Cost-Effectiveness Analysis vs Budget Impact Analysis

Purpose and Questions Addressed:

Cost-effectiveness studies answer the question of how to allocate resources efficiently. They provide insights into the long-term value for money of new treatments, helping to determine their cost-effectiveness compared to the current standard of care. This is crucial for making informed decisions about which treatments provide the best health outcomes relative to their costs.

BIA on the other hand addresses the issue of affordability. It answers the question of whether a healthcare system can afford to implement a new treatment within its existing budget constraints. This analysis is essential for short-term financial planning and ensuring that new treatments can be adopted without exceeding available resources.

Both cost effectiveness studies and BIA rely on similar clinical assumptions and data sources. For instance, they might assume that Medicine B leads to 20% fewer complications and has a 15% better response rate compared to an existing treatment. Both analyses may use similar model structures, such as decision trees or Markov models, to simulate the outcomes of different treatment strategies.
 

Key Differences:

There are some key differences between both analyses that stem from the different questions both analyses aim to answer.

1. Time Horizons:

   • Cost-effectiveness studies typically consider a long-term perspective, often spanning a patient’s lifetime, to evaluate the cost-effectiveness of a treatment. This long-term view is essential to capture the full range of costs and health outcomes associated with a treatment.
   • BIA, on the other hand, usually adopts a shorter time horizon, typically between 3 to 5 years. This shorter perspective is more relevant for budgeting and financial planning purposes within healthcare systems.
      

2. Cohort Structure:

   • Cost-effectiveness studies often work with a closed cohort model where a fixed group of patients is followed over time without new entrants.
   • BIA uses an open cohort model, which allows for the entry of new patients into the analysis in subsequent years. This approach reflects the dynamic nature of healthcare populations and the continuous introduction of new patients eligible for treatment.
      

3. Complexity and Model Structure:

   • Due to the open cohort structure, BIA models tend to be more complex than those used in CEA. BIA must account for factors such as the incidence and prevalence of conditions, the epidemiology of subgroups, and current market shares of treatments.
      

4. Comparators:

   • Cost-effectiveness studies usually compare a new treatment directly against the current standard of care.
   • BIA often compares a new treatment against a mix of current treatments. This comparison reflects the real-world scenario where multiple therapies are available, and a new treatment might partially or wholly replace existing options.
      

5. Additional Data Requirements for BIA:

   • BIA requires extra data that are not typically needed for cost-effectiveness studies. This includes the number of eligible patients, incidence and prevalence rates, the epidemiology of subgroups, current market shares, and forecasted market shares of treatments. Additionally, forecasting techniques are crucial in BIA to estimate the financial impact over the specified time horizon. 
     
      

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