Recreational Drug Use in the Year After Initiation

April 16, 2008

This week’s fax from the Center for Substance Abuse Research at the University of Maryland touches on an issue of continual interest, namely the determination of “how addictive” different drugs of abuse may be. As I have mentioned a time or two (or three) before, I believe this is an area where the scientific research tends to skirt a key issue. From my perspective, this is one of the hardest questions to answer on the basis of the available human data and the animal models tend to drive right over the essential concepts.
This week’s CESAR fax (sign up here) reports rates of discontinuation, continued use without dependence and dependence for most major drugs of abuse. These data can help us to answer the question of “how addictive” are various recreational drugs.

The first measure which might answer a question of “how addictive” is the rate of discontinuation or non-use in the year after first trying a substance. On this scale, crack cocaine is “least risky” with 76% of individuals who tried it no longer taking it a year later. Inhalants (73%) and heroin (69%) also result in high quit rates. On this measure, alcohol (26% discontinuing- wow, this high?) is the “most risky” by some margin over the nearest competition (marijuana, 42%; pain relievers, 57%). This is a decent start. From a population point of view if a drug has properties (inherent, environmental or circumstantial) that result in high percentages of those who try them failing to continue use, it is “less addictive”.
I’m not going to get into the question of why people might try specific substances once or a few times and then not continue using because these data can’t illuminate the question. I’m sure the commenters will have fun proposing the likely testable hypotheses. And of course, “use” is not that congruent with “addiction” although, of course, discontinuation of a substance is a fantastic way to prevent addiction. So it is somewhat relevant to the human condition to consider this factor. And taking the canonical self-administration laboratory model of drug use as an example, it should be obvious that the major governing hypothesis is “with enough exposure, in the right pattern, all individuals will become dependent/addicted”. Charles “Bob” Schuster, who is one of the major figures behind the long history of self-administration, drugs-as-reinforcers laboratory animal models reiterated this concept in his Dews award lecture I recently attended. That pole of the drug-abuse research community is not backing down one bit, apparently.


Notes: Data are from persons aged 12 or older responding to the 2004 to 2006 National Survey on Drug Use and Health who reportedinitiation of a substance 13 to 24 months prior to the interview. Pain reliever, sedative, tranquilizer, and stimulant use refers to nonmedical use of prescription-type drugs not prescribed for the respondent or used only for the experience or feeling they caused. Percentages may not sum to 100 due to rounding.

Source: Substance Abuse and Mental health Services Administration, “Substance Use and Dependence Following Initiation of Alcohol or Illicit Drug Use,”The NSDUH Report, March 27, 2008. Available online at

The second measure which might answer the “how addictive” question is the dependence rate. For many people, the idea of drug dependence is essential to the question of whether or not we should be concerned about recreational drug use. On this scale, the “most risky” substances are heroin (13% dependent) and crack cocaine (9%) with a second grouping of marijuana (6%), stimulants (5%) and powder cocaine (4%).
Now we move to the final measure which helps us to arrive at the crux of the matter. The percentage of the sample that continues to use a substance and yet does not meet criteria for dependence. Of course, a failure to meet dependence criteria does not mean that one is not at risk from continued substance use. Not at all. There are a host of other health risks that can result from continued drug use other than dependence per se. However, this non-dependent use measure helps us to fill in the gaps between those who discontinue use and those who become substance dependent. Here we find that 71% of those who initiated alcohol use in the past year continue to use alcohol without dependence. The next-closest substance is marijuana (52% still using without dependence) with almost all of the rest hitting the 34-40% range. Interesting, but we can go a bit further along the path.
The most interesting comparison to my eye is to take the population of drug-initiators who are still using a year later and compare the relative rates of dependent and non-dependent use. Heroin (13% dependent/17% nondependent) and crack cocaine (9%/15%) are the “most risky” by this measure. One way to express this might be to say that given continued use of the substance, 43% will become dependent on heroin, 38% on crack cocaine and 10% on marijuana. On the other end, only 4% of continued users will become dependent on alcohol.
The CESAR figure fits well with a favorite paper of mine to which I refer in discussions of relative “addictiveness”. It is a bit old, but presents some interesting data. The study population in the National Comorbidity Survey was:

“based on a stratified, multistage area probability sample of persons 15 to 54 years old in the noninstitutionalized civilian population in the 48 coterminous United States, including a representative sample of students living in campus group housing. …between September 14, 1990 and February 6, 1992.”

