I have become a little frustrated lately with the
barrage of inaccurate statements that are posted all over the Internet. The
statements that I find the most frustrating are those that purport to cite
medical research and then have attention grabbing headlines that are phrased to
excite peoples' fears about their environment. Many times they will quote
an article from a respected journal but will somehow misconstrue the actual
intent of that article. In any case, it has driven me to this. I
want to begin a discussion here of the types of epidemiological evidence and help
you learn how to judge how much belief you could reasonably attach to a study
that might be cited in one of the articles. I thought I would begin at
the bottom of the ladder, so to speak, of weight of evidence. Today's
posting will discuss cross-sectional studies.
First, as you probably know, a cross-sectional
study is like taking a snapshot on a particular day. Our
national census is cross-sectional in that you report your address on the first
day of April of census year. It doesn't matter where you resided the day
before or where you are moving to the next day. It is only for that day.
When a cross-sectional analysis is performed you are only looking at the
characteristics that apply for the date and time the evidence is gathered.
This means that both the outcome and the exposure are from the same time
period.
There are a lot of questions you should ask
yourself about this type of analysis. For example, if you were trying to
make an inference about a long-term exposure, you would need to have a window
of exposure to justify the connection between exposure and outcome. It might
also help if you could quantify the exposure. The classic example is the father
of modern epidemiology John Snow. He deduced the cause of the Broad St. cholera
epidemic was water from a particular well. He removed the pump handle and
the epidemic subsided.
If you are looking at a highly stable
population where nobody moves in or out the error induced by people moving
around is minimal. If your population is going through an upheaval of any
kind in which there is migration of populations, from the rural areas to the
cities, for example, any exposure that is focused on urban population might be
biased due to what is basically an inaccurately measured exposure. So you
can ask yourself questions about the population stability where the
cross-sectional analysis subjects are situated. Also, the outcome might
be associated with the place the subjects live. If you are doing a cross
sectional study in Rochester, MN, where the Mayo Clinic is located, there are
some outcomes that will show up differently here if people can move to
Rochester, MN, to be treated for the particular disease you are researching.
I know this can sound very complicated and when I
first began my graduate program I almost threw my hands up in the air when
these questions arose because they just seemed insurmountable. That is
why these types of studies should be carefully done and all of these questions
addressed. So it would be expected any issue that would make
cross-sectional studies invalid or biased should be addressed in the article publishing
these results.
If you doubt these biases were adequately
addressed, you can see what direction the analysis results would be biased in
the worst case. If you are satisfied the investigators dealt adequately
with these possible biases, the next question would be, what does it all mean?
The evidence gathered in cross-sectional studies is of value to generate
hypotheses for studies to be conducted that would provide better evidence.
What those studies could be is the subject for another day.
