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1. Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction

Author

Liu, Yinan, Dong, Xinyu, Lyu, Weimin, Rosenthal, Richard N., Wong, Rachel, Ma, Tengfei, and Wang, Fusheng

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models. Most techniques to alleviate the problem rebalance class proportions and they predominantly assume the rebalanced proportions should be a function of the original data and oblivious to the model one uses. This work challenges this prevailing assumption and proposes that links the optimal class proportions to the model complexity, thereby tuning the class proportions per model. Our experiments on the opioid overdose prediction problem highlight the performance gain of tuning class proportions. Rigorous regression analysis also confirms the advantages of the theoretical framework proposed and the statistically significant correlation between the hyperparameters controlling the model complexity and the optimal class proportions.

Published
2023

2. Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learning

Author

Dong, Xinyu, Deng, Jianyuan, Rashidian, Sina, Abell-Hart, Kayley, Hou, Wei, Rosenthal, Richard N, Saltz, Mary, Saltz, Joel, and Wang, Fusheng

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society, Statistics - Machine Learning
Abstract

The United States is experiencing an opioid epidemic, and there were more than 10 million opioid misusers aged 12 or older each year. Identifying patients at high risk of Opioid Use Disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to predict OUD patients among opioid prescription users through analyzing electronic health records with machine learning and deep learning methods. This will help us to better understand the diagnoses of OUD, providing new insights on opioid epidemic. Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner Health Facts database between January 1, 2008 and December 31, 2017. Long Short-Term Memory (LSTM) models were applied to predict opioid use disorder risk in the future based on recent five encounters, and compared to Logistic Regression, Random Forest, Decision Tree and Dense Neural Network. Prediction performance was assessed using F-1 score, precision, recall, and AUROC. Our temporal deep learning model provided promising prediction results which outperformed other methods, with a F1 score of 0.8023 and AUCROC of 0.9369. The model can identify OUD related medications and vital signs as important features for the prediction. LSTM based temporal deep learning model is effective on predicting opioid use disorder using a patient past history of electronic health records, with minimal domain knowledge. It has potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic., Comment: 20 pages, 6 figures

Published
2020

5. Rapid Initiation of Injection Naltrexone for Opioid Use Disorder

Author

Shulman, Matisyahu, primary, Greiner, Miranda G., additional, Tafessu, Hiwot M., additional, Opara, Onumara, additional, Ohrtman, Kaitlyn, additional, Potter, Kenzie, additional, Hefner, Kathryn, additional, Jelstrom, Eve, additional, Rosenthal, Richard N., additional, Wenzel, Kevin, additional, Fishman, Marc, additional, Rotrosen, John, additional, Ghitza, Udi E., additional, Nunes, Edward V., additional, and Bisaga, Adam, additional

Published
2024
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8. Gabapentin Enacarbil Extended‐Release for Alcohol Use Disorder: A Randomized, Double‐Blind, Placebo‐Controlled, Multisite Trial Assessing Efficacy and Safety

Author

Falk, Daniel E, Ryan, Megan L, Fertig, Joanne B, Devine, Eric G, Cruz, Ricardo, Brown, E Sherwood, Burns, Heather, Salloum, Ihsan M, Newport, D Jeffrey, Mendelson, John, Galloway, Gantt, Kampman, Kyle, Brooks, Catherine, Green, Alan I, Brunette, Mary F, Rosenthal, Richard N, Dunn, Kelly E, Strain, Eric C, Ray, Lara, Shoptaw, Steven, Tiouririne, Nassima Ait‐Daoud, Gunderson, Erik W, Ransom, Janet, Scott, Charles, Leggio, Lorenzo, Caras, Steven, Mason, Barbara J, Litten, Raye Z, and Group, Institute on Alcohol Abuse and Alcoholism Clinical Investigations Group Study

