pilot study on biological markers of chronic cannabis...

Masterthesis Simon Vanhoutte

Academic Year 2012-2014

Pilot study on biological markers of chronic cannabis use

Simon VANHOUTTE

Promotor: Prof. Dr. Alain Verstraete

Dissertation presented in the 2nd

Master year in the programme of

MASTER OF MEDICINE IN MEDICINE


Academic Year 2012-2014

Pilot study on biological markers of chronic cannabis use

Simon VANHOUTTE

Promotor: Prof. Dr. Alain Verstraete

Dissertation presented in the 2nd

Master year in the programme of

MASTER OF MEDICINE IN MEDICINE


“The author and the promotor give the permission to use this thesis for consultation and to

copy parts of it for personal use. Every other use is subject to the copyright laws, more

specifically the source must be extensively specified when using results from this thesis.”

Date:

Simon Vanhoutte Prof. Dr. Alain Verstraete


FOREWORD

The realization of this thesis and the implementation of the study required a lot of support and

help from different people allied to the Ghent University. All of these individuals deserve my

gratitude and respect.

First I would like to thank Prof. Dr. Alain Verstraete of the department of Clinical Biology,

Microbiology and Immunology of the Ghent University Hospital. He has been involved very

closely in all steps that led to the writing of this thesis, and the execution of the actual study.

His help, advice and knowledge have been irreplaceable. Pauline Meersseman, co-assistant

Clinical Biology, and Kenneth Asselman also deserve a word of gratitude for the analysis of

all blood and hair samples in this same department.

Secondly the departments of Public Health and Psychiatry and Medical Psychology were also

necessary for the execution of the study. Prof. Dr. Lea Maes, Joris Van Damme (PhD student)

and Miriam de Vreugd (secretary) of the Public Health Department and Prof. Dr. Gilbert

Lemmens of the department of Psychiatry and Medical Psychology, each played key parts in

the experimental design and practical implementation, and were valuable aids in the process

of statistical analysis and writing of the thesis. Lien De Smet, co-assistant Clinical Biology,

should also be thanked for a period of daily visiting the UPSIE department for inclusion of

possible study participants.

Furthermore a lot of my fellow medicine students (Gaelle Moerman, Emilie Janssens, Levi

Hoste, Louise Achten, Toon Allaeys, Freek Verstraete, Leonie Vandoorne, Astrid

Moeyersoms, Kim Van der Burght, Eveline Van Mulders, Heleen De Vuyst and Karen

Jacobs) helped out with the blood and hair sample withdrawals, their help was also much

appreciated.

Last but not least my family should be thanked for their support during these two years which

it took to complete this experiment and thesis.


TABLE OF CONTENTS

ABSTRACT ............................................................................................................................... 1

English .................................................................................................................................... 1

Nederlands: ............................................................................................................................. 1

INTRODUCTION ...................................................................................................................... 3

Validity of self-report and precedents of questionnaire studies ............................................. 4

Choice of biomarkers .............................................................................................................. 7

MATERIALS AND METHODS ............................................................................................. 11

Participants and selection procedure .................................................................................... 11

Questionnaires ...................................................................................................................... 12

Biomarkers ............................................................................................................................ 13

Screening .......................................................................................................................... 13

Quantitation of THCCOOH in serum samples ................................................................ 14

Quantitation of THC in hair samples ............................................................................... 15

Statistical Analysis ............................................................................................................... 15

Blinding and prevention of bias ............................................................................................ 16

RESULTS ................................................................................................................................. 17

Participants ........................................................................................................................... 17

Biomarkers ............................................................................................................................ 17

Questionnaires ...................................................................................................................... 19

CAGE-AID questionnaire ................................................................................................ 21

Single-Question ................................................................................................................ 24

Severity of Dependence Scale (SDS) ............................................................................... 25

ProbCannabis-DT questionnaire ...................................................................................... 28

Correlations between all questionnaires screening results ............................................... 33


DISCUSSION .......................................................................................................................... 36

Recruitment of participants .............................................................................................. 36

Biomarker results ............................................................................................................. 36

Questionnaire results ........................................................................................................ 37

REFERENCES ......................................................................................................................... 40

ATTACHMENTS .................................................................................................................... 43


1

ABSTRACT

English

Main target of this study was the evaluation of a new questionnaire (ProbCannabis-DT)

designed to screen for problematic cannabis abuse. The ProbCannabis-DT questionnaire was

developed by the Ghent University but has never yet been validated, and an optimal cut-off

score has never been determined.

In this study a sample of 97 subjects was composed. All received three questionnaires

(ProbCannabis-DT, CAGE-AID and SDS) and one single question (“How many times in the

past year have you used an illegal drug or used prescription medication for non-medical

reasons?”). A blood and hair sample were taken from each subject, for comparison of

cannabinoid traces in these samples with the questionnaire answers. Based on the serum

THCCOOH concentration subjects were subdivided into no or very light cannabis users (<5

ng/mL), regular cannabis users (5 – 75 ng/mL), and heavy cannabis users (≥75 ng/mL).

Single-question results didn’t differ significantly between cannabis abusers and subjects who

didn’t use cannabis. For all other questionnaires significant differences were calculated. The

optimal cut-off score for the ProbCannabis-DT questionnaire was determined at 1 positive

answer, this corresponds with a sensitivity of 0.87 and a specificity of 0.68. It was seen that

the ProbCannabis-DT and CAGE-AID ≥ 1 (with a cut-off at one positive answer) yield very

similar results and psychometric properties. The same was concluded for the SDS and CAGE-

AID ≥ 2 (with a cut-off at two positive answers).

By determining the optimal cut-off score and corresponding sensitivity and specificity this

study can serve as a scientific support for future use of this ProbCannabis-DT questionnaire,

when it is used for assessment of the prevalence of problematic cannabis abuse within a

certain population.

Nederlands

Het hoofddoel van deze studie was het onderzoeken van een nieuwe vragenlijst (de

ProbCannabis-DT) die ontworpen is om te screenen naar problematisch cannabis gebruik. De

ProbCannabis-DT vragenlijst is ontwikkeld door Universiteit Gent maar is nog nooit


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gevalideerd geweest, en er is nog nooit een optimale cut-off score voor bepaald.

In deze studie werd daarom een steekproef van 97 deelnemers opgesteld. Elke deelnemer

kreeg 3 vragenlijsten (ProbCannabis-DT, CAGE-AID en SDS) en één alleenstaande

screeningsvraag (“Hoeveel maal in het laatste jaar hebt u een illegale drug of medicatie

zonder voorschrift gebruikt voor niet-medische redenen?”) . Vervolgens werd van de

deelnemers ook een bloed- en haarstaal afgenomen, om de cannabinoid concentraties in deze

stalen te vergelijken met de antwoorden op de vragenlijsten. Gebaseerd op de THCCOOH

concentraties in serum werden deelnemers dan vervolgens ingedeeld in verschillende

groepen: zij die geen cannabis gebruiken of het maar zeer licht gebruiken (<5 ng/mL),

regelmatige cannabis gebruikers (5-75 ng/mL), en zware cannabis gebruikers (≥75 ng/mL).

De resultaten op de alleenstaande vraag bleken niet significant te verschillen tussen cannabis

gebruikers en niet-gebruikers. Voor alle andere vragenlijsten werden wel significante

verschillen berekend. De optimale cut-off score voor de ProbCannabis-DT vragenlijst werd

bepaald op 1 positief antwoord, dit komt overeen met een sensitiviteit van 0.87 en een

specificiteit van 0.68. Het bleek ook dat de CAGE-AID ≥ 1 (met een cut-off op één positief

antwoord) en de ProbCannabis-DT zeer gelijke resultaten en sensitiviteit en specificiteit

opleverden. Hetzelfde gold voor de CAGE-AID ≥ 2 (met een cut-off op 2 positieve resultaten)

en de SDS vragenlijst.

Door de bepaling van de optimale cut-off score voor deze vragenlijst, en de daarmee

overeenkomende sensitiveit en specificiteit kan deze studie worden gebruikt als

onderbouwing voor toekomstig gebruik van de ProbCannabis-DT. Dan kan deze vragenlijst

gebruikt worden voor bepaling van het voorkomen van problematisch cannabis gebruik

binnen een zeker populatie.


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INTRODUCTION

According to the European Monitoring Centre for Drugs and Drug Addiction the prevalence

of cannabis use (CU) among European adolescents is rising (or stagnating) to high levels,

with great differences between different countries. The prevalence of having smoked cannabis

in the last year varies from 2% up to 35% and the prevalence of having smoked cannabis in

the last month varies from 1% to 20% depending on the country (1). In a national health

survey in Belgium, it was reported that the prevalence of having ever used cannabis among

Belgian adults is 13%. The prevalence of having used cannabis in the past 12 months and past

1 month was 5% and 3% respectively. Of all adults 1 % admitted that they smoked cannabis

on a daily basis (2). Thus CU may be considered as a problem, growing in importance.

Adequately assessing the intensity of cannabis use of individuals seems to be difficult, both

through biomarkers (mostly in hair, blood and urine) and through self-report. Nevertheless it

is important to estimate the extent of the cannabis dependence within a population, knowing

that cannabis dependence in adolescents is associated with increased juvenile offending,

unemployment, school dropout and the use of other illicit drugs (3). Evidence also suggests

that there might be high comorbidity with mood and anxiety disorders (4). Cannabis

dependence is a situation following compulsive use that leads to behavioral and physiologic

symptoms, this use continues despite any physical and psychological problems caused by the

cannabis use. According to DSM-IV cannabis dependence can be diagnosed when a person

meets at least three of the following six criteria (5).

Table 1: DSM-IV criteria for cannabis dependence

DSM-IV-TR diagnostic criteria for cannabis dependence

A maladaptive pattern of cannabis use resulting in clinically significant impairment or distress

as indicated by three or more of the following at any time during the same 12 month period:

1. Tolerance, defined by either one of the following:

a. using markedly increased amounts of cannabis to achieve the desired effect or

intoxication.

b. markedly diminished effect with continued use of the same amount of cannabis.

2. Cannabis is often taken in larger amounts or over a longer period than was intended.

3. There is a persistent desire or there are unsuccessful efforts to cut down or control

cannabis use.

4. A great deal of time is spent obtaining cannabis, using it, or recovering from its effects.

5. Important social, occupational or recreational activities are neglected because of

cannabis use.


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6. Persistent cannabis use despite knowledge of having a recurrent or ongoing physical or

psychological problem that is probably caused or exacerbated by cannabis, such as

chronic cough related to smoking or a decrease in goal related activities.

Cannabis use, abuse, dependency and associated comorbidity are related to a wide variety of

factors. It is stated that certain factors influence the probability of occasional CU becoming

CU disorders. Gender is an important factor, and CU seems to be more frequent in males than

in females (6). Furthermore higher frequency of use (7-9), smoking cannabis alone (8), upon

rising (8) and CU before going to sleep (8) are all associated with an increased risk of

developing cannabis dependence.

