Clinical outcomes for psychotic and co-morbid patients admitted to acute psychiatric wards in four European centres – follow-up to 6 months

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Clinical Outcomes for Psychotic and Co-morbid Patients

Admitted to Acute Psychiatric Wards in Four European

Centres - Follow up to 6 months

Journal: Mental Health and Substance Use

Manuscript ID: Draft

Manuscript Type: Original Article

Keywords: comorbidity, dual diagnosis, mental health, psychosis

URL: http://mc.manuscriptcentral.com/RMHS Email: [email protected]

Mental Health and Substance Use

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Clinical Outcomes for Psychotic and Co-morbid Patients Admitted to Acute Psychiatric

Wards in Four European Centres - Follow up to 6 months.

Abstract

Background: This study was conducted with the aim of identifying the outcome and

characteristics of patients admitted to acute psychiatric wards with a diagnosis of psychotic

illness and substance misuse. Methods: In this multi-centre collaborative and opportunistic

study patients were allocated into 4 cohorts based on the type of drug use and then were followed

up 1 and 6 months later. Results: The total PANSS score improved significantly by the 6

month follow up in all but the cannabis using cohort, which initially had the lowest symptom

level. All cohorts improved significantly in levels of general symptoms, and 3 out of the 4

groups improved in the levels of positive symptoms (the cannabis and other drug cohort was

nearing significance at p=0.02). The biggest contrast between the groups was in the levels of

negative symptoms. These improved only in the non-drug using cohort; however even with this

significant improvement, levels did not reach the lower levels initially evident in the cannabis

only using cohort. Conclusions: Psychopathology and recovery processes of psychotic patients

admitted to acute psychiatric wards will differ depending on their use of illicit substances. In

particular those who use cannabis but not other illicit substances are likely to have lower levels

of negative symptoms.

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Clinical Outcomes for Psychotic and Co-morbid Patients Admitted to Acute Psychiatric

Wards in Four European Centres - Follow up to 6 months.

Introduction

Patterns of co-morbidity of drug use and psychiatric illness have been the subject of much

research taking many approaches. These range from individual case studies (Chaudry et al.,

1991) to the association between specific drugs (Arendt et al.,2005; McGuire et al.,1994;

Zammit et al.,2008; Degenhardt et al.,2007) looking for causal links (Ferdinand et al.,2005;

Fergusson et al.,2005), or substance use patterns in psychotic patients (Verdoux et al.,2005;

Virgo et al.,2001). Much focus on the subject of co-morbidity has been on connections between

drug use and psychosis; at predictors and patterns of drug use and patterns of psychopathology in

co-morbid versus non-co-morbid patients (Dalmau et al.1999; Grace et al.,2000; Kaiser et

al.2005; Pencer & Addington,2003; Sevy et al.,2001). Where patients are co-morbid the

evidence suggests there is a poorer prognosis (Grech et al., 2005). There is also evidence for

poorer treatment compliance in this patient group (Pencer & Addington, 2003). In spite of this

there has been little work done on the clinical outcomes and course of illness in this group of

patients.

The aim of this multi-centre collaborative and opportunistic study was to identify and compare

the clinical characteristics of patients diagnosed with a psychotic illness depending on their illicit

substance use at admission to acute psychiatric wards. In addition to this, patients were followed

up at 1 and 6 months. Outcomes were investigated based on the patterns of illicit substance use

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at baseline. Detailed information on the baseline data is reported elsewhere (Baldacchino et al.,

2009).

Methods

Design and settings

Patients were recruited from acute general psychiatric wards during a 12 month period between

2002 and 2005. Four centres participated in the Denmark, Germany, the UK, and Italy. In the

UK this was Springfield Psychiatric Hospital, London serving a population of just over 1 million.

In Germany this was the Department for Psychiatry and Psychotherapy, LVR-Hospital Essen,

serving a population of 210,000. In Italy, this was the Department of Addiction, Ospedali

Riuniti di Bergamo (combined hospitals of Bergamo) serving a population of 500,000. In

Denmark this was the Embedslaege Institutionen, Ringkobing recruiting patients in Holstebro

and Herning, serving populations of 85,000 and 115,000 respectively.

