Rapidly rising health care spending is a great concern of policy makers, and the diffusion and use of new treatment technologies are generally viewed as the primary drivers of spending increases (1). Antipsychotic medications represent one of the most important new mental health treatment technologies of the past several decades. Beginning in 1989, several second-generation antipsychotics were introduced, followed subsequently by several reformulations of those drugs, for example, extended-release formulations. At the time, a large body of research concluded that second-generation antipsychotics were more efficacious and had a lower incidence of extrapyramidal symptoms, such as tardive dyskinesia (2,3) than first-generation antipsychotics (4,5). Second-generation antipsychotics quickly became first-line treatment for psychotic disorders (6).
More recently, two publicly funded trials in the United States and the United Kingdom showed that with the exception of clozapine, second-generation antipsychotics are no more effective than their predecessors (7,8), causing some experts to question the wholesale shift away from first-generation antipsychotics (9,10). Evidence associating use of second-generation drugs with a substantially increased risk of weight gain and metabolic side effects (11–13) and with a far smaller advantage with regard to the risk of tardive dyskinesia (14) has intensified the reassessment of the role of these medications in schizophrenia treatment (15,16). The cost-effectiveness of using second-generation antipsychotics is particularly salient to Medicaid and other payers because of the drugs’ high prices and the strain they place on state budgets (17).
Little is known about the factors that contribute to physicians’ adoption of second-generation antipsychotics. Studies of other medications indicate that most of the variation in prescribing is explained not by patient clinical characteristics but by physician preferences for a particular drug (18–21). Few empirical studies have identified influences on physicians’ adoption of medications, other than small effects of physician age and gender (22,23). For example, there has been little study of the role of medical training or practice setting.
We used data on prescriptions dispensed over the period 1996–2008 by a large random sample of physicians from multiple specialties that prescribe antipsychotics to examine physicians’ adoption of second-generation drugs and to identify physician-level factors associated with early adoption.
We used the IMS Xponent prescription database to obtain monthly physician-level data on the number of prescriptions for every first- and second-generation antipsychotic dispensed between January 1, 1996, and September 30, 2008. Xponent directly captures over 70% of all outpatient prescriptions filled in the United States and uses a patented projection methodology to represent 100% coverage of outpatient prescriptions. [A description of Xponent is available online as a data supplement to this article.] We obtained data for a 10% national random sample of physicians from each of the ten specialties with the highest antipsychotic-prescribing volume who prescribed at least one dispensed antipsychotic prescription in 1996. To this sample we added 10% of antipsychotic prescribers from the same specialties in each subsequent year of the study who had not prescribed an antipsychotic in any previous year. The sample included 30,369 physicians. The prescribing data were linked to data on physician characteristics from the American Medical Association Physician Masterfile, which includes current and historical information on physicians, residents, and medical students in the United States, including foreign medical school graduates (24).
We examined three primary outcomes: the proportion of physicians who had adopted a drug at different points in time, for example, a year after a drug became commercially available; the number of months between availability of a new drug product and physician adoption; and the median number of different antipsychotics prescribed by physicians per year.
Each model adjusted for physicians’ demographic characteristics (age in 1996 and sex), education and training (specialty, attendance at a medical school ranked among the top 25 by U.S. News and World Report in 2010, and graduation from a foreign medical school), practice setting (solo, other, or unknown versus group practice), part- or full-time hospital practice, and total prescribing volume of first- and second-generation antipsychotics in the year before a drug became available by use of dummy variables for volume in each quartile. For specialty, we used four categories: general practice, including internal medicine, family medicine, and family practice; general, child and adolescent, and geriatric psychiatry; pediatrics; and general and child neurology. To adjust for characteristics of the area in which a physician practiced, the models included data from the 2002 Area Resource File (http://research.archives.gov/description/1685175) about the percentage of persons living in the same zip code as the physician’s practice who were black or Hispanic, were enrolled in a health maintenance organization, had completed high school, and were 65 years or older. We also included state fixed effects to control for time-invariant characteristics of the state where a physician practiced.
