Eur J Clin Pharmacol (2005) 61: 855–861DOI 10.1007/s00228-005-0034-6

PHARMACOEPIDEMIOLOGY AND PRESCRIPTION

E. Acquaviva . I. Gasquet . B. Falissard

Psychotropic combination in schizophrenia

Received: 1 June 2005 / Accepted: 7 September 2005 / Published online: 8 November 2005# Springer-Verlag 2005

Abstract Objective: To study adjunctive medicationsused with antipsychotic agents in schizophrenia via com-parisons of antidepressant, anxiolytic and antiparkinsonianco-prescribing. Method: In the context of a national naturalisticprospective observational study, a database containing allthe prescriptions from 100 French psychiatrists during theyear 2002 was analysed. The inclusion criteria were adiagnosis of schizophrenia or schizoaffective disorder andage over 18. A log-linear model and generalised linearmixed models were used. Results: In all 5,257 prescrip-tions for 922 patients were analysed. The proportion ofpatients who were prescribed an antiparkinsonian drug was32.9%. Amisulpride, haloperidol, phenothiazines with asedative action and depot typical antipsychotics provedmore likely to be prescribed with antiparkinsonians. Thefrequency of antidepressant and anxiolytic prescriptionswas 51.2% and 52.3%, respectively. Associations betweenatypical antipsychotics (except clozapine) and antidepres-sants were positive while associations between typicalantipsychotics and antidepressants were not. There were nodifferences among antipsychotics for the prescription ofanxiolytics. Conclusions: Atypical antipsychotics can beexpected to be less likely associated with antiparkinsonians.This result is indeed found for olanzapine, clozapine and toa limited extent for risperidone. Furthermore, a trendtowards a positive association between atypical antipsy-chotics and antidepressants appears. In view of the an-

tidepressive action of certain atypical antipsychotics, thisresult is surprising. The increase in the prescriptions ofanxiolytics concerns all types of antipsychotics. In view ofthe increase in associated medications in schizophrenia andthe difficulty of estimating it in randomised trials, this studyunderlines the contribution of naturalistic studies on thisscore.

Keywords Schizophrenia . Antipsychotics .Pharmaco-epidemiology

Introduction

Over the last decade, new trends in the treatment ofschizophrenia have appeared. A new generation of anti-psychotics has emerged and their prescriptions have in-creased at the expense of typical antipsychotics [1–9]. Atthe same time, antidepressants and anxiolytics have beenextensively used in the treatment of schizophrenia. Anti-parkinsonian prescriptions used to treat or to prevent antipsy-chotic-induced extrapyramidal syndromes have decreased[1–10]; and, except for antiparkinsonians, guidelines arenot clear [11, 12]. Moreover, despite the scale of such co-prescriptions, there is little information regarding the kindof associated medication prescribed [1, 13]. Are thesemodifications in the frequencies of adjunctive prescriptionsexplained by the existence of atypical antipsychotics? Anattempt has been made to answer this question by studyingthe differences in antiparkinsonian, antidepressive and an-xiolytic co-prescribing according to different classes of an-tipsychotics. The first step of this analysis was to determinethe frequencies of prescription in schizophrenia of threepharmacological groups: antiparkinsonians, antidepressantsand anxiolytics. The second step consisted in comparing thefrequencies of co-prescription for several antipsychotics byexploring the structure for psychotropic associations using agraphic model and by testing associations between thedifferent types of antipsychotic and the three types of co-prescription studied, using generalised linear mixed models.

E. Acquaviva (*) . I. Gasquet . B. FalissardInserm U669 PSIGIAM (Paris Sud Innovation GroupIn Adolescent Mental health Methodology),Cochin Hospital,97 boulevard de Port Royal,75679, Paris cedex 14, Francee-mail: EAcqua7129@aol.comTel.: +33-66-2179812Fax: +33-14-0032263

Method

Sources of data

Data for the study are derived from the Thales companydatabase. This company has developed an exhaustive com-puterized medical file system used by a panel of random-ised French psychiatrists. For each consultation, treatmentand diagnosis were entered directly by the psychiatrist.Diagnoses were made in the context of day-to-day practice,without the use of standardized interviews but on the basisof the DSM-IV classification. The software providedprintouts of prescriptions and recorded data in a centraldatabase: this guaranties the quality of data concerningtreatments. Other variables such as adverse events andmedical history were not used because their quality was notguaranteed with the same level. The exhaustiveness of thedata on the variables used in our study was checkedmonthly. The practitioners were voluntary and not paid fortheir participation, but they received for free the computersystem used for the database.

