Socio-economic- and sex-related disparities in rates of hospital admission among patients with HIV infection in Ontario: a population-based study.
Bibliographic record
Abstract
BACKGROUND: Among people living with HIV infection in the era of combination antiretroviral therapy (cART), admission to hospital may indicate inadequate community-based care. As such, population-based assessments of the utilization of inpatient services represent a necessary component of evaluating the quality of HIV-related care. METHODS: We used a validated algorithm to search Ontario's administrative health care databases for all persons living with HIV infection aged 18 years or older between 1992/93 and 2008/09. We then conducted a population-based study using time-series and longitudinal analyses to first quantify the immediate effect of cART on hospital admission rates and then analyze recent trends (for 2002/03 to 2008/09) in rates of total and HIV-related admissions. RESULTS: The introduction of cART in 1996/97 was associated with more pronounced reductions in the rate of hospital admissions among men than among women (for total admissions, -89.9 v. -60.5 per 1000 persons living with HIV infection, p = 0.003; for HIV-related admissions, -56.9 v. -36.3 per 1000 persons living with HIV infection, p < 0.001). Between 2002/03 and 2008/09, higher rates of total hospital admissions were associated with female sex (adjusted relative rate [RR] 1.15, 95% confidence interval [CI] 1.05-1.27) and low socio-economic status (adjusted RR 1.21, 95% CI 1.14-1.29). Higher rates of HIV-related hospital admission were associated with low socio-economic status (adjusted RR 1.30, 95% CI 1.17-1.45). Recent immigrants had lower rates of both total admissions (adjusted RR 0.70, 95% CI 0.61-0.80) and HIV-related admissions (adjusted RR 0.77, 95% CI 0.61-0.96). INTERPRETATION: We observed important socio-economic- and sex-related disparities in rates of hospital admission among people with HIV living in Ontario, Canada.
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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 | 0.000 |
| 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 | 0.001 |
| 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".