When do diagnostic tests improve mortality?

I thought this post, originally published last May, was worth revisiting apropos this paper that came out in this week's Archives of Internal Medicine. Below I discuss some of the data in the paper, as they were presented in an abstract at a meeting last year, as the context for understanding various mortality statistics. 

The question the title of this post poses is well worth asking, particularly as we argue about the merits of mammography screening. The USPSTF has really stirred up the hornet's nest with this one, and the politicians cannot help but get on their populist pulpit, ignoring the facts completely. Oh well, what else is new?

But the question remains: do screening or diagnostic tests that are more sensitive save lives? A great talk on pulmonary embolism detection and outcomes by a recent graduate from the Dartmouth group at the American Thoracic Society last week prompted me to clarify this. We all hear that mortality from many diseases has decreased over the last few decades. But is this true? In order to answer this question, one has to ask what is meant by mortality. Even people well versed in epidemiology and biostatistics occasionally blur the lines between mortality and case fatality, and to our question the distinction is critical. Case fatality is defined as the proportion of patients with the disease that dies, while mortality is a population-based measure, a proportion of all of the population at risk for the disease that dies. The difference lies in our old friend the denominator, which will always keep us honest.

Let's go through a simple example to illustrate this concept. Let's pretend that the total number of cases of disease D diagnosed using stone-age test T 30 years ago was 100 in a population of 10,000 people. Of these cases, 90 died, giving us the case fatality of 90% and mortality of 9 per 1,000 population. Now, we have a new test for D, a super-Doppler-MRI-PET-cyberscan called über-T, a much more sensitive test than the old "gold standard" test T. And now we detect 1,000 cases of D in the population of 10,000 people. Of the 1,000 cases detected by über-T, 90 have died. The case fatality now has decreased dramatically from 90% to 9%, and we can pat ourselves on the back for a job well done, right? Not so fast, the population mortality from disease D has remained a steady 9 per 1,000 population!

So, what does this mean? Does it mean that über-T, which costs 2 orders of magnitude more than its predecessor, is worthless? Well, decide for yourselves. What it means to me is that the additional cases detected by über-T, though finding earlier stage disease, thus increasing the denominator for the case-fatality calculation, has had no impact on the numerator and therefore has not in fact improved the only mortality that matters: population mortality related to the disease.

So, next time a politician tells you how well we are doing with technological innovation in disease management, ask this simple question: Has all the money and innovation really altered the important outcomes, or is this all smoke in mirrors, a mirage created by our irrational belief that technology is our salvation? This may be an uncomfortable epiphany for some. But think about the 900 excess cases of the pseudo-disease diagnosed in our example above -- how many people could have been saved becoming a chronically ill person, how many complications of follow-up procedures could have been avoided, and yes, how much money could have been spent on something other than healthcare? And asking these questions may help us to identify technological advances that actually improve our lives, as opposed to those that merely create attractive business opportunities and stimulate the economy.