Nonmedical Use Of Prescription Medications Research Articles

When Can Physicians Fire Patients with Opioid Use Disorder for Nonmedical Use of Prescription Medications?

AbstractThe opioid crisis has greatly increased the number of patients who are illegally injecting drugs while hospitalized for other conditions. Physicians face a difficult decision in these circumstances: when is it appropriate to involuntarily discharge or "fire" a patient with opioid use disorder for their continued nonmedical use of opioids? This commentary on a case analyzes physicians' responsibilities to their patients and argues that physicians should fire nonadherent patients only when every other option has been exhausted and the expected benefits of firing the patient outweigh the expected harms.

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications

BackgroundSubstance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.MethodsWe collected Twitter data for four medications—fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.ResultsThe quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.ConclusionsNLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

Association of State Drug Laws with Nonmedical Use of Prescription Medications in Adolescents.

The aim of the study is to examine the associations between mandatory access prescription drug monitoring programs (PDMPs), pain management clinic (PMC) laws, and doctor shopping (DS) laws with adolescent nonmedical use of prescription medications (NUPM). We linked 2011-2015 Youth Risk Behavior Survey data on 364,103 adolescents across 40 states with PDMP, PMC laws, and DS laws. We conducted a 2-way fixed effects logistic regression model to examine the associations between state drug laws and adolescent self-reported NUPM. We found some evidence that implementation of a mandatory access PDMP was associated with a decrease in nonmedical use of prescription drugs at the P = 0.079 level (average marginal effect: -0.017, 95% confidence interval = -0.036 to 0.002), while there were no associations with the implementation of PMC and DS laws. Our findings suggest that current state drug laws to combat NUPM are inadequate for adolescents.

Psychological distress, non-medical use of prescription medications, and perceived unmet mental health care needs: a cross-sectional study of Ontario students.

The non-medical use of prescription medications among adolescents has become a concerning public health issue. This study assessed the prevalence of the non-medical use of prescription medications in Ontario high school students, and explored the moderating effect of this use on the relationship between psychological distress and unmet mental health needs. Cross-sectional data for 4896 students, age 14-18, were drawn from the 2019 Ontario Student Drug Use and Health Survey. Psychological distress was measured using the Kessler-6 Distress Scale, unmet mental health needs were defined by self-report (yes/no), and non-prescription medication use was defined by self-reported frequency of use. Using logistic regression, we explored the effect of the non-medical use of prescription medications on the relationship between psychological distress and unmet mental health needs. High proportions of Ontario students reported serious psychological distress (22%), some degree of unmet mental health need (38%), and/or non-medical use of prescription medications (13%). While there were strong associations between psychological distress and unmet mental health need, this association was weaker among those reporting non-medical use of prescription medications (OR = 3.3, 95% CI 1.9-5.7) compared to non-users (OR = 5.6, 95% CI 4.5-7.1). Our findings suggest that Ontario students experiencing distress and using non-prescribed medications are less likely to identify a need for mental health support, highlighting the consequences of apparent self-medication through misuse of prescription medications. To assist in the redirection of adolescent perceptions of healthy coping strategies, population-based educational programming, with targeted promotion of both formal and informal mental health care resources, should be considered.

Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?

Traditional substance use (SU) surveillance methods, such as surveys, incur substantial lags. Due to the continuously evolving trends in SU, insights obtained via such methods are often outdated. Social media-based sources have been proposed for obtaining timely insights, but methods leveraging such data cannot typically provide fine-grained statistics about subpopulations, unlike traditional approaches. We address this gap by developing methods for automatically characterizing a large Twitter nonmedical prescription medication use (NPMU) cohort (n = 288,562) in terms of age-group, race, and gender. Our natural language processing and machine learning methods for automated cohort characterization achieved 0.88 precision (95% CI:0.84 to 0.92) for age-group, 0.90 (95% CI: 0.85 to 0.95) for race, and 94% accuracy (95% CI: 92 to 97) for gender, when evaluated against manually annotated gold-standard data. We compared automatically derived statistics for NPMU of tranquilizers, stimulants, and opioids from Twitter with statistics reported in the National Survey on Drug Use and Health (NSDUH) and the National Emergency Department Sample (NEDS). Distributions automatically estimated from Twitter were mostly consistent with the NSDUH [Spearman r: race: 0.98 (P< 0.005); age-group: 0.67 (P< 0.005); gender: 0.66 (P= 0.27)] and NEDS, with 34/65 (52.3%) of the Twitter-based estimates lying within 95% CIs of estimates from the traditional sources. Explainable differences (e.g., overrepresentation of younger people) were found for age-group-related statistics. Our study demonstrates that accurate subpopulation-specific estimates about SU, particularly NPMU, may be automatically derived from Twitter to obtain earlier insights about targeted subpopulations compared to traditional surveillance approaches.

