A q for our resident research assistant...

[syk613]

Here is the next bit of info I found, from the link I PM you, keyword search THC, its not the global effects but the molecular effects, while not as educational for the laymen it is a lot more informative for others, and there is a link to the full article from the database link.

 

Tetrahydrocannabinol-induced neurotoxicity depends on CB1 receptor-mediated c-Jun N-terminal kinase activation in cultured cortical neurons.

 

Downer EJ, Fogarty MP, Campbell VA.

 

Department of Physiology, Trinity College, Trinity College Institute of Neuroscience, Dublin 2, Ireland.

 

Delta9-Tetrahydrocannabinol (THC), the main psychoactive ingredient of marijuana, induces apoptosis in cultured cortical neurons. THC exerts its apoptotic effects in cortical neurons by binding to the CB1 cannabinoid receptor. The CB1 receptor has been shown to couple to the stress-activated protein kinase, c-Jun N-terminal kinase (JNK). However, the involvement of specific JNK isoforms in the neurotoxic properties of THC remains to be established. The present study involved treatment of rat cultured cortical neurons with THC (0.005-50 microM), and combinations of THC with the CB1 receptor antagonist, AM 251 (10 microM) and pertussis toxin (PTX; 200 ng ml-1). Antisense oligonucleotides (AS) were used to deplete neurons of JNK1 and JNK2 in order to elucidate their respective roles in THC signalling. Here we report that THC induces the activation of JNK via the CB1 receptor and its associated G-protein, Gi/o. Treatment of cultured cortical neurons with THC resulted in a differential timeframe of activation of the JNK1 and JNK2 isoforms. Use of specific JNK1 and JNK2 AS identified activation of caspase-3 and DNA fragmentation as downstream consequences of JNK1 and JNK2 activation. The results from this study demonstrate that activation of the CB1 receptor induces JNK and caspase-3 activation, an increase in Bax expression and DNA fragmentation. The data demonstrate that the activation of both JNK1 and JNK2 isoforms is central to the THC-induced activation of the apoptotic pathway in cortical neurons.

 

PMID: 14522843 [PubMed - in process]

[syk613]

And another: These aren't the exact answer you were looking for but I will keep looking, have you had a look?

 

Effects of the synthetic cannabinoid nabilone on spatial learning and hippocampal neurotransmission.

 

Diana G, Malloni M, Pieri M.

 

Laboratory of Pharmacology, Istituto Superiore di Sanita', Viale Regina Elena 299, 00161 Roma, Italy. giovanni.diana@iss.it

 

Cannabinoids, the active components of marijuana, affect memory and hippocampal neurotransmission. It has been claimed that nabilone, a synthetic cannabinoid endowed with antiemetic properties, has a peculiar profile of actions. We studied the effects of the drug on spatial learning and in vitro hippocampal CA1 electrophysiology in the rat. Nabilone (0.1, 0.5, and 1.0 mg/kg ip) does not impair place learning in a water maze task, whereas Delta(8)-tetrahydrocannabinol (Delta(8)-THC) disrupts this function. At concentrations ranging from 1 nM to 10 microM nabilone does not influence basal glutamatergic neurotransmission, which is decreased by Delta(8)-THC. Although cannabinoids have been consistently reported to affect synaptic plasticity, nabilone 1 microM does not change paired-pulse facilitation, long-term potentiation and the magnitude of long-term depression. However, the time course of the latter phenomenon is significantly changed by the drug, the depression being lower than in control experiments from 7 to 35 min postinduction. Altogether, our data indicate that there might be differences in the effects of agonists for central cannabinoid receptors, which could help to understand the pharmacology of this class of molecules. The results also suggest that amnesia induced by cannabinoids be possibly related to their effects on hippocampal neurotransmission. The study supports the use of nabilone in conditions the course of which is complicated by cognitive impairment.

 

PMID: 12895676 [PubMed - in process

[Guest Wilderbud]

Haha, this all might make sense later.

 

Damn leaf oil is good.

[syk613]

Here is some OZ info on drugs and driving , It is the full text but after reading the sites conditions of use I don't think this kind of reproduction is allowed. Maybe a mod could clear up the ramifications of these kinds of posts for me, thanks.

 

 

The incidence of drugs in drivers killed in Australian road traffic crashes

 

Olaf H. Drummer, , a, Jim Gerostamoulosa, Helen Batzirisa, Mark Chua, John R. M. Caplehornb, Michael D. Robertsonc and Philip Swannd

 

a Department of Forensic Medicine, Victorian Institute of Forensic Medicine, Monash University, 57-83 Kavanagh Street, Southbank, Vic., Australia

b Department of Public Health and Community Medicine, University of Sydney, Sydney, Australia

c Department of Forensic Medicine, Monash University, 57-83 Kavanagh Street, Southbank, Vic., Australia

d Road Safety Section, VicRoads, Denmark St., Kew 3101, Australia

 

Received 20 May 2002; accepted 26 March 2003. ; Available online 13 May 2003.

 

 

 

 

Abstract

The incidence of alcohol and drugs in fatally injured drivers were determined in three Australian states; Victoria (VIC), New South Wales (NSW) and Western Australia (WA) for the period of 1990–1999. A total of 3398 driver fatalities were investigated which included 2609 car drivers, 650 motorcyclists and 139 truck drivers. Alcohol at or over 0.05 g/100 ml (%) was present in 29.1% of all drivers. The highest prevalence was in car drivers (30.3%) and the lowest in truckers (8.6%). WA had the highest rate of alcohol presence of the three states (35.8%). Almost 10% of the cases involved both alcohol and drugs. Drugs (other than alcohol) were present in 26.7% of cases and psychotropic drugs in 23.5%. These drugs comprised cannabis (13.5%), opioids (4.9%), stimulants (4.1%), benzodiazepines (4.1%) and other psychotropic drugs (2.7%). 8.5% of all drivers tested positive for 9-tetrahydrocannabinol (THC) and the balance of cannabis positive drivers were positive to only the 11-nor-9-tetrahydrocannabinol-9-carboxylic acid (carboxy-THC) metabolite. The range of THC blood concentrations in drivers was 0.1–228 ng/ml, with a median of 9 ng/ml. Opioids consisted mainly of morphine (n=84), codeine (n=89) and methadone (n=33), while stimulants consisted mainly of methamphetamine (n=51), MDMA (n=6), cocaine (n=5), and the ephedrines (n=61). The prevalence of drugs increased over the decade, particularly cannabis and opioids, while alcohol decreased. Cannabis had a larger prevalence in motorcyclists (22.2%), whereas stimulants had a much larger presence in truckers (23%).

 

Author Keywords: Drugs; Alcohol; Driving; Australia; Forensic toxicology

 

 

Article Outline

1. Introduction

2. Materials and methods

2.1. Study population

2.2. Drug analysis

2.3. Categorization of drugs

3. Results

3.1. Characteristics of drivers

3.2. Prevalence of alcohol

3.3. Prevalence of drugs

3.4. Prevalence of alcohol and drugs by year

4. Discussion

Acknowledgements

References

 

 

1. Introduction

There is increasing interest throughout the world concerning the incidence of drugs in driving and in their contribution to road trauma specifically. The most common drugs (other than alcohol) found in fatally injured drivers have been cannabis, benzodiazepines, amphetamine-like stimulants and opioids. A number of reports have detailed the incidence of drugs in fatally injured drivers around the world [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14]. A number of jurisdictions have reported increase in the proportion of drivers using drugs [15, 16, 17 and 18]. Preliminary data has also suggested similar trends in Australia [19 and 20].

