Russel S. Falck

The prevalence of psychiatric disorder among a community sample of crack cocaine users: An exploratory study with practical implications

The Diagnostic Interview Schedule for DSM-IV was used to assess the lifetime prevalence of psychiatric disorder among not-in-treatment crack cocaine users (N = 313). The most common dependencies involved cocaine (59.7%), alcohol (37.7%), and cannabis (12.1%). The most common nondependency disorders were antisocial personality disorder (ASPD; 24%), depression (17.8%), and posttraumatic stress disorder (PTSD; 11.8%). Comorbidity was present in 36.4% of the sample. Proportionately more white than black users were dependent on cocaine, alcohol, amphetamine, and sedative-hypnotics. More white than black users experienced ASPD, depression, PTSD, and attention deficit disorder. Proportionately fewer black users suffered comorbid disorders. Proportionately more men had ASPD, whereas more women had PTSD and phobias. Multinominal modeling revealed that black users and users with a high school education were less likely to have comorbid disorders; married users were less likely to have nondependency disorders. White crack users were more likely to have comorbidity, complicating their treatment.

The Prevalence and Correlates of Depressive Symptomatology Among a Community Sample of Crack-Cocaine Smokers

Abstract Depression has been identified as a disorder of clinical significance among cocaine users. Even so, its prevalence in cocaine-abusing populations is uncertain. This research employed a crosssectional design to determine the prevalence of current depressive symptomatology among a sample of 430 not-in-treatment crack-cocaine users. Depression was assessed with the Beck Depression Inventory (BDI). The mean BDI score was 19.1. Overall, 80% of the sample had BDI scores that suggested more than minimal depression, and 55% had symptoms of moderate to severe depression. The results of a cumulative logistic regression analysis showed that men, African-Americans, and individuals with some college education were less likely to be depressed. Individuals with higher Addiction Severity Index composite scores for family/social, medical, legal, and alcohol problems had a greater likelihood of reporting symptoms of depression. Frequency of cocaine use and perceived need for treatment were also positively associated with higher BDI scores. The results suggest that the prevalence of depression among crack users is higher than has been previously reported. Treatment programs should routinely screen crack-cocaine users for depression. The BDI may be a suitable tool for such efforts since it has an acceptable level of internal consistency when employed with crack users (a = 0.89).

The Validity of Injection Drug Users Self-Reported Use of Opiates and Cocaine

In a study examining the correspondence between self-reported drug use and drug urinalysis, 128 injection drug users (IDUs), who were subjects in an AIDS prevention research project, were asked to provide urine samples, which would be tested for the presence of the metabolites of opiates and cocaine. Ninety-five IDUs provided samples for testing. Of these, twenty IDUs (21.1%) who reported abstinence from opiates and cocaine for the six months prior to the completion of a follow-up questionnaire had their claims contradicted by urinalysis results. Logit regression analyses revealed that IDUs whose primary drug of choice was both crack and injected cocaine and those who were black were significantly more likely to have misrepresented their current drug use status than other groups. The implications of these findings are discussed.

CRACKING THE CORNFIELDS: Recruiting Illicit Stimulant Drug Users in Rural Ohio

This article describes the process of recruiting research subjects for a natural history study of illicit stimulant use in rural Ohio using respondent-driven sampling and ethnographic methods. Participant observation, qualitative interviews, and focus groups were used to establish the project and to evaluate and modify the sampling process as it unfolded. We outline the steps taken in several different rural counties, using ethnographic data to illustrate local differences and obstacles that were faced. The article concludes that respondent-driven sampling is a promising method for identifying and recruiting members of hidden populations in rural areas. However, adequate time must be allotted to establish ethnographic footholds and to reach various networks in separate communities.

Predictors of drug abuse treatment entry among crack-cocaine smokers

The goal of this study was to identify factors that predicted drug abuse treatment program entry among a community sample of 430 crack-cocaine users. Subjects were recruited using a targeted sampling methodology and responded to interviewer-administered questionnaires at 6 months intervals over a 3-year period. At baseline, 40.5% (n=174) reported they had never been in a drug abuse treatment program. During the observation period, 37.7% (n=162) of the sample reported they had entered a program. Of these, 43.8% (n=71) reported that their treatment was court-ordered. Slightly more than one-quarter (n=44) entered treatment for the first time. A host of variables, including individual characteristics, frequency and duration of crack use, frequency of drunkenness, Addiction Severity Index (ASI) family/social, medical, and psychiatric status composite scores, perceived need for treatment, history of treatment, and medical insurance coverage, was explored. The results of Cox proportional hazards model suggested that younger people, users with more severe legal problems, people who perceived a need for treatment, and individuals with prior treatment experience had a greater likelihood of entering treatment. Developing a strategy to practically apply these findings may facilitate treatment entry among a population involved with a dangerous and debilitating drug.

