Updated direct costs of medical care for HIV‐infected patients within a regional population from 2006 to 2017
Bibliographic record
Abstract
OBJECTIVES: The aim of the study was to reappraise the precise costs of HIV care and cost drivers, to determine the optimal tools for modelling costs for HIV care, and to understand the implications of changing medical management of HIV-infected patients for both subsequent outcomes and health care budgets. METHODS: We obtained all drug, laboratory, out-patient and in-patient care costs for all HIV-infected patients followed between 1 January 2006 and 31 December 2017 (2017 Cdn$). Mean cost per patient per month (PPPM) was used as the standard comparator value. Patients were stratified based on CD4 count: (1) ≤ 75, (2) 76-200, (3) 201-500 and (4) > 500 cells/μL. We determined the cost for only HIV-related expenses. We compared current costs with costs previously reported for the same population. RESULTS: The number of HIV-infected patients in care doubled from 2006 to 2017; total costs increased from $12.4 to $30.1 million, with antiretroviral (ARV) drugs accounting for 78.8% of costs by 2017. Out-patient/laboratory costs declined from 12% to 8.5%, while in-patient costs exhibited more annual variation. Mean PPPM costs increased from $1316 in 2006 to $1712 in 2014, declining to $1446 in 2017. Higher PPPM costs were associated with CD4 counts < 200 cells/μL. Costs have shifted. While the cost of ARV drugs increased by 32%, the costs of out-patient and in-patient services decreased by 80% and 71%, respectively. Most of the decrease for in-patient costs was attributable to a substantial decrease in HIV-related hospitalizations. CONCLUSIONS: Although antiretroviral therapy (ART) provides immense benefits, it is not inexpensive. ARV drugs remain the largest cost driver. Hospital costs have remained low. Substantial costs of lifelong ART necessitate innovative, locally applicable strategies for ARV selection and use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".