 

Injured drivers also show a high prevalence of drugs. Cannabinoids were found in 13.9% of French injured drivers, while opioids, cocaine and amphetamines were found in 10.5, 1.0 and 1.4%, respectively [21]. Impairing drugs were found in 32% of injured drivers presented to an urban emergency center in Colorado in which cannabis was the most frequent detected drug (17%), followed by alcohol (14%) [22]. In a South Australian study on injured drivers, cannabis was found in 10.8%, benzodiazepines in 2.7% and stimulants in 1.0% [23].

 

There are significant variations in the type and frequency of detected drugs between jurisdictions. In New York, 20% of drivers killed in traffic crashes were positive to cocaine amongst drivers in the 16–45-year group [24]. A high incidence of cocaine use in impaired drivers was also observed in The Netherlands (33%). Benzodiazepines (33%) and opioids (19%) were also commonly seen [25]. In France, cannabinoids (13.9%) and opioids (10.5%) were most frequently drugs in drivers admitted to hospital as a result of their injuries [26]. Cocaine (1.0%) and amphetamines (1.4%) were infrequently detected. In contrast, Norwegian drugged drivers show a high incidence of benzodiazepines use of about 31% followed by THC (30%) and amphetamine (28%) [27]. In Scotland, benzodiazepines tend to be even more dominant with over 85% of fatally injured drivers testing positive to benzodiazepines [17].

 

The most controversial aspect of the involvement of drugs in accident causation is that of cannabis. Previous reports of Australian drivers have relied on coroners' records in which forensic laboratories only measured the inactive form of cannabis (carboxy-9-THC). Following the use of cannabis, this species is present in blood for up to several days and therefore its presence cannot be used to imply recent use of cannabis, and therefore likely impairment. Since 1998, the Australian forensic laboratories used in the study have measured THC routinely in fatal road crashes.

 

Australia is a modern community with a large and evolved network of roads in both urban and rural settings, and with a long history of a focus on road safety. The national legal limit for blood alcohol concentration (BAC) is 0.05%. The blood alcohol limit for commercial drivers and non-full license holders is zero. The population of Australia approaches 20 million and the states of Victoria (VIC), New South Wales (NSW) and Western Australia (WA) represent some 69% of Australian drivers.

 

The purpose of this study was to establish the incidence and extent to which drugs contributed to fatal motor vehicle accidents. This study presents the results of a 10-year research project involving a number of collaborating centers over three Australian states. In total 3398 drivers were included in this study. Due to the large amount of data, the results of the culpability analyses of alcohol and the various drug types have been described elsewhere [28].

 

2. Materials and methods

2.1. Study population

The study population consisted of drivers killed in motor vehicle accidents in the three Australian states of VIC, NSW and WA. In VIC these data were obtained from records kept at the Victorian Institute of Forensic Medicine and the State Coroner's Office at Southbank. Drivers were identified on the basis of records obtained from the Victorian Institute of Forensic Medicine. These cases included Victorian drivers killed in road crashes from 1990 to 1999.

 

In NSW, Coroner's case numbers and names of persons killed in motor vehicle accidents between January 1991 and March 1993, and from 1995 to 1999 were obtained from records kept at the Coroners' Courts in Glebe and at Westmead, Sydney. Drivers were identified on the basis of records obtained from the State Coroner's Office and included regional cases, except for cases accessed in 1995 and 1996 which only included the wider Sydney district.

 

In WA, information on drivers killed in motor vehicle crashes between 1990 and 1992 and from 1995 to end of 1999 was obtained from records kept at the Perth Coroner's Office. Drivers were identified on the basis of records obtained from the toxicology section of the Chemistry Centre. These cases included all Western Australian drivers killed in road crashes in these periods. Ethics permission to conduct these WA studies was obtained from the Perth Coroners Office.

 

2.2. Drug analysis

Most analyses (>90%) were conducted on blood taken at autopsy. In the remainder of cases blood specimens were taken in hospital prior to the death of the victim. In all three states toxicology testing on these deaths was similar and included testing for alcohol, drugs of abuse (amphetamines and related stimulants, benzodiazepines, cannabinoids, cocaine, opioids) and included a screen for neutral and basic psychotropic drugs. Other than confirmation of THC, there was no change in the overall pattern or extent of drug testing over this period of time.

 

All drugs detected were confirmed and quantified by appropriate techniques. Cannabis testing was based on the presence of 9-tetrahydrocannabinol (THC) and/or the metabolite 11-nor-9-tetrahydrocannabinol-9-carboxylic acid (carboxy-THC). Since circa 1998 all three state laboratories conducted routine confirmations of cannabinoid presence by measurement of THC. Prior to this period, with the exception of NSW, not all cannabinoids detections were confirmed for THC, rather the metabolite carboxy-THC. The detection and confirmation limits for target drugs were comparable between the laboratories.

 

All the Australian laboratories took part in proficiency trials during this period, and all have been accredited by the national accreditation body in Australia (National Accreditation Testing Authority, NATA) in Forensic Science (toxicology). BAC was expressed as grams alcohol per 100 ml blood (%).

 

Drugs administered to the deceased as part of medical treatment were not included in the analysis. In most cases involving hospitalization, cases were excluded from the study since often toxicology testing had either not been conducted at all, or was not conducted on relevant antemortem specimens.

 

2.3. Categorization of drugs

To simplify the statistical analysis of drug-effects, drugs were categorized into drug families. All substances acting as stimulants were placed into the "amphetamine" group. This included amphetamine, methamphetamine, methylenedioxymethamphetamine, ephedrine, pseudoephedrine, phentermine and cocaine. Benzodiazepine drugs were placed into a single group. The "opioid" group included morphine, 6-acetylmorphine, codeine, methadone, propoxyphene and meperidine (pethidine). The cannabinoid group included cases found to contain either THC or carboxy-THC. All other impairing drugs were placed into the "other" drug group. This included sedating antihistamines, phenothiazine antipsychotics, tricyclic antidepressants, the anticonvulsants phenytoin and carbamazepine. Any non-impairing drug was placed into the "miscellaneous" group. This included acetaminophen, salicylate, quinine, theophylline, serotonin reuptake inhibitors etc.

 

Those cases classified as having died from natural causes or as a result of suicide, were excluded from the analysis. Cases were also excluded if there were long delays (>4 h) from the crash to their death or a relevant specimen was not obtained in hospital within this time frame.

 

3. Results

3.1. Characteristics of drivers

The total number of drivers included in the study was 3398. On average this represented over 85% of all drivers killed in this period. This included Victorian drivers (47.4% of study population), NSW (30.3%) and WA (22.3%). The breakdown of crash types (single and multiple vehicle crashes) and type of vehicle is shown in Table 1. Car drivers, motorcyclists and truckers represented 76.7, 19.1, and 4.1% of the study group, respectively. Single vehicle crashes represented 50.7% of the cases.

