Technology’s effect of survival rates and health care spending

How do new technologies affect longevity and health care cost? A working paper by Chandra and Skinner investigates just this question.

The authors categorize medical innovations into three broad categories.

• Category I. These are the home run treatments. The treatments are highly cost effective for all patients with the disease. For instance, the development of antibiotics was highly effective in reducing pneumonia mortality.
• Category II. These treatments are cost effective for some patients, but not others. Angioplasty, for instance, dramatically improves survival after a heart attack if administered within 24 hours, but yields no survival benefit and only modest functioning improvements for those with stable coronary disease.
• Category III. These treatments have small or unproven benefits. Arthroscopic surgery for osteoarthritis of the knee, for instance, was found to have no medical value in an RCT compared to a “placebo surgery.” Nevertheless, 650,000 such surgeries were being performed annually at a cost of more than $5,000 each.

Using this taxonomy, the authors aim to determine how survival and cost change over time due to each type of innovation.

Using cardiovascular disease as an example, they note that 44 percent of the reduction in mortality from 1980 to 2000 was due to improved health behaviors. Another 22 percent of the decline was due to inexpensive Category I treatments such as aspirin and beta blockers, 12 percent was due to Category II treatments like angioplasty, and perhaps 10 percent was due to Category III treatments. On the cost side, the spread of Category I and II treatments appears to have contributed only modestly to cost growth, suggesting a larger role for Category III spending. Despite the rapid diffusion of “home run” technologies like beta blockers during this period, the average cost of saving an additional life-year tripled, to nearly $250,000.

Source:

• Amitabh Chandra and Jonathan Skinner. “Technology Growth and Expenditure Growth in Health Care“, NBER Working Paper #16953. (see also NBER Bulletin on Aging and Health)