PharmacoEconomics

This paper provides an educational review covering the consideration of costs for cost-effectiveness analysis (CEA), summarising relevant methods and research from the published literature. Cost data are typically generated by applying appropriate unit costs to healthcare resource-use data for patients. Trial-based evaluations and decision analytic modelling represent the two main vehicles for CEA. The costs to consider will depend on the perspective taken, with conflicting recommendations ranging from focusing solely on healthcare to the broader ‘societal’ perspective. Alternative sources of resource-use are available, including medical records and forms completed by researchers or patients. Different methods are available for the statistical analysis of cost data, although consideration needs to be given to the appropriate methods, given cost data are typically non-normal with a mass point at zero and a long right-hand tail. The choice of covariates for inclusion in econometric models also needs careful consideration, focusing on those that are influential and that will improve balance and precision. Where data are missing, it is important to consider the type of missingness and then apply appropriate analytical methods, such as imputation. Uncertainty around costs should also be reflected to allow for consideration on the impacts of the CEA results on decision uncertainty. Costs should be discounted to account for differential timing, and are typically inflated to a common cost year. The choice of methods and sources of information used when accounting for cost information within CEA will have an effect on the subsequent cost-effectiveness results and how information is presented to decision makers. It is important that the most appropriate methods are used as overlooking the complicated nature of cost data could lead to inaccurate information being given to decision makers.

Objectives: To perform an evaluation from the societal perspective of the cost of treatment with enoxaparin sodium versus unfractionated heparin (UFH) in patients with unstable angina and non-Q wave myocardial infarction in France. Design: Four complementary cost-minimisation analyses based on the results of the Efficacy and Safety of Subcutaneous Enoxaparin in Non-Q wave Coronary Events (ESSENCE) international trial were conducted. We assessed differences in medical resource consumption and in duration of hospital stay in the whole study population (n = 3171) and for the French patients (n = 133). Results: Resultswere consistent for the study group as a whole and for the French subgroup. Among patients treated with enoxaparin sodium, there was a statistically significant reduction in the use of angiography and percutaneous transluminal coronary angioplasty (whole group study: p = 0.024 and 0.006, respectively) and a trend towards shorter lengths of hospital stay. The differences in angiography and angioplasty rates led to estimated average net cost savings with enoxaparin sodium of French Francs (FF)1555 per treated patient (whole study population) and FF9993 (French subgroup) [1996 values]. The analyses based on the duration of hospital stay resulted in estimated net cost savings with enoxaparin sodium of between FF1014 per treated patient (whole study population) and FF2804 (French subgroup). Conclusion: Our study confirmed earlier results which show that enoxaparin sodium is cost saving in the treatment of unstable angina.

Cost effectiveness is becoming an increasingly important factor for stakeholders faced with decisions about adding a new vaccine into national immunization programmes versus alternative use of resources. Evaluating cost effectiveness, taking into account the relevant biological, clinical, epidemiological and economic factors of a vaccination programme, generally requires use of a model. This review examines the modelling approaches used in cost-effectiveness analyses (CEAs) of vaccination programmes. After overviewing the key attributes of models used in CEAs, a framework for categorising theoretical models is presented. Categories are based on three main attributes: static/dynamic; stochastic/deterministic; and aggregate/individual based. This framework was applied to a systematic review of CEAs of all currently available vaccines for the period of 1976 to May 2007. The systematic review identified 276 CEAs of vaccination programmes. The great majority (83%) of CEAs were conducted in the setting of high-income countries. Only a few vaccines were widely studied, with 57% of available CEAs being focused on the varicella, influenza, hepatitis A, hepatitis B or pneumococcal vaccine. Several time trends were evident, indicating that the number of vaccine CEAs being published is increasing; the main health outcome measures are moving away from the number of cases prevented towards quality-adjusted and unadjusted life-years gained, and more complex models are beginning to be used. The modelling approach was often not adequately described. Of the 208 CEAs that could be categorized according to the framework, around 90% were deterministic, aggregate-level static models. Although a dynamic transmission model is required to account for herd-immunity effects, only 23 of the CEAs were dynamic. None of the CEAs were individual based. To improve communication about the cost effectiveness of vaccination programmes, we believe the first step is for analysts to be more transparent with each other. A clear description of the model type using consistent terminology and justification for the model choice must begin to accompany all CEAs. As a minimum, we urge modellers to provide an explicit statement about the following attributes: static/dynamic; stochastic/deterministic; aggregate/individual based; open/closed. Where relevant, time intervals (discrete/continuous) and (non)linearity should also be described. Enhanced methods of assessing model performance and validity are also required. Our results emphasize the need to improve modelling methods for CEAs of vaccination programmes; specifically, model choice, construction, assessment and validation.