To assess time to adoption, we first used Kaplan-Meier analysis (the procedure that computes the empirical survival curve for the sample) to tabulate the proportion of physicians who had not adopted a given drug during the first 12 months after the drug became available. The Kaplan-Meier calculation accounted for censored observations by restricting the risk set at each point in time to providers who had yet to adopt a drug. The adoption rate was calculated by subtracting the nonadoption rate from 1.
Next, we estimated drug-specific Cox proportional-hazard models of the number of months between the date a drug became available and a physician’s first prescription of each orally administered second-generation antipsychotic, including four original formulations (olanzapine, quetiapine, ziprasidone, and aripiprazole) and five reformulations (Zyprexa Zydis, Risperdal M-Tab, Seroquel XR, Symbyax, and Invega) (25). To focus on physicians who prescribed antipsychotics with some regularity, we excluded physicians with fewer than ten antipsychotic prescriptions in the year before the drug was released. We did not study clozapine or risperidone because they were introduced before 1996. Physicians who died or retired from clinical practice were censored at the point of their last prescription.
The Cox proportional-hazards regression models simultaneously accounted for the effects of the predictors, isolating their independent effects (25). For ease of visual presentation, we computed survival probabilities over a range of values of the predictor of interest, with the value of other, continuously valued predictors set to their mean and of other, discrete-valued predictors set to their most common value. The corresponding survival curves showed the probability that a physician remained a nonadopter at a given point in time as a function of a single predictor, with the other predictors fixed at realistic values for the sample.
These curves adjusted separately for the characteristics that we hypothesized would be the most important determinants of adoption speed: age, sex, specialty, practice setting, antipsychotic prescribing volume, attendance at a top-25 medical school, and graduation from a foreign medical school. Because physicians with certain characteristics adopted new products at a much faster rate than other physicians—for example, those whose specialty was psychiatry—for ease of presentation, for some curves we truncated the vertical axes near the low point of the survival curve for the second fastest type of adopter in order to depict more clearly differences in adoption rates among providers with various characteristics, including those with characteristics associated with nonadoption.
The Cox models themselves yield parameter estimates with exponentials that represent the change in the hazard ratio of a unit change in the predictor. In the case of a categorical variable, a unit change in the predictor corresponds to changing from the baseline level to the level of interest. We used the statistical inferences and associated tests (confidence intervals and p values) to assess the level of statistical evidence of a nonzero effect on time to adoption for each predictor.
Finally, using data from the last 12 months of the study period (October 1, 2007–September 30, 2008), we examined the median number of different antipsychotic products, including reformulations, prescribed by specialty.
+
Characteristics of the sample
Approximately two-thirds (68%) of the sample’s prescribers of antipsychotics were men, and most (56%) were between the ages of 30 and 49 (Table 1). Approximately two-thirds (66%) were general practitioners, 16% psychiatrists, 14% pediatricians, and 4% neurologists. Including pediatricians, 16% (N=27,597) of physicians specialized in treating children. Approximately one-fifth (21%) worked in solo practices, 42% in groups, and 18% in other types of setting, such as the Veterans Health Administration. No classification was listed for 19%. In terms of medical training, 12% graduated from a U.S. medical school ranked among the top 25, and 27% were foreign medical graduates.
During its first year on the market, each of the four original formulations was prescribed by a minority of antipsychotic prescribers (13%–31%) (Table 2). Olanzapine, on the market third, after clozapine and risperidone, was adopted fastest, with 31% of physicians having prescribed it during the first year that it was on the market, 48% during the first two years, and 61% during the first three years. After ten years on the market, olanzapine had been prescribed by almost all prescribers (91%). Adoption of the other three original formulations was slightly slower, although after five years on the market, even aripiprazole (the last new molecule approved) had been prescribed by 59% of prescribers. In contrast, several years after their introduction, the reformulations had been adopted by only a minority of prescribers.
