Prevalence of Psychiatric and Medical Comorbidities in HIV-Positive Middle-Aged and Older Adults: Findings From a Nationally Representative Survey
Bibliographic record
Abstract
Objective : In order to better serve older individuals with HIV, more information is needed regarding the prevalence of medical and psychiatric comorbidities, and its influence on quality of life within a nationally representative sample. The aims of this study were to assess associations of HIV among middle-aged and older Americans with lifetime Axis I and II psychiatric disorders and suicide attempts, past-year Axis I psychiatric disorders, past-year medical conditions, obesity, and health-related quality of life. Method : Data were drawn from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), which included 15,456 adults aged 50 and older. Results : Respondents with self-reported HIV were more likely than those without to report any lifetime Axis I disorder, any lifetime anxiety disorder, any lifetime substance and drug use disorder, any past-year Axis I and substance use disorders, hypertension, diabetes, and arthritis. HIV infection was also associated with poorer physical health-related quality of life. Discussion : Middle-aged and older adults with HIV have an increased risk of psychiatric disorders and a number of medical conditions that impair physical but not mental health-related quality of life. Comprehensive and integrated medical treatment should be designed and delivered to this underappreciated but growing group of patients.
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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 | 0.000 | 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) | 0.001 | 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".