Statins Utilization in Adults With HIV: The Treatment Gap and Predictors of Statin Initiation
Bibliographic record
Abstract
BACKGROUND: We characterized trends in statin eligibility and subsequent statin initiation among people with HIV (PWH) from 2001 to 2017 and identified predictors of statin initiation between 2014 and 2017. SETTING: PWH participating in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) enrolled in 12 US cohorts collecting data on statin eligibility criteria/prescriptions from 2001 to 2017. METHODS: We determined the annual proportion eligible for statins, initiating statins, and median waiting time (from statin eligibility to initiation). Eligibility was defined using ATP III guidelines (2001-2013) and ACC/AHA guidelines (2014-2017). We assessed initiation predictors in 2014-2017 among statin-eligible PWH using Poisson regression, estimating adjusted prevalence ratios (aPRs) with 95% confidence intervals (95% CIs). RESULTS: Among 16,409 PWH, 7386 (45%) met statin eligibility criteria per guidelines (2001-2017). From 2001 to 2013, statin eligibility ranged from 22% to 25%. Initiation increased from 13% to 45%. In 2014, 51% were statin-eligible, among whom 25% initiated statins, which increased to 32% by 2017. Median waiting time to initiation among those we observed declined over time. Per 10-year increase in age, initiation increased 46% (aPR 1.46, 95% CI: 1.29 to 1.67). Per 1-year increase in calendar year from 2014 to 2017, there was a 41% increase in the likelihood of statin initiation (aPR 1.41, 95% CI: 1.25 to 1.58). CONCLUSIONS: There is a substantial statin treatment gap, amplified by the 2013 ACC/AHA guidelines. Measures are warranted to clarify reasons we observe this gap, and if necessary, increase statin use consistent with guidelines including efforts to help providers identify appropriate candidates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".