Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions
Duilio Balsamo, Paolo Bajardi, Alberto Salomone, Rossano Schifanella
PPatterns of Routes of Administration and Drug Tamperingfor Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions
DUILIO BALSAMO,
Mathematics Department, University of Turin
PAOLO BAJARDI,
ISI Foundation
ALBERTO SALOMONE,
Chemistry Department, University of Turin
ROSSANO SCHIFANELLA,
Computer Science Department, University of Turin and ISI Foundation
Background:
The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of alarge body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on howto implement interventions and policies to effectively control its impact on public health.
Objectives:
This study leverages Reddit, a social media platform, as the primary data source to investigate theopioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, tracethe temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumptionof opioids, with a focus on routes of administration and drug tampering.
Methods:
We used a semiautomatic information retrieval algorithm to identify subreddits discussing non-medical opioid consumption and developed a methodology based on word embedding to find alternativecolloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tamperingmethods. We modeled the preferences of adoption of substances and routes of administration, estimating theirprevalence and temporal unfolding. Ultimately, through the evaluation of odds ratios based on co-mentions, wemeasured the strength of association between opioid substances, routes of administration, and drug tampering.
Results:
We identified 32 subreddits discussing nonmedical opioid usage from 2014 to 2018 and observed theevolution of interest among over 86,000 Reddit users potentially involved in firsthand opioid usage. We learnedthe language model of opioid consumption and provided alternative vocabularies for opioid substances, routesof administration, and drug tampering. A data-driven taxonomy of nonmedical routes of administration wasproposed. We modeled the temporal evolution of interest in opioid consumption by ranking the popularity ofthe adoption of opioid substances and routes of administration, observing relevant trends, such as the surge insynthetic opioids like fentanyl and an increasing interest in rectal administration. In addition, we measured thestrength of association between drug tampering, routes of administration, and substance consumption, findingevidence of understudied abusive behaviors, like chewing fentanyl patches and dissolving buprenorphinesublingually.
Conclusions:
This work investigated some important consumption-related aspects of the opioid epidemicusing Reddit data. We believe that our approach may provide a novel perspective for a more comprehensiveunderstanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and controlof the public health effects.Additional Key Words and Phrases: routes of administration; drug tampering; Reddit; word embedding;social media; opioid; heroin; buprenorphine; oxycodone; fentanyl
Authors’ addresses: Duilio Balsamo, [email protected], Mathematics Department, University of Turin, Turin, Italy;Paolo Bajardi, [email protected], ISI Foundation, Turin, Italy; Alberto Salomone, [email protected], Chemistry De-partment, University of Turin, Turin, Italy; Rossano Schifanella, [email protected], Computer Science Department,University of Turin, Turin, Italy, ISI Foundation, Turin, Italy. a r X i v : . [ c s . C Y ] F e b Duilio Balsamo, Paolo Bajardi, Alberto Salomone, and Rossano Schifanella
In the last decade, the United States witnessed an unprecedented growth of deaths due to opioiddrugs [1], which sparked from overprescriptions of semisynthetic opioid pain medication such asoxycodone and hydromorphone and evolved in a surge of abuse of illicit opioids like heroin [2, 3]and powerful synthetic opioids like fentanyl [4, 5]. Alongside traditional medical, pharmacological,and public health studies on the nonmedical adoption of prescription opioids [6–14], severalphenomena related to the opioid epidemic have recently been successfully tackled through a digitalepidemiology [15–18] approach. Researchers have used digital and social media data to performvarious tasks, including detecting drug abuse [19, 20], forecasting opioid overdose [21], studyingtransition into drug addiction [22], predicting opioid relapse [23], and discovering previouslyunknown treatments for opioid addiction [24]. A few recent studies investigated the temporalunfolding of the opioid epidemic in the United States by leveraging complementary data sourcesdifferent from the official US Centers for Disease Control and Prevention data [2, 25–28] and usingsocial media like Reddit [29, 30].Pharmacology research is interested in understanding the consequences of various routes ofadministration (ROA), that is, the paths by which a substance is taken into the body [6, 31, 32], dueto the different effects and potential health-related risks tied to them [10, 33, 34]. Researchers haveestimated the prevalence of routes of administration for nonmedical prescription opioids [9, 31, 32,35] and opiates [36, 37]; however, they rarely consider less common ROA, such as rectal, transdermal,or subcutaneous administration [32, 38], leaving the mapping of nonmedical and nonconventionaladministration behaviors greatly unexplored [39, 40]. Many of these studies [31, 32, 35] acknowledgethat drug tampering, that is, the intentional chemical or physical alteration of medications [41],is an important constituent of drug abuse. The alteration of the pharmacokinetics of opioidsthrough drug-tampering methods, together with unconventional administration, may potentiallylead to very different addictive patterns and ultimately have unexpected health-associated risks[33]. Research has also been focused on developing tamper-resistant and abuse-deterrent drugformulations. However, to the best of our knowledge, no large-scale empirical evidence has beenfound to unveil the relationships between substance manipulation, unconventional ROA, andnonmedical substance administration.
This paper seeks to complement current studies widening the understanding of opioid consumptionpatterns by using Reddit, a social content aggregation website, as the primary data source. Thisplatform is structured into subreddits, user-generated and user-moderated communities dedicatedto the discussion of specific topics (Multimedia Appendix, Figure 6) . Due to fair guarantees ofanonymity, no limits on the number of characters in a post, and a large variety of debated topics,this platform is often used to uninhibitedly discuss personal experiences [42]. Reddit constitutes anonintrusive and privileged data source to study a variety of issues [43, 44], including sensitivetopics such as mental health [45], weight loss [46], gender issues [47], and substance abuse [22, 24].This study’s contributions are manifold. First, leveraging and expanding a recent methodologyproposed by Balsamo et al [30], we identified a large cohort of opioid firsthand users (ie, Redditusers showing explicit interest in firsthand opioid consumption) and characterized their habits ofsubstance use, administration, and drug tampering over a period of 5, years. Second, using wordembeddings, we identified and cataloged a large set of terms describing practices of nonmedicalopioids consumption. These terms are invaluable to performing exhaustive and at-scale analyses ofuser-generated content from social media, as they include colloquialisms, slang, and nonmedical atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 3 terminology that is established on digital platforms and hardly used in the medical literature. Weprovided a longitudinal perspective on online interest in the opioids discourse and a quantitativecharacterization of the adoption of different ROA, with a focus on the less-studied yet emerging andrelevant practices. We have made available the ROA taxonomy and the corresponding vocabularyto the research community. Third, we quantified the strength of association between ROA anddrug-tampering methods to better characterize emerging practices. Finally, we investigated theinterplay between the previous 32, dimensions, measuring odds ratios to shed light on the “how”and “what” facets of the opioid consumption phenomenon. We studied a wide spectrum of opioidforms, referred to as “opioids” throughout, ranging from prescription opioids to opiates and illegalopioid formulations. To the best of our knowledge, our contributions are original in both breadthand depth, outlining a detailed picture of nonmedical practices and abusive behaviors of opioidconsumption through the lenses of digital data.