Quantitative Survey on Prevalence of Prescription Pain Medications and Stimulants Use in Young Adults

Prescription drug use including pain relievers and stimulants has been a growing problem in young adults and the issue has been a public health concern for years. This Quantitative Cross-sectional study aimed to collect preliminary data regarding prescription opioid use, prescription stimulant drug use and overdose treatment knowledge in young adults between 18 and 24 years old in a southern New Jersey university through administering an online survey. Of the 1663 students who completed the survey, 33% reported using prescription pain relievers and 15% reported using prescription stimulant drugs. Stimulant drug users were found to be more likely to use prescription pain relievers (49%) as compared to non-stimulant users (30%). Furthermore, students with overdose treatment knowledge of opioids were more likely to report misuse of prescription drugs (15%) than students with less knowledge (8%). This study reiterates the increasing usage of prescription drugs and stimulant use among college students. There is a need for effective education strategies to inform students about the prescription medication uses and misuse to reduce nonmedical use of prescription medications.

A Survey of Illegal Medication Trading through Twitter in Japan

Non-medical use of prescription medications is a serious public health crisis. The black market for prescription medications should be routinely surveyed to encourage their appropriate use. Herein, we focused on Twitter to investigate the possibility of illicit drug trading in Japan. From March 1 to 8, 2021, we examined the characteristics of Twitter posts, identified using the search term "Okusuri Mogu Mogu", a Japanese argot used for trading of medications. The captured data included the date of the posts, whether with a hashtag was used, an indication of the trades type (buy, sell, self-administration, and unknown), and the name of the mentioned pharmaceutical products. The number of named medications in the posts was counted and further categorized according to the Anatomical Therapeutic Chemical (ATC) classification. Two hundred and thirty-eight posts were identified with the searching term "Okusuri Mogu Mogu", of which 154 (64.7%) named specific medications. Of note, 73 posts (30.7%) were associated with buying or selling medications. We examined the 73 posts. These posts included 118 medications (26 types), of which 107 (88.4%) were classified as nervous system drugs. Hypnotics and sedatives were the most frequently mentioned medications. The present study sheds light on pharmaceutical medication trading via Twitter. Reinforcing the surveillance practices or cracking down on traders by authorities may be insufficient. We consider the possible effectiveness of socially supportive approaches to help those who lack support to access the appropriate psychiatric care.

Gender-specific correlates of nonmedical use of prescription medications in a diverse primary care sample

BackgroundNonmedical use of prescription medications (NUPM) is a growing problem but little is known about its gender-specific mechanisms despite NIDA’s call for gender-stratified research over a decade ago. We explored gender differences in NUPM in a diverse sample of primary care patients. MethodsN = 4458 participants participated in an anonymous health survey in urban primary care clinics. The primary outcome was past month NUPM. All analyses were stratified by gender. Bivariate relationships among NUPM and demographic, medical, psychological, and substance use-related variables were analyzed. Stepwise multivariate logistic regression models (LRMs) were estimated by gender. ResultsMore men (9.5%) reported NUPM than women (7.4%). The final LRM among men included age (OR=0.98), race (OR=0.49), chronic pain diagnosis (OR=1.73), hepatitis (OR=1.78), depression diagnosis (OR=1.77), positive alcohol misuse screen (OR=1.58), and mood disturbance (OR=1.04). Among women, the model included mood disturbance (OR=1.04), illicit drug use (OR=2.22), family history of drug problems (OR=1.41), and heart disease diagnosis (OR=0.48). Effect sizes ranged from small to moderate. ConclusionsAmong a sample of primary care patients, gender-stratified analyses indicated differential presentation of NUPM by gender. Demographic factors were more relevant correlates among men, with younger, White men at higher risk. Chronic pain and depression were more notable risk factors for men. Recent illicit drug use and family history of drug problems were uniquely associated among women, while recent distress was a strong correlate among both men and women. A better understanding of gender-specific correlates of NUPM can inform gender-tailored prevention and treatment efforts.

