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Adherence to Antipsychotic and Nonpsychiatric Medications in Middle-Aged and Older Patients With Psychotic Disorders

From the Department of Psychiatry (C.R.D., J.P.L., D.V.J.), University of California, San Diego; and the Pharmacy Service (J.P.L., C.R.D.) and Psychiatry Service (D.V.J.), Veterans Affairs San Diego Healthcare System, San Diego, California.

Address reprint requests to: Christian R. Dolder, PharmD, Geriatric Psychiatry Intervention Research Center, VA San Diego Healthcare System, 116A-1, 3350 La Jolla Village Dr., San Diego, CA 92161. Email: cdolder{at}ucsd.edu

	ABSTRACT

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OBJECTIVE: The prevalence and consequences of nonadherence to antipsychotic medications in schizophrenia and related psychotic disorders have been well described; however, little is known about adherence to medications for nonpsychiatric conditions in patients with psychotic disorders. We wished to determine medication adherence in nondemented middle-aged and older Veterans Affairs outpatients with schizophrenia or other psychotic disorders who had been prescribed oral medications for hypertension, hyperlipidemia, or diabetes.

METHODS: Medication adherence was assessed by review of medication fill records for 76 patients aged 40 years and older who had been prescribed an oral antipsychotic in addition to an oral agent for hypertension (N = 60), hyperlipidemia (N = 28), or diabetes (N = 24). Up to 12 months of therapy was reviewed, and a compliant fill rate (the number of adherent fills in proportion to the total number of prescription fills) and cumulative mean gap ratio (the number of days when medication was unavailable in relation to the total number of days) were calculated for each medication.

RESULTS: The 12-month mean compliant fill rates for antipsychotics, antihypertensives, antihyperlipidemics, and antidiabetics ranged from 52% to 64%. Nonpsychiatric medication adherence rates were similar in patients on typical vs. atypical antipsychotics and did not correlate significantly with antipsychotic adherence rates.

CONCLUSIONS: Nonadherence rates were found to be equally problematic for both antipsychotic and nonpsychiatric medications in middle-aged and older patients with psychotic disorders. Interventions to improve adherence to both antipsychotic and nonpsychiatric medications are needed.

Key Words: medication adherence • psychosis • hypertension • hyperlipidemia • diabetes.

Abbreviations: CFR = compliant fill rate; CMGR = cumulative mean gap ratio.

	INTRODUCTION

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The prevalence of antipsychotic nonadherence and its consequences are well known. Deviation from antipsychotic maintenance treatment leads to numerous clinical and economic burdens, including psychotic relapse, increased clinic visits, emergency room visits, and rehospitalization (1, 2). Furthermore, nonadherence to prescribed antipsychotic regimens compromises the ability to enjoy a satisfactory quality of life and function optimally (3). Examining adherence to medications besides antipsychotics in patients with schizophrenia is important because comorbid medical conditions are common (4), yet there is a lack of studies examining this issue (5, 6). In general, adherence with medication regimens for physical disorders has been demonstrated to be problematic in patients with nonpsychiatric conditions (7–11).

Cramer and Rosenheck (7) reviewed studies of medication adherence for antipsychotics, antidepressants, and a variety of nonpsychiatric medications. Patients receiving antipsychotics took an average of 58% of the recommended quantity of medication, and patients prescribed antidepressants took 65% of the recommended amount. Studies involving patients with nonpsychiatric disorders (eg, hypertension, hyperlipidemia, or epilepsy) reported an average adherence rate of 76%, although the use of microelectronic monitoring in these studies might have led to an overestimation of adherence (7). Improving adherence to nonpsychiatric medications for disease states such as hypertension, hyperlipidemia, and diabetes is important because inadequate treatment of these disorders can have long-term negative consequences (12–16).

Reports suggest that mortality in persons with schizophrenia is two to four times greater than that in the general population (17–19). Although this difference in mortality is due in part to the increased risk of suicide in people with schizophrenia, undertreated physical illness, to which nonadherence can contribute, may also be a factor in increased mortality (4).

