Healthcare Cost-Effectiveness for a Population, Not Just One Patient

A new decision-making tool aims to make effective treatments available to more patients within a set budget.

Recent research in the International Journal for Equity in Health proposes disrupting the common practice of cost-effectiveness analysis (CEA) by providing treatment that is effective enough to a broad population, which might be preferable over providing optimal effectiveness on an individual basis.

Authors introduced the need for their approach in the context of the global economic crisis, which put new demands on many nations to provide healthcare within stringent budget constraints.

In detail, the article deconstructed the limitations of CEA, pointing out that the addition of quality-adjusted-life-years (QALY) almost always comes with higher costs. Within this method of analysis, the authors argued, healthcare costs must rise endlessly to adopt “new and improved” innovations and improve outcomes.

For similar reasons, the authors believe that the incremental cost-effectiveness ratio (ICER) could be an inappropriate and unsustainable tool to allocate limited resources across a wide population.

Health technology assessment (HTA) typically uses a combination of CEA and budget impact analysis to inform policy makers of the short- and long-term consequences of choices under their consideration. Traditional HTA, authors argued, is a better tool to find the best treatment for one patient than it is to find effective treatment for the most patients.

For example, they said, new therapies for hepatitis C virus (HCV) demonstrate high cure rates and favorable ICERs, but using them at a cost of $66,000 to $84,000 per patient to treat 184,000,000 infected individuals worldwide seems untenable.

New economic realities compel policy makers to improve health outcomes without increasing healthcare costs, authors asserted, and so they created a new tool for that job.

How Does the New Decision-Making Tool Work?

Researchers created a mathematical model composed of 5 steps of equations.

The first equation starts with the set budget limit (B) and the cost of treatment per patient (C) to determine the potentially treated population (PTP, defined as the upper limit of the number of patients that could receive the treatment/intervention within budget). This model assumes that C includes all intervention costs and potential cost offsets.

The second step factors in effectiveness (E), measured by added QALYs per patient, to determine the total number of adverse events prevented.

The third step calculates QALYs added for the entire intended-use population (PEP) as a function of effectiveness, cost, and budget.

The fourth step uses the constant value of B to determine the ratio between PEPs of two treatments/interventions decision makers want to compare. The implication of this step is that in order for a treatment to be more effective for the entire population, its relative effectiveness must be higher than its relative market price. Step 4 can also calculate the breakeven cost of a new intervention.

In the final step, knowing the ratio of superiority between the effiacy of two treatment options, a breakeven price is calculated. The idea here is that the price difference should not exceed the efficacy difference. This distinguishes the new approach from traditional methods. Whereas a high-cost, highly effective treatment may have acceptable ICER, it could provide inferior outcomes within budget, depending on the ratios between efficacies and costs for two options.

Implications of the New Decision-Making Tool

The new model described here resembles approaches taken by the Prospective Urban Rural Epidemiological (PURE) study and by the German Institute for Quality and Efficiency in healthcare, authors said. This new policy-making tool could be applied to various technologies in various countries, to achieve optimal distribution of health care resources and outcomes within a fixed budget.