Prevalence and health consequences of nonmedical use of tramadol in Africa: A systematic scoping review
Bibliographic record
Abstract
Tramadol is a widely prescribed painkiller around the world. As a synthetic opioid, it offers a valuable substitute for morphine and its derivatives in African countries. However, the adverse health effects of tramadol use resulting from illicit trafficking, like those caused by fentanyl and methadone in North America, have not been well-documented in Africa. This scoping review aims to shed light on the nature and scope of the nonmedical use (NMU) of tramadol in Africa and its associated health consequences. To carry out our scoping review, we used Arksey and O'Malley's (2005) five-step approach for exploratory analysis and followed Joanna Briggs Institute guidelines for scoping reviews to ensure systematic and replicable studies. We then searched six databases: Medline, Global Health (EBSCO), Scopus, Web of Science, the African Journals online database, and for grey literature via Google Scholar without any time restriction. The articles were imported into Covidence and reviewed by two independent researchers. Eighty-three studies on NMU of tramadol's prevalence or health consequences were selected from 532 titles/abstracts screened, including 60 cross-sectional and six qualitative studies from 10 African countries. Findings from the included studies highlighted five distinct groups significantly affected by the NMU of tramadol. These groups include: 1) young adults/active populations with varying degrees of prevalence ranging from 1.9% to 77.04%, 2) professionals, where drivers exhibit a relatively high prevalence of tramadol NMU, ranging from 7.2% to 35.1%, and commercial motorcyclists, with a prevalence of 76%, 3) patients, who have a high rate of tramadol NMUs, with prevalence rates ranging from 77.1% to 92%, 4) academics, with a considerable rate of tramadol misuse among substance-using undergraduates (74.2%) and substance-using high school students (83.3%), and 5) other individuals impacted in various ways. The health consequences are classified into four distinct types: intoxication, dependence syndrome, withdrawal syndrome and other symptoms. Despite providing a comprehensive global overview of the phenomenon described in the African literature, this systematic scoping review's main limitations stem from the relatively limited exploration of various consequences of the NMU of tramadol, notably those of a social and economic nature. Our review shows that tramadol misuse affects diverse populations in Africa. The prevalence of misuse varies within sub-populations, indicating the complexity of the issue. Professional and academic groups have different rates of misuse across regions. This highlights the need for targeted interventions to address unique challenges contributing to tramadol misuse. Future studies should focus on the social and economic costs of abuse on households to better understand the impact on well-being. Systematic review registration: Open Science Framework: https://osf.io/ykt25/.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".