I’ve graphed some data presented in tabular form (Table 2 from the publication) and added one additional analysis. The data from the paper include the proportion of the sample that reported extramedical use of the various substances in his/her lifetime as well as the proportion that reported sufficient symptoms to meet criteria for dependence (including at any time, not just when surveyed). The estimate of life-time prevalence of use is quite familiar if you browsed any further into the Monitoring the Future data after reading this post which included one of the annual prevalence graphs. The dependence rate given extramedical use from Anthony, et al (1994) is quite similar to the data from the CESAR fax, above. Strikingly similar given that it is derived from a different sample and gated on the one-year interval after initiation instead of considering the whole lifetime.
To my eye, the most striking outcome of these types of data is the realization that it is only a minority of individuals who sample a drug that will become dependent. From the Anthony et al (1994) data, about 23% of the population who has ever sample heroin becomes dependent. As outlined above, only 43% of the population of heroin initiators become dependent in the first year.


Anthony JC, Warner LA, Kessler RC.
“Comparative Epidemiology of Dependence on Tobacco, Alcohol, Controlled Substances, and Inhalants: Basic Findings from the National Comorbidity Survey”.
Experimental and Clinical Psychopharmacology. 1994;2:244-68.

My interpretation of these analyses is that we are doing something wrong with our “usual” drug abuse models in the laboratory. Those models are usually predicated on 100% of the animals developing the “phenotype” of interest by, e.g., self-administering the drug. This leads to a theoretical approach that drug abuse is simply a matter of “sufficient exposure” to a given drug. And in some ways this may very well be true. From a certain perspective.
It overlooks the fact that the human population of interest consumes drugs in highly variable patterns, influenced by any number of factors. If the development of addiction is a result of the “correct” repeated exposure to drug, this just begs the question of why some individuals end up exposing themselves according to the addiction protocol and some do not! It is my belief that a more interesting way to move forward in understanding drug abuse would be to concentrate our models on population outcomes that mimic the human data more closely. With appropriate subsets of the sample discontinuing use after a few samples, using repeatedly without dependence and developing drug dependence.
The final point results from the analysis I added to the graph above to reflect the population prevalence of dependence on the respective substances. The point is to illustrate the respective scope of public health concern related to various recreational drugs. For example the estimate suggests that the chances of becoming dependent on heroin (23% of lifetime users) are greater than the chances for alcohol (15%) or cannabis (9%). However, the much greater population prevalence of alcohol (91.5%) or cannabis (46.3%) in comparison with heroin (1.5%) means that the fraction of the entire population that is dependent on heroin (0.3%) is miniscule in comparison with the fraction dependent on cannabis (4%) or alcohol (14%). It is true that the personal and societal impact of heroin dependence may be much greater than that of alcohol or cannabis. Nevertheless it is clear that the number of individuals affected by alcohol or cannabis dependence dwarfs the number affected by heroin addiction.
return to hunt
Those who think that cannabis is perfectly benign from a public health standpoint should grapple with the data suggesting that population dependence rates for cannabis (4%) exceed those for cocaine (2.7%), stimulants (1.7%) and anxiolytics (1.2%) as well as that for heroin (0.3%).

