Antiretroviral Utilization and Adherence Among Provincial Beneficiaries with HIV in Saskatchewan
Bibliographic record
Abstract
Background: HIV has been an alarming issue in the province of Saskatchewan. In 2018, the provincial drug insurance plan expanded their program to provide free antiretroviral medications to all beneficiaries living with HIV. The objective of this study was to examine antiretroviral therapy (ART) medication utilization and adherence before and after the implementation of the provincial coverage program. Methods: A retrospective study was conducted using administrative databases to examine beneficiaries of the provincial drug plan who were living with HIV. Aggregate ART drug claims and associated costs were measured in four-month intervals between 2015 and 2021. A linear regression model was developed to test the impact of the program using interrupted time series analysis. Also, the probability of achieving optimal adherence to ART medications was compared before and after the program's implementation. The effect of the provincial ART coverage program on achieving optimal adherence outcomes was measured using a random effects logistic regression model. Results: Among 4,994 Saskatchewan residents with at least one diagnosis of HIV, 1,828 were exclusive beneficiaries of the provincial drug plan and satisfied all other inclusion criteria. Almost two-thirds (57%) were male and one-third were aged between 40-59. A statistically significant increase in the number of active users (i.e., individuals with at least one ART claim) and the total number of ART prescription claims was observed immediately following program implementation. Total spending associated with increased utilization was attenuated due to a historical trend of declining ART costs. In contrast to the aggregate increase in drug utilization, no significant difference in the probability of optimal adherence was observed before versus after program implementation (53.6% vs 55.1%). However, certain subgroups of beneficiaries living with HIV may have been more likely to benefit than others. The two factors most strongly associated with optimal adherence were older age and no history of substance use. Discussion: In summary, our study showed a small increase in utilization following the implementation of an ART coverage program without a detectable effect on the frequency of optimal adherence among beneficiaries living with HIV. Although certain subgroups may have benefited, additional support measures are needed to help combat this important public health issue.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".