MétaCan
Menu
Back to cohort

Crack use in North American cities: the neglected ‘epidemic’

2007· editorial· en· W1483493677 on OpenAlexaffabout
Benedikt Fischer, Michelle Coghlan

Bibliographic record

VenueAddiction · 2007
Typeeditorial
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsBC Centre for Disease ControlUniversity of Victoria
Fundersnot available
KeywordsContext (archaeology)Psychological interventionPoliticsCriminologyHistorySociologyPsychologyDevelopment economicsDemographyPsychiatryPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

When claims of crack use as a new ‘drug epidemic’ emerged in the scientific and mass media in the 1980s, experts were quick to dismiss such suggestions as politically motivated fear-mongering within the context of Reagan's ‘drug war’[1,2]. A quarter-century later, we wished those initial observations had been less prophetic. In many North American cities today, crack is a ‘staple . . . in the street drug pharmacopeia’, and its users often far outnumber injection drug user populations [3,4]. Yet the problem is not mainly one of quantity. The phenomenon's devastating traits are that its users (disproportionately African American men, at least in US settings) feature some of the worst and most detrimental characteristics with regard to health and social consequences of their drug use. Equally debilitating is the fact that in terms of interventions (whether prevention or treatment) the crack problem has been treated like the proverbial stepchild's ugly cousin. A further heavy cloud is blown into this already gloomy sky by Falck et al.'s paper (published in this issue) [4] which confirms, based on a longitudinal sample of urban crack users from a mid-western US city, that most crack users will engage in their drug use habit for extensively long times (i.e. many years) without interruptions or even phases of abstinence. This, among other things, means that even under optimistic circumstances crack use is here to stay in our cities for a long time to come, and will continue to impose a heavy toll on users and communities unless dramatic changes occur on key fronts. In recent years, a sizeable body of literature has documented that crack users typically feature severely compromised somatic health status and a large proportion suffer from severe mental health problems, including both affective and personality disorders [3,5,6]. In fact, crack users are probably one of the highest-risk drug-user populations in which the interactive dynamics of comorbidity and substance use as an act of self-medication for undiagnosed and untreated psychiatric problems are most detrimentally pronounced [5,7,8]. While often not involved actively in injection drug use, crack users (often at a young age) have been shown to be at highly elevated risk for human immunodeficiency virus (HIV) and other blood-borne viruses/sexually transmitted infections (BBV/STI), as the ‘protective’ effects of less or absent involvement in injection drug use are frequently far outweighed by increased intensities of sexual risk behavior, whether related to sex work, sex-for-money exchanges or high-risk sex practices in the context of stimulant use [9–11]. More recent epidemiological warning indicators suggest that populations of crack users are at elevated risk for hepatitis C virus (HCV), prompting the (biologically) as yet unanswered question of whether HCV transmission may, in addition to drug use and sex-related risk, also occur through crack use paraphernalia sharing [12–15]. Several studies show crack users to be the most socially disadvantaged—the ‘marginalized among the marginalized’—even when compared to their street drug-using peers with the highest rates of homelessness, extreme poverty or lack of basic subsistence and highest barriers to social or health care: a picture amplified by the fact that a large proportion of crack users come from disadvantaged socio-ethnic backgrounds [16–18]. At the same time, it is crack users who typically stand out among street drug users in terms of crime involvement—regretfully involving disproportionate levels of violent crime, as also evidenced by systematic studies on longitudinal crime patterns in US cities [19–21]. It is both frustrating and distressing to see how empty the armory of targeted interventions for the high-risk populations of crack users has remained. While needle-exchange services and opioid maintenance treatment programs are widely available mainstay interventions in most western countries, and some jurisdictions are going as far as offering costly medical heroin prescription programs, the main offers to crack users may be scorn or pity. Pharmacotherapeutic treatment options for cocaine/crack dependence appear to indicate ‘no evidence’ for efficacy [22], with new agents being experimented within early and speculative stages at best, and other approaches (whether cognitive, psychotherapeutic or contingency management) have not demonstrated convincing long-term effects [23–25]. Rudimentary preventive interventions—such as community-based ‘safer crack use kits’ initiatives launched in several Canadian cities—have not yet been allowed to demonstrate their potential public health impact and have remained largely socio-politically controversial and under-resourced [3,15]. In North America and elsewhere, researchers and policy makers need to embrace the fact that both quantitatively and qualitatively crack use is one of the largest and most destructive pieces in the overall picture of our cities' illicit drug problem, and is likely going to be around for some time. Over the period of a quarter-century, we have made little if any progress concerning effective interventions. It is time to recalibrate our aim and focus drastically, and devote concerted energy todeveloping knowledge and measures that will make crack users less prone to disease, crime and marginalization and more likely to be offered effective therapeutic interventions, so that in years from now this commentary will have no need to be repeated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.423
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.287
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueAddictionSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207