The inclusion criteria were: age between 16 and 65 years; the presence of psychotic symptoms as

defined by ICD-10; ability to provide three locators; not involved in harmful or hazardous

drinking (indicated by an AUDIT score>16); no acute physical pathology; no involvement with

criminal justice systems; and no history of psycho-surgery, epilepsy, or head injury. In the four

countries it was legal to smoke tobacco and drink alcohol at the age of 16, and so the term illicit

substance excludes these.

This study was given the necessary ethical and research governance approvals for each of the

four European sites.

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Instruments used and selection process

The selection process is outlined in figure 1. The screening instruments were completed within

24 hours of admission. These were the Mini Mental State Examination (MMSE) (Folstein,

Folstein,& McHugh, 1975), Alcohol Use Disorders Identification Test (AUDIT); and the

Schedules for Clinical Assessment in Neuropsychiatry (SCAN) (Bebbington, 1992). All

instruments were administered in the native language of the participant by a researcher trained in

these instruments. Patients with psychotic symptoms were asked for a urine sample within a 48

hour period.

Insert Figure 1 here

The diagnostic instruments were completed within 7 days of admission. This included the

Fagerstrom test for nicotine dependence (Pomerleau et al.,1994), the Positive and Negative

Symptom Severity Scale (PANSS) (Kay, Fizbein & Opler, 1987), the Composite International

Diagnostic Interview (CIDI) (Pull & Wittchen,1991; Wittchen,1994) and the Addiction

Severity Index (Leonhard et al., 2000). These instruments were repeated at 1 and 6 month follow

ups.

Patients were allocated into one of four cohorts depending on their urine tests at baseline. This

was done by automated screening of urine samples by enzyme immunoassay (EMIT) or enzyme-

linked immunosorbent assay (ELISA) to classify any substance present. In the event a positive

finding thin layer (TLC), gas (GC) or liquid (LC) chromatography (Guitton et al., 1993) were

used for confirmation of a specific substance (Simpson et al., 1997).

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Those testing negative for all illicit substances (cannabis, amphetamines, cocaine, opiates and

benzodiazepines) were allocated into cohort 1. Those testing positive for cannabis only were

allocated into cohort 2. Those testing positive for illicit substances but not cannabis were

allocated into cohort 3. Those testing positive for cannabis and also other illicit substances were

allocated into cohort 4.

Analyses

Analyses were completed using SPSS, version 18.0. In order to minimise Type 1 error as a

result of multiple testing of effects that could be independent to each other, the significance level

was lowered from p<0.05 to p<0.01.

Repeated measures ANOVA were used to compare changes between baseline, 1 and 6 month

follow up. One-way ANOVA were used when comparing data between cohorts at the same time

point of the study. When comparing the means of only two groupings, t-tests were used in place

of the ANOVA. Chi squares were used to compare categorical data, except where small

numbers did not allow for this when Fishers exact Test was used.

Analyses were completed only on patients who were recruited and followed up to 6 months to

allow comparisons made to be as accurate as possible.

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Results

Participants

A total of 196 patients were allocated into cohorts at baseline based on urine samples. These were

recruited from a total of 1104 eligible patients who were admitted to the 4 participating centres

during the recruitment period. The level of recruitment was therefore 17.7%. Reasons for non-

participation and exclusion are included in table 1. Further details of the baseline characteristics

of these patients can be found elsewhere (Baldacchino et al., 2009).

Insert Table 1 here

One hundred and nineteen of the original 196 patients were followed up to 6 months (61%). The

numbers of patients recruited in each centre and in each cohort, with their mean ages are

presented in table 2. No significant differences were found between the socio-demographic

characteristics of those patients who were followed up and those who were not.

Insert Table 2 here

Psychopathology

Figure 2 shows the PANSS scores in each cohort over the 3 study time points. No significant

differences were found between the cohorts at baseline in any of the symptoms measured by the

PANSS.

