Statistics- Page 2
But hold on. Look at the difference between the positives and the negatives. The positives
were about twice as likely to be black and to lack a high school diploma. Why not blame poor education or cultural factors correlated with race for their high discharge rate? (In
fact, civil rights groups have complained that the Navy uses testing to keep blacks out of
the service.)
Actually, there is an even simpler explanation. One of the conditions for being admitted
to the Navy after testing positive for marijuana was agreeing to submit to rigorous
"surveillance" thereafter, including unusually frequent drug tests (all naval personnel
undergo three such tests a year). A third of the positives were discharged because they
failed another drug test. There are two problems here. One is that these sailors had no
documented loss of productivity and exhibited no misbehavior except for drug use. So the
natural conclusion isn't that people who have used drugs are more likely to be deficient
workers (the only conclusion that would justify workplace testing), but rather that people
who have used drugs are more likely than others to use drugs in the future. But even this
conclusion is undermined by the second problem with the study: these sailors, facing
more frequent testing than sailors in the control group, stood a better chance of getting
caught using drugs.
If one discounts the sailors discharged for failing subsequent drug tests the difference in
the discharge rates between the positives and the negatives shrinks from twenty-four to
ten percent. Other forms of extra surveillance could easily account for that difference.
Obviously, the more closely you watch someone, the more likely you are to see him
misbehaving, especially if you think he is a troublemaker to begin with.
Another study cited by Walsh, conducted by the Utah Power and Light Company, makes
the Navy experiment look like a paragon of scientific rigor. The Utah study compared the
work history of employees who tested positive for drugs with a control group of
employees whose ages and jobs were similar. The data showed a "significant difference
between drug users and nonusers in terms of being involved in accidents, being absent
from work, and overutilization of health benefits," Walsh told me.
When one reads the study two flaws quickly stand out: there were only twelve positives
in all (eleven for marijuana and one for cocaine), an absurdly small sample, and the
control group was never tested for drugs. The study's conclusion could be rejected on
these grounds alone.
But there is an even bigger problem. Eight of the twelve "drug abusers" (to use the Utah
researchers' term) were tested because they were in accidents, and some were injured and
needed time off to recuperate. Of the four remaining positives, two were tested for other
performance-related problems and two because they had enrolled in a substance-abuse
program. High absenteeism almost invariably precedes -- and precipitates -- both
performance-related testing and submission to a substance-abuse program. Moreover, all
employees who undergo a test are suspended until the results come back, which usually
takes three or four days.
Incredibly, the Utah researchers included in their calculations the accidents and
absenteeism directly associated with the testing of the twelve subjects. By this logic, you
could link any trait to accidents and absenteeism. Round up employees who have been in
an accident or have been absent a lot, test them for, say, type 0 blood, and send them
home for a few more days of absenteeism. Then compare the accident and absenteeism
rates within the type 0 group with those for a "control" group with no particular history of
accidents or absenteeism -- and whose blood type, in keeping with the Utah
methodology, wouldn't even be tested; it would just be assumed not to be type 0.
Surprise, surprise: type Os had more accidents and missed work more often than people
whose blood wasn't tested. Better get rid of everyone with type 0 blood.
And what about the "overutilization of health benefits" that Walsh had mentioned? He
apparently misspoke. In the introduction to the NIDA monograph he calls the health
benefits data "inconclusive." In fact, the positives consumed almost fifty percent less in
health benefits than the control group. If the positives had used fifty percent more, would
Walsh have found that "inconclusive"?
The other utility study cited by Walsh, which was done at the Georgia Power Company,
also focused on employees tested "for cause." But the Georgia researchers used a
different control group: the 116 people who came up positive were compared with 713
who passed the test. This comparison, ostensibly fairer than that in the Utah study, found
that the positives missed about five more days of work per year than the negatives.
But even the methodology that yielded this modest finding is flawed. The authors of the
study note, with refreshing candor, that "the primary subject for the database is the
problem employee." Indeed, the negatives and positives all missed work much more
often than the company average. It's kind of like testing burglars for marijuana and
concluding that -- because those who tested positive had robbed a few more houses than
those who did not -- marijuana makes ordinary people more likely to steal.
Moreover, couldn't alcohol, which is often consumed in conjunction with illegal drugs,
contribute to the higher absenteeism of the positives? None of the employees was tested
for alcohol. Of course, tests wouldn't prove much anyway, since alcohol is detectable in
body fluids for only six hours or so, whereas cocaine persists for two or three days and
marijuana for up to a month. That may be one reason that this and some other studies fail
to consider the possibility that alcohol abuse is sometimes responsible for misbehavior
attributed to illegal drugs.