1394. Clinical and Sociodemographic Characteristics Associated with Poor Self-rated Health across Multiple Domains among Older North American Adults Living with HIV
Bibliographic record
Abstract
Abstract Background Older adults living with HIV (OALHIV) are a growing demographic in North America. We summarized baseline characteristics and associated self-rated suboptimal health in North American OALHIV. Methods OALHIV aged ≥ 50 years from the United States, Canada, and Mexico who participated in the 25-country 2019 Positive Perspectives survey were included. Self-rated health was assessed across 4 domains (physical, mental, sexual, overall); each was dichotomized as optimal (Good/Very good) or suboptimal (Neither good nor poor/Poor/Very poor). Multinomial logistic regression was measured for associations between domains and relevant sociodemographic and clinical characteristics. Results Of 583 participants from North America, 161 were aged ≥ 50 years and included in this analysis. Most were male (73%), 14% were aged ≥ 65 years, 66% were diagnosed with HIV ≥ 10 years ago, median disease duration was 20 years, and 55% took ≥ 5 non-HIV pills daily. Most OALHIV (n=128; 80%) had switched antiretroviral therapy (ART) at least once, 14% (18/128) because of potential drug-drug interactions and 22% (28/128) because it was no longer effective or for resistance, and 6% were very treatment experienced (changed ART ≥ 4 times, with ≥ 1 switch in the past year for resistance or poor tolerability). Among OALHIV, 86% reported ≥ 1 comorbidity; the most common were hypertension (42%), hypercholesterolemia (39%), mental illness (32%), and insomnia (29%). Overall, 48% (77/161) of OALHIV reported suboptimal physical health, 35% (56/161) suboptimal mental health, 60% (97/161) suboptimal sexual health, and 47% (75/161) suboptimal overall health. On all domains, 19% (31/161) reported suboptimal health; 24% (38/161) reported optimal health on all domains, 23% (37/161) on 3 domains only, 13% (21/161) on 2 domains only, and 21% (34/161) on 1 domain only. Comorbidities with a > 15% difference between OALHIV reporting suboptimal health in all domains vs those reporting optimal health in at least 1 domain, respectively, were mental health (48% vs 28%), substance abuse (26% vs 5%, as self-reported in the survey), bone disease (26% vs 8%), and insomnia (42% vs 25%). Conclusion Most OALHIV in North America reported comorbidities and polypharmacy, with approximately one-quarter reporting suboptimal health on all domains. Disclosures Megan Dominguez, PharmD, GlaxoSmithKline: Stocks/Bonds|ViiV Healthcare: Employee|ViiV Healthcare: Stocks/Bonds Chinyere Okoli, MSc, DIP, ViiV Healthcare: I am an employee of ViiV healthcare|ViiV Healthcare: Stocks/Bonds Patricia de los Rios, MSc, GlaxoSmithKline: Stocks/Bonds|ViiV Healthcare: Employment Manyu Prakash, PhD, GlaxoSmithKline: Stocks/Bonds|ViiV Healthcare: Employee Andrew Clark, MD, ViiV Healthcare: Employee|ViiV Healthcare: Stocks/Bonds.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".