Challenges of Incorporating Life Cycle Drug Pricing in Cost-Effectiveness Models: A Review of Methods and Modeling Suggestions

OBJECTIVES

This study aimed to conduct a review of existing methods used to incorporate life cycle drug pricing (LCDP) in cost-effectiveness analyses (CEAs), identify common methodological challenges, and suggest modeling approaches for prospectively implementing LCDP in CEA.

METHODS

Two complementary searches were conducted in PubMed, combined with hand searching and reference mining, to identify English language full-text articles that explored (1) how drug prices change over time and (2) methods used to apply dynamic pricing in cost-effectiveness models (CEMs). Relevant articles were reviewed, and authors discussed the common methodological practices used in the literature and their associated challenges on prospectively implementing LCDP in CEMs. For each key challenge identified, we provide modeling suggestions to address the issue.

RESULTS

We screened 1200 studies based on title and abstract; 117 were reviewed for eligibility, and 47 individual studies were included across both searches. Variations in prices over a product's life cycle are complex and multifactorial, and models applying LCDP in CEA varied in their methodology. We identified 4 key challenges to modeling LCDP in CEA, including how to model price trends before and after loss of exclusivity, how to capture the effect of price changes on future patient cohorts, and how to report results.

CONCLUSION

Accurately quantifying the impact of LCDP requires careful consideration of multiple aspects pertaining to both the evolution of drug prices and how to reflect these in CEA. Although uncertainties remain, our findings can aid implementation and evaluation of LCDP in economic evaluations.

The Effect of the Drug Life Cycle Price on Cost-Effectiveness: Case Studies Using Real-World Pricing Data

OBJECTIVES

Cost-effectiveness analyses (CEAs) generally assume constant drug prices throughout the model time horizon, yet it is known that prices are not constant, often with price decreases near loss of exclusivity (LOE). This study explores the impact of using dynamic drug-specific prices on the incremental cost-effectiveness ratio (ICER) using selected reproduced case studies.

METHODS

Case studies were selected following explicit criteria to reflect a variety of drug characteristics. For each drug, a published CEA model was identified, replicated, and modified with dynamic real-world pricing data, to compare ICERs based on constant drug prices with estimates obtained when including drug life cycle pricing. The impact of dynamic real-world pricing-inclusive LOE-was analyzed using a single patient cohort and multiple cohorts over time.

RESULTS

Fluvastatin, alendronic acid + colecalciferol combination therapy, letrozole and clopidogrel were selected as case studies. Inclusion of real-world pricing data compared with applying constant prices reduced the ICER in a single-cohort setting up to 43%. In the multicohort analyses, further reductions of the ICERs were observed of up to 113%. The ICERs were sensitive to the period of drug usage relative to the models' time horizons, the relative proportions of drug costs in the overall treatment costs, and timing of LOE compared with the cost year of the original analysis.

CONCLUSIONS

Assuming dynamic drug prices may lead to more representative ICER estimates. Future CEAs for drugs could account for predicted and disaggregated life cycle price developments based on retrospective data.

Risk Stratification in Cost-Effectiveness Analyses of Cancer Screening: Intervention Eligibility, Strategy Choice, and Optimality

Introduction

There is increasing interest in risk-stratified approaches to cancer screening in cost-effectiveness analysis (CEA). Current CEA practice regarding risk stratification is heterogeneous and guidance on the best approach is lacking. This article suggests how stratification in CEA can be improved.

Methods

I use a simple example of a hypothetical screening intervention with 3 potential recipient risk strata. The screening intervention has 6 alternative intensities, each with different costs and effects, all of which vary between strata. I consider a series of alternative stratification approaches, demonstrating the consequences for estimated costs, effects, and the choice of optimal strategy. I supplement this analysis with applied examples from the literature.

Results

Adopting the same screening policy for all strata yields the least efficient strategies, where efficiency is understood as the volume of net health benefit generated across a range of cost-effectiveness threshold values. Basic stratification that withholds screening from lower-risk strata while adopting a common strategy for those screened increases efficiency. Greatest efficiency is achieved when different strata receive separate strategies. While complete optimization can be achieved within a single analysis by considering all possible policy combinations, the resulting number of strategy combinations may be inconveniently large. Optimization with separate strata-specific analyses is simpler and more transparent. Despite this, there can be good reasons to simulate all strata together in a single analysis.