 

 

 

Table 1. Breakdown of case type by state

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The gender distribution varied with vehicle type. The proportion of female car drivers, motorcyclists and truckers was 26.8, 2.0 and 0.7%, respectively. The proportion of females killed was highest in the >60 years age group (29.5%). The corresponding mean age and age range (in parentheses) in these three vehicle types was 38.5 (13–92), 28.8 (12–79) and 37.7 (17–68) years. Truckers were represented most in the 31–40 age group, while the highest proportion of motorcyclists killed were in the 22–30 years age group (Table 2).

 

 

 

Table 2. Breakdown of drivers by age, gender and driver type

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3.2. Prevalence of alcohol

The prevalence of alcohol in all driver groups at 0.05% was 29.1% (Table 3) with a median BAC of 0.17%. The highest prevalence was in car drivers (30.3%), followed closely by motorcyclists (28.9%) and with only 8.6% of truckers positive to alcohol at or over the legal limit (Table 4). A further 3.2–5.2% of drivers were below 0.05%. 2.1% of drivers had a BAC of 0.05–0.079%, and 3.4% had a BAC of 0.08–0.099%. 2.9% of truckers, 10.5% of motorcyclists and 9.8% of drivers contained both alcohol and a drug.

 

 

 

Table 3. Prevalence of alcohol and drugs in all drivers and by crash type

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Table 4. Prevalence of alcohol and drugs in drivers by type

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WA had the highest prevalence of alcohol 0.05% in drivers (35.8%), and VIC the lowest (26.2%), whilst NSW drivers had an incidence of 28.8% (Fig. 1). Females were less likely to use alcohol than males (19.2% versus 36.4%).

 

 

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Fig. 1. Incidences of alcohol in car, truck and motor cycle drivers by blood alcohol category (1: 0.010–0.049%, 2: 0.050–0.099%, 3: 0.100–0.149%, 4: 0.150–0.199%, 5: 0.200–0.249%, 6: 0.250%).

 

 

 

For automobile drivers and truckers the highest proportion of alcohol positive drivers occurred in the 22–30 age group (46.8 and 16.7%, respectively). In motorcyclists the highest proportion occurred in the 31–40 age group (42.7%).

 

3.3. Prevalence of drugs

For the whole data set impairing drugs (other than alcohol) were present in 23.5% of drivers. This was made up of cannabinoids (13.5% of drivers), opioids (4.9%), stimulants and benzodiazepines (each 4.1%) (Table 3). The incidence of miscellaneous impairing drugs was 2.7%. The majority of all drug-positive cases involved more than one impairing substance. The largest group was combinations with alcohol (9.3%). Other combinations included cannabis with opioids (1.1%), amphetamines (0.8%) and benzodiazepines (0.7%), and amphetamine combinations with opioids (0.05%) and benzodiazepines (0.03%). Benzodiazepines with opioids represented 0.7% of the driving cohort. 0.7% of cases involved three or more impairing drugs.

 

Table 3 also shows the prevalences of alcohol and drugs in the single and multiple vehicle crashes. The incidences of chemical substances were higher in single vehicle crashes for alcohol (44.1% versus 13.7%) and cannabis (15.9% versus 11.1%). On the other hand, opioids (5.8% versus 4.1%) and miscellaneous drugs (7.4% versus 5.1%) were more frequently detected in multiple vehicle crashes. There was no appreciable difference in the two crash types for stimulants and benzodiazepines.

 

The incidence of THC in driver fatalities for the years it was tested in cases was 8.5%. This included 58 cases of THC-only (no other drugs or alcohol detected), 43 cases with alcohol and 20 cases with other impairing drugs. The range of THC concentrations detected in blood in all positive cases was 0.1–228 ng/ml (Table 5). The median blood concentration was 10 ng/ml in the THC-only group, 9 ng/ml in the THC plus alcohol group and 6 ng/ml in the THC plus other drugs group. In the THC-only group (n=58 cases), 49 drivers (3.4%) were 5 ng/ml or higher. These included three females, one trucker, 13 motorcyclists and 35 car drivers.

 

 

 

Table 5. Prevalence of alcohol and drugs in drivers by year

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The most common stimulants were pseudoephedrine/ephedrine (n=61), methamphetamine (n=51), MDMA (n=6), and phentermine (n=12) (Table 5). Cocaine or the metabolite benzoylecgonine was detected in five drivers (4 cars, 1 motor cyclists, all male). It was of interest that stimulants were present in 23% of all truckers. The most common opioids were morphine (n=84), codeine (n=89) and methadone (n=33), while the most common benzodiazepines were diazepam (n=75), temazepam (n=27) and oxazepam (n=28). A number of cases for each major drug exceeded the upper limit of concentrations normally associated with therapeutic use (Table 6).

 

 

 

Table 6. Prevalences and blood concentrations of selected drugs and drug combinations

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The prevalence of cannabis was highest in the 22–30 age group for both motor cyclists (26.8%), and drivers of automobiles (19.7%). The prevalences in the over 50 age groups were zero and 0.3%, respectively. Stimulants were present in the greatest proportion in the 22–30 age group of truckers (44.4%), but only 5.4% in the corresponding age group of automobile drivers.

 

3.4. Prevalence of alcohol and drugs by year

The data was grouped into 3-year blocks to establish if any trends in the prevalence of alcohol and drugs occurred in the 10-year period (Table 6). The prevalence of alcohol dropped from 33.0 in 1990–1993 to just under or just over 27% in the following 6 years to 1999. In contrast, the prevalence of drugs increased in all 3-year blocks. For impairing drugs the increase was from 20.0% (1990–1993) to 26.7% (1997–1999). This increase was most marked for cannabis (10.9–15.6%) and for opioids (3.4–6.6%). However, smaller increases occurred for all other key drug groups ( Table 6). The overall proportion of drug-free drivers involved in fatalities dropped steadily from 50.9% (1990–1993) to 48.7% (1997–1999).

 

4. Discussion

This 10-year multi-center study of drug-involved driving has shown a substantial incidence of drugs other than alcohol. In the last 3 years, over one-quarter of all drivers had used an impairing drug, an overall increase of 6.7% compared to the 1990–1993. The increasing prevalence of drug presence in fatally injured drivers was associated with a decline in the involvement of alcohol. Overall, the proportion of drivers with a BAC of 0.05% or higher fell over 8% between 1990 and 1999, and was similar in prevalence to drug-positive drivers in 1999. It was of interest that few drivers were positive to alcohol below 0.05% (3.7% of drivers), and even fewer between 0.05 and 0.079% (2.1%). This is because most drivers had a BAC over 0.10% (25.7% of all drivers).

 

The prevalence of drugs in Australian fatal crashes is similar to previously published studies in other countries (Table 7). As in most countries, cannabis was the most frequently detected drug. Benzodiazepines, amphetamine-like stimulants and opioids represented the next class of frequently detected drugs. However, one notable difference is the absence of any significant presence of cocaine in fatally injured drivers in Australia. This is not unexpected since relatively few coroners' cases involve cocaine due to its relative scarcity (OH Drummer, personal communication).