17 Responses to “Recreational Drug Use in the Year After Initiation”

  1. PhysioProf Says:

    Those who think that cannabis is perfectly benign from a public health standpoint should grapple with the data suggesting that population dependence rates for cannabis (4%) exceed those for cocaine (2.7%), stimulants (1.7%) and anxiolytics (1.2%) as well as that for heroin (0.3%).

    I think you gotta also factor in the level of harm that comes from dependence, and that is not solely due to the illegality of the drug.
    Sounds like your post is an argument for the irrelevance of animal studies of drug-seeking behavior to human drug abuse.


  2. Lucas Says:

    If I was a mouse trapped in a cage with wires in my brain, I would probably want to do as much cocaine or heroin as they were willing to give me too šŸ˜‰
    Seriously, though, I have known several people who snorted heroin or oxycontin and thought something along the lines of “I like this so much that if I continue, I will surely become dependent,” and then didn’t use again. I realize that anecdotes don’t make good science, but I find it hard to believe that a mouse would run through a train of thought anything like this.


  3. DrugMonkey Says:

    I think you gotta also factor in the level of harm that comes from dependence
    Of course. I guess I was mostly discussing the scope of the “problem”. Trying to balance relative severity of harm is another thing altogether. Think of it this way. You could have one employee (of many) in your lab that’s dependent on heroin. A severe screw up, consequently. No problem, you just get the person hooked up with medical leave, turn it over to HR, bada bing. You could also have half or three quarters of your employees under more “benign” addictions. They still make in to work. Now, they tend to take a boat load of sick days. Arrive late and leave early. Spend all morning reading blogs (!) instead of actually getting sheist done. etc. Which is “worse”? Depends on perspective.
    Sounds like your post is an argument for the irrelevance of animal studies of drug-seeking behavior to human drug abuse.
    If I was a mouse trapped in a cage with wires in my brain, I would probably want to do as much cocaine or heroin as they were willing to give me too šŸ˜‰
    I critique the irrelevance of some animal studies. Ironically, Lucas’ crack about being trapped in a cage actually increases the validity of animal studies somewhat. There is a good argument that a lack of alternative reinforcers is a driving factor for sustained drug use. [I will note that self-administration models do not typically feature “wires in the brain” however.] Of course, this enriched/ipoverished environment is rarely systematically modeled. Keven Beck blogged a paper from Serge Ahmed showing that rats may not prefer cocaine to sweet tastes. For those who pay attention to detail this was no news since response rates for appetitive reinforcers are frequently as high or higher than for classic self-administered drugs like cocaine and heroin. Nevertheless, this is one example of designing a study that has a chance of getting into the real meat of the issue- individual differences in preference for drug versus more natural reinforcers.
    Ultimately my complaint is about analysis. I’d like to see the situations set up so that some animals take the drug and some do not. Then we can ask questions about what makes the difference between a drug-preferring and non-preferring individual. Then we’ll be on to something.
    This sort of thing is possible with many existing preparations it is just that they have been optimized to get all members of the group expressing the target behavior.
    …people who snorted heroin or oxycontin and thought … “I like this so much that if I continue, I will surely become dependent,” and then didn’t use again….I find it hard to believe that a mouse would run through a train of thought anything like this
    It isn’t as absurd as you think. While one cannot get a rat or mouse to think ahead abstractly like this, it might be possible to model in immediate adverse consequences. There is a literature showing that animals will put up with an aversive shock to self-administer although I’d have to track down the refs on that one. Not a mainstream model at all.


