Heterogeneity in the costs of medical care among people living with HIV/AIDS in the United States
Bibliographic record
Abstract
OBJECTIVE: The costs of medical care for people with HIV/AIDS (PWH) vary substantially across demographic groups, stages of disease progression and regionally across the United States. We aimed to estimate medical costs for PWH and examine the heterogeneity in costs within key patient groups typically distinguished in cost-effectiveness analyses. DESIGN: Retrospective cohort study using health administrative databases for diagnosed PWH in care at 17 HIV Research Network sites across the United States. METHODS: We estimated mean quarterly costs for key patient groups using multivariable generalized linear mixed effects models. We used quantile regression to highlight differences in the effect of covariates within each patient group (difference between covariate estimates at the mean versus the 90th percentile of quarterly costs), identifying covariates with a larger effect among the highest cost PWH, or generating greater uncertainty in mean cost estimates. RESULTS: Our sample included 40 022 patients with a median age of 39 years. Mean quarterly costs were highest for people who inject drugs with advanced disease progression and for PWH on antiretroviral treatment (ART). Within patient groups, we found the most heterogeneity at different levels of resource use for PWH on ART and PWH off ART with CD4 cell counts less than 200 cells/μl, people who inject drugs, as well as PWH in the South. CONCLUSION: The study quantifies heterogeneity in costs both across and within key PWH patient groups. Our results highlight the need for sensitivity analysis on cost estimates and may inform decisions on model structure in cost-effectiveness analyses on HIV/AIDS treatment and prevention strategies.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".