 

 

 

Table 7. Selected summaries of the incidence of drugs in fatally injured drivers

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Stimulants generally represent a diverse group of drugs and make up a reasonable proportion of drug detected in fatally injured drivers. These include amphetamines and ecstasy (MDMA), cocaine as well as the ephedrines and the anorexiants (e.g. phentermine). The greatest use of these drugs was by longer-distance drivers (e.g. truckers). The finding that stimulants were detected in 23% of truckers is in accord with other studies showing a relatively high use of stimulants by this group [9 and 29].

 

The largest increase in drug detection was in the cannabinoids and opioid groups. In Australia, both drug types have shown an increasing prevalence. National household surveys show increases in current and lifetime use of many of these drugs, particularly cannabis and amphetamines [30]. The markedly rising heroin death toll in Victoria and other states attests to the rising prevalence of the use of opioids. This is reflected by the relatively high number of cases using heroin or morphine (often only detected as morphine in blood analyses), and the relatively high number of methadone cases. There is, however, no direct evidence that methadone itself causes motor vehicle crashes including those cases using diverted methadone [31 and 32].

 

THC was detected in 8.5% of cases in which it was measured. This represents a THC confirmation rate of about 52%, i.e. THC was detected in just over half of the cases in which carboxy-THC was identified. It is the THC-positive drivers who are likely to have recently used cannabis and to have been impaired at the time of the crash [33 and 34]. Analysis of these cases by responsibility analyses shows THC-positive drivers to have a statistically higher culpability rate than drug-free drivers [28].

 

It was of interest that cannabis was more frequently detected in single vehicle crashes than in multiple vehicle crashes (16% versus 11%). This was more true for alcohol in which the incidence was over double that of single vehicle crashes. The difference for other drugs was less dramatic, although opioids and miscellaneous drugs showed a slightly greater prevalence in multiple vehicle crashes.

 

The selection of cases is unlikely to have introduced significant bias as this was done by independent persons, the clerk of court or another official. However, the exclusion of many cases who survived for a time in hospital may have tended to exclude more young drivers. This may have resulted in a slight under-estimation of the prevalence of cannabis/THC and illicit drugs among fatally injured drivers. Any under-estimation is unlikely to have affected year-to-year or state-by-state comparisons.

 

In this study, 65% of opioid-positive drivers were using other drugs, predominantly benzodiazepines (34%) and cannabis (24%). The additional use of other impairing drugs is likely to have increased the drivers' impairment and the likelihood of significant driver errors compared with drivers who used only opioids.

 

For some drugs some increases in blood concentration may have occurred due to postmortem redistribution [35, 36 and 37]. This applies particularly to methamphetamine and to methadone, but other drugs are likely to have some alteration in concentration due to postmortem artifacts. However, even allowing for possible postmortem changes many of the cases had drugs detected at concentrations that reflected misuse of the drug.

 

The results of the present study strongly suggest the problem posed by drug-affected drivers is much greater than that suggested by the number of prosecutions for drug-impaired driving. Indeed, standard field sobriety tests or medical evaluations tend to only detect drivers with substantial clinical impairment. As responsibility analyses suggest impairing drug use contribute to road trauma, there is a need to recognize that recreational drugs are a likely significant cause of road trauma, and therefore there is a need to provide effective counter-measures to reduce the use of recreational drugs by drivers.

 

 

Acknowledgements

We thank the staff of the Victorian Institute of Forensic Medicine for their assistance in these investigations. We also thank the toxicologists in each state for their support and assistance, particularly Mr. Robert Hansson (WA) and Mr. Allan Hodda (NSW), as well as coroners, registrars and clerical assistants at the respective coroners' courts. We acknowledge the financial assistance of VicRoads for financing many of these studies, as well as AustRoads and the NSW roads and traffic authority.

 

 

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22. S.R. Lowenstein and J. Koziol-Mclain, Drugs and traffic crash responsibility: a study of injured motorists in Colorado. J. Trauma 50 (2001), pp. 313–320. Abstract-MEDLINE | Abstract-EMBASE | Full Text via CrossRef

 

23. C. Hunter, R. Lokan, M. Longo, J. White, M. White, The prevalence and role of alcohol, cannabinoids, benzodiazepines and stimulants in non-fatal crashes, Department for Administrative and Information Services, Adelaide, South Australia, 1998.

 

24. P.M. Marzuk, K. Tardiff, A.C. Leon, M. Stajic, E.B. Morgan and J.J. Mann, Prevalence of recent cocaine use among motor vehicle fatalities in New York city. JAMA 263 (1990), pp. 250–256. Abstract-EMBASE | Abstract-MEDLINE

 

25. B.E. Smink, B. Ruiter, K.J. Lusthof and P.G.M. Zweipfenning, Driving under the influence of alcohol and/or drugs in the Netherlands 1995–1998 in view of the German and Belgium legislation. Forensic Sci. Int. 120 (2001), pp. 195–203. SummaryPlus | Full Text + Links | PDF (199 K)

 

26. P. Marquet, P.A. Delpla, S. Kerguelen, J. Bremond, F. Facy, M. Garnier et al., Prevalence of drugs of abuse in urine of drivers involved in road accidents in France: a collaborative study. J. Forensic Sci. 43 (1998), pp. 806–811. Abstract-EMBASE | Abstract-BIOTECHNOBASE | Abstract-MEDLINE

 

27. S. Skurtveit, B. Abotnes and A.S. Christophersen, Drugged drivers in Norway with benzodiazepine detections. Forensic Sci. Int. 125 (2002), pp. 75–82. SummaryPlus | Full Text + Links | PDF (287 K)

 

28. O.H. Drummer, J. Gerostamoulos, M. Chu, JRN. Caplehorn, M.D. Robertson, P. Swann, The involvement of drugs in drivers of motor vehicles killed in Australian road traffic crashes, Accident Analysis and Prevention, in press.

 

29. N.A. Mabbott, L.R. Hartley, Patterns of stimulant drug use in the WA transport industry, in: Proceedings of the Third International Conference on Fatigue and Transportation, Fremantle, 1998, 15 pp.

