A 20-year population-based study of all-cause and cause-specific mortality among people with concurrent HIV and psychotic disorders
Bibliographic record
Abstract
OBJECTIVE: We aimed to characterize mortality among people with HIV (PWH) and psychotic disorders (PWH/psychosis+) vs. PWH alone (PWH/psychosis-). METHOD: A population-based analysis of mortality in PWH (age ≥19) in British Columbia (BC) from April 1996 to March 2017 was conducted using data from the Seek and Treat for Optimal Prevention of HIV/AIDS (STOP HIV/AIDS) study. Deaths were identified from the Vital Statistics Data (classified as HIV vs. non-HIV causes). Mortality trends across all fiscal years were examined. Cox models assessed the hazard of psychotic disorders on mortality; possible differences between schizophrenia and nonschizophrenia types of psychotic disorders were also evaluated. RESULTS: Among 13 410 PWH included in the analysis, 1572 (11.7%) met the case definition for at least one psychotic disorder. Over the study period, 3274 deaths (PWH/psychosis-: n = 2785, PWH/psychosis+: n = 489) occurred. A decline over time in all-cause mortality and HIV-related mortality was observed in both PWH/psychosis+ and PWH/psychosis- ( P value <0.0001). A decline in non-HIV mortality was observed among PWH/psychosis- ( P value = 0.003), but not PWH/psychosis+ ( P value = 0.3). Nonschizophrenia psychotic disorders were associated with increased risk of mortality; adjusted hazard ratios with (95% confidence intervals): all-cause 1.75 (1.46-2.09), HIV-related 2.08 (1.60-2.69), non-HIV-related 1.45 (1.11-1.90). Similar associations between schizophrenia and mortality were not observed. CONCLUSION: People with co-occurring HIV and nonschizophrenia psychotic disorders experienced a significantly higher risk of mortality vs. PWH without any psychotic disorder. Implementing care according to syndemic models considering interactions between HIV and particularly episodic psychotic disorders could help manage mortality risk more effectively among PWH/psychosis+.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".