Self-reported and Documented Substance Use Among Adolescents in the Pediatric Hospital.

Adolescent substance use is associated with numerous adverse health outcomes. A hospitalization represents an opportunity to identify and address substance use. We sought to describe self-reported and documented substance use among hospitalized adolescents. We conducted a cross-sectional survey of adolescents aged 14 to 18 years old admitted to two pediatric hospitals between August 2019 and March 2020. Using previously validated questions, we assessed the proportion of adolescents reporting ever, monthly, and weekly use of alcohol, marijuana, tobacco, electronic cigarettes, and other illicit drugs and nonmedical use of prescription medications. We reviewed medical records for substance use documentation. Among 306 respondents, 57% were older (16-18 years old), 53% were female, and 55% were of non-Hispanic white race and ethnicity. The most frequently reported substances ever used were alcohol (39%), marijuana (33%), and electronic cigarettes (31%); 104 (34%) respondents reported ever use of >1 substance. Compared with younger adolescents, those aged 16 to 18 years were more likely to report ever use of alcohol (29% vs 46%; P = .002), marijuana (22% vs 41%; P < .001), and ≥2 drugs (26% vs 40%; P = .009). A positive substance use history was rarely documented (11% of records reviewed), and concordance between documented and self-reported substance use was also rare. In this study of hospitalized adolescents, the most commonly reported substances used were alcohol, marijuana, and electronic cigarettes. Positive substance use documentation was rare and often discordant with self-reported substance use. Efforts to improve systematic screening for substance use and interventions for prevention and cessation in hospitalized adolescents are critically needed.

Automatic gender detection in Twitter profiles for health-related cohort studies.

ObjectiveBiomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user’s demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study.Materials and MethodsWe used a large Twitter dataset composed of public, gender-labeled users (Dataset-1) for training and evaluating the gender detection pipeline. We experimented with machine learning algorithms including support vector machines (SVMs) and deep-learning models, and public packages including M3. We considered users’ information including profile and tweets for classification. We also developed a meta-classifier ensemble that strategically uses the predicted scores from the classifiers. We then applied the best-performing pipeline to Twitter users who have self-reported nonmedical use of prescription medications (Dataset-2) to assess the system’s utility.Results and DiscussionWe collected 67 181 and 176 683 users for Dataset-1 and Dataset-2, respectively. A meta-classifier involving SVM and M3 performed the best (Dataset-1 accuracy: 94.4% [95% confidence interval: 94.0–94.8%]; Dataset-2: 94.4% [95% confidence interval: 92.0–96.6%]). Including automatically classified information in the analyses of Dataset-2 revealed gender-specific trends—proportions of females closely resemble data from the National Survey of Drug Use and Health 2018 (tranquilizers: 0.50 vs 0.50; stimulants: 0.50 vs 0.45), and the overdose Emergency Room Visit due to Opioids by Nationwide Emergency Department Sample (pain relievers: 0.38 vs 0.37).ConclusionOur publicly available, automated gender detection pipeline may aid cohort-specific social media data analyses (https://bitbucket.org/sarkerlab/gender-detection-for-public).

Drugs of Abuse

The term “drugs of abuse” lacks a formal medical definition. Historically, discussions of drugs of abuse focused on “street drugs”; however, the adverse effects of the nonmedical use of prescription medications, such as opiates, benzodiazepines, and therapeutic amphetamines, are increasingly seen. The purpose of this review is to aid the clinician in identifying and treating a broad representation of drugs of abuse, which may include those illicitly produced in laboratories (e.g., methamphetamine), diverted pharmaceuticals (oxycodone), and herbal products (marijuana). This review covers stimulants, hallucinogens, cannabinoids, and sedative-hypnotics. Figures show substances ranked according to weighted harm score on a normalized scale from 0 being no harm to 100 being extreme harm to self and others, a treatment algorithm for sympathomimetic toxicity, a treatment algorithm for sedative-hypnotic overdose, and a treatment algorithm for opioid overdose. Tables list commonly abused sympathomimetic agents, modern novel drugs of abuse, commonly abused sedative-hypnotic agents, commonly abused opiates, and pitfalls of the drug screen. This review contains 4 highly rendered figures, 6 tables, and 90 references. Key Words: street drugs, prescription medications, opiates, benzodiazepines, amphetamines, stimulants, hallucinogens, oxycodone, marijuana, overdose