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Depression, Behavioral and Social Science, Clinical Research, Alcoholism, Alcohol Use and Health, Mental Health, Substance Misuse, Clinical Trials and Supportive Activities, Brain Disorders, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Mental health, Oral and gastrointestinal, Cancer, Stroke, Good Health and Well Being, Adult, Alcoholism, Behavior Therapy, Carbamates, Combined Modality Therapy, Delayed-Action Preparations, Double-Blind Method, Female, Humans, Male, Middle Aged, Prodrugs, Therapy, Computer-Assisted, Treatment Outcome, Young Adult, gamma-Aminobutyric Acid, Alcohol Use Disorder, Gabapentin Enacarbil Extended-Release, HORIZANT, Randomized Placebo-Controlled Clinical Trial, National Institute on Alcohol Abuse and Alcoholism Clinical Investigations Group (NCIG) Study Group, Clinical Sciences, Neurosciences, Substance Abuse, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

BackgroundSeveral single-site alcohol treatment clinical trials have demonstrated efficacy for immediate-release (IR) gabapentin in reducing drinking outcomes among individuals with alcohol dependence. The purpose of this study was to conduct a large, multisite clinical trial of gabapentin enacarbil extended-release (GE-XR) (HORIZANT® ), a gabapentin prodrug formulation, to determine its safety and efficacy in treating alcohol use disorder (AUD).MethodsMen and women (n = 346) who met DSM-5 criteria for at least moderate AUD were recruited across 10 U.S. clinical sites. Participants received double-blind GE-XR (600 mg twice a day) or placebo and a computerized behavioral intervention (Take Control) for 6 months. Efficacy analyses were prespecified for the last 4 weeks of the treatment period.ResultsThe GE-XR and placebo groups did not differ significantly on the primary outcome measure, percentage of subjects with no heavy drinking days (28.3 vs. 21.5, respectively, p = 0.157). Similarly, no clinical benefit was found for other drinking measures (percent subjects abstinent, percent days abstinent, percent heavy drinking days, drinks per week, drinks per drinking day), alcohol craving, alcohol-related consequences, sleep problems, smoking, and depression/anxiety symptoms. Common side-effects were fatigue, dizziness, and somnolence. A population pharmacokinetics analysis revealed that patients had lower gabapentin exposure levels compared with those in other studies using a similar dose but for other indications.ConclusionsOverall, GE-XR at 600 mg twice a day did not reduce alcohol consumption or craving in individuals with AUD. It is possible that, unlike the IR formulation of gabapentin, which showed efficacy in smaller Phase 2 trials at a higher dose, GE-XR is not effective in treating AUD, at least not at doses approved by the U.S. Food and Drug Administration for treating other medical conditions.

Published
2019

11. Association of Opioid Use Disorder With 2016 Presidential Voting Patterns: Cross-sectional Study in New York State at Census Tract Level

Author

Xiang, Anthony, Hou, Wei, Rashidian, Sina, Rosenthal, Richard N, Abell-Hart, Kayley, Zhao, Xia, and Wang, Fusheng

Subjects
Public aspects of medicine, RA1-1270
Abstract

BackgroundOpioid overdose-related deaths have increased dramatically in recent years. Combating the opioid epidemic requires better understanding of the epidemiology of opioid poisoning (OP) and opioid use disorder (OUD). ObjectiveWe aimed to discover geospatial patterns in nonmedical opioid use and its correlations with demographic features related to despair and economic hardship, most notably the US presidential voting patterns in 2016 at census tract level in New York State. MethodsThis cross-sectional analysis used data from New York Statewide Planning and Research Cooperative System claims data and the presidential voting results of 2016 in New York State from the Harvard Election Data Archive. We included 63,958 patients who had at least one OUD diagnosis between 2010 and 2016 and 36,004 patients with at least one OP diagnosis between 2012 and 2016. Geospatial mappings were created to compare areas of New York in OUD rates and presidential voting patterns. A multiple regression model examines the extent that certain factors explain OUD rate variation. ResultsSeveral areas shared similar patterns of OUD rates and Republican vote: census tracts in western New York, central New York, and Suffolk County. The correlation between OUD rates and the Republican vote was .38 (P

Published
2021
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12. Buprenorphine implants for opioid dependence

Author

Rosenthal, Richard N, Ling, Walter, Casadonte, Paul, Vocci, Frank, Bailey, Genie L, Kampman, Kyle, Patkar, Ashwin, Chavoustie, Steven, Blasey, Christine, Sigmon, Stacey, and Beebe, Katherine L

