April 1, 2009

3 Min Read
How Much Is a Life Worth?

FROM THE EDITORS

sherrie_conroy.jpg

The world is struggling with how to deal with aging populations. Our longer life spans certainly contribute to our climb-ing healthcare costs (due, in part, to increasingly sophisticated treatment options). The result is a need to find an equitable way to distrib-ute healthcare with fixed economic resources. Along comes comparative effectiveness as a solution. It is getting a lot of media coverage, and rightfully so. As people seek to understand this term, what they might find is that there are varying degrees of comparative effective-ness, and there are some ideas float-ing out there that are, quite frankly, a bit scary.


One method for comparing effec-tiveness might very well affect your business if it is implemented on any significant level. It's called quality-adjusted life years, or QALYs. The idea is based on research that is being studied in a number of coun-tries, including Norway, Sweden, the UK, and the United States. The UK's Na-tional Institute for Health and Clinical Excel-lence (NICE) uses a QALY method to compare treatments. Its Web site says, "Having used the QALY measurement to compare how much someone's life can be extended and improved, we then consider cost-effectiveness--that is, how much the . . . treatment costs per QALY." NICE notes that if a treatment costs more than £20,000-£30,000 per QALY, then that treat-ment would not be considered cost-effective.
One idea of QALY is that it assumes that a year lived in perfect health is worth 1 QALY (1 year of life × 1 utility = 1 QALY). This for-mula is taken from a 2003 article, "Problems and Solutions in Calculating Quality-Adjusted Life Years (QALYs)," by Luis Prieto and Jose Sacristan of Eli Lilly's health outcomes research unit. A half year lived in perfect health equals 0.5 QALY, as is a year of life lived in a situation with utility of 0.5 (e.g., bedridden).
Proponents say that QALYs, which Prieto and Sacristan say have become increasingly used as a measure of health outcomes, allow the effec-tiveness and cost-effectiveness of interventions in different disease areas to be compared. It seems that such an approach could become too generalized.
What if an insurance company, group pur-chasing organization, or even the 15-member Federal Coordinating Council for Compara-tive Effectiveness Research decide that such a numerical economic evaluation model must also be taken a step further? For example, sup-pose that a 50-year-old woman is diagnosed with cervical cancer. For argument's sake, say it will cost $200,000 to treat the disease, and that treatment will add one additional year of life over the life expectancy without treatment. Using QALY, the healthcare system would be spending $200,000 for only a year of productiv-ity that woman would add back to the world. (They may consider that a borderline return on investment (ROI), but they may opt to provide the treatment anyway.)
Let's look at another example. A 10-year-old is diagnosed with appendicitis, and it will cost $30,000 to treat the child. Treatment will add, say, 60 years to his or her life expectancy. So, doing the math means that the healthcare sys-tem would spend $30,000 to gain much more in productivity. That's a great ROI, so the decision would be a definite go.
Now let's look at someone's grandfather. He's 78 and he too has just been diagnosed with appendicitis, and without treatment he will certainly die. Let's say the cost for surgery and follow-up treatment is $80,000, but it will add little to his life expectancy. In this scenario, it may be deemed more economical --or cost- effective--to let him die.
It is essential that people making these deci-sions exercise great care when implementing a system meant to compare treatments. There is a very real difference between comparative effectiveness and cost-effectiveness. It could be quite easy--even with a formula--to give more weight to the cost side of the equation. The device industry shouldn't be caught off guard as to where comparative effectiveness research must logically lead.

Sherrie Conroy for the Editors

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