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Published Online: 30 September 2021

Variation in Psychotropic Medication Prescription for Adults With Schizophrenia in the United States

Natalie Bareis, L.M.S.W., Ph.D. https://orcid.org/0000-0002-9067-2075 [email protected], Mark Olfson, M.D., M.P.H., Melanie Wall, Ph.D., and T. Scott Stroup, M.D., M.P.H.Authors Info & Affiliations
Publication: Psychiatric Services
Volume 73, Number 5
https://doi.org/10.1176/appi.ps.202000932
PDF/EPUB

Abstract

Objective:

Variation in prescription of psychotropic medications to patients with schizophrenia spectrum disorders may underlie health inequities. Using a national U.S. Medicaid sample, the authors examined prescription patterns of psychotropic medications commonly used for managing schizophrenia.

Methods:

Data from the 2011–2012 Medicaid Analytic eXtract were examined for demographic predictors of and variation across states in psychotropic medication prescription among adult patients diagnosed as having schizophrenia spectrum disorders (N=357,914). Percentages of patients in each state who filled prescriptions of at least 15 days of any antipsychotic, clozapine, antidepressant, benzodiazepine, mood stabilizer, or long-acting injectable (LAI) antipsychotic medication were determined after adjustment for demographic and clinical covariates. Multivariate regressions of clinical and demographic factors predicting prescription patterns were conducted.

Results:

Prescribing patterns for all types of psychotropic medications varied across states. Clozapine and LAI prescriptions showed the most dramatic differences across states and among patients with different demographic characteristics. Across states, adjusted proportions of prescriptions ranged from 4% to 22% for LAIs and from 1% to 11% for clozapine. Non-Hispanic Blacks and people of other race-ethnicities were more likely than non-Hispanic Whites to fill prescriptions for LAIs, and non-Hispanic Whites were more likely than individuals from other racial-ethnic groups to fill prescriptions for clozapine and all other medications.

Conclusions:

Considerable variation in prescribing patterns of LAIs and clozapine by race-ethnicity and across states suggests uneven quality of care for individuals with schizophrenia spectrum disorders in the United States. A better understanding of what causes this variation could inform policy makers to improve treatment for this vulnerable population.

HIGHLIGHTS

•
This nationwide study reports variation in prescribing patterns for major psychotropic medication classes used to treat patients with schizophrenia.
•
Significant variation was found among states in prescription of each psychotropic medication class, with 10-fold and fivefold variations in clozapine and long-acting injectable (LAI) antipsychotic medications, respectively.
•
Compared with non-Hispanic Whites, individuals from other racial-ethnic groups were more likely to fill prescriptions of any oral and LAI antipsychotic medications, and non-Hispanic Whites were more likely to fill prescriptions for clozapine and all other medications.
•
A better understanding of the reasons for the divergence of clozapine and LAI prescriptions by race-ethnicity is needed to identify the causes of these inequities.
Several classes of psychotropic medications are commonly prescribed to people with schizophrenia spectrum disorders, but only antipsychotic medications have strong evidence for effectiveness in treating such patients (1, 2). Among these medications, clozapine and long-acting injectable (LAI) formulations have distinctive roles. Clozapine is the established medication of choice for treatment-resistant schizophrenia and is the only drug approved for this indication (3). LAI antipsychotic medications are important options to help ensure medication adherence (4), but their benefits and how they are used are controversial (5, 6). Evidence for an important role for antidepressants in schizophrenia treatment has been increasing (1, 7), whereas mood stabilizers and benzodiazepines have limited evidence for effectiveness in this disorder (8–12).
Treatment variation refers to differences in treatment that are not attributable to differences in clinical presentation for persons with the same condition. Variation in treatment patterns is thought to reflect professional uncertainty—a lack of consensus or evidence regarding the best treatment choice for a condition (13). Other factors contributing to such variation are uncertainty of sequential timing of treatments, differences in availability of providers and services, and provider preferences (13, 14). Regulations, variations in health insurance benefits, pharmacy benefit management, and consumer preferences are other possible contributors (13, 14). In some cases, treatment variations may reflect inequities due to socioeconomic status, race, or ethnicity.
In a review of the literature, variations in prescription of psychotropic medications for schizophrenia have been documented despite treatment guidelines designed to reduce this variation (15). These studies provide evidence of variation by geographic location (16), within specific U.S. states (17–19), for specific medications (clozapine vs. other antipsychotic medications) (20) and medication classes (antidepressants vs. anxiolytics), or by demographic characteristics (African Americans vs. non-Hispanic Whites) (21). Previous studies, however, have not provided a portrait of variation across the major psychotropic medication classes used to treat patients with schizophrenia in the United States.
This study of variations in psychotropic medication prescriptions for adults diagnosed as having schizophrenia spectrum disorders extends these previous investigations. In addition to antipsychotic medications, we included antidepressants, mood stabilizers, and benzodiazepines because of clinical interest regarding the appropriate role of these medication classes in the management of schizophrenia (2, 7, 22, 23). We analyzed Medicaid claims data from 47 states and the District of Columbia with two complementary goals: to identify patient-specific factors that predict prescription of these medications and to determine whether variation in psychotropic medication prescription rates across U.S. states remains significant after controlling for clinical, demographic, and service use factors.