More information specifically addressing nonpsychiatric medication adherence in patients with psychotic disorders is needed, especially in older patients, because they are more likely to be prescribed medications for concomitant medical disorders (15) and have more complex medication regimens, cognitive and sensory deficits, and ageist beliefs that impede adherence (21, 22). Furthermore, symptoms of schizophrenia such as lack of insight likely hinder adherence to a variety of medications in addition to antipsychotics.

The purpose of this study was to determine medication adherence in veterans 40 years of age and older with psychotic disorders who had been prescribed maintenance treatments for hypertension, hyperlipidemia, and/or diabetes in addition to antipsychotic therapy. Adherence rates reported for antihypertensives, antihyperlipidemics, and antidiabetics range from 20% to 75% (9, 23–30). Although these rates are similar to the adherence rate for antipsychotics, the published study samples were not composed of psychotic individuals. Considering the side effect profile of antipsychotics and results from Cramer and Rosenheck (7) demonstrating widespread but possibly different levels of adherence among various agents, we wanted to test the hypothesis that antipsychotic nonadherence would be greater than nonadherence with nonpsychiatric medications. Based on previous findings (31–33) that antipsychotic adherence was moderately greater with atypical than with typical agents, and the suggestion that atypical agents can improve some domains of cognition (34–36) and modestly improve negative symptoms (37), we wanted to test the hypothesis that patients prescribed atypical antipsychotics would have greater adherence to nonpsychiatric medications than those receiving conventional agents. In a secondary analysis, we assessed whether adherence to nonpsychiatric medications was correlated with antipsychotic adherence and whether nonadherence was associated with medication quantity (ie, total number of medications and total number of dosages consumed per 24 hours).

	METHODS

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Patient Selection
This protocol was approved by the University of California, San Diego, Human Subjects Committee. Subjects were identified from a previous study in which antipsychotic adherence for up to 12 months was examined in 288 outpatients from the VA San Diego Healthcare System (31). Patients were receiving chronic maintenance therapy with one of the following antipsychotics: haloperidol (N = 57), perphenazine (N = 60), risperidone (N = 80), olanzapine (N = 63), or quetiapine (N = 28). In the earlier study, all patients prescribed one of these antipsychotics within a 3-month time period were identified using computerized records, and consecutive patients meeting selection criteria were included. In the current study, all patients 40 years of age and older receiving an oral antihypertensive, antihyperlipidemic, or antidiabetic agent were identified. We included only those at least 40 because middle-aged and older adults were more likely to be receiving medications for the nonpsychiatric conditions we were studying. The sample was restricted to patients with 1) a DSM-IV–based chart diagnosis of schizophrenia, schizoaffective disorder, mood disorder with psychotic features, or psychosis not otherwise specified; and 2) a minimum of two prescription fills accounting for at least 60 days’ worth of medication (for both the antipsychotic and nonpsychiatric medication). Excluded were individuals with nonpsychotic mood disorders or behavioral disturbances secondary to dementia or other general medical conditions. In addition, patients who, according to chart review, appeared to be receiving even part of their medical care outside the VA system were excluded. These selection criteria led to a total sample of 76, with 60 patients prescribed an antihypertensive, 28 receiving an antihyperlipidemic, and 24 taking an antidiabetic agent. Of the three nonpsychiatric medications examined, 28 patients were prescribed two of the agents, and 5 were receiving all three of the medications.

Data Collection
Demographic and relevant clinical information, including age, gender, ethnicity, psychiatric diagnosis, and psychotropic medications, was obtained. Medication profiles were examined for the use of oral agents for hypertension, hyperlipidemia, and diabetes with fill dates and the supplied quantity of medications (in days) recorded.

Adherence to prescribed regimens was determined by examining computerized medication fill records covering a 12-month period. Rates of adherence were calculated separately for each patient’s antipsychotic, antihypertensive, antihyperlipidemic, and/or antidiabetic agent at 6 and 12 months. Adherence was computed using two methods: compliant fill rate (CFR) and cumulative mean gap ratio (CMGR) (38, 39).

