Google to Pay $500 Million in FDA Investigation of Pharmacy Ads Settlement
August 25, 2011 9:50 AM
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The Department of Justice stated that Google was aware that these ads were illegal since as early as 2003
The U.S. Food and Drug Administration (FDA) launched an investigation against Google in 2009 regarding whether the search leader knowingly accepted pharmacy ads online that were illegal. But Google is now paying a hefty sum in order to
settle these allegations
According to the Department of Justice (DOJ), shipping prescriptions to the United States from outside of the country is "generally illegal" because they are not FDA-approved for safety. The DOJ stated that Google was aware of this since as early as 2003, concluding that it knowingly accepted these
Only when the FDA investigation began in 2009 did Google make an effort to put a stop to the unlawful drug sales, said the DOJ. Google began accepting U.S. Canadian ads from certified pharmacies only at that point, and in 2010, it joined Microsoft and Yahoo as well as others in developing a nonprofit organization for the fight against illegal Internet pharmacies.
In May 2011, Google said it was
setting aside $500 million USD
for antitrust settlements, and now, that's exactly the amount it is paying to settle these FDA-related allegations.
"We banned the advertising of prescription drugs in the U.S. by Canadian pharmacies some time ago," said Google in a statement. "However, it's obvious with hindsight that we shouldn't have allowed these ads on Google in the first place."
The DOJ said that along with the $500 million payment would be "a number of compliance and reporting measures" as well.
"This settlement ensures that Google will reform its improper advertising practices with regard to these pharmacies while paying one of the largest financial forfeiture penalties in history," said Deputy Attorney General James Cole.
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8/25/2011 4:37:26 PM
I've critiqued that report before. The biggest problem with it is that it includes a weighted factor for "fairness" - how equally available health care is to the population. The more equitable the availability of health care, the higher the rank in that factor. And that factor was used to partly determine the overall ranking of each country.
The problem is, inequality in availability of health care is
factored into the data. If a quarter of your population is denied adequate health care, their poor health will drag down all your national health statistics. If you then add a
factor measuring equality of availability, you've double-counted it. Either you can't add it as a second factor, or you can add it but must eliminate it from the general data set by
the quarter of the population without adequate health care from the national health statistics.
To draw a simplified analogy, say you have two classrooms with 20 students each. In class A, each student receives 1 cookie (20 overall). In class B, only 3/4 the class receives a cookie (15 overall). The average for class A is 1 cookie per student. The average for class B is 0.75 cookies per student.
It makes no sense to then add
factor for the unequal distribution of cookies in class B. The unequal distribution already shows up in the lower average. Adding another factor for it is like saying only 3/4 the students in class B got cookies, so we need to ding class B's average by 0.75. Class B's average was 0.75 cookies per student, ding it by 0.75, so class B should get a ranking of 0.56 cookies per student.
If you do want to add a factor for unequal distribution, you have to look at
the portion of students who actually got cookies. 15 students got 15 cookies for an average of 1 cookie per student, ding it by 0.75, to get an overall ranking of 0.75 cookies per student in class B.
I'm not saying it's wrong to have equality as a factor. It can be useful for distinguishing between, say, class C which has 20 students but only 1 student got all 20 cookies, which yields 1 cookie per student which is the same average as class A.
But that factor has to be carefully weighted. Otherwise you can get a situation like class D, where 15 students got 1.25 cookies and 5 got 1 cookie. That's 23.75 cookies overall, or 1.19 per student. Considerably better than class A, and even the students who got the fewest cookies got as many as the students in class A (1 each). But the equality of distribution is such that only 3/4 of the students got the most cookies. So if you ding 1.19 by 0.75, you get an overall ranking of 0.89, which is lower than class A's 1.00. It's really easy to screw things up like this if you add equality as a separate factor
and don't control for it in your original data set
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