Predictors of compliance with neuroleptic medication among inpatients with schizophrenia: a discriminant function analysis

Predictors of compliance with neuroleptic medication among inpatients with schizophrenia: a discriminant function analysis

Eur Psychiatry 2001 ; 16 : 293-8 © 2001 Éditions scientifiques et médicales Elsevier SAS. All rights reserved S0924933801005818/FLA ORIGINAL ARTICLE ...

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Eur Psychiatry 2001 ; 16 : 293-8 © 2001 Éditions scientifiques et médicales Elsevier SAS. All rights reserved S0924933801005818/FLA

ORIGINAL ARTICLE

Predictors of compliance with neuroleptic medication among inpatients with schizophrenia: a discriminant function analysis G. Donohoe1,2, N. Owens1, C. O’Donnell1, T. Burke2, L. Moore1, A. Tobin3, E. O’Callaghan1* 1 2

Department of Adult Psychiatry, Cluain Mhuire Service, Hospitaller Order John of God, Dublin, Ireland; Department of Psychology, University College Dublin, Dublin, Ireland; 3 Eli Lilly Pharmaceuticals ( Ireland )

(Received 23 November 1999; revised 27 April 2001; accepted 8 June 2001)

Summary – Objective. To identify clinically useful predictors of adherence to medication among persons with schizophrenia. Method. We evaluated levels of compliance with neuroleptic medication among 32 consecutive admissions with DSM-III-R schizophrenia from a geographically defined catchment area using a compliance interview. We also assessed symptomatology, insight, neurological status and memory. Results. Less than 25% of consecutive admissions reported being fully compliant. Drug attitudes were the best predictor of regular compliance, symptomatology the best predictor of noncompliance, and memory the best predictor of partial compliance with neuroleptic medication. Conclusions. These data emphasise the complexity of factors that influence whether a person adheres to his medication regimen. Furthermore, they suggest that these factors may vary within the same person over time. © 2001 Éditions scientifiques et médicales Elsevier SAS adherence / compliance / drug attitudes / insight / neuroleptics / schizophrenia

INTRODUCTION Despite the overwhelming evidence that neuroleptic medication is effective in the acute treatment and prophylaxis of schizophrenia, many patients do not take their medication [6, 12, 13]. Several studies have shown that approximately one-third of patients are fully compliant, one-third partially compliant, and the final onethird entirely noncompliant [4, 9, 20]. Noncompliance is now considered more relevant than ever since untreated schizophrenia has a ‘neurotoxic’ effect on psychological functioning [16].

In their influential review, Fenton et al. [8] cite several factors that have been variously associated with adherence to medication. These include insight, drug attitudes, symptomatology, and side effects of medication. However, few of the studies have attempted to explore the interaction between these factors and nonadherence to medication. Furthermore, most of the studies were conducted in research settings among selected patient populations. We set out to examine potential predictors of compliance among a consecutive series of admissions with schizophrenia from a geographically defined catchment area service. Additionally, we sought to establish whether memory dis

*Correspondence and reprints: Cluain Mhuire Service, Newtownpark Avenue, Blackrock, Co. Dublin, Ireland. E-mail address: [email protected] (E. O’Callaghan).

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turbance, a recognised deficit among some patients with schizophrenia, also influenced compliance. SUBJECTS AND METHOD After we received ethical committee approval, we interviewed consecutively admitted patients to St John of God Hospital using the Structured Clinical Interview for the DSM-111-R Diagnosis (SCID) [2]. We included those aged between 18–65 years, with an Intelligence Quotient (IQ) greater than 80 and no evidence of organic disturbance. We evaluated symptomatology using the Positive and Negative Symptoms Scale (PANSS) [15], and the Global Assessment of Functioning [7]. We measured compliance using the Compliance Interview [1]. We defined poor compliance as 0–25% compliance over the preceding 3 months. Partial compliance was defined as 26–74% compliance and regular compliance as 75% or higher compliance over the preceding 3 months. Insight was evaluated using the Schedule for Assessment of Insight (SAI) [5], and drug attitudes with the Drug Attitude Inventory (DAI) [14]. General cognitive functioning was measured in the Mini Mental State Exam [10], and pre-morbid IQ using the National Adult Reading Test (NART) [17]. We assessed memory using the Recognition Memory Test [19]. We measured akathisia using the Barnes rating scale for akathisia [3], and tardive dyskinesia with the Abnormal Involuntary Movements Scale (AIMS) [11]. Educational attainment was rated in terms of state exams and third-level qualifications obtained. Statistics Differences between the groups were analysed using one-way ANOVA’s and discriminant analysis. Significant differences observed were analysed using Tukey post hoc comparison tests. We used the χ2 test to analyse categorical variables. Statistical significance was taken at P < 0.05. The statistical package used was SPSSt for Windows t version 7.0 [18]. RESULTS Of the 32 participants, 19 (59%) had paranoid schizophrenia, seven (22%) undifferentiated, three (10%) disorganised, two (6%) residual, and one (3%) catatonic. Sixteen (50%) participants had a history of the illness lasting 5 years or less, seven (22%) between 6 and