CFR represents the proportion of total fills that are adherent, ie, filled at time-appropriate intervals, for a specified period of time. Adherence was assessed by comparing the number of days of medication supply to the number of calendar days between fills. Those fills obtained within 20% of the previous prescription’s completion were considered adherent (38, 39). An exception to this rule occurred when a prescription became invalid because of a change in therapy, eg, dose change or new medication. In such cases medication was prematurely filled but was deemed adherent because the provider had altered the original therapy. CMGR was calculated by dividing the number of days of medication that were unavailable for consumption (because of delayed refill) by the total number of days during the same interval (38). Thus, CFR is based on a series of dichotomous assessments of adherence, whereas CMGR provides a continuous assessment detecting the magnitude of gaps in therapy. Medication profiles were also examined to calculate the number of scheduled oral daily medications and the total number of prescribed tablets or capsules consumed per day. If a patient was prescribed more than one agent for a disorder (eg, two medications for hypertension), the agent with the highest nonadherence was recorded and used for data analysis.

Data Analysis
Descriptive statistics were used to determine patient demographics, medication information, and adherence rates for antipsychotic, antihypertensive, antihyperlipidemic, and antidiabetic agents. Independent samples t tests and chi-square analyses were used to compare characteristics (ie, age, gender, ethnicity, diagnosis, and quantity of medications) between individuals prescribed typical vs. atypical antipsychotics. Because of the small expected frequencies with some of these analyses, Fisher’s exact test was performed to compare the two groups on gender. Univariate analysis of variance was conducted to compare adherence rates among the four types of medications (ie, antipsychotic, antihypertensive, antihyperlipidemic, and antidiabetic) and to evaluate nonpsychiatric medication adherence rates among patients prescribed typical vs. atypical antipsychotics. Pearson correlations were computed to determine 1) relationships between medication adherence and quantity of medication and 2) adherence between antipsychotic and nonpsychiatric medications. All statistical tests were two-tailed with significance set at .05.

	RESULTS

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Patient Characteristics
Seventy-six patients met selection criteria. The mean age of the subjects was 54 years (SD = 11). In general, the sample was comprised of whites (57%, N = 43) and men (95%, N = 72). Seventy-eight percent of the patients had a diagnosis of schizophrenia (N = 41) or schizoaffective disorder (N = 18). The remaining patients had a diagnosis of mood disorder with psychotic features (N = 11) or psychosis not otherwise specified (N = 6). The mean number of prescribed oral medications (5.6) and the total number of dosages taken per day (9.9) showed that most patients received a relatively high number of medications. The number of medications was actually greater for many patients since inhalers and PRN (as needed) medications were not included in the total number of daily dosages calculated. No significant differences in age (t = 0.69, df = 74, p = .53), gender (Fisher exact test = 1.00), ethnicity (² = 0.57, df = 1, p = .75), diagnosis (² = 0.32, df = 1, p = .57), mean number of total medications (t = -0.27, df = 74, p = .79), total number of dosages consumed per day (t = -0.74, df = 74, p = .46), or medication adherence (F range = 0.002–0.010, df = 1,74, p range 0.91–0.97) were found between patients prescribed typical agents compared with those receiving atypical antipsychotics. The mean total days of adherence assessments conducted for antipsychotics, antihypertensives, antihyperlipidemics, and antidiabetics were 305 (SD = 81), 333 (SD = 78), 285 (SD = 87), and 332 (SD = 93), respectively (F = 3.01, df = 3,184, p = .032; no significant differences among individual agents using the Scheffe test).

The percentages of subjects who were receiving maintenance therapy (ie, prescribed the medication for at least 6 months before our measurement of adherence) for the four classes of medication were also calculated. There were no significant differences on this variable among the medication classes (range = 58% to 78% on maintenance therapy: ² = 3.86, df = 3, p = .28).