We refer to a publicly available Reddit data set [48] that contains all the subreddits published onthe platform since 2007 [44, 49]. In this work, we analyzed the textual part of the submissionsand the comments collected from 2014 to 2018. We preprocessed each year separately, filteringout the subreddits with less than 100 comments in a year. We used spaCy [50] to remove Englishstop words, inflectional endings, and tokens with less than 100 yearly appearances. We adopted abag-of-words model, resulting in a vocabulary of different lemmas for each year. Vocabulary sizesranged from 300,000 to 700,000 lemmas, with a size growth of approximately 30% each year. InTable 1, the number of unique comments and unique active users per year is reported. A steadygrowth of approximately 30%, per year both in the volume of conversations and in the active userbase is observed.All the analyses in this work were performed on a subset of subreddits related to opioid consumption,which were identified using the procedure described here. For space constraints, we restricted theanalyses of odds ratios to comments and submissions created during 2018. Similar to a vast body ofusers’ activities on social media platforms [51–53], the distribution of posts per user shows a heavytail, with the majority of users posting few comments and the remaining minority (eg, core usersand subreddit moderators) producing a large portion of the content. Moreover, a nonnegligiblepercentage of posts, respectively 25%, and 7%, of submissions and comments, were produced byauthors who deleted their usernames.Year Redditcomments, n Redditauthors, n Opiatessubreddits, n Opiatescomments, n Opiatesauthors, n Authors’prevalence2014 545,720,071 8,149,234 19 386,984 12,381 0.00152015 699,245,245 10,673,990 19 470,609 15,888 0.00152016 840,575,089 12,849,603 25 612,619 21,791 0.00172017 1,045,425,499 14,219,062 30 866,023 28,358 0.00202018 1,307,123,219 18,158,464 25 919,036 33,700 0.0019
Table 1. Dataset Statistics.
Duilio Balsamo, Paolo Bajardi, Alberto Salomone, and Rossano Schifanella
The methodology adopted in this paper consists of several steps. First, we identified a cohort ofopioid firsthand users and the subreddits related to opioid consumption through a semiautomaticalgorithm. Second, we trained a word-embedding language model to capture the latent semanticfeatures of the discourse on the nonmedical use of opioids. Third, we exploited the embeddedvectors to extend an initial set of medical terms known from the literature, (eg, opioid substancenames, ROA, and drug-tampering methods) to nonmedical and colloquial expressions. The termswere organized in a taxonomy that provides a conceptual map on the topic. Moreover, we studiedthe temporal evolution of the popularity of the main opioid substances and ROA. Ultimately, wemeasured the strength of the associations between opioid substances, routes of administration, anddrug-tampering techniques in 2018.
We leveraged a semiautomaticinformation retrieval algorithm developed to identify relevant content related to a topic of interest[30] to collect opioid-related conversations on Reddit yearly. This approach aims at retrievingtopic-specific documents by expressing a set of initial keywords of interest; here, it identifiedrelevant subspaces of discussion via an iterative query expansion process, retaining a list of terms 𝑄 𝑦 and a list of subreddits 𝑆 𝑦 ranked by relevance for each year. We merged all the query termsin a set ¯ 𝑄 = (cid:208) 𝑦 𝑄 𝑦 containing 67 terms. To ensure that the sets 𝑆 𝑦 were effectively referring tothe opioid-related topics and in particular to nonmedical opioid consumption, we performed amanual inspection on the union of the top 150 subreddits for each year, for a total of 554 subreddits.Three independent annotators, including a domain expert specialized in antidoping analyses, reada random sample of 30, posts, checking for subreddits (1), mostly focused on discussing the use ofopioids, (2) mostly focused on firsthand usage, and (3), not focused on medical treatments. Thisyielded a total of 32, selected subreddits, with a Fleiss’ 𝑘 interrater agreement of 𝑘 = . The methodology to extend the vocabulary on opioid-related domainswith user-generated slang and colloquial forms was implemented in 2, steps. First, we trained aword-embedding model (word2vec [58]), which learns semantic relationships in the corpus duringtraining and maps their terms to vectors in a latent vectorial space, with all the comments andsubmissions in our subreddit data set (relevant training parameters are displayed in MultimediaAppendix Table 7). Second, starting from a set of seed terms K (eg, a list of known opioid substances),we expanded the vocabulary by navigating the semantic neighborhood 𝐸 𝑛𝑤 = 𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑢𝑟𝑠 ( 𝑤, 𝑛 ) ofeach element 𝑤 ∈ ¯ 𝑄 in the embedded space, considering the 𝑛 =
20, semantically closest elementsin terms of cosine similarity. We merged the results in a candidate expansion set, ¯ 𝐸 = (cid:208) 𝑤 𝐸 𝑛𝑤 ,together with the seed terms 𝐾 if not already included. Based on the knowledge of a domain expert(a clinical and forensic toxicologist) and with the help of search engine queries and a crowdsourced atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 5 acetylfentanyl acryladminister affinity agonistanalgesia analgesicanaloganalogueantagonistantidote bitartrate bthbupbupebuprenorphine butyrbutyrfentanylcarfcarfent carfentanilcarfentanyl clinic cocaine cocodamol codein codeinecodienecodine codone crushablecwedarvocetdemeroldesomorphine dhcdiacetyldiacetylmorphinediamorphinedihydrocodeinedilaudeddilaudiddilliesdillysdope drugefficacyepi fentfentanylfentynalfetanyl formulationfuf furanyl g74 gabapentinguaifenesin h hcl herion heroinheroine hydrohydrocodonehydrocodoneshydromorphhydromorphone illicitimpure insufflatedintranasally irivived lortab lortabs lyricamaint maintainencemaintancemaintenance matinence mdone meth methadone methamphetamine methodone microgrammmt morph morphin morphinenalaxonenaloxonenaltrexonenarcannarcon norconorcos nri nucynta opanaopanas opiateopioid opiumoralorallyort otc oxy oxycodon oxycodoneoxycontinoxycotinoxymorphoneoxys painkillerparacetamolparacetemol percocetpercocets pethidinephosphatepotentprecip precipitate prodrug prometh promethazinepwdreversal roxicodone snri somassristabilize sub sublingualsublingually suboxone substance subutex sufentanil tapentadoltaper tartratethebaine tramtramadol tussionexultram unreactedvenlafaxine vicodinvivitrol wean zubsolv buprenorphinecodeinefentanylheroinhydrocodonehydromorphonemethadonemorphineoxycodoneoxymorphonetramadolnaloxone discarded Fig. 1. Two-dimensional projection of the word2vec embedding, modeling the semantic relationships amongterms in the Reddit opioids data set. Filled markers represent the seed terms K. Expansion terms, representedwith hollow markers, are colored according to their respective initial term if accepted or in gray if discarded.The nature of the relationships between neighboring terms varies, representing (1) equivalence (eg, synonyms),(2) common practices (eg, the use of methadone for addiction maintenance), or (3) co-use (eg, the cluster ofheroin, cocaine, and methamphetamine). online dictionary for slang words and phrases (Urban Dictionary [59]) to understand the mostunusual terms, we manually selected and categorized the relevant neighboring terms, obtainingan extended vocabulary 𝑉 . Figure 1 shows an example of the expansion procedure in which thehigh-dimensional vectors are projected to 2 dimensions using the uniform manifold approximationand projection (UMAP) algorithm [60]. As a sensitivity analysis, we compared the effectivenessof an alternative embedding model (GloVe [61]) for topical coherence. In the case of vocabularyexpansion of opioid substance terms, that is, using 𝐾 = ¯ 𝑄 as seeds, the 2 models captured 100terms in common out of their respective candidate terms, with word2vec showing a higher numberand a larger percentage of accepted terms (2) . Moreover, the volume of comments that included Duilio Balsamo, Paolo Bajardi, Alberto Salomone, and Rossano Schifanella an accepted term was almost double when using the vocabulary of word2vec rather than thevocabulary of GloVe. Hence, we chose word2vec as the reference word-embedding model.Candidateterms, n Acceptedterms, n (%) Comments a ,n word2vec
297 128 (43.1) 225165
GloVe
369 110 (29.8) 144564
Table 2. Comparison of term expansions of opioid substances for the 2 trained models. a Comments in thecorpus mentioning at least one term of the respective accepted terms for vocabulary expansion.
