Multimorbidity among people with HIV in regional New South Wales, Australia
Bibliographic record
Abstract
UNLABELLED: Background Multimorbidity is the co-occurrence of more than one chronic health condition in addition to HIV. Higher multimorbidity increases mortality, complexity of care and healthcare costs while decreasing quality of life. The prevalence of and factors associated with multimorbidity among HIV positive patients attending a regional sexual health service are described. METHODS: A record review of all HIV positive patients attending the service between 1 July 2011 and 30 June 2012 was conducted. Two medical officers reviewed records for chronic health conditions and to rate multimorbidity using the Cumulative Illness Rating Scale (CIRS). Univariate and multivariate linear regression analyses were used to determine factors associated with a higher CIRS score. RESULTS: One hundred and eighty-nine individuals were included in the study; the mean age was 51.8 years and 92.6% were men. One-quarter (25.4%) had ever been diagnosed with AIDS. Multimorbidity was extremely common, with 54.5% of individuals having two or more chronic health conditions in addition to HIV; the most common being a mental health diagnosis, followed by vascular disease. In multivariate analysis, older age, having ever been diagnosed with AIDS and being on an antiretroviral regimen other than two nucleosides and a non-nucleoside reverse transcriptase inhibitor or protease inhibitor were associated with a higher CIRS score. CONCLUSION: To the best of our knowledge, this is the first study looking at associations with multimorbidity in the Australian setting. Care models for HIV positive patients should include assessing and managing multimorbidity, particularly in older people and those that have ever been diagnosed with AIDS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".