >$130000 per additional QALY gained for the tenth day. Cost-effectiveness was poorer for infants who were born at gestational ages >30 weeks. Results were sensitive to the proportion of charted apneas requiring stimulation that would actually progress, without intervention, to respiratory arrest.

CONCLUSIONS

In this model, the cost-effectiveness of predischarge monitoring for apnea of prematurity declined significantly as the duration of monitoring was increased. Consideration should be given to alternative uses for resources in formulating neonatal discharge guidelines.

Health economics and cervical cancer prevention: a global perspective

Recent scientific advances are providing an opportunity to revisit strategies for cervical cancer prevention. How to invest health resources wisely, such that clinical benefits are maximized-and opportunity costs are minimized-is a critical question in the setting of new technology. In addition to an intervention's effectiveness, public health decision-making requires consideration of its feasibility, sustainability and affordability. No clinical trial or single cohort study will be able to simultaneously consider all of these components. A mathematical simulation model can be a useful tool with which to evaluate alternative cervical cancer control strategies by extending the knowledge from empirical studies to real-world situations. Models combine information about the natural history of disease with other relevant demographic, epidemiological, and economic characteristics. We describe a comprehensive Cervical Cancer Policy Model with a flexible structure that may be modified as new data on the biology of disease become available. This model provides an analytic framework to synthesize data on costs and benefits, to help design clinical guidelines, and to inform development of sound health policy. Examples of cost-effectiveness analyses conducted in the US and South Africa illustrate inevitable tradeoffs when choosing among a variety of interventions to decrease cervical cancer mortality, and demonstrate how these methods can facilitate a bridge between research and health policy.

Development of a decision-analytic model of stroke care in the United States and Europe

OBJECTIVE

Stroke places a huge burden on society in terms of premature death, disability, and costs of care. Increasingly, the cost-effectiveness of new interventions needs to be demonstrated before their widespread implementation. Clinical trials are unable to measure the long-term impact of such new interventions in stroke care, and a modeling approach is necessary. The Stroke Outcome Model has been developed in four countries: France, Germany, the United Kingdom, and the United States as a flexible tool for this purpose.

METHOD

The decision-analytic model represents the management of acute stroke and long-term care and prevention of recurrence for stroke survivors. The latter consists of semi-Markov state-transition processes, with health states defined by therapy, disability, and occurrence of further stroke. Sources of clinical data include trials, meta-analyses, and prospective cohort studies such as the Oxfordshire Community Stroke Project and the Northern Manhattan Stroke Study. Resource use data were obtained from published sources and expert clinician panels. Outcome measures used were strokes averted, life years, and quality-adjusted life-years gained.

RESULTS

The model has been used to undertake economic analyses of antiplatelet therapy for the prevention of recurrent strokes, and of stroke unit care and thrombolytic therapy in acute stroke. From a health- and social-care perspective, new interventions were found to be cost saving or to provide health benefits at modest additional cost. Results were sensitive to the cost perspective, time horizon, baseline risk of stroke recurrence, and choice of effectiveness measure.

CONCLUSION

The development of this model highlights the need for improved information on prognosis and resources used by stroke survivors and the importance of differentiating between economically distinct end points such as death, disabled survival and nondisabled survival, which may be combined as outcomes in clinical trials.

Tamoxifen plus chemotherapy versus tamoxifen alone as adjuvant therapies for node-positive postmenopausal women with early breast cancer: a stochastic economic evaluation

BACKGROUND

There remains uncertainty around the appropriate choice of adjuvant therapies to offer postmenopausal women with node-positive early breast cancer.

OBJECTIVE AND STUDY DESIGN

To present the results derived from a discrete event simulation (DES) model that compared tamoxifen plus chemotherapy versus tamoxifen alone in node-positive postmenopausal women diagnosed with early breast cancer.

METHODS

The data populating the model were mainly derived from the existing literature, which was analysed to specify probability distributions describing the uncertainty around the true value of each input parameter. The specified probability distributions facilitated the stochastic analysis of the decision model, whereby distributions of the model's outputs [aggregate costs and quality-adjusted life years (QALYs)] were estimated.

RESULTS

The baseline results show that the addition of chemotherapy to tamoxifen in this patient group is relatively cost effective (under pound 4000 per additional QALY), but the distribution of the incremental cost-effectiveness ratio shows a wide range, including 10% of observations in which tamoxifen dominates tamoxifen plus chemotherapy.

CONCLUSIONS

The results demonstrate the intuitive nature of stochastic evaluations of healthcare technologies, which may ease decision-makers' interpretation of cost-effectiveness results.

Development of the health and economic consequences of smoking interactive model

OBJECTIVE

To describe the health and economic consequences of smoking model, a user friendly, web based tool, designed to estimate the health and economic outcomes associated with smoking and the benefits of smoking cessation.