Methods

Data Source

The data for this study came from the 2011–2012 national Medicaid Analytic eXtract Inpatient, Other Services, Prescription Drug and Personal Summary data sets; Hawaii, Idaho, and Maine were excluded because of missing data. Data from individuals dually covered by Medicaid and Medicare were excluded, but we included data from individuals covered by Medicaid fee-for-service (FFS) and managed care plans. The institutional review board of New York State Psychiatric Institute approved this study.

Cohort Selection

We limited the final analytic sample to data from patients with one inpatient or two outpatient diagnoses of schizophrenia spectrum disorders (ICD-9-CM 295.xx), hereafter referred to as schizophrenia, by using the principal and second listed diagnosis codes in the 2011 Inpatient and Other Services data sets, a previously validated method (24). To improve interpretation of prescription of mood stabilizers, which are often used as anticonvulsant medications, data from patients with 2011 principal and second listed diagnoses of epileptic disorders (ICD-9-CM 345.xx) were excluded. The sample was limited to adults ages 18–64 years who filled at least a 15-day prescription of at least one oral psychotropic medication or prescription of an LAI medication and who were eligible for Medicaid for all months of 2012 (N=357,914).

Analysis of Prescription Patterns

The outcomes of interest were prescribing patterns for six types of psychotropic medications (see table in the online supplement to this article) by state in 2012: any antipsychotic, clozapine, LAI antipsychotic medication, antidepressant, mood stabilizer, and benzodiazepine medication. Filled prescriptions were defined as at least a 15-day supply of any oral antipsychotic, antidepressant, benzodiazepine, or mood stabilizer or any LAI antipsychotic medication. To examine practice variation in psychotropic medication prescription by state, indicator variables of each state were the primary predictor of interest in these analyses.

Demographic, Clinical, and Service Use Covariates

Because our sample included Medicaid FFS and managed care plans that could affect prescribing patterns, we included a covariate indicating the financing arrangement (FFS, managed care, or other). Demographic covariates included age, sex, race-ethnicity (non-Hispanic Black; non-Hispanic White; Hispanic; and an “other” race category including American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, more than one race, or unknown). Clinical covariates included previous year (2011) schizoaffective subtype (defined as >50% of diagnosis code 295 coded as 295.7x) or schizophreniform subtype (defined as >50% of diagnosis code 295 coded as 295.4x) and psychiatric comorbid conditions (intellectual disability, substance use disorders, depression, anxiety disorders, or self-harm) (see table in the online supplement). Clinical covariates also included previous year (2011) comorbid general medical conditions that may affect antipsychotic or other treatment choices, such as diabetes; cardiovascular disease, excluding essential hypertension diagnosis code 401.xx (25–27); white blood cell disease; intracranial injury (28); hyperlipidemia; drug-induced dyskinesia (29); and HIV (30) (see table in the online supplement). Previous year (2011) service uses, including psychiatric outpatient visits, any mental health emergency department visits, and inpatient psychiatric admissions (see table in the online supplement), were also included as covariates.