We evaluatedthe odds ratios (ORs) to quantify the pairwise strength of the association between substance useand ROA, substance use and drug-tampering methods, and ROA and drug-tampering methods.Under the assumption that co-mention was a proxy for associating a substance to its ROA (ordrug-tampering method), we focused on the posts that contained a reference to terms in eachdomain, evaluating contingency tables and odds ratios. Odds ratios, significance, and confidenceintervals were estimated using chi-square tests implemented in the statsmodel Python package[62], with the significance level set to 𝛼 = .
01. As a sensitivity analysis, we assessed the effect ofthe proximity of terms on the characterization of odds ratios. We modified the definition of co-occurrence, introducing a distance threshold 𝜌 at sentence level. We explored the range 𝜌 ∈ { , ..., } ,, where 𝜌 = 𝜌 > 𝜌 =
1, for the preceding and consecutive sentences). The value 𝜌 = ∞ indicates the scenario in which we considered the entire post as reference. Accordingly,given a threshold 𝜌 in the construction of the contingency table, the co-occurrence event betweentwo terms is conditioned to their distance being less than or equal to 𝜌 . Conversely, we consideredterms to be separate events in cases of distance above the threshold. It is important to consider thatthe OR measures do not imply any form of causation but rather surface correlations that could beused in hypothesis formation. To better interpret the results of this analysis, in some cases, manualinspection of the comments mentioning the variables under investigation was performed followingthe directives on privacy and ethics (see the “Ethics and Privacy” section). We applied the methodology described in the “Vocabulary Expansion” section to extract andexpand domain-specific vocabularies and to characterize the temporal unfolding of interest indifferent opioid substances, routes of administration, and drug-tampering methodologies. Westarted from a review of the relevant medical research, collecting an initial set of terms referringto the most common opioid substances, ROA [6, 10, 31, 34, 38, 39, 41, 63, 64], and drug-tamperingmethods [41, 63]. We expanded the original set with neighboring terms in a low-dimensionalembedding space, and the outputs were reviewed and organized by a domain expert. The resultingvocabulary for opioid substances is shown in Table 3. It is worth noting that the vocabularyexpansion procedure considerably increased the richness of the terminology related to the domainof interest and, consequently, the volume of conversations on Reddit that contained these terms. Forexample, for the heroin category, we observed a 62%, growth in the retrieved relevant conversations(Table 3). We investigated the temporal unfolding of the popularity of the opioid substances, atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 7 measured as the fraction of authors mentioning a substance over the entire opioid firsthand userbase, for each trimester from 2014 to 2018. A binary characterization of the mentioning behaviorat the user level was considered to discount potential biases due to users with high activity. Wealso provided a relative measure of popularity to account for the constantly increasing volume ofactive users on Reddit. Figure 2 shows a decrease in the usage of heroin and a rise in fentanyl andcodeine. The resulting vocabulary for routes of administration was further organized in a 2-levelhierarchical structure, reported in Table 4. It is worth noting that the taxonomy does not have astrict medical interpretation, nor was it intended to be a comprehensive review. However, it cangive structure to otherwise unstructured collections of words and help in the interpretation of theresults. Figure 3 shows the estimated temporal evolution of the relative popularity of the routesof administration from 2014 to 2018, measured in quarterly snapshots. Finally, we extracted andorganized the vocabulary related to drug-tampering techniques, as shown in Table 5. In this paper,we considered the act of chewing pills a second-level route of administration under the ingestioncategory [8, 31, 32] instead of a drug-tampering method, as some research might suggest [41].
Substance Terms Δ 𝑉𝑜𝑙𝑢𝑚𝑒, %Heroin bth , diacetylmorphine, diamorphine,dope, ecp , goofball, goofballs,gunpowder, h, herion , heroin , heroine, heron, smack,speedball, speedballing, speedballs , tar 62Buprenorphine bup, bupe , buprenorphine , butrans , sub, suboxone , subutex , zub, zubsolv hydrocodone , hydrocodones , lortab , lortabs , norco , norcos , tuss, tussionex , vic,vicoden, vicodin , vicodins , vicoprofen , vics ,vikes, viks, zohydro codein , codeine , codiene , codine,dhc, dihydrocodeine , prometh, sizzurp, syrup 28Oxymorphone g74, opana , opanas, oxymorphone , panda 25Tramadol desmethyltramadol, dsmt, tram, tramadol , ultram dilaudid , dilaudids, dillies , dilly, dillys, diluadid , hydromorph , hydromorphone oxy , oxycodone , oxycontin , oxycontins, oxycotin , oxys , perc , percocet , percocets , percoset, percosets, percs , perk, roxi , roxicodone , roxie , roxies , roxis , roxy , roxycodone , roxys morphine acetylfentanyl , butyr, butyrfentanyl, carf, carfent, carfentanil , carfentanyl, duragesic , fent , fentanyl , fents,fentynal, fetanyl, furanyl, sufentanil, u47700 4Antagonist nalaxone , naloxone , naltrexone, narcan , narcon, revia,viv, vivitrol methadone , methodone Table 3. Vocabulary of opioid substances. Starting from a candidate expansion set ¯ 𝐸 , comprising 297 uniqueterms, the final expansion terms considered equivalent to a substance were gathered in the same class. Termsin ¯ 𝑄 are highlighted in bold. The increase in the volume of occurrences of a substance using the terms in theexpanded vocabulary compared with only using the terms in ¯ 𝑄 . Duilio Balsamo, Paolo Bajardi, Alberto Salomone, and Rossano Schifanella A u t h o r s , % HeroinBuprenorphineOxycodoneFentanylMethadoneCodeineHydrocodoneMorphineAntagonistTramadolHydromorphoneOxymorphone
Fig. 2. Popularity of opioid substances among opioid firsthand users on Reddit. Each line represents theshare of opioid firsthand users mentioning an opioid substance, measured quarterly from 2014 to 2018. A u t h o r s , % Injection 2014 2015 2016 2017 2018Time0.100.150.200.250.30 A u t h o r s , % Inhalation2014 2015 2016 2017 2018Time0.050.100.150.200.25 A u t h o r s , % Ingestion 2014 2015 2016 2017 2018Time0.000.010.020.030.040.05 A u t h o r s , % Rectally and other ROA
Injection
InjectionGeneral injectionIntramuscularIntravenous Subcutaneous
Inhalation
InhalationGeneral inhalationIntranasal Smoking
Ingestion
IngestionGeneral ingestionChewDrinkOralSublingual
Rectally and other ROA
Other ROADermalUrogenitalRectally
Fig. 3. Popularity of routes of administration among opioid firsthand users on Reddit. Each line representsthe fraction of opioid firsthand users mentioning an ROA-related term, measured quarterly from 2014 to2018. Thick lines represent the share of authors mentioning primary ROA, evaluated by aggregating thecontributions of all the corresponding secondary ROA. ROA: routes of administration. atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 9
Primary ROA Secondary ROA TermsIngestion Oral bolus, buccal, gulp, mouth, mouthful, oral ,orally, swallow
Sublingual sublingual , sublingually, tongue, toungeDrink chug, drink, pour, pourin, sip , sipper, sippin, swig, swishChew chew , chewy, chomp, gumGeneral Ingestion ingest , ingestionInhalation Intranasal intranasal, intranasally, nasal, nasally, nose, nostril,rail, sniff , sniffer, sniffin, snoot, snooter, snort , snorter,tooterGeneral Inhalation breath, breathe,dab, exhale, inhalation, inhale , insufflate,insufflated, insufflating, insufflation, puff, toke, tokes, vap,vape, vaped, vapes, vaping, vapor, vaporise, vaporize,vaporizer, vapourSmoking bong, fume, hookah, pipe, smoke , smoker, smokin, spliffInjection Intramuscular deltoid, imed, iming, intramuscular , intramuscularlySubcutaneous subcutaneous , subcutaneously, subqIntravenous arterial, bloodstream, intravenous , intravenously, iv , ivd, ived, iving, ivs, vein, venousGeneral Injection bang, inject , injectable, injection,parenteral, shoot, shotRectally Rectally anal, anally, boof , boofed, boofing, bunghole, butt,pooper, rectal , rectallyOther ROA Dermal cutaneous, dermis, transdermal , transdermallyUrogenital vaginalIntrathecal intrathecal Table 4. Taxonomy defining the ROA categories and their corresponding terms. Primary ROA include all theexpansion terms considered for the appropriate secondary ROA (original candidate expansion set comprised199 unique terms). Seeds in 𝐾 are highlighted in bold. To investigate the strength of association between routes of administration, drug tampering, andopioid substances and to shed light on the interplay between the “how” and the “what” dimensionsof opioid consumption, we estimated the ORs, 95% confidence intervals, P values, and volumeof the co-mentions among substances, routes of administration, and drug-tampering methods.The number of sentences in Reddit posts vary greatly, but the posts are generally quite short(approximately 50% of them have 2 sentences or less, as seen in Multimedia Appendix Figure 7).However, as about 20% of posts have more than 10 sentences, one should be cautious in adopting abag-of-words approach to measure co-occurring terms. To limit the chance of including spuriouscorrelations due to the co-occurrences of terms far apart in the posts, we conservatively selected 𝜌 =