Conclusions

If the benefits of risk stratification are to be fully realized, policy makers need to consider the extent to which stratification is feasible, and modelers need to simulate those choices adequately. It is hoped this analysis will clarify those policy and modeling choices and therefore lead to improved population health outcomes.

Interpreting cost-effectiveness ratios in a cost-effectiveness analysis of risk-tailored prostate screening: A critique of Callender et al

Callender et al. recently published a model-based cost-effectiveness analysis of a risk-tailored approach to prostate cancer screening. It considers the costs and effects of prostate cancer screening offered to all men aged 55-69 without any risk selection and, alternatively, over a range of risk-tailored strategies in which screen eligibility is determined by a varying threshold of disease risk. The analysis finds that the strategy of screening men once they reach a 10-year absolute risk of disease of 5% or more is cost-effective in a UK context. I believe there are several problems with the study, mostly stemming from an incorrect interpretation of the cost-effectiveness estimates. I show that one reinterpretation of their results indicates that screening is much less cost-effective than the original analysis suggests, indicating that screening should be restricted to a much smaller group of higher risk men. More broadly, I explain the challenges of attempting to meaningfully reinterpret the originally published results due to the simulation of non-mutually exclusive intervention strategies. Finally, I consider the relevance of considering sufficient alternative screening intensities. This critique highlights the need for appropriate interpretation of cost-effectiveness results for policymakers, especially as risk stratification within screening becomes increasingly feasible.

Interpreting cost-effectiveness ratios in a cost-effectiveness analysis of risk-tailored prostate screening: A critique of Callender et al

Callender et al. recently published a model-based cost-effectiveness analysis of a risk-tailored approach to prostate cancer screening. It considers the costs and effects of prostate cancer screening offered to all men aged 55-69 without any risk selection and, alternatively, over a range of risk-tailored strategies in which screen eligibility is determined by a varying threshold of disease risk. The analysis finds that the strategy of screening men once they reach a 10-year absolute risk of disease of 5% or more is cost-effective in a UK context. I believe there are several problems with the study, mostly stemming from an incorrect interpretation of the cost-effectiveness estimates. I show that one reinterpretation of their results indicates that screening is much less cost-effective than the original analysis suggests, indicating that screening should be restricted to a much smaller group of higher risk men. More broadly, I explain the challenges of attempting to meaningfully reinterpret the originally published results due to the simulation of non-mutually exclusive intervention strategies. Finally, I consider the relevance of considering sufficient alternative screening intensities. This critique highlights the need for appropriate interpretation of cost-effectiveness results for policymakers, especially as risk stratification within screening becomes increasingly feasible.

The Interaction between Price Negotiations and Heterogeneity: Implications for Economic Evaluations

Economic evaluation is an important element of the decision making process for the reimbursement of drugs. Heterogeneity can be considered an explained variation in clinical or economic outcomes based on the clinical and sociodemographic characteristics of patients. However, to our knowledge, the relationship between price negotiations and population heterogeneity has not been considered in the literature to date. If a company offers a conditional discount that is dependent on obtaining reimbursement in 2 subgroups or indications, an interaction is generated between groups that should be accounted for in economic evaluations. Critically, where the drug has 2 indications but is only cost-effective in 1 indication at the full price (herein "indication 1"), the cost savings realized from implementation of the discount in indication 1 can be used to offset the incremental cost of extending reimbursement to indication 2 at the discounted price. This reduces the incremental cost-effectiveness ratio and increases the probability of positive reimbursement compared to a stratified approach. Given the additional complexity that this introduces, we introduce a framework deemed the "hybrid approach" to guide the economic assessment. We present 2 worked examples. We show that failure to account for the interaction can lead to inaccurate conclusions regarding a drug's cost effectiveness and that adoption of strategic behavior could theoretically increase the reimbursement price of drugs. By adopting this framework, cost-effective interventions are identified that may have been previously misclassified as not being cost-effective and vice versa. Recognition of the interaction in the literature by pharmaceutical companies may influence the forms of discounts offered to decision makers. Therefore, we expect this research to have far-reaching effects on medical decision making.