Analysis Strategy

Medicaid programs differ among states in the demographic characteristics of the Medicaid-covered populations and in covered mental health and other medical services. We examined variation in psychotropic medication prescription by state, controlling for individual-level demographic, clinical, and service use characteristics and financing arrangement. Logistic regressions with filled prescriptions of each psychotropic class (yes or no) as the outcome were predicted by using dummy variables for each state and all other covariates. We included a Bonferroni adjustment for multiple comparisons of these six models, with p<0.008 (0.05 divided by 6) or a confidence interval (CI) of 99.2% (hereafter rounded to 99%). SAS, version 9.4 (31), was used to conduct these analyses. Odds ratios (ORs) and 99% CIs assessed the association of each demographic and clinical covariate. We calculated adjusted proportions of prescriptions for each medication for each state from the logistic regressions (by using the SAS Proc GENMOD with a binomial distribution and logit link) as the overall population mean values adjusted for all other covariates. Tests of variation in filled prescriptions among states were performed with the chi-square likelihood ratio test from the fully adjusted logistic regression models.

Results

Sample Characteristics

Table 1 shows the general characteristics of the study population, providing context for our analyses of state variation in prescribing patterns. The composition of the sample was 49% women, 35% non-Hispanic Black, 40% non-Hispanic White, and 12% Hispanic. Patients with schizoaffective disorder made up 33% of the sample, and <1% of patients had schizophreniform disorder. Past-year depression (34%) was the most common comorbid mental health condition, and cardiovascular disease (35%), hyperlipidemia (24%), and diabetes mellitus (21%) were the most common comorbid general medical conditions. Almost 61% of the patients had nine or fewer outpatient visits during 2011, 26% had a mental health emergency department visit, and 81% had no past-year psychiatric inpatient admissions. Almost 70% of patients had FFS, and 31% had managed care financing arrangements. Only 30% of patients were prescribed psychotropic monotherapy, and the highest rates of polypharmacy prescription were for two types of medications (38%), highlighting the need to study other psychotropic medications in addition to antipsychotic medications commonly prescribed for schizophrenia.
TABLE 1. Demographic and service use characteristics of a national sample of persons with schizophrenia spectrum diagnoses (N=357,914)a
CharacteristicN%
Age (M±SD years)43.2±12.6 
Women174,27248.7
Race-ethnicity  
 White, non-Hispanic141,26139.5
 Black, non-Hispanic125,25635.0
 Hispanic or Latino41,26211.5
 Otherb50,13514.0
Schizophrenia subtypes  
 Schizoaffective118,93033.2
 Schizophreniform2,007.6
Psychiatric comorbid conditions (2011)  
 Substance use disorder54,89515.3
 Depression119,73833.5
 Anxiety55,46815.5
 Deliberate self-harm2,796.8
General medical comorbid conditions (2011)  
 Diabetes75,83321.2
 Serious cardiovascular disease126,63635.4
 Hyperlipidemia86,15324.1
Financing arrangement  
 Fee for service246,64068.9
 Managed care109,01730.5
 Otherc2,257.6
Outpatient visits for schizophrenia  
 0–9217,25260.7
 10–2964,77418.1
 30–4924,8526.9
 ≥5051,03614.3
Mental health emergency department visit (2011)92,55225.9
Inpatient admission for schizophrenia (N visits)  
 0288,87680.7
 134,4539.6
 215,8194.4
 36,2971.8
 ≥412,4693.5
N of psychotropic medication types taken per year  
 1107,53830.1
 2137,17838.3
 387,44024.4
 424,4206.8
 51,338.4
Psychotropic medication typed  
 Any antipsychotic306,10385.5
 LAI antipsychotic51,42714.4
 Clozapine15,6794.4
 Antidepressant207,53158.0
 Mood stabilizer99,75827.9
 Benzodiazepine97,46827.2
a
Data were from Medicaid Analytic eXtract years 2011–2012. Data were missing for Hawaii, Idaho, and Maine. LAI, long-acting injectable.
b
Other race-ethnicity category included American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, more than one race, and unknown.
c
Other financing arrangements included capitation plan, lump-sum payment to provider, supplemental payment above capitation fee, unknown, or missing information.
d
Not mutually exclusive categories.