HIV infection and risk behaviors among intravenous drug users in low seroprevalence areas in the Midwest.

We studied behavioral factors that place intravenous drug users at risk for the acquisition and transmission of the human immunodeficiency virus (HIV) in a sample of 855 individuals not in drug treatment, living in central and southwestern Ohio. The HIV seropositivity rate for the sample was 1.5%. Three factors were significantly related to HIV infection: homeless shelter residence (odds ratio [OR] = 7.7, 95% confidence interval (CI) = 3.0-20.0), travel to northeastern HIV hyperendemic areas (OR = 5.2, 95% CI = 1.8-15.4), and recent male homosexual or bisexual behavior (OR = 11.2, 95% CI = 2.9-43.9).

PREDOSE: A semantic web platform for drug abuse epidemiology using social media

OBJECTIVES The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel semantic web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO--pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC), through combination of lexical, pattern-based and semantics-based techniques. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. METHODS Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, and routes of administration. The DAO is also used to help recognize three types of data, namely: (1) entities, (2) relationships and (3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information, which facilitate search, trend analysis and overall content analysis using social media on prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. RESULTS A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. CONCLUSION A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future.

The epidemiology of physical attack and rape among crack-using women.

This prospective study examines the epidemiology of physical attack and rape among a sample of 171 not-in-treatment, crack-cocaine using women. Since initiating crack use, 62% of the women reported suffering a physical attack. The annual rate of victimization by physical attack was 45%. Overall, more than half of the victims sought medical care subsequent to an attack. The prevalence of rape since crack use was initiated was 32%, and the annual rate was 11%. Among those women having been raped since they initiated crack use, 83% reported they were high on crack when the crime occurred as were an estimated 57% of the perpetrators. Logistic regression analyses showed that duration of crack use, arrest for prostitution, and some college education were predictors of having experienced a physical attack. Duration of crack use and a history of prostitution were predictors of suffering a rape. Drug abuse treatment programs must be sensitive to high levels of violence victimization experienced by crack-cocaine using women. Screening women for victimization, and treating the problems that emanate from it, may help make drug abuse treatment more effective.

Examining factorial structure and measurement invariance of the Brief Symptom Inventory (BSI)-18 among drug users

The purpose of this study is to examine the factorial structure of the Brief Symptom Inventory 18 (BSI-18) and test its measurement invariance among different drug using populations. A total sample of 710 drug users was recruited using respondent-drive sampling (RDS) from three states: Ohio (n=248), Arkansas (n=237), and Kentucky (n=225). The results of confirmatory factor analysis (CFA) show: 1) the BSI-18 has a three-factor structure (somatization, depression, and anxiety) with an underlying second-order factor (global severity index of distress); and 2) its factorial structure and metric (factor loadings) are invariant across populations under study. However, the scalars (intercepts) of the BSI-18 items are not invariant, and the means of the latent factors also varied across populations. Our findings provide evidence of a valid factorial structure of the BSI-18 that can be readily applied to studying drug using populations.

Crack-Cocaine Use and Health Status as Defined by the SF-36

The Medical Outcomes Study Short Form 36 (SF-36) was administered to 443 not-in-treatment, crack-cocaine smokers to explore the relationship between frequency of crack, alcohol, and tobacco use, addiction to these drugs, and health status. The reliability of the SF-36 with crack smokers was also assessed. Statistically significant, negative associations emerged between frequency of crack use and all SF-36 subscales except physical functioning. There were not significant associations between frequency of alcohol or tobacco use and any SF-36 subscale. Self-assessed addiction to crack was strongly and negatively associated with all SF-36 subscales: alcohol and tobacco addiction were also negatively associated with health status, but not to the degree of crack. The SF-36 produces reliable data on the health status of crack users and, as such, may have a useful role in assessments involving crack-using populations.