Insert Figure 2 here

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At the 1 month follow up there remained no significant differences in the majority of symptoms

between the four cohorts. However, there were significant differences for the symptoms

‘uncooperativeness’ (F(3,113)=4.03, p=0.01) and ‘disorientation’ (F(3,113)=6.72, p<0.001). In

both of these the highest scores were in cohort 4, cannabis and other illicit substances (2.13

(SD=1.13) and 2.13 (SD=1.36) respectively), with the lowest scores in cohort 2, the cannabis

only cohort (1.00 (SD=0.00) and 1.08 (SD=0.29). The scores in cohort 1, no illicit drugs were

1.26 (SD=0.83) and 1.24 (SD=0.59); and in cohort 3, illicit drugs but not cannabis were 1.57

(SD=0.84) and 1.78 (SD=0.95).

At the 6 months follow up there was again no significant differences between the cohorts in the

levels of psychotic symptoms as measured by the PANSS.

Differences in PANSS scores across the study

The total PANSS scores decreased across the study in cohorts 1, 3 and 4. This difference was

significant in cohort 1 (F(2, 144)=40.79, p<0.001), cohort 3 (F(2, 40)=7.41, p=0.002), and

cohort 4 (F(2, 14)=17.95, p<0.001), but not cohort 2 (F(2, 22)=2.21, p=0.13).

Refer to Table 3 for the mean total scores for; the PANSS as a whole, the positive symptoms, the

negative symptoms and the general symptoms of patients in each cohort at the different points of

the study.

Insert Table 3 here

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Positive Symptoms

The total of positive scores decreased significantly over the 3 points of study (F(2, 230)=74.92,

p<0.001). The level of positive symptoms reduced in all cohorts over the 6 months, the biggest

reduction being between baseline and 1 month. This difference was significant in cohorts 1, 2

and 3 but not in cohort 4, cannabis and other illicit substances (cohort 1; F(2, 146)= 43.52,

p<0.001; cohort 2 F(2, 22)=22.33, p<0.001; cohort 3: F(2, 42)=10.33, p<0.001; cohort 4; F(2,

14)=5.60, p=0.02).

In cohort 1; 6 of the 7 positive symptoms improved significantly (‘delusions’ (p<0.001),

‘conceptual disorganisation’ (p<0.01), ‘hallucinatory behaviour’ (p<0.001), ‘excitement’

(p<0.001), ‘grandiosity’ (p<0.001), and ‘suspiciousness and persecution’ (p<0.001). In cohort 2

those which improved were ‘delusions’ (p<0.001), ‘hallucinatory behaviour’ (p<0.01) and

‘suspiciousness and persecution’ (p<0.001). In cohort 3 the only symptoms improving

significantly over the study were ‘delusions’, and ‘hallucinatory behaviour’ (p<0.001), and

‘excitement’ (p<0.01). In cohort 4 the symptoms which improved significantly were ‘delusions’

(p<0.01) and ‘excitement’ (p<0.01).

Negative Symptoms

The general trend for the negative total across the points of study was a decrease in symptoms.

This was found to differ significantly from baseline to 6 month follow up (F(2, 230)=7.45,

p<0.001). However, the only cohort that individually showed a statistically significant

difference was cohort 1 (F(2, 148)= 8.69, p<0.001). The symptoms which differed in cohort 1

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were ‘emotional withdrawal’ (p=0.01), ‘poor rapport’ (p<0.01), and ‘difficulty in abstract

thinking’ (p<0.01).

It should be noted here that although cohort 2 did not differ significantly from the other cohorts

in the levels of negative symptoms it did have lower levels. In comparison to cohort 1 (no drug

use) it had lower levels of negative symptoms even after cohort 1 had made a significant

reduction in levels (see figure 2).