  4. Liam Says:

    I’m a first time poster, so take it easy on me!
    The disconnect between laboratory self-administration models and the “real” world does not escape those of us striving in the labs to keep our work relevant to the human condition. But the truth is, and we all know this, that no model system is perfect.
    The thread of this post seems to be developing around the really interesting topic of individual differences in drug administration behaviors. Individual differences in self-administration behaviors, however, are often the bain of the experimenter’s existence because variable responding can lead to difficulty supporting the hypothesis (via statistical findings). Many researchers, myself included, have tried to examine individual differences and turn a “weakness” of the study into a strength.
    Still, if your scientific question involves an outcome that is a consequence of the drug, say neurotransmitter release, then individual differences in responding can really wreak havoc on the data and make interpretation difficult. Thus, it seems that most folks, myself included, go for the simplistic approach so that their neurobiological outcome is not overly complicated by variable behavior. But that doesn’t always work either!!
    Just my 2 cents…


  5. DuWayne Says:

    I have to really question how dependence is being defined here. At thirty-two, I have been smoking marijuana on and off for eighteen years. While I have chosen to abstain here and there over the years, given opportunity and less of a need for sobriety in general, I would happily smoke weed every day. Indeed, pre-fatherhood, I pretty much did.
    The thing is, I have never had any problems with abstaining when necessary, or when I chose to. Since my oldest started taking more notice of the world around him, I have drastically scaled by my intake. Nowadays, I smoke pot two to four times a month. Make no mistake though, were it not for the responsibility of supporting my family and raising my boys, I would be smoking on a daily basis.
    Would I fit into the definition of dependent? After all, given the right circumstances, I would (and at some point in the future probably will) smoke pot daily.
    I too would like to see studies that took a more realistic approach to drug addiction. I think that the addition of adverse stimuli is a very good method, but still misses out on the complexity of related justifications.
    I have tried a pretty wide range of drugs over the years, in a pretty good range of methods. (i.e. I tried snorting heroin first, then smoked it and finally used it IV) Crack was an amazing buzz, but the comedown was enough to turn me off from future use. I really liked powder cocaine, but never had an issue with it. Then I tried it IV and not only decided never to try it that way again (it was an incredibly euphoric, mind bending experience), I decided that cocaine had become too much to risk using ever again. Heroin was never a real contender, because IV and inhaled, it made me vomit (something I just couldn’t get into) and smoking it wasn’t much different than smoking opium, except the price was higher. Were it not for the vomiting though, the high might have been more tempting.
    Even the psychology behind my interactions with the drug that I actually did have serious problems with, doesn’t really fit into a “normal” dependence paradigm. I had/have very significant problems with LSD. It is not like what I generally think of as dependence, but it was certainly an addiction. But that is rather unique to LSD’s interaction with the brain.
    I guess my point is that I really question the validity of an abuse/addiction paradigm that tries to compare drug A with drug B. Different drugs have different interactions and different mechanisms for dependence and/or addiction. My addiction to and experience with LSD was very different than my experience with IV cocaine. I can reasonably assert that had I not chosen to stop using cocaine altogether, after my IV experience, I would have ended up with a serious dependence problem. But the motivation for it and mechanism behind it, was an entirely different beast than my experience with LSD.


  6. DrugMonkey Says:

    DSM-IV still uses the generic substance criteria for dependence and abuse, as far as I know. As with most psychiatric diagnoses, the devil is in the detail which is why you need a qualified clinician to make a diagnosis. Yes, there are a lot of overlapping distributions and gray areas in the diagnosis of mental and behavioral disorders. Things that are “maladaptive” or an “impairment” are not always the same across individuals. And then there is the question of the “characteristic withdrawal syndrome” which in the case of cannabis and caffeine is still being hashed out.
    I should point out that the laboratory research is not really concentrated on comparing “addictiveness” across drug classes in a comprehensive, real world way. This is (perhaps rightfully) not the goal. The closest it comes is to ask about “reinforcer efficacy”, which I think is frankly beside the point.
    From this link:

