...One big-picture aspect of this huge transformation in American health care which seems to be receiving little or no attention is its heavy emphasis on preventive medicine. We have been hearing for some time about how preventive medicine will save substantial sums of money and thereby make the overall health care system far less costly. Of course, such rhetoric has an enormous appeal at a surface level — after all, if you can prevent diseases, you certainly don’t need to spend money to cure them.
Who could argue with this?
But this innocent-sounding, simplistic Trojan horse will prove deadly for American health care, and end up empowering the bureaucrats and politicians who will, in fact, gain the most from this change in direction.
When we talk about preventive medicine, we are generally speaking of two general areas: the screening and early detection of diseases, and lifestyle changes and therapy to reduce long-term medical risk. Screening and early detection of diseases is appealing concept, but devilishly difficult in practice. The idea sounds wonderful: do a simple, inexpensive test; detect the disease earlier, when it is simpler and less expensive to treat; and you will be healthier in the future, requiring far fewer health resources. The problem lies, as I have discussed elsewhere, in the malignant mathematics and sickening statistics of applying medical screening to large populations. Simply put, no screening test is perfect, and all such tests generate both false positives — telling you that you have a disease, when you do not — and false negatives — telling you you’re fine when you really have the disease. Even with an extraordinarily accurate test the problem lies in applying it to large populations. If you have a cancer screening test with a 1% false positive rate (an extraordinarily low number in the screening business), and have a disease which occurs in one patient out of every 10,000, applying the test to 10,000 patients will generate 100 false alarms (false positives) for every patient with the disease. These false positives all require additional testing or procedures to determine whether in fact the abnormal test really means you have the disease. And herein lies the economic trap: you will in fact spend an extraordinary amount of money on patients without the disease for every patient detected who does have the disease. This phenomenon has been well demonstrated in almost every study of screening — to wit: screening actually increases rather than reduces medical costs.
Of course many simple screening tests and procedures are used every day in medicine. When you go to the doctor, your blood pressure is checked, your cholesterol is measured, you stand on the scale and are weighed, and asked whether or not you smoke. If your blood pressure is high, you will likely be started on medication, and it is also likely that you will need to stay on this medication indefinitely. If your cholesterol is elevated, will be encouraged to exercise, make dietary changes, and lose weight (most of which you won’t do), but will also likely will be started on cholesterol-reducing medication, likely for the long-term. Of course, we recognize that this is appropriate for the reduction of risk from high blood pressure or high cholesterol. What may not be recognized, is that many people with high blood pressure or high cholesterol, unrecognized and untreated, may not have significant problems from these disorders for many years, if ever.
Suppose that 100 people with high cholesterol levels take statins, a common treatment for high cholesterol. Of them, about 93 wouldn’t have had heart attacks even if they had not taking the medication. Five people, on the other hand, will have heart attacks despite taking the statin. Only the remaining two out of the original 100 avoided a heart attack by taking the daily pills. In the end, 100 people needed to be treated to avoid two heart attacks during the study period—so, the number of people who must get the treatment for a single person to benefit is 50. This is known as the “number needed to treat” — and is a common way in which health researchers determine the cost and effectiveness of preventive therapy. Ideally, we will get better at selecting those patients at the front end who actually will benefit from taking the drug, and therefore avoid administering it in those who ultimately will not need it. But such health forecasting is far, far from perfect, and there will always be a need to treat patients perceived to be at risk even though time will ultimately find them not to be at risk at all. The human organism in health and disease is far too complex to eliminate this reality...
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