Blogs
CATEGORIES [ GENERAL ]

Comparative effectiveness: rationing health care or reforming it?

BY DEBU TRIPATHY | JULY 22, 2009

"Comparative effectiveness" has become a buzzword in the health field and a political powder keg--a big ticket item to be financed by the federal stimulus package and endorsed by the Institute of Medicine. Opponents of this discipline say that it is the first step toward rationing health care, while proponents say this is necessary to save our health system from total collapse. What exactly does this term mean and why is it so polarizing?

Comparative effectiveness basically means the study of how much cost and resources a given medical test, procedure, or treatment consumes in relationship to how much it improves health, which is usually measured by how long someone lives and with what quality of life and ability to function. In essence, it is the "bang for the buck" you get from health care dollars, for which we all pay for one way or another.

The reason that it has negative connotations is in part due to the fact that in England the National Institute for Health and Clinical Excellence (NICE) has used cost calculations per "quality adjusted life-year (QALY)" saved as an index for what the National Health Service (the nationalized health program) will and will not cover anything that costs more than 30,000 British pounds per QALY saved. While there are exceptions to this rule, many have considered this to represent health care rationing. Not only might this disallow a potentially life-saving treatment, but it might discourage the private sector from investing in medical technology and stymie the advances we have enjoyed over the recent years.

However, research in comparative effectiveness might not be such a bad thing after all. For one, we know that spending more money on tests and treatment can improve health, but only to a certain point. Many health strategies, particularly those that prevent disease in the first place (exercise, smoking cessation, vaccines, hypertension treatment), improve longevity at lower costs, but you get diminishing returns for every extra dollar spent (see figure below). It only makes sense that the line has to be drawn somewhere since we have finite health care dollars to spend. As you can in the figure, the difference between the two dashed lines represents a very large outlay for a small potential benefit.

On the other hand, the problem with benefit-cost curves is that they represent the average of all patients in the analysis. They hide the fact that a few people who get a "low yield" therapy might actually see spectacular results. Of course, when you or your loved one has cancer, you may roundly object if the treatment plan your oncologist outlines is beyond the threshold for payment. The mindset changes to wanting to take the gamble when it applies to you. As we develop better tools to personalize therapy and better identify more precisely who benefits from low yield therapies, we can take less gambles and make more informed decisions.

Comparative effectiveness research allows us to know the shape of the curve, but it does not force us to adopt rules. It can help us make more informed decisions as to how to spend health dollars for the best collective health of the population--these decisions cannot rest with just lawmakers, policy wonks, or even physicians and scientists. They must be made with representation from all who pay into health care, so a broad consortium must deliberate on what to do with the data. This is much better than many of today's arbitrary coverage rules made in the dark.

What we need in addition to comparative research is more study on biomarkers and predictive tests that will focus expensive therapies where they are most effective, significantly lowering the cost per QALY saved and thereby lowering overall health care costs. One compromise could be that a new expensive technology or drug could be conditionally approved if the sponsor agrees to conduct further studies to define subsets of patients who benefit the most--otherwise, the cost (or covered amount) would then have to fall in line with an agreed-upon cost per QALY saved. This might give us the best of both worlds--an environment that supports the development and availability of new technology, but also requires efforts to ensure the costs (and side effects) be applied in cases where the chances of success are reasonably good.

Additional information on comparative effectiveness can be found in the New England Journal of Medicine:

A Time for Revolutions -- The Role of Clinicians in Health Care Reform July 22, 2009

Comparative Effectiveness -- Thinking beyond Medication A versus Medication B July 23, 2009

Prioritizing Comparative-Effectiveness Research -- IOM Recommendations July 30, 2009

comparative effectiveness figure

RELATED POSTS

COMMENTS

I featured this today at ZDNet's SmartPlanet. http://tinyurl.com/nxquxm
- Posted by Dana Blankenhorn 7/23/09 6:44 AM

I think one good recent example of Comparative Effectiveness Research in cancer medicine was a Duke University cost savings study on the impact of a chemoresponse assay (which I advocate) on treatment costs for recurrent ovarian cancer. They sought to estimate mean costs of chemotherapy treatment with or without use of a chemoresponse assay.

They estimated mean costs for 3 groups: (1) assay assisted: 75 women who received oncologist's choice of chemotherapy following chemoresponse testing (65% adherence to test results), (2) assay adherent: modeled group assuming 100% adherence to assay results, and (3) empiric: modeled from market share data on most frequently utilized chemotherapy regimens. Cost estimates were based on commercial claims database reimbursements.

The most common chemotherapy regimens used were topotecan, doxorubicin, and carboplatin/paclitaxel. Mean chemotherapy costs for 6 cycles were $48,758 (empiric), $33,187 (assay assisted), and $23,986 (assay adherent). The cost savings related to the assay were associated with a shift from higher- to lower-cost chemotherapy regimens and lower use of supportive drugs such as hematopoiesis-stimulating agents.

Conclusion of the study was that assay-assisted chemotherapy for recurrent ovarian cancer may result in reduced costs compared to empiric therapy. What most medical oncologists do now (PMID: 20417480).
- Posted by Gregory D. Pawelski 5/5/10 1:35 PM

ADD A COMMENT

Your comment will appear once approved by CURE staff:
* Required fields