Examining the substance use, violence, and HIV and AIDS (SAVA) syndemic among urban refugee youth in Kampala, Uganda: cross-sectional survey findings
Bibliographic record
Abstract
BACKGROUND: Interactions between substance use, violence, HIV and AIDS, known as the 'SAVA' syndemic, are understudied among refugee youth. We assessed the synergistic effects of frequent alcohol use, depression and violence on HIV vulnerability among urban refugee youth aged 16-24 years in Kampala, Uganda. METHODS: We conducted a cross-sectional survey between January and April 2018 with a convenience sample of refugee youth aged 16-24 years living in informal settlements in Kampala (Kabalagala, Rubaga, Kansanga, Katwe, Nsambya). We assessed non-communicable health conditions (frequent [≥3 times per week] alcohol use [FAU]; depression); violence (young adulthood violence [YAV] at age ≥16 years, intimate partner violence [IPV]), and HIV vulnerability (past 12-month transactional sex; recent [past 3-month] multiple [≥2] sex partners). We calculated the prevalence and co-occurrence of non-communicable health conditions, violence and HIV vulnerability variables. We then conducted multivariable logistic regression analyses to first create unique profiles of FAU, depression, YAV and IPV exposures, and second to assess for interactions between exposures on HIV vulnerability outcomes. RESULTS: Most participants (n=445; mean age: 19.59, SD: 2.6; women: n=333, 74.8%, men: n=112, 25.2%) reported at least one non-communicable health condition or violence exposure (n=364, 81.8%), and over half (n=278, 62.4%) reported co-occurring exposures. One-fifth reported FAU (n=90; 20.2%) and one-tenth (n=49; 11%) major depression. In logistic regression models including all two-way product terms, adjusted for sociodemographics, we found (a) multiplicative interaction for joint effects of FAU and IPV (adjusted OR (aOR)=4.81, 95% CI: 1.32 to 17.52) on multiple sex partners, and (b) multiplicative interaction for joint effects of FAU and IPV (aOR=3.72, 95% CI: 1.42 to 9.74), and YAV and depression (aOR=7.13, 95% CI: 1.34 to 37.50), on transactional sex. CONCLUSION: Findings signal the importance of addressing the SAVA syndemic among urban refugee youth in Uganda. Synergistic interactions indicate that addressing FAU, depression or violence may concomitantly reduce HIV vulnerability with urban refugee youth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".