1, (ie, considering only the co-occurrence of terms within a sentence or in the first adjacentsentences) for computing the OR. Figure 4 shows in blue the results of the analysis at 𝜌 = 𝜌 = 𝜌 = ∞ for the same categories. Multimedia Transformation TermsBrew brew , brewer, homebrew
Concentrate concentrate , concentrate,concentration, purifyDissolve desolve, dilute, disolve, disolved, disolves, dissolve ,solute, solution, soluble, soluable,Evaporate evap, evaporateExtract cwe , extract , extractionGrind chop, crush , crushable, crusher, grind , grinded, grinder,ground, pulverizeHeat boil, heat , melt, microwave, overheat, simmerInfusion infuse, infusion , tea, tincturePeel peal, peel, shaveSoak soak , submergeWash rewash, rinse, wash Table 5. Vocabulary of drug-tampering methods. Expansion terms referring to the same drug-tamperingmethod are grouped in the corresponding transformation classes (original candidate expansion set comprised179 unique terms). Seed terms 𝐾 are highlighted in bold. Appendix Figures 8,9,10, provide the complete set of results for all the substances identified and thesecondary ROA. Due to the low representativeness of intrathecal and urogenital ROA with mostof the tampering-related terms, we omitted those categories from the analysis. In the plots, theassociations that are not statistically significant (P>.01) are reported in gray, and the horizontallines indicate the OR and the 95% confidence interval. The radius of the circle is proportional to thesample of co-mentions and the dashed vertical line corresponds to an OR of 1, for reference.
In this work, we identified over 3 million comments on 32 subreddits focused on discussing practicesand implications of firsthand opioid use. We also selected a cohort of over 86,000 Reddit usersinterested in this topic. Such a large data set allowed us to assess the magnitude of the onlineinterest in opioids and model its evolution during the 5 years of study, sadly verifying its rapidlyincreasing popularity. By the end of 2018, the opioid epidemic remained an escalating public healththreat, and at the time of writing, the opioid crisis is still calling for countermeasures at scale.Hence, we believe our large data set may constitute a valid alternative source to advise decisionmaking and a valuable starting point for future infodemiology research.
By observing the vocabularies in Tables 3,4,5 resulting from the expansion algorithm, we canascertain the importance of enriching domain expertise with user-generated content and observethat some common features are captured across categories. Our method was able to detect synonymsand common short names, very specific acronyms (eg, “cwe” for cold water extraction [65]), slangexpressions like “sippin” (often used when referring to the act of drinking codeine mixtures[63]), nicknames (eg, “panda” for oxymorphone), and polypharmacy instances (eg, “speedball” and“goofball” [66]). The vocabulary expansion underlines the use of prescription dosages (usuallystamped on the tablets) in place of the commercial names of the substances (eg, "30s” for oxycodone).Moreover, we deduced that opioid firsthand users discussed variants of the substances (eg, “bth” and atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 11 “ecp” for black tar heroin and East Coast powder), research chemical equivalents (eg, “u47700” [67]),and formulations intended for veterinary use (eg, sufentanil, carfentanil). ROA vocabulary includedand categorized both medical terms, adding terms scarcely considered in previous studies, like“vaping,” and nonmedical or unconventional administration terms, such as “chewing,” “snorting,”“smoking,” and “boofing” [39]. Our taxonomy also enabled us to disambiguate common primary ROA,such as injection and ingestion, into specific secondary ones, like subcutaneous [39] and sublingualadministrations. Finally, the drug-tampering vocabulary captured tampering methods that modifythe physical status of the substances, like crushing and peeling, and some methods aiming ataltering the chemical characteristics of the substances, like dissolving, washing, and heating [41].We believe that even if this vocabulary might not be exhaustive of all drug-tampering methods, itoffers a novel evidence-based perspective on the topic compared with the existing literature. Theexpanded vocabularies proved essential to fully incorporating the language complexity of online
Fig. 4. Odds ratios of the most widespread opioid substances with routes of administration (top row) anddrug-tampering methods (bottom row). Labels on the right axis report the confidence interval at 𝜌 = . OR:odds ratio. OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Injection OR Ingestion OR Inhalation OR Rectally (2.5-3.9) (2.4-4.0) (1.9-3.2) (1.9-4.0) (2.2-3.2) (2.4-3.0) (1.8-2.3) (4.6-5.4) (4.0-4.8) (3.0-3.4) (6.5-7.3) 95% CI (1.0-1.7) (0.2-0.6) (2.8-4.2) (3.9-6.6) (2.0-2.8) (0.8-1.1) (2.6-3.1) (0.7-0.9) (0.9-1.2) (3.8-4.3) (2.6-3.0) 95% CI(1.3-2.2) (0.4-0.9) (0.6-1.2) (1.7-3.3) (1.6-2.4) (1.0-1.3) (2.4-2.8) (0.7-1.0) (1.5-1.8) (1.4-1.7) (3.4-3.8) 95% CI (1.5-4.8) (0.2-2.2) (0.5-4.4) (0.7-2.4) (1.3-2.5) (1.1-2.0) (0.2-0.7) (0.6-1.4) (2.4-3.4) (4.0-5.4) 95% CI
Sample (N)
20 50 200 500 1K 2K 5K
Sample (N)
20 50 200 500 1K 2K 5K
Sample (N)
20 50 200 500 1K 2K 5K
Sentence threshold
Fig. 5. Odds ratios of the primary routes of administration (excluding other routes of administration) anddrug-tampering methods. Labels on the right axis report the confidence interval at 𝜌 = . OR: odds ratio. discussions and taboo behaviors [68] into at-scale analyses. Hopefully, our contribution mightbe useful in the future to find and understand new abusive behaviors that are discussed online,ultimately driving future research to yield more effective prevention methods. Considering the share of users mentioning a term to be a proxy of firsthand involvement in opioid-related activities and including topic-specific terminology, the longitudinal views in Figures 2 and3 can be used to rank the popularity of nonmedical usage of opioid substances and ROA and theiradoption trends. Ranking the substances by average share, we can see that heroin is by far themost popular substance, mentioned on average by 1, in every 3 users. Its share of users, though,is steadily decreasing, with a loss of 10% reported in state-specific findings by Rosenblum et al[27]. Buprenorphine and oxycodone were the most mentioned prescription opioids; they showedfairly static behavior, while hydrocodone importance decreased over time [28], possibly due tomore stringent prescription regulation starting in 2014 [69]. Fentanyl showed the most abruptbehavior, dramatically increasing since 2016. Its volume of mentions in 2018 increased by almost1.5 times compared with 2014, confirming it as one of the most recent threats [5, 28]. In contrast,we did not find evidence of drastic changes in oxymorphone adoption after its partial ban in2017 [70]. ROA adoption was led by injection and inhalation, which were the most popular ROAacross the years, mentioned by 1 of every 3 authors at their peak. These were followed closelyby ingestion. Rectal use and other ROA involved, on average, a significantly lower share of users,around 5% and less than 1%, respectively. Nevertheless, rectal administration has shown a sharpincrease in popularity since 2016, almost doubling its share. Administration through inhalation wasequally staggered by the intranasal and smoking categories of secondary ROA, strong indicatorsthat this route of administration is indeed capturing nonmedical use of opioids. This work onunderstanding which substances are currently gaining popularity may give prevention programs astrategic advantage, especially if consumption trends can be localized geographically [12, 30, 71],focusing the interventions needed to prevent early adoption of emerging dangerous substances atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 13 like fentanyl. Moreover, tracking the evolution of interest in prescription opioids might be usefulfor evaluating the efficacy of ban policies, as in the case of oxymorphone. Understanding whichROA are the most adopted might eventually help address targeted campaigns informing users onsafer practices, develop better tamper-resistant prescription drugs, and ultimately better informthe health system of the health risks specific to opioid adoption.