General Symptoms

General scores improved significantly for all patients over the course of the study with an

improvement at each of the follow-up points (F(2, 230)=47.92, p<0.001). Individually all

cohorts also demonstrated significant changes (cohort 1; F(2, 146)= 28.08, p<0.001; cohort 2; (2,

22)=5.64, p=0.01; cohort 3; F(2, 42)= 9.57, p<0.001; cohort 4; F(2, 14)=6.14, p=0.01).

The number of general symptoms showing a significant difference across the points of the study

was greatest in cohort 1 with 6 out of the 16 symptoms. These were ‘anxiety’ (p<0.001),

‘tension’ (p<0.001), ‘depression’ (p<0.001), ‘motor retardation’ (p<0.001), ‘unusual thought

content’ (p<0.001). In cohort 2 no individual general symptoms differed significantly across the

study time points. In cohort 3, 3 out of the 16 general symptoms differed across the points of the

study. These were ‘unusual thought content’ (p<0.001), ‘poor attention’ (p<0.01), and ‘lack of

judgement and insight’ (p<0.001). In cohort 4 no individual general symptom differed

significantly.

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Use of illicit substances

At baseline 32.5% of patients in cohort 3 tested positive for opiates with less use of cocaine and

benzodiazepines. This differed from cohort 4 where higher levels of cocaine and amphetamines

were used.

The current illicit substance use by patients at the times of their follow up interviews was tested

by urine analysis (see table 4). In the illicit substance using cohorts, there was a noticeable

reduction in the number of patients testing positive for illegal substances, although it should be

noted that some patients not using drugs at baseline tested positive at follow up.

No differences were found between the socio-demographic characteristics of patients who

changed their drug used patterns and those who did not. These results do, however, show the

potentially transient nature of co-morbidity.

Insert Table 4 here

Discussion

Patients in all four cohorts improved in their levels of psychotic symptoms over the course of the

study. Results from this study suggest that the most notable difference in the outcome of patients

with a diagnosis of psychosis depending on whether or not they use drugs is in the levels of

negative symptoms. A significant improvement in the levels of negative symptoms was only

found in those patients who were not using drugs, despite there being a lack of significant

differences between the cohorts at baseline. There was also a significant improvement in more

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positive and general symptoms in patients not using drugs. Cannabis using patients showing an

improvement in the fewest symptoms, but they do tend to have the lowest symptom levels

initially.

Psychopathology

Extreme caution should be used when interpreting the results from this study due to the small

numbers; however some interesting findings seem to be emerging especially with regards to the

levels of negative symptoms.

All cohorts show an improvement in their levels of psychopathological symptoms as indicated

by the PANSS total score with the exception of cohort 2 (cannabis only). Levels of general

symptoms improve significantly in all cohorts at the follow up points. Positive symptoms also

improve in each of the four cohorts, although not significantly in cohort 4 (cannabis and other

illicit substances). However the numbers in this cohort were particularly low (n=8) and the

significance level was nearing significance at p=0.02.

Negative symptoms only improved significantly in cohort 1 (no illicit substances). However, the

level of negative symptoms in cohort 1 were consistently higher than those in cohort 2 (cannabis

only) even after a significant improvement had been made (see figure 2). Interpretation of these

findings are complicated by the fact that a statistically significant difference was not found

between the cohorts at any point of the study, but this is likely to be due to a lack of power owing

to small numbers, especially considering that there is a significant improvement in the levels of

negative symptoms in cohort 1. Lower levels of negative symptoms in psychotic patients using

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cannabis reflect previous findings (Bersani et al., 2002; Dubertret et al., 2006; Rottanburg et al.,

1982) which lend support to the idea that there are real differences in the symptoms and recovery

of psychotic patients dependent on their use of illicit substances.

The levels of negative symptoms remained high in those patients who were using other illicit

substances, including those who were using cannabis alongside other substances. This suggest

that cannabis does not provide a protective factor from the negative symptoms of psychosis,

rather there is a difference in the way psychosis manifests in those individuals who are using

cannabis alone.