In this study the aim was to investigate the validity of 3 specific questionnaires and one single

question designed to screen for problematic cannabis abuse in populations. Questionnaire and

self-report answers have been compared to cannabinoid findings in biological samples, to

determine which questionnaire answers are best correlated with cannabinoid concentrations in

biological specimens. In consequence one will be able to link certain questionnaire answers

(scores) to a certain intensity of use so that problematic cannabis use can be determined in

certain populations (e.g. university students). For this purpose a sample consisting of cannabis

users and non-users will be used, with a variety of light to heavy cannabis use.

Validity of self-report and precedents of questionnaire studies

Many studies comparing self-report and results in biological samples have been carried out,

sometimes with conflicting results. Williams and Nowatzki (1) investigated self-reported

cannabis use by comparing answers given in an interview with urinalysis. A difference in

sensitivity was found depending on subject’s awareness of the urine testing. When the subject

was unaware of the subsequent urine testing on the moment of the interview, the agreement

between self-reported cannabis use and urine testing was lower than when subjects were

aware that their answers would be checked later by urinalysis. A positive predictive value

(PPV) of self-report of 66% was demonstrated, meaning that 66% of all people that reported

cannabis use had cannabis detectable in urine (with a cut-off for cannabinoids of 50 ng/mL).

Sensitivity and specificity were 64% and 83% respectively. Another study reported

correspondence between urinalysis and self-report in a sample of 248 subjects (10). A GAIN

(Global Appraisal of Individual Needs) structure was implemented for the self-report, this

measures recent use, days of use and symptoms associated with problematic cannabis abuse.


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The aim of the study was to compare self-report, and results of laboratorial and on-site urine

testing. Overall a 65% agreement between laboratorial urine testing and self-report was

calculated. It was shown that the rate of agreement decreased when the use occurred longer

ago. In a report by Robertson et al. different types of self-report were compared to markers in

oral fluid for different types of drugs in a sample of 271 subjects (11). The authors also tried

to identify those factors that seem to be related with differences between self-report and oral

fluid testing. A greater similarity between self-report and test results was achieved when

subjects were ensured confidentiality and no negative consequences (e.g. legal proceedings).

Overreporting sometimes even seemed to be correlated with less predictable factors like being

younger, being homeless and not hoping to achieve abstinence.

Several studies on the use of different questionnaires and self-report instruments have been

carried out, but unlike with alcohol abuse (Alcohol Use Disorders Identification Test

questionnaire) there is no real consensus about what questionnaire is best used. The CUDIT

test (Cannabis Use Disorder Identification Test) is derived from this AUDIT to identify

subjects with cannabis use disorder within a certain population. In New Zealand this CUDIT

questionnaire was used to screen for cannabis dependent subjects within an alcohol-dependent

sample (n=115), which can be seen as a population at higher risk for cannabis dependence (3).

The CUDIT appeared to be a rather good predictor of cannabis dependence. When a cut-off

value of 8 points was used this resulted in a sensitivity and a positive predictive value of

73.3% and 84.3% respectively. Further interpretation of these tests made it possible to

calculate which factors questioned in the questionnaire were best predictors of cannabis use

disorder, and the authors concluded that frequency of use appeared to be the best predictor for

cannabis use disorder. Other questionnaires have also been developed aiming to evaluate

cannabis related problems, for example the CAST and CPQ-A-S tests (12). A study

comparing these two questionnaires was carried out by Fernandez-Artamendi et al. at the

University of Oviedo in Spain, using a sample of 144 students that had all used cannabis in

the past month. This resulted in 14,5% of them being diagnosed as cannabis abuser and 31,9%

cannabis dependent, when following DSM IV criteria. Major differences in targets of both

questionnaires are important to keep in mind. The CAST seems to be a shorter test, with

higher specificity, designed to detect cannabis use disorder over a period of 12 months. CPQ-

A-S is more sensitive to detect psychological distress, and a broader spectrum of problems

commonly associated with the use of the drug over a period of 3 months. CPQ-A-S has a


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higher predictive capacity with regard to recent use, intensive use, and cannabis dependence.

In this study three questionnaires (SDS, CAGE-AID and ProbCannabis-DT) and one single

question are used. The proportions of cannabis abusing subjects that is identified by the

particular questionnaire is investigated, which gives an idea about the accuracy of the

questionnaire. The optimal cut-off score for the ProbCannabis-DT questionnaire will be

determined, the other questionnaires have been included for comparison. All questionnaires

are included as attachments, in the Dutch version.

1. The SDS (Severity of Dependence Scale) is a 5-item scale that measures the degree of

psychological dependence. It is based on the feelings of impaired control, anxiety and

preoccupation towards the drug consumption. It was investigated in a review

conducted by Piontek et al. (13). Authors reported that moderate to high positive

correlations (r = .32 to .76) were found between the SDS total score and frequency of

cannabis use, amount of cannabis use and the number of DSM-IV dependence criteria

in different studies. A good ability to discriminate between dependent and non-

dependent individuals was found, correctly classifying at least 85% of cases for

general population. Just one study reported a very low PPV for SDS when used in

general population.

2. The single question is based on a study by Smith et al. (14), who validated the

question “How many times in the past year have you used an illegal drug or used

prescription medication for non-medical reasons?”. A response of at least once, is

considered as positive for drug use disorder. These answers were compared to the

results of a DAST-10 test, oral fluid testing and the presence or absence of a current

(past year) drug use or drug use disorder. Results showed good psychometric

properties, with 100% sensitivity and 73.5% specificity for the detection of drug use

disorder. For detection of self-reported drug use and oral fluid testing a sensitivity of

92.9% and 81.8% was reported respectively. In our study this question will be

translated into Dutch.

3. The CAGE-AID questionnaire was investigated by Brown et al., who validated a cut-

off of one and two positive answers. Sensitivity and specificity were calculated for

both cut-of scores. For a cut-off of one positive answer sensitivity was 0.79 and

specificity was 0.77; for two positive answers this was 0.7 and 0.85 respectively (15,

16). Couwenbergh et al. investigated the CAGE-AID questionnaire in a sample of


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Dutch teenagers (17). They calculated a probability of 99,6% that someone with a

substance use disorder has a higher score on the CAGE-AID compared to someone

without a substance use disorder. It was concluded that the CAGE-AID is a good

measure for detection of substance use disorder. In our study the CAGE-AID

questions will be limited to cannabis-related questions only.

4. The 6-item ProbCannabis-DT questionnaire developed by Decorte et al. of Ghent

University was used in a university study for the use of illegal substances, but has

never been properly validated. It is based on the criteria for cannabis dependence as

stated in DSM-IV. A respondent can answer ‘yes’, ‘no’ or ‘no answer’ to all six

questions. A cut-off of one positive answer indicating problematic cannabis abuse was

always used, but never validated; this validation was the primary aim of this study

(18). So the one most important question that needed to be answered in this study was:

“Which cut-off score should be used for the ProbCannabis-DT questionnaire to

differentiate between problematic cannabis users and non-users?”

Choice of biomarkers

The three most important ways of detecting cannabis are detection of THC and/or its

metabolites in hair, blood and urine.

In urine mostly THC, 11-OH-THC and THC-COOH are detected. Studies where THC was

detected in urine report similar results. The THC excretion differs greatly inter-individually,

and is not constant over time. Huestis et al. (19) reported that 50% of the totally excreted THC

is excreted on the first day. The excretion half-life is dependent on the intensity of use, being

long (2.6 to 7.2 days) in heavy, chronic users and short (several hours) in light users. An

important limit to the use of THC as a biological marker for cannabis abuse is that little is

known about how to differentiate between recent use or delayed excretion of past use (20),

and that sometimes it is not detected in light users (21). Despite these limitations concerning

detection times Manno et al. stated that urinary concentrations >1.5 ng/mL suggest cannabis

use within an 8 hour time frame (21, 22). One could conclude that when detecting THC

concentrations in urine it is better to evaluate the concentration (e.g. a 1.5 ng/mL cut-off) and

not the detection time, because detection times can not provide certain information on

intensity of use. Determining 11-OH-THC concentrations in urine is not commonly used as

marker for cannabis smoking, although it has a detection rate of 99.7% (20). In the past it was

thought that 11-OH-THC was a good marker to determine recent use of cannabis, but recent


8

studies have shown that this is not the case. 11-OH-THC is excreted much more slowly than

previously thought, with extended excretion times and it could be detected up to 24 days in a

study by Lowe et al. (20, 23). These findings were also confirmed for THC itself. Thus 11-

OH-THC and THC are not to be used to indicate recent cannabis exposure, except for the cut-

off for THC of 1.5 ng/mL. For THCCOOH the total amount excreted, peak concentration and

detection times also vary greatly between subjects, whilst the percentage of total THCCOOH

excreted every day seems to be comparable. The detection time seems to be dependent of the

frequency of cannabis use, being longer detectable when the subject smokes cannabis more

frequently (up to weeks and months). In urine mostly the THCCOOH glucuronide metabolite

is detected (21). There is one major benefit to THCCOOH, namely that monitoring the

urinary THCCOOH to creatinine ratio may provide additional information on whether the

excretion is due to recent use or due to remaining excretion from past use. With a

THCCOOH/creatinine ratio ≥ 0.5 compared to the previous ratio, measured 24h later, one can

assume that this predicts new marijuana use. This cut-off would yield an overall prediction

accuracy of 85,4%, with a false positive ratio of 5,6% and a false negative ratio of 7,4% (23-

26).

It general the elimination of cannabinoids is an unpredictable process, with great variations in

detection times and concentrations. It is not possible to certainly link urine concentrations to a

certain intensity of cannabis use.

Whole-blood, plasma and serum seem to give a more accurate view of a person’s cannabis

use. THCCOOH is the most frequently traced metabolite in blood, knowing that it

accumulates in the blood of frequent users with half-lives of 20-57h in occasional users and 3

to 13 days in frequent users (27). Even a half-life of up to 25 days has been reported for this

metabolite (27). Measuring THCCOOH concentrations in blood allows one to distinguish

between regular use and heavy use. In Germany serum THCCOOH concentrations ≥ 75

ng/mL are assumed to be associated with heavy cannabis consumption, whereas

concentrations < 5ng/mL indicate no or very light cannabis smoking (21, 28, 29). But just like

in urine great inter-individual differences in plasma and whole-blood concentrations are

observed (21, 27, 30, 31). In New-Caledonia a study was carried out where different groups of

known cannabis consumers were tested for the presence of THC and CBD (cannabidiol).

