Diagnosis is Not Enough, Measuring Medical Outcomes is Critical | Big Think

Diagnosis is Not Enough, Measuring Medical Outcomes is Critical

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Biochemist Paul Nurse says that if you make incorrect assumptions in analysis, things can go bad quickly for public health.

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Paul Nurse:

Paul Nurse, Ph.D, is a British biochemist. He was awarded the 2001 Nobel Prize in Physiology or Medicine with Leland H. Hartwell and R. Timothy Hunt for their discoveries regarding cell cycle regulation by cyclin and cyclin dependent kinases. He became Rockefeller University's ninth president in 2003.

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TRANSCRIPT:

Question: How important is measuring outcomes?

Paul Nurse: Measuring outcomes, particularly in cancer, is actually crucial because if you make incorrect assumptions in the way you're doing analysis, things go bad rather quickly actually because you get statistics that show certain cancers, or certain diseases, are dramatically increasing, when it may be only that we are diagnosing them better, and then everything goes wrong in trying to think about it.

What I think about this public health issue is that by having better diagnosis, I have talked a little bit about how you can use molecular tools to get better diagnosis, which I think will classify disease better, by using more modern techniques and applying them in sensible ways, I believe we are likely to get better information upon which public health statistics can be acquired. I do think it's important because that public health information is going to be important for the population-based studies that are necessary to understand prevention better, the genetic effects and environmental effects. If we have imprecise information there, we're going to get deeply misled. And, once again, will give rise to quackery because people will read, maybe allergies have increased tenfold or something, and then they say this is due to pollution or whatever, because this is how the way people think when in fact it may have nothing to do with that, and we get campaigns in favor of certain approaches which is simply totally misplaced.

One example, which was really close to a tragedy, was the so-called "triple jab" for immunization of young children against several diseases. There was very flawed research which suggested that giving the three vaccines together would cause autism. This was barely taken seriously by the clinical community because the data was not very good. But because it got such public support, this changed the way immunizations were being carried out, and then led to a rise of the very diseases that you're trying to eliminate. That's a consequence of not reporting properly the data that you have, because autism was seen to be on the rise, in particular circumstances, when in fact it was simply being diagnosed better and it appeared at a certain time in life when these inoculations were given.

Accurate information about disease and disease onset is crucial for good prevention and good heath care delivery.