Estimated Lifetime Economic Burden of Type 1 Diabetes

Background: The financial strain of type 1 diabetes on the United States health care system, patients, and employers underscores the importance of developing novel treatments for the disease. This study estimated the lifetime economic burden attributable to type 1 diabetes in the United States. Methods: A patient-level, Markov state/transition simulation model was developed to compare cumulative societal costs among patients with and without type 1 diabetes. For each patient type, 1 prevalent and 10 incident cohorts were constructed and followed annually over a lifetime horizon. The 1 prevalent cohort with type 1 diabetes entered in the first year of the model and at the current age of each patient, whereas the 10 incident cohorts entered in each of 10 subsequent years and at the age of diagnosis of each patient. Patients were assigned age-specific annual medical expenditures and lost wages. Model outputs included the total cumulative medical and lost productivity costs attributable to type 1 diabetes, defined as the difference in costs between patients with and without type 1 diabetes. Results: The model consisted of 1,630,317 patients with type 1 diabetes and an equal number of patients without type 1 diabetes. The difference in lifetime costs was $813 billion (95% confidence interval: $682-$1037 billion), representing a high burden of illness compared with patients without type 1 diabetes. Sensitivity analyses demonstrated robustness in model results. Conclusions: Our findings suggest significant investment in research and development of novel treatments for type 1 diabetes is justified, given the high burden of illness associated with the disease.

Optimal Information Collection Policies in a Markov Decision Process Framework

BACKGROUND

The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change over time may be optimal.

METHODS

We present a stochastic dynamic programming approach to simultaneously identify the optimal intervention and information collection policies. We use our framework to evaluate birth cohort hepatitis C virus (HCV) screening. We focus on how the presence of a time-varying parameter (HCV prevalence) affects the optimal information collection policy for a parameter assumed constant across birth cohorts: liver fibrosis stage distribution for screen-detected diagnosis at age 50.

RESULTS

We prove that it may be optimal to delay information collection until a time when the information more immediately affects decision making. For the example of HCV screening, given initial beliefs, the optimal policy (at 2010) was to continue screening and collect information about the distribution of liver fibrosis at screen-detected diagnosis in 12 years, increasing the expected incremental net monetary benefit (INMB) by $169.5 million compared to current guidelines.

CONCLUSIONS

The option to delay information collection until the information is sufficiently likely to influence decisions can increase efficiency. A dynamic programming framework enables an assessment of the marginal value of information and determines the optimal policy, including when and how much information to collect.

Estimating Future Health Technology Diffusion Using Expert Beliefs Calibrated to an Established Diffusion Model

OBJECTIVES

Estimates of future health technology diffusion, or future uptake over time, are a requirement for different analyses performed within health technology assessments. Methods for obtaining such estimates include constant uptake estimates based on expert opinion or analogous technologies and on extrapolation from initial data points using parametric curves-but remain divorced from established diffusion theory and modeling. We propose an approach to obtaining diffusion estimates using experts' beliefs calibrated to an established diffusion model to address this methodologic gap.

METHODS

We performed an elicitation of experts' beliefs on future diffusion of a new preterm birth screening illustrative case study technology. The elicited quantities were chosen such that they could be calibrated to yield the parameters of the Bass model of new product growth, which was chosen based on a review of the diffusion literature.

RESULTS

With the elicitation of only three quantities per diffusion curve, our approach enabled us to quantify uncertainty about diffusion of the new technology in different scenarios. Pooled results showed that the attainable number of adoptions was predicted to be relatively low compared with what was thought possible. Further research evidence improved the attainable number of adoptions only slightly but resulted in greater speed of diffusion.

CONCLUSIONS

The proposed approach of eliciting experts' beliefs about diffusion and informing the Bass model has the potential to fill the methodologic gap evident in value of implementation and research, as well as budget impact and some cost-effectiveness analyses.

Emergent Challenges in Determining Costs for Economic Evaluations

This paper describes methods of determining costs for economic evaluations of healthcare and considers how cost determination is being affected by recent developments in healthcare. The literature was reviewed to identify the strengths and weaknesses of the four principal methods of cost determination: micro-costing, activity-based costing, charge-based costing, and gross costing. A scoping review was conducted to identify key trends in healthcare delivery and to identify costing issues associated with these changes. Existing guidelines provide information on how to implement various costing methods. Bottom-up costing is needed when accuracy is paramount, but top-down approaches are often the only feasible approach. We describe six healthcare trends that have important implications for costing methodology: (1) reform in payment mechanisms; (2) care delivery in less restrictive settings; (3) the growth of telehealth interventions; (4) the proliferation of new technology; (5) patient privacy concerns; and (6) growing efforts to implement guidelines. Some costs are difficult to measure and have been overlooked. These include physician services for inpatients, facility costs for outpatient services, the cost of developing treatment innovations, patient and caregiver costs, and the indirect costs of organizational interventions. Standardized methods are needed to determine social welfare and productivity costs. In the future, cost determination will be facilitated by technological advances but hindered by the shift to capitated payment, to the provision of care in less restrictive settings, and by heightened concern for medical record privacy.

