Food Insecurity in HIV-Hepatitis C Virus Co-infected Individuals in Canada: The Importance of Co-morbidities
Bibliographic record
Abstract
While research has begun addressing food insecurity (FI) in HIV-positive populations, knowledge regarding FI among individuals living with HIV-hepatitis C virus (HCV) co-infection is limited. This exploratory study examines sociodemographic, socioeconomic, behavioral, and clinical factors associated with FI in a cohort of HIV-HCV co-infected individuals in Canada. We analyzed longitudinal data from the Food Security and HIV-HCV Co-infection Study of the Canadian Co-infection Cohort collected between November 2012-June 2014 at 15 health centres. FI was measured using the Household Food Security Survey Module and classified using Health Canada criteria. Generalized estimating equations were used to assess factors associated with FI. Among 525 participants, 59 % experienced FI at their first study visit (baseline). Protective factors associated with FI (p < 0.05) included: enrolment at a Quebec study site (aOR: 0.42, 95 % CI: 0.27, 0.67), employment (aOR: 0.55, 95 % CI: 0.35, 0.87), and average personal monthly income (aOR per $100 CAD increase: 0.98, 95 % CI: 0.97, 0.99). Risk factors for FI included: recent injection drug use (aOR: 1.98, 95 % CI: 1.33, 2.96), trading away food (aOR: 5.23, 95 % CI: 2.53, 10.81), and recent experiences of depressive symptoms (aOR: 2.11, 95 % CI: 1.48, 3.01). FI is common in this co-infected population. Engagement of co-infected individuals in substance use treatments, harm reduction programs, and mental health services may mitigate FI in this vulnerable subset of the HIV-positive population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".