Impact of Age on Retention in Care and Viral Suppression
Bibliographic record
Abstract
BACKGROUND: Retention in care is important for all HIV-infected persons and is strongly associated with initiation of antiretroviral therapy and viral suppression. However, it is unclear how retention in care and age interact to affect viral suppression. We evaluated whether the association between retention and viral suppression differed by age at entry into care. METHODS: Cross-sectional analysis (2006-2010) involving 17,044 HIV-infected adults in 14 clinical cohorts across the United States and Canada. Patients contributed 1 year of data during their first full-calendar year of clinical observation. Poisson regression examined associations between retention measures [US National HIV/AIDS Strategy (NHAS), US Department of Health and Human Services (DHHS), 6-month gap, and 3-month visit constancy] and viral suppression (HIV RNA ≤200 copies/mL) by age group: 18-29 years, 30-39 years, 40-49 years, 50-59 years, and 60 years or older. RESULTS: Overall, 89% of patients were retained in care using the NHAS measure, 74% with the DHHS indicator, 85% did not have a 6-month gap, and 62% had visits in 3-4 quarters of the year; 54% achieved viral suppression. For each retention measure, the association with viral suppression was significant for only the younger age groups (18-29 and 30-39 years): 18-29 years [adjusted prevalence ratio (APR) = 1.33, 95% confidence interval (CI): 1.03 to 1.70]; 30-39 years (APR = 1.23, 95% CI: 1.01 to 1.49); 40-49 years (APR = 1.06, 95% CI: 0.90 to 1.22); 50-59 (APR = 0.92, 95% CI: 0.75 to 1.13); ≥60 years (APR = 0.99, 95% CI: 0.63 to 1.56) using the NHAS measure as a representative example. CONCLUSIONS: These results have important implications for improving viral control among younger adults, emphasizing the crucial role retention in care plays in supporting viral suppression in this population.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".