Determinants of health among people who use illicit drugs in the conflict-affected countries of Afghanistan, Colombia and Myanmar: a systematic review of epidemiological evidence
Bibliographic record
Abstract
Abstract Background Afghanistan, Colombia and Myanmar are the world’s leading heroin and cocaine producers and have also experienced prolonged periods of armed conflict. The link between armed conflict and drug markets is well established but how conflict impacts on the health and social determinants of people who use drugs is less clear. The aim was to investigate health outcomes and associated factors among people who use illicit drugs in Afghanistan, Colombia and Myanmar. Methods We conducted a systematic review searching Medline, EMBASE, PsychINFO and Global Health databases using terms relating to Afghanistan, Colombia and Myanmar; illicit drug use (all modes of drug administration); health and influencing factors. Quality assessment was assessed with the Newcastle–Ottawa-Scale and papers were analysed narratively. Results 35 studies were included in Afghanistan (n = 15), Colombia (n = 9) and Myanmar (n = 11). Health outcomes focused predominantly on HIV, Hepatitis C (HCV), Hepatitis B and sexually transmitted infections (STIs), with one study looking at human rights violations (defined as maltreatment, abuse and gender inequality). Drug use was predominantly injection of heroin, often alongside use of amphetamines (Myanmar), cocaine and cocaine-based derivatives (Colombia). Only one study measured the effect of a period of conflict suggesting this was linked to increased reporting of symptoms of STIs and sharing of needles/syringes among people who inject drugs. Findings show high levels of external and internal migration, alongside low-income and unemployment across the samples. External displacement was linked to injecting drugs and reduced access to needle/syringe programmes in Afghanistan, while initiation into injecting abroad was associated with increased risk of HCV infection. Few studies focused on gender-based differences or recruited women. Living in more impoverished rural areas was associated with increased risk of HIV infection. Conclusions More research is needed to understand the impact of armed-conflict and drug production on the health of people who use drugs. The immediate scale-up of harm reduction services in these countries is imperative to minimize transmission of HIV/HCV and address harms associated with amphetamine use and other linked health and social care needs that people who use drugs may face.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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".