    A maladaptive pattern of substance use, leading to clinically significant impairment or distress, as manifested by three (or more) of the following, occurring at any time in the same 12-month period:
    1. Tolerance, as defined by either of the following:
    a. A need for markedly increased amount of the substance to achieve intoxication or desired effect.
    b. Markedly diminished effect with continued use of the same amount of the substance.
    2. Withdrawal, as defined by either of the following:
    a. The characteristic withdrawal syndrome for the substance.
    b. The same (or a closely related) substance is take to relieve or avoid withdrawal symptoms.
    3. The substance is often taken in larger amounts or over a longer period than was intended.
    4. There is a persistent desire or unsuccessful efforts to cut down or control substance use.
    5. A great deal of time is spent in activities necessary to obtain the substance (e.g., visiting multiple doctors or riving long distances), to use the substance (e.g., chain smoking), or to recover from its effects.
    6. Important social, occupational, or recreational activities are given up or reduced because of substance use.
    7. The substance use is continued despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been cause or exacerbated by the substance (e.g., depression or continued drinking despite recognition that an ulcer was made worse by alcohol consumption).


  7. NeuroStudent Says:

    So, did they lump nicotine with the stimulants or just ignore it?
    it actually has an opposite issue from that described above…my understanding is that it tends to have a relatively high rate of dependence in humans and, interestingly, people have a hard time getting animals to self-administer it (although, this makes it ideal for individual differences studies)


  8. Neuro-conservative Says:

    Thanks for this very interesting post, DM. Your approach here reminds of the current rage for real-world effectiveness studies (e.g., CATIE, STEP-BD, and STAR*D) as opposed to classic efficacy studies in clinical psychiatry.
    A classical efficacy study would examine the effects of a psychotropic drug (e.g, the SSRI citalopram) in a refined group of carefully-selected patients known to be receiving the drug under controlled conditions. One can then make inferences about the effects of drug on the brain and behavior with high internal validity (assuming appropriate controls, etc). Patients who drop out of treatment are viewed as a nuisance to the analysis and are statistically controlled in various ways.
    By contrast, an effectiveness study such as STAR*D takes an “all-comers” approach, and then looks to see what might happen to them in the real world after being assigned to treatment with citalopram. This might commonly include dropping out of treatment, which is one of the most common outcomes in clinical practice. Thus, drop out becomes a key dependent measure. These studies deliberately sacrifice certain controls of internal validity in exchange for generalizability.
    Does this analogy work for you, DM? I think you should consider your model as a potential EUREKA proposal, assuming they renew the mechanism for the coming year. Sounds like the kind of thing they are looking for with that mechanism.


  9. DrugMonkey Says:

    So, did they lump nicotine with the stimulants or just ignore it?
    tch, tch NeuroStudent, this is why I included the link to the original source

    NSDUH measures the nonmedical use of prescription-type pain relievers, sedatives, stimulants, or tranquilizers. Nonmedical use is defined as the use of prescription-type drugs not prescribed for the respondent by a physician or used only for the experience or feeling they caused. Nonmedical use of any prescription-type pain reliever, sedative, stimulant, or tranquilizer does not include over-the-counter drugs. Nonmedical use of stimulants includes methamphetamine use.

    getting to your real point, it is true that while rats will self-admin coc and heroin and amp type stimulants easily, nicotine, thc and alcohol have posed challenges. I dunno what to say about mice, they have a lot of “issues” from the behavioral to the metabolic (mouse = liver wrapped in fur). it could get lengthy. monkeys OTOH, will self admin alc and thc readily by way of comparison. i’d have to refresh myself on who has tried nicotine and how that’s gone. nobody’s been able to get animals to self administer classical hallucinogens.
    I don’t think this means we just generically dismiss all animal models as uninformative. it is actually possible that the “difficulty” establishing good self-administration (remember, in all individuals is the target here) for various drugs may have been a massive hint that went overlooked. there may have been a minority of individuals who did prefer. this is clearly the case for alcohol, where preferring/nonpreferring strains were bred from ends of the “normal” distribution…


