Point of Diagnosis and Patient Retention in HIV Care in Western Kenya
Bibliographic record
Abstract
BACKGROUND: Home-based counseling and testing (HBCT) achieves earlier HIV diagnosis than other testing modalities; however, retention in care for these healthier patients is unknown. The objective of this study was to determine the association between point of HIV testing and retention in care and mortality. SETTING: Academic Model Providing Access to Healthcare (AMPATH) has provided HIV care in western Kenya since 2001. METHODS: AMPATH initiated HBCT in 2007. This retrospective analysis included individuals 13 years and older, enrolled in care between January 2008 and September 2016, with data on point of testing. Discrete-time multistate models were used to estimate the probability of transition between the following states: engaged, disengaged, transfer, and death, and the association between point of diagnosis and transition probabilities. RESULTS: Among 77,358 patients, 67% women, median age: 35 years and median baseline CD4: 248 cells/mm. Adjusted results demonstrated that patients from HBCT were less likely to disengage [relative risk ratio (RRR) = 0.87, 95% CI: 0.83 to 0.91] and die (RRR = 0.65, 95% CI: 0.55 to 0.75), whereas those diagnosed through provider-initiated counseling and testing were more likely to disengage (RRR = 1.09, 95% CI: 1.07 to 1.12) and die (RRR = 1.13, 95% CI: 1.06 to 1.20), compared with patients from voluntary counseling and testing. Once disengaged, patients from HBCT were less likely to remain disengaged, compared with patients from voluntary counseling and testing. CONCLUSIONS: Patients entering care from different HIV-testing programs demonstrate differences in retention in HIV care over time beyond disease severity. Additional research is needed to understand the patient and system level factors that may explain the associations between testing program, retention, and mortality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".