Healthcare resource utilization and costs associated with renal, bone and cardiovascular comorbidities among persons living with HIV compared to the general population in Quebec, Canada
Bibliographic record
Abstract
There is limited understanding on healthcare utilization and costs of age-related comorbidities such as cardiovascular, bone and renal disease/disorder in people living with human immunodeficiency virus, so we compared comorbidity prevalence and associated healthcare utilization and costs. Through the Quebec health insurance database, people living with human immunodeficiency virus on antiretroviral therapy for ≥6 months from January 2006 to June 2012 were categorized by their comorbidity status using International Classification of Diseases (ICD)-9 codes, and controls without human immunodeficiency virus diagnosis or antiretroviral therapy use were age and gender matched. We compared healthcare utilization and costs. A total of 3,905 people living with human immunodeficiency virus and 11,715 control individuals were included. The mean age of people living with human immunodeficiency virus was 45.3 years and 77.3% were men. Prevalence of comorbidities was higher and occurred earlier in people living with human immunodeficiency virus and increased with older age regardless of human immunodeficiency virus status. Interestingly, bone comorbidity was high (37%) and 5-fold greater in people living with human immunodeficiency virus <20 years than the controls. Polypharmacy and comorbidity scores were greater in people living with human immunodeficiency virus than controls (p<0.01), as were cardiovascular, bone and renal comorbidities (40.3%, 26.0% and 5.5%, respectively; p<0.01). People living with human immunodeficiency virus had higher healthcare utilization and costs than controls largely due to longer hospital stays and prescriptions. Mean total healthcare cost/person/year for people living with human immunodeficiency virus was CAD$6,248 and was highest for those with renal disease (CAD$19,617). Comorbidities in people living with human immunodeficiency virus are more prevalent, occur earlier and incur a higher burden on the healthcare system; earlier screening and improved preventative and management strategies may reduce the burden to people living with human immunodeficiency virus and to the healthcare system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".