 

30. Statistics on drug use in Australia, 1998, Australian Institute of Health and Welfare, Canberra, 2000.

 

31. J.R.N. Caplehorn and O.H. Drummer, Fatal methadone toxicity: signs and circumstances, and the role of benzodiazepines. Aust. N. Z. J. Public Health 26 (2002), pp. 358–363. Abstract-MEDLINE

 

32. J.R. Caplehorn and O.H. Drummer, Mortality associated with New South Wales methadone programs in 1994: lives lost and saved. Med. J. Aust. 170 (1999), pp. 104–109. Abstract-EMBASE | Abstract-MEDLINE

 

33. M.A. Huestis, J.E. Henningfield and E.J. Cone, Blood cannabinoids. I. Absorption of THC and formation of 11-OH-THC and THCCOOH during and after smoking marijuana. J. Anal. Toxicol. 16 (1992), pp. 276–282. Abstract-EMBASE | Abstract-MEDLINE

 

34. M.A. Huestis, J.E. Henningfield and E.J. Cone, Blood cannabinoids. II. Models for the prediction of time of marijuana exposure from plasma concentrations of delta 9-tetrahydrocannabinol (THC) and 11-nor-9-carboxy-delta 9-tetrahydrocannabinol (THCCOOH). J. Anal. Toxicol. 16 (1992), pp. 283–290. Abstract-EMBASE | Abstract-MEDLINE

 

35. B. Levine, S.C. Wu, A. Dixon and J.E. Smialek, Site dependence of postmortem blood methadone concentrations. Am. J. Forensic Med. Pathol. 16 (1995), pp. 97–100. Abstract-MEDLINE | Abstract-EMBASE

 

36. R.W. Prouty and W.H. Anderson, The forensic science implications of site and temporal influences on postmortem blood-drug concentrations. J. Forensic Sci. 35 (1990), pp. 243–270. Abstract-MEDLINE | Abstract-EMBASE

 

37. F.E. Barnhart, J.R. Fogacci and D.W. Reed, Methamphetamine––a study of postmortem redistribution. J. Anal. Toxicol. 23 (1999), pp. 69–70. Abstract-Elsevier BIOBASE | Abstract-MEDLINE | Abstract-EMBASE

 

38. B.K. Logan and E.W. Schwilke, Drug and alcohol use in fatally injured drivers in Washington State. J. Forensic Sci. 41 (1996), pp. 505–510. Abstract-MEDLINE | Abstract-EMBASE

 

 

Corresponding author. Tel.: +61-3-9684-4334; fax: +61-3-9682-7353.

[sols]

g'day; have a look here.... http://www.cleartest.com/

 

maybe cannabis helped man get to, and walk on the moon!!!

did you know.....Dr Carl Sagen was a cannabis user? he was regular user during the 1st space travel experiments,if not for Dr Carl Sagen, man may not have walked on moon. he was 1 of the scientists involved with the project at the time.

 

so to answer the question? i do not believe that cannabis use contributes to lower Intelligence Quotiant

[Guest Wilderbud]

Another myth is the memory loss. Its not marijuana - people just start forgetting things because they dont excercise their brains IMHO. I have great memory [short and long] and its better than most non-smokers.

 

I think my alcoholism when I was 18-21 screwed with my memory a lot but since I stopped drinking I have regained my brain power heh.

 

How can you blame the drug when other factors are almost always involved? Blah.

 

Eddy McGuire should have Boozers V Stoners on the show sometime but itd probably end up turning into a Stoner blood-bath as soon as people started laughing at the Boozers.

Edited November 26, 2003 by Wilderbud

[Luke Skywalker]

Short term mem loss in heavy users is a fact mate, sorry, there are more than enough studies which show this link. It's okay tho, as the effect is only temporary, and not a permanent effect at all like alcohol (which actually destroys brain cells) or some other drugs.

 

Brain Cells don't grow back, so you can get pissed and kill em, or get stoned, in which case they'll just forget where they put their keys for a while, . I know which I prefer. ::

[syk613]

something on IQ

 

Title Current and former marijuana use: preliminary findings of a longitudinal study of effects on IQ in young adults

 

 

© 2002 Canadian Medical Association; Association médicale canadienne

 

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Volume 166(7) 2 April 2002 pp 887-891

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Current and former marijuana use: preliminary findings of a longitudinal study of effects on IQ in young adults

[Research]

Fried, Peter; Watkinson, Barbara; James, Deborah; Gray, Robert

 

From the Department of Psychology, Carleton University, Ottawa, Ont.

This article has been peer reviewed.

Contributors: Peter Fried has been the Director of Ottawa Prenatal Prospective Study since its inception. He designed the protocol for the study, as well as the questionnaire that was administered to the subjects. He also made a substantial contribution to the writing and editing of the manuscript. Barbara Watkinson was the primary statistical analyst and made a substantial contribution to the writing and editing of the manuscript. Deborah James assisted in the analyses and made a substantial contribution to the writing and editing of the manuscript. Robert Gray was in charge of data management and extrapolated the data reported in this manuscript. He also assisted in writing the manuscript.

Acknowledgements: This work was supported by a grant to Peter Fried from the National Institute on Drug Abuse, Washington, DC. The authors thank Heather Linttell for her testing of the subjects over the past 15 years and all the families who have participated in the Ottawa Prenatal Prospective Study for the past 2 decades. The urinalysis was carried out under the direction of Dr. Sherry Perkins, Head, Division of Biochemistry, Ottawa Hospital, Ottawa.

Competing interests: None declared.

Correspondence to: Dr. P.A. Fried, Department of Psychology, Carleton University, 1125 Colonel By Drive, Ottawa ON K1S 5B6; fax 613 520-3667; peter_fried@carleton.ca

 

 

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Abstract

Background: Assessing marijuana's impact on intelligence quotient (IQ) has been hampered by a lack of evaluation of subjects before they begin to use this substance. Using data from a group of young people whom we have been following since birth, we examined IQ scores before, during and after cessation of regular marijuana use to determine any impact of the drug on this measure of cognitive function.

 

Methods: We determined marijuana use for seventy 17- to 20-year-olds through self-reporting and urinalysis. IQ difference scores were calculated by subtracting each person's IQ score at 9–12 years (before initiation of drug use) from his or her score at 17–20 years. We then compared the difference in IQ scores of current heavy users (at least 5 joints per week), current light users (less than 5 joints per week), former users (who had not smoked regularly for at least 3 months) and non-users (who never smoked more than once per week and no smoking in the past two weeks).

 

Results: Current marijuana use was significantly correlated (p < 0.05) in a dose- related fashion with a decline in IQ over the ages studied. The comparison of the IQ difference scores showed an average decrease of 4.1 points in current heavy users (p < 0.05) compared to gains in IQ points for light current users (5.8), former users (3.5) and non-users (2.6).

 

Interpretation: Current marijuana use had a negative effect on global IQ score only in subjects who smoked 5 or more joints per week. A negative effect was not observed among subjects who had previously been heavy users but were no longer using the substance. We conclude that marijuana does not have a long-term negative impact on global intelligence. Whether the absence of a residual marijuana effect would also be evident in more specific cognitive domains such as memory and attention remains to be ascertained.

 

 

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Marijuana produces well-documented, acute cognitive changes that last for several hours after the drug has been ingested.1,2,3 Whether it produces cognitive dysfunction beyond this period of acute intoxication is much more difficult to establish. Approaches to investigating long-lasting effects include clinical assessment of long-term users,4,5,6 observations of subcultures in countries where long-term daily use of cannabis has been the cultural norm for decades 7,8,9 and marijuana administration studies in which subjects with a history of use ranging from infrequent to extensive are given the drug in controlled laboratory settings after various periods of abstinence.10,11,12 As discussed in several reviews of the literature,1,13,14 the findings have been equivocal.