These groups differed in mental status: there was a control group, a group with acute

psychoses caused by cannabis use, a group with chronic psychiatric problems (mainly


9

schizophrenia) and with other mental illnesses. The authors concluded that subjects with

chronic psychiatric problems had a higher mean THC blood concentration compared to other

groups. Furthermore subjects suffering from an acute psychosis caused by cannabis had lower

mean THC and CBD concentrations when compared to other groups (32).

Hair testing has some major advantages. Firstly it allows much longer retrospective analysis,

depending on the length of the hair, offering a cumulative reflection of long-term abuse (27,

33). Secondly hair can be stored at room temperature and it does not need to be analyzed

directly after collection(21). The most important downside is that of contamination. Most

cannabis metabolites can also be incorporated in hair by passive exposure, i.e. by

contamination (21, 33). Also the concentrations measured in hair are often very low (even at

the pg-level) so that sometimes cannabinoids are not detected, giving false negative results

(33).

In the literature a lot of different ways to test hair for the presence of cannabinoids have been

reported. For example an extensive method has been developed with a preceding screening

test using an ELISA immunoassay. This is then followed by exposure proof using GC/MS

(detecting THC and CBN) and finally proving active consumption by detecting THCCOOH

using GC/MS/MS (34-36). In a study by Uhl et. al the usefulness of this preceding ELISA

screening method was assessed and based on their results one could conclude that this extra

test is not really very relevant. When screened by ELISA there were no cases where the

immunoassay was negative and the consequent THC/CBN detection was positive. But 3 cases

out of 66 were described where ELISA resulted in a negative test but subsequent GC/MS/MS

for THCCOOH was positive (36). In this way one can see that the ELISA screening was

neither sensitive nor specific enough to rule out any cases. Several different methods of hair

preparation to avoid false positive results due to contamination have been investigated, but

none is completely reliable, making the THCCOOH presence the best marker for active

consumption(21, 31, 36). In a consensus by the Society Of Hair Testing it is stated that for

testing hair THC can be detected using an immunoassay or chromatographic procedure,

possibly followed by THCCOOH determination to prove active consumption(37). In the study

by Huestis et al. (34) it was also confirmed that when intensity of cannabis abuse increases

also the concentrations of THC and THCCOOH in hair will increase, with differences

between concentrations in hair of daily users vs. non-daily users. Nevertheless the differences

were not significant (P > 0.2) so it is not possible to determine a certain cut-off value that can


10

distinguish between daily and non-daily users.

Only one study could be found where authors mentioned a certain cutoff value (0.1-1 ng

THC/mg hair) which maybe could be used to indicate weekly/daily cannabis consumption. A

concentration above 0.1ng/mg would suggest regular cannabis use, and a concentration above

1 ng/mg would indicate daily use. But authors couldn’t say this is an absolute certainty, and

this cut-off has never been validated properly (18).


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MATERIALS AND METHODS

Participants and selection procedure

The purpose was to perform the study on a study sample containing students and psychiatric

patients. At the beginning of the study the aim was a total study sample of 150 participants,

with 75% cannabis users and 25% non-users, 75 psychiatric patients and 75 students.

Eventually our study was conducted with 97 participants, because of an insufficient number

of included psychiatric patients. For recruitment of the students flyers and posters were

distributed in different University facilities (restaurants, auditoria, bars,…) in the Belgian city

of Ghent. In our selection campaign we did not include any persons under the age of 18.

Students interested in participation could subscribe by visiting a temporary website

www.cannabis.ugent.be. By answering several questions students were enrolled in the

database of possible student study participants. The questions were: “How often did you use

cannabis in the last 12 months?”, “What is your gender?”, and “Is your hair longer than 3

cm?”. Students were also asked to fill in their e-mail address and cell phone number, and the

date on which they could be available for participating in the study. These data were all

encrypted (SSL 128 bits encryption), so that anonymity was insured. Investigators were blind

to which subjects used cannabis. All these data were saved in an independent database, by a

person who was not involved in the study. All possible study subjects were divided into

groups based on the intensity of their cannabis use (no or very light cannabis use, regular use

and heavy use). In this way 173 students responded and completed the online questions. All

students who had registered on line and had declared to be a cannabis user were contacted.

Out of the large number of students that had declared that they were not a cannabis user, a

random sample was selected. In this way it was possible to include a sufficient number of

actual cannabis users. These selected subjects were contacted and asked to be present at one

of four sessions, where questionnaires were answered and blood and hair samples were

collected by the authors and medical students. An incentive of 15 euro was provided, in the

form of a coupon that could be used in a vast number of popular shops. Sessions were

organized on 21, 22, 24 and 29 October 2013 on different locations in the city of Ghent. A lot

of subjects that were contacted did not show up for the session, and these were reminded by

sending them a text message on their mobile phone. A physician was always present in case

of complications during the blood withdrawal. During the filling out of the questionnaires a

study collaborator was present at all times in both groups to clarify possible uncertainties or

doubts concerning the questions. Prior to completing the questionnaire subjects were asked

http://www.cannabis.ugent.be/


12

about their drug use of the past year again, subjects responding that they had not used any

cannabis in the past 12 months did not have to fill in the rest of the questionnaire, because this

would not be relevant. This way 94 students participated. Their data (names, questionnaires,

blood and hair samples) were coded.

Patients recruited from the psychiatric ward were recruited at the UPSIE (University

Psychiatric Emergency Intervention Unit). Patients eligible for participation in the study were

asked by the UPSIE doctors and nurses if they were willing to participate. If they agreed a

blood and hair sample was taken and the questionnaires were filled out. No incentive was

provided, because hospitalized patients are not allowed to receive an incentive for

participating in studies. Envelopes were prepared for each patient individually, these

contained: an informed consent document, a plastic bag for hair collection together with hair

collection instructions, the four questionnaires, and two 2 mL fluoride/oxalate tubes for blood

collection. Per patient one envelope was used, and each envelope had a unique code. In

contrast to the high number of students that cooperated, the response rate amongst psychiatric

patients was very low, only 3 patients could be included; this was far from our objective. In

total the recruiting procedure lasted for 4-5 months in this department.

Once arrived at the laboratory where the analyses of the samples would take place the blood

and hair samples were separated from the informed consent document and the questionnaires.

Questionnaires

Study participants filled out three questionnaires and one single question about their cannabis

use. The questionnaires used were the Severity of Dependence Scale, the ProbCannabis-DT

questionnaire and the CAGE-AID questionnaire (see addendum 1). The single question was:

“How many times in the past year have you used an illegal drug or used prescription

medication for non-medical reasons?” (14). All questionnaires were presented in Dutch (38,

39). Because this study only concerned cannabis abuse, the CAGE-AID questionnaire was

limited to cannabis use only. These questionnaires were all validated as mentioned above,

except for the ProbCannabis-DT. For all questionnaires interpretation of the total scores is in

the same direction, meaning that all question are formulated as such that a higher score is the

result of more cannabis use. For SDS scores vary between 0 and 15 (5 questions on a 4 point

Likert scale), CAGE-AID scores vary from 0 to 4 (4 questions, answered with ‘yes’ or ‘no’)


13

and the ProbCannabis-DT questionnaire comprises 6 ‘yes’ or ‘no’ questions (total score

ranging from 0 to 6). For the ProbCannabis-DT questionnaire no specific cut-off score has

been studied in the literature, so the main goal of the study was evaluating the ProbCannabis-

DT questionnaire’s value in predicting problematic cannabis abuse, and assessing a proper

cut-off score. For all questionnaire assessments the serum THCCOOH based classes of

cannabis abuse were used as gold standard, for knowing whether or not a person uses

cannabis or not. Thus when sensitivity, specificity, PPV and NPV are calculated this was

always based on blood THCCOOH determined user status.

When examining the results of the answers on the single-question it appeared that one person

did not answer correctly on this question, the person just answered that he smoked 5 to 10 gr

cannabis a day. The highest reported value on this question was 500 times (joints), by another

subject. In a study by Hunault et. al it is mentioned that a classic European ‘joint’ would

contain about 300 mg of cannabis and 700 mg of tobacco (40). So if the first subject smokes

about 5-10 gr cannabis per day, this would correspond with 16 to 32 joints per day, that is

more than a lot. Thus we also changed the answer of the first subject into 500 times.

Biomarkers

Cannabinoid concentrations were determined in hair and serum samples. The blood samples

were collected in two 2mL fluoride tubes per subject. They were centrifuged and the serum

stored at -20°C till analysis. Hair samples were taken by cutting of a lock of hair close to the

scalp. These samples were then transported in a plastic bag at room temperature. All

laboratory analyses were conducted at the Toxicology department of the Ghent University

Hospital.

Screening

The serum samples were firstly screened by Cloned Enzyme Donor ImmunoAssays (CEDIA)

on a Thermo Fisher Scientific Indiko plus analyzer. The sample volume necessary for

immunoassay analysis is 100 µl. Reagent kit is the CEDIA THC Plus Assay (by Microgenics ,

Germany) consisting of two reconstitution buffers and two reagents:

- 1 EA Reconstitution Buffer (buffering salts 0.9 µl, monoclonal antibodies against 11-

nor-Δ9-THC-THCCOOH, stabilizer and preservative)

- 1a EA Reagent (0.171 g/L Enzyme acceptor, buffering salts, detergent and

preservative)

- 2 ED Reconstitution Buffer (buffering salts, stabilizer and preservative)


14

- 2a ED Reagent (microbial Enzyme Donor conjugated with 11-nor-Δ9-THC-COOH,

1.67 g/L chlorophenolred-β-D-galactopyranoside, stabilizer and preservative)

R2 Enzyme donor solution is prepared first, afterwards the R1 enzyme acceptor solution.

Preparation of the solution is by connection between the ‘2a bottle’ and the ‘2 bottle’ through

an included adapter, for the R2 Enzyme Donor solution. The same method is used for

preparation of the R1 enzyme acceptor solution. These are stored at 2-8°C.

A four point calibration curve is made automatically by the instrument, by combining

different quantities of negative calibrator THC 25 S calibrator, resulting in concentrations of

0, 6.25, 12.5 and 25 ng/mL.

Samples with a THC concentration in serum above 6 ng/mL on immunoassay were analyzed

further by GC/MS.

Quantitation of THCCOOH in serum samples

Of these serum samples 1000 µl was transferred to a salinized stoppered glass tube together

with: 1000 µl of water, 50 µl of methanol, 25 µl Internal Standard-MIX (containing 100 µl

solution of THC.D3, 11-OH-THC.D3 and THCCOOH.D3, diluted with methanol in a flask of

10 mL) of 1000 ng/mL in MeOH (25 ng) and 100 µl of 10% acetic acid. This was vortexed.

