Despite their intuitive appeal and apparent simplicity, DM programs are highly variable in design, complex in implementation, and have proven difficult to evaluate. PE, in the strictest sense, evaluates cost-effectiveness of drug therapy in terms of the long-term costs and benefits to the patient, to the payer, or to the system. PE principles can be applied to DM programs, inasmuch as the DM program could be viewed like a pharmaceutical treatment and the costs and economic impacts of the treatment can be calculated. Unlike a medication, however, a DM program has multiple targets, including the behavior of patients and multiple providers, each of which have multiple different impacts on healthcare utilization, costs, and health outcomes. This makes a typical PE approach to DM programs difficult. The following section takes a look at different applications of PE principles to DM evaluation.

The Central Role of Pharmaceuticals

Many DM programs target the appropriate use of evidence-based drug therapy as a way to improve outcomes and reduce costs related to disease exacerbations or progression. For example, heart failure and asthma DM programs all include guidelines that specify the routine use of drugs such as angiotensin-converting enzyme (ACE) inhibitors for congestive heart failure (CHF) or inhaled corticosteroids for asthma, since these have been shown to reduce emergency room visits and hospitalizations. For depression and diabetes, guidelines promote treatments that have been proven to improve symptoms and prevent worsening of the disease and attendant hospitalizations. Effective DM programs assess whether patients are on appropriate therapy and dose, whether they are taking medications as directed, and whether they are responding as hoped.

Cost Analyses of Pharmaceutical Interventions

The crudest justification for DM programs and for pharmaceutical interventions is simple cost of illness (COl) studies (see Chapter 3 for more information on COl). Although COI studies can be useful in identifying candidate conditions with potential for reducing costs, they do not define alternative choices. Using average costs in patients with a given diagnosis (as opposed to marginal costs associated with having the diagnosis on top of other conditions) to assign direct costs of an illness often leads to overestimation of burden attributable to the disease in question and overestimation of the savings from better management of that single condition. Such studies have relatively limited roles in evaluating DM programs themselves, but articulating the burden of illness in financial terms has often been effective in justifying the need for some intervention, especially among healthcare purchasers.

Cost-minimisation analysis (CMA) compares the costs of alternative interventions that are assumed to achieve the same target outcome (see Chapter 6 for more information on CMA). This analysis is most easily applied to pharmaceuticals where there may be evidence that several alternatives are equivalent in relieving symptoms or improving some physiologic endpoint, for example, a specific improvement in blood pressure. A DM program designer or manager may generate a list of all pharmaceuticals approved for use in a particular application within a DM program and identify the least expensive, accounting for direct, indirect, and intangible costs while considering time horizon and discounting to present value (see Chapter 11 on discounting). An example would be to analyze currently approved HMG CoA reductase inhibitors (commonly referred to as statin drugs). While there are distinctions among these drugs in terms of cost, dosing, and evidence on long-term outcomes, if one assumes that there is no clear superiority among available statins (or among a selection of statins) on important outcomes, a simple CMA comparing the various drugs could identify cost-saving strategies for disease managers.

Cost-effectiveness analysis (CEA) calculates both the costs for a series of equivalent treatment or preventive options and the effectiveness expressed as change in a single common dimension of health outcomes, for example, cases avoided, admissions avoided, life-years gained, deaths avoided, cases identified, etc. (see Chapter 7 for more information on CEA). Researchers in the United Kingdom have compared a group of statin medications with regard to the cost to achieve a certain reduction in low-density lipoprotein cholesterol and total cholesterol [10, 11]. In these studies, researchers were able to name a specific drug as being the most cost-effective in the cohort examined. Such information can be useful in choosing among different interventions that may vary in effectiveness (e.g., in formulary decisions). CEA can also be used to decide if a new intervention, such as a DM intervention, provides reasonable “value” relative to other health programs, even if it is not strictly cost-saving.

Cost-benefit analysis is distinct from the previous analytic methods described as it strictly adheres to costs and benefits in monetary terms [12]. These tend to be comprehensive comparisons of all social costs and consequences, taking a societal perspective to maximize social welfare; these are not routinely used in the evaluation of DM programs as they require assigning monetary values to all health outcomes.

Cost-utility analysis (CUA) compares alternative interventions using the health outcome of individual “utility” based on preferences for different states of well-being (see Chapter 7 for more information on CUA). As mentioned previously, the quality-adjusted life year (QALY) is a common unit of measurement in North American studies. Unlike CEA. CUA can account for a variety of disparate outcomes, such as effects on symptoms, mortality, and unanticipated harms of treatment. Several challenges complicate the use of CUAs: utilities must be assigned to a comprehensive set of outcomes; a small change in the disutility assigned to a common outcome (e.g., the inconvenience of monitoring one’s blood sugar regularly) can have big effects on overall assessments, and finally, the results can be difficult for lay people to interpret. There is also no consensus about what cost per QALY represents a “reasonable” value. That is, there are generally no hard cut-offs for an acceptable cost to save one QALY. A common cut-off in the U.S. has been $50,000. although these have been changing (see Chapter 15), but different thresholds may be used in the United Kingdom and other European countries [13]. A conference on evaluation of DM sponsored by the Agency for Healthcare Research and Quality (AHRQ) in 2002 recommended the use of natural history models that combine the expected benefits of improvement from multiple outcomes measures into a single composite measure (the QALY), with the need for data validation and appropriate case-mix adjustments [14].

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