Drug use by patients at follow-up

Differences in drugs used by patients from baseline to follow-up are noted, but were not taken

into account for the purposes of analysing the differences in the outcomes of the different

cohorts. It is possible that the changes in drug use that do occur at the follow up points of this

study will have some impact on outcome. These findings highlight the fact that co-morbidity of

drug use and psychotic illness is a chronic relapsing condition, making the changing use of illicit

substances a particularly difficult factor to control for.

Limitations of study; methodological dilemmas

This study was designed to provide comprehensive information about the course of illness of co-

morbid patients following admission to an acute psychiatric ward. However, there were some

issues that arose, or, need consideration when interpreting the results.

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Firstly, the population being studied is very specific, and while this was to gain as pure a data-set

as possible about this specific patient group it does mean that there is little scope for

generalisation beyond acute psychiatric admissions. Small numbers of patients did not allow for

grouping by country as well as cohort meaning that cultural differences can not be taken into

account which otherwise may have influenced outcome. An issue relating to the cross-cultural

aspect is the differences between service use and provision that may occur in different cultures

(Nilson, Fridell & Griffiths, 2006). Bearing this in mind, interpretation of results should

consider the possibility of these differences, which due to the opportunistic nature of this

research it was not possible to control for. There may have been differences in the treatment

received by participants both on admission and during the follow-up periods. Continued use of

anti-psychotic and anti-depressant medication, or simply inpatient status, at follow up was not

taken into account.

General problems that are evident in identifying a population for this type of study were

applicable, in that the definition of co-morbidity is unclear (Corkery & Baldacchino, 2006;

Crome,2006). In order to provide diagnosis that used standardised and validated measures

providing a consistent diagnostic system across the different centres a combination of measures

were used. This led to some problems as the training of researchers led to delays, as well as

finding measures that were translated and validated in the languages required for this study.

The main weakness of this study is the small numbers recruited. This is not unusual in this

population as they generally have a chaotic life-style and are difficult to follow-up (Corkery &

Baldacchino, 2006) . In this study, rigorous inclusion criteria may have exacerbated this

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problem. Studying patients with specific diagnosis, ruling out poor cognitive functioning or

hazardous alcohol use, among other things will mean lower numbers. This problem is difficult

to address, as there is the need to compromise between inclusion criteria and recruitment of

adequate numbers.

Other limiting factors include the lack of monitoring of drug use throughout the follow up period

with only 3 set points of analysis available. This might have shown a change of drug patterns

within the groups and making baseline cohort criteria redundant. Throughout the study period the

study did not monitor treatment care plans including prescribing which might have influenced

the improvement observed in all cohort groups. Given that previous research has found that co-

morbid mental health and substance misuse populations can lead to decreased adherence to

treatment, both drug status and treatment status throughout the study period are important pieces

of information that could help explain the positive result of improved level of psychotic

symptoms (Corkery & Baldacchino, 2006).

Conclusions

The psychopathology and recovery of patients admitted to acute psychiatric wards with

psychosis is likely to be influenced by their drug use on admission. In this study, those using

cannabis had lower levels of negative symptoms, with little change during their recovery period.

Patients not using drug use on admission showed a significant improvement in levels of negative

symptoms by the 6 month follow up, whereas the levels of negative symptoms remained high in

those patients who were using other illicit substances (including those using cannabis alongside

other substances).

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Acknowledgements

The authors would like to thank:

• The EU Quality of Life and Management of Living Resources Programme: 1998-2002

(Ref: QLRT-2000-01550) for their funding of this research.

• Researchers in the centres: Billy Boland, Tracy Burrows, Martin Bland, Survjit Cheeta,

Sanjoo Chengappa, Laurence Church, Caterina Criaco, Hamid Ghodse, Rosemary

Jambert-Grey, Ignatius Loubser, Anand Mathilakath, M-C Marelli, Annie McLeod and

Karine Aslanian-MacMillan.

• All the patients who participated in this study and who hopefully sustained their recovery.

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17 Folstein, M., Folstein, S., McHugh, P.(1975). 'Mini-mental state': A practical method for

grading the cognitive state of patients for the clinician. J Psychiatry Res, 12,189-198.

