Changes in Causes of Death, the Impact on Life Expectancy, and the Risk of Myocardial Infarction Among People Living with and without HIV
Bibliographic record
Abstract
Background: Within the context of aging with HIV in the combination antiretroviral therapy (ART) era, the set of four papers that make up this dissertation aimed to: summarize the existing evidence on the risk of myocardial infarction (MI) among people living HIV (PLHIV) (Chapter 2); characterize changes over time in rates and causes of death among HIV-positive and HIV-negative individuals (Chapter 3); assess the impact of the changing causes of death on life expectancy and potential gains in life expectancy over time (Chapter 4); estimate the incidence of MI and its association with HIV infection, ART, and other explanatory variables, among HIV-positive and HIV-negative individuals (Chapter 5).Methods: A systematic review and meta-analysis of MI risk among PLHIV was initially performed. Next, data from the COAST studya linked population-based, retrospective cohort study containing longitudinal data on over half a million HIV-positive and HIV-negative adults in British Columbia (BC)were assessed to investigate several issues pertinent to aging with HIV. With the hope of producing evidence to inform relevant programmatic and clinical guidelines/policies among aging HIV-positive individuals, a series of analyses were performed to examine mortality changes over time, cause-deleted life expectancy, and the risk of MI among HIV-positive compared to HIV-negative individuals. Results: We observed significant declines in mortality and dramatic shifts in the causes of death between 1996 and 2012 among HIV-positive compared to HIV-negative individuals. Although HIV/AIDS continues to account for the greatest burden of mortality among PLHIV, other non-AIDS-defining conditions have become increasingly relevant. Consequently, our results suggest that managing cardiovascular diseases and non-AIDS-defining cancers among PLHIV has the same effect on life expectancy in this population as in HIV-negative individuals. Increasing age, male sex, and HIV infection (including exposure to some ART regimens) were found to be associated with a higher risk of MI.Conclusion: Taken together, our findings highlight the increasing need to concurrently consider multiple factors, including HIV infection itself, other emerging non-HIV-related conditions, exposure to ART, and demographic and clinical risk factors, as part of the effort to address and improve the care of aging HIV-positive individuals.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".