Subjects
Substance Misuse, Drug Abuse (NIDA only), Clinical Research, Neurosciences, Clinical Trials and Supportive Activities, Brain Disorders, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Good Health and Well Being, Administration, Sublingual, Adolescent, Adult, Aged, Buprenorphine, Double-Blind Method, Drug Implants, Female, Humans, Male, Middle Aged, Naloxone, Narcotic Antagonists, Opiate Substitution Treatment, Opioid-Related Disorders, Tablets, Treatment Outcome, Young Adult, drug addiction, drug implants, maintenance therapy, opioid dependence, treatment adherence, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse
Abstract

AimsTo evaluate the safety and efficacy of buprenorphine implants (BI) versus placebo implants (PI) for the treatment of opioid dependence. A secondary aim compared BI to open-label sublingual buprenorphine/naloxone tablets (BNX).DesignRandomized, double-blind, placebo-controlled trial. Subjects received either four buprenorphine implants (80 mg/implant) (n = 114), four placebo implants (n = 54) or open-label BNX (12-16 mg/day) (n = 119).SettingTwenty addiction treatment centers.ParticipantsAdult out-patients (ages 18-65) with DSM-IV-TR opioid dependence.MeasurementsThe primary efficacy end-point was the percentage of urine samples negative for opioids collected from weeks 1 to 24, examined as a cumulative distribution function (CDF).FindingsThe BI CDF was significantly different from placebo (P < 0.0001). Mean [95% confidence interval (CI)] proportions of urines negative for opioids were: BI = 31.2% (25.3, 37.1) and PI = 13.4% (8.3, 18.6). BI subjects had a higher study completion rate relative to placebo (64 versus 26%, P < 0.0001), lower clinician-rated (P < 0.0001) and patient-rated (P < 0.0001) withdrawal, lower patient-ratings of craving (P < 0.0001) and better subjects' (P = 0.031) and clinicians' (P = 0.022) global ratings of improvement. BI also resulted in significantly lower cocaine use (P = 0.0016). Minor implant-site reactions were comparable in the buprenorphine [27.2% (31 of 114)] and placebo groups [25.9% (14 of 54)]. BI were non-inferior to BNX on percentage of urines negative for opioids [mean (95% CI) = 33.5 (27.3, 39.6); 95% CI for the difference of proportions = (-10.7, 6.2)].ConclusionsCompared with placebo, buprenorphine implants result in significantly less frequent opioid use and are non-inferior to sublingual buprenorphine/naloxone tablets.

Published
2013

14. Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach

Author

Yao, Hannah, Rashidian, Sina, Dong, Xinyu, Duanmu, Hongyi, Rosenthal, Richard N, and Wang, Fusheng

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundIn recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. ObjectiveThis study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. MethodsReddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. ResultsClassification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. ConclusionsOpioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

Published
2020
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23. Comparison of Methods for Alcohol and Drug Screening in Primary Care Clinics

Author

McNeely, Jennifer, primary, Adam, Angéline, additional, Rotrosen, John, additional, Wakeman, Sarah E., additional, Wilens, Timothy E., additional, Kannry, Joseph, additional, Rosenthal, Richard N., additional, Wahle, Aimee, additional, Pitts, Seth, additional, Farkas, Sarah, additional, Rosa, Carmen, additional, Peccoralo, Lauren, additional, Waite, Eva, additional, Vega, Aida, additional, Kent, Jennifer, additional, Craven, Catherine K., additional, Kaminski, Tamar A., additional, Firmin, Elizabeth, additional, Isenberg, Benjamin, additional, Harris, Melanie, additional, Kushniruk, Andre, additional, and Hamilton, Leah, additional

Published
2021
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26. Buprenorphine implants for treatment of opioid dependence

Author

Ling, Walter, Casadonte, Paul, Bigelow, George, Kampman, Kyle M., Patkar, Ashwin, Bailey, Genie L., Rosenthal, Richard N., and Beebe, Katherine L.