The types of antihypertensives for which adherence was analyzed were relatively evenly distributed among angiotensin-converting enzyme inhibitors (33%, N = 20), ß-blockers (25%, N = 15), calcium channel blockers (20%, N = 12), and diuretics (22%, N = 13), whereas sulfonylureas (58%, N = 14) and hydroxymethylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (71%, N = 20) were the primary medications included in diabetes and hyperlipidemia medication adherence analyses, respectively.

Adherence Rates
Figure 1 shows the 12-month CFR and CMGR results for antipsychotic, antihypertensive, antihyperlipidemic, and antidiabetic agents, covaried for total days of adherence assessments. Twelve-month CFRs ranged from 52% to 64% across the four medication categories. Thus, patients filled their medications at appropriate intervals only slightly more than half of the time. Twelve-month CMGRs ranged from 5% to 15%, resulting in average gaps in therapy varying from 2 to 5 days per month. No significant differences were found among these medication groups in terms of CFR or CMGR at 12 months (F = 1.39, df = 3,184, p = .25 and F = 2.24, df = 3,184, p = .086, respectively) or 6 months (F = 0.94, df = 3,184, p = .42 and F = 1.56, df = 3,184, p = .20, respectively). No significant differences in adherence were found when adherence rates were compared by gender.

View larger version (47K): [in this window] [in a new window]   Fig. 1. Twelve-month mean medication adherence rates. Bars show standard deviations. a, Proportion of total fills that are adherent among medications (F = 1.39, df = 3,184, p = .25). b, Number of days of medication that were unavailable for consumption (because of delayed refill) divided by the total number of days during the same interval among medications (F = 2.24, df = 3,184, p = .09).

	DISCUSSION

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This study revealed that adherence to nonpsychiatric medications in middle-aged and older patients with schizophrenia or related disorders is problematic. Our hypothesis that patients prescribed atypical agents would have higher rates of adherence with nonpsychiatric medications compared with those receiving conventional agents was not proven because no differences in adherence were found. This suggests that nonadherence for nonpsychiatric agents is a problem despite the use of atypical agents. In addition, our hypothesis that nonadherence with antipsychotics would be greater than nonadherence with nonpsychiatric medications was also not proven because the rates of nonadherence for antipsychotic, antihyperlipidemic, and antidiabetic agents were similar. Of note was the finding that none of the CFRs or CMGRs for the four medication classes were significantly correlated with one another. Although the small sample size makes such a finding preliminary, it does suggest that adherence to one medication does not guarantee adherence with another agent. As part of our secondary questions, we found that medication quantity (ie, total number of medications and the total number of dosages consumed per 24 hours) was significantly related to antipsychotic nonadherence, thus reinforcing the importance of simplifying medication regimens whenever possible in an attempt to improve adherence. We also did not find significant differences in antipsychotic adherence among patients prescribed typical vs. atypical agents; however, our limited sample size might have contributed to this lack of a significant difference. Our finding of no significant difference in adherence rates by gender is not surprising. Although definitive conclusions about the effect of gender on adherence cannot be made from this mostly male sample, previous examinations of gender as a risk factor for nonadherence (40–45) did not find it to be a significant factor.

To the best of our knowledge, our findings are unique in that we specifically examined adherence to nonpsychiatric medications in a sample of middle-aged and older patients with psychotic disorders. Similar results regarding nonadherence correlating to greater numbers of prescribed medications were reported in a sample of community-dwelling elderly patients with chronic medical disorders such as hypertension, osteoarthritis, and diabetes (46). In general, previous examinations of adherence to such medications as antihypertensives and other nonpsychiatric medications have tended to include medical patients and did not specifically address adherence in patients with psychiatric disorders (7–11, 47–50). The findings that nonadherence rates were not highly correlated with one another becomes logical in the context of the Health Belief Model. This model considers health behavior to be a result of the interplay among a number of construct factors, such as perceived susceptibility to illness, perceived severity of illness, perceived benefits of taking health action, perceived barriers to taking action, and cues to action (51–54). In this model patients determine their adherence behavior by weighing the perceived benefits and costs of each treatment, which may vary for different medications. For example, the side effect profile of an antipsychotic is different than that of an antihypertensive or antidiabetic agent. Furthermore, hypertension and hypercholesterolemia are usually chronic, painless conditions perceived by the patient as having deleterious health consequences in the distant future (55), whereas schizophrenia has more immediately noticeable symptoms.