  10. NeuroStudent Says:

    I thought that I had looked at the original last night and I didn’t see nicotine/tobacco anywhere and I guess that I just skimmed over it
    aha! found something… did
    although, once a month is not likely to be indicative of dependence…
    yeah, you pretty much can’t get a mouse to self-admin nicotine because of their metabolism (t1/2 = 5-7 min) (there’s probably 2 labs in the world that have done it), rats are better (t1/2 = about an hour) (humans t1/2 = around 2 hours) and people gets rats to self-admin, but apparently it’s still difficult…my guess is that it has to do with the salient concentration window being really tight and highly individual for nicotine and human smokers can titrate their nicotine on a per puff basis, while rats are being given an administrator set dose every time they lever press which may be right for some of the animals, but not all
    I don’t think that it makes the animal models invalid, its just interesting to think about why it’s harder to get animals to self-admin some drugs than others…and maybe it’s good to think about why it’s difficult so that new self-admin paradigms can be developed to more closely mimic addiction in humans… (wait, I think that is is what you just said in your last paragraph–sorry to be a parrot)


  11. bsci Says:

    Drugmonkey noted that the dependence rates for most of the drugs seems fairly low compared to the “war on drugs” party line. I’m curious if there are factors that bias these numbers lower than reality.
    Persons excluded from the survey include persons with no fixed household address (e.g., homeless and/or transient persons not in shelters), active-duty military personnel, and residents of institutional group quarters, such as correctional facilities, nursing homes, mental institutions, and long-term hospitals.
    They definitely exclude some populations here with much higher drug dependency rates, although many are probably beyond their first year of exposure.
    The interviews were conducted in person and DSM diagnoses where based based solely on survey responses, as opposed to direct observation by a clinician. Since some people probably downplay drug use, I suspect this might also bias the survey results against the dependence stats.
    That said, I’d be very surprised if these biases increased dependence rates more than 5-10% for each drug. That’s still much lower than the “war on drugs” fear tactics.


  12. DuWayne Says:

    DM –
    Thanks, you muddied the waters even more for me. I’m trying to formulate a post on this, because I have some more questions and some more responses.
    bsci –
    The interviews were conducted in person and DSM diagnoses where based based solely on survey responses, as opposed to direct observation by a clinician. Since some people probably downplay drug use, I suspect this might also bias the survey results against the dependence stats.
    Now I have to wonder about that. I am not saying that the figures aren’t probably off, one way or the other, but I would tend to think that if they used the DSM criteria that DM listed above, there would be a tendency for bias in the other direction. However, I would also agree that not including people in correctional facilities and the homeless, is ignoring a substantial segment of the population that is at much higher risk for drug addiction.
    I just think that the criteria that DM listed is really overinclusive. A fairly high percentage of college students are addicts, according to that criteria. This in spite of the fact that most of them settle down after college and many become moderate users of alcohol and maybe marijuana.
    It also misses folks who are addicts, but never fit into more than one or two of those categories. While they are assuredly rare, there are people who are certainly addicted to their drug of choice, who never really have a problem with it, excepting that it doesn’t get them the same buzz it used to. Hell, I worked for a painting contractor who slammed smack every morning before getting out of bed to pee. Did the same thing at lunch time and the minute he was done working for the day. Runs a very successful business and is an active member of his community. He’s an addict without question, but he definitely doesn’t fit the criteria.
    OTOH, there are also addicts who recognize it early on and decide that they will just avoid using drugs altogether. From a statistical standpoint, their inclusion is probably not necessary, but from a treatment/prevention standpoint, I would say the they are extremely important. Mainly, what differentiates them, from addicts who do not choose to abstain early on?
    Ultimately, I think this is indicative of the scope of the problem. I really don’t think that animal models can get us there. Even coming up with a reasonable computer model would be a herculean task. There are just so many variables to consider, in assessing motivations for recreational versus dependency related drug use. And for most people, it is a variety of these variables, with many conflicting with another one.