Hexane/ethyl acetate (5.5 mL) was added, and the sample was placed on the roller for 30

minutes. Afterwards the sample was centrifuged. The organic layer was then transferred to a

silanized tube. At 56°C this was evaporated under a continuous flow of N2, and resuspended

in 200 µl Tetramethylammoniumhydroxide/Dimethylsulfoxide. This residue was vortexed

and incubated at room temperature for 5 minutes. This was vortexed again after addition of 50

µl of methyl iodide. An hour of incubation at room temperature preceded addition of 200 µl

HCL 0.1 N and vortexing again. This residue was incubated at room temperature for 5

minutes, 2 mL of iso-octane was added and the tube was shaken manually. This was

centrifuged at 3000 rpm for 5 minutes, the remaining organic layer was transferred to a

silanized tube and evaporated under a continuous flow of N2 at 56°C. By vortexing this was

resuspended in 35µl of ethyl acetate. Finally this was transferred to silanized autosampler

inserts.

The GC/MS analysis was performed on a Shimadzu QP 2010 Plus GC/MS (Shimadzu; Kyoto

Japan) .The column was a HP-5ms (12m x 0.2 mm x 0.33 µm) (Agilent Technologies; Santa

Clara California, USA). The carrier gas (helium) flowed at 0.72 mL/min. Two microliters

were injected in the 1/5 split mode with the injector at a temperature of 270°C. The


15

temperature program was as follows: 100°C for 3 minutes, raising to 310 °C in 7 minutes

(30°C rise per minute), this was retained for 5 minutes. Using GC/MS THC (retention time

8.895 minutes; target ion 328; qualifier ions 313, 285), 11-OH-THC (retention time 9.417;

target ion 313) and THCCOOH (retention time 9.828; target ion 357; qualifier ion 313, 372)

concentrations were determined. In each run a 6-step calibration curve was run with 0, 2, 4, 8,

16 and 32 ng/mL. LOQ was set at 1 ng/mL for THC and 11-OH-THC, and at 2 ng/mL for

THCCOOH.

Quantitation of THC in hair samples

The analysis of hair was performed as follows. First the hair was decontaminated and

pulverized. In the pulverization process only the 3 proximal centimeters were used, so the

results obtained in the hair analysis reflect the use of only the past three months. Hair samples

were washed with 3 mL MeOH and then mixed by gentle rolling for 10 minutes. After drying

the hair on a Kleenex it was pulverized in a ball mill for 15 minutes. For extraction samples

were incubated at 100°C for 10 minutes, prior to addition of hexane/ethyl acetate and 10

minutes mixing by rolling. Afterwards they were centrifuged for 5 minutes at 3000 rpm and

the samples were evaporated at 56°C under a continuous flow of N2. For the derivatisation

procedure the extraction residue was resuspended in 300 µl of acetone, with subsequent

addition of 100 mg of K2CO3 and 30 µl of iodomethane. This was incubated at 56°C for 3

hours. After cooling down, the residue was again evaporated under a continuous flow of N2.

This was then resuspended in 30 µl of ethyl actetate and transferred to a silanized autosampler

crimp vial. For determination of THC the exact same GC/MS method was used as in blood. In

each run a three-step calibration curve was run with 0, 0.1, 0.5 ng/mg. Only THC was

measured in hair (LOQ 0.01 ng/mg)(41). These are also the cut-offs used in this study to

define hair positive and negative results. Only 95 hair samples could be analyzed, one sample

was too small (only 3mg) and one sample was lost during the washing procedure.

Statistical Analysis

All analyses were conducted using IBM SPSS Statistics 21 (manufactured by IBM Corp.,

Armonk NY). The total score on each questionnaire was calculated, and the proportion of

subjects who had a total score higher than the cut-off value (found in literature) was

computed. THCCOOH measurements in serum were used to define three classes of intensity


16

of cannabis use, as mentioned above. A dichotomous ‘THCCOOH positive or negative’

variable was also created. THC concentrations in hair were also recoded into non users (THC

<0.1 ng/mg) and certain cannabis users (THC ≥ 0.1 ng/mg). For the analysis of the optimal

cut-off for the ProbCannabis-DT questionnaire 6 new dichotomous variables were defined.

For variable 1 the total score on the ProbCannabis-DT questionnaire was redefined with a

score of 0 being recoded as value ‘0’, and all scores of 1 or higher were recoded into ‘1’. So

this considered a total score of at least 1 positive answer as a positive screening result, coded

as ‘1’ in the new variable. The same method was then used to determine variables

corresponding to cut-offs of 2, 3, 4, 5 and 6 points on the ProbCannabis-DT questionnaire.

For further evaluation Fisher’s Exact test was used for determining differences between

categorical variables and Mann Whitney-U test for comparison of continuous variables

between males and females.

Furthermore correlations, sensitivity, specificity and Cohen’s Kappa were calculated when

appropriate and for evaluation of the ProbCannabis-DT questionnaire.

All statistical significance levels were set at 0.05, and confidence intervals were set at 95%.

All statistical tests for significance were 2-sided.

Blinding and prevention of bias

The informed consent document was the only document in the envelope that contained the

name of the subject. By separating the informed consent document from the questionnaires

and the biological samples, the investigators could not know the name of the participant, this

made the study anonymous. The selection of the students from the online database was

conducted by a person who had no interest in the study and did not participate in the analyses

of the questionnaires and biological samples. She was also responsible for contacting all

students, and linking all samples to the same study subject. In the psychiatric ward it was

always an assistant-doctor who recruited the study subjects, and collected the samples.

Therefore the investigators could not know a subject’s identity or possible cannabis abuse.


17

RESULTS

Participants

In total 97 participants could be included, 3 of them were included in the psychiatric ward.

There were 61 male participants, 36 were female. Mean age was 21.2 years, with a standard

deviation of 2.5 years. These are values of the student sample, the age of the three psychiatric

patients has not been recorded.

Biomarkers

THCCOOH concentrations in serum are used to classify study subjects in three different

classes. THCCOOH concentrations <5ng/mL are labeled as not using cannabis to using

cannabis very lightly. The highest number of the sample (66 subjects) is part of this first class.

Concentrations between 5 and 75 ng/mL are considered as regular cannabis users, and heavy

cannabis users have serum THCCOOH concentrations above 75ng/mL (see table 2). The

mean THCCOOH concentration in serum was 11.43 ng/mL with a standard deviation (SD) of

25.63 ng/mL. The Mann Whitney-U test proves that the difference between males and

females is significant (P<0.001), and the bar chart clarifies this difference, showing that males

generally have higher THCCOOH concentrations than females. Of course a potential bias

could be that due to the selection procedure (which was voluntary) a higher amount of non-

cannabis using females participated in the study, and a lot less non-using males participated.

So it can’t be concluded that also in the general student population a lot less females smoke

cannabis, compared to males.


18

Figure 1: Distribution of male and female subjects over the different classes of intensity of cannabis

use, based on THCCOOH measurement in serum.

When considering only the subjects that are labeled as cannabis users, the mean concentration

was 34.8 ng/mL with a standard deviation of 35.63.

Hair THC concentrations are considered positive when 0.1 ng/mg or higher. Of all samples,

13 samples had a positive screening result (≥0.1 ng/mg), indicating daily to weekly use of

cannabis. The mean concentration was 0.047 (SD 0.14). No subject had a THC concentration

above 1 ng/mg, which would suggest daily use.

Cohen’s Kappa was calculated as a measure of agreement between hair and blood screening

results. A value of 0.437 was concluded, indicating a moderate agreement (42, 43), this value

was proven significant (P< 0.001) . Table 2 illustrates the differences in serum and hair

testing outcomes.


19

Table 2: Comparison of results of hair and serum testing. Amount of subjects per class is displayed.

THC concentrations in hair (n =

95)

<0.1 ng/mg

(negative

screen)

(n = 82)

0.1-1 ng/mg

(weekly – daily

use)

(n = 13)

THCCOOH concentrations

in serum (n = 97)

<5 ng/mL (no or very light

use)

(n = 66)

63 1

5 – 75 ng/mL (regular use)

(n = 28)

18 10

> 75 ng/mL (heavy use)

(n = 3)

1 2

Questionnaires

The aim of this study was evaluating the validity of the questionnaire ProbCannabis-DT

developed by the department of Criminology of Ghent University. The other questionnaires

are all yet validated, and were included for comparison.

Based on the question about the cannabis use in the past 12 months, we can have an accurate

view on the intensity of cannabis use in our sample.

Table 3: Subjects’ answers on the question “How often did you use cannabis (marijuana, weed,

hash,…) in the past 12 months?” For each outcome class for the serum and hair samples the

percentage of the class that answered ‘yes’ on the question is presented (e.g. 25% of all subjects that

had a negative hair screening result report that they have NEVER used cannabis).

The amount of subjects answering the question with ‘yes’ is presented in parentheses.


93)

THCCOOH concentrations in serum (n =

97)

<0.1 ng/mg

(negative

screen)

(n = 80)

0.1-1 ng/mg

(weekly – daily

use)

(n = 13)

<5 ng/mL (no

or very light

use)

(n = 66)

5 – 75 ng/mL

(regular use)

(n = 28)

> 75

ng/mL

(heavy

use)

(n = 3)

I NEVER used cannabis 25% (20) 0 31.3% (20) 0 0


20

I ever used cannabis, but

NOT in the last 12

months

6.3% (5) 0 7.8% (5) 0 0

less than once a month 17.5% (14) 0 21.9% (14) 0 0

once a month 2.5% (2) 0 3.1% (2) 0 0

less then once a week,

more than once a month 11.3% (9) 0 10.9% (7) 7.1% (2) 0

once a week 6.3% (5) 0 4.7% (3) 7.1% (2) 0

more than once a week,

less than daily 21.3% (17) 38.5% (5) 15.6% (10) 42.9% (12) 0

daily 10% (8) 61.5% (8) 4.7% (3) 42.9% (12) 100%

(3)

Table 3 shows that 25 students did not smoke any cannabis in the past 12 months, they did not

complete the rest of the questionnaires, as they do not smoke (sufficient) cannabis. So all

other subjects reported smoking cannabis less than once a month or more frequently.

Nevertheless, only in 31 subjects a THCCOOH concentration ≥ 5ng/mL was detected. The

three psychiatric patients weren’t asked this question, because they were already known users

when they were enrolled in the study. But one of these patients mentioned on the single

question that he smoked cannabis every day, so he was also included in the above analysis.

The table suggests a good concordance between the answers on the question and the

THCCOOH-based classes of use. Namely, the regular users all declared to use cannabis more

than once a month and the heavy users all declared to use cannabis daily. This difference was

proven significant by the Fisher’s Exact test (P<0.001). Nevertheless the table also shows that

amongst the THCCOOH determined non-cannabis users and very light cannabis users there is

also a large number that reports using cannabis very often (once a week, daily,…).

For hair results, all cannabis users that have a THC concentration ≥ 0.1 ng/mg report smoking

cannabis at least several times a week. Fisher’s Exact test proved that the subjects indicated as

regular cannabis users by the hair testing differ significantly from the ones with a negative

hair test, indicating more cannabis use in the last 12 months (P<0.001).