 

Most studies that examined heavy marijuana users for possible cognitive dysfunction lasting beyond the acute intoxication period assessed subjects after an abstinence period of only a day or two.10,12,15,16 The fact that cannabinoid metabolites have been detected in the urine of long-term marijuana users after weeks or even months of abstinence 17,18,19 compromises the interpretation of these studies. To account for potential pre-existing differences between users and non-users, studies have typically matched the comparison group with the user group in terms of non-marijuana variables.6,20 Suggestions for improving study designs 13,14 have emphasized both the need for comparison groups to be as similar as possible to the drug-using group and the need for a prolonged abstinence period. The most desirable procedure would involve a longitudinal, prospective design in which cognitive measures were available for all non-using and using subjects before and after marijuana consumption had been initiated by the users.15

 

The Ottawa Prenatal Prospective Study (OPPS), underway since 1978, satisfies these criteria. This study permits both within-subject and between-subject comparisons among relatively low-risk non-users and users before, during and after quitting regular marijuana use. The primary objective of the OPPS is the neuropsychologic assessment of children exposed prenatally to marijuana or cigarettes. Women who used and did not use marijuana and cigarettes volunteered to participate during their pregnancy, and their children, now between the ages of 17 and 20 years, have been assessed since birth. Details of the recruitment of the largely middle-class families, the assessment procedures and the findings for the children from birth to adolescence have been summarized elsewhere.21,22

 

The objectives of the current study were as follows: to determine if current, regular marijuana use is predictive of decline in IQ from pre-usage levels, to determine if a differential effect on IQ occurs with heavy versus light current, regular marijuana use, and to determine if any IQ effects persist after subjects cease using marijuana for at least 3 months.

 

Methods

A potential pool of 74 young adults with urinalysis results, self-reports of marijuana use and a broad measure of IQ obtained at both a preteen (9–12 years) and a young adult (17–20 years) assessment was available. Two subjects with inconsistencies between the self- report of marijuana use and the urine screening results were excluded, as were one subject who tested positive for cocaine and another who was taking methylphenidate. Consequently, the final sample comprised 70 subjects whose self-report of marijuana use and absence of hard drug use had been validated by urinalysis results.

 

During the preteen period and before initiation of marijuana use, IQ was measured by means of the Wechsler Intelligence Scale for Children-III (WISC).23 When the subjects were young adults, IQ was evaluated with the Wechsler Adult Intelligence Scale-III (WAIS).24 The outcome variable for the examination of potential marijuana effects was an IQ difference score, derived by subtracting the preteen WISC IQ score from the young adult WAIS IQ score. Thus a positive difference score reflects an increase in IQ over the approximately 10-year period, whereas a negative score reflects a decrease.

 

Marijuana use was determined by 2 procedures that were part of an extensive neuropsychologic battery given to the 17– to 20-year-olds. The first consisted of a questionnaire completed by the subject, which asked for details of current and past marijuana use, as well as other drug use. The second was a urine sample analyzed for the presence of cannabinoids, amphetamines, opiates, cocaine and cotinine (a metabolite of nicotine). All metabolite concentrations were adjusted for creatinine to control for urine dilution. Although these procedures did not assess the strength of the marijuana used by the OPPS subjects, an estimate was suggested by Health Canada's analysis of marijuana seized by police between 1996 and 1999, which revealed an average of 5% to 6% tetrahydrocannabinol (THC).

 

Marijuana measures treated as continuous variables were self-report of mean number of joints currently smoked per week, self-report of length of time (months) that marijuana had been smoked and total estimated number of joints smoked (mean number of joints smoked per week multiplied by number of weeks of use). The mean number of joints currently smoked per week was also treated as a categorical variable, as follows: the subjects were grouped as light current regular users, heavy current regular users, former regular users or non-users.

 

Categorization of the current marijuana users as light or heavy users was based on both the self-report and the urinalysis data. The urinalysis data were bimodally distributed: 11 subjects had cannabinoid to creatinine ratios between 4 and 54 ng/mg, and 13 subjects had ratios between 147 and 705 ng/mg. These 2 groups of subjects were used to validate the categorization based on self-reports. Defining heavy regular use as at least 5 joints per week (n = 15) and light regular use of any amount less than 5 joints at least once a week (n = 9) optimized concordance with the bimodal urine division as indicated by &b.chi;2 analysis. Eight (73%) of the 11 subjects with the lower metabolite values smoked fewer than 5 joints per week, and 12 (92%) of the 13 subjects with the higher metabolite values smoked an average of 5 or more joints per week (p = 0.001).

 

Of the 70 subjects, 37 were non-users who had never used marijuana regularly (where regular use was defined as at least once a week) and who had not used any marijuana in the past 2 weeks; 9 were former users who had smoked marijuana regularly in the past but had not smoked for at least 3 months before the young adult assessment; 9 were light current users; and 15 were heavy current users.

 

The assessments were conducted in laboratories at Carleton University, Ottawa. Given that the testing sessions commenced in the early morning and that all subjects reported no use of marijuana on the day of testing, it is unlikely that the subjects were assessed while in an acute state of intoxication.

 

The validity of self-reporting for current marijuana use was examined with 2 approaches. The initial selection of the 70 subjects involved a criterion of concordance between self-reports of marijuana use and urine screening results (see above). The second measure of concordance was a high correlation between reported current marijuana use and the cannabinoid to creatinine ratio found with urinalysis (r = 0.70, p < 0.001). Although self-reports of earlier use could not be directly confirmed pharmacologically, their reliability is enhanced by the validity of the self-reporting for current marijuana use.

 

In examining the relation between marijuana use and IQ difference scores, we considered a variety of potentially confounding variables, including variables related to socioeconomic status, such as family income and parental education; the subject's education level (number of years of education at the time of the young adult assessment); age and sex of the subject; mother's age at the time of the subject's birth; maternal use of cigarettes, marijuana and alcohol during pregnancy; and the subject's use of tobacco and alcohol and exposure to secondhand marijuana smoke. In the subsequent analyses, we controlled for any potential confounding factor that was related to both the marijuana independent variable (at ? = 0.1) and the IQ difference score (at ? = 0.05).25

 

Hierarchical regression (a statistical approach to measure the impact of marijuana use after considering potential confounders) was used to examine the predictive relation of quantity (both mean number of joints per week and total joints over lifetime) and duration (period of use) of current marijuana use to the IQ difference score. Differential effects on the IQ difference score of light current use, heavy current use and former use as contrasted to non-use were examined with Dunnett's 2-sided multiple comparison procedure 26 with analysis of variance (ANOVA) and analysis of covariance (ANCOVA) when required to control for confounding variables.

 

Results

Analyses in which number of joints smoked per week was used both as a continuous and as a categorical variable revealed significant associations of this variable with the IQ difference score.

 

When number of joints smoked per week was treated as a continuous variable, regression analyses revealed a significant negative association with the IQ difference score (r = –0.24, p < 0.05) after accounting for potentially confounding variables. In these analyses, no predictive relation with the IQ difference score was found for the self-reported period of marijuana use or the estimated total number of joints smoked.