RESULTS

An overview of the development of the model equations and user interface is given, and data from the UK are presented as an example of the model outputs. These results show that a typical smoking cessation strategy costs approximately 1200 pounds sterling per life year saved and 22,000 pounds sterling per death averted.

CONCLUSIONS

The model successfully captures the complexity required to model smoking behaviour and associated mortality, morbidity, and health care costs. Furthermore, the interface provides the results in a simple and flexible way so as to be useful to a variety of audiences and to simulate a variety of smoking cessation methods.

The economics of selective serotonin reuptake inhibitors in depression: a critical review

The prevalence of depression and the high costs associated with its treatment have increased interest in pharmacoeconomic evaluations of drug treatment, particularly in the 1990s as the use of selective serotonin (5-hydroxytryptamine; 5-HT) reuptake inhibitors (SSRIs) expanded substantially. This review presents results from specific studies representing the key study designs used to address the pharmacoeconomics of SSRI use: retrospective administrative database analyses, clinical decision analysis models, and randomised clinical trials. Methodological considerations in interpreting results are highlighted. In retrospective administrative database analyses, most comparisons have been made between SSRIs and tricyclic antidepressants (TCAs). A few studies have addressed differences between SSRIs. The studies focused on healthcare cost (to payer) and cost-related outcomes (e.g. treatment duration, drug switching). Although SSRIs are generally associated with higher drug acquisition costs than are TCAs, total healthcare costs are at least offset, if not decreased, by reductions in costs associated with use of SSRIs. Although studies from the early 1990s show some advantage for fluoxetine, the results are limited by use of data from shortly after the introduction of paroxetine and sertraline; studies from the mid- 1990s on that compare drugs within the SSRI class show general equivalence in terms of cost. Important methodological advances are occurring in retrospective studies, with selection bias and other design limitations being addressed statistically. Clinical decision analysis models permit flexibility in terms of ability to specify different alternative treatment scenarios and varying durations. Sensitivity analysis aids interpretability, although model inputs are limited by data availability. Results from short term (1 year duration or less) studies comparing SSRIs and TCAs suggest that SSRIs are more cost effective or that there is no difference. Longer term studies (lifetime Markov models) focus more on the impact of maintenance antidepressant therapy and show more mixed results, generally favouring SSRIs over TCAs. The results indicate that the effect of SSRIs is mainly through prevention of relapse. Important assumptions of these models include fewer serious adverse effects and lower treatment discontinuation rates with SSRIs. Naturalistic clinical trials provide greater generalisability than traditional randomised clinical trials. One naturalistic trial found that nearly half of TCA-treated patients switched to another antidepressant within 6 months; only 20% of SSRI-treated patients switched. Cost differences between groups were minimal. These studies indicate few differences in medical costs, depression outcomes and health-related quality of life between TCAs and fluoxetine, although fewer fluoxetine-treated patients switched treatment.

Expert judgement in pharmacoeconomic studies. Guidance and future use

Research in the field of pharmacoeconomics has increased substantially during the past decade. Much of this research has been on the design and analysis of data concerning the relative merits of one drug or device compared with another in terms of costs and effects. Concomitant with these evaluations has been the development of guidelines for the conduct of economic evaluations in several countries. However, despite an increase in research, little attention has been paid to how different study designs may influence the results of a study. The use of expert judgement in decision analytic modelling is one area where design issues may influence the findings of a study. This issue is examined for the case of modified Delphi and Delphi panels. Although the use of expert opinion in modelling studies seems to be widespread, there is little consistent application, understanding or reporting of the techniques used. In particular, the definitions of techniques vary between studies, the criteria for determining when consensus is reached vary, and the reporting of these criteria is absent. Future studies using expert judgement should be more aware of the controversies surrounding the issue and provide more reporting of the techniques used. It is proposed that future validation exercises may assist researchers in determining the most appropriate application of methods.

Assessing quality in decision analytic cost-effectiveness models. A suggested framework and example of application

