Antiretroviral utilization and adherence before vs. after expansion of a provincial drug insurance policy
Bibliographic record
Abstract
OBJECTIVE: To compare antiretroviral therapy (ART) utilization and adherence before and after expansion of a drug coverage program. METHODS: A retrospective study was conducted using administrative databases in Saskatchewan, Canada. Beneficiaries with at least one diagnostic claim for HIV infection or AIDS between 1999 and 2021 were eligible. An interrupted time series analysis described trends for three indicators of ART utilization before and after drug coverage expansion in 2018: number of active users (defined by at least one ART claim), number of ART claims, and ART spending. A random-effects logistic regression model, controlling for confounders, was used to evaluate the likelihood of achieving at least 95% adherence measured by the proportion of days covered (PDC) before vs. after coverage expansion. RESULTS: A total of 519 individuals received at least one ART claim during the study period and met all other inclusion criteria. Time series models detected statistically significant increases in the number of active ART users and ART claims within 4 months following coverage expansion. Corresponding increases in ART spending were offset by decreases over prior years. No statistically significant changes were detected in the likelihood of achieving at least 95% PDC between the pre vs. postcoverage periods (adjusted odds ratio 1.26, 95% confidence interval: 0.71-2.25, P = 0.423). CONCLUSION: ART coverage expansion was associated with a higher number of claims, more active users, and a change in spending pattern; however, we did not detect a difference in the likelihood of achieving optimal adherence. Addressing additional gaps in HIV management remains a priority.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".