Our findings provide mixed support for our hypotheses regarding the extent of customization. On the one hand, most physicians were using a variety of class combinations, not starting every patient on the same combination. This is compatible with some degree of customization, although not proof of it, since use of a variety of regimens could occur for other reasons. In particular, patient clinical status did not appear to matter as much of the time as one would expect, if physicians were customizing their prescribing to patients’ clinical presentations. In fact, only four (of 11) clinical status variables (e.g. depression, roughening) ever affected class choice in either model. Other conditions that one would have expected to matter, such as mania, had no apparent effect on the choice among medication class combinations. This accords with an earlier study examining medication use at study intake for this population. Busch et al (2009)
found that neither the use of antimanics nor the choice between AP only and any mood stabilizer was affected by various medical conditions (e.g. thyroid, hepatic, renal) that had been expected to affect prescribing.
The results of our simulation of the effect of assigning hypothetical patients to each psychiatrist highlight the pronounced variation across physicians in which medications are prescribed. It is clearly not the case that all physicians are following the same algorithms, given this finding of substantial differences in class choice between physicians treating observationally similar patients. This is compatible with what Frank and Zeckhauser (2007)
call ‘My Way’ prescribing: behavior where physicians use very different algorithms based on their own experience and training. To the extent they underweight broader evidence on what works, the resulting variation would not be customization in the desirable sense. One could argue that the patient characteristics we used are not sensitive enough, and that the variation in prescribing we observe could be a response to unobserved heterogeneity, implying that customization is occurring after all. For example, our data did not include information on patient preferences or patient history of treatment and treatment-related side effects. However, given the importance of the clinical status variable, it is striking how little key clinical conditions appear to affect choice of drug class. Of course, the same conditions could have a stronger effect on choice of medications within a class, which we did not study.
Our finding that nonwhites were less likely than whites to receive the MS/AD combination warrants further analysis, particularly since the preferred treatment for severe depressive episodes without psychosis is the combination of mood stabilizer and antidepressant (Sachs et al., 2000
). Recent work has shown that black patients with bipolar disorder are less likely to be prescribed the newer SSRI-type antidepressants (Kilbourne and Pincus, 2006
), a prescribing choice which may play a role in the decreased likelihood of the nonwhites in our study receiving the MS/AD combination. African-Americans and Latinos with bipolar disorder have been found to be less likely than non-Latino whites to receive mood stabilizers or antipsychotics (Depp et al., 2008
), which raises questions about care quality.
Our finding that 16% of patients were prescribed no mood stabilizer is lower than the 41% reported by Blanco et al (2002)
or the 52% in the NCS-R study (Merikangas et al., 2007
), but similar to the rate in Unutzer et al (2000)
. All these findings are at variance with the recommendation that this class of medication be a vital part of pharmacotherapy with bipolar patients throughout all phases of treatment.
Interestingly, when we stratified patients by clinical status, 10.3% of manic patients were not prescribed any mood stabilizer. Also, although the first-line preference is for mood stabilizing medication alone, or (if psychosis is present), mood stabilizer and antipsychotic (Sachs et al., 2000
), nearly half (48.2%) of patients with mania were receiving something other than these two combinations.
Several limitations of our study should be kept in mind. First, the data used come from a study administered chiefly in academic medical settings, where physicians may have been more exposed to recent research than physicians elsewhere. Similarly, the physicians in this study received more guidance about prescribing than is probably typical elsewhere. These features limit generalizability of our findings to the wider universe of physicians prescribing for bipolar disorder. A second important limitation is that, despite the richness of clinical detail available, we lack information about some other potentially important influences on medication choice that may have been observable to the physician, such as prior medication and disease history, patient preferences, history of side effects, and other clinical issues such as pregnancy. The available data do not record which patients either had previously experienced an inadequate response to any preferred or first-line medications initially prescribed, or had rejected such treatment recommendations. Ideally, psychiatrists’ decision-making is guided not only by the patient’s current clinical status but also their knowledge of the frequency, severity and consequences of the patient’s past episodes. Also, guidelines recommend medications but also emphasize the necessity of monitoring patients’ response to medications, and augmenting or switching as needed, so some proportion of prescribing that at first blush seems unaligned with guidelines may not be. Inability to control for such influences could help explain the nonsignificance of the clinical variables we did include. On the other hand, these variables may be less relevant to the choice of medication classes, which is what we study. For example, knowing that a patient once reacted badly to lamotrigine would discourage future prescribing of that drug, but would not discourage future prescribing of the entire mood stabilizer class.
Another omission is what kind of insurance coverage patients had for psychotropic medications (e.g. what prior authorization requirements or cost-sharing tiers applied). These limitations are less serious for a study of choice among drug classes (as opposed to among medications). The reason is that while insurers often use copayments or administrative controls to limit access to specific medications within a class, they rarely use those controls to bar access to an entire class. Thus, insurance features should be less important for the present study than for studies seeking to explain the specific drug chosen. Third, while we used physician fixed effects to control for differing practice styles, we were not able to use these for all physicians, as many were low volume. Fourth, the sample size (1,759 patients) may limit the ability to detect statistically significant effects, given the number of parameters being estimated. Finally, it is unlikely that most of these patients were truly new to treatment, given the chronic nature of the disorder, and physicians may have been influenced by patients’ medication experiences prior to the study, which we do not observe.
The results show substantial variation among physicians in their prescribing for bipolar disorder, even after controlling for our observed measure of clinical status. In addition, our measure of clinical status did not appear to strongly affect prescribing, although our study had limited power to detect such effects. The study does not find evidence for a high degree of customization in physicians’ selection among medication classes, although customization could still have been occurring between medications within each class. Further research is needed, using larger datasets with additional information on variables such as medication history and patient preferences.