Monday, July 2, 2012

What Is the Most Dangerous Drug?

The recent Miami cannibal attack brought "bath salts" to the attention of the general public.  Bath salts are not the Epsom salts that sit unused in bathrooms nationwide, but are actually designer drugs that are sold as bath salts in order to circumvent regulation of the component chemical (typically MDPV) and avoid legal responsibility for the results of human consumption.  These designer drugs are frequently legal for possession or consumption since they are not controlled substances, but instead are chemically similar to controlled substances on the theory that similar chemicals should have similar recreational effects.

While reading about these so-called "research chemicals" at Erowid, a project that purports to provide honest information about drug effects and dosage for users and health care providers, I became interested in the stories of user experiences with these poorly-understood substances.  Assessing the similarities and differences between user accounts, it appeared that despite the small sample size there were indeed typical presentations of the effects of each drug.  If the thematic saturation from just a dozen or so accounts were enough to assemble a list of common experiences and side effects, then given the enormous number of user accounts at Erowid can I draw some conclusions about the relative danger of drugs in spite of the obvious self-selection bias?  A few notes before I start:
  • I will discard all drugs with fewer than 100 total user accounts from my study.  If you would like to repeat this study from the R code with a different threshold, source is provided.
  • The reports at Erowid are self-selected.  For example, you will see in Table 2 that alcohol use is less reported than cocaine.  Obviously, a smaller proportion of alcohol users see fit to report their experiences than cocaine users since most adults drink alcohol.  I cannot account for this self-selection bias, and it certainly taints all of my results.
  • Data is collected from all user accounts at Erowid on June 27, 2012.
  • I have made the R code for this web scraping experiment available here.  It includes web scraping code, an example of elastic net models in R with the glmnet package, labeled scatterplots, and how to produce normalized word cloud data.
  • The dose makes the poison, and I haven't done anything to account for the size of the dose nor the method of drug delivery.  Each case in Erowid should be considered a recreational or abusive dose.  
  • I in no way account for "fatally bad experiences" versus just "didn't enjoy it."  You will learn below that the risk for caffeine is quite high because the users take absurdly high amounts of it and are merely uncomfortable during the experience.  There are other drugs where a high dose will do far worse than make you uncomfortable.
  • There is no intrinsically safe substance.  None of these results should be construed as an endorsement of any illegal drug use.
First, I used Erowid's search feature to retrieve all user experiences, the drugs that were taken during the session, and the category of report.  I assembled these into large binary matrices, where the category of the report is the response variable and the drugs taken were the features.  I did not discriminate between large and small doses, and only counted whether or not a drug was taken at all during an experience.

Next, I decided to group the response variables a bit more coarsely. In my study, I will refer to number of reports, reward, risk, and addictiveness.

Table 1:  Categories of experiences in my study versus Erowid.


My StudyErowid Category
Number of ReportsTotal number of data points in all categories
Reward"Glowing Experiences", "Mystical Experiences", "Health Benefits"
Physical Risk"Health Problems", "Train Wrecks & Trip Disasters", "Addiction & Habituation"
Addictive"Addiction & Habituation"


Finally, I will build an elastic net model to determine which drugs are most associated with each category.  Because the majority of experience reports at Erowid involve a cocktail of drugs, it is necessary to use a predictive model that will be able to give me some idea as to the risks of individual elements when several are taken together.  In R, the code looks like this:

require(glmnet)
rewardModelGLMNET <- glmnet(featureTable, rewardTable, family="binomial")


This is all that is required to extract coefficients for each of reward, risk and addictiveness.  I present the coefficients in a model using all of the drugs with at least 100 experiences in Table 2.

Table 2:  Reward, Risk, Addictiveness and number of reports of drugs from Erowid.  Unitless model coefficients are given, which gauge the relative reward, risk or addictive nature compared to other drugs on the list.  Note the obvious self-selection bias visible in the list of reported drugs, where cocaine and opiates are ranked more highly than the relatively ubiquitous alcohol. 