Subjects
Buprenorphine -- Usage, Buprenorphine -- Health aspects, Opioids -- Drug use, Opioids -- Patient outcomes
Abstract

A study was conducted to evaluate the efficacy and safety of buprenorphine implants as a form of treatment for individuals with opioid dependence. Results indicated that in the use of such implants resulted in less opioid use over 16 weeks.

Published
2010

32. Trends and Patterns of Opioid Epidemic: A Large-Scale Retrospective Study of Hospital Visits with Opioid Poisoning in New York State, 2010-2016

Author

Chen, Xin, primary, Hou, Wei, additional, Rashidian, Sina, additional, Wang, Yu, additional, Zhao, Xia, additional, Rosenthal, Richard N, additional, Saltz, Mary, additional, Saltz, Joel H, additional, Schoenfeld, Elinor Randi, additional, Leibowitz, George Stuart, additional, and Wang, Fusheng, additional

Published
2020
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35. Contributors

Author

Abrams, Charles S., primary, Abzug, Mark J., additional, Adrogué, Horacio E., additional, Aeby, Tod C., additional, Akst, Lee, additional, Alam, Mahboob, additional, Albertson, Brian K., additional, Almeida, Madson Q., additional, Anand, Girish, additional, Anderson, Deverick J., additional, Anderson, Kelley P., additional, Andrès, Emmanuel, additional, Anstead, Gregory M., additional, Arici, Aydin, additional, Aring, Ann M., additional, Arita, Isao, additional, Azar, Cecilio, additional, Azodi, Masoud, additional, Bagley, Adrianne Williams, additional, Bailey, Justin, additional, Balagué, Federico, additional, Balasubramanyam, Ashok, additional, Banerjee, Arna, additional, Baykam, Nurcan, additional, Begany, Meg, additional, Bernstein, David I., additional, Bilezikian, John P., additional, Bilotta, Federico, additional, Blevins, Natalie C., additional, Bogaev, Roberta C., additional, Bolotin, Diana, additional, Bonilla, Mary Ann, additional, Bonilla-Martinez, Zuleika L., additional, Borenstein, David, additional, Borgen, Patrick, additional, Boyle, Krystene I., additional, Brecher, Mark E., additional, Brice, Sylvia L., additional, Brown, Patricia D., additional, Brown, Patrick, additional, Brown, Richard B., additional, Buckley, Peter, additional, Burd, Irina, additional, Cadavid, Diego, additional, Caddy, Grant R., additional, Caraccio, Thomas R., additional, Carbajal, Enrique V., additional, Carpenter, Steve, additional, Carvounis, Petros E., additional, Castell, Donald O., additional, Cervera, Alvaro, additional, Chan, Lawrence, additional, Chan, Miriam M., additional, Chen, Emery L., additional, Cherukumilli, Venkata Sri, additional, Chitlur, Meera, additional, Chohan, Saima, additional, Clark, Peter E., additional, Claussen, Claus-Frenz, additional, Colburn, Keith K., additional, Coleman, Gary C., additional, Cornett, Patricia A., additional, Costello, Fiona, additional, Coyle, John F., additional, Crawford, Lester M., additional, Cunha, Burke A., additional, Danby, F. William, additional, Daniel, Ralph C., additional, Daniolos, Athena, additional, Dantas, Stella, additional, Dascal, Andre, additional, Davids, Susan, additional, Davidson, Susan A., additional, Davis-Malesevich, Melinda V., additional, de Paula, Francisco J.A., additional, Deedwania, Prakash C., additional, Dennery, Phyllis A., additional, Deputy, Stephen R., additional, deShazo, Richard D., additional, Dessinioti, Clio, additional, Dickson, Gretchen M., additional, DiOrio, Douglas, additional, Dogra, Sunil, additional, Dokuzoguz, Basak, additional, Domachowske, Joseph, additional, Donnan, Geoffrey A., additional, Donnelly, Craig L., additional, Dorsch, John, additional, Drevets, Douglas A., additional, Dudler, Jean, additional, Duggan, Peter R., additional, Eagle, Kim, additional, Egnatios, Genevieve L., additional, Elliott, Julian, additional, Elliott, Sean P., additional, Elston, Dirk M., additional, Embil, John M., additional, Engel, Tobias, additional, Epstein, Scott K., additional, Evens, Andrew M., additional, Farhat, Walid A., additional, Feldman, Dorianne, additional, Feldman, Gregory, additional, Feldman, Steven R., additional, Fessler, Barri J., additional, Fife, Terry D., additional, Finley, David, additional, Fisher, Robert S., additional, Fisher, William E., additional, Fleischer, Alan B., additional, Flores, Raja, additional, Flynn, Brian J., additional, Fountain, Nathan B., additional, Frank, Jennifer, additional, Freelove, Robert S., additional, Freeman, Ellen W., additional, Freeman, Theodore