Appropriate measurement of medication adherence remains a fundamental issue when determining adherence. Presently, there is no single measure accepted as the "gold standard" because all the commonly employed methods have drawbacks (56). Patient interviews, while straightforward and inexpensive, are clearly limited by their subjective nature. Pill counts are frequently utilized, inexpensive, and can provide information about the number of pills taken; however, it is difficult to determine actual medication consumption, and patients can intentionally or unintentionally manipulate this measure. Electronic adherence monitors, while providing detailed information regarding medication administration, are expensive and do not measure actual medication consumption. Medication refill records provide unobtrusive information regarding refill histories and can be valuable in determining gaps in therapy, but this method, similar to pill counts and electronic monitors, is indirect and cannot confirm actual medication consumption. Blood and urine medication levels, while direct measures of adherence, may be unpopular with patients and can be manipulated (56).

Strengths of our study include the 12-month assessment period and use of objective definitions of adherence (CFR and CMGR) based on medication refill records. Steiner and Prochazka (38) reviewed the literature on the assessment of refill adherence using pharmacy records. From the 41 studies reviewed, the authors concluded that refill records are a useful source of adherence information when direct measurement of medication consumption is not feasible, such as in the current study. Applying identical adherence criteria to different classes of drugs can be problematic as it could be argued that different cutoffs for adherence exist among various medications. For this reason, we calculated adherence using both a dichotomous assessment (CFR) and a continuous measure (CMGR). The unobtrusive nature of obtaining pharmacy refill information to assess adherence allowed for a naturalistic estimation of adherence (56). Additionally, rates of adherence based on pharmacy refill records have been reported to correlate with other adherence behaviors (eg, appointment keeping), serum drug levels, and drug effects such as blood pressure control (57–60). Furthermore, although refill records provided only an indirect measure of adherence, they allowed us to calculate gaps in therapy (61), which demonstrated underuse of medications. Our decision to exclude patients who had likely received medical care outside the VA system increased the completeness of our pharmacy records. The very low cost of medications to patients in the VA system, at the time the study was conducted, minimized the effects of financial burden on refill rates. Thus, in our study setting, CFR and CMGR were probably the most useful practical measures of adherence.

In addition to the drawbacks with refill records that was previously discussed, we should also point out the limitations of this study. One of the limitations was the nonrandomized study design. We attempted to decrease selection bias by assessing everyone who met selection criteria. Furthermore, this investigation was an attempt to measure adherence in a naturalistic "real-world" setting. Because the study used a retrospective design, we were limited in our ability to measure and analyze potentially relevant factors such as medication efficacy, side effects, insight, medication supervision status, therapist alliance, or psychiatric hospitalization. Another limitation of this study was the small sample size. Finally, our results may not generalize to non-VA patients. Future studies should examine larger numbers of individuals and patients with other psychiatric disorders. Additional investigations should also compare nonpsychiatric medication adherence rates between a schizophrenic group and a nonpsychiatric group.

In summary, we found that adherence to antihypertensive, antihyperlipidemic, and antidiabetic agents was problematic in middle-aged and older patients with schizophrenia or related disorders, even in those taking atypical agents. The long-term consequences of nonadherence to these classes of medications, in addition to antipsychotics, underscore the need to improve adherence for both psychiatric and nonpsychiatric medications in this population.

	ACKNOWLEDGMENTS

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This work was supported in part by the National Institute of Mental Health, Grants MH19934, MH49671, MH43693, and MH59101, and by the Department of Veterans Affairs. Portions of this work were presented at the American Association for Geriatric Psychiatry Annual Meeting in Orlando, Florida, February 24–26, 2002.

Received for publication December 5, 2001.

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