Place of death for people with HIV: a population-level comparison of eleven countries across three continents using death certificate data
Bibliographic record
Abstract
BACKGROUND: With over 1 million HIV-related deaths annually, quality end-of-life care remains a priority. Given strong public preference for home death, place of death is an important consideration for quality care. This 11 country study aimed to i) describe the number, proportion of all deaths, and demographics of HIV-related deaths; ii) identify place of death; iii) compare place of death to cancer patients iv), determine patient/health system factors associated with place of HIV-related death. METHODS: In this retrospective analysis of death certification, data were extracted for the full population (ICD-10 codes B20-B24) for 1-year period: deceased's demographic characteristics, place of death, healthcare supply. RESULTS: i) 19,739 deaths were attributed to HIV. The highest proportion (per 1000 deaths) was for Mexico (9.8‰), and the lowest Sweden (0.2‰). The majority of deaths were among men (75%), and those aged <50 (69.1%). ii) Hospital was most common place of death in all countries: from 56.6% in the Netherlands to 90.9% in South Korea. The least common places were hospice facility (3.3%-5.7%), nursing home (0%-17.6%) and home (5.9%-26.3%).iii) Age-standardised relative risks found those with HIV less likely to die at home and more likely to die in hospital compared with cancer patients, and in most countries more likely to die in a nursing home. iv) Multivariate analysis found that men were more likely to die at home in UK, Canada, USA and Mexico; a greater number of hospital beds reduced the likelihood of dying at home in Italy and Mexico; a higher number of GPs was associated with home death in Italy and Mexico. CONCLUSIONS: With increasing comorbidity among people ageing with HIV, it is essential that end-of-life preferences are established and met. Differences in place of death according to country and diagnosis demonstrate the importance of ensuring a "good death" for people with HIV, alongside efforts to optimise treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".