When Future Change Matters: Modeling Future Price and Diffusion in Health Technology Assessments of Medical Devices

BACKGROUND

Health technology assessments (HTAs) that take account of future price changes have been examined in the literature, but the important issue of price reductions that are generated by the reimbursement decision has been ignored.

OBJECTIVES

To explore the impact of future price reductions caused by increasing uptake on HTAs and decision making for medical devices.

METHODS

We demonstrate the use of a two-stage modeling approach to derive estimates of technology price as a consequence of changes in technology uptake over future periods on the basis of existing theory and supported by empirical studies. We explore the impact on cost-effectiveness and expected value of information analysis in an illustrative example on the basis of a technology in development for preterm birth screening.

RESULTS

The application of our approach to the case study technology generates smaller incremental cost-effectiveness ratios compared with the commonly used single cohort approach. The extent of this reduction in the incremental cost-effectiveness ratio depends on the magnitude of the modeled price reduction, the speed of diffusion, and the length of the assumed technology life horizon. Results of value of information analysis are affected through changes in the expected net benefit calculation, the addition of uncertain parameters, and the diffusion-adjusted estimate of the affected patient population.

CONCLUSIONS

Because modeling future changes in price and uptake has the potential to affect HTA outcomes, modeling techniques that can address such changes should be considered for medical devices that may otherwise be rejected.

Economic evaluation of the breast cancer screening programme in the Basque Country: retrospective cost-effectiveness and budget impact analysis

Background

Breast cancer screening in the Basque Country has shown 20 % reduction of the number of BC deaths and an acceptable overdiagnosis level (4 % of screen detected BC). The aim of this study was to evaluate the breast cancer early detection programme in the Basque Country in terms of retrospective cost-effectiveness and budget impact from 1996 to 2011.

Methods

A discrete event simulation model was built to reproduce the natural history of breast cancer (BC). We estimated for lifetime follow-up the total cost of BC (screening, diagnosis and treatment), as well as quality-adjusted life years (QALY), for women invited to participate in the evaluated programme during the 15-year period in the actual screening scenario and in a hypothetical unscreened scenario. An incremental cost-effectiveness ratio was calculated with the use of aggregated costs. Besides, annual costs were considered for budget impact analysis. Both population level and single-cohort analysis were performed. A probabilistic sensitivity analysis was applied to assess the impact of parameters uncertainty.

Results

The actual screening programme involved a cost of 1,127 million euros and provided 6.7 million QALYs over the lifetime of the target population, resulting in a gain of 8,666 QALYs for an additional cost of 36.4 million euros, compared with the unscreened scenario. Thus, the incremental cost-effectiveness ratio was 4,214€/QALY. All the model runs in the probabilistic sensitivity analysis resulted in an incremental cost-effectiveness ratio lower than 10,000€/QALY. The screening programme involved an increase of the annual budget of the Basque Health Service by 5.2 million euros from year 2000 onwards.

Conclusions

The BC screening programme in the Basque Country proved to be cost-effective during the evaluated period and determined an affordable budget impact. These results confirm the epidemiological benefits related to the centralised screening system and support the continuation of the programme.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2386-y) contains supplementary material, which is available to authorized users.

The value of innovation under value-based pricing

OBJECTIVE

The role of cost-effectiveness analysis (CEA) in incentivizing innovation is controversial. Critics of CEA argue that its use for pricing purposes disregards the 'value of innovation' reflected in new drug development, whereas supporters of CEA highlight that the value of innovation is already accounted for. Our objective in this article is to outline the limitations of the conventional CEA approach, while proposing an alternative method of evaluation that captures the value of innovation more accurately.

METHOD

The adoption of a new drug benefits present and future patients (with cost implications) for as long as the drug is part of clinical practice. Incidence patients and off-patent prices are identified as two key missing features preventing the conventional CEA approach from capturing 1) benefit to future patients and 2) future savings from off-patent prices. The proposed CEA approach incorporates these two features to derive the total lifetime value of an innovative drug (i.e., the value of innovation).