Demographic, Clinical, and Service Use Predictors of Psychotropic Medication Prescriptions

Table 2 presents the results from the logistic regression models predicting filled prescriptions of psychotropic classes and formulations; models adjusted for all covariates, including state. The most notable differences were observed for race-ethnicity and sex. Compared with prescriptions for non-Hispanic Whites, any antipsychotic and LAI prescriptions were more likely to be filled for non-Hispanic Blacks (adjusted OR [AOR]=1.16 for antipsychotic and AOR=1.39 for LAI), Hispanics (AOR=1.10 for antipsychotic and AOR=1.26 for LAI), and Native Hawaiians or other Pacific Islanders (AOR=1.55 for antipsychotic and AOR=1.26 for LAI). However, non-Hispanic Whites were more likely than all other racial-ethnic groups to fill prescriptions of clozapine (AOR range 1.17–1.60), antidepressants (AOR range 1.05–1.40), benzodiazepines (AOR range 1.37–1.49), and mood stabilizers (AOR range 1.26–1.31). Compared with men, women were less likely to fill prescriptions for any antipsychotic medications (AOR=0.81 for antipsychotic, AOR=0.78 for LAI, and AOR=0.82 for clozapine) but were more likely to fill prescriptions for antidepressants (AOR=1.47) and benzodiazepines (AOR=1.38) (Table 2).
TABLE 2. Likelihood of prescription of psychotropic medications for persons with schizophrenia spectrum diagnosesa
 Antipsychotic medicationsLAIClozapineAntidepressantsBenzodiazepinesMood stabilizers
Demographic characteristicAOR99% CIbAOR99% CIbAOR99% CIbAOR99% CIbAOR99% CIbAOR99% CIb
Age (continuous).99*.99–.99.99*.99–.99.98*.98–.981.01*1.00–1.011.01*1.01–1.01.98*.98–.98
Women (reference: men).81*.79–.83.78*.76–.80.82*.78–.861.47*1.44–1.501.38*1.35–1.411.00.98–1.02
Race-ethnicity (reference: White, non-Hispanic)            
 Black, non-Hispanic1.16*1.12–1.191.39*1.35–1.44.40*.38–.42.71*.69–.73.51*.49–.52.69*.67–.71
 Asian1.26*1.13–1.411.10.99–1.23.83*.72–.96.60*.56–.65.63*.58–.69.72*.66–.78
 Hispanic1.10*1.03–1.171.26*1.19–1.34.63*.57–.69.95*.92–.991.00.95–1.04.74*.71–.78
 Native Hawaiian or other Pacific Islander1.55*1.30–1.841.26*1.11–1.44.80*.66–.97.69*.63–.75.63*.57–.70.72*.65–.80
Schizophrenia subtype (reference: schizophrenia)            
 Schizoaffective1.00.98–1.03.77*.75–.80.91*.87–.961.35*1.32–1.381.14*1.11–1.161.60*1.57–1.64
 Schizophreniform1.15.96–1.37.91.76–1.09.77.56–1.07.93.82–1.051.04.90–1.201.00.87–1.15
Psychiatric comorbid condition            
 Substance use disorder (reference: none).82*.79–.85.83*.80–.87.45*.41–.501.41*1.37–1.451.09*1.06–1.13.88*.86–.91
 Depression (reference: none).72*.70–.74.65*.63–.67.56*.53–.602.06*2.01–2.101.17*1.14–1.20.92*.90–.94
 Anxiety (reference: none).69*.67–.72.66*.63–.69.65*.60–.701.41*1.37–1.452.33*2.27–2.40.88*.86–.91
 Deliberate self-harm (reference: none)1.03.89–1.191.02.88–1.19.92.66–1.291.46*1.29–1.661.07.95–1.191.05.94–1.17
Comorbid general medical condition            
 Diabetes (reference: none)1.08*1.04–1.121.06*1.03–1.101.48*1.40–1.571.10*1.07–1.131.08*1.05–1.111.12*1.09–1.15
 Serious cardiovascular disease (reference: none).86*.83–.89.84*.81–.86.98*.93–1.031.19*1.16–1.211.30*1.27–1.331.06*1.04–1.09