Sample

The psychiatrists in the panel were private practitioners.They were chosen at random with stratification for regionof practice, gender and age. Other characteristics such assalary and the kind of practice (private only, private andpublic practice) were compared with the French nationalsocial security register. Regarding all these characteristics,the panel of 100 psychiatrists was representative of theFrench population of private practitioners.

All the subjects included were outpatients. Inclusioncriteria were as follow: patients older than 18 who con-sulted any psychiatrist on the panel at least once from 1January 2002 to 31 December 2002 and who had at leastonce received a diagnosis of schizophrenia or schizoaffec-tive disorder.

The diagnoses were divided into four groups, using theDSM-IV classification [14]: (1) schizophrenia, (2) schizo-affective disorder, (3) affective disorders, which groupbipolar disorders and depressive disorders and (4) otherdisorders. These variables were binary.

Concerning the encoding of prescriptions, the mainpreoccupations were: (1) not to increase the number ofvariables which could have led to data being unexploitableand loss of power in analysis, (2) to avoid missing majorinformation relevant to the study aims and (3) to describethe patterns of co-prescription in terms of targeted action. Itwas therefore decided to individualise each atypical an-tipsychotic in order to detail associations with these drugs.The choice was also made to individualise haloperidolbecause it is the typical antipsychotic most frequentlyprescribed. Typical antipsychotics were then classified intotwo groups according to specificity of clinical action. Sed-ative antipsychotics were individualised in one group (thesewere phenothiazines with a sedative action: cyamemazine,chlorpromazine, levomepromazine, thioridazine, pericia-

zine), and other typical antipsychotic usually used as along-term specific treatment of psychosis were assigned toanother group (benzamides: sulpiride, tiapride, sultopride;butyrophenones other than haloperidol: pimozide, pipam-perone, penfluridol, droperidol; and phenothiazines with anantiproductive action: loxapine, flupentixol, pipothiazine,zuclopenthixol, thioproperazine, fluphenazine, carpipra-mine). The specificities of depot antipsychotics also neededto be taken into account. Therefore a variable was createdgrouping the depot forms of several antipsychotics: hal-operidol, fluphenazine, flupentixol and zuclopenthixol.

Quetiapine was not considered because it is not com-mercialised in France.

Thus the group of medications appears as follows: (1)olanzapine; (2) risperidone (non-depot); (3) amisulpride;(4) clozapine; (5) haloperidol (non-depot); (6) sedative an-tipsychotics (non-depot); (7) other typical antipsychotics(non-depot); (8) depot antipsychotics; (9) antidepressants;(10) anxiolytics (benzodiazepines other than hypnoticsand hydroxyzine); (11) antiparkinsonians; (12) hypnotics(zolpidem, zopiclone, alimenazine, flunitrazepam, nitraze-pam, triazolam, lormetazepam and loprazolam); and (13)mood stabilizers (carbamazepine, valpromide and lithium).

Analysis

The first step of the analysis was descriptive in order tocalculate the frequency in prescription of the differentmedications. Demographic characteristics of the sampleof patients were also studied in order to check theirrepresentativeness.

The second step aimed to illustrate the structure of as-sociations between prescriptions. A log-linear model [15–17] was used. This model is a multivariate analysis whichdoes not contain any dependent variable. It is a rep-resentation of the correlations between medications. It wasobtained after eliminating less significative associations tomaximise the Bozdogan indicator, which is a criterion ofmodel fit. This provided a picture of the most significantassociations between medications. The log-linear model isoriginal because it is less intended to test any relationshipthan to describe the strength and the structure of the linksbetween different variables.

The third step aimed to statistically test associationsbetween the type of antipsychotic medication and eachdependent variable: antiparkinsonian, antidepressant andanxiolytic. Three multivariate models were used. Sincethere were multiple prescriptions for one subject, general-ised linear mixed models were also used [15–18]. Con-founding variables such as diagnosis and demographicvariables were entered into the models. Mixed models aremultivariate analyses using dependant variables. This ex-plains the need to complement the log-linear model withgeneralised linear mixed models to compare the associa-tions of prescriptions. A stepwise descending methodologywas applied to retain the most significant variables in termsof statistical and clinical relevance. For the three finalmodels, the strength of the associations with the dependent

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variables was represented by the odds ratio. The p valuerepresents the probability of obtaining an odds ratio valueother than 1.