By jointly considering the results of the odds ratios in Figures 4 and 5 and Multimedia AppendixFigures 8,9,10, we can outline complex preferences for the nonmedical use of opioids, triangulatingsubstance use, ROA, and drug-tampering methods. We noticed that the majority of substancesexhibited more than one high odds ratio, both with ROA and drug-tampering methods, meaningthat such substances might be consumed by users in multiple nonexclusive ways. Our analysisshows that for the most part, the expected medical and nonmedical routes of administration ofeach substance (ie, intended ROA or known abusive administration) had high odds ratios. Forprescription opioids, oral (medical) use was often confirmed (eg, oxycodone: OR 3.6, 95% CI 3.4-3.8),while intranasal administration was often the preferred nonmedical ROA, followed by injection,especially intravenous administration (eg, hydromorphone: OR 9.1, 95% CI 8.6-9.8) [32, 72]. Asexpected, heroin appeared to be most likely consumed through injection (OR 3.3, 95% CI 3.2-3.4) orsmoking, if heated up on aluminum foil (OR 3.1, 95% CI 3.0-3.2). Heroin was the only substance thatshowed high correlations with this administration route. It was also reported to be snorted [64].Besides confirming and quantifying some known behaviors, our analysis can provide additionalinsights on the nonmedical use of intended routes of administration. In accordance with the lit-erature [31, 32, 40, 73], we found evidence that abuse of prescription opioids may be associatedwith chewing the pills (eg, oxycodone: OR 2.7, 95% CI 2.4-3.0). From the analysis of ROA anddrug-tampering methods, it appears that nonmedical oral administration was correlated withdissolving (OR 9.7, 95% CI 9.0-10.4), grinding, and washing the substances. In some cases, oral andchewing-related misuse of prescription opioids simply consisted of peeling (OR 5.1, 95% CI 2.6-9.9)the external coating, which is usually hard to chew or responsible for the extended-release effect.Even though some formulations, such as Opana ER (oxymorphone hydrochloride extended-releasetablets; Endo Pharmaceuticals), are known to be tamper resistant to crushing, users can peel thetablets to get rid of the extended release coating for higher recreational effects. Injection usuallyrequires that the substance be dissolved (OR 3.5, 95% CI 3.2-3.7), while inhalation requires that thesubstance be ground to powder, especially for intranasal abuse (OR 6.7, 95% CI 6.3-7.1).Our method ultimately found evidence of unconventional nonmedical administration for most ofthe substances. We found a high correlation between dissolving and intranasal administration (OR4.1, 95% CI 3.8-4.4), which may indicate the adoption of “monkey water,” the practice of dissolvingsoluble substances, like tar heroin and fentanyl patches, and using the resulting liquid as a nasalspray [36]. Fentanyl patches were also consumed in other unforeseen ways; an unexpectedly highOR of fentanyl and chewing (OR 2.6, 95% CI 2.2-3.0) suggests that prescription patches intendedfor transdermal use may be chewed for nonmedical use. Our analyses revealed the high odds ofabuse of codeine via drinking (OR 4.0, 95% CI 3.7-4.3) codeine syrup, made by extracting or brewingthe cough suppressants (OR 14.1, 95% CI 11.5-17.2) and forming the so-called lean or purple drank[7, 63, 74].Buprenorphine, usually administered sublingually in its formulations without an antagonist, suchas Subutex (buprenorphine; Indivior), and orally in combination with naloxone in the form ofpills, such as Suboxone (buprenorphine-naloxone; Indivior) and Zubsolv (buprenorphine-naloxone;Orexo), measured exceptionally high odds of sublingual administration (OR 7.6, 95% CI 7.0-8.2).
Evidence of nonmedical use of buprenorphine was also found in the association between dissolv-ing and sublingual use (OR 18.9, 95% CI 16.8-21.3). Opioid firsthand users know that the opioidantagonist in buprenorphine-naloxone compounds has low bioavailability if dissolved under thetongue; hence, to achieve higher opioid effects and eliminate the antagonist, these compoundsare generally taken sublingually and not through other ROA, with which buprenorphine showsnegative associations. Finally, our study shows that rectal administration is a viable and unforeseenoption for the nonmedical use of some opioids, resulting in higher recreational effects, especiallywith hydromorphone (OR 5.2, 95% CI 4.6-6.0), morphine, and oxymorphone. Rectal administrationshowed high correlations with the dissolving, grinding, and soaking drug-tampering methods,possible indicators of an unconventional route of administration, largely overlooked, which involvesdissolving the substances in liquid water or alcohol (ie, “butt-chugging”) [39, 75]. Subcutaneousadministration was only weakly associated with morphine, suggesting that the practice of “skinpopping” [38], which consists of injecting the substance in the tissues under the skin, is potentiallynot widespread.The complex interactions between substance use, routes of administration, and drug tampering thatcan be unveiled with our methodology provide a broad yet detailed perspective on the nonmedicaluse of opioids, evidencing abusive behaviors in which unconventional ROA and drug tamperingplay a key role. Knowledge about abusive behaviors could be taken into consideration by physiciansduring treatment programs, allowing them to favor opioid medications that are less likely to betransformed and abused. Our results should be addressed with effective health policies, drivingfuture clinical research to better focus its efforts on understanding health-related risks and guidingthe production of new tamper-resistant and abuse-deterrent opioid formulations.
We acknowledge some limitations in the present research. The population sampled on Reddit mighthave intrinsic social media biases, and it is likely not representative of the general population(eg, for gender, age, or ethnicity). Moreover, since we enrolled the users in our cohort based ontheir engagement in subcommunities focusing on firsthand use of opioids, we cannot excludethe possibility that in some cases, such users might have been reporting secondhand experiences,disseminating general news, or discussing intended medical drug use for pain management. We mustalso consider that the selected individuals were not clinically diagnosed with opioid use disorder.Future work will be devoted to building a classifier at the user level to identify individuals withopioid use disorder. We are aware that Reddit data have some gaps [76], but since the incompletenessmostly affects the years before 2010, we consider the overall results of our work to not be significantlybiased. Other limitations are related to the analytic pipeline, where we narrowed our text analysisto term counts and co-occurrences, which might have produced spillover effects in commentsdiscussing multiple topics and could have amplified the strength of cross-associations. Future workwill include n-grams and more context-based language models. Finally, it is worth stressing that themeasure of association through odds ratios should not be intended by any means as an indicationof causal effects. This work is an observational study focusing on the characterization of a complexand faceted social phenomenon rather than the identification of determinants or interventions, andit shares the strengths and limitations of correlational studies, especially in medical research.
