Global Trends in CD4 Cell Count at the Start of Antiretroviral Therapy: Collaborative Study of Treatment Programs
Bibliographic record
Abstract
Background: Early initiation of combination antiretroviral therapy (cART), at higher CD4 cell counts, prevents disease progression and reduces sexual transmission of human immunodeficiency virus (HIV). We describe the temporal trends in CD4 cell counts at the start of cART in adults from low-income, lower-middle-income, upper-middle-income, and high-income countries (LICs, LMICs, UMICs, and HICs, respectively). Methods: We included HIV-infected individuals aged ≥16 years who started cART between 2002 and 2015 in a clinic participating in the International epidemiology Databases to Evaluate AIDS (IeDEA) or the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE). Missing CD4 cell counts at the start of cART were estimated through multiple imputation. Weighted mixed-effect models were used to smooth trends in median CD4 cell counts. Results: A total of 951855 adults from 16 LICs, 11 LMICs, 9 UMICs, and 19 HICs were included. Overall, the modeled median CD4 cell count at the start of cART increased from 2002 to 2015, from 78/µL (95% confidence interval, 58-104/µL) to 287/µL (250-328/µL) in LICs, from 99/µL (71-140/µL) to 234/µL (192-285/µL) in LMICs, from 71/µL (49-104/µL) to 311/µL (255-379/µL) in UMICs, and from 161/µL (143-181/µL) to 327/µL (286-372/µL) in HICs. In LICs, LMICs, and UMICs, the increase was more pronounced in women; in HICs, the opposite was observed. Conclusions: Median CD4 cell counts at the start of cART increased in all income groups, but generally remained below 350/μL in 2015. Substantial additional efforts and resources are required to achieve earlier diagnosis, linkage to care, and initiation of cART.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".