RESULTS

The conventional CEA approach tends to underestimate the value of innovative drugs by disregarding the benefit to future patients and savings from off-patent prices. As a result, innovative drugs are underpriced, only allowing manufacturers to capture approximately 15% of the total value of innovation during the patent protection period. In addition to including the incidence population and off-patent price, the alternative approach proposes pricing new drugs by first negotiating the share of value of innovation to be appropriated by the manufacturer (>15%?) and payer (<85%?), in order to then identify the drug price that satisfies this condition.

CONCLUSION

We argue for a modification to the conventional CEA approach that integrates the total lifetime value of innovative drugs into CEA, by taking into account off-patent pricing and future patients. The proposed approach derives a price that allows manufacturers to capture an agreed share of this value, thereby incentivizing innovation, while supporting health-care systems to pursue dynamic allocative efficiency. However, the long-term sustainability of health-care systems must be assessed before this proposal is adopted by policy makers.

Estimating the Cost-Effectiveness of Implementation: Is Sufficient Evidence Available?

BACKGROUND

Timely implementation of recommended interventions can provide health benefits to patients and cost savings to the health service provider. Effective approaches to increase the implementation of guidance are needed. Since investment in activities that improve implementation competes for funding against other health generating interventions, it should be assessed in term of its costs and benefits.

OBJECTIVE

In 2010, the National Institute for Health and Care Excellence released a clinical guideline recommending natriuretic peptide (NP) testing in patients with suspected heart failure. However, its implementation in practice was variable across the National Health Service in England. This study demonstrates the use of multi-period analysis together with diffusion curves to estimate the value of investing in implementation activities to increase uptake of NP testing.

METHODS

Diffusion curves were estimated based on historic data to produce predictions of future utilization. The value of an implementation activity (given its expected costs and effectiveness) was estimated. Both a static population and a multi-period analysis were undertaken.

RESULTS

The value of implementation interventions encouraging the utilization of NP testing is shown to decrease over time as natural diffusion occurs. Sensitivity analyses indicated that the value of the implementation activity depends on its efficacy and on the population size.

CONCLUSIONS

Value of implementation can help inform policy decisions of how to invest in implementation activities even in situations in which data are sparse. Multi-period analysis is essential to accurately quantify the time profile of the value of implementation given the natural diffusion of the intervention and the incidence of the disease.

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

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

Evaluation of health benefits and harms of the breast cancer screening programme in the Basque Country using discrete event simulation

BACKGROUND

Since the breast cancer screening programme in the Basque Country (BCSPBC) was started in 1996, more than 400,000 women aged 50 to 69 years have been invited to participate. Based on epidemiological observations and simulation techniques it is possible to extend observed short term data into anticipated long term results. The aim of this study was to assess the effectiveness of the programme through 2011 by quantifying the outcomes in breast cancer mortality, life-years gained, false positive results, and overdiagnosis.

METHODS

A discrete event simulation model was constructed to reproduce the natural history of breast cancer (disease-free, pre-clinical, symptomatic, and disease-specific death) and the actual observed characteristics of the screening programme during the evaluated period in the Basque women population. Goodness-of-fit statistics were applied for model validation. The screening effects were measured as differences in benefits and harms between the screened and unscreened populations. Breast cancer mortality reduction and life-years gained were considered as screening benefits, whereas, overdiagnosis and false positive results were assessed as harms. Results for a single cohort were also obtained.

RESULTS

The screening programme yielded a 16 % reduction in breast cancer mortality and a 10 % increase in the incidence of breast cancer through 2011. Almost 2 % of all the women in the programme had a false positive result during the evaluation period. When a single cohort was analysed, the number of deaths decreased by 13 %, and 4 % of screen-detected cancers were overdiagnosed. Each woman with BC detected by the screening programme gained 2.5 life years due to early detection corrected by lead time.

CONCLUSIONS

Fifteen years after the screening programme started, this study supports an important decrease in breast cancer mortality due to the screening programme, with reasonable risk of overdiagnosis and false positive results, and sustains the continuation of the breast cancer screening programme in the Basque population.

Impact on total population health and societal cost, and the implication on the actual cost-effectiveness of including tumour necrosis factor-α antagonists in management of ankylosing spondylitis: a dynamic population modelling study

Background

Sequential treatment of ankylosing spondylitis (AS) that includes tumour necrosis factor-α antagonists (anti-TNF agents) has been applied in most of the Western countries. Existing cost-effectiveness (CE) models almost exclusively presented the incremental CE of anti-TNF agents using a closed cohort while budget impact studies are mainly lacking. Notwithstanding, information on impact on total population health and societal budget as well as on actual incremental CE for a given decision time span are important for decision makers. This study aimed at quantifying, for different decision time spans starting from January 1, 2014 in the Dutch society, (1) impact of sequential drug treatment strategies without and with inclusion of anti-TNF agents (Strategies 1 and 2, respectively) on total population health and societal cost, and (2) the actual incremental CE of Strategy 2 compared to Strategy 1.

