Assessing the Impact of Food Insecurity on HIV Medication Adherence in the Context of an Integrated Care Facility for People Living with HIV in Vancouver, Canada
Bibliographic record
Abstract
Objective\nFood insecurity, or self-reports of inadequate food access due to limited financial resources, remains prevalent among people living with HIV (PLHIV). We examined the impact of food insecurity on combination antiretroviral therapy (cART) adherence within an integrated care facility that provides services to PLHIV, including two meals per day.\nDesign\nAdjusted odds ratios (aOR) were estimated by generalized estimating equations, quantifying the relationship between food insecurity (exposure) and cART adherence (outcome) with multivariable logistic regression.\nSetting\nWe drew on survey data collected between February 2014-March 2016 from the Dr. Peter Centre Study based in Vancouver, Canada.\nParticipants\nThis study included 116 PLHIV at baseline, with 99 participants completing a 12-month follow-up interview. The median age was 46 years (IQR: 39-52) at baseline, and 82% (n=95) were biologically male at birth.\nResults\nAt baseline, 74% (n=86) of participants were food insecure (≥ 2 affirmative responses on Health Canada’s Household Food Security Survey Module) and 67% (n=78) were adherent to cART ≥ 95% of the time. In the adjusted regression analysis, food insecurity was associated with suboptimal cART adherence (aOR=0.47, 95% confidence interval = 0.24-0.93).\nConclusions\nWhile food provision may reduce some health-related harms, there remains a relationship between this prevalent experience and suboptimal cART adherence in this integrated care facility. Future studies that elucidate strategies to mitigate food insecurity and its affects on cART adherence among PLHIV in this setting and in other similar environments are necessary.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".