Use of Academic Detailing With Audit and Feedback to Improve Antipsychotic Pharmacotherapy

In New Hampshire, the state public mental health authority contracts with 10 private, nonprofit community mental health centers to deliver rehabilitation services and comprehensive care to people with severe mental illness. During the period of study, the mental health authority implemented a quality initiative to improve health among people with severe mental illness (18). State leaders engaged community mental health center leaders and other community stakeholders, such as the National Alliance on Mental Illness, in the initiative with education and discussion. Subsequently, all psychiatric care providers at the community mental health centers (psychiatrists and advance practice nurses) were invited to participate. From 2009 to 2013, all 85 psychiatric care providers were informed of the health initiative and sent a quarterly letter describing the initiative’s goals and strategies for prescribers to support health among people with severe mental illness. Each letter included articles describing optimal, evidence-based antipsychotic prescribing and cardiometabolic side-effect monitoring.

During the study period, the state’s Medicaid insurance policy covered risperidone, quetiapine, ziprasidone, clozapine, and first-generation antipsychotics without requiring a copayment or prior authorization. Other second-generation antipsychotics were covered if prior authorization was obtained.

Contemporaneously, the community mental health centers expanded delivery of a health promotion intervention, In Shape, to improve fitness for people with severe mental illness (24). This intervention included a gym membership, regular meetings with a fitness coach, and regular celebrations of progress toward health goals. The education intervention described below was timed to take place as each community mental health center implemented In Shape.

The state mental health medical director (MFB) engaged mental health center leaders to elicit support for the program. Educators, who were experienced psychiatrists, then provided a sequence of four 50-minute, in-person visits over two years to the group of psychiatric care providers at each site during the group’s regular, mandatory administrative meeting. The intervention included providing data on rates of polypharmacy and prescribing of medications with high cardiometabolic risk in conjunction with a curriculum supporting recommended treatment at the initial session and three follow-up sessions (see box). Staff who did not attend one or more visits because of vacation or an emergency were provided the materials. The education intervention began as the mental health center initiated its In Shape health promotion program. The rollout was planned to occur in two phases: three centers began in 2010, and five centers began in 2012. Two centers in the state declined to participate in the education intervention because of competing organizational quality improvement projects. The study was reviewed and approved by the Dartmouth Institutional Review Board. Because no patient-identifiable data were utilized, informed consent was not obtained from patients. Research procedures were in compliance with the Declaration of Helsinki.

From state Medicaid medical and pharmaceutical claims (2009–2013), we defined our study cohort as adult beneficiaries (age 18 or older as of January 1, 2009) with at least two rehabilitation service claims separated by at least six months in each year, indicating they were likely to be receiving longitudinal services for severe mental illness. Among these recipients, we utilized data for each month in which an antipsychotic prescription was filled (67,721 person-months). We categorized beneficiary age into three groups (18–29, 30–39, and ≥40) to enable us to describe prescribing patterns by age, given that younger and older individuals have different side effect susceptibilities. For example, younger patients are more likely to develop cardiometabolic side effects (25), and older patients are more likely to experience adverse drug events associated with polypharmacy (26). Psychiatric diagnoses were classified by ICD-9 code.

Mental health centers that received the intervention beginning in 2010 were designated as the early cohort, and those that received the intervention beginning in 2012 were designated as the late cohort. This designation was used to control for a potential priming effect of the overall health initiative received by the late cohort. The two community mental health centers that did not receive the intervention contributed filled claims and were designated as preintervention only. [A description of the complete data preparation procedures is available as an online supplement to this article.]

The outcomes of interest were the monthly proportions of all beneficiaries with antipsychotic fills who had a polypharmacy fill (two or more types of antipsychotics filled for more than 60 consecutive days), a fill for an antipsychotic with high cardiometabolic risk (“high risk”), and a fill for an antipsychotic with low cardiometabolic risk (“low risk”). We defined date of service in the Medicaid pharmacy file as a prescription fill date and defined the “days’ supply” field as a marker of duration of pharmacotherapy per each medication fill. On the basis of existing literature, antipsychotic medications in use over this period were divided into groups with high cardiometabolic risk (olanzapine, quetiapine, chlorpromazine, and thioridizine), medium risk, or low risk (ziprasidone, aripiprazole, asenapine, lurasidone, fluphenazine, haloperidol, molindone, and pimozide) (27–29). Because clozapine was recommended for treatment-resistant psychosis, people on clozapine were not candidates for switching; thus clozapine fills were omitted. Prescription fills for medium-risk agents were included in the denominators for the analyses, but they were not an outcome of focus for this study.

