Factors Associated With Gaps in Medicaid Enrollment Among People With HIV and the Effect of Gaps on Viral Suppression
Bibliographic record
Abstract
INTRODUCTION: Gaps in Medicaid enrollment may affect HIV outcomes. We evaluated factors associated with Medicaid enrollment gaps and their effect on viral suppression (VS) within the HIV Research Network. METHODS: We used a combined data set with Medicaid enrollment files from 2006 to 2010 and HIV Research Network demographic and clinical data. A gap was defined as ≥1 month without Medicaid and gap length was determined. We used multivariable logistic regression to determine factors associated with a gap and multivariable logistic regression with generalized estimated equations to evaluate factors associated with VS after gap. RESULTS: Of 5836 participants, the majority were male, of black race, and aged 25-50 years. More than half had a gap in Medicaid. Factors associated with a gap included male sex [adjusted odds ratio (aOR) 1.79, (1.53, 2.08)] and younger age (aORs ranging from 1.50 to 4.13 comparing younger age groups to age >50, P < 0.05 for all). About a quarter of gaps had VS information before and after gap. Of those, 53.7% had VS both before and after gap and 25.8% were unsuppressed both before and after gap. The strongest association with VS after gap was VS before gap [aOR 15.76 (10.48, 23.69)]. Transition into Ryan White HIV/AIDS Program coverage during Medicaid gaps was common (28% of all transitions). CONCLUSIONS: Gaps in Medicaid enrollment were common and many individuals with pre-gap VS maintained VS after gap, possibly due to accessing other sources of antiretroviral therapy coverage. Implementing initiatives to maintain Medicaid enrollment and to expedite Medicaid reenrollment and having alternate resources available in gaps are important to ensure continuous antiretroviral therapy to optimize HIV outcomes.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".