What Can We Learn From Cross-Sectional Studies?

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.

It is wise to keep in mind that correlation is not causation, but as Edward Tufte has said, 'it sure is a hint'.    

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.