10 years, and nine (28%) 11 years or more. On entry to the study, 12 (38%) were on traditional neuroleptics and 20 (62%) on atypical neuroleptics. Only 11 participants (less than 25%) reported being fully compliant, 12 reported being partially compliant and nine admitted to being completely noncompliant with their prescribed neuroleptic within the last month. The compliance groups could not be distinguished statistically in terms of age, gender, years since diagnosis, educational attainment, domestic situation or schizophrenia subtype. Poor, partial and regular compliers were prescribed similar doses of neuroleptics, and showed similar rates of substance abuse. Differences between poor, partial, and regular compliers in mean scores on clinically significant variables are presented in table I. Symptomatology On the PANSS activation subscale, poor compliers had significantly higher scores than regular compliers (Q = 2.58; df = 29; P = .039), with no difference between partial and regular compliers. On the PANSS composite subscale poor compliers had significantly higher scores than partial compliers (Q = 9.03; df = 29; P = .021). Mean scores for the three compliance groups suggest that poor compliers had more positive symptoms than negative symptoms, whereas both partial and regular compliers had more negative symptoms than positive symptoms. Interestingly, no significant differences were observed between partial compliers and regular compliers on either of the symptomatology subscales or on global assessment of function. Drug attitudes and insight From among what might be described as ‘cognitive variables’, we found a linear relationship between levels of compliance and both drug attitudes and insight. Poor compliers had significantly lower levels of insight than regular compliers (Q = 4.85; df = 31; P = .013), with partial compliers failing to show significant differences from the other two groups. Differences in attitudes to drugs resulted from differences between regular compliers and both poor compliers (Q = 13.79; df = 31; P = .001) and partial compliers (Q = 10.12; df = 31; P = .01), with no differences between poor and partial compliers. Eur Psychiatry 2001 ; 16 : 293–8

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Table I. Differences between poor partial and regularcompliers (N = 34) on symptomatology, cognitive, and medication-related side effects.

Symptomatology Global Functioning PANSS-positive PANSS-negative PANSS-paranoid belligerence PANSS-thought disturbance PANSS-activation PANSS-anergia PANSS-composite PANSS-depression PANSS-total Cognitive variables Drug attitudes (DAI) Insight (SAI) Premorbid IQ (Nart) Warrington-faces Warrington-words Medication-related variables Akathisia (Barnes)

Poor (N = 9) Mean SD

Compliance group Partial (N = 12) Mean SD

Full (N = 11) Mean SD

30.67 19.56 17.22 7.22 10.44 6.67 8.22 4.44 6.22 60.33

7.43 9.65 7.46 5.26 6.31 2.87 4.09 9.68 2.17 30.95

33.00 14.42 19.00 6.25 8.08 6.00 9.25 –4.58 8.17 66.50

14.61 6.01 5.86 2.73 2.81 2.37 2.96 6.54 3.51 18.90

45.00 13.27 15.73 4.55 7.36 4.09 8.55 –2.45 9.82 59.45

18.71 3.74 6.47 1.37 2.77 1.22 4.44 5.18 4.64 19.88

2.88 2.45 .72 1.70 1.515 3.760 .202 4.292 2.387 .313

2, 29 2, 29 2, 29 2, 29 2, 29 2, 29 2, 29 2, 29 2, 29 2, 29

.07 .72 .50 .20 .24 .04* .82 .02* .11 .73

41.67 5.33 97.22 5.5 10.29

9.90 4.33 37.2 .93 3.25

45.33 7.92 111.2 9.67 12.00

8.47 3.12 7.69 3.5 1.95

55.45 10.18 114.4 7.5 9.5

3.39 3.19 4.18 3.27 3.37

9.075 4.708 1.981 4.838 2.242

2, 29 2, 29 2, 29 2, 27 2, 26

.001** .02* .16 .02* .13

.33

.71

4.08

3.09

3.00

3.52

4.626

2, 29

.02*

F

df

P

* significant at .05; **significant at .01.

