Changes in the Prevalence of Non-AIDS Conditions Among Hospitalized Persons With HIV in the United States and Canada, 2008–2018
Bibliographic record
Abstract
BACKGROUND: Hospitalization causes among persons with HIV (PWH) have shifted to non-AIDS conditions, but the complete disease profile of hospitalized PWH has not been well described. To inform hospitalization and readmission prevention efforts, we examined non-AIDS disease prevalence among PWH hospitalized in 4 US cohorts and 1 Canadian cohort. METHODS: Among PWH with ≥1 hospitalization from 2008 to 2018, we used log-binomial regression with generalized estimating equations to estimate trends in the annual prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV), hypertension, hyperlipidemia, diabetes mellitus, chronic kidney disease (CKD) stage ≥3, and multimorbidity (≥2 and ≥3 conditions), defined using longitudinal diagnosis, medication, and laboratory data. RESULTS: We examined 6781 hospitalized PWH who were 75% cisgender men, 40% White, and 38% Black. From 2008 to 2018, the proportion of PWH in care who had ≥1 hospitalization decreased from 9.6% to 6.3%. Age- and cohort-adjusted prevalence increased for hyperlipidemia (relative change per year: 3.6% [95% CI: 2.5%-4.7%]), diabetes mellitus (2.8% [1.3%-4.4%]), CKD (3.3% [1.7%-4.9%]), ≥2 conditions (1.3% [0.6%-2.0%]), and ≥3 conditions (3.0% [1.7%-4.3%]), decreased for HCV infection (-2.0% [-3.0%, -0.9%]), and remained stable for HBV infection (1.6% [-1.1%, 4.3%]) and hypertension (0.4% [-0.2%, 1.1%]). CONCLUSIONS: Hospitalized PWH had an increasing burden of several non-AIDS conditions and multimorbidity not accounted for by aging alone. Further work is needed to understand these conditions' role in hospitalization risk among PWH. Our findings reinforce that hospital discharge planning in PWH should include efforts to ensure chronic conditions are adequately managed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".