Among physicians who adopted each product, there was considerable variation among products in the speed of adoption. Among adopters of the four original formulations, the median time to adoption was 22 months for olanzapine, 24 months for aripiprazole, 27 months for ziprasidone, and 43 months for quetiapine. The median number of months to adoption varied more among adopters of reformulations (Seroquel XR, eight months; Invega, nine months; Symbyax, 11 months; Risperdal M-Tab, 25 months; and Zyprexa Zydis, 38 months).
+
Predictors of time to adoption
Results from the drug-specific Cox models were consistent across drugs (Tables 3 and 4). A hazard ratio greater than 1.0 indicated that on average, physicians with a particular characteristic adopted the drug faster than the reference group. A hazard ratio less than 1.0 indicated that a physician with that characteristic was slower than the reference group to adopt the drug. Each model adjusted for all of the other variables.
For eight of nine products, physicians under age 50 were faster to adopt new products than physicians who were 50 years old or older; results were null for the ninth product. Female physicians were slower than male physicians to adopt new products. For eight of nine products, psychiatrists were much faster than general practitioners, pediatricians, and neurologists to adopt new products, although physicians in general practice were significantly faster than psychiatrists to adopt Symbyax. For all nine products, physicians in the top volume quartile adopted the drug much faster than physicians in lower volume quartiles.
Solo practitioners were slower than physicians practicing in groups to adopt five of nine antipsychotics; results were null for the other four products. Physicians who graduated from a top-25 medical school were slower than physicians who attended other schools to adopt six of nine products; results were null for the other three products. Foreign medical graduates were faster than U.S. medical graduates to adopt eight of nine products; results were null for the other product. Physicians who practiced in a hospital setting were faster than physicians who had no hospital practice to adopt quetiapine and Zyprexa Zydis; results were null for the other seven products. [Survival curves for adjusted time to adoption of olanzapine among physicians with selected characteristics are available online as a data supplement to this article.]
+
Number of agents prescribed
Psychiatrists tended to prescribe a much broader set of antipsychotic medications than the other types of specialists. In the last year of the study, psychiatrists prescribed a median of six different antipsychotic products versus a median of two for general practitioners and neurologists and one for pediatricians.
In this study of a large, national sample of prescribers of antipsychotics, we found that a vast majority of prescribers (two-thirds of whom were general practitioners) did not adopt new drugs immediately after they became available. We also found substantial heterogeneity in adoption speed across physicians. In particular, physician specialty and prescribing volume were key drivers of time to adoption, although other factors, such as physicians’ practice setting, training, and demographic characteristics, were also important influences.
Although most second-generation antipsychotics were eventually adopted by a majority of antipsychotic prescribers, a majority of prescribers waited two or more years before prescribing a new product. This behavior could be due to a variety of factors, such as a lack of awareness of a drug’s introduction, a change in prescribing after the U.S. Food and Drug Administration approved new clinical indications, or an intentionally cautious approach to adopting new drugs in an effort to ensure patient safety. Rates of adoption varied by product, however. Variation in adoption rates can be influenced by order of entry and the number of alternatives available in the class. In fact, olanzapine, the third second-generation antipsychotic on the market, was adopted relatively quickly, although we can’t compare its adoption to that of its predecessors, which were introduced before the study period. Variation in adoption by drug can also be influenced by perceived clinical advantages; for example, the relatively rapid adoption of aripiprazole may have been influenced by its relatively low incidence of metabolic side effects (11,26). Rates of reformulation adoption were generally much lower than rates of original formulation adoption, although the physicians who adopted these products did so relatively quickly.
Psychiatrists adopted new antipsychotics much sooner on average and generally prescribed a much broader set of antipsychotics than physicians from other specialties that commonly prescribe antipsychotics. These other specialists may be more likely to prescribe antipsychotics for off-label indications, such as sleep disorders. These results were consistent with those of a study by Taub and colleagues (27), who used a similar data set. The results were also consistent with evidence that physicians often follow norms to guide treatment decisions, for example, prescribing one or two drugs to all patients with a condition, to avoid the substantial costs in time and cognition associated with customized treatment (21,28). Nonpsychiatrists may use norms more commonly than psychiatrists because antipsychotic treatment may represent a much smaller proportion of their prescribing and thus impose greater costs in cognition and time spent learning the nuances of treatment.