Despite the growing use of decision analytic modelling in cost-effectiveness analysis, there is a relatively small literature on what constitutes good practice in decision analysis. The aim of this paper is to consider the concept of 'validity' and 'quality' in this area of evaluation, and to suggest a framework by which quality can be demonstrated on the part of the analyst and assessed by the reviewer and user. The paper begins by considering the purpose of cost-effectiveness models and argues that the their role is to identify optimum treatment decisions in the context of uncertainty about future states of the world. The issue of whether such models can be defined as 'scientific' is considered. The notion that decision analysis undertaken at time t can only be considered scientific if its outputs closely predict the results of a trial undertaken at time t + 1 is rejected as this ignores the need to make decisions on the basis of currently available evidence. Rather, the scientific characteristic of decision models is based on the fact that, in principle at least, such analyses can be falsified by comparison of two states of the world, one where resource allocation decisions are based on formal decision analysis and the other where such decisions are not. This section of the paper also rejects the idea of exact codification of scientific method in general, and of decision analysis in particular, as this risks rejecting potentially valuable models, may discourage the development of novel methods and can distort research priorities. However, the paper argues that it is both possible and necessary to develop a framework for assessing quality in decision models. Building on earlier work, various dimensions of quality in decision modelling are considered: model structure (disease states, options, time horizon and cycle length); data (identification, incorporation, handling uncertainty); and consistency (internal and external). Within this taxonomy a (nonexhaustive) list of questions about quality is suggested which are illustrated by their application to a specific published model. The paper argues that such a framework can never be prescriptive about every aspect of decision modelling. Rather, it should encourage the analyst to provide an explicit and comprehensive justification of their methods, and allow the user of the model to make an informed judgment about the relevance, coherence and usefulness of the analysis.

Testing the validity of cost-effectiveness models

A growing body of recent work has identified several problems with economic evaluations undertaken alongside controlled trials that can have potentially serious impacts on the ability of decision makers to draw valid conclusions. At the same time, the use of cost-effectiveness models has been drawn into question, due to the alleged arbitrary nature of their construction. This has led researchers to try and identify ways of improving the quality of cost-effectiveness models through identifying 'best practice', producing guidelines for peer review and identifying tests of validity. This paper investigates the issue of testing the validity of cost-effectiveness models or, perhaps more appropriately, whether it is possible to objectively measure the quality of a cost-effectiveness model. A review of the literature shows that there is much confusion over the different aspects of modelling that should be assessed in respect to model quality, and how this should be done. We develop a framework for assessing model quality in terms of: (i) the structure of the model; (ii) the inputs of the model; (iii) the results of the model; and (iv) the value of the model to the decision maker. Quality assessment is investigated within this framework, and it is argued that it is doubtful that a set of objective tests of validity will ever be produced, or indeed that such an approach would be desirable. The lack of any clearly definable and objective tests of validity means that the other parts of the evaluation process need to be given greater emphasis. Quality assurance forms a small part of a broader process and is best implemented in the form of good practice guidelines. A set of key guidelines are presented.

Cost-effectiveness of cesarean section delivery to prevent mother-to-child transmission of HIV-1

OBJECTIVE

To evaluate costs and outcomes of cesarean section performed before onset of labor and before rupture of membranes (elective cesarean section) compared to vaginal delivery among HIV-infected women.

DESIGN

Cost-effectiveness and cost-benefit analysis.

PARTICIPANTS AND SETTING

Pregnant HIV-infected women in the US who refrain from breastfeeding.

INTERVENTION

Elective cesarean section versus vaginal delivery by antiretroviral therapy regimen.

MAIN OUTCOME MEASURES

Pediatric HIV cases avoided, years of life saved, and direct medical costs for maternal interventions and pediatric HIV treatment.

RESULTS

Elective cesarean section (versus vaginal delivery) was cost-effective among women receiving zidovudine prophylaxis (US$1131 per case avoided, US$17 per year of life saved) and combination antiretroviral therapy (US$112693 per case avoided, US$1697 per year of life saved), and cost saving among women receiving no antiretroviral therapy during pregnancy (benefit-cost ratio of 2.23). Although elective cesarean section remained cost-effective, results were sensitive to variations in vertical transmission rates and to pediatric HIV treatment costs. Population-based analyses indicated that elective cesarean section could prevent 239 pediatric HIV cases annually with a savings of over US$4 million.

CONCLUSIONS

Elective cesarean section is a cost-effective intervention to prevent vertical transmission of HIV among women receiving various antiretroviral therapy regimens, who refrain from breastfeeding.

Modeling economic evaluations of pharmaceuticals: manipulation or valuable tool?

Controversy surrounds the use of models in economic evaluations of pharmaceuticals. Many believe that modeling is a way of manipulating results and is not credible, whereas others consider modeling a valuable tool in economic evaluations. The purpose of this article is to provide a historical perspective on modeling, focus on the controversy and policy implications of using models, and review the suggested framework and guidelines for modeling practices. Models can be used to extrapolate beyond intermediate end points, predict costs and consequences of alternative therapies, generalize data to other settings, pose questions instead of providing answers when no data exist, design an evaluation to reduce uncertainty, and perform direct comparisons that are not currently available. We believe that a useful model should document the detailed inner workings, assumptions, and inherent bias during production (and at publication time), so that its reviewers and users can evaluate the appropriateness of the model's outcomes. The acceptability of models in the future rests with the researchers constructing them. If constructed appropriately, modeling economic evaluations is not a manipulation but rather a valuable tool.