Text classification models for the automatic detection of nonmedical prescription medication use from social media

BackgroundPrescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.MethodsWe experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class.ResultsOur proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.ConclusionsBERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

Misuse of Prescription Pain Relievers, Stimulants, Tranquilizers, and Sedatives Among U.S. Older Adults Aged ≥50 Years

Misuse of prescription medications has led to higher mortality and morbidity in the U.S. This study investigates medical and nonmedical use of prescription medications (prescription pain relievers, stimulants, prescription tranquilizers, and sedatives) among adults aged ≥50 years. Data were from the 2015-2017 National Survey of Drug Use and Health. For each prescription medication, medical and nonmedical use were mutually exclusive (prescription pain relievers, stimulants, tranquilizers, and sedatives). Nonmedical use (misuse) was defined as the use of any prescription medications in greater amounts, frequency, or duration than prescribed or the use of medications without a prescription of the individual's own. Descriptive analysis was performed to assess prescription medication use and misuse among adults aged ≥50 years (pooled N=26,322). Correlates of misuse were assessed within multivariable logistic regression analyses at p<0.05. All analyses were conducted in 2019. In the past 12 months, 46.7% (51.7 million) of adults reported using any prescription medication, of which 7.7% (4.0 million) reported misuse. The proportion of past 12-month users reporting misuse was 5.4% for sedatives, 6.4% for prescription pain relievers, 6.5% for prescription tranquilizers, and 8.1% for stimulants. Overall, 16.2% (17.9 million) reported using 2 or more prescription medications within the past 12 months. Multiple prescription medication use was associated with female sex; White race; younger age; and use of nonprescription substances, including alcohol, tobacco, marijuana, and heroin. It is important to monitor medication misuse behaviors among older adults who may be living with several chronic conditions requiring pain management. The strong associations with serious psychological distress underscore the need for increased access to mental health services among this key population.

Associations Between Substance Use and Instagram Participation to Inform Social Network-Based Screening Models: Multimodal Cross-Sectional Study.

BackgroundTechnology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools.ObjectiveThis paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity.MethodsParticipants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram’s approval, participants’ Instagram photo posts were downloaded with an application program interface. Participants’ past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having “had more than a few alcoholic drinks a day,” drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level.ResultsCompared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts.ConclusionsResearchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

If kids ruled the world, how would they stop non-medical use of prescription drugs?

We examined ideas about how youth would mitigate non-medical use of prescription medications among their peers. The National Monitoring of Adolescent Prescription Stimulants Study (N-MAPSS) interviewed 11,048 youth10-18 years of age between 2008 and 2011 from entertainment venues of 10 US urban, suburban, and rural areas. Using a mixed-methods approach, participants completed a survey culminating in open ended questions asking: 1) How should kids your age be told about prescription drugs and their effects?; 2) If you ran the world, how would you stop kids from taking other people's prescription medicines?; 3)Why do people use prescription stimulants without a prescription? Responses from a random sample of 900 children were analyzed using qualitative thematic analyses. The random sample of 900 youth (52% female, 40% white, with a mean age was 15.1 years) believed they should be educated about prescription drugs and their negative effects at schools, at home by parents, through the media, and health professionals. Youth would stop kids from using other people's prescription drugs through more stringent laws that restricted use, and education about negative consequences of use. Peer pressure was the most common reason youth gave for using other's pills, though some reported using for curiosity. This analysis shows the importance of considering youth's opinions on non-medical use of prescription medications, which are often overlooked. Studies should disseminate this data from youth to stop the illicit use of prescription drugs among teens and youth.

Substance use disorders in pregnancy: clinical, ethical, and research imperatives of the opioid epidemic: a report of a joint workshop of the Society for Maternal-Fetal Medicine, American College of Obstetricians and Gynecologists, and American Society of Addiction Medicine

Substance use disorders in pregnancy: clinical, ethical, and research imperatives of the opioid epidemic: a report of a joint workshop of the Society for Maternal-Fetal Medicine, American College of Obstetricians and Gynecologists, and American Society of Addiction Medicine

Validation of the Adult Substance Abuse Subtle Screening Inventory-4 (SASSI-4)

