Neuropsychiatric disorders in HIV infection: impact of diagnosis on economic costs of care
Bibliographic record
Abstract
BACKGROUND: HAART is associated with a growing prevalence of HIV-associated neuropsychiatric disorders (NPD) despite improved overall survival. OBJECTIVE: To investigate the added direct costs of medical care for patients with and without NPD. METHODS: Nine dimensions of patient-specific costs [as costs per patient per month (CPM)] were followed prospectively between 1997 and 2003 in a community-based HIV/AIDS clinic for HIV-1-seropositive patients with a diagnosis of NPD (n = 188) and without (n = 153). Patients with NPD were stratified into subgroups of cognitive impairment (CI), peripheral neuropathies (PN), or other neuropsychiatric disorders (OND). RESULTS: Compared with the non-NPD group ($916), patients in the NPD group showed an increased mean CPM during the 12-month intervals immediately preceding and subsequently following NPD diagnosis [$1371 (P < 0.001) and 1463 US dollars (P < 0.001), respectively], but not at 18 months prior to diagnosis (1061 US dollars; P > 0.05). Intragroup comparisons between 12 month post-diagnosis and 18 month pre-diagnosis showed a mean CPM increased of 67% (1613 US dollars; P < 0.001) with CI, 31% (1490 US dollars; P < 0.01) with PN, and 33% (1362 US dollars; P < 0.01) with OND. Increased numbers of clinic and physician visits, non-antiretroviral drugs and home care accounted for the higher mean CPM (P < 0.05) both pre-and post-diagnosis within the NPD group. CONCLUSIONS: Neuropsychiatric disorders in patients with HIV/AIDS increase medical costs both before and after diagnosis, primarily owing to the management of the neuropsychiatric illness. Cost analyses offer useful measures of evolving patient needs, and provide a basis for allocation of healthcare resources.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".