Non-adherence to antiretroviral treatment and associated factors among people living with HIV in Iran: a retrospective cohort study
Bibliographic record
Abstract
Introduction:Although combination of antiretroviral therapy (cART) has been successful in improving health outcomes of people living with HIV (PLWH), optimal treatment adherence is required to maintain the benefits.This study aimed to determine factors associated with treatment non-adherence among PLWH in Iran. Material and methods:In this cohort study, we included 988 PLWH (1997PLWH ( -2017) ) living in Southern Iran, Fars Province.Required demographic and clinical data was collected from patients' files.Non-adherence was defined by a physician of the center as skipping a visit or less than 90% intake of prescribed medicines (antiretroviral drugs) in the month preceding to the date of data collection.Results: Of the 988 participants, 70.54% were males.Mean (SD) age of the participants was 35.80 (SD = 8.58) years and treatment non-adherence was found in 17.81% of patients (n = 176).Multiple regression model showed that injection drug use (IDU) (AOR = 2.53, 95% CI: 1.11-5.74%),and history of incarceration (AOR = 4.20, 95% CI: 1.65-10.66%)increased the likelihood of treatment non-adherence, while taking medications for pneumocystis pneumonia (AOR = 0.34, 95% CI: 0.22-0.52%),duration of being under ART (AOR = 0.13, 95% CI: 0.08-0.21%)for 1-5 years, and (AOR = 0.06, 95% CI: 0.02-0.16%)for more than 5 years, decreased the likelihood of treatment non-adherence. Conclusions:These findings show that one in five PLWH did not adhere to cART.On the other hand, the likelihood of non-adherence was directly associated with IDU and incarceration history.Based on the results, tailored programs should be developed to improve adherence among individuals with a history of IDU or incarceration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".