 

For analyses in which number of joints smoked per week was treated as a categorical variable, ANOVA with Dunnett's procedure 26 indicated that the mean IQ difference score for the heavy current user group was significantly different from that for non-users (–4.0 v. 2.6, p < 0.05), whereas no significant differences were evident in comparisons with the light current users and former users (5.8 v. 2.6 and 3.5 v. 2.6 respectively) (Table 1). The characteristics of the 4 groups (light current users, heavy current users, former users and non-users) are presented in Table 1. Of particular importance to the present study is the fact that preteen IQ, assessed before marijuana use, did not differ across the groups. Although some characteristics did differ across the 4 groups (such as father's and mother's education), none of these was associated with the IQ difference score; therefore, they were not used as covariates.

 

 

Although there was no overall difference in IQ difference score between former users and non-users, a subgroup of former users, those who had used at least 5 joints per week (heavy use), was analyzed separately; again, there was no significant difference relative to non-users (t-test, p = 0.7). This lack of a negative impact among the former heavy users is striking, as they had smoked, on average, an estimated 5793 joints over 3.2 years (mean of 37 joints per week); in contrast, the current heavy users had smoked, on average, an estimated 2386 joints over 3.1 years (mean of 14 joints per week).

 

Interpretation

In the present work, the use of commensurable IQ measures obtained before and after initiation of marijuana use permitted examination of the consequences of marijuana use in the context of pre-drug performance. Of all the marijuana and non-marijuana variables considered, only the quantity of current marijuana use, in terms of number of joints smoked per week, was negatively related to change in IQ from preteen to young adult. Not associated with change in IQ were duration of marijuana use, the total quantity of marijuana used and former use of marijuana. In addition, variables such as socioeconomic status (family income and parental education), age of mother at time of subject's birth, subject's prenatal exposure to drugs (nicotine, marijuana and alcohol), preteen IQ score, age, sex, academic history, other drug use and passive marijuana exposure were not predictive of change in IQ score.

 

The IQ difference score for the heavy current users differed from that for non-users, but no such differences were apparent between light current users and non-users. The clinical significance for an individual of such an effect on IQ scores is difficult to ascertain, but the impact on society might be substantial. IQ scores are considered normally distributed, with a mean of 100 and a standard deviation of 15, and it is therefore estimated that 2.3% of individuals will score 70 or below (2 standard deviations [sD]), and 6.7% will score 77.5 or below (1.5 SD) on global intelligence tests. These are cutoff points at which intervention and special education have typically been provided.27 Any factors in a population that result in a 4-point decrease in IQ, as was found with the heavy current marijuana users, would increase to 5.5% the proportion of individuals with an IQ of 70 or below and to 11.0% those with an IQ of 77.5 or below. A corresponding decrease in proportions would be expected on the other end of the distribution (people with higher IQ scores). For comparison, an IQ decrement of 5 points has been observed in children exposed prenatally to 3 alcoholic drinks per day,28 of 3.75 points in offspring exposed prenatally to cocaine 27 and of 2.6 points after low lead exposure.29

 

The IQ deficit among heavy current users in the present study likely reflected residue of the drug in their bodies.10 Assuming use of at least 5 joints per week by subjects in this group and given the elimination half-life of THC in the plasma of long-term marijuana users,30,31 such quantities and patterns of smoking are likely to result in an accumulation of THC in the body.

 

Although the heavy current users experienced a decrease in IQ score, their scores were still above average at the young adult assessment (mean 105.1). If we had not assessed preteen IQ, these subjects would have appeared to be functioning normally. Only with knowledge of the change in IQ score does the negative impact of current heavy use become apparent.

 

There were no differences in IQ score at the preteen assessment among the future groups of users and the future non-users. This finding suggests that, at least in a low-risk, white, predominantly middle-class sample, IQ score before any marijuana use is not a predictor of future marijuana use.

 

We investigated the possibility of a longer-lasting deficit, perhaps representing a neurotoxic consequence on the central nervous system (CNS), using data for the former users. The mean IQ difference score for the former users did not differ significantly from that for the non-users, which suggests a lack of long-term effects. Similarly, there was no negative impact on IQ difference among former heavy users relative to non-users (in contrast to the situation for current heavy users). This lack of a long-lasting negative impact suggests the absence of any CNS alteration as reflected by global IQ performance.

 

Both the negative effects of use of at least 5 joints weekly and the lack of long-term effects found in this study should be interpreted cautiously. The relatively small number of subjects for whom data were available, the length of time that the drug was used, the estimated total number of joints smoked and the young age of the subjects may serve, individually or collectively, to moderate effects. Smoking at least 5 joints weekly should not be interpreted as a definitive threshold, as subjects were at low risk for other factors that could have a negative synergistic effect on IQ score. It is also important to emphasize that broad intellectual functioning may be less vulnerable to the consequences of marijuana use than more specific cognitive domains, such as attention and memory.7,13,14

 

The popularity of marijuana among youth has been increasing during the past 4 years,32,33 and pressure on governmental agencies to assess the medical uses of the drug and to reassess the legal status of the drug has been growing.34 These trends emphasize the need to continue investigating the cognitive consequences of both current and previous marijuana use.

 

References

1. Hall W, Solowij N, Lemon J. The health and psychological consequences of cannabis use. Canberra: Australian Government Publishing Service; 1994. [Context Link]

 

2. Klonoff H. Acute psychological effects of marijuana in man, including acute cognitive, psychomotor and perceptual effects on driving. In: Fehr KO, Kalant H, editors. Cannabis and health hazards. Toronto: Addiction Research Foundation; 1983. p. 433-74. [Context Link]

 

3. Beardsley PM, Kelly TH. Acute effects of cannabis on human behavior and central nervous system functions. In: Kalant H, Corrigall W, Hall W, Smart R, editors. The health effects of cannabis. Toronto: Addiction Research Foundation; 1999. p. 129-69. [Context Link]

 

4. Kolansky H, Moore RT. Toxic effects of chronic marihuana use. JAMA 1972; 222:35-41. Bibliographic Links [Context Link]

 

5. Lundqvist T. Specific thought patterns in chronic cannabis smokers observed during treatment. Life Sci 1995;56:2141-4. Full Text Bibliographic Links [Context Link]

 

6. Schwartz RH, Gruenewald PJ, Klitzner M, Fedio P. Short-term memory impairment in cannabis-dependent adolescents. Am J Dis Child 1989;143:1214-9. Bibliographic Links [Context Link]

 

7. Rubin V, Comitas L. Psychological assessment. In: Rubin V, Comitas L, editors. Ganja in Jamaica: a medical anthropological study of chronic marijuana use. The Hague: Mouton; 1975. p. 111-9. [Context Link]

 

8. Carter WE, Coggins W, Doughty PL. Cannabis in Costa Rica: a study of chronic marihuana use. Philadelphia: Institute for the Study of Human Issues; 1980. [Context Link]

 

9. Varma VJ, Malhotra AK, Dang R, Das K, Nehra R. Cannabis and cognitive functions: a prospective study. Drug Alcohol Depend 1988;21:147-52. Bibliographic Links [Context Link]

 

10. Pope HG Jr, Yurgelun-Todd D. The residual cognitive effects of heavy marijuana use in college students. JAMA 1996;275:521-7. [Context Link]

 

