Rates and predictors of injury in a population-based cohort of people living with HIV
Bibliographic record
Abstract
OBJECTIVES: Injuries are responsible for 10% of the global burden of disease; however, the epidemiology of injury among people living with HIV (PLHIV) has not been well elucidated. This study seeks to characterize rates and predictors of injury among PLHIV compared to the general population in British Columbia (BC), Canada. DESIGN: A population-based dataset was created via linkage between the BC Centre for Excellence in HIV/AIDS and PopulationDataBC. METHODS: PLHIV aged 20 years and older were compared to a random 10% sample of the adult general population. The International Classification of Diseases 9 and 10 codes were used to classify unintentional and intentional injuries based on the external cause of the injury from 1996 to 2013. Generalized estimating equation (GEE) Poisson regression models were fit to estimate the effect of HIV status on rates of unintentional and intentional injury, and to identify correlates of injury among PLHIV. RESULTS: The crude incidence rate of unintentional injury was 18.56/1000 person-years [95% confidence interval (CI) 17.77-19.39] among PLHIV and 8.51/1000 person-years (95% CI 8.42-8.59) in the general population. Among PLHIV, 13.45% of deaths were due to injury, compared to 5.52% of deaths in the general population. In adjusted models, PLHIV were more likely to report unintentional (incidence rate ratio 1.42, 95% CI 1.32-1.52) and intentional injury (incidence rate ratio 1.93, 95% CI 1.70-2.18) compared to the general population. CONCLUSIONS: We identified elevated rates of intentional and unintentional injury among PLHIV. Injuries are largely preventable; as such, targeted efforts are needed to decrease the burden of injury-related disability and death among PLHIV.
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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) | 0.000 | 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) | 0.000 | 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".