All-cause hospitalisation among people living with HIV according to gender, mode of HIV acquisition, ethnicity, and geographical origin in Europe and North America: findings from the ART-CC cohort collaboration
Bibliographic record
Abstract
BACKGROUND: Understanding demographic disparities in hospitalisation is crucial for the identification of vulnerable populations, interventions, and resource planning. METHODS: Data were from the Antiretroviral Therapy Cohort Collaboration (ART-CC) on people living with HIV in Europe and North America, followed up between January, 2007 and December, 2020. We investigated differences in all-cause hospitalisation according to gender and mode of HIV acquisition, ethnicity, and combined geographical origin and ethnicity, in people living with HIV on modern combination antiretroviral therapy (cART). Analyses were performed separately for European and North American cohorts. Hospitalisation rates were assessed using negative binomial multilevel regression, adjusted for age, time since cART intitiaion, and calendar year. FINDINGS: Among 23 594 people living with HIV in Europe and 9612 in North America, hospitalisation rates per 100 person-years were 16·2 (95% CI 16·0-16·4) and 13·1 (12·8-13·5). Compared with gay, bisexual, and other men who have sex with men, rates were higher for heterosexual men and women, and much higher for men and women who acquired HIV through injection drug use (adjusted incidence rate ratios ranged from 1·2 to 2·5 in Europe and from 1·2 to 3·3 in North America). In both regions, individuals with geographical origin other than the region of study generally had lower hospitalisation rates compared with those with geographical origin of the study country. In North America, Indigenous people and Black or African American individuals had higher rates than White individuals (adjusted incidence rate ratios 1·9 and 1·2), whereas Asian and Hispanic people living with HIV had somewhat lower rates. In Europe there was a lower rate in Asian individuals compared with White individuals. INTERPRETATION: Substantial disparities exist in all-cause hospitalisation between demographic groups of people living with HIV in the current cART era in high-income settings, highlighting the need for targeted support. FUNDING: Royal Free Charity and the National Institute on Alcohol Abuse and Alcoholism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".