But the table shows that the hair test results don’t always provide a clear view on one’s

cannabis abuse status: 27 subjects reported smoking cannabis several times per week, but only

5 of them had a positive hair result. In contrast 14 of these 27 subjects had a positive blood

result. Only half of all daily cannabis users had a THC concentration in hair of ≥ 0.1 ng/mg.


21

CAGE-AID questionnaire

The CAGE-AID questionnaire comprises 4 items. The answers on all different questions are

summarized in table 5. For the CAGE-AID questionnaire a possible drug abuse is suspected

when a person answers at least one of these questions positively, drug abuse surely is present

when at least two questions are answered positive (15-17, 38, 44, 45). Overall score on the

CAGE-AID ranged from 0 to 3, so no subject answered all questions positively.

Table 4: Distribution of total scores on the CAGE-AID questionnaire.

Frequency

0 39

1 24

2 18

3 16

Total 97

Table 5: Overview of answers provided on the CAGE-AID questions. For each outcome class for

the serum and hair samples the percentage of the class that answered ‘yes’ on the question is

presented (e.g. 37% of all subjects that had a negative hair screening result answered ‘yes’ on the

first question).

The total amount of subjects that answered ‘yes’ on the question is also displayed.

THC concentrations in hair

(n = 95)

THCCOOH concentrations in

serum (n = 97)

<0.1 ng/mg

(negative

screen)

(n = 82)

0.1-1 ng/mg

(weekly –

daily use)

(n = 13)

<5 ng/mL

(no or very

light use)

(n = 66)

5 – 75

ng/mL

(regular

use)

(n = 28)

> 75

ng/mL

(heavy

use)

(n = 3)

Have you ever felt you ought to

cut down on your cannabis

use?

yes 37% 92% 29% 76% 100%

Total 30 12 19 22 3

Have people annoyed you by

criticizing your cannabis use?

yes 11% 15% 8% 18% 67%

Total 9 2 5 5 2

Have you felt bad or guilty

about your cannabis use?

yes 26% 31% 21% 39% 0%

Total 21 4 14 11 0

Have you ever used cannabis

first thing in the morning to

steady your nerves or to get rid

of a hangover ?

yes 23% 54% 15% 50% 100%

Total 19 7 10 14 3


22

Table 5 shows that that the first question is the one most answered positively, and that the

second question is the one that is least frequent answered positively. Fisher’s Exact test was

used to determine the differences in answers between the different classes of use. When

considering differences based on THCCOOH measurement, Fisher’s Exact test calculated that

there is a statistical difference between the classes for questions 1 (P<0.001), 2 (P<0.05) and 4

(P<0,001). When considering only the THCCOOH < 5ng/mL and the THCCOOH ≥ 5ng/mL

group, so only the users and non-users, again the Fisher’s Exact test showed no significant

difference in answers on that third question. For the other questions the table clearly shows

that as subjects belong to a higher class of intensity of cannabis use, they are more likely to

answer questions 1,2 and 4 positively. This is also what one would hope for as the screening

questionnaire’s target is that those groups can be distinct from the group that doesn’t have a

cannabis problem (THCCOOH <5ng/mL). Odds Ratios were computed to further investigate

the strength of the difference, they were calculated between the non-users group (THCCOOH

<5ng/mL) and the users group (THCCOOH ≥ 5ng/mL). For question 1 a significant OR of

10.3 (CI: 3.65-29.1) was calculated, meaning that when a subject has THCCOOH ≥ 5ng/mL

in serum, they have a tenfold higher risk of answering ‘yes’ on that question. For questions 2,

3 and 4 calculated OR were 3,558 (CI: 1,029-12,309), 2,043 (CI: 0,795-5,246) and 6,800

(CI:2,562-18,051). All CI are 95% confidence intervals, and they suggest that the OR are

significant for questions 1, 2 and 4. When comparing the questionnaire results to hair analysis

significant differences in questionnaire answers between the THC positive and negative group

can be found for questions 1 (P<0.001) and 4 (P<0.05). OR for answering positively on this

questions when having a positive hair THC result were 20.8 (CI: 2,58 – 168) and 3,868 (CI:

1,159 - 12,909) respectively.

So above an analysis of the questionnaire was made per specific question. Now in table 6 the

overall score on the questionnaire is considered.

Table 6 shows that 39 subjects answered none of the CAGE-AID questions positively. Of all

subjects 58 had at least one positive answer; according to the literature this would indicate a

positive screening (15, 38, 44). These subjects probably have a cannabis abuse problem. A

certain drug abuse problem can be concluded in 16 of all study subjects, who answer two or

more questions positively(17). Thus 42 of all subjects answered ‘yes’ on only one question,

and therefore they probably have a drug abuse problem, but not certainly.


23

Table 6: CAGE-AID screening results. For each outcome class for the serum and hair samples the

percentage of the class that had a score above the cut-off is presented (e.g. 52.4% of all subjects that

had a negative hair screening result answered at least one question positively).

The total amount of subjects that had a score above the cut-off is also displayed.


(n = 95)

THCCOOH concentrations in serum

(n = 97)

<0.1 ng/mg

(negative

screen)

(n=82)

0.1-1 ng/mg

(weekly –

daily use)

(n=13)

<5 ng/mL (no

or very light

use)

(n=66)

5 – 75 ng/mL

(regular use)

(n=28)

> 75

ng/mL

(heavy

use)

(n=3)

CAGE-

AID ≥ 1

Positive screen

(probably regular

cannabis use)

52.4% 100% 40.9% 100% 100%

Total 43 13 27 28 3

CAGE-

AID ≥ 2

Problematic cannabis

use 13.4% 30.7% 10% 40% 67%

Total 11 4 6 8 2

Again Odds Ratios were calculated for users and non-users. OR for the screening was 2.15

(CI: 1.63-2.83),indicating a higher probability of being a cannabis user when answering at

least one question positively. In this sample these results corresponded to a sensitivity of 1,

specificity of 0.59 and a PPV of 0.53. These were different from the properties calculated by

Brown et al. (sensitivity 0.79; specificity 0.77)(15). But they were rather similar to results

from an investigation by Hinkin et al. (sensitivity: 0.92; specificity 0.48)(16). For the ‘certain

drug abuse’ (≥2 positive answers) cut-off an OR of 4,76 (CI:1,54-14,71) was calculated, so

drug users (>5 ng/mL THCCOOH in serum) have a four times higher chance of being labeled

as ‘certain drug abusers’ by the questionnaire. Sensitivity was 0.32, specificity 0.9 and PPV

0.63. Brown et al. computed a sensitivity and specificity of 0.70 and 0.85(15); for Hinkin et

al. this was 0.81 and 0.72 respectively(16). Our results seem to differ from both study’s

calculations. The concordance between THC hair results and questionnaire outcome is low, of

all 56 subjects with a positive screening on CAGE-AID, only 13 had a positive hair sample.

Subjects identified as problematic drug abusers by CAGE-AID are not indicated as such by

the THC hair testing in two out of three cases (15 certain drug users according to CAGE-AID,

11 not identified by hair testing). Only for the screening group (cut-off at 1) a significant

difference (P<0.01) was shown in distribution of cannabis abusers along the CAGE-AID ≥1


24

and <1 groups.

Single-Question

The single-question yielded widely dispersed results. The question was not limited to

cannabis use alone, but was about ‘all illegal drugs or medicines without medical

prescription’. Range of people’s answers varied from 0 to 500 times. The mean was 68.51

(SD 128.12). Correlations were computed between THCCOOH based class of use and

single-question outcome (Spearman coefficient 0.253; P<0.05) and between hair results and

single-question answer (Spearman coefficient 0.274; P<0.01). Both were positive and

significant, but not strong. When class of use is replaced by whether or not a subject is a

cannabis user, the correlations don’t get stronger.

Smith et al (14) have stated that an answer of using a drug or medicine not on medical

prescription of at least once in the past twelve months should be considered as a positive

screening result, this indicates 55 of all participants as cannabis users in this sample. Another

study by Saitz et al. concluded that a cut-off of 3 times or more should be used, this would

indicate 44 of all subjects as problematic cannabis abusers (46).

Table 7: screening results of the single-question. For each outcome class for the serum and hair

samples the percentage of the class that had a score above the cut-off is presented (e.g. 51.2% of all

subjects that had a negative hair screening result answered that they had at least once used a drug

or medicine without prescription in the past 12 months).



95)

THCCOOH concentrations in serum (n

= 97)

<0.1 ng/mg

(negative

screen)

(n= 82)

0.1-1 ng/mg

(weekly – daily

use)

(n = 13)

<5 ng/mL (no

or very light

use)

(n = 66)

5 – 75 ng/mL

(regular use)

(n = 28)

> 75

ng/mL

(heavy

use)

(n= 3)

single question

=>1

positive

screen 51.2% 76.9% 51.5% 67.9% 66.7%

Total 43 10 34 19 2

Single question ≥ 3 positive

screen 41.5% 69.2% 39.4% 60.7% 33.3%

Total 34 9 26 17 1

Fisher’s Exact test and OR’s proved that when using the ‘1 time’ cut-off, there was no


25

significant difference in distribution of positive and negative screening results between the

THCCOOH classes of use, and neither between the positive and negative hair THC groups.

The same conclusion was computed when using the ‘3 times’ cut-off. Illustrated by table 7

where one can see that amongst all subjects that scored higher than 1 on the single-question,

more than half still didn’t seem to be cannabis abusers based on the biomarker outcome. This

means that the amount of positive screening results was not distributed differently across

cannabis users and non-users. So, the single-question seemed not to be capable to discriminate

between cannabis users and non-users. In fact this would mean that, in this sample, the single-

question had no diagnostic value with a ‘1 time’ and ‘3 times’ cut-off value.

Severity of Dependence Scale (SDS)

The Severity of Dependence Scale comprises 5 questions, all concerning the last 3 months. A

minimal score of 4 would indicate problematic hash or cannabis abuse (13, 47, 48). The

questions are not answered with ‘yes’ or ‘no’ but a 4 point Likert scale is used (‘never or

almost never’, ‘sometimes’, ‘often’ or ‘always or almost always’). Total scores can range

from 0 to 12 points. Distribution of total scores is pictured in table 8. Of all subjects 44

answered ‘never’ on all questions.

Table 8: Distribution of total scores on the SDS questionnaire

Frequency

0 44

1 20

2 13

3 9

4 2

5 1

6 1

7 1

8 3

9 2

10 1

Total 97


26

Table 9: overview of answers provided on the SDS questions. For each outcome class for the serum

and hair samples the percentage of the class that gave a specific answer is shown (e.g. 81.7% of all

subjects that had a negative hair screening result answered ‘never or almost never’ on question 1 ).

The total number of subjects that corresponds with this percentage is displayed in parentheses.