11. Jones RT, Benowitz N. The 30 day trip — clinical studies of cannabis tolerance and dependence. In: Braude MC, Szara S, editors. The pharmacology of marijuana. New York: Raven Press; 1976. p. 627-42. [Context Link]

 

12. Chait LD. Subjective and behavioral effects of marijuana the morning after smoking. Psychopharmacology (Berl) 1990;100:328-33. Bibliographic Links [Context Link]

 

13. Pope HG Jr, Gruber AJ, Yurgelun-Todd D. The residual neuropsychological effects of cannabis: the current status of research. Drug Alcohol Depend 1995; 38:25-34. Full Text Bibliographic Links [Context Link]

 

14. Solowij N. Long-term effects of cannabis on the central nervous system. In: Kalant H, Corrigall W, Hall W, Smart R, editors. The health effects of cannabis. Toronto: Addiction Research Foundation; 1999. p. 195-265. [Context Link]

 

15. Block RI, Farnham S, Braverman S, Noyes R Jr, Ghoneim MM. Long-term marijuana use and subsequent effects on learning and cognitive functions related to school achievement: preliminary study. NIDA Res Monogr 1990; 101: 96-111. Bibliographic Links [Context Link]

 

16. Block RI, Ghoneim MM. Effects of chronic marijuana use on human cognition. Psychopharmacology (Berl) 1993;110:219-28. [Context Link]

 

17. Ellis GM Jr, Mann MA, Judson BA, Schramm NT, Taschian A. Excretion patterns of cannabinoid metabolites after last use in a group of chronic users. Clin Pharmacol Ther 1985;38:572-8. Bibliographic Links [Context Link]

 

18. Cridland JS, Rottanberg D, Robins AH. Apparent half-life of excretion of cannabinoids in man. Hum Toxicol 1983;2:641-4. Bibliographic Links [Context Link]

 

19. Heustis MA, Mitchell JM, Cone EJ. Detection times of marijuana metabolites in urine by immunoassay and GC–MS. J Anal Toxicol 1995;19:443-9. [Context Link]

 

20. Millsaps CL, Azrin RL, Mittenberg W. Neuropsychological effects of chronic cannabis use on the memory and intelligence of adolescents. J Child Adolesc Subst Abuse 1994;3:47-55. [Context Link]

 

21. Fried PA. Behavioral evaluation of the older infant and child. In: Slikker W Jr, Chang LW, editors. Handbook of developmental neurotoxicology. San Diego: Academic Press; 1998. p. 469-86. [Context Link]

 

22. Fried PA, Smith AM. A literature review of the consequences of prenatal marijuana exposure. An emerging theme of deficiency in aspects of executive function. Neurotoxicol Teratol 2001;23:1-11. Full Text Bibliographic Links [Context Link]

 

23. Wechsler D. Wechsler Intelligence scale for Children. 3rd ed. New York: The Psychological Corporation; 1991. [Context Link]

 

24. Wechsler D. Wechsler Adult Intelligence Scale. 3rd ed. San Antonio (TX): The Psychological Corporation; 1997. [Context Link]

 

25. Jacobson SW, Jacobson JL. Prospective, longitudinal assessment of developmental neurotoxicity. Environ Health Perspect 1996;104:275-83. Bibliographic Links [Context Link]

 

26. Dunnett CW. A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 1955;50:1096-121. [Context Link]

 

27. Lester BM, LaGasse LL, Seifer R. Cocaine exposure and children: the meaning of subtle effects. Science 1998;282:633-4. Bibliographic Links [Context Link]

 

28. Streissguth AP, Barr HM, Sampson PD, Darby BL, Martin DC. IQ at age 4 in relation to maternal alcohol use and smoking during pregnancy. Dev Psychol 1989;25:3-11. Bibliographic Links [Context Link]

 

29. Bellinger DC. Interpreting the literature on lead and child development: the neglected role of the “experimental system.” Neurotoxicol Teratol 1995;17:201-12. Full Text Bibliographic Links [Context Link]

 

30. Johansson E, Agurell S, Hollister LE, Halldin MM. Prolonged apparent half-life of D1-tetrahydrocannabinol in plasma of chronic marijuana users. J Pharm Pharmacol 1988;40:374-5. Bibliographic Links [Context Link]

 

31. Martin BR, Cone EJ. Chemistry and pharmacology of cannabis. In: Kalant H, Corrigal W, Hall W, Smart R, editors. The health effects of cannabis. Toronto: Addiction Research Foundation; 1999. p. 21-68. [Context Link]

 

32. Ogborne AC, Smart RG, Adlaf EM. Self-reported medical use of marijuana: a survey of the general population. CMAJ 2000:162;1685-6. Available: www.cma.ca/cmaj/vol-162/issue-12/1685.htm [Context Link]

 

33. Johnston LD, O'Malley PM, Bachman JG. The Monitoring the Future national survey results on adolescent drug use: overview of key findings, 2000. Bethesda (MD): National Institute on Drug Abuse; 2001. NIH Publ No. 01-4923. Available: http://monitoringthefuture.org/pubs/monogr...verview2000.pdf (accessed 20 Feb 2002) [Context Link]

 

34. Marijuana: federal smoke clears, a little [editorial] CMAJ 2001;164:1397. Available: www.cma.ca/cmaj/vol-164/issue-10/1397.asp [Context Link]

Edited November 26, 2003 by syk613

[Guest niall]

The comparison of the IQ difference scores showed an average decrease of 4.1 points in current heavy users (p < 0.05) compared to gains in IQ points for light current users (5.8), former users (3.5) and non-users (2.6).

 

Interpretation: Current marijuana use had a negative effect on global IQ score only in subjects who smoked 5 or more joints per week. A negative effect was not observed among subjects who had previously been heavy users but were no longer using the substance. We conclude that marijuana does not have a long-term negative impact on global intelligence. Whether the absence of a residual marijuana effect would also be evident in more specific cognitive domains such as memory and attention remains to be ascertained.

Note the IQ gain for light current users, greater than any other group including non-users. Now why (rhetorical) did all of the news coverage have titles like "It's official: pot makes you dumb" etc. The most significant result of their study was that light use actually increases IQ. And it doesn't even come up in their interpretation, let alone as a comment pointing out the potential for inaccurate results with such a "rediculous" finding.

 

Who funded this research, and who sent out the press releases?!?

[syk613]

Who funded this research,

 

Acknowledgements: This work was supported by a grant to Peter Fried from the National Institute on Drug Abuse, Washington, DC

This is not a press release it is a published article in a scientific journal written by the researchers.

 

Interpretation: Current marijuana use had a negative effect on global IQ score only in subjects who smoked 5 or more joints per week. A negative effect was not observed among subjects who had previously been heavy users but were no longer using the substance. We conclude that marijuana does not have a long-term negative impact on global intelligence. Whether the absence of a residual marijuana effect would also be evident in more specific cognitive domains such as memory and attention remains to be ascertained.

 

AA that finding doesn't sound that bad to me, actually I read go news from this article. And relly the point that 5 or more joints eaffects IQ is a statistical interpretation of the population sampled so for any1 person it will differ to some degree. What have you seen in the mass media regarding this research?