In keeping with a previously described approach (13), antipsychotic polypharmacy was defined as overlapping use of two or more different antipsychotic medications for longer than 60 days. Because prescriptions may overlap to allow for periods of switching from one antipsychotic to another, concurrent treatment for less than 60 days was not considered antipsychotic polypharmacy. For each beneficiary with antipsychotic fills, we labeled each month as polypharmacy or monotherapy based on these calculations.

In this filled claims–based method, all Medicaid beneficiaries receiving rehabilitation services and antipsychotic fills during the five-year study period contributed data to the analyses. The unit of analysis was a month of pharmacotherapy. A beneficiary could contribute an antipsychotic medication fill in any category in the same month (high risk, low risk, and polypharmacy); thus the categories of fills associated with an individual were not mutually exclusive. Some beneficiaries were associated with fills in the preintervention period but not in the postintervention period and vice versa. Monthly antipsychotic fills were aligned according to the time of education intervention at each mental health center and were assigned a status of preintervention or postintervention. They were also assigned a time designation based on the point in time during the five-year study window. Because prescribing patterns change over time (30), we included time in our modeling.

Using chi-square or t test, as appropriate, we assessed whether beneficiary characteristics (e.g., gender, age group, diagnosis group, and hospitalization during the five-year study period) (31) were associated with pharmacotherapy category (antipsychotic polypharmacy, high-risk antipsychotic, and low-risk antipsychotic) for beneficiaries contributing filled claims before the intervention.

To examine the effect of the intervention on monthly prescribing, we calculated raw rates of filled claims for each antipsychotic category before and after the intervention. We then ran three models, one each for antipsychotic polypharmacy, high-risk antipsychotic pharmacotherapy, and low-risk antipsychotic pharmacotherapy. We used a segmented regression approach within generalized estimating equation (GEE) models (32,33), which accounts for autocorrelation between monthly pharmacotherapy within beneficiaries. We first conducted a series of exploratory analyses. Based on research questions of interest and model fit, our final models included the time trend prior to intervention, time trend change after intervention during the five-year study period, beneficiary diagnosis and demographic characteristics, late-intervention cohort status, and actual time (e.g., December 2012) during the five-year period to enable measurement of secular trends. We expected that antipsychotic fills for participants with markers of potentially lower acuity of antipsychotic need (diagnosis other than schizophrenia or bipolar disorder and no hospital use) and those with potentially higher risk of metabolic adverse effects (youngest and oldest age groups) would be more likely to change. The intervention effect was assessed by whether there was a significant trend change after the intervention compared with before the intervention. If the intervention effect was significant, the interaction between beneficiary characteristics and intervention was added into the model, and significant interaction effects became the focal point of interpretation for that model. Thus the three models included different variables. The results were interpreted as a relative increase or decrease in the outcomes compared with the relevant reference group. Data management and analyses were performed by using SAS, version 9.3.

The analysis group included 4,986 adult Medicaid beneficiaries with severe mental illness who had received rehabilitation services over at least six months and who had also filled at least one antipsychotic prescription paid for by Medicaid during the study period (67,721 person-months) (Table 1). Compared with beneficiaries who filled antipsychotic prescriptions before the intervention, those who filled antipsychotic prescriptions after the intervention were less likely to have diagnoses of bipolar disorders and were more likely to have had a psychiatric hospitalization or an emergency department (ED) visit during the five-year study period, although the actual magnitude of differences was small.

Table 2 shows antipsychotic fills by beneficiary characteristic before the intervention, showing that patterns of prescription fills varied depending upon beneficiary characteristic. High-risk antipsychotic pharmacotherapy was associated with a diagnosis of depression, older age, and treatment at early-cohort centers. Low-risk antipsychotic pharmacotherapy was associated with schizophrenia and bipolar disorder diagnoses, and antipsychotic polypharmacy was associated with schizophrenia diagnoses and older age.

In aggregate, the raw proportions of people with Medicaid-filled claims for antipsychotic polypharmacy decreased after the intervention. Before the intervention, 13.1% of beneficiaries who had filled an antipsychotic prescription were users of polypharmacy, compared with 10.9% in the second year after the intervention, an absolute reduction of 2.2% and a relative reduction of 16.8%. The proportion of beneficiaries who filled prescriptions for high-risk antipsychotics and low-risk antipsychotics did not change between the year before the intervention and the years after the intervention (Table 3).

Segmented GEE models were specified to evaluate trends in prescribing over time before and after the intervention. The models adjusted for participant age group, gender, diagnosis, hospital and ED utilization, and intervention cohort.