 Hyperlipidemia (reference: none)1.13*1.09–1.17.91*.88–.941.44*1.37–1.521.07*1.05–1.101.04*1.01–1.071.06*1.03–1.08
Financing arrangement (reference: FFS)c            
 Managed care.46*.44–.48.81*.77–.84.77*.72–.831.24*1.20–1.27.99.96–1.02.89*.86–.92
 Other.23*.20–.26.61*.50–.74.16*.08–.31.24*.21–.27.19*.14–.24.20*.16–.25
Psychiatric service use            
 Outpatient schizophrenia visits (reference: 0–9)            
  10–291.94*1.86–2.022.65*2.55–2.742.36*2.22–2.51.78*.76–.80.86*.84–.89.89*.86–.92
  30–492.272.13–2.424.25*4.06–4.453.10*2.86–3.36.75*.72–.78.83*.79–.87.97.93–1.01
  ≥502.752.61–2.885.32*5.13–5.524.88*4.59–5.18.73*.71–.75.82*.79–.851.15*1.12–1.19
 Mental health ED visit (reference: none)1.18*1.14–1.221.44*1.39–1.49.77*.72–.82.95*.92–.971.12*1.09–1.151.36*1.32–1.40
Inpatient schizophrenia admission visit (reference: 0)            
  11.13*1.08–1.191.36*1.30–1.43.92.84–1.00.91*.88–.951.04*1.00–1.081.30*1.26–1.35
  21.32*1.23–1.421.58*1.48–1.691.01.90–1.15.83*.79–.871.04.99–1.101.45*1.38–1.52
  31.53*1.36–1.711.80*1.64–1.981.12.93–1.34.83*.77–.901.01.93–1.091.71*1.59–1.84
  ≥41.51*1.39–1.641.80*1.67–1.941.22*1.06–1.41.90*.85–.961.08*1.01–1.151.75*1.65–1.85
a
Selected results from the full statistical model, adjusted for all covariates. All covariates included state, age, sex, race-ethnicity, substance use disorder, depression, anxiety, self-harm, HIV infection, financing arrangement, schizophrenia spectrum disorder, diabetes, cardiovascular disease, white blood cell disease, intellectual disability, intracranial injury, hyperlipidemia, drug-induced dyskinesia, outpatient visit count, mental health emergency department visit, and inpatient admission count. Some adjusted odds ratio (AOR) figures are identical to those in their 99% confidence intervals because of rounding. ED, emergency department; FFS, fee for service; LAI, long-acting injectable antipsychotic.
b
Confidence interval is 99.2% to adjust for multiple comparisons.
c
Other financing arrangements included capitation plan, lump-sum payment to provider, supplemental payment above capitation fee, unknown, or missing information.
*p<0.008.
Compared with patients with other schizophrenia spectrum disorders, patients with schizoaffective disorder were less likely to fill prescriptions of LAIs or clozapine but more likely to fill prescriptions for antidepressants, benzodiazepines, and mood stabilizers. Those with comorbid substance use disorder, depression, or anxiety were less likely to fill prescriptions for antipsychotic medications and mood stabilizers but more likely to fill prescriptions for antidepressants (AOR range 1.41–2.06) and benzodiazepines (AOR range 1.09–2.33) than were patients without these comorbid conditions. Deliberate self-harm was associated with increased odds of antidepressant prescriptions (AOR=1.46) (Table 2) but not with prescriptions of other medication classes. All general medical comorbid conditions were associated with increased likelihood of benzodiazepine and mood stabilizer prescription. Those under managed care were more likely to be prescribed antidepressants (AOR=1.24) and less likely to be prescribed all other medication types than those under FFS financing arrangements.