18 Bebbington, P. (1992). Welcome to ICD-10 and welcome to SCAN. Soc Psychiatry and

Psychiatric Epidemiol, 27,255-257.

19 Pomerleau, C., Carlton, S., Lutzke, M., Flessland, K., Pomerleau, O.(1994). Reliability of

the Fragerstrom tolerance questionnaire and the Fragerstrom test for Nicotine Dependence.

Addict Behav, 19(1), 33-39.

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20 Kay, S., Fizbein, A., Opler, L.(1987). The Positive and Negative Syndrome Scale for

Schizophrenia (PANNS). Schizophr Bull, 13(2), 261-279.

21 Pull, C., Wittchen, H. (1991). CIDI, SCAN and IPDE: structured diagnostic interviews for

ICD-10 and DSM-III-R. Eur Psychiat, 6,277-285.

22 Wittchen, H. (1994).Reliability and validity studies of the WHO-Composite International

Diagnostic Interview (DIDI): A critical review. J Psychiatry Res, 28(1), 57-84.

23 Leonhard, C., Mulvey, K., Gastfriend, D.R., Shwartz, M. (2000).The Addiction Severity

Index - A field study of internal consistency and validity. Journal of Substance Abuse,

Treatment 18(2),129-135.

24 Guitton, J., David, O., Mialon, A., Manchon, M.(1993). Evaluation of an automated liquid

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25 Simpson, D., Brathwaite, R., Jarvie, D., Stewart, M., Walker, S., Watson, I., et al.

(1997).Screening for drugs of abuse (II): Cannabinoids, lysergic acid diethylamide,

buprenorphine, methadone, barbiturates, benzodiazepines and other drugs. Ann Clin

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26 Bersani, G., Orlandi, V., Kotzalidis, G., Pancheri, P.(2002). Cannabis and schizophrenia:

Impact on onset, course, psychopathology and outcomes. Eur Arch Psychiatry Clin

Neurosci, 252, 86-92.

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27 Dubertret, C., Bidard, I., Ades, J., Gorwood, P.(2006). Lifetime positive symptoms in

patients with schizophrenia and cannabis abuse are partically explained by co-morbid

addiction. Schizophr Res, 86,284-90.

28 Rottanburg, D., Ben-Arie, O., Robins, A., Teggin, A., Elk, R.(1982). Cannabis-Associated

Psyhosis with Hypomanic Features. Lancet,18;320,1364-1366.

29 Nilson, M., Fridell, M., Griffiths, P. (2006) Definition and Classification in comorbidity.

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30 Corkery, J., Baldacchino, A.(2006). Definition and Classification in co-morbidity. In:

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31 Crome I. An epidemiological perspective of psychiatric co-morbidity and substance

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Figure 1: Graphical Description of the procedure used in study

Figure 2: The mean total positive score, mean total negative score, mean total general score,

and mean total PANSS score in each cohort at baseline and then the 1-month and 6-month

follow-up.

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Table 1: Reasons for non participation

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UK Denmark Germany Italy TOTAL

Clients available 361 343 203 197 1104

Recruited into study 40 62 53 41 196

Physical illness 11 13 3 20 47

Refused urine sample 101 91 7 43 242

Sample/seen to late 48 16 10 23 97

Refused to take part 34 13 48 17 112

MMSE score <24 6 17 7 9 39

AUDIT score >16 9 37 11 5 62

Negative SCAN 7 11 7 4 29

Too ill 47 48 19 15 129

Language barrier 17 7 6 7 37

Out of area 10 5 3 0 18

Self-discharge 19 0 18 3 40

Other exclusion criteria

(including no symptoms or diagnosis; and

treatment order from courts)

12 23 11 10 56

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Table 2: Numbers of patients recruited and followed up to 6 months in each centre and into

each cohort, including their mean ages.

SD= Standard Deviation; IQR= Inter-Quartile Range.