Methods

Dynamic population modelling was used to capture total population health and cost, and the actual incremental CE. Distinguishing the prevalent AS population on January 1, 2014 and the incident AS cohorts in the subsequent 20 years, the model tracked individually an actual number of AS patients until death or end of the simulation time. During the simulation, data on patient characteristics, history of drug use, costs and health at discrete time points were generated. In Strategy 1, five nonsteroidal anti-inflammatory drugs (NSAIDs) were available but anti-TNF agents withdrawn. In Strategy 2, five NSAIDs and two anti-TNF agents continued to be available.

Results

The predicted size of the prevalent AS population in the Dutch society varied within the range of 67,145–69,957 with 44–46 % of the patients receiving anti-TNF agents over the period 2014–2034. The use of anti-TNF agents resulted in an increase in the annual drug costs (168.54–205.28 million Euros), but at the same time caused a decrease in the annual productivity costs (12.58–31.21 million Euros) and in annual costs of healthcare categories other than drugs (7.23–11.90 million Euros). Incremental cost (Euros) per QALY gained in Strategy 2 compared to Strategy 1 corresponding to decision time spans of 5, 10, 15 and 20 years improved slightly from 75,379 to 67,268, 63,938 and 61,129, respectively. At willingness-to-pay thresholds of 118,656, 112,067, 110,188 and 110,512 Euros, it was 99 % certain that Strategy 2 was cost-effective for decision time spans of 5, 10, 15 and 20, respectively.

Conclusions

Using the dynamic population approach, the present model can project real-time data to inform a healthcare system decision that affects all actual number of AS patients eligible for anti-TNF agents within different decision time spans. The predicted total population costs of different categories in the present study can help plan the organization of the healthcare resources based on the national budget for the disease.

Characterizing the financial burden of pulmonary arterial hypertension within an integrated healthcare delivery system

PURPOSE

Financial burden associated with providing healthcare to patients with pulmonary arterial hypertension (PAH) is poorly characterized. This study sought to quantify 3-year healthcare expenditures and determine whether expenditures differed between incident and prevalent PAH cases.

METHODS

This was a retrospective cohort study of Kaiser Permanente Colorado (KPCO) patients with confirmed diagnosis of PAH. Included patients were followed from study entry until 3 years, death, or termination of KPCO membership, whichever came first. All expenditures were reported in 2011 US dollars from the KPCO perspective.

RESULTS

In total, 157 patients were included: 44 (28%) prevalent and 113 (72%) incident cases. Mean age (prevalent vs incident cases) was 61 years vs 67 years and 13.6% vs 27.4% were males. The majority of patients (55%) were classified as WHO Group 1 PAH. Prevalent cases had less follow-up (843 vs 975 days; p = 0.033). Overall, median total per patient per day (PPPD) and 3-year total expenditures were $56 (interquartile range (IQR = $29-$166) and $50,599 (IQR = $25,958-$135,535), respectively. After adjustment for patient characteristics and chronic disease burden, median PPPD ($54 vs $56; p = 0.950) and 3-year ($37,340 vs $55,073; p = 0.111) total expenditures were equivalent between prevalent and incident cases; however, the risk of death during the 3-year follow-up was lower among incident cases (hazard ratio = 0.41, 95% CI = 0.18-0.91). No significant differences were detected in pharmacy, inpatient, medical office, emergency department, or other expenditures. Median PAH specialty medication PPPD expenditures were also equivalent, also ($226 vs $223 among specialty medication users; p = 0.861).

CONCLUSION

Healthcare expenditures related to PAH represent substantial financial burden. Significant differences according to prevalent or incident case status appeared to be driven by median ED and inpatient expenditures; however, PAH specialty medication expenditures represented a substantial cost-driver overall. Future efforts should focus on optimizing care for patients with PAH to avoid unnecessary harm or waste.

Transcatheter aortic valve replacement in nonsurgical candidates with severe, symptomatic aortic stenosis: a cost-effectiveness analysis

BACKGROUND

Transcatheter aortic valve replacement (TAVR) seems to improve the survival and quality of life of patients with aortic stenosis ineligible for surgical aortic valve replacement.

