Cost-benefit analysis: how biobanks and registers add value
Using empirical data and biological samples gathered and housed by biobanks and registers provides an essential and unmatched resource in performing a cost-benefit analysis for new healthcare treatments or protocols.
In today’s increasingly competitive market, a cost-benefit analysis is an essential tool in determining whether commercialization of a discovery or moving forward with a new health care initiative makes sense. Governments, funding agencies and policy makers must factor in long-term economic growth and return on investment when considering which projects to support. Companies may have questions about the size of a target population, potential to recapture R&D expenditures, or whether a start-up has traction. Is there a crystal ball that helps predict which investments are the right ones?
From empirical evidence to cost-benefit analysis
The ability to evaluate the potential of any new bioscience discovery or medical treatment across populations rests heavily on having the right data. Using empirical data and biological samples gathered and housed by biobanks and registers provides an essential and unmatched resource in performing a cost-benefit analysis for new healthcare treatments or protocols.
“Data gathered by biobanks and registers is a useful resource to governments, agencies, businesses and startups looking to quantify the potential for a new technology or initiative,” said Inna Feldman, statistician at the City Council in Uppsala, Sweden. “This sort of analysis is also helpful when comparing a new idea to what is already on the market. It helps answer the question: compared to what we do now, is the new idea or treatment worth it?”
Putting a monetary value on the question of whether a potential treatment is worth the cost doesn’t rest just in expenditures by governments, agencies or other payers. It also considers the total impact on a society, including issues such as loss of productivity, marketplace competitiveness or even the “value of a statistical life.”¹ (This refers to the ability to establish the amount a society is willing to invest to save the life of one person whose identity we don’t know).²
Cost-effectiveness analyses compare the costs and outcomes for a new intervention with an existing alternative treatment, strategy or intervention.
Accessing biobank data for analyzing cost effectiveness
Biobanks house structured collections of biological samples and associated data for the purpose of current and future research. Biobanks store human biospecimens—such as tissue, blood, urine—along with information pertaining to the donors: demography and lifestyle, history of present illness, treatment and clinical outcomes. Similarly, registers provide access to the empirical, demographic and disease-related data, but not the actual biological samples. Biobanks and registers may focus on regional or disease-specific areas, or offer broad samples of data across various populations. The collected sources provide powerful data for analytical studies such as cost effectiveness.
“The two questions [cost-benefit] analyses aim to answer are: how much does the new intervention cost compared with current practice and is it more effective, and if so, how much more?”
The first step in conducting an effective cost-benefit analysis is to ensure there is a clear description of the product or process being proposed.
“The analysis should start with a very clear question,” said Feldman. “What are you trying to solve? What is the aim of the product in the marketplace? Make sure it’s clear where to focus: is it lives saved, reduction in treatments costs, long-term improvement, or something else?”
A cost-benefit analysis is not just beneficial for evaluating new treatments for rare disease. It’s also extremely useful when evaluating treatments for common diseases. “For example, in evaluating a new screening protocol for breast cancer, you can evaluate the how many lives are saved and evaluate the cost savings for treatment,” said Feldman. “Or for cardiovascular disease, evaluating which new treatments prevent complications and consumption of healthcare resources.”
What cost-benefit analysis measures
Some factors that are considered during evaluation of a policy based on costs vs. benefits:
What cost-benefit analysis provides
Conducting a cost-benefit analysis typically takes about 1-2 months. What can it provide?
- An understanding of the effectiveness of the product or treatment (how many lives it can save, what complications are reduced, treatment times shortened)
- Calculations for the cost of consequences the treatment can prevent
- The total savings the product provides
Providing data to authorities or decision-makers about the financial impact and cost savings associated with a new treatment can make the difference between a great idea that never gets funded and a ground-breaking new protocol that helps patients and society.
Find out more
To learn more about developing a cost-benefit analysis for your project, visit (EIT PAGE) or contact (name).
1. Australian Government. THE HEALTH OF NATIONS: THE VALUE OF A STATISTICAL LIFE (2008).
2. Abelson, P. (2008). ‘Establishing a Monetary Value for Lives Saved: Issues and Controversies’, WP 2008-02 in cost-benefit analysis, Office of Best Practice Regulation, Department of Finance and Deregulation, Sydney University.
3. Abelson, P. (2010). ‘The Value of Life and Health for Public Policy’, Macquarie University.
4. EXPLORING COST-BENEFIT ANALYSIS OF RESEARCH, DEVELOPMENT AND INNOVATION INFRASTRUCTURES: AN EVALUATION FRAMEWORK, Florio Massimo, etc al Milan, Feb. 2019 European Investment Bank.
5. Detsky AS, Naglie IG: A clinician’s guide to cost-effectiveness analysis. Ann Intern Med. 1990, 113: 147-154.
6. Hill, BMC Medicine 2012, 10:10.
Uses for biobank & register data
Companies, researchers and public organisations use data available from biobanks and registers in a number of ways. These include:
- Multivariate data analysis of cause/effect
- Cost-benefit analysis of treatments
- Genome-wide association studies
- Clinical response or outcome studies
- Biomarker identification
- Phenotype studies
- Disease prevalence by population or genome groups
- Disease pattern studies
- Drug effectiveness by population or genome types
- Correlation studies
- Translational research
- Identification of new proteins or molecules for drug targets
- Molecular aspects of disease progression
- Disease inheritance patterns