Even after controlling for specialty, we found that the highest-volume prescribers were much faster to adopt new products than low-volume prescribers. It could be that high-volume prescribers are disproportionately likely to treat patients with treatment-refractory symptoms and thus more likely to try new products soon after they come on the market. Alternatively, high-volume prescribers may be more likely to be targeted by marketing efforts by drug manufacturers (29,30).
Speed of adoption also varied on the basis of the physician’s practice setting and training. Physicians in solo practice were often slower to adopt new products than those practicing in group settings, although the differences were relatively small. We were unable to isolate the features of solo practice that may contribute to slower adoption. However, physicians who practice alone may have less exposure to a variety of influences on prescribing, including quality improvement initiatives, guideline dissemination, and contact with pharmaceutical sales representatives, than physicians who practice in groups. In addition, social influences within organizations have long been acknowledged as an important determinant of technology diffusion (18,31,32); physicians are likely to be influenced by their peers within their practice organizations, and solo practitioners may have fewer interactions with peers that could influence prescribing behavior.
Interestingly, physicians who graduated from the highest-ranked medical schools were slower to adopt most new antipsychotics. It could be that higher-ranked medical schools are more likely to emphasize a more “conservative” approach to adopting new drugs (33) or grant less exposure to pharmaceutical representatives, but there is no evidence to support this conjecture.
Our study had several limitations. First, we lacked information on the patients’ filling the prescriptions, including the specific disorder for which an antipsychotic was prescribed or the disorder’s severity. Psychiatrists treating patients with treatment-resistant mood or psychotic disorders may be faster adopters than those treating less severely ill patients. Second, we were unable to study the adoption of clozapine and risperidone, although our data allowed us to look at adoption patterns during a 13-year period in which the other second-generation original formulations and most reformulations became available on the market. Third, we lacked data on prescriptions filled by in-hospital pharmacies. Further, to the extent that we did not have data on prescriptions written but not dispensed, our results were confounded by factors affecting patients’ decisions to fill prescriptions. Fourth, we lacked data on the number of free samples distributed by each physician and on use of patient assistance programs, although use of such programs is quite low (34).
In addition, we were unable to identify a physician’s residency training program, which may have more influence on prescribing than medical school attended. Finally, because of a lack of data, we were unable to adjust for some of the external influences on prescribing behavior, such as manufacturer promotional efforts directed at physicians, characteristics of the specific organizations in which physicians practice, and health plan coverage of different antipsychotics.
Physician decisions about whether to adopt new drugs into practice can have profound implications for patient care, in terms of both the quality and the safety of care. These decisions also have important implications for health care spending. As policy makers and payers grapple with how to control rising health care expenditures, there will likely be increased pressure to maximize the value of care received by patients, including individuals with mental disorders. By identifying physician characteristics associated with decisions to adopt new medications, our findings enable the targeting of efforts to increase high-value, evidence-based prescribing through training and education programs, academic detailing (35), guideline dissemination, financial incentives, utilization management, or other initiatives.
This work was supported by a grant from the National Institute of Mental Health (R01 MH093359), by a Robert Wood Johnson Foundation Investigator Award in Health Policy Research to Dr. Huskamp, and by grants to Dr. Donohue from the Agency for Healthcare Research and Quality (R01HS017695) and the National Institute of Mental Health (NIMH) (R34 MH082682 and P30 MH090333), to Dr. Horvitz-Lennon from NIMH (R01 MH087488), and to Dr. Berndt from the National Science Foundation (0915674). The study is based in part on data obtained under license from IMS Health, Inc. (Xponent, 1996–2008). The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IMS Health Inc. or any of its affiliated or subsidiary entities.
Dr. Donohue consulted for the National Railways Labor Conference, which was considering changes to its health benefits for railway workers, in 2011. The other authors report no competing interests.