Severe mental illness at <scp>ART</scp> initiation is associated with worse retention in care among <scp>HIV</scp>‐infected <scp>U</scp>gandan adults
Bibliographic record
Abstract
OBJECTIVE: The impact of severe mental illness (SMI) on retention in HIV care remains uncertain. We aimed to measure the association between SMI at antiretroviral therapy (ART) initiation and subsequent retention in care in HIV-infected Ugandan adults. METHOD: We conducted cohort study of 773 patients who initiated ART between January 2005 and July 2009 at the Butabika HIV clinic in Kampala, Uganda. SMI was defined as any clinically diagnosed organic brain syndrome, affective disorder or psychotic disorder. We used Kaplan-Meier and Cox proportional hazards analysis to evaluate the association between SMI and retention in care. RESULTS: The prevalence of SMI at ART initiation was 23%. Patients with SMI at baseline were similar to those without SMI in terms of age (median [IQR]: 35 [28-40] vs. 35 [30-40], P = 0.03), sex (36% vs. 35% female, P = 0.86) and baseline CD4+ T-cell count (112 [54-175] vs. 120 [48-187] cells/mm3, P = 0.86). At 12 months after ART initiation, Kaplan-Meier estimates of continuous retention in care were 65% (95% confidence interval, CI: 31-39%) among patients without SMI, vs. 47% (95% CI: 39-55%) among those with SMI (P < 0.001). All-cause mortality in the two groups was similar: 1.2% vs. 2.0% (P > 0.05). In multivariable analysis, the only baseline variable independently associated with breakage of continuous care was SMI (HR = 1.58, 95% CI: 1.06─2.33). CONCLUSIONS: Severe mental illness at ART initiation is associated with worse retention in HIV care in this urban Ugandan referral hospital. As ART is scaled up across sub-Saharan Africa, greater attention must be paid to the burden of mental illness and its impact on retention in care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".