M., additional, Friedman, Aaron, additional, Gallagher, R. Michael, additional, Garber, John, additional, Ghanem, Khalil G., additional, Gilbert, Donald L., additional, Giusti, Robert, additional, Gladwin, Mark T., additional, Goddard, Andrew W., additional, Gold, Mark S., additional, Goldstein, Robert, additional, Goldstein, Robert C., additional, González-Fernández, Marlís, additional, Gormley, E. Ann, additional, Gotuzzo, Eduardo, additional, Gradoni, Luigi, additional, Grant-Kels, Jane M., additional, Greene, William, additional, Greensher, Joseph, additional, Gregory, David, additional, Grewal, Priya, additional, Grose, Charles, additional, Grossberg, Robert, additional, Groves, Michael, additional, Guinan, Eva C., additional, Gumbo, Tawanda, additional, Gunkel, Juliet, additional, Gupta, Amita, additional, Hadley, David, additional, Halder, Rebat M., additional, Hall, Ronald, additional, Hanania, Nicola A., additional, Haque, Rashidul, additional, Harnisch, David R., additional, Harris, George D., additional, Herndon, Emily J., additional, Hill, David G., additional, Hillis, L. David, additional, Hillyer, Christopher D., additional, Hinderliter, Stacey, additional, Hinshaw, Molly, additional, Ho, Bryan, additional, Hohl, Raymond J., additional, Holstein, Sarah A., additional, Holubar, Marisa, additional, Hoque, M. Ekramul, additional, Hossani-Madani, Ahmad Reza, additional, Hsieh, Christine, additional, Hübschen, Judith M., additional, Hudak, Christine, additional, Hueston, William J., additional, Hughes, Joseph M., additional, Hundahl, Scott A., additional, Hunger, Stephen P., additional, Husain, Khawaja O., additional, Isenberg, Gerald A., additional, Jackson, Alan C., additional, Jacobs, Danny O., additional, Jacobson, Kurt M., additional, Jacobson, Robert M., additional, James, James J., additional, Jamieson, Katarzyna, additional, Jarvis, James N., additional, Jellinek, Nathaniel, additional, John, Roy M., additional, Jones, James F., additional, Judson, Marc A., additional, Kadhiravan, Tamilarasu, additional, Kalia, Harmit, additional, Kao, Walter, additional, Karnad, Dilip R., additional, Katsambas, Andreas, additional, Katz, Philip O., additional, Kavanaugh, Arthur, additional, Kearon, Clive, additional, Keegan, B. Mark, additional, Kelley, Paul R., additional, Kemp, Stephen F., additional, Kim, Haejin, additional, Kingma, Paul S., additional, Kirsner, Robert S., additional, Kiss, Joseph E., additional, Klein, Joel D., additional, Kolodny, Luciano, additional, Kolski, Gerald B., additional, Korley, Frederick K., additional, Kozakowski, Kristin, additional, Kratzke, Robert A., additional, Kraut, Jeffrey A., additional, Kremer, Jacques, additional, Krieger, John N., additional, Krilov, Leonard R., additional, Krishnamurti, Lakshmanan, additional, Kulkarni, Roshni, additional, Kumar, Bhushan, additional, Kumar, Seema, additional, Kuritzky, Louis, additional, Kyle, Robert A., additional, Laffel, Lori M.B., additional, Lange, Richard A., additional, Larioza, Julius, additional, Larkin, Jerome, additional, Lassman, Andrew B., additional, Latenser, Barbara A., additional, Lau, Christine L., additional, Lawrence-Hylland, Susan, additional, Leal, Miguel A., additional, Lee, Paul J., additional, Leikin, Jerrold B., additional, Lewis, Jana, additional, Lin, Albert P., additional, Lindbaek, Morten, additional, Lindemann, Janet C., additional, Linder, Jeffrey A., additional, Lipscomb, Gary H., additional, Litch, James A., additional, Lock, James, additional, Lowe, Robert C., additional, Luft, Benjamin J., additional, Lynch, Michael F., additional, Lyons, Kelly E., additional, Lyznicki, James M., additional, Mace, Kimberly E., additional, Mackall, Judith, additional, Malaeb, Bahaa S., additional, Mantyh, Christopher R., additional, Manuskiatti, Woraphong, additional, Margesson, Lynne, additional, Martin, Paul, additional, Martin, Vickie, additional, Mascarenhas, Maria, additional, Maxwell, Pinckney J., additional, Mazloom, Ali, additional, McCall, Anthony L., additional, McCarley, Jill D., additional, McCloskey, Laura J., additional, McGuigan, Michael, additional, McNeil, Donald, additional, Melton, Genevieve B., additional, Mendez, Mario F., additional, Mercado, Moises, additional, Milks, Jeffrey Wm., additional, Miller, Brian, additional, Millward, Peter A., additional, Mofenson, Howard C., additional, Morales, Enrique, additional, Morales-Arias, Jaime, additional, Morgenthaler, Timothy I., additional, Morison, Warwick