METHODS AND RESULTS

We used a decision analytic Markov model to estimate lifetime costs and benefits in a hypothetical cohort of patients with severe, symptomatic aortic stenosis who were ineligible for surgical aortic valve replacement. The model compared transfemoral TAVR with medical management and was calibrated to the Placement of Aortic Transcatheter Valves (PARTNER) trial. TAVR increased life expectancy from 2.08 to 2.93 years and quality-adjusted life expectancy from 1.19 to 1.93 years. TAVR also reduced subsequent hospitalizations by 1.40 but increased complications, particularly stroke (from 1% to 11% lifetime risk), and also increased lifetime costs from $83,600 to $169,100. The incremental cost-effectiveness of TAVR was $116,500 per quality-adjusted life-year gained ($99,900 per life-year gained). Results were robust to reasonable changes in individual variables but were sensitive to the level of annual healthcare costs caused by noncardiac diseases and to the projected life expectancy of medically treated patients.

CONCLUSIONS

TAVR seems to be an effective but somewhat expensive alternative to medical management among patients with symptomatic aortic stenosis ineligible for surgery. TAVR is more cost-effective for patients with a lower burden of noncardiac disease.

Multicohort models in cost-effectiveness analysis: why aggregating estimates over multiple cohorts can hide useful information

BACKGROUND

Models used in cost-effectiveness analysis (CEA) of screening programs may include 1 or many birth cohorts of patients. As many screening programs involve multiple screens over many years for each birth cohort, the actual implementation of screening often involves multiple concurrent recipient cohorts. Consequently, some advocate modeling all recipient cohorts rather than 1 birth cohort, arguing it more accurately represents actual implementation. However, reporting the cost-effectiveness estimates for multiple cohorts on aggregate rather than per cohort will fail to account for any heterogeneity in cost-effectiveness between cohorts. Such heterogeneity may be policy relevant where there is considerable variation in cost-effectiveness between cohorts, as in the case of cancer screening programs with multiple concurrent recipient birth cohorts, each at different stages of screening at any one point in time.

OBJECTIVE

The purpose of this study is to illustrate the potential disadvantages of aggregating cost-effectiveness estimates over multiple cohorts, without first considering the disaggregate estimates. Analysis. We estimate the cost-effectiveness of 2 alternative cervical screening tests in a multicohort model and compare the aggregated and per-cohort estimates. We find instances in which the policy choices suggested by the aggregate and per-cohort results differ. We use this example to illustrate a series of potential disadvantages of aggregating CEA estimates over cohorts.

CONCLUSIONS

Recent recommendations that CEAs should consider the cost-effectiveness of more than just a single cohort appear justified, but the aggregation of estimates across multiple cohorts into a single estimate does not.

Population- versus cohort-based modelling approaches

While no single type of model can provide adequate answers under all circumstances, any modelling endeavour should incorporate three fundamental considerations in any decision-making question: the target population, the disease and the intervention characteristics. A target population is likely to be characterized by various types of heterogeneity and a dynamic evolution over time. It is therefore important to adequately capture these population effects on the results of a model. There are essentially two different approaches in modelling a population over time: a cohort-based approach and a population-based approach. In a cohort-based model, a closed group of individuals who have at least one specific characteristic or experience in common over a defined period of time is run through a state transition process. The cohort is generally composed of a hypothetical number of representative or 'average' individuals (i.e. the target population is considered to be a homogeneous group). The population-based approach projects the evolution of the estimated prevalent target population and intends to reflect as much as possible the demographic, epidemiological and clinical characteristics of the prevalent target population relevant for the decision problem. A cohort-based approach is generally used in most published healthcare decision models. However, this choice is rarely discussed by modellers. In this article, we challenge this assumption. To address the underlying decision problem, we affirm it is crucial that modellers consider the characteristics of the target population. Then, they could opt for using the most appropriate approach. Decision makers should also understand the impact on the results of both types of models in order to make informed healthcare decisions.

Practical implications of differential discounting in cost-effectiveness analyses with varying numbers of cohorts

OBJECTIVE

To call attention to the influence of the number of birth-cohorts used in cost-effectiveness analysis (CEA) models on incremental cost-effectiveness ratios (ICERs) under differential discounting.

