2571. Higher Rates of Hospitalization and Infection-Related Hospitalization Among HIV-Exposed Uninfected Infants Compared with HIV Unexposed Uninfected Infants in the United States
Bibliographic record
Abstract
Abstract Background Studies from multiple countries have suggested impaired immunity in perinatally HIV-exposed uninfected (HEU) children, with elevated rates of all-cause hospitalization and infections. We estimated the incidence of all-cause hospitalization and infection-related hospitalization in the first 2 years of life among HEU children and compared this with HIV-unexposed uninfected (HUU) children in the US Among HEU children, we evaluated associations of maternal HIV disease-related factors during pregnancy with risk of infant hospitalization. Methods We evaluated HEU children enrolled in the Surveillance Monitoring for ART Toxicities (SMARTT) Study dynamic cohort of the Pediatric HIV/AIDS Cohort Study (PHACS) network who were born 2006–2017 and followed from birth. Data on HUU children were obtained from the Medicaid Analytic Extract database, restricted to states participating in SMARTT. We compared rates of first hospitalization, total hospitalizations, first infection-related hospitalization, total infection-related hospitalizations, and mortality between HEU and HUU children using Poisson regression. Among HEU children, multivariable Poisson regression models were fit to evaluate associations of maternal HIV factors with risk of hospitalization. Results Our analysis included 2,404 HEU and 3,605,864 HUU children. HEU children had approximately 2 times greater rates of first hospitalization, total hospitalizations, first infection-related hospitalization, and total infection-related hospitalizations compared with HUU children (figure). There was no significant difference in mortality. Among HEU children, maternal HIV disease factors, including viral load, CD4 count, antiretroviral regimen, and mode of HIV acquisition, were not associated with hospitalization rates. Conclusion Compared with HUU, HEU children in the United States have nearly twice the rate of hospitalization and infection-related hospitalization in the first 2 years of life, consistent with studies in other countries. Closer monitoring of HEU infants for infection and further elucidation of immune mechanisms is needed. Disclosures E. G. Chadwick, Abbott Labs: Shareholder, stock dividends. AbbVie: Shareholder, stock dividends. R. Van Dyke, Giliad Sciences: Grant Investigator, Research grant.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".