Most Rewarding
DMT 0.94
Ayahuasca 0.89
Cacti 0.83
2C-I 0.79
Mushrooms 0.76
Pharms - Paroxetine 0.72
MDMA 0.72
Nitrous Oxide 0.62
5-MeO-DMT 0.52
Morning Glory 0.48
Methylone 0.45
H.B. Woodrose 0.43
DPT 0.4
Hydrocodone 0.39
AMT 0.36
LSD 0.32
Salvia divinorum 0.29
Mimosa spp. 0.28
Kava 0.27
Kratom 0.26
2C-E 0.25
2C-T-2 0.23
Harmala Alkaloids 0.22
Melatonin 0.22
Ketamine 0.21
2C-B 0.16
Opioids 0.13
Pharms - Buprenorphine 0.12
Cannabis 0.11
Pharms - Venlafaxine 0.08
Modafinil 0
Pharms - Alprazolam -0.02
Pharms - Tramadol -0.02
2C-T-7 -0.05
Heroin -0.05
Anadenanthera spp. -0.07
Pharmaceuticals -0.07
5-MeO-DiPT -0.12
Pharms - Clonazepam -0.12
Amphetamines -0.12
Tobacco -0.13
Pharms - Oxycodone -0.24
Opiates -0.27
Amanitas -0.31
Alcohol -0.33
Benzodiazepines -0.33
Syrian Rue -0.34
Pharms - Methylphenidate -0.41
Codeine -0.42
Cannabinoid Receptor Agonists -0.43
Methamphetamine -0.51
Pharms - Bupropion -0.54
Absinthe -0.57
GHB -0.6
DXM -0.6
Spice and Synthetic Cannabinoids -0.78
Nutmeg -0.82
Pharms - Zolpidem -0.82
Cocaine -0.84
Datura -0.95
Caffeine (extreme dose)-0.98
5-MeO-AMT -1
Inhalants -1.1
Piperazines -1.1
SSRIs -1.12
Diphenhydramine -2.01
Dimenhydrinate -2.44
Least Rewarding
Most Risky
GHB 1.62
Methamphetamine 1.51
Heroin 1.35
Cocaine 1.16
Pharms - Venlafaxine 1.15
Inhalants 1.09
Opioids 0.93
Tobacco 0.91
Caffeine (extreme dose)0.9
Syrian Rue 0.85
Pharms - Buprenorphine 0.82
Amphetamines 0.76
Pharms - Tramadol 0.73
Pharms - Paroxetine 0.69
Pharmaceuticals 0.68
DXM 0.64
Alcohol 0.48
Datura 0.47
Pharms - Zolpidem 0.45
Dimenhydrinate 0.42
Pharms - Methylphenidate 0.4
Diphenhydramine 0.38
5-MeO-AMT 0.32
Benzodiazepines 0.25
Nitrous Oxide 0.2
Opiates 0.19
AMT 0.18
MDMA 0.16
Pharms - Bupropion 0.13
SSRIs 0.1
2C-T-7 0.05
Piperazines 0.02
Kratom -0.01
Modafinil -0.08
Pharms - Oxycodone -0.11
Pharms - Alprazolam -0.15
Spice and Synthetic Cannabinoids -0.16
Cannabis -0.19
Cannabinoid Receptor Agonists -0.2
Ketamine -0.22
LSD -0.29
Codeine -0.3
Morning Glory -0.32
Amanitas -0.39
Pharms - Clonazepam -0.39
5-MeO-DMT -0.42
Nutmeg -0.5
Hydrocodone -0.5
DPT -0.53
5-MeO-DiPT -0.63
H.B. Woodrose -0.65
2C-T-2 -0.7
Harmala Alkaloids -0.73
Melatonin -0.76
2C-E -0.78
Methylone -0.83
2C-B -0.85
Kava -0.92
Ayahuasca -0.93
2C-I -0.94
Mushrooms -1.02
Mimosa spp. -1.09
Anadenanthera spp. -1.27
Absinthe -1.31
DMT -2.14
Cacti -2.17
Salvia divinorum -2.19
Least Risky

Most Addictive
Methamphetamine 2.21
Cocaine 2.05
Opioids 1.53
Pharms - Buprenorphine 1.46
Heroin 1.43
Pharms - Venlafaxine 1.38
Amphetamines 1.33
Tobacco 1.31
Pharms - Paroxetine 1.26
Pharms - Tramadol 1.2
GHB 1.08
Pharms - Methylphenidate 0.93
Opiates 0.67
Benzodiazepines 0.67
Kratom 0.65
Pharms - Clonazepam 0.65
Inhalants 0.55
Caffeine (extreme dose)0.52
Nitrous Oxide 0.39
Pharms - Alprazolam 0.3
Modafinil 0.07
DXM 0.03
Pharms - Zolpidem -0.03
Ketamine -0.08
Pharms - Bupropion -0.1
Alcohol -0.15
Pharms - Oxycodone -0.16
MDMA -0.16
Dimenhydrinate -0.22
Diphenhydramine -0.26
Cannabinoid Receptor Agonists -0.3
Pharmaceuticals -0.31
Codeine -0.45
Harmala Alkaloids -0.51
Methylone -0.52
Cannabis -0.67
Melatonin -0.81
Absinthe -0.83
Hydrocodone -0.83
5-MeO-AMT -0.88
SSRIs -1.04
2C-T-2 -1.23
Piperazines -1.24
Spice and Synthetic Cannabinoids -1.28
5-MeO-DMT -1.54
Kava -1.64
AMT -1.7
2C-E -1.83
Datura -1.85
LSD -1.88
2C-T-7 -2.16
5-MeO-DiPT -2.31
DPT -2.32
Mushrooms -2.86
Mimosa spp. -3.35
Syrian Rue -3.81
Salvia divinorum -3.87
Ayahuasca -4.88
Anadenanthera spp. -5.03
Amanitas -5.38
Cacti -5.43
DMT -5.49
H.B. Woodrose -5.6
Nutmeg -5.7
2C-B -5.7
Morning Glory -5.82
2C-I -5.96
Least Addictive
Most Reported Experiences
Mushrooms 1839
Cannabis 1637
Salvia divinorum 1577
MDMA 1354
LSD 1232
DXM 730
Opiates 589
Pharmaceuticals 582
Opioids 544
Cocaine 529
Alcohol 491
Morning Glory 463
2C-I 458
Amphetamines 425
Harmala Alkaloids 414
Methamphetamine 353
5-MeO-DiPT 318
DMT 315
Ketamine 314
5-MeO-DMT 310
SSRIs 305
H.B. Woodrose 304
Syrian Rue 301
2C-E 292
Datura 291
Nutmeg 289
AMT 274
Nitrous Oxide 258
Cacti 256
Diphenhydramine 248
Benzodiazepines 236
2C-T-7 235
Pharms - Tramadol 232
2C-B 229
Caffeine (extreme dose)213
Kratom 209
Dimenhydrinate 207
Pharms - Oxycodone 205
Amanitas 194
Hydrocodone 194
Heroin 191
DPT 185
Inhalants 185
Ayahuasca 179
GHB 172
Kava 172
Pharms - Zolpidem 165
2C-T-2 164
Codeine 161
Pharms - Alprazolam 150
5-MeO-AMT 138
Pharms - Methylphenidate 138
Mimosa spp. 136
Tobacco 132
Pharms - Bupropion 128
Pharms - Paroxetine 124
Pharms - Venlafaxine 124
Modafinil 111
Methylone 109
Absinthe 108
Anadenanthera spp. 107
Melatonin 105
Spice and Synthetic Cannabinoids 104
Pharms - Buprenorphine 101
Piperazines 98
Pharms - Clonazepam 96
Cannabinoid Receptor Agonists 90
Least Reported Experiences