L., additional, Moses, Scott, additional, Mostaghimi, Ladan, additional, Moul, Judd W., additional, Muller, Claude P., additional, Murphy, Michael, additional, Mutasim, Diya F., additional, Nader, Nicole, additional, Nagji, Alykhan S., additional, Neil, Tara J., additional, Neschis, David G., additional, Neustadt, David H., additional, Ney, Douglas E., additional, Nieves-Arriba, Lucybeth, additional, Novelli, Enrico M., additional, Okeson, Jeffrey P., additional, Olive, David L., additional, Ong, Peck Y., additional, Orengo-Nania, Silvia, additional, Ortel, Bernhard, additional, Oughton, Matthew T., additional, Overturf, Gary D., additional, Ozer, Kerem, additional, Pacak, Karel, additional, Page, Richard L., additional, Pahwa, Rajesh, additional, Pandharipande, Pratik, additional, Park, Sangtae, additional, Pasipanodya, Jotam, additional, Patel, Manish R., additional, Paulman, Paul, additional, Perez, Alexander, additional, Perkins, Allen, additional, Petri, William A., additional, Petronic-Rosic, Vesna, additional, Pichichero, Michael E., additional, Pierach, Claus A., additional, Pietrangelo, Antonello, additional, Podolsky, Daniel K., additional, Posencheg, Michael A., additional, Praga, Manuel, additional, Prasad, Abhiram, additional, Pratt, Daniel, additional, Prinz, Richard A., additional, Puchalsky, David, additional, Quillen, David M., additional, Rackow, Beth W., additional, Rahko, Peter S., additional, Rajkumar, S. Vincent, additional, Ramin, Kirk D., additional, Ramirez, Julio A., additional, Raoult, Didier, additional, Ravindran, Lakshmi, additional, Reddy, Elizabeth, additional, Reeder, Guy S., additional, Reid, Ian R., additional, Reid, Robert L., additional, Reveille, John D., additional, Rho, Robert W., additional, Roberts, Jason R., additional, Robinson, Malcolm K., additional, Rodriguez, Nidra, additional, Rosa, Giovanni, additional, Rosand, Jonathan, additional, Rose, Peter G., additional, Rosen, Clifford J., additional, Rosenthal, Richard N., additional, Rosin, Anne E., additional, Ruha, Anne-Michelle, additional, Samson, Susan L., additional, Saunders, J. Terry, additional, Schaitkin, Barry M., additional, Schapira, Ralph M., additional, Schatz, Michael, additional, Scheib, Stacey A., additional, Schiller, Lawrence R., additional, Schlechte, Janet A., additional, Schoffer, Kerrie, additional, Schroeder, Kevin, additional, Schuller, Dan, additional, Seas, Carlos, additional, Seifert, Steven A., additional, Septimus, Edward, additional, Sexton, Daniel J., additional, Shah, Beejal, additional, Shammo, Jamile M., additional, Sharafkhaneh, Amir, additional, Sharara, Ala I., additional, Sheppard, Chelsea A., additional, Shott, Julie, additional, Silasi, Dan-Arin, additional, Smith, Michael J., additional, Sood, Suman L., additional, St. Louis, Erik K., additional, Stein, Murray B., additional, Stephens, Todd, additional, Stevens, Dennis L., additional, Stokes, Brenda, additional, Stratakis, Constantine A., additional, Strokoff, Harris, additional, Swaroop, Prabhakar P., additional, Swartout, Jessica P., additional, Takashima, Masayoshi, additional, Taylor, Matthew D., additional, Teng, Edmond, additional, Teng, Joyce M.C., additional, Thielman, Nathan, additional, Thomas, David R., additional, Tobin, Kenneth, additional, Trachtenbarg, David E., additional, Trent, Maria, additional, Tristram, Debra, additional, Trujillo, Elaine B., additional, Underman, Arvid E., additional, Uysal, Utku, additional, Duin, David van, additional, Vance, Mary Lee, additional, Vanness, Erin, additional, Vartanian, Vahan, additional, Velasco, Brenda R., additional, Vinh, Donald C., additional, Vitaz, Todd W., additional, Wakefield, Thomas W., additional, Wald, Ellen R., additional, Walling, Anne, additional, Wang, Andrew, additional, Ward, Bryan K., additional, Weber, Ruth, additional, Weetman, Anthony P., additional, Weinstein, Arthur, additional, Weissman, David N., additional, Welliver, Robert C., additional, Westergaard, Ryan, additional, Wetzler, Meir, additional, Williams, Kimberly, additional, Williams, Steven R., additional, Williams, Tracy L., additional, Winkel, Elaine, additional, Wipperman, Jennifer, additional, Wolfe, Michael, additional, Wood, Gary S., additional, Wood, Jamie R.S., additional, Woods, Jon B., additional, Wu, Steve W., additional, Yeu, Elizabeth, additional, Yiannias, James A., additional, Young, Ronald F., additional, Zeltzer, Jami Star, additional, Zhou, Wei, additional, and Zupanc, Mary, additional

