Geographic variation in the costs of medical care for people living with HIV in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Regional variation in medical care costs can indicate heterogeneity in clinical practice, inequities in access, or inefficiencies in service delivery. We aimed to estimate regional variation in medical costs for people living with HIV (PLHIV), adjusting for demographics and case-mix. METHODS: We conducted a retrospective cohort study using linked health administrative databases of PLHIV, from 2010 to 2014, in British Columbia (BC), Canada. Quarterly health care costs (2018 CAD) were derived from inpatient, outpatient, prescription drugs, antiretroviral therapy (ART), and HIV diagnostics. We used a two-part model with a logit link for the probability of incurring costs, and a log link and gamma distribution for observations with positive costs. We also estimated quarterly utilization rates for hospitalization-, physician billing- and prescription drug-days. Primary variables were indicators of individuals' Health Service Delivery Area (HSDA). We adjusted cost and utilization estimates for demographic characteristics, HIV-disease progression, and comorbidities. RESULTS: Our cohort included 9577 PLHIV (median age 45.5 years, 80% male). Adjusted total quarterly costs for all 16 HSDAs were within 20% of the provincial mean, 8/16 for hospitalization costs, 16/16 for physician billing costs and 10/16 for prescription drug costs. Northern Interior and Northeast HSDAs had 38 and 44% lower quarterly non-ART prescription drug costs, and 2 and 5% higher quarterly inpatient costs, respectively. CONCLUSIONS: We observed limited variation in medical care costs and utilization among PLHIV in BC. However, lower levels of outpatient care and higher levels of inpatient care indicate possible barriers to accessing care among PLHIV in the most rural regions of the province.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".