Given the sensitive nature of the information shared, including users’ vulnerabilities and personalinformation, privacy and ethical considerations are paramount. In this work, we followed theguidelines and directives in Eysenbach and Till [77], which describe recommendations to ethicallyconduct medical research with user-generated online data, and we relied on the vast experience of atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 15 research works dealing with sensitive data gathered on social media [47, 78–81]. The researchershad no interactions with the users and have no interest in harming any, and the analyses wereperformed and reported in the spirit of knowledge, prevention, and harm reduction. In this direction,it is worth noting that the subreddits under study are of public domain, are not password protected,and have thousands of active subscribers; users were fully aware of the public nature of the contentthey posted and of its free accessibility on the web. Moreover, Reddit offers pseudonymous accountsand strong privacy protection, making it it unlikely that the true identity of a user can be recovered.Nevertheless, in order to further protect the privacy and anonymity of the users in our data set, allinformation about the names of the authors was anonymized before using the data for analysis.Moreover, every analysis performed was intended to provide aggregated estimates aimed at researchpurposes, and this work did not include any quotes or information that focused on single authors.Following the directives in Eysenbach and Till [77], our research did not require informed consent.
In this work, we characterized opioid-related discussions on Reddit over 5 years, involving morethan 86,000 unique users, and focused on firsthand experiences and nonmedical use. To address thecomplexity of the language in social media communications, especially in the presence of taboobehaviors such as drug abuse, we gathered a large set of colloquial and nonmedical terms thatcovered most opioid substances, routes of administration, and drug-tampering methods. We wereable to characterize the temporal evolution of the discourse and identify notable trends, such asthe surge in the popularity of fentanyl and the decrease in the relative interest in heroin. Focusingon routes of administration, we extended pharmacological and medical research with an in-depthcharacterization of how opioids substances are administered, since different practices imply differenteffects and potential health-related risks. We proposed a 2-layer taxonomy and correspondingvocabulary that enabled us to study both medical and recreational routes of administration. Wedemonstrated the presence of conventional nonmedical ROA (eg, intranasal administration andintravenous injection) and the spread of less conventional practices (eg, an increasing trend inrectal use). In particular, with reference to nonconventional ROA, we characterized for the firsttime at scale the phenomenon of drug tampering, which could have an impact on health outcomes,since it alters the pharmacokinetics of medications. The interplay between these dimensions wassystematically characterized by quantitatively measuring the odds ratios, providing an insightfulpicture of the complex phenomenon of opioid consumption as discussed on Reddit.
ACKNOWLEDGMENTS
PB acknowledges support from the Intesa Sanpaolo Innovation Center. The funder had no role inthe study design, data collection, analysis, decision to publish, or preparation of the manuscript. RSwas partially supported by the project Countering Online Hate Speech Through Effective On-lineMonitoring, funded by Compagnia di San Paolo.
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MULTIMEDIA APPENDIX
Fig. 6. Schematic representation of the structure of Reddit. Reddit’s most common access point is the frontpage, where the most relevant content of the moment is collected. The users can post on already-existingsubreddits or they can create and moderate new ones on any topic of choice. In a subreddit, users can eithercreate a new thread via a submission or indefinitely expand the conversation tree by commenting on anexisting thread. The level of content moderation in a subreddit is solely decided by its moderators.
Subreddits 2014 2015 2016 2017 2018opiates x x x x xOpiatesRecovery x x x x xlean x x x x xheroin x x xsuboxone x x x x xPoppyTea x x x x xMethadone x x x x xOpiatewithdrawal x x x x xfentanyl x x x xcodeine x x x x xHeroinHeroines xheroinaddiction x x x xoxycodone x x x x xopiatescirclejerk x x x x xloperamide x x xOpiate_Withdrawal x xOpiateAddiction x x xPoppyTeaUniversity x xrandom_acts_of_heroin x x x x xNorco x x xGetClean x x0piates x x x x xzubsolv x x xoxycontin x x xCodeineCowboys x x xOurOverUsedVeins x x x x xLeanSippersUnited xHopelessJunkies x xKetamineCuresOPIATES xAnarchyECP x x xPSTea xglassine x x x x
