Racial, ethnic, and gender disparities in hospitalizations among persons with HIV in the United States and Canada, 2005–2015
Bibliographic record
Abstract
OBJECTIVE: To examine recent trends and differences in all-cause and cause-specific hospitalization rates by race, ethnicity, and gender among persons with HIV (PWH) in the United States and Canada. DESIGN: HIV clinical cohort consortium. METHODS: We followed PWH at least 18 years old in care 2005-2015 in six clinical cohorts. We used modified Clinical Classifications Software to categorize hospital discharge diagnoses. Incidence rate ratios (IRR) were estimated using Poisson regression with robust variances to compare racial and ethnic groups, stratified by gender, adjusted for cohort, calendar year, injection drug use history, and annually updated age, CD4+, and HIV viral load. RESULTS: Among 27 085 patients (122 566 person-years), 80% were cisgender men, 1% transgender, 43% White, 33% Black, 17% Hispanic of any race, and 1% Indigenous. Unadjusted all-cause hospitalization rates were higher for Black [IRR 1.46, 95% confidence interval (CI) 1.32-1.61] and Indigenous (1.99, 1.44-2.74) versus White cisgender men, and for Indigenous versus White cisgender women (2.55, 1.68-3.89). Unadjusted AIDS-related hospitalization rates were also higher for Black, Hispanic, and Indigenous versus White cisgender men (all P < 0.05). Transgender patients had 1.50 times (1.05-2.14) and cisgender women 1.37 times (1.26-1.48) the unadjusted hospitalization rate of cisgender men. In adjusted analyses, among both cisgender men and women, Black patients had higher rates of cardiovascular and renal/genitourinary hospitalizations compared to Whites (all P < 0.05). CONCLUSION: Black, Hispanic, Indigenous, women, and transgender PWH in the United States and Canada experienced substantially higher hospitalization rates than White patients and cisgender men, respectively. Disparities likely have several causes, including differences in virologic suppression and chronic conditions such as diabetes and renal disease.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".