Question 1 (Q 1): did you IN THE PAST 3 MONTHS ever think your use of hash or weed was out

of control?

Question 2 (Q 2): IN THE PAST 3 MONTHS: did the prospect of missing a smoke ever make you

very anxious or worried?

Question 3 (Q 3): did you IN THE PAST 3 MONTHS ever worry about your hash or weed use?

Question 4 (Q 4): did you IN THE PAST 3 MONTHS ever wish you could stop using hash or weed?

Question 5 (Q 5): IN THE PAST 3 MONTHS: how difficult would you have found it to stop using

hash or weed, or to live without?


(n = 95)


(n = 97)

<0.1 ng/mg

(negative screen)

(n = 82)

0.1-1 ng/mg

(weekly – daily

use)

(n = 13)

<5 ng/mL (no or

very light use)

(n = 66)

5 – 75 ng/mL

(regular use)

(n = 28)

> 75

ng/mL

(heavy

use)

(n = 3)

Q 1 never or

almost never 81.7% (67) 53.8% (7) 87.9% (58) 57.1% (16)

33.3%

(1)

sometimes 11% (9) 46.2% (6) 6% (4) 39.3% (11)

33.3%

(1)

often 7.3% (6) 0 6% (4) 3.6% (1)

33.3%

(1)

always or

almost

always

0 0 0 0 0

Q 2 never or

almost never 82.9% (68) 92.3% (12) 89.3% (59) 78.6% (22)

33.3%

(1)

sometimes 12.2% (10) 7.7% (1) 9% (6) 14.3% (4)

33.3%

(1)

often 3.7% (3) 0 1.2% (1) 7.1% (2) 0

always or

almost

always

1.2% (1) 0 0 0 33.3%

(1)

Q 3 not at all 63.4% (52) 23% (3) 77.3% (51) 17.9% (5) 0

a little bit 28% (23) 76.9% (10) 15.2% (10) 75% (21) 100% (3)

quite a lot 7.3% (6) 0 6% (4) 7.1% (2) 0

a lot 1.2% (1) 0 1.5% (1) 0 0


27

Q 4 never or

almost never 80.5% (66) 84.6% (11) 87.9% (58) 71.4% (20)

33.3%

(1)

sometimes 14.6% (12) 15.4% (2) 9% (6) 21.4% (6)

66.7%

(2)

often 2.4% (2) 0 3% (2) 0 0

always or

almost

always

2.4% (2) 0 0 7.1% (2) 0

Q 5 not difficult 78% (64) 38.5% (5) 84.8% (56) 50%(14) 0

quite difficult 18.3% (15) 61.5% (8) 12.1% (8) 50%(14)

66.7%

(2)

very difficult 3.7% (3) 0 3% (2) 0

33.3%

(1)

impossible 0 0 0 0 0

Fisher’s Exact test proved certain significant differences. When considering the several

classes of cannabis use intensity, all questions were answered significantly different

(significance levels for all five questions were P<0.001, P<0.05, P< 0.001, P<0.05 and

P<0.001 respectively). The table shows that classes of higher cannabis use reported higher

Likert scores on these questions. For hair significant differences in answering patterns were

found between the THC positive and negative groups for questions 1 (P<0.05), 3 (P<0.05)

and 5 (P<0.01).

For SDS cut-off scores table 10 was calculated, a cut-off score of 4 positive answers was used

(13, 47).

Table 10: SDS screening results. For each outcome class for the serum and hair samples the

percentage of the class that had a score above the cut-off is presented (e.g.12.2% of all subjects that

had a negative hair screening result answered have a total score higher than the cut-off score).



(n = 95)


(n = 97)

<0.1 ng/mg

(negative screen)

(n = 82)

0.1-1 ng/mg

(weekly – daily

use)

(n = 13)

<5 ng/mL (no or

very light use)

(n = 66)

5 – 75 ng/mL

(regular use)

(n = 28)

> 75

ng/mL

(heavy

use)

(n = 3)

SDS ≥ 4 Positive

screen

12.2% 7.7% 6% 17.9% 66.7%

Total 10 1 4 5 2


28

It was computed that the distribution of the subjects of the different classes of cannabis use

intensity in the ‘SDS problematic’ and ‘SDS non-problematic’ groups is statistically

significantly different when considering the THCCOOH values as gold standard (P<0.01). It

was calculated that in the ‘SDS non-problematic’ group 24 of the 86 subjects were incorrectly

classified, i.e. they have a negative SDS result but a positive THCCOOH result. In the ‘SDS

problematic use’ group 4 of 11 subjects was incorrectly classified, they had a positive SDS

result but no positive THCCOOH blood result. Thus in this sample actually the SDS didn’t

have a high PPV (0.63) but higher NPV (0.72). Sensitivity was very low at 0.23 (only 7 of all

31 cannabis users are identified), specificity was 0.91. These results can be compared to

psychometric properties calculated by Martin et al. (sensitivity: 0.65; specificity: 0.94)(47),

and Van der Pol et al. (sensitivity: 0.61; specificity: 0.63)(48). Specificity in our sample

seemed to be comparable with the results of Martin et al., but the sensitivity achieved in our

sample was very low When comparing these results to the CAGE-AID results (58 positive

screening results) the SDS identified a lower number of cannabis users (positive screening

results). However, when comparing the SDS result to the CAGE-AID ≥2 a greater similarity

is seen, namely 11 and 16 positive results respectively. For hair the table shows surprising

results: of all 11 subjects screened positively with the SDS questionnaire, actually only 1 also

had a positive hair result. This also means that of the 13 positive hair samples, 12 had a

negative screening score on the SDS. The distribution of the hair positive and hair negative

subjects over the SDS problematic and non-problematic group seemed not to be significantly

different, according to a Fisher’s Exact test.

ProbCannabis-DT questionnaire

The ProbCannabis-DT questionnaire was the questionnaire that needed to be evaluated in our

study. Before the evaluation of this questionnaire it was thought that a cut-off of one positive

answer would be ideal for determining if a person had a risk for a cannabis abuse problem or

not. We investigated optimal cut-off points, sensitivity and specificity for this questionnaire.

When looking at the answers given on the different questions (see table 11) it seemed that

only 10 participants answered the last 4 questions positively, which was much lower

compared to the first questions. Off course a questionnaire that screens for problematic

cannabis abuse would not be useful if the cannabis using subjects don’t yield different scores

on the questions, when compared with non-cannabis using subjects. Fisher’s Exact test proved

that the difference in answers between the several classes of intensity of cannabis abuse were


29

significant for question 1 (P<0.05), 3 (P<0.01), 5 (P<0.001) and 6 (P<0.01). So for question 2

and 4 no significant difference in answering pattern has been shown for different classes of

intensity of cannabis abuse. Cannabis users answered ‘yes’ on the other questions a lot more

often than non-cannabis users. Considering hair results, for question 5 subjects with a

negative hair result were likely to also have a negative screening outcome in the

questionnaire. This is the only question where a significant difference (P<0.05) was shown in

answers between subjects with positive and negative hair results.

Table 11: Overview of answers provided on the ProbCannabis-DT. For each outcome class for the

serum and hair samples the percentage of the class that answered ‘yes’ on each question is

presented (e.g.34.1% of all subjects that had a negative hair screening result answered ‘yes’ on the

first question).

The total amount of subjects that answered ‘yes’ on each question is also displayed.

Question 1 (Q 1): Did you EVER experience you used more cannabis than intended or wanted for

longer than a week??

Question 2 (Q 2): Did you EVER feel the need to reduce your use of cannabis for longer than a

week, or did you ever try - without success - to stop the use of cannabis for longer than a week?

Question 3 (Q 3): Did you EVER reduce or stop social activities, hobby's or work for longer than a

week, because of your use of cannabis?

Question 4 (Q 4): Did you EVER continue to use cannabis for longer than a week, despite having

psychological or physical problems caused or worsened by the use of cannabis?

Question 5 (Q 5): Did you EVER, for longer than a week, not perform your work or study because

of the use of cannabis?

Question 6 (Q 6): Did you EVER continue to use cannabis for longer than a week, despite having

relational problems caused or worsened by the use of cannabis?


(n = 95)


(n = 97)

<0.1 ng/mg

(negative screen)

(n = 82)

0.1-1 ng/mg (weekly

– daily use)

(n = 13)

<5 ng/mL (no or

very light use)

(n = 66)

5 – 75 ng/mL

(regular use)

(n = 28)

> 75

ng/mL

(heavy

use)

(n = 3)

Q 1 yes 34.1% 38.5% 27.2% 57.1% 33.3%

Total 28 5 18 16 1

Q 2 yes 15.9% 15.4% 13.6% 21.4% 33.3%

Total 13 2 9 6 1

Q 3 yes 9.8% 15.4% 4.5% 17.9% 66.7%

Total 8 2 3 5 2

Q 4 yes 9.8% 15.4% 7.8% 14.3% 33.3%

Total 8 2 5 4 1

Q 5 yes 7.3% 30.8% 1.5% 28.6% 33.3%

Total 6 4 1 8 1


30

Q 6 yes 11% 7.7% 4.5% 17.9% 66.7%

Total 9 1 3 5 2

Because this questionnaire had not yet been validated Cronbach’s alpha was calculated as a

measure of internal consistency. This was determined at 0.693, and ‘Cronbach’s alpha based

on standardized items’ was 0.717. This is a good value, although generally it is agreed that a

Cronbach’s alpha should be >0.7 (49, 50). If question 1 would be deleted the Cronbach’s

alpha would actually rise to 0.727.

Calculation of the inter-item correlation matrix yielded table 12. This allows one to assess the

correlations between all the questions reciprocally, indicated as Spearman correlation

coefficients. All correlations were positive, this is also logical because they all measure the

same concept. Ideally all correlations should be at least between 0.3 and 0.5. The correlations

between question 1 and questions 4 and 5 seemed to be very low (0,098 and 0,028

respectively). Some other questions were also associated weakly with each other, as is clear in

the table.

Table 12: inter-item correlation for ProbCannabis-DT questionnaire. Spearman correlation

coefficients are displayed for correlations between all questionnaires.

Questions are the same as in table 9.

Correlations

Spearman Correlation Coefficient Q 1 Q 2 Q 3 Q 4 Q 5 Q 6

Q 1 1,000 ,360** ,239

* ,098 ,028 ,239

*

Q 2 1,000 ,489** ,215

* ,306

** ,489

**

Q 3 1,000 ,331** ,443

** ,554

**

Q 4 1,000 ,220* ,220

*

Q 5 1,000 ,220*

Q 6 1,000

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

To determine the perfect cut-off score for the Decorte questionnaire binary logistic regression

techniques were used to investigate which cut-off score was optimal for predicting whether or

not a subject is a cannabis user.