State Variation in the Prescription of Psychotropic Medications

Adjusted proportions of patients in each state who filled prescriptions for LAI medications, clozapine, and benzodiazepines are shown in Figures 1–3, and information for each psychotropic medication is provided in the online supplement. States significantly differed in prescribing for each of the medication types (LAIs, χ2=4,893.0, df=47, p<0.001; clozapine, χ2=2,709.1, df=47, p<0.001; antidepressants, χ2=4,092.1, df=47, p<0.001; benzodiazepines, χ2=8,186.2, df=47, p<0.001; and mood stabilizers, χ2=858.5, df=47, p<0.001).
FIGURE 1. Adjusted proportions of long-acting injectable antipsychotic prescription, by U.S. statea
aDifference among states (error bars indicate 99% confidence intervals): χ2=4,892.99, df=47, p<0.001. Standard two-letter state abbreviations are used on the x-axis.
FIGURE 2. Adjusted proportions of clozapine prescription, by U.S. statea
aDifference among states (error bars indicate 99% confidence intervals): χ2=2,709.06, df=47, p<0.001. Standard two-letter state abbreviations are used on the x-axis.
FIGURE 3. Adjusted proportions of benzodiazepine prescription, by U.S. statea
aDifference among states (error bars indicate 99% confidence intervals): χ2=8,186.17, df=47, p<0.001. Standard two-letter state abbreviations are used on the x-axis.
Proportions of LAI prescriptions ranged from 4% (in Colorado) to 22% (in Rhode Island) (Figure 1), an adjusted fivefold difference and interquartile range (IQR) of 4%. For clozapine, some states (North Dakota, South Dakota, and Vermont) with few patients were outliers, but prescriptions in most states ranged from 1% (in Nevada) to 11% (in South Dakota), representing an adjusted 10-fold difference and IQR of 2% (Figure 2). Adjusted proportions for antidepressants ranged from 47% (in Vermont) to 75% (in Missouri), representing a 55% difference and IQR of 8%. Two states (Tennessee and North Dakota) were outliers in benzodiazepine prescriptions, but prescription in the other states ranged from about 20% to 35%, representing a 75% difference and IQR of 8% (Figure 3). Mood stabilizer prescriptions ranged from 19% (in Montana) to 33% (in Pennsylvania), representing a 73% difference and an IQR of 5%.

Post Hoc Analyses

As a result of the adjustment for all factors used in these analyses to identify whether heterogeneity in prescribing patterns persisted, identification of statewide differences in these factors and their possible implications was limited. To probe whether patient race-ethnicity or other characteristics contributed to interstate variation in prescribing patterns, we partitioned states by tertiles of LAI medication and clozapine prescriptions (i.e., one record per state for highest, middle, and lowest levels of LAI and clozapine prescriptions). We then examined the composition of characteristics of patients in these state-level tertiles of LAI and clozapine prescriptions (see table in the online supplement).
Results from these analyses revealed that states in the highest compared with the lowest tertile of LAI medication prescription had higher percentages of non-Hispanic Whites (43.8% [N=34,831 of 79,442] vs. 36.6% [N=59,296 of 161,860]) and non-Hispanic Blacks (43.5% [N=34,542 of 79,442] vs. 31.7% [N=51,351 of 161,860]) and lower percentages of Hispanics (2.8% [N=2,216 of 79,442] vs. 15.9% [N=25,670 of 161,860]). States in the highest compared with the lowest tertile of clozapine prescription had lower percentages of non-Hispanic Blacks (32.0% [N=26,777 of 83,683] vs. 41.9% [N=42,594 of 101,734]) and higher percentages of Hispanics (18.6% [N=15,547 of 83,683] vs. 7.1% [N=7,180 of 101,734]).
As a measure of access to psychiatrists, we calculated the number of psychiatrists per capita with Census Bureau data from 2012 for the states in our study. States with higher rates of clozapine prescription (Colorado, Connecticut, Delaware, Illinois, Iowa, Massachusetts, Montana, Nebraska, New Hampshire, New York, North Dakota, Pennsylvania, Rhode Island, South Dakota, Utah, Vermont) had more psychiatrists per capita (11 psychiatrists per 100,000 residents) than those with lower rates (Alabama, Alaska, Arizona, Arkansas, District of Columbia, Georgia, Indiana, Kansas, Kentucky, Louisiana, Mississippi, Missouri, Nevada, South Carolina, Tennessee, Texas) (five psychiatrists per 100,000 residents).