Cognitive functioning

Discriminant analysis

We found no relationship between measures of intelligence, educational attainment, or cognitive assessment and compliance. One variable related to cognitive functioning, however, on which poor, partial, and regular compliers were observed to differ significantly, was recognition memory. Poor compliers had significantly lower mean memory scores than partial compliers as measured by facial recognition (Q = 4.17; df = 27; P = .013). No difference was observed between partial compliers and regular compliers. Poor, partial, and regular compliers did not differ on the word subscale of the recognition memory test.

The emphasis of this analysis was on understanding how these variables related to each other to determine compliance. We used discriminant analysis to determine the linear combination of predictor variables that best classifies cases into each of the compliance groups. We derived the analysis on the basis of a forced entry of all variables on which the compliance groups (above) differed significantly: insight, drug attitudes, symptomatology (activation and composite subscales), side effects (akathisia), and recognition memory (faces subscale). On the basis of a discriminant analysis of these variables two significant functions were derived. The eigenvalues, relative variance, canonical correlations, and significance tests are shown in table II. In the classification of patients on the basis of these discriminant functions 90.0% (27/30) of all cases were correctly classified as compared with a chance classification of 33%. Of the poor group, 87.5% (7/8) were correctly classified, 83.3% (10/12) of the partial group, and 100% (10/10) of the regular group. In short, only three patients were misclassified. See table III for a summary of classification results.

Medication-related variables Those receiving depot medication were no more compliant than those not receiving depot. However, in terms of side effects, mean scores differed significantly between the compliance groups; partial compliers had significantly higher levels of akathisia than poor compliers (Q = 3.75; df = 29; P = .015). Eur Psychiatry 2001 ; 16 : 293–8

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Table II. Statistical significance of discriminant functions observed. Function 1 2

Eigenvalue

% variance

Canonical correlations

Wilks’ lambda

χ2

df

sig.

1.5032 .9011

62.52% 37.48%

.7749 .6885

.210 .526

38.22 15.74

12 5

.0001 .0076

Function 1 was described as the PANSS-composite, akathisia, recognition memory, and insight, while Function 2 was described as drug attitudes and PANSSactivation. Function 1 is the most important, representing 62.5% of the discrimination power. To interpret the contribution of each of the variables to the discrimination of each group, group centroids and stretched vectors for each variable (discriminate loading for each variable multiplied by univariate F value) were plotted (see figure 1).

On the basis of figure 1, the composite subscale of the PANSS (measuring symptomatology) appears to be the best predictor of membership of the noncompliance group, the Warrington-faces subscale (measuring recognition memory) the best predictor of the partial compliance group, and the DAI (measuring drug attitudes) of the regular compliance group. In summary, on the basis of knowledge about patients’ symptoms, drug attitudes, insight, memory and side effects, patients can be accurately categorised as poor, partial, or regular

Table III. Summary of classification results.

Original group membership

Compliance categories

Predicted group Membership poor

Total partial

full

poor partial full

6 1 0

0 10 0

1 1 9

7 12 9

90.00% of original grouped cases correctly classified.

Figure 1. Mapping of poor, partial, and regular compliance group centroids and stretched vectors for variables discriminating group membership: drug attitudes (DAI), insight (SAI), akathisia (Barnes), symptomatology (PANSS-C and AC), and recognition memory (RMT). Eur Psychiatry 2001 ; 16 : 293–8