Several new variables were calculated to use in the regression analysis. Six new variables

were computed, after the following logic. Variable 1 was a recoding of the total score on the

ProbCannabis-DT questionnaire, recoding a total score of 0 into the value ‘0’. Higher scores,


31

equal to or higher then 1, were coded as ‘1’. Thus this ‘1’ value represents all cannabis users,

if a cut-off score of 1 would be applied. For variable 2 the recoding was: total scores of 0 and

1 were coded as ‘0’ and scores of 2 or higher were recoded as value ‘1’. The same method

was used to calculate another 4 variables until variable 6, characterized by a value ‘0’ for total

scores 5 or lower, and value ‘1’ for a total score of 6. Frequency tables showed that no

subjects had a total score of 4 nor 6. So the variables corresponding with those possible cut-

off concentrations weren’t used in the following analysis in the search for an optimal cut-off

score.

Logistic regression analysis was conducted for every new variable. The regression results

proved which cut-off score formed the best model for predicting cannabis abuse.

For assessment of the cut-off at a score of 1 positive answer, a Nagelkerke R Square of 0.352

was calculated, meaning that 35.2% of variance in the dependent variable (cannabis user

status) was accounted for by the explaining variable (the ProbCannabis-DT cut-off score).

The overall percentage correctly classified when using this ProbCannabis-DT score would be

74.2%. A sensitivity of 0.87 and a specificity of 0.68 were computed. OR was 14.464 (CI:

4,485 - 46,644). ROC curve AUC was 0.776, indicating reasonably good discriminatory

power of the model (43).

When investigating a cut-off score of 2, the overall percentage correct was just the same,

namely 74,2%. But the sensitivity and specificity had changed: they were 45,2% and 87,9%

respectively. OR was calculated to be 5,971 (CI: 2,146-16,609). Now this cut-off seems to

have a higher positive predictive value than a cut-off set at a score of 1. But it is clear that the

sensitivity is a lot lower, this is not ideal for a screening questionnaire. Subjects that had a

score ≥ 2 had a lot lower risk that this would be a false positive result, meaning that the

chance that they actually were cannabis abusers would be pretty high. But subjects that had a

score <2 would be considered as non-cannabis users, whereas a large proportion of these

subjects could actually still be cannabis users, not identified by the questionnaire because of

the low sensitivity . The Nagelkerke R Square decreased to 0,168.

Trying the 3 and 5 score as a cut-off produced unsatisfying results, with overall correct

percentages of 69,1% (sensitivity 16,1% and specificity 93,9%) and 68% (sensitivity 9,7%

and specificity 95,5%) respectively. A cut-off set at a 3 or 5 point score seems not to be an

option, because of the very low sensitivity.

So in conclusion it is correct to define a cut-off score of 1 positive answer, for differentiating


32

between cannabis users and non-cannabis users. In this study, this resulted in 48 subjects

having a positive screening result. Setting the cut-off at 2 positive answers has a much higher

specificity, but lower sensitivity. This could maybe be useful in some cases (e.g. when

evaluating a sample of already known drug users, with higher a priori chance of being a

problematic cannabis abuser), but might not be ideal for screening purposes.

Regression analysis and OR’s for each question separately showed that actually in this sample

only two questions seemed to be related significantly with the cannabis abuser outcome.

When being a cannabis user one was more likely to answer questions 1 and 5 positively, with

OR of 5,014 (CI: 1,654 - 15,206) and 59,295 (CI: 4,557 - 771,558) respectively.

Table 13: Overview of screening results on the ProbCannabis-DT questioannire. For each outcome

class for the serum and hair samples the percentage of the class that had a score above the cut-off is

presented (e.g. 41.5 % of all subjects that had a negative hair screening result were indicated as

cannabis users by the questionnaire).

The total amount of subjects that had a score of at least the cut-off is also displayed.

THC concentrations in hair (n=

95)

THCCOOH concentrations in serum (n

= 97)

<0.1 ng/mg

(negative

screen)

(n = 82)

0.1-1 ng/mg

(weekly – daily

use)

(n= 13)

<5 ng/mL (no

or very light

use)

(n = 66)

5 – 75 ng/mL

(regular use)

(n = 28

> 75

ng/mL

(heavy

use)

(n = 3)

ProbCannabis-DT

results

Positive

screen

41.5% 92.3% 31.8% 85.7% 100%

Total 34 12 21 24 3

Table 13 illustrates screening outcome results based on the 1 point cut-off score for the

ProbCannabis-DT questionnaire. For hair results 12 out of all 13 positive samples were also

withheld by the questionnaire as problematic cannabis abusers. Difference in distribution was

proven significant with Fisher’s Exact test (P<0.01), and Cohen’s Kappa for agreement

between hair and questionnaire conclusions was 0.246 and significant (P<0.01). This indicates

a low value for agreement (42, 43), illustrated in the table by the 34 subjects that have a

positive questionnaire outcome but no THC levels traceable in hair. Agreement between

questionnaire outcome and blood results (higher or lower than 5 ng/mL) was a lot higher, with

a Cohen’s Kappa of 0.483 (P<0.001), indicating moderate agreement (42, 43). Again

differences in distribution of positive questionnaire results across all classes of cannabis use


33

intensity were proven significant with Fisher’s Exact test (P<0.001).

Correlations between all questionnaires screening results

Computing correlations between all questionnaire’s screening results yields table 14. All

correlations were positive, this means that all factors were associated in the same direction,

thus all questionnaires largely formulated the same conclusion (cannabis abuser or not) about

most subjects.

Table 14: Correlations computed between all questionnaire outcome results

Correlations

Spearman correlation coefficient

CAGE-AID

>= 1

CAGE-AID

>= 2

SDS ProbCannabis-

DT

single-

question >= 1

single-

question >= 3

CAGE-AID >= 1 1,000 ,364** ,293

** ,601

** ,387

** ,367

**

CAGE-AID >= 2 1,000 ,542** ,449

** ,164 ,265

**

SDS 1,000 ,361** ,050 ,066

ProbCannabis-DT 1,000 ,366** ,382

**

single-question >= 1 1,000 ,796**

single-question >= 3 1,000

**. Correlation is significant at the 0.01 level (2-tailed).

Significant correlations between questionnaire outcomes varied from 0.265 to 0.796. Highest

correlations (>0.6) were seen between the ProbCannabis-DT and CAGE-AID ≥ 1 and

between the single-question ≥ 1 and single-question ≥ 3. Correlations between CAGE-AID ≥2

and SDS were also rather strong (>0,5). This is an important analysis; because it shows that

when a person had a positive score on one questionnaire he probably also had a positive score

on the other questionnaires. Of course this is also what one would expect, knowing that all

questionnaires are designed to identify the same problem.

Lowest correlations were seen between CAGE-AID ≥ 1 and SDS, and between CAGE-AID ≥

2 and single-question ≥ 3. Knowing that the CAGE-AID ≥ 1 indicated 58 subjects as

problematic cannabis users, and SDS only 11, this was easily understood. A big discrepancy

was also seen between CAGE-AID ≥ 2 (16 subjects screened positively) and single-question

≥ 3 (54 subjects screened positively). In conclusion there seemed to be two pairs of

questionnaires that were inter-correlated highly. The CAGE-AID ≥ 1 and ProbCannabis-DT

were very strongly correlated, indicating 58 and 48 subjects as cannabis users respectively.

The SDS and CAGE-AID ≥ 2 indicated 11 and 16 subjects as cannabis users respectively, and


34

were also correlated highly/moderately. These numbers suggested that probably the first two

questionnaires resulted in a positive screening outcome easier than the other two, probably

already measuring less frequent cannabis use than the CAGE-AID ≥2 and SDS. This is

proven by the difference in psychometric properties in our study. CAGE-AID ≥ 1 and

ProbCannabis-DT both had a high sensitivity (1 and 0.87 respectively), while SDS and

CAGE-AID ≥ 2 had a higher specificity (0.91 and 0.9 respectively). A positive CAGE-AID ≥

2 screening result requires one more question answered positively than CAGE-AID ≥ 1,

supposedly indicating more intensive cannabis abuse than only one positive answer.

ICC as a measure of reliability gives an accurate view of the agreement in outcome between

the different questionnaires. It was calculated between the dichotomous outcomes for the

different questionnaires, namely if the questionnaire indicated the particular subject as a

cannabis user or not. A Cronbach’s alpha of 0.778 was calculated, which was a good value for

internal consistency. This means that all questions largely measure the same concept. This is

what one would hope for because they are designed for exactly the same purpose. The Intra-

Class Correlation for Single Measures was 0.305 (CI: 0,206 - 0,416), according to the Fleiss

criteria this would be a low value (43).This means that the inter-rater reliability was not good

but this is quite logical, when we consider that the inter-questionnaire correlations were rather

dispersed, with some questionnaires clustering together. So, ICC between ProbCannabis-DT –

CAGE-AID ≥1 indicated higher inter-rater reliability (Single-Measures correlation of 0,591

(CI: 0,443 - 0,707)), and a little lower between SDS – CAGE-AID ≥2 (Single-Measures

correlation of 0,532 (CI:0,374 - 0,660). All these ICC’s were significant, but none of them

was really high. So, in conclusion, it is seen that the inter-rater reliability between all

questionnaires together was rather low, but this seems largely to be due to the fact that the

results on the CAGE-AID ≥1 and ProbCannabis-DT questionnaire are very similar, but very

different from the CAGE-AID ≥2 and SDS, and vice versa. Calculation of ICC between

ProbCannabis-DT and CAGE-AID ≥1 showed a higher inter-rater reliability, and so does the

ICC between CAGE-AID ≥2 and SDS.

Kappa statistics can be calculated to elaborate the agreement in outcome between the different

questionnaires. Based on the above analysis it is supposed that probably the pairs

ProbCannabis-DT – Cage-AID ≥1 and SDS – Cage-AID ≥2 will yield the best kappa.

Calculating kappa yielded a significant kappa value of 0,588 for the agreement between the

outcome of the ProbCannabis-DT questionnaire and the CAGE-AID screening (≥1) results


35

(P<0.001). Agreement between CAGE-AID ≥2 and SDS yielded a significant kappa of 0,529

(P<0.001). These two coefficients indicated moderate agreement between the two pairs of

questionnaires when following the Landis and Koch interpretation(42). This measure shows

to what extent the final ‘cannabis abuse diagnosis’ based on the questionnaire cut-off is the

same between different questionnaires.


36

DISCUSSION

Recruitment of participants

Main practical problem in this study was the recruitment of psychiatric patients. This

recruitment process was nothing like we had expected it to be, because of the very low

amount of patients actually willing to cooperate in our study. Several reasons are responsible

for this low number of participants. Patients at this emergency unit were often in psychosis,

and very often the treating psychiatrist considered it not safe to go through the entire study

intake procedure at that moment. Because this is an emergency unit patients don’t tend to stay

long after recovering from their psychosis, so they could not be included in a later safer

period. Also some of the patients refused to cooperate. An assistant from the Toxicology

department visited the UPSIE daily, for a period of about 3 weeks, but still it seemed not to be

possible to recruit a sufficient number of patients. If one would try to conduct a similar study,

it would possibly be better not to run the recruitment procedure on an emergency department.