UK Denmark Germany Italy TOTAL

Mean age 37.71

(SD=11.89;

IQR=17)

33.72

(SD=10.23;

IQR=19)

29.75

(SD=7.91;

IQR=14)

39.12

(SD=13.70;

IQR=18)

34.30

(SD=10.29;

IQR=18)

Urine Drug

Screen/ cohort:

Mean Age:

Cohort 1:

(negative urine

test)

11 21 20 22 35.69

(SD=11.50;

IQR=18)

74

Cohort 2: (urine

positive for

cannabis only)

6 2 5 0 33.54

(SD=10.32;

IQR=19)

13

Cohort 3: (urine

positive for illicit

drugs not

including

cannabis)

3 18 3 0 29.54

(SD=7.33;

IQR=13)

28

Cohort 4: (urine

positive for other

illicit drugs and

cannabis)

1 5 0 2 37.00

(SD=10.81;

IQR=17)

8

TOTAL 21 46 28 24 119

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Table 3: Means and standard deviations for the totals of positive, negative and overall total

PANSS scores in each of the four cohorts at the 3 study time points (baseline, 1 month and 6

months). Significant levels are shown for the difference across the three time points (n=120).

Mean and SD at

baseline

Mean and SD at 1

month

Mean and SD at 6

months

Sig level for

difference across

the time-points

Positive Total

Cohort 1 (n=75)

Cohort 2 (n=13)

Cohort3 (n=24)

Cohort 4 (n=8)

19.27 (6.24)

20.62 (4.70)

20.61 (7.15)

21.25 (8.07)

14.85 (6.24)

12.5 (4.34)

15.91 (6.54)

16.75 (7.32)

13.27 (5.26)

13.23 (7.12)

15.04 (6.54)

13.88 (6.24)

***

***

***

p=0.02 n/s

Negative total 16.58 (7.26)

13.15 (7.79)

17.00 (8.07)

18.87 (9.96)

14.84 (5.95)

12.50 (5.60)

16.83 (7.83)

18.13 (10.16)

13.86 (5.40)

14.38 (7.68)

14.67 (6.74)

18.13 (10.16)

***

p=0.90 n/s

p=0.08 n/s

p=0.93 n/s

General Total 34.05 (8.20)

33.62 (10.59)

36.83 (9.06)

42.38 (15.46)

30.12 (8.53)

27.42 (8.85)

32.56 (7.86)

39.88 (15.18)

27.69 (7.42)

28.31 (10.34)

29.25 (7.52)

31.25 (10.17)

***

p=0.01 **

***

p=0.001 **

PANSS TOTAL 74.84 (17.20)

68.23 (14.56)

77.75 (24.57)

90.50 (34.06)

63.80 (18.94)

56.33 (16.10)

70.27 (18.55)

76.88 (26.63)

59.04 (16.66)

60.85 (22.72)

61.83 (18.65)

64.00 (28.52)

***

p=0.13 n/s

p=0.002 **

***

SD=Standard Deviation; p=significance levels; **= p<0.01; ***=p <0.001.

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Table 4: The percentage of patients in each cohort using illicit substances at 1 and 6 months

follow-up.

1 month follow up 6 months follow up

No

dru

gs

Ca

nn

ab

is

on

ly

Illi

cit

dru

gs

no

t ca

nn

ab

is

Ca

nn

ab

is a

nd

oth

er

dru

gs

No

dru

gs

Ca

nn

ab

is

on

ly

Illi

cit

dru

gs

no

t ca

nn

ab

is

Ca

nn

ab

is a

nd

oth

er

dru

gs

Cohort 1: No drug use at

baseline (N=74)

58 1 14 0 57 1

15

0

Cohort 2: cannabis use

only at baseline (N=12)

6

5

0 1

5

6

0 1

Cohort 3: illicit drug use

and cannabis at baseline

(N=23)

14 2

6

1

14 3

5

1

Cohort 4: cannabis and

other illicit substances at

baseline (N=8)

4

1 2

1 5 0 2

1

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