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Editorial: Beware of Studies Claiming that Social Factors are "Independently Associated" with Biological Complications of Surgery.

How often have we read claims like, “After controlling for confounding variables, patients with Medicaid were more likely to develop infection after surgery than were patients with commercial insurance,” or its evil twin, “Even after controlling for all relevant confounding patient characteristics and risk factors, patient insurance status predicts the risk of infection, and should be considered when assessing patient risk factors prior to surgery”? Some details have been changed to protect the guilty, but if you spend much time with your nose in orthopaedic journals, you have seen claims just like those. As analytic approaches become more robust, it is easy to become more trusting. We know that the old highschool statistics approach—using a chisquared test to compare the proportion of patients with one kind of insurance who develop an infection and compare it to the proportion of patients with another kind of insurance—is insufficient. Researchers and readers alike know that we need to control for confounding variables and so clinician scientists throw additional factors into the mix, like smoking or BMI, and run some sort of multivariable analysis. After the experiment is over, they dragoon an unsuspecting statistician, ask that poor soul whether the whole exercise was kosher, and assert a claim of “independent association.” Here’s why this is a problem. The corporate logo on a check—whether Medicaid, Medicare, or some commercial payor—is a social factor that has no biological component to it. It cannot possibly increase the risk of a biological complication like infection. And with rare exceptions [4], neither can race. That being so, any study claiming that insurance status or race predict, influence, or even are “independently associated” with medical or surgical complications—and many studies have done so—almost certainly has not controlled for all relevant variables. The best it is likely to be able to claim is that the list of factors considered in the statistical model, which almost never includes the complete roster of potentially confounding variables that matter, was unlikely to contain the culprit. Social deprivation often goes handin-hand with poorer health and inadequate social support, as well as with poorer payor status. That being so, if a study evaluates endpoints that might be influenced by poor health or inadequate social support, poverty is, by definition, a confounding variable. Imagine a study that evaluates whether patients with yellow fingers are more likely to develop lung cancer. That study would need to do more than control for patient age, sex, and hair color; if that study were to fail to include cigarette smoking in its statistical model, it will draw the misleading conclusion that yellow fingers are independently associated withmalignancy. Poor healthmay result in poverty (or vice versa), and povertymay result in a patient being insured byMedicaid, butMedicaid itself cannot cause surgical complications. Claiming otherwise may exacerbate access-to-care problems, and even nudge surgeons who accept patients with Medicaid to note the increased risk (and perhaps even counsel patients differently; The author certifies that neither he, nor any members of his immediate family, have any commercial associations (such as consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article. The opinions expressed are those of the writer, and do not reflect the opinion or policy of CORR or The Association of Bone and Joint Surgeons.