A normal psychiatric ward or a psychiatric department specialized in drug addictions could be

a better choice for a similar study.

In contrary the recruitment of students was conducted without any problems, a lot of students

responded quickly to the flyers and posters and registered online. A lot of these registered

students didn’t show up at the contact sessions and had to be reminded by an extra e-mail or

phone message, but eventually the target of at least 75 participants was easily achieved.

Biomarker results

Rather surprising result in the biomarker analysis would be the low amount of positive hair

samples. Prior to the analysis of data we had expected that the amount of positive results

amongst the hair samples would be higher than amongst the blood samples. This because the

detection time of cannabinoids in serum is a lot shorter than in hair, as mentioned above. We

expected that some subjects that reported drug use would possibly yield negative blood results

but positive hair detection, because their self-reported drug use would date from a few

days/weeks back so cannabinoids would no longer be detectable in serum but only in hair.

Now given the fact that there are only 13 positive screening results in hair and 31 positive

blood samples, this assumption seems to be incorrect. Calculation of Cohen’s Kappa also

proved a moderate agreement between the blood and hair results.

Several reasons are possible for this surprising finding, for example the cut-offs yielded for

hair concentrations could be wrong. It could be that the cut-off concentration should be lower,

so that a higher amount of users would be identified in this sample. The study in which these


37

hair cut-off concentrations were proposed was conducted in a sample of presumed intensive

drug users who lost their drivers’ license or were involved in crimes, maybe not similar to our

sample of students who maybe use less cannabis(51).Authors also declared that these cut-off

concentrations were just propositions and weren’t yet validated. Nevertheless the 0.1 ng/mg

cut-off is also suggested by the Society Of Hair Testing, but not yet validated(37).

Only one sample had a THC hair positive result and a THCCOOH negative serum result. All

other samples with positive hair analysis also had a positive blood result. We used

THCCOOH serum concentrations as “gold standard” for comparing the results of the

ProbCannabis-DT and determination of an optimal cut-off score, because the THCCOOH

concentration cut-off values are yet validated in studies, and the hair cut-off values are not.

Also because the amount of positive THC samples is also very low (13).

Questionnaire results

The question about the cannabis use in the past 12 months gave an overview about the degree

of cannabis abuse in our sample. Answers to this question indicated that 56 subjects used

cannabis at least once a month, this is without the two psychiatric patients who weren’t asked

this question. But only 31 subjects were identified as problematic cannabis abusers in this

sample. So, a lot of subjects reported smoking cannabis regularly, but aren’t identified on the

blood samples. Their low THCCOOH concentrations can probably be explained by the fact

that their last cannabis smoking occurred a few days or weeks before the blood sampling,

knowing that THCCOOH concentrations in serum don’t remain high for a long time. So they

appear not to be identified because of the limit in THCCOOH detection time. In this study

design this can only be assumed, not concluded surely.

Most important results of this study are all concerning the ProbCannabis-DT questionnaire,

evaluation of an optimal cut-off for this questionnaire was the main aim. It was proven that a

cut-off score of one positive answer is the optimal choice for the screening of a sample similar

to ours. It holds a high sensitivity, specificity is slightly lower. These are characteristics one

would prefer for a screening instrument, designed not to yield a lot of false negative results.

Cronbach’s alpha got higher when the first question is left out. This doesn’t mean that this is a

irrelevant question, but it means that actually the question shouldn’t be part of this particular

scale. Statistical analysis showed that the CAGE-AID ≥1 and ProbCannabis-DT

questionnaires yield rather similar screening results, and are inter-correlated highly. The


38

ProbCannabis-DT questionnaire had the highest sensitivity and specificity. The same can be

concluded for the CAGE-AID ≥2 and SDS questionnaires. The single-question showed no

significant results in this sample, and seemed not to be capable of significantly differentiating

between non cannabis users and cannabis abusers.

For CAGE-AID it was proven that there is no statistical difference in answers of cannabis

users and non-users on question 3. So a participant’s answer on question 3 seems not to be

related significantly with the intensity of his cannabis use in this sample, thus one could

dispute the relevance of question 3 in this questionnaire, based on the results of this sample.

In fact the table 5 shows that the 3 heaviest cannabis users responded to this question that they

did not feel guilty about their cannabis use. Calculated sensitivity and specificity differ

somewhat from results of other studies (Brown et al. (15) and Hinkin et al. (16)). The reasons

for these differences can’t be concluded out of this study design.

Evaluation of the single-question yielded no relevant results. No significant differences could

be demonstrated in answering between cannabis users and non-cannabis users, for both cut-

off scores. This is in conflict to the study by Smith et al., en further investigation of this

questionnaire might be necessary in the future.

For the SDS questionnaire a very low sensitivity was achieved in our sample, it was also very

different from results calculated by other studies (Martin et al. (47) and Van der Pol et

al.(48)). The cut-off score at 4 positive answers was perhaps too high in this sample, although

most studies suggest this cut-off (39, 45, 47, 48, 52), others suggest a lower cut-off (13) . If

lower cut-offs would be implemented this could yield a higher sensitivity. CAGE-AID ≥ 2

also had a similar low sensitivity. Both seemed to be unsatisfying for using as a screening

instrument in this sample.

Answering the questionnaires not truthfully by respondents could be a possible bias in our

study design. Therefore all participants were aware that the processing of the results of the

questionnaires and biomarker analyses would be conducted anonymously. This would

encourage a participant to answer all questionnaires honestly, but one can never be sure about

this fact. The ProbCannabis-DT questionnaire will probably be used in general populations in

the future, while in our study a sample was used with participants with a higher a priori

chance of being a cannabis abuser. This could induce spectrum bias where sensitivity and

specificity could be overestimated. Nevertheless this is very dependent on the samples that

will be screened by this questionnaire, and one can not really predict this without already


39

knowing the extent of cannabis abuse in a population. It is also important to state that our

sampling process was designed to include enough actual cannabis abusers, so the prevalence

of cannabis abusers in this sample of students is not equal to the prevalence of cannabis abuse

in the general student population, this would probably be an overestimation.

In conclusion it is correct to state that the ProbCannabis-DT questionnaire is a very good

choice for evaluation of cannabis abuse in a sample. In this sample it actually corresponded

best (of all questionnaires) to the THCCOOH based cannabis user status. As mentioned above

the questionnaire’s main purpose is to indicate subjects that have a problematic cannabis use

problem, for example the DSM criteria of cannabis abuse and dependence. In this study we

could not really identify subjects with a problematic cannabis use problem based on the

THCCOOH measurements. But one can assume that when a subject smokes cannabis very

regularly (THCCOOH ≥ 5 ng/mL), this might correspond to problematic use. So if a patient

has a positive screening result on the ProbCannabis-DT questionnaire one would also have to

take other clinical information into account to be sure that this regular cannabis use can also

be considered as really ‘problematic’. Or the questionnaire could maybe be evaluated in the

future using a clinical abuse or dependence diagnosis as gold standard, instead of the

THCCOOH measurement.

Depending on what target population the ProbCannabis-DT questionnaire will be used for in

the future, assessment of the questionnaire’s psychometric properties could maybe be

conducted in certain other types of samples. Samples where there will be few subjects that use

cannabis intensively will probably be screened effectively when a cut-off of one positive

answer is used, but in some situations a cut-off of 2 positive answers could maybe be

investigated when one would want less false positive outcomes and a higher specificity.


40

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ATTACHMENTS

ATTACHMENT 1: questionnaires (Dutch versions)

Afhankelijkheidsschaal (Severity of Dependence Scale, SDS)

Heb je in de afgelopen 3 maanden…

1. Wel eens gedacht dat je hasj of wietgebruik uit de hand gelopen was?

Nooit of bijna nooit □ 0

Soms □ 1

Vaak □ 2

Altijd of bijna altijd □ 3

2. Je angstig of bezorgd gevoeld bij het vooruitzicht niet te kunnen blowen?


Soms □ 1

Vaak □ 2


3. Je zorgen gemaakt over je hasj of wietgebruik?

Helemaal niet □ 0

Een beetje □ 1

Vrij veel □ 2

Heel veel □ 3

4. Gewenst dat je kon stoppen?


Soms □ 1

Vaak □ 2


5. Hoe moeilijk zou je het gevonden hebben te stoppen of zonder te moeten

doen?


44

Niet moeilijk □ 0

Vrij moeilijk □ 1

Heel moeilijk □ 2

Onmogelijk □ 3

SDS5score /15

De optimale discriminante score die als indicator voor hasj of wietafhankelijkheid

gebruikt wordt, is een SDS5score van 4.

CAGE-AID (“Cut down Annoyed Guilty Eye-opener” - Adapted to Include Drugs)

1. Heeft u wel eens het gevoel gehad te moeten minderen met het gebruik van cannabis?

Nee Ja

2. Raakt u ge rriteerd door opmerkingen van anderen op uw cannabisgebruik?

Nee Ja

3. Heeft u zich wel eens schuldig gevoeld over uw cannabisgebruik?

Nee Ja

4. Heeft u wel eens direct na het opstaan cannabis gebruikt om de zenuwen de baas te worden

of om van een kater af te komen?

Nee Ja

Mogelijk alcohol- en/of drugmisbruik = 1 ‘Ja’ Waarschijnlijk alcohol- en/of drugmisbruik =

2 ≤ ‘Ja’

ProbCannabis-DT

Heb je ooit ondervonden dat je langer dan een week meer cannabis gebruikte dan je van plan

was, of dat je het product langer gebruikte dan je bedoeling was?

Heb je ooit langer dan een week een behoefte gevoeld om je gebruik van cannabis te

verminderen of heb je ooit langer dan een week – zonder succes – met cannabis willen

stoppen?


45

Heb je ooit langer dan een week sociale activiteiten, hobby’s of werk verminderd of gestaakt

vanwege je gebruik van cannabis?

Ben je ooit langer dan een week cannabis blijven gebruiken terwijl je te kampen had met een

psychisch of lichamelijk probleem veroorzaakt of verergerd door het gebruik van cannabis?

Heb je ooit langer dan een week je verplichtingen jegens werk of studie niet kunnen nakomen

door het gebruik van cannabis?

Ben je ooit langer dan een week cannabis blijven gebruiken, terwijl je te kampen had met

problemen in de relationele sfeer veroorzaakt door of verergerd door het gebruik van

cannabis?

Op deze vragen kan men ja/nee/geen antwoord antwoorden.

Meer vragen met ‘ja’ beantwoorden zou gepaard gaan met een frequenter cannabisgebruik.

pilot study on biological markers of chronic cannabis...

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