  13. bsci Says:

    There’s one common flaw in your understanding of the use of DSM. In theory (unfortunately, often not in reality since insurers also use these diagnoses), DSM criteria are primarily a research tool. Clinicians should use them as a guide when designing treatment, but shouldn’t be locked to them.
    For research, when you are designing a large study or comparing studies, you want to be able to be able to consistantly group the same types of people together. No definition is perfect, but one needs to have a definition to be able to do studies like this one. In theory, they have the data for each of these criteria points and can break them down even more, but for a study of dependence vs. not, but you need a clear definition.
    The issue on not having clinical interviews is that clinicians should be able to more consistently apply these categories since they’ve seen more of the spectrum of behaviors.


  14. MattXIV Says:

    As DuWayne points out, acute adverse experiences (either direct or observed in others) is a significant factor in modeling drug use.
    Also, I think the 26% discontinuation rate for alcohol can be explained in part by it having a low initiation age and distinct circumstances. The first time I ingested enough alcohol to actually get a buzz was when I decided to try some of my parent’s cognac when I was 13. I drank about a shot and a half’s worth neat; I didn’t know that it was a sipping drink, so I glupped half dixie cup’s worth down as a trial dose. The taste and the burning sensation weren’t very appealing and I was fairly afraid of my parents noticing I had got into the liquor, so it was several years before I tried alcohol again. I’d wager a good fraction of that 26% is early adolescents who made similarly naive runs on their parent’s liquor cabinets.


  15. DuWayne Says:

    BSCI –
    No, I totally get that. What I am talking about is it’s use in research and how accurate it is as a research tool. I understand that it is necessary to set absolutes for research purposes, but using this criteria misses a lot and would include those who don’t really fit.
    unfortunately, often not in reality since insurers also use these diagnoses
    It’s not just used in insurance either. I was once on probation for possession of marijuana. Part of my probation included taking a drug abuse assessment survey. It was over one hundred questions each of which were asking in different ways, about one of those seven criteria. It was virtually impossible not to be considered an addict, because you already have arrested for it, working against you. They made it simple to lie and apparently most people do. That I didn’t and was marginal (i.e. if I hadn’t gotten caught with a loaded pipe and arrested, I would have passed) he didn’t put me in outpatient treatment.
    I think that anywhere that absolutes are required, you will see this criteria used, which is not an insignificant problem. But even were it just a research tool, the flaws are still a problem.
    I think that certain aspects should probably carry more importance than others. Four and seven, for example, should be given more weight than the others, while one should probably carry the least. I would also note something that I see as a glaring omission, honesty. Has the user lied to family or other loved ones, about using the substance in question? Like all of them, this too could have a non-addiction explanation, but it would be a very good indicator nonetheless.


  16. DrugMonkey Says:

    Liam said: individual differences in responding can really wreak havoc on the data and make interpretation difficult. Thus, it seems that most folks, myself included, go for the simplistic approach so that their neurobiological outcome is not overly complicated by variable behavior.
    Yeh. To be clear, I understand all too well the ways we can all be seduced by the models that “work” as opposed to those that are actually important. My basic science friends who trumpet “We’re getting down to the biological mechanism” I’m looking in your direction too.
    OTOH, I am fully cognizant of the fact that completely artificial models can tell us a lot that eventually does end up being relevant to something important about public health.
    with respect to Liam’s point about some scientists being interested in individual differences, this is true. But this is only recently. In fact I have the sneaking suspicion that the tide may in fact be turning a little bit on this. So we may very well indeed be looking at the beginnings of some really interesting revisions of traditional drug abuse models. I certainly hope so.


  17. Casey Says:

    I think self reported data of this nature shows us very little.


  18. […] You will recall from my posts trying to work out the conditional probability of dependence, that I am not a fan of simple, drug-feels-good models of drug reinforcement; even […]


  19. […] Roughly speaking the conditional probability of alcohol dependence is on the order of 4%, for cannabis on the order of 8% and for stimulants, including cocaine, on the order of 15%. […]


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