Here’s a question for all of us trying to apply scientific research and disseminate evidence-based programs:  How does one move the complex findings of a particular research study into useful knowledge on which non-scientists can make decisions? And to what extent are the media friend or foe in such efforts?

We’ve been treated to a great example in a widely-publicized study that came out last week.. Becky Jungbauer  at Scienceblogging [1. http://www.scientificblogging.com/truth_universally_acknowledged/depression_placebos_and_paxil],  summarized the news coverage of this finding on the effectiveness of antidepressant medication. With millions of Americans taking antidepressants, the question of whether they work or not is by no means just an academic one.

The media frenzy over this study could leave a casual reader with a simple (and wrong) impression: That antidepressants don’t work. As Jungbauer notes, many people reading headlines or half-listening to the news might assume that  “antidepressants in general don’t really work unless you’re standing on a ledge.” Headlines such as these appeared: “Study: Antidepressant lift may be all in your head,” and “Placebos: Pretty Good for Depression.”

I’m not going to comment on all the details of the study, which is summarized well in the New York Times [2. http://www.nytimes.com/2010/01/06/health/views/06depress.html],  and on Jungbauer’s blog. Rather, it’s great example for those us translating research because it shows just what a hard job this can be. How can a lay person take findings like these reported in the media and use them?

Let’s begin with the article itself, “Antidepressant Drug Effects and Depression Severity: A Patient-Level Meta-Analysis,” published in the prestigious Journal of the American Medical Association. The first clue to potential problems with translating the findings lies in that subtitle: What the heck is a meta-analysis?
Most people probably assumed that the authors themselves did a study and reported the results. That isn’t the case – they used statistical methods to merge the findings of six previous studies done by various research groups. The results of their analysis are entirely dependent on the quality of the studies reviewed.

What if a reader decided to go to to JAMA itself, to read the article? I can imagine a depressed acquaintance of mine (now worried whether she should be taking antidepressants) trying to make sense of statements like: “Mean intake severity did not differ as a function of treatment condition (F1,711= 0.05, P=.82), but the 6 studies did show different mean intake severity levels, reflecting differences in inclusion criteria (F5,711=79.56, P_.001).” How many concerned readers can explain what “a randomized, double-blind, placebo-controlled trial” is?

It would take some time and effort to see that  the study doesn’t say the anti-depressants don’t work – they did. But just not more than a placebo (a sugar pill).

And it would take even more careful reading  to note that the drug used in three of the six studies is no longer widely used, and that people in the placebo groups typically received some form of counseling and interaction (even if not formal therapy), which might have had an anti-depressant affect. And few people have easy access to JAMA to begin with, even if they wanted to read the article.

The moral of this story  is how hard we need to work to make sure people understand the science behind any generalization. Is there some way that we could ensure that “ordinary” citizens can obtain a minimal level of scientific literacy, so they can discern what they should be influenced by and what they should ignore? Might words like “meta-analysis,” and “randomized-controlled trial” need to become generally understood terms?

I am an ignoramus about car repair. You wouldn’t find me looking under the hood of my car unless I suspected a small animal was trapped inside. But I have learned enough of the language so I can communicate with my mechanic and evaluate in an elementary way what he plans to do to my car. That’s what we need with science.