Table 6. Subreddits discussing firsthand nonmedical use of opioids. An X marks the presence of a subredditin a specific year.
Min term count Vector size Context window Negative Sampling Training Epochsword2vec 5 256 5 10 200GloVe 5 256 10 - 300
Table 7. Relevant training parameters of the word embeddings. All the other parameters are set to defaultvalues. Two state-of-the-art word embedding models, namely word2vec, and GloVe, have been trained withall the comments and submissions in our subreddits dataset. After a-posteriori validation by a domain expertin terms of topical coherence, we choose word2vec as the reference word embedding model. atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 21
Fig. 7. Cumulative probability of finding n or fewer terms in a sentence for submissions and comments (leftpanel). Cumulative probability of having n or fewer sentences in a submission or a comment (right panel).Plots refer to the selected subreddit in 2018. OR RectallyDermalSubcutaneousIntramuscularIntravenousInjectionIntranasalSmokingInhalationDrinkChewOralSublingualIngestion Heroin OR Buprenorphine OR Oxycodone OR Fentanyl OR RectallyDermalSubcutaneousIntramuscularIntravenousInjectionIntranasalSmokingInhalationDrinkChewOralSublingualIngestion Hydrocodone OR Methadone OR Hydromorphone OR Morphine OR RectallyDermalSubcutaneousIntramuscularIntravenousInjectionIntranasalSmokingInhalationDrinkChewOralSublingualIngestion Codeine OR Oxymorphone OR Tramadol OR Antagonist 0.7 (0.5-0.9) 3.6 (1.7-7.6) 1.1 (0.2-7.8) 5.9 (3.0-11.6) 2.1 (1.9-2.3) 6.1 (5.8-6.4) 1.9 (1.7-2.1) 0.4 (0.3-0.5) 2.9 (2.5-3.4) 0.6 (0.5-0.7) 0.7 (0.4-1.2) 2.9 (2.6-3.3) 9.6 (8.4-11.0) 3.2 (2.2-4.6) OR (95% CI) 0.4 (0.4-0.5) 4.5 (3.2-6.2) 2.0 (1.1-3.7) 0.7 (0.3-1.4) 1.0 (1.0-1.1) 1.2 (1.1-1.2) 0.8 (0.7-0.8) 0.6 (0.5-0.6) 0.5 (0.4-0.6) 0.6 (0.5-0.6) 0.5 (0.4-0.6) 0.9 (0.8-1.0) 7.6 (7.0-8.2) 1.1 (0.8-1.4) OR (95% CI) 0.8 (0.6-1.0) 1.4 (0.5-3.8) 0.8 (0.1-5.4) 0.7 (0.6-0.8) 0.5 (0.5-0.6) 0.5 (0.4-0.6) 0.5 (0.5-0.6) 0.8 (0.6-1.0) 4.0 (3.7-4.3) 0.5 (0.3-0.8) 2.2 (2.0-2.4) 0.3 (0.1-0.5) 3.3 (2.4-4.4) OR (95% CI) 0.4 (0.3-0.5) 17.2 (12.8-23.1) 0.4 (0.1-2.8) 0.9 (0.3-2.5) 1.8 (1.7-1.9) 1.7 (1.6-1.8) 1.8 (1.7-1.9) 1.6 (1.5-1.7) 1.4 (1.2-1.6) 0.3 (0.3-0.4) 2.6 (2.2-3.0) 1.1 (1.0-1.2) 1.4 (1.1-1.7) 3.1 (2.5-3.9) OR (95% CI) 0.7 (0.7-0.8) 0.8 (0.5-1.3) 1.0 (0.5-2.0) 2.0 (1.4-3.0) 3.0 (2.9-3.1) 3.3 (3.2-3.4) 2.3 (2.2-2.4) 3.1 (3.0-3.2) 1.9 (1.7-2.0) 0.7 (0.7-0.8) 0.5 (0.4-0.6) 0.8 (0.8-0.9) 0.6 (0.5-0.7) 1.4 (1.1-1.6) OR (95% CI) 0.8 (0.7-1.1) 0.7 (0.2-2.8) 0.8 (0.1-5.4) 0.4 (0.1-3.1) 1.1 (0.9-1.2) 0.7 (0.6-0.8) 1.5 (1.4-1.6) 0.7 (0.6-0.8) 0.7 (0.6-0.9) 1.4 (1.3-1.6) 1.6 (1.2-2.1) 2.3 (2.1-2.5) 0.6 (0.4-0.9) 1.5 (1.0-2.4) OR (95% CI) 5.2 (4.6-6.0) 2.3 (0.9-6.3) 2.6 (0.6-10.4) 6.1 (3.0-12.4) 9.1 (8.6-9.8) 4.3 (4.0-4.6) 4.9 (4.6-5.3) 0.6 (0.5-0.8) 0.9 (0.7-1.2) 0.5 (0.4-0.6) 0.8 (0.5-1.3) 4.3 (3.9-4.8) 0.8 (0.5-1.3) 1.1 (0.5-2.0) OR (95% CI) 0.2 (0.1-0.3) 2.6 (1.5-4.7) 0.8 (0.3-2.5) 1.2 (1.1-1.3) 1.0 (0.9-1.1) 0.4 (0.3-0.4) 0.7 (0.6-0.7) 0.5 (0.4-0.6) 1.0 (0.9-1.1) 0.4 (0.3-0.7) 1.1 (1.0-1.3) 0.6 (0.4-0.8) 0.8 (0.5-1.3) OR (95% CI) 5.9 (5.3-6.6) 11.1 (7.4-16.8) 4.3 (1.7-10.6) 4.5 (2.3-8.8) 5.1 (4.8-5.5) 2.4 (2.2-2.5) 1.7 (1.6-1.9) 0.8 (0.7-0.9) 1.1 (0.9-1.3) 0.8 (0.7-1.0) 2.5 (2.0-3.2) 9.4 (8.8-10.0) 0.8 (0.5-1.1) 4.8 (3.7-6.3) OR (95% CI) 1.2 (1.0-1.3) 1.6 (1.0-2.7) 0.4 (0.1-1.7) 1.0 (0.5-2.0) 1.3 (1.2-1.4) 0.9 (0.9-1.0) 3.4 (3.3-3.5) 1.2 (1.2-1.3) 0.9 (0.8-1.0) 0.8 (0.8-0.9) 2.7 (2.4-3.0) 3.6 (3.4-3.8) 0.7 (0.6-0.9) 1.5 (1.2-1.9) OR (95% CI) 2.0 (1.6-2.4) 3.4 (1.4-8.2) 2.5 (2.2-2.8) 1.7 (1.6-1.9) 4.9 (4.6-5.3) 0.5 (0.4-0.7) 1.1 (0.9-1.5) 0.3 (0.2-0.4) 0.7 (0.4-1.2) 3.0 (2.7-3.4) 0.3 (0.1-0.7) 1.2 (0.6-2.3) OR (95% CI) 0.4 (0.2-0.7) 2.2 (0.7-6.9) 0.9 (0.8-1.1) 0.4 (0.4-0.5) 0.7 (0.6-0.8) 0.5 (0.4-0.7) 0.6 (0.4-0.8) 0.5 (0.4-0.7) 1.0 (0.6-1.6) 1.0 (0.8-1.2) 0.6 (0.3-1.1) 1.2 (0.6-2.4) OR (95% CI)
Sample (N)
20 50 200 500 1K 2K 5K
Fig. 8. Odds Ratios of opioid substances and Secondary Routes of Administration. The central line and thebar mark the OR and the 95% confidence interval respectively, while the size of the circle is proportional tothe sample of co-mentions. Measures that are not statistically significant (P >.01) are reported in gray. Labelson the right axis report the Odds Ratio and the confidence interval. atterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining andContent Analysis of Reddit Discussions 23 OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Heroin OR Buprenorphine OR Oxycodone OR Fentanyl OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Hydrocodone OR Methadone OR Hydromorphone OR Morphine OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Codeine OR Oxymorphone OR Tramadol OR