Abstract. The study objective was to develop a revision of the adult Substance Abuse Subtle Screening Inventory-3 to include new items to identify nonmedical use of prescription medications, as well as additional subtle and symptom-related identifiers of substance use disorders (SUDs) and to evaluate its psychometric properties and screening accuracy against a criterion of DSM-5 diagnoses for SUD. Clinical professionals throughout the nine US Census Bureau regions and two Canadian provinces who used the SASSI Online screening tool submitted 1,284 completed administrations of the provisional SASSI-4 along with their independent DSM-5 diagnoses of SUD. Validation sample findings demonstrated SASSI-4 sensitivity of 93% and specificity of 90%, AUC = .91. Items added to identify respondents who were abusing prescription medications showed 94% overall screening accuracy. Logistic regression showed no significant effects of client demographic characteristics or type of screening setting on the accuracy of SASSI-4 screening outcomes. In Study 2, 120 adults in recovery from SUD completed the SASSI-4 under instructions to fake good. Sensitivity of 79% was demonstrated for the full scoring protocol and was 47% when only face valid scales were utilized. Clinical utility is discussed.

Drugs of Abuse

The term “drugs of abuse” lacks a formal medical definition. Historically, discussions of drugs of abuse focused on “street drugs”; however, the adverse effects of the nonmedical use of prescription medications, such as opiates, benzodiazepines, and therapeutic amphetamines, are increasingly seen. The purpose of this review is to aid the clinician in identifying and treating a broad representation of drugs of abuse, which may include those illicitly produced in laboratories (e.g., methamphetamine), diverted pharmaceuticals (oxycodone), and herbal products (marijuana). This review covers stimulants, hallucinogens, cannabinoids, and sedative-hypnotics. Figures show substances ranked according to weighted harm score on a normalized scale from 0 being no harm to 100 being extreme harm to self and others, a treatment algorithm for sympathomimetic toxicity, a treatment algorithm for sedative-hypnotic overdose, and a treatment algorithm for opioid overdose. Tables list commonly abused sympathomimetic agents, modern novel drugs of abuse, commonly abused sedative-hypnotic agents, commonly abused opiates, and pitfalls of the drug screen. This review contains 4 highly rendered figures, 5 tables, and 89 references

Nonmedical Use of Prescription Drugs among Medical Students and the Relationship With Illicit Drug, Tobacco, and Alcohol Use.

Nonmedical use of prescription medications is a growing health problem including not only the opioid painkiller overdose epidemic but also benzodiazepine and stimulants misuse. Medical students express considerable high percentages of prescription drug misuse but also of illicit drug use, with cannabis being the frequently used illicit drug. We report the final results of a cross-sectional study among medical students that examined prevalence and motivation for use and suggest that medical students display similar patterns of misuse, for both licit and illicit drugs, to other students. Furthermore, a correlation was found between illicit drugs use with tranquilizers misuse and between smoking and alcohol misuse with opioids, sleeping drugs, and stimulants use. Considering that substance use during medical school affects students’ personal and professional lives and has potential consequences for their patients, further studies are needed to elucidate the prevalence but also the motivation for that use.

Nonmedical prescription drug use among African Americans who use MDMA (ecstasy/molly): Implications for risk reduction

Recent data suggest that both nonmedical prescription drug and MDMA (ecstasy/molly) use have risen among African Americans. However, studies investigating these two forms of drug use among African Americans are rare. As a result, very little is known about African-American MDMA users and their nonmedical use of prescription medications. The primary goal of this study, therefore, was to describe patterns of nonmedical prescription drug use among African Americans who use MDMA. We also assessed alcohol and illicit drug use among the sample. Surveys (n=100) and in-depth interviews (n=15) were conducted with African-American young adults in Southwest Florida between August 2014 and November 2015. Survey results show that a significant proportion of the sample used MDMA in conjunction with prescription medications (benzodiazepines=59%; opioids=35%; stimulants=13%). Qualitative findings suggest that benzodiazepine medications were used to alleviate MDMA comedown symptoms, opioids were used to achieve a different quality high, and stimulants were used to provide added energy throughout the night. These results suggest that treatment practitioners and harm reduction professionals should pay particular attention to informing users of the potential hazards of combining MDMA with prescription medications. Although additional research is clearly needed, these findings are an important first step towards understanding both nonmedical prescription drug and MDMA use among African Americans, and could be used to tailor treatment and risk reduction interventions to this population.