Published
2012
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49. Correlates to the variable effects of cannabis in young adults: a preliminary study

Author

Camera Ariella A, Tomaselli Veronica, Fleming Jerry, Jabbar Gul A, Trachtenberg Melissa, Galvez-Buccollini Juan A, Proal Ashley C, Rosenthal Richard N, and DeLisi Lynn E

Subjects
Public aspects of medicine, RA1-1270
Abstract

Abstract Background Cannabis use can frequently have adverse affects in those that use it and these can be amplified by various characteristics of an individual, from demographic and environmental variations to familial predisposition for mental illnesses. Methods The current study of 100 individuals, who were cannabis users during their adolescence and may still be users, was a survey of the self perceived effects of cannabis and their correlates. A reliable family member was also interviewed for determination of family history of various major mental illnesses and substance use. Results As many as 40% of cannabis users had paranoid feelings (suspiciousness) when using cannabis, although the most frequent effect was feeling relaxed (46%). Having a familial background for mental illnesses such as depression or schizophrenia did not determine the effects of cannabis nor its pattern of use, although the number of subjects with such a history was small. An age at which an individual began using cannabis did have an effect on how heavily it was used and the heavier the cannabis use, the more likely the individual was also to have had psychotic symptoms after use. There were no sex differences in effects of cannabis. These results are tempered by the reliance on self-report for many of the variables ascertained. Conclusion Cannabis can frequently have negative effects in its users, which can be amplified by certain demographic and/or psychosocial factors. Thus, users with a specific profile may be at a higher risk of unpleasant effects from cannabis use and caution should be noted when cannabis is administered to young people for medicinal purposes.

Published
2012
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