Antagonist 0.4 (0.1-1.3) 0.6 (0.2-1.6) 0.1 (0.0-1.1) 0.3 (0.0-2.1) 1.4 (0.8-2.2) 0.3 (0.2-0.6) 0.3 (0.1-0.5) 0.5 (0.3-0.7) 0.3 (0.2-0.6) 1.2 (1.0-1.4) 1.4 (1.2-1.7) OR (95% CI) 0.5 (0.3-0.7) 0.4 (0.3-0.7) 0.5 (0.4-0.8) 0.1 (0.0-0.4) 0.9 (0.7-1.2) 0.3 (0.2-0.4) 0.3 (0.2-0.3) 1.0 (0.9-1.2) 0.5 (0.4-0.6) 0.4 (0.4-0.5) 1.1 (1.0-1.3) OR (95% CI) 0.5 (0.2-1.2) 14.1 (11.5-17.2) 0.3 (0.1-1.0) 1.9 (1.0-3.6) 2.4 (1.8-3.3) 15.7 (14.3-17.2) 0.6 (0.5-0.9) 1.6 (1.3-1.9) 0.7 (0.5-1.0) 1.1 (0.9-1.3) 2.8 (2.5-3.2) OR (95% CI) 0.7 (0.4-1.2) 1.2 (0.7-1.8) 0.5 (0.3-1.0) 2.7 (1.8-4.1) 1.9 (1.5-2.4) 0.7 (0.5-0.9) 1.0 (0.9-1.2) 0.4 (0.3-0.6) 0.4 (0.3-0.6) 0.8 (0.7-0.9) 2.1 (1.9-2.3) OR (95% CI) 0.7 (0.6-1.0) 0.3 (0.2-0.4) 0.9 (0.7-1.1) 2.1 (1.6-2.7) 1.2 (1.0-1.4) 0.5 (0.4-0.6) 2.1 (1.9-2.2) 0.8 (0.7-0.9) 0.7 (0.6-0.8) 0.8 (0.8-0.9) 1.7 (1.6-1.8) OR (95% CI) 0.6 (0.3-1.3) 1.8 (1.1-2.9) 0.2 (0.1-0.8) 1.0 (0.4-2.5) 0.9 (0.5-1.4) 6.6 (5.8-7.5) 0.3 (0.2-0.5) 1.0 (0.8-1.3) 0.6 (0.4-0.8) 1.1 (0.9-1.3) 0.8 (0.7-1.0) OR (95% CI) 0.2 (0.0-1.1) 0.7 (0.3-1.9) 0.3 (0.1-1.4) 1.0 (0.3-3.2) 0.8 (0.4-1.6) 0.8 (0.5-1.2) 0.9 (0.7-1.3) 0.7 (0.5-1.0) 0.5 (0.3-0.8) 1.9 (1.6-2.3) 1.6 (1.3-1.9) OR (95% CI) 0.8 (0.4-1.3) 0.2 (0.1-0.6) 0.1 (0.0-0.5) 0.1 (0.0-0.9) 1.4 (1.0-1.9) 0.4 (0.3-0.6) 0.3 (0.2-0.4) 1.0 (0.8-1.2) 0.4 (0.3-0.5) 0.3 (0.3-0.4) 1.0 (0.8-1.1) OR (95% CI) 1.9 (1.2-3.0) 1.8 (1.1-3.0) 0.3 (0.1-1.1) 3.2 (1.9-5.5) 5.1 (4.0-6.3) 3.7 (3.1-4.3) 1.9 (1.6-2.3) 3.2 (2.7-3.7) 1.9 (1.6-2.4) 1.9 (1.7-2.2) 3.5 (3.2-4.0) OR (95% CI) 0.5 (0.3-0.8) 0.4 (0.2-0.6) 1.8 (1.4-2.3) 1.3 (0.8-2.0) 0.9 (0.7-1.1) 1.9 (1.7-2.1) 0.5 (0.4-0.6) 0.7 (0.6-0.8) 0.4 (0.3-0.5) 2.6 (2.5-2.8) 0.8 (0.8-0.9) OR (95% CI) 0.9 (0.4-2.2) 0.2 (0.0-1.4) 4.3 (2.8-6.6) 2.8 (1.3-5.8) 0.5 (0.2-1.3) 1.7 (1.2-2.2) 0.8 (0.5-1.1) 0.3 (0.1-0.5) 0.3 (0.2-0.6) 2.7 (2.3-3.1) 0.8 (0.6-1.1) OR (95% CI) 0.4 (0.1-1.6) 0.4 (0.1-1.8) 0.2 (0.0-1.6) 1.3 (0.7-2.3) 1.5 (1.1-2.1) 0.2 (0.1-0.4) 1.1 (0.8-1.6) 0.3 (0.2-0.7) 0.7 (0.5-0.9) 0.4 (0.3-0.6) OR (95% CI)
Sample (N)
20 50 200 500 1K 2K 5K
Fig. 9. Odds Ratios of opioid substances and Drug Tampering Methods. The central line and the bar markthe OR and the 95% confidence interval respectively, while the size of the circle is proportional to the sampleof co-mentions. Measures that are not statistically significant (P >.01) are reported in gray. Labels on theright axis report the Odds Ratio and the confidence interval. OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Injection OR Intravenous OR Intramuscular OR Subcutaneous OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Inhalation OR Smoking OR Intranasal OR Ingestion OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Oral OR Chew OR Drink OR Sublingual OR SoakBrewPeelEvaporateConcentrateExtractHeatInfusionWashGrindDissolve Rectally OR Dermal 4.2 (2.1-8.4) 5.1 (2.6-9.9) 1.1 (0.2-7.9) 0.9 (0.3-2.8) 2.3 (1.5-3.5) 3.2 (2.3-4.4) 1.1 (0.6-1.9) 3.9 (2.8-5.5) 6.3 (5.2-7.5) 3.9 (3.1-5.0) OR (95% CI) 14.3 (5.3-38.5) 3.9 (1.4-10.4) 0.6 (0.1-4.3) OR (95% CI) 2.9 (2.1-4.0) 5.3 (4.1-6.9) 0.7 (0.4-1.4) 4.0 (2.6-6.1) 1.7 (1.2-2.3) 2.8 (2.4-3.3) 1.9 (1.6-2.2) 8.1 (7.4-8.9) 4.9 (4.3-5.5) 1.6 (1.4-1.8) 3.5 (3.2-3.9) OR (95% CI) 2.4 (0.6-9.8) 2.7 (0.7-10.7) 9.3 (5.4-16.2) 8.6 (6.0-12.3) 0.2 (0.0-1.4) 5.0 (3.3-7.6) 3.9 (2.4-6.5) 2.0 (1.2-3.2) 3.6 (2.5-5.2) OR (95% CI) 1.8 (1.0-3.3) 0.2 (0.0-1.4) 3.1 (1.9-5.2) 3.9 (2.1-7.3) 7.9 (6.3-10.1) 1.2 (0.9-1.8) 5.5 (4.8-6.4) 1.2 (0.9-1.7) 0.8 (0.5-1.3) 1.6 (1.3-1.9) 1.8 (1.5-2.2) OR (95% CI) 1.6 (1.2-2.2) 0.6 (0.4-1.0) 0.9 (0.6-1.4) 2.9 (2.0-4.1) 1.6 (1.3-2.0) 1.1 (0.9-1.3) 2.8 (2.5-3.1) 0.7 (0.6-0.8) 1.9 (1.7-2.2) 1.6 (1.5-1.8) 3.5 (3.2-3.7) OR (95% CI) 4.9 (1.8-13.1) 1.4 (0.3-5.5) 2.3 (0.7-7.2) OR (95% CI) 1.5 (1.0-2.1) 0.2 (0.1-0.5) 4.7 (3.7-5.9) 4.3 (3.0-6.1) 1.1 (0.8-1.6) 0.9 (0.7-1.2) 1.5 (1.3-1.8) 0.4 (0.3-0.5) 1.3 (1.1-1.6) 6.7 (6.3-7.1) 4.1 (3.8-4.4) OR (95% CI) 1.5 (1.0-2.3) 0.4 (0.2-0.9) 0.8 (0.4-1.4) 1.3 (0.7-2.6) 2.7 (2.1-3.4) 1.3 (1.1-1.7) 1.8 (1.6-2.2) 1.1 (0.9-1.4) 1.0 (0.8-1.3) 1.3 (1.2-1.5) 3.6 (3.3-3.9) OR (95% CI) 3.3 (2.3-4.7) 1.1 (0.6-2.1) 3.9 (2.7-5.5) 2.0 (1.0-3.9) 3.7 (2.9-4.8) 2.2 (1.8-2.7) 1.9 (1.6-2.3) 1.4 (1.1-1.7) 4.1 (3.5-4.8) 5.1 (4.6-5.6) 9.7 (9.0-10.4) OR (95% CI) 2.7 (1.5-4.8) 0.7 (0.2-2.2) 1.4 (0.5-4.4) 1.3 (0.7-2.4) 1.8 (1.3-2.5) 1.5 (1.1-2.0) 0.3 (0.2-0.7) 0.9 (0.6-1.4) 2.9 (2.4-3.4) 4.6 (4.0-5.4) OR (95% CI) 1.0 (0.6-1.6) 0.5 (0.3-1.0) 1.8 (1.2-2.6) 5.4 (3.8-7.5) 2.1 (1.6-2.7) 0.7 (0.6-1.0) 3.7 (3.4-4.2) 1.0 (0.8-1.3) 0.7 (0.5-0.9) 1.4 (1.2-1.6) 1.5 (1.3-1.7) OR (95% CI) 15.6 (3.8-63.3) 2.1 (0.3-14.9) 2.3 (0.3-16.6) 2.4 (0.6-9.7) 13.0 (6.6-25.9) OR (95% CI) 2.4 (1.0-5.7) 0.5 (0.1-3.7) 2.6 (1.1-6.2) 2.7 (1.5-5.1) 0.5 (0.2-1.2) 2.4 (1.7-3.4) 1.8 (1.2-2.8) 4.8 (3.6-6.4) 2.7 (2.1-3.4) 18.9 (16.8-21.3) OR (95% CI)
Sample (N)
20 50 200 500 1K 2K 5K20 50 200 500 1K 2K 5K