Antidepressants and Suicide

Whether antidepressant use increase suicide risk in the short-term is an ongoing debate in the clinical medicine and health policy worlds. A few years back, based on some evidence that antidepressant use was correlated with a higher risk of suicide, the FDA issued a "black box" warning, forcing manufacturers to acknowledge the increased risks on packaging and materials related to the drugs. The public responded predictably: antidepressant use dropped notably after the warning. (See this 2006 article for more on the issue)

The biomedical model that links antidepressant use to suicide is the following. Depressive symptoms involve both mood and reduced activity. Antidepressants, it is thought, start working by increasing activation before mood. As a result, the hypothesis is that, in the short term, people who have suicidal thoughts may actually carry it out because they are now "activated."

But is there another explanation that could explain the link between anti-depressants and suicide? An important possibility is selection: anti-depressants are taken by people with depressive symptoms, who are more likely to commit suicide. The fact that the association between anti-depressant use and suicide only exists in the short-run could be explained by this selection model as well: those who would commit suicide would do so, and those who are left may have been unlikely to do so in the first place or were prevented from doing so by the medication.

The overall literature on anti-depressants and suicide gives some support to the selection hypothesis. First off, the relationship between use and suicide seems to vary from study to study and across countries. We would not expect this if the biological model were correct. Second, the "black box" warning provides an interesting time series test. In several countries, the use of anti-depressants dropped after the public was informed about the potential risks, and the incidence of suicides actually increased. This runs counter to what we would expect from the biological mechanism model.

A recent paper (forthcoming in the Journal of Health Economics) provides what I think is the most careful analysis of the causal relationship between anti-depressant use and suicide, taking explicitly into account the potential selection bias issue. The authors, Jens Ludwig, Dave Marcotte and Karen Norberg, utilize an instrumental variables (IV) approach:

In this paper we present what we believe to be the first estimates for the effects of SSRIs on suicide using both a plausibly exogenous source of identifying variation and adequate statistical power to detect effects on mortality that are much smaller than anything that could be detected from randomized trials. We construct a panel dataset with suicide rates and SSRI sales per capita for 26 countries for up to 25 years. Since SSRI sales may be endogenous, we exploit institutional differences across countries that affect how they regulate, price, distribute and use prescription drugs in general (Berndt et al., 2007). Since we do not have direct measures for these institutional characteristics for all countries, we use data on drug diffusion rates as a proxy. We show that sales growth for SSRIs is strongly related to the rate of sales growth of the other major new drugs that were introduced in the 1980s for the treatment of non-psychiatric health conditions. This source of variation in SSRI sales helps overcome the problem of reverse causation and many of the most obvious omitted-variables concerns with past studies. Our research design may also have broader applications for the study of how other drug classes affect different health outcomes.

Using this strategy, they find that a 12% increase in anti-depressant sales is associated with a 5% decrease in suicides. Interesting stuff.

While the main innovation in the paper is the use of instrumental variables, this may also the main weakness. First, as discussed in previous posts, in order for the IV approach to work, the instruments should only affect the outcome through the exposure of interest. The authors in this paper go through some trouble to establish the validity of their IVs. Its all carefully done and compelling, but, depending on your priors about institutional differences in pricing strategies, you may still have qualms about the IV.

The other issue with IVs, is that the effect it computes applies to those people (or here, groups of people) that are most affected or sensitive by the instrument (see this earlier post for more on this). Thus, it is very important to note that the finding in this paper does not rule out the possibility that anti-depressant use might have adverse impacts on some populations. I think this is of particular interest to clinicians, and there are new methods in econometrics that can help uncover heterogeneity in treatment effects (see this paper on the heterogeneous impacts of treatment on breast cancer, utilizing methods developed by Heckman and co-authors).