Discussion

The results of this study indicated variation across states in prescription patterns for psychotropic medications to treat people diagnosed as having schizophrenia. The most dramatic interstate differences were in prescription of clozapine and LAIs, which have distinctive roles in medication management—clozapine has efficacy in managing treatment-resistant schizophrenia and reducing suicidal behaviors and has low rates of prescription by clinicians (15, 32–39), and LAI medications address nonadherence, but they require clinician administration and pose risks for coercion (5, 40). Significant variation across states held for all the psychotropic medications studied, even after the analysis was adjusted for several relevant covariates. This national analysis expands previous investigations of specific city (41), state (42), and regional (43) variations in prescriptions for individuals with schizophrenia by including a range of psychotropic medications.
Identification of individual-level predictors of psychotropic medication prescription aids our understanding of state-level variation in prescribing practices. Non-Hispanic Whites were more likely to fill clozapine and less likely to fill LAI prescriptions than were non-Hispanic Blacks, Hispanics, and people of all other race-ethnicities. These findings were based on a national data set and are consistent with those from previous reports (15, 20, 21), further highlighting the need to understand drivers of disparities and variation in state-level prescription of these medications. One hypothesis is that availability of providers that prescribe clozapine, and that offer regular monitoring of neutrophil counts required for clozapine maintenance, may be unevenly distributed in states where significant numbers of people from racial-ethnic minority groups receive services. These providers may recognize treatment-resistant schizophrenia and other indications for clozapine better among non-Hispanic Whites than among other racial-ethnic groups. Conversely, the divergent patterns of LAI medication prescription may be due to interstate differences in the type of insurance, clinical and demographic factors of patients or psychiatrist demographic characteristics (44). For example, some providers may be better at recognizing indications for LAIs, including medication nonadherence or risk factors such as substance use disorder, poor insight, or risk for relapse associated with aggression, violence, or self-harm (45), among non-Hispanic Blacks, Hispanics, and other non-White groups. Interest in addressing medication nonadherence (44) and awareness that LAIs may be stigmatizing from the patient’s perspective (46) may influence decision making of clinicians qualified to administer LAI medications.
That racial-ethnic minority groups were consistently less likely than non-Hispanic Whites to fill prescriptions for antidepressants and mood stabilizers may have reflected receipt of care in settings with fewer resources and receipt of narrower evaluations leading to poorer recognition of mood and anxiety problems. State regulation of benzodiazepines may have affected their use—Tennessee’s Medicaid program had strict limits on benzodiazepine prescriptions (47). Concern regarding benzodiazepine abuse liability that has resulted in changes in labeling by the U.S. Food and Drug Administration (48) may be associated with disproportionately lower numbers of prescriptions of these medications to patients from racial-ethnic minority groups compared with non-Hispanic Whites (49). These prescription patterns raise questions about the equity and quality of care provided to different racial-ethnic groups, and if rooted in implicit biases or systemic racism, disparities in prescription must be addressed to ensure equal access to evidence-based treatments for everyone.
The variability in prescribing patterns of psychotropic medications across states should be viewed in the context of several limitations. Because this was an observational study, we could not account for unmeasured variation in several comparisons. The data reflected filled prescriptions, not medication ingestion. We were also limited by using clinical rather than research diagnoses, although clinical diagnoses are appropriate for describing real-world community practice patterns. This study used the latest year that included data from almost all states; practice patterns may have changed since 2012, including expansion of Medicaid coverage through the Affordable Care Act. Our analysis was limited to individuals diagnosed as having schizophrenia in 2011 and to 2012 data from these individuals; therefore, we could not account for the length of schizophrenia diagnosis that could influence initiation and use of LAI medications after multiple oral antipsychotic medications had failed to improve patients’ conditions. The data did not include patients dually eligible for Medicaid and Medicare, those privately insured or insured by the U.S. Department of Veterans Affairs, or uninsured adults. Previous studies have found that type of practitioner (e.g., psychiatrist vs. general practitioner) is related to selection of psychotropic medications, but Medicaid data do not include a consistent provider type variable for prescriptions (50).
Some of the variation in psychotropic prescribing patterns might be explained by state preferred drug lists (PDLs). Pennsylvania, for example, which had the fifth highest adjusted odds of LAI prescribing, has no current prior authorization requirements for LAI medications (51). By contrast, Colorado, which had the lowest adjusted odds of LAI prescription, requires prior authorization for all LAIs (52). Without access to 2012 preferred drug lists from each of the states, however, a formal analysis of the contribution of state PDLs to prescribing practices for people with schizophrenia is beyond the scope of our analysis.

