Severe food insecurity is associated with elevated unprotected sex among HIV-seropositive injection drug users independent of HAART use
Bibliographic record
Abstract
OBJECTIVE: Despite emerging evidence of a significant adverse relationship between food insecurity and sexual risk-taking, data have been primarily derived from resource-constrained settings and HIV-negative populations. To our knowledge, this study is the first to longitudinally evaluate the relationship between food insecurity and unprotected sex among HIV-seropositive people who inject drugs [injection drug users (IDUs)] both on and not on HAART. DESIGN: Longitudinal analyses were restricted to HIV-positive IDUs who completed baseline and at least one follow-up visit in a prospective cohort (AIDS Care Cohort to evaluate Exposure to Survival Services, 2005-2009). METHODS: We constructed a multivariate logistic model using generalized estimating equations (GEEs) to assess an independent relationship between severe food insecurity (e.g., hunger due to lack of access or means to acquire food) and unprotected vaginal/anal sex. RESULTS: Among 470 HIV-positive IDUs, the median age was 42 years (interquartile range 36-47) with 61% men and 39% women. The prevalence of severe food insecurity was 71%, with no differences by HAART use. Severe food insecure IDUs were marginally less likely to have a suppressed HIV-1 RNA viral load (31 vs. 39%, P = 0.099). In multivariate GEE analyses, severe food insecurity [adjusted odds ratio = 2.68, 95% confidence interval 1.49-4.82] remained independently correlated with unprotected sex among HIV-positive IDUs, controlling for age, sex/gender, married/cohabitating partner, binge drug use, homelessness, and HAART use. CONCLUSION: These findings highlight a crucial need for structural HIV interventions that incorporate targeted food assistance strategies for IDUs. Given recent evidence of poor virological response among food insecure individuals on HAART, innovative HIV care models should integrate targeted food security programs and early access to HAART.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".