Table 2 gives lists of the most rewarding, risky, addictive and most reported common drugs on Erowid.  I found it interesting that the most rewarding and least addictive drugs tended to be hallucinogens and entheogens, and that the most stereotypically abused drugs (cocaine, heroin, methamphetamine) had higher risk and addiction coefficients but generally lower rates of rewarding experiences. This trend makes me think of the Rat Park Experiment where it was shown that rats that lived happy lives did not become addicts despite easy availability of drugs in their environment.  This supports the idea that methamphetamine, cocaine, and heroin may indeed be more of an avenue of escape than a positive experience in their own right.  The bath salts which I wondered about from the cannibal attack in the news typically contain MDPV, a substance which did not have enough documented experiences on Erowid to pass my criterion for inclusion of having more than 100 user reviews.  

Figure 1:  Risk versus Reward in common drugs on Erowid as calculated by an elastic net model.  Please note that this plot is greatly affected by self-selection bias.  For example, normal users of caffeine think nothing of it, but do not report their experiences.   
Most remarkably, Figure 1 shows that the risk-to-reward ratio of hallucinogens and entheogens tended to be in the "more rewarding, less risky" category, and drugs categorized as research chemicals tended to offer a better reward-to-risk ratio than alcohol, tobacco, cocaine or heroin.

Overall, I was a bit disappointed with the results of this study.  Not shown are numerous plots and statistics that I thought would be enlightening, but in the end were worthless due to the corruption of the self-selection bias.  For example, I was sure there would be a correlation between number of reports and reward coefficient, but in fact I could find nothing significant.  It's possible that availability is a bigger factor in selecting a drug than reward, or that users of some drugs are simply more inclined to share their story.  In order to get better data on the effects of illegal drugs, it would require a survey designed to avoid self-selection bias.

In conclusion,
  • This study was enormously biased.  The popularity section of Table 2 shows a colossal self-selection bias because cocaine is reported far more than alcohol.  I did not account for dose nor for the type of risk, which is obvious because high doses of caffeine were more associated with risk than alcohol.
  • There was not enough data at Erowid to satisfactorily assess the outcomes associated with MDPV, a principal component of many "bath salts".  The R code for this study is provided in case you would like to check for yourself with another threshold.
  • The user experiences at Erowid indicate that that the most rewarding and least risky drugs tend to be hallucinogens and entheogens.  
  • The user experiences at Erowid indicate that the most dangerous drug is GHB, followed by Methamphetamine, Heroin and Cocaine.
  • I do not partake in, endorse or recommend the use of any illegal substance.  There is no intrinsically safe substance.      

As a bonus, I added some code to the R script to produce word cloud data for use with programs such as the freeware program Wordaizer.  To produce these word clouds, I first counted all of the words in a random sample of Erowid experiences, then counted the words in the experiences of just the drug of interest.  By dividing the frequency of each word in the drug-specific set by the frequency of each word in the control sample, I was able to get an idea of which keywords were truly more common and unique to each drug as opposed to Erowid experiences in general.  I then used Wordaizer under Wine in order to produce these attractive word clouds.  The code I used to produce these is freely available.




Ayahuasca
Caffeine
Cannabis
GHB
Inhalants
LSD
Cocaine
Heroin
Meth
Figure 2:  Word clouds produced using Wordaizer with Erowid experiences for a selection of drugs, normalized and prepared with R code available above.

Thanks to Simiao for proofreading and advice regarding the design of this study.  

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