Cause-specific mortality among HIV-infected people in Ontario, 1995–2014: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Risk factors for cause-specific mortality have not been widely studied among people with HIV infection. Our objectives were to estimate rates of and risk factors for all-cause and cause-specific mortality from 1995 to 2014 among HIV-infected people in Ontario. METHODS: We conducted a population-based retrospective cohort study using provincial health databases of people with HIV infection who were aged 16 years or more, were residents of Ontario between 1995 and 2014, and had HIV infection according to a previously validated algorithm. We used International Classification of Diseases codes to classify the underlying cause of death and estimated age-adjusted mortality rates per 100 person-years for 1995 to 2014. We used descriptive statistics to characterize the cohort at baseline and calculated adjusted mortality rate ratios (RRs) using generalized estimating equations. RESULTS: Among 23 043 people, the all-cause mortality rate declined from 6.69 to 1.53 per 100 person-years over the study period, and the rate of death from HIV/AIDS declined from 4.75 to 0.46 per 100 person-years. Concomitantly, the proportions of deaths due to cancer, cardiovascular disease and other noncommunicable diseases rose; however, rates remained constant or declined. Compared to males, females had higher mortality due to cardiovascular disease (adjusted RR 1.36, 95% confidence interval [CI] 1.04-1.77), noncommunicable causes (adjusted RR 1.75, 95% CI 1.39-2.20) and, by 2010-2014, any cause (adjusted RR 1.19, 95% CI 1.02-1.38). Residing in a low-income neighbourhood was associated with increased risk for most causes, including HIV/AIDS (adjusted RR in 2010-2014 1.86, 95% CI 1.49-2.31). Rural residence was associated with increased mortality due to malignant disease (adjusted RR 1.60, 95% CI 1.10-2.34) and noncommunicable disease (adjusted RR 1.86, 95% CI 1.25-2.77). Being an immigrant was associated with lower risk of death from all causes. INTERPRETATION: Over the study period, death was increasingly due to common chronic conditions rather than to HIV infection itself. Care should incorporate the prevention and management of these conditions, especially among females and those residing in rural and low-income areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".