Lost but not forgotten: A population‐based study of mortality and care trajectories among people living with HIV who are lost to follow‐up in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Selection as a consequence of volunteer participation in, and loss to follow-up from, cohort studies may bias estimates of mortality and other health outcomes. To quantify this potential, we estimated mortality and health service use among people living with HIV (PLWH) who were lost to cohort follow-up (LTCFU) from a volunteer clinical HIV-infected cohort, and compared these to mortality and health service use in active cohort participants and non-cohort-participants living with HIV in Ontario, Canada. METHODS: We analysed population-based provincial health databases from 1995 to 2014, identifying PLWH ≥ 18 years old; these included data from participants in the Ontario HIV Treatment Network Cohort Study (OCS), a volunteer, multi-site clinical HIV-infected cohort. We calculated all-cause mortality, hospitalization and emergency department (ED) visit rates per 100 person-years (PY) and estimated hazard ratios (HRs) of mortality, adjusting for age, sex, income, rurality, and immigration status. RESULTS: Among 23 043 PLWH, 5568 were OCS participants. Compared with nonparticipants, participants were younger and less likely to be female, to be an immigrant and to reside in a major urban centre, and had lower comorbidity. Mortality among active participants, participants LTCFU and nonparticipants was 2.52, 3.30 and 2.20 per 100 PY, respectively. After adjustment for covariates, mortality risk was elevated among participants LTCFU compared with active participants (HR 2.26; 95% confidence interval 1.91, 2.68). Age-adjusted hospitalization rates and ED visit rates were highest among participants LTCFU. CONCLUSIONS: Mortality risk and use of health care resources were lower among active cohort participants. Our findings may inform health outcome estimates based on volunteer cohorts, as well as quantitative bias adjustment to correct for such biases.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".