A case-control study of anaemia in patients with rheumatoid arthritis treated with disease-modifying antirheumatic drugs in an adult population in the US: prevalence and impact on healthcare utilisation.

OBJECTIVE: The objective of this study was to estimate the prevalence of anaemia and its impact on healthcare utilisation in patients with rheumatoid arthritis (RA). METHODS: Patients with claims for moderate-to-severe RA (ICD-9 code 714.x) treated with disease-modifying antirheumatic drugs as well as controls without RA matched for age, gender and time in plan were selected from the MarketScan Research Database. Anaemia was identified by ICD-9 codes 280.x, 285.2x, 281.9, 285.9 and 284.8. The prevalence ratio and 95% confidence interval (CI) for anaemia among RA patients versus controls were estimated. Overall disease burden was measured using the Elixhauser Comorbidity Index (ECI). RESULTS: The prevalence ratio for anaemia in RA patients was 2.2 (95% CI 2.1-2.4). Mean ECI was higher in RA (2.26) compared with control (1.02) patients (p<0.001), and RA patients with anaemia had a higher ECI compared with those without anaemia (3.95 vs. 2.08; p<0.001). Total healthcare costs in RA patients with anaemia were approximately twice those of RA patients without anaemia. CONCLUSIONS: The prevalence of clinically diagnosed anaemia in RA patients in the claims database was 2.2 times higher than that in the comparable non-RA control group. RA patients with anaemia had significantly higher levels of co-morbidity and healthcare costs than RA patients without anaemia.

Presenting results of probabilistic sensitivity analysis: the incremental benefit curve.

Cost-effectiveness acceptability curves have become a common way of presenting the results of probabilistic sensitivity analysis. However, these curves do not provide information on what the loss of welfare or net benefit (NB) is for cases where a given intervention is not the optimal one. We describe an alternate approach to presenting the results of probabilistic sensitivity analysis called the incremental benefit curve that presents the entire distribution of incremental NB of each intervention for a given WTP value. The incremental benefit curve provides the decision maker information regarding the potential welfare loss with a given intervention for scenarios in which it is not the optimal intervention, and thus would be a useful complement to the acceptability curve.

Overview of the pharmacoeconomics of pharmacogenetics.

Pharmacoeconomics and pharmacogenetics are two fields converging together as it is increasingly recognized that genetic markers predicting efficacy and toxicity to drugs can cost-effectively improve patient care. While pharmacogenetics aims at identifying genetic markers underlying the response to drugs, pharmacoeconomics aims at delivering healthcare cost-effectively. Several studies have investigated the potential cost-effectiveness of pharmacogenetic-based approaches. Recent evidences include screening for thiopurine methyltransferase gene polymorphisms to prevent azathioprine-induced myelosuppression, or screening for human leukocyte antigen (HLA)B5701 to prevent hypersensitivity reactions to abacavir therapy. Furthermore, examples suggesting a cost-effectiveness of markers predicting drug efficacy include screening the angiotensin-converting enzyme gene polymorphisms for statins therapy, the alpha-adducin gene variant for diuretic therapy and the assessment of human epidermal growth factor receptor (HER2) expression for trastuzumab therapy. However, thus far, all these pharmacoeconomic analyses are exploratory and validations in prospective randomized clinical trials are warranted.

Cardiovascular disease and risk factors in patients with rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis.

