Is Data Secrecy a Prescription for Health, Wealth and Innovation?

5154759492_115c871fdb_zIn the US Pharmaceutical manufacturers currently receive a five-year exclusivity period for the clinical trial data they submit for a successful new drug approval. New applications and new indications allow for extensions of that period for an additional 6 months to 3 years. This means that for up to 8 years, generic drug manufacturers are not allowed to review the proprietary data. (Presumably non-government safety researchers are also not allowed access.)

This would of course be the period when a new drug is still under patent protection and only available in its higher cost branded (vs. generic) form. Some have argued that such data secrecy negatively impacts public health by hampering competition and research. Others argue that it is a necessary trade secret that protects and promotes drug innovation. Everyone, meanwhile, seems to agree that tackling drug-expenditures needs to be a central part of health reform. In Europe, the data exclusivity period is even longer: 10 to 11 years. Some have argued that US regulations should be extended to match that longer term. But until now, no one has really studied the financial and health impacts of these policies with any rigor.

A new study from the Schaeffer Center for Health Policy and Economics at USC, published in Health Affairs has reached some interesting conclusions. Like many health policy issues, the findings are nuanced and by no means simplistic. According to the study:

Would extending data-exclusivity increase drug costs? Yes, and also revenue. Increasing the exclusivity period would increase drug revenues by five percent, on average. At least in the short term this would mean higher drug costs.

Would extending data-exclusivity increase drug innovation? Yes. It would likely lead to an additional 228 drug approvals in the next 50 years.

Would extending data-exclusivity increase life expectancy? Yes. By an estimated 1.7 months over the next 50 years.

While any predictive statements such as the number of new innovations over 50 years can be challenged, this is one of those interesting findings that forces a delicate and complex, values-based balancing act. To begin with, the shorter term costs would be born by those who need drugs (or are paying for them) in the 2020’s, while the benefits would be seen by those needing drugs (or paying for them) in 2060.

Further, how much are we willing to pay now for marginal increases in life expectancy later? How much is a 1.7 month increase in average life-expectancy worth, especially when it may not be your life expectancy that you are paying to increase?

Naturally having some data upon which to inform health policy decisions is important. If nothing else it reminds us that these are not always clear cut, simplistic decisions where one perspective is clearly right and the other wrong.

Presuming that these findings are robust, what is your take on them, and what opportunities or pitfalls might they foreshadow?

Photo credit: The author, via Flickr.