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 Study	Erowid Category
Number of Reports	Total 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.