OBJECTIVE: To compare the prevalence of cardiovascular diseases and their risk factors between patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) and control subjects. METHODS: Data for patients continuously enrolled in an integrated outcomes database between January 1, 2001, and December 31, 2002, with International Classification of Diseases, 9th Revision codes of 714.x (RA), 696.0 (PsA), or 720.0 (AS) were evaluated in this cross-sectional comparative study. Control groups were established for each patient group (1:4 ratio) by matching on the basis of age, sex, geographic region, and length of time in plan. Age- and sex-adjusted prevalence of cardiovascular comorbidities and risk factors were calculated; the prevalence ratio of the comorbidities and risk factors for the patient groups compared with the control population were estimated. Use of selected cardiovascular medications was also compared between patient and control groups. RESULTS: The RA, PsA, and AS cohorts comprised 28,208, 3066, and 1843 patients, respectively. The prevalence ratio of ischemic heart disease (1.5, 1.3, 1.2), atherosclerosis (1.9, 1.4, 1.5), peripheral vascular disease (2.4, 1.6, 1.6), congestive heart failure (2.0, 1.5, 1.8), cerebrovascular disease (1.6, 1.3, 1.7), type II diabetes (1.4, 1.5, 1.2), hyperlipidemia (1.2, 1.2, 1.2), and hypertension (1.3, 1.3, 1.3) were higher in patients than controls. For RA, PsA, and AS, use of angiotensin-converting enzyme inhibitors, calcium channel blockers, diuretics, nitrates/vasodilators, anticoagulants, and antihyperlipidemia agents was significantly higher in patients than controls. CONCLUSION: Cardiovascular diseases and their risk factors were more common in patients with RA, PsA, and AS than in matched controls.

Optimal assignment of treatments to health states using a Markov decision model: an introduction to basic concepts.

Assessing the cost effectiveness of a new health intervention often requires modelling to estimate the impact of the intervention on cost, survival and quality of life over the lifetime of a cohort of patients. Markov modelling is a methodology that is commonly employed to estimate these long-term costs and benefits. As commonly used, these models assume that the patients continue to get the treatments assigned regardless of the change in health states. In this paper, we describe an extension to the Markov modelling approach, called Markov decision modelling. Such a model starts with a set of health states and treatments and optimally assigns treatments to each of the health states. A Markov decision model can be used to identify the optimal treatment strategy not just for the initial disease state, but also as the disease state changes over time. We present a dynamic programming approach to identifying the optimal assignment of treatments, and illustrate this methodology using an example. The Markov decision modelling approach provides an efficient way of identifying optimal assignment of treatments to health states, but, like the standard Markov model, may be of limited use when probabilities of future events depend on past history in a complex fashion. Even with its limitations, Markov decision models offer an opportunity for health economists to inform healthcare decision-makers on how to modify current treatment pathways to incorporate new treatments as they become available.

Pharmacogenomics and the evolution of healthcare : is it time for cost-effectiveness analysis at the individual level?

The efficacy and toxicity of any given drug can vary substantially from one individual to another. The heterogeneity in individual genetics contributes, in part, to this variability. Pharmacogenomics uses each patient's individual genetic information to identify the drug with the best efficacy-safety profile for that patient. However, heterogeneity is also present in individuals' preferences for alternate efficacy-safety profiles. We argue that as healthcare evolves towards individualised drug therapy, preference elicitation and cost-effectiveness analysis should also be performed at the individual level to maximise societal welfare.

Conditions for the near equivalence of cost-effectiveness and cost-benefit analyses.

The equivalence of cost-effectiveness analysis (CEA) and cost-benefit analysis (CBA) has been vigorously debated in the health economic literature. In this paper we review and refine the conditions for the equivalence of CEA and CBA. The previously stated conditions require that 1) each individual's willingness to pay (WTP) per quality-adjusted life year (QALY) is constant and does not vary with the magnitude of QALY gains, and 2) the WTP per QALY is identical for every individual in society. Based on mathematical programming formulations of CEA and CBA, we note that condition 2 can be replaced with two other conditions, which together are less restrictive than the requirement that every individual have the same WTP per QALY. Even with this less restrictive set of conditions, CEA and CBA are unlikely to be equivalent under real world conditions. When CEA and CBA do lead to different resource allocation decisions, the most appropriate framework for health economic analysis depends on the perspective regarding distribution issues. We also examine the equivalence of two different definitions of CEA provided in the literature and discuss the problems that could arise when there are multiple optima.

Application of cost-effectiveness analysis to multiple products: a practical guide.

The appropriate interpretation of cost-effectiveness results and its use in treatment decision making have been debated vigorously. In this report we have summarized the decision rules described in the health economics literature to determine which intervention should be chosen from among multiple treatment options for a given disease, using a graphic framework. The implications of different means of expressing budget constraints on treatment choice are examined.