For statistical analyses, SAS 8.2 software was used. Proccat mod was used for the log-linear models and proc genmod for the mixed models.

Results

During 2002, 2,523 patients consulted a psychiatrist fromthe panel of 100 psychiatrists at least once; 922 subjectssuffering from schizophrenia or schizoaffective disorderwere included. Of these 85.6% received a diagnosis ofschizophrenia at least once in 2002, 31.9% a diagnosis ofschizoaffective disorder, and 11.8% a diagnosis of affectivedisorders; 28.9% of patients had at least two differentdiagnoses. All subjects included were prescribed medica-tion at least once.

Demographic characteristics

There were no missing data for the variable gender and twomissing data for age. Mean age was 39.7 (min.=16, max.=90, SD=12.0); 26.9% of the patients were younger than 35and 22.3% were older than 55. Men were younger thanwomen (38.2 vs 41.8; p<0.01 ) and represented 57.9% ofthe population.

Prescriptions

On average, a patient received six prescriptions in the year2002 (SD=2.2). The mean number of psychotropic med-

ications per prescription was 2.6 (SD=1.1). The distribu-tion of treatments is presented in Table 1; 70.8% of thepatients were prescribed either an antidepressant or ananxiolytic or both. About one-third of the subjects receivedan antiparkinsonian drug. Of the patients 80.2% were pre-scribed at least one of the three following adjunctivepharmacological agents: antiparkinsonian, antidepressantor anxiolytic; 31% of the patients were prescribed at leastfour different treatments.

Log-linear model

The structure of the associations between the medicationsis graphically represented with a log-linear model (seeFig. 1).

Concerning adjunctive medications:

– AntiparkinsoniansTypical antipsychotics (except the class “other typicalantipsychotics”), haloperidol, depot antipsychoticsand amisulpride were more likely to be co-prescribedwith antiparkinsonians than were olanzapine, risper-idone and clozapine.

– AntidepressantsThree atypical antipsychotics (olanzapine, risperidoneand amisulpride) were significantly though only slight-ly linked to antidepressants whereas typical ones werenot.

– AnxiolyticsAnxiolytics were not preferentially prescribed with aspecific class of antipsychotic.

Table 1 Distribution of treatments according to subject and prescriptiona

Treatment % of patients who have receivedthe treatment at least once during 2002(n=922 patients)

% of prescriptions includingthe treatment during 2002(n=5712 prescriptions)

% n % n

Atypical antipsychotics Olanzapine 28.6 264 22.4 1278Risperidone 19.1 176 12.5 713Amisulpride 20.5 189 16.9 967Clozapine 0.8 7 0.8 44

Typical antipsychotics Haloperidol 16.6 153 8.1 462Other typical antipsychotics 8.6 79 8.6 492Depot antipsychotics 14.1 130 8.0 455Sedative antipsychotics 34.3 317 26.2 149

Other psychotropic drugs Antiparkinsonians 32.9 303 27.3 1564Antidepressants 51.1 471 45.1 2574Anxiolytics 52.3 482 42.6 2585Hypnotics 26.1 241 20.1 1150Mood stabilizers 19.4 179 16.9 967

aTreatments were considered whether combined or not with another treatment; 100% of the antiparkinsonians, 97% of the antidepressants,95% of the anxiolytics, 98% of the hypnotics and 97% of the mood stabilizers were co-prescribed with at least one neuroleptic

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General linear mixed models

One mixed model was performed for each dependentvariable (antiparkinsonian, antidepressant, anxiolytic) con-

sidered as an adjunctive medication. Results are presentedin Tables 2, 3, 4. Three atypical antipsychotics (olanzapine,risperidone and clozapine) and other typical antipsychoticswere not associated with antiparkinsonian prescription for

Table 2 Factors influencing antiparkinsonian prescriptions. Multi-variate mixed model

ORa pb

Olanzapine 0.9 0.6Risperidone 1.3 0.07Amisulpride 1.4 0.05Clozapine 0.4 0.2Haloperidol 3.3 <0.0001Depot antipsychotics 3.9 <0.0001Other typical antipsychotics 1.2 0.3Sedative antipsychotics 1.4 0.003Age 1c 0.7 0.07Age 2d 2.0 0.01Anxiolytics 1.3 0.008aOR indicates the odds ratio of the association between antiparkin-sonian co-prescriptions and the corresponding medication in thefirst columnbp indicates the probability for an OR other than 1cAge 1=1 if age <35, otherwise age=0dAge 2=1 if age >55, otherwise age=0