Conclusions

Our results indicate significant variation across states and among racial-ethnic groups in prescription patterns of six types of psychotropic medications, even after we had adjusted for multiple patient factors. Variations in state policies and differences in psychiatrist training and prescribing behaviors may have contributed to these variations. Psychiatric training that is culturally sensitive and seeks to minimize disparities by race or ethnicity and that requires competency in the prescription of clozapine and LAI antipsychotic medications may reduce variation. The use of standardized PDLs that have fewer LAI medications requiring prior authorization and that list more LAIs and oral antipsychotic medications may be a concrete way to improve prescribing patterns. A better understanding of the causes of wide variation in LAI and clozapine prescriptions is needed to improve access to these important treatment options.

Supplementary Material

File (appi.ps.202000932.ds001.docx)

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Information & Authors

Information

Published In

Go to Psychiatric Services
Go to Psychiatric Services
Psychiatric Services
Volume 73 • Number 5 • May 2022
Pages: 492 - 500
PubMed: 34587788

History

Received: 23 December 2020
Revision received: 24 May 2021
Revision received: 6 July 2021
Accepted: 16 July 2021
Published online: 30 September 2021
Published in print: May 2022

Keywords

  1. Antipsychotics
  2. Drug treatment
  3. Psychopharmacology
  4. Schizophrenia
  5. Psychotropic medication

Authors

Affiliations

Natalie Bareis, L.M.S.W., Ph.D. https://orcid.org/0000-0002-9067-2075 [email protected]
Department of Psychiatry, Columbia University, and the New York State Psychiatric Institute, New York City.
View all articles by this author
Mark Olfson, M.D., M.P.H.
Department of Psychiatry, Columbia University, and the New York State Psychiatric Institute, New York City.
View all articles by this author
Melanie Wall, Ph.D.
Department of Psychiatry, Columbia University, and the New York State Psychiatric Institute, New York City.
View all articles by this author
T. Scott Stroup, M.D., M.P.H.
Department of Psychiatry, Columbia University, and the New York State Psychiatric Institute, New York City.
View all articles by this author

Notes

Send correspondence to Dr. Bareis ([email protected]).

Funding Information

This study was supported by a pilot project of the Administrative Core of the National Institute of Mental Health Optimizing and Personalizing Interventions for Schizophrenia Across the Lifespan (OPAL) Center (P50-MH-115843).Dr. Stroup reports receiving royalties from APA Publishing and UpToDate, grants from the National Institutes of Health, and continuing medical education support from Intra-Cellular Therapeutics. The other authors report no financial relationships with commercial interests.

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