METHODS

The consequences of increasing the number of birth-cohorts are demonstrated using a CEA of cervical cancer prevention as an example. The cost-effectiveness of vaccinating 12-year-old girls against the human papillomavirus is estimated with the MISCAN microsimulation screening analysis model for 1, 10, 20, and 30 birth-cohorts. Costs and health effects are discounted with equal rates of 4% and alternatively with differential rates of 4% and 1.5% respectively. The effects of increasing the number of cohorts are shown by comparing the ICERs under equal and differential discounting.

RESULTS

The ICER decreases as the number of cohorts increases under differential discounting, but not under equal discounting.

CONCLUSIONS

The variation of ICERs with the number of cohorts under differential discounting prompts questions regarding the appropriate specification of CEA models and interpretation of their results. In particular, it raises concerns that arbitrary variation in study specification leads to arbitrary variation in results. Such variations could lead to erroneous policy decisions. These findings are relevant to CEA guidance authorities, CEA practitioners, and decision makers. Our results do not imply a problem with differential discounting per se, yet they highlight the need for practical guidance for its use.

Accounting for the drug life cycle and future drug prices in cost-effectiveness analysis

Economic evaluations of health technologies typically assume constant real drug prices and model only the cohort of patients currently eligible for treatment. It has recently been suggested that, in the UK, we should assume that real drug prices decrease at 4% per annum and, in New Zealand, that real drug prices decrease at 2% per annum and at patent expiry the drug price falls. It has also recently been suggested that we should model multiple future incident cohorts. In this article, the cost effectiveness of drugs is modelled based on these ideas. Algebraic expressions are developed to capture all costs and benefits over the entire life cycle of a new drug. The lifetime of a new drug in the UK, a key model parameter, is estimated as 33 years, based on the historical lifetime of drugs in England over the last 27 years. Under the proposed methodology, cost effectiveness is calculated for seven new drugs recently appraised in the UK. Cost effectiveness as assessed in the future is also estimated. Whilst the article is framed in mathematics, the findings and recommendations are also explained in non-mathematical language. The 'life-cycle correction factor' is introduced, which is used to convert estimates of cost effectiveness as traditionally calculated into estimates under the proposed methodology. Under the proposed methodology, all seven drugs appear far more cost effective in the UK than published. For example, the incremental cost-effectiveness ratio decreases by 46%, from £61, 900 to £33, 500 per QALY, for cinacalcet versus best supportive care for end-stage renal disease, and by 45%, from £31,100 to £17,000 per QALY, for imatinib versus interferon-α for chronic myeloid leukaemia. Assuming real drug prices decrease over time, the chance that a drug is publicly funded increases over time, and is greater when modelling multiple cohorts than with a single cohort. Using the methodology (compared with traditional methodology) all drugs in the UK and New Zealand are predicted to be more cost effective. It is suggested that the willingness-to-pay threshold should be reduced in the UK and New Zealand. The ranking of cost effectiveness will change with drugs assessed as relatively more cost effective and medical devices and surgical procedures relatively less cost effective than previously thought. The methodology is very simple to implement. It is suggested that the model should be parameterized for other countries.

Historical lifetimes of drugs in England: application to value of information and cost-effectiveness analyses

OBJECTIVES

The expected lifetime of a health technology is a critical parameter in value of information analysis and in two methodologies for cost-effectiveness analysis which have recently been suggested. The first method allows for the possibility that a superior technology will become available in the future. The second advocates modeling both the prevalent and all future incident patient cohorts. Unfortunately, for value of information analysis, the period of time over which information about the decision problem would be useful is very uncertain, and existing estimates are seemingly arbitrary. Furthermore, there is very little literature on the historical lifetimes of technologies. Here, I quantify and analyze the historical lifetimes of drugs in England. I then apply this information to inform the value of further research and the cost-effectiveness of health technologies.

METHODS

A Weibull regression model was fitted to the historical drug lifetimes of 455 drugs. These represented all British National Formulary drugs in England which were launched from 1981 to 2007, and which did not have very low sales volumes.

RESULTS

The mean drug lifetime was 57 years (95% confidence interval 39-79 years), and the median was 46 years (35-60 years). Drugs with low sales volumes tended to have shorter lifetimes. Under certain assumptions, the ratio of population level to per-year expected value of information is 21. Drug lifetimes are used to parameterize the two models of cost-effectiveness.

CONCLUSIONS

The distribution function of the historical lifetimes of drugs can inform suitable time horizons for: 1) value of information; and 2) cost-effectiveness analyses related to drugs.