Table 3 Factors influencing antidepressant prescriptions. Multivar-iate mixed model

ORa pb

Olanzapine 1.1 0.7Risperidone 1.2 0.1Amisulpride 1.2 0.3Clozapine 0.5 0.01Haloperidol 0.7 0.08Depot antipsychotics 0.8 0.3Other typical antipsychotics 0.8 0.3Sedative antipsychotics 0.9 0.5Age 1c 0.7 0.01Age 2d 1.0 0.9Anxiolytics 1.4 0.002Schizoaffective disorder 1.5 0.04aOR indicates the odds ratio of the association between antide-pressant co-prescriptions and the corresponding medication in thefirst columnbp indicates the probability for an OR other than 1cAge 1=1 if age <35, otherwise age=0dAge 2=1 if age >55, otherwise age=0

olanzapine

risperidone

amisulpride

clozapine

haloperidol

APoth

APdepot

APsed

APK

Mood

Hypno

ANX

ATD

APtra= other typical antipsychotics

APdepot = depot antipsychotics

APsed = sedative antipsychotics

χ2> 100

χ2< 100 Above each line : Odds Ratio of the association.

1,5 2,6

5,8

1,9

9,3

2,2

1,9

1,3

1,6

1,6

1,5

2,5

2,1

1.6

1,4

2,4

2,0

ATD = antidepressants

ANX = anxiolytics

APK = antiparkinsonians

Hypno = hypnotics

Mood = mood stabilizers

atypical antipsychotics

typical antipsychotics

Fig. 1 Log-linear model. The coefficient above the link betweentwo psychotropics represents the odds ratio of the associationbetween these two drugs. All the links represented in the figure arestatistically significant at a 5% level. This model was obtained after

a maximisation of the Bozdogan criterion. Chi-sqaure cut-off of 100was arbitrarily chosen to differentiate the most substantial associa-tions (bold lines) from other associations (thin lines)

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p<0.05. However, the p value for risperidone was close to5% (p=0.07). Concerning antidepressants, odds ratios foratypical antipsychotics were higher than 1 (except cloza-pine) whereas odds ratios for typical ones were smallerthan 1. This confirms the results of the log-linear model.None of the antipsychotics were preferentially associatedwith anxiolytics.

Discussion

This naturalistic study provides data in the context of real-world practice. Therefore, these data are likely to be morerepresentative of day-to-day practice than randomisedtrials, in terms of selection of population, number ofprescriptions studied and time of follow-up [21]. This kindof study nevertheless has limitations, among which is thelack of randomisation. Therefore, in order to check therepresentativeness of the sample of patients, demographicdata and frequencies of prescriptions were compared in thisstudy with referential data either from other naturalisticstudies or from the French social security [3, 6–8, 20]. Forexample, in the French cohort of outpatients of Bruno et al.[3], mean age was 39.2; 60.9% were men. In the samecohort, the mean number of non-neuroleptic psychotropicdrugs was 1.9 [3]. The results found in our study provedconsistent with these data.

Although only private practitioners are represented inthis study, half also have a public practice too. Thus, thebehaviour of private as compared to public practitioners isnot so different. Private practitioners represent approxi-mately 60% of French psychiatrists [19]. They treat alltypes of psychiatric pathologies. Private practice differsfrom public practice mainly because public consultation is

more often affiliated with a hospital facility with differenttypes of structure: hospitalisation, day hospitalisation.

Another important point is the choice of variables used toexplain the dependent variables in the models. Only a fewvariables were retained (prescriptions, demographic dataand diagnosis) because of the poor quality of data con-cerning medical and psychiatric history and side effects.

Two statistical methodologies that are not in widespreaduse were chosen in view of the aims of the study. First, thelog-linear model provides a simple visual representation ofa complicated network of associations [15–17]. It proved tobe a useful means of visualising patterns of co-prescrip-tions. To our knowledge, it is the first time that this kind ofmodel has been used in the pharmaco-epidemiology ofpsychotropics. Secondly, mixed models were chosen be-cause they take into account the repetition of prescriptionsfor the same patient and they provide statistical tests with adependent variable, which is not the case with log-linearmodels [15, 17].

