Food insecurity may lead to incomplete HIV viral suppression and less immune reconstitution among HIV/hepatitis C virus‐coinfected people
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the impact of food insecurity (FI) on HIV viral load and CD4 count among people coinfected with HIV and hepatitis C virus (HCV). METHODS: This study was conducted using data from the Food Security & HIV-HCV Sub-Study of the Canadian Co-Infection Cohort study. FI was measured using the adult scale of Health Canada's Household Food Security Survey Module and was classified into three categories: food security, moderate food insecurity and severe food insecurity. The association between FI, HIV viral load, and CD4 count was assessed using a stabilized inverse probability weighted marginal structural model. RESULTS: A total of 725 HIV/HCV-coinfected people with 1973 person-visits over 3 years of follow-up contributed to this study. At baseline, 23% of participants experienced moderate food insecurity and 34% experienced severe food insecurity. The proportion of people with undetectable HIV viral load was 75% and the median CD4 count was 460 [interquartile range (IQR): 300-665] cells/μL. People experiencing severe food insecurity had 1.47 times [95% confidence interval (CI): 1.14, 1.88] the risk of having detectable HIV viral load and a 0.91-fold (95% CI: 0.84, 0.98) increase in CD4 count compared with people who were food secure. CONCLUSIONS: These findings provide evidence of the negative impact of food insecurity on HIV viral load and CD4 count among HIV/HCV-coinfected people.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".