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compliers, with positive symptoms the best predictor of poor, memory the best predictor of partial, and drug attitudes the best predictor of good compliance. Insight, akathisia, and PANSS-activation scores were each only modestly predictive of compliance classification. DISCUSSION This study illustrates the complexity of factors that influence whether a person adheres to his medication regimen. While noncompliance is already understood to be related to drug attitudes, insight, symptom severity, and side effects, this study underlines how specific variables are important for specific compliance groups. We found that whereas the main differences between regular and partial compliers were in ‘cognitive variables’ such as drug attitudes and insight, the main differences between partial and poor compliers were in symptomatology and memory. These differences also highlight the probability that factors determining compliance may vary within the same person over time. For example, while higher levels of positive symptoms and memory impairment may determine compliance when a patient is unwell, attitudes to medication and insight may be stronger determinants of compliance when a patient is well. In symptomatology, the PANSS subscales on which poor compliers significantly differed from partial and regular compliers suggests that poor compliers have higher levels of ‘positive’ symptoms of schizophrenia. None of the ‘negative’ subscales differed significantly between the groups. This suggests that, rather than the relationship between symptoms and compliance being a general one, the relationship appears to be specific to symptoms of aggression and hostility in particular. To our knowledge this is the first report that identifies memory impairment as an important factor influencing compliance. Of all the variables considered in our discriminant analysis, memory impairment was the variable which best discriminated the partial compliance group. This association between memory impairment and compliance is relatively independent of general cognitive functioning because we found no relationship between compliance and pre-morbid IQ. This is consistent with behavioural theories which suggest that poor compliance results in part from a failure to remember to take medication. These data highlight the importance of facilitating patient compliance by simplifying medication regimes and using complianceenhancing strategies such as reminder cards. Eur Psychiatry 2001 ; 16 : 293–8

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Given the large number of symptomatic, cognitive, and medication-related variables considered, an argument could be made for making a Bonferroni correction to avoid a type I error by setting a more conservative critical value. On the basis of such a correction (reducing the critical value to .014), only the DAI would achieve the restrictive level of statistical significance. However, we considered that there would be an even greater risk of making a type II error given the modest sample size. This approach is supported both by the fact that variables observed as significant are consistent with previous studies on compliance, and by the fact that patients’ compliance could be so accurately classified on the basis of these variables. A second potential weakness of this study is that assessments of compliance, insight, and medicationrelated side effects were clinician-rated and thus vulnerable to observer bias. However, each of these tools has been used extensively in previous research on compliance and are ‘industry standard’. Also, by choosing similar measures, our findings can be more easily compared with other studies, a comparison which until recently has been difficult given the lack of uniformity in assessments used. This sample is drawn from consecutively admitted inpatients with schizophrenia rather than from a selected patient population, allowing us to avoid the selection bias inherent in many studies in this area. However, the study is based on a small sample, thus limiting the degree to which its findings can be generalised. Clearly, these findings need to be replicated in a larger sample. Additionally, the cross-sectional design limits interpretations of time-related variables; a prospective design would have been preferable. By underlining the complexity of compliance, both in the way that each of the three compliance groups are determined by different factors, and in the probability that factors determining compliance may vary within the same person over time, these data have some implications for our efforts to develop effective interventions to improve compliance. Atypical neuroleptics are better tolerated than conventional neuroleptics with less propensity to induce unpleasant extrapyramidal sides effects. This improved tolerability is likely to go some way toward improving compliance, although little data is yet available on the issue. However, these findings suggest that no single strategy is likely to solve the problem of noncompliance, and that different aspects will be important for patients at different stages. Only by taking a multi-faceted approach to noncompliance,

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therefore, will we be effective in dealing with the world’s ‘other drug problem’. ACKNOWLEDGEMENTS This work was supported by an educational grant from Eli Lilly Pharmaceuticals, Ireland. REFERENCES 1 Adams SG, Howe JT. Predicting medication compliance in a psychotic population. J Nerv Ment Dis 1993 ; 181 : 558-60. 2 American Psychiatric Association. Structured Clinical Interview for the DSM-III-R. Washington DC: APA; 1992. 3 Barnes TR. Rating scale for drug induced akathisia. Br J Psychiatry 1989 ; 154 : 672-6. 4 Buchanan A. A two year prospective study of treatment compliance in patient with schizophrenia. Psychol Med 1992 ; 22 : 787-97. 5 David A. Insight and psychosis. Br J Psychiatry 1990 ; 156 : 798-808. 6 Dencker SJ, Liberman RP. From compliance to collaboration in the treatment of schizophrenia. Int Clin Psychopharmacol 1995 ; 9 (Suppl5) : 75-8. 7 Endicott J, Spitzer RL, Fleiss JL, Cohen J. The Global Assessment Scale: a procedure for measuring overall severity of psychiatric disturbance. Arch Gen Psychiatry 1976 ; 33 : 766-71. 8 Fenton WS, Blyer CR, Heinssen RK. Determinants of medication compliance in schizophrenia: empirical and clinical findings. Schizophr Bull 1997 ; 23 : 637-51.

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