The considerable scale of co-prescription of anxiolyticsand antidepressants is confirmed here. In 2002, more thanhalf of the patients were prescribed an antidepressant. Thesame observation occurs for anxiolytics. It confirms thetrend towards an increase in co-prescriptions frequencyover the past 10 years [1–6]. Concerning antiparkinso-nians, a frequency of approximately one-third of the pa-tients has also been observed in the most recent studies[1–7].

As stated in the introduction the use of antiparkinsoniansfor neurological side effects is a clear recommendation [11,12]. Regarding antidepressants, our findings do not fit withgeneral recommendations: they are prescribed to approxi-mately 50% of the schizophrenic patients since their ef-fectiveness has not been proven in this illness [22]. Generalrecommendations for anxiolytics are not clear, and it isdifficult to assess our results concerning this co-prescrip-tion in relation to general guidelines [23].

For antiparkinsonians, the decrease in prescriptions doesnot seem to be linked to an increase in the prescriptions foratypical antipsychotics as a whole, but only to certainclasses (markedly in the case of olanzapine and clozapineand with only minor significance in that of risperidone).These observations are in line with recent meta-analyses[24, 25] which show the advantages of these two atypicalagents in terms of lesser adverse neurological effects.

The increase in antidepressant prescriptions does notappear to be solely determined by the rise of atypical anti-psychotics. Nevertheless, the fact that odds ratios arehigher than 1 with atypical and smaller than 1 with typicalones is interesting, since many randomised trials tend toshow an improvement of depressive and negative symp-toms in schizophrenia with atypical antipsychotics [26–29]. This association has been explored in a few studies andcertain results are along the same lines. Many hypothesescould be suggested. First, because of the effectiveness ofatypical antipsychotics on depressive symptoms, it appearsquite logical that practitioners prescribe more atypicalantipsychotics to patients at risk for depression. Secondly,atypical antipsychotics induce less side effects which

Table 4 Factors influencing anxiolytic prescriptions. Multivariatemixed model

ORa pb

Olanzapine 0.9 0.4Risperidone 0.9 0.6Amisulpride 0.9 0.8Clozapine 0.7 0.07Haloperidol 0.8 0.2Depot antipsychotics 0.5 0.0005Other typical antipsychotics 0.8 0.2Sedative antipsychotics 1.1 0.1Antidepressants 1.4 0.006Antiparkinsonians 1.3 0.002Age 1c 0.5 <0.0001Age 2d 1.0 0.9Hypnotics 1.3 0.01aOR indicates the odds ratio of the association between anxiolyticco-prescriptions and the corresponding medication in the firstcolumnbp indicates the probability for an OR other than 1cAge 1=1 if age <35, otherwise age=0dAge 2=1 if age >55, otherwise age=0

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enables the prescription of antidepressants. Another hy-pothesis is psychopathological: the more marked improve-ment in the negative and positive symptoms obtained byatypical antipsychotics could lead to post-schizophrenicdepression. In addition a psychosocial interpretation ispossible: the improvement of psychotic symptoms leads tochanges in the social life of the patient. These changes,involving new social relationships and new obligations,could cause depressive symptoms. It could therefore beuseful to study this association taking into account both theclinical response to the antipsychotics in terms of symp-toms of schizophrenia and the social response.

Concerning anxiolytics, the increase in prescription ofanxiolytics does not appear to be linked to a particular classof antipsychotics, but would rather appear to illustrate ageneral evolution in prescriptions in schizophrenia thatdoes not solely concern atypical antipsychotics but also allneuroleptics, thus constituting a new approach to the treat-ment of schizophrenia.

As a conclusion, the use of this naturalistic database andan unusual methodology has made it possible to describeand test associations between medications in schizophreniain the context of day-to-day practice. Three aspects of theevolution of co-prescriptions have been highlighted. First adecrease in antiparkinsonian prescriptions explained by twoatypical antipsychotics (olanzapine and clozapine) and to alimited extent risperidone. A second aspect is a trendtowards a link between atypical antipsychotics and anti-depressants which was not expected in view of the anti-depressive action of atypical antipsychotics in certainrandomised trials. A third aspect is the increase in anxiolyticprescriptions which are found in association with allantipsychotics. All these aspects illustrate new trends andentail consequences in the management of schizophrenia.Furthermore, the effectiveness of these associations whichare used more and more has not been widely studiedbecause of methodological difficulties. An observationalstudy including clinical score along these lines wouldenable better understanding of the determinants of pre-scription associations. This methodology could contributeto the study of the effectiveness of associations of med-ication and then to provide clear guidelines for these ther-apeutic strategies.

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