Alzheimer’s disease or dementia can impose a significant burden on family and other informal caregivers. This study investigated how the inclusion of family/informal caregiver spillover effects in a cost-utility analysis may influence the reported value of Alzheimer’s disease/dementia interventions. We used PubMed to identify Alzheimer’s disease or dementia cost-utility analyses published from 1 January, 2000 to 31 March, 2018. We reviewed and abstracted information from each study using a two-reader consensus process. We investigated the frequency and methods in which family/caregiver spillover costs and health effects were incorporated into cost-utility analyses, and examined how their inclusion may influence the reported incremental cost-effectiveness ratios. Of 63 Alzheimer’s disease/dementia cost-utility analyses meeting inclusion criteria, 44 (70%) considered at least some family/caregiver spillover costs or health effects. Thirty-two studies incorporated spillover costs only, two incorporated spillover health effects only, and ten incorporated both. The most common approach for accounting for spillover was adding informal caregiving time costs to patient costs (n = 36) and adding informal caregiver quality-adjusted life-years to patient values (n = 7). In a subset of 33 incremental cost-effectiveness ratio pairs from 19 studies, incorporating spillover outcomes made incremental cost-effectiveness ratios more favorable (n = 15; 45%) or kept the intervention cost saving (n = 13; 39%) in most cases. In fewer cases, including spillover increased incremental cost-effectiveness ratios (n = 2; 6%), kept the intervention dominated [more costs/less quality-adjusted life-years] (n = 2; 6%), or changed incremental cost-effectiveness ratio from dominated to less cost/less quality-adjusted life-years (n = 1; 3%). In 11 cases (33%), adding spillover effects into analyses resulted in a lower incremental cost-effectiveness ratio that crossed a common cost-effectiveness threshold, which could have downstream implications for programs or policies that are adopted based on cost-effectiveness analysis results. Most Alzheimer’s disease/dementia cost-utility analyses incorporated spillover costs, often as caregiver time costs, but considered spillover health impacts less often. In about 85% of the analyses, including Alzheimer’s disease/dementia spillover cost or health effects decreased incremental cost-effectiveness ratios or kept the intervention cost saving. The broader value of an Alzheimer’s disease/dementia intervention to society may in some cases be underestimated without considering these spillover effects on family and informal caregivers.

Objective: We compared the estimated costs of coronary interventions from our hospital’s cost accounting system with data from the French Diagnosis Related Group (DRG) cost database, taking the perspective of our hospital. Design: Cost data on hospital resources used by patients hospitalised for acute myocardial infarction (MI), with and without complications, including deceased patients, were collected in a tertiary care university hospital located in Paris, France. The data were collected using the hospital’s cost accounting system and then compared with the estimates provided by the DRG reimbursement schedule for similar conditions. Main outcome measures and results: The estimated costs were 849 euro (EUR) for coronary angiography, EUR4762 for coronary angioplasty with stenting, and EUR4978 to 8067 for MI. The DRG reimbursement schedule provided for acute MI was EUR3920 to 5709. Conclusions: Although the current cost of treating acute MI in a teaching hospital is reasonably close to that in the current reimbursement schedule, rapid technological changes regarding both drugs and devices renders necessary a close monitoring of costs associated with the management of these acute care patients.

Widely used risk equations for cardiovascular outcomes for individuals with type 2 diabetes mellitus (T2DM) have been incapable of predicting cardioprotective effects observed in recent cardiovascular outcomes trials (CVOTs) involving individuals with T2DM at high risk for or with established cardiovascular disease (CVD). We developed cardiovascular and mortality risk equations using patient-level data from the CANVAS (CANagliflozin cardioVascular Assessment Study) Program to address this shortcoming. Data from 10,142 patients with T2DM at high risk for or with established CVD, randomized to canagliflozin + standard of care (SoC) or SoC alone and followed for a mean duration of 3.6 years in the CANVAS Program were used to derive parametric risk equations for myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and death. Accumulated knowledge from the widely used UKPDS-OM2 (United Kingdom Prospective Diabetes Study Outcomes Model 2) was leveraged, and any departures in parameterization were limited to those necessary to provide adequate goodness of fit. Candidate explanatory covariates were selected using only the placebo arm to minimize confounding effects. Internal validation was performed separately by study treatment arm. UKPDS-OM2 predicted CANVAS Program outcomes poorly. Recalibrating UKPDS-OM2 intercepts improved calibration in some cases. Refitting the coefficients but otherwise preserving the UKPDS-OM2 structure improved the fit substantially, which was sufficient for stroke and death. For MI, reselecting UKPDS-OM2 covariates and functional form proved sufficient. For HHF, selection from a broad set of candidate covariates and inclusion of a canagliflozin indicator was required. These risk equations address some of the limitations of widely used risk equations, such as the UKPDS-OM2, for modeling cardioprotective treatments for individuals with T2DM and high cardiovascular risk, including derivation from overly healthy patients treated with agents that lack cardioprotection and have been described as reflecting a different therapeutic era. Future work is needed to examine external validity.

Objective: To describe the pharmacoeconomic assessment process in Ireland and to provide examples of recent appraisals and the subsequent impact on pricing and reimbursement decisions. Method: The pharmacoeconomic appraisals conducted by the National Centre for Pharmacoeconomics (NCPE) between September 2006 and February 2009 were reviewed. The NCPE recommendations and subsequent reimbursement decisions by the Health Service Executive (HSE) were recorded. Recommendations made by the NCPE were compared with those of UK agencies. The duration of the NCPE pharmacoeconomic process and the time from marketing authorization to reimbursement was estimated. The budget impact assessments from the pharmaceutical companies were reviewed and compared for consistency. Results: The NCPE conducted 12 single technology appraisals during the study period. Eight of the medicines assessed were either recommended as a cost-effective use of resources or recommended with certain restrictions, and were funded by the HSE. Of the four medicines that were not considered cost effective, two were reimbursed after a price reduction was negotiated and the remaining two were not. The NCPE recommendations concurred with those of the UK agencies for the majority of appraisals, with the exception of sunitinib and lapatinib. The average duration of the NCPE process was 2.7 months. The average time from marketing authorization to reimbursement was 7 months. The review of budget impact assessments highlighted a high degree of variability between submissions. Conclusions: The findings of this review highlight the efficiency of the pharmacoeconomic process and the acceptance of the NCPE recommendations by the HSE for pricing and reimbursement decisions. NCPE recommendations broadly concurred with those of UK agencies for the majority of appraisals.