Incidence of diabetes mellitus among people living with and without HIV in British Columbia, Canada between 2001 and 2013: a longitudinal population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: People living with HIV (PLHIV) are increasingly at risk of age-related comorbidities such as diabetes mellitus (DM). While DM is associated with elevated mortality and morbidity, understanding of DM among PLHIV is limited. We assessed the incidence of DM among people living with and without HIV in British Columbia (BC), Canada, during 2001-2013. METHODS: We used longitudinal data from a population-based cohort study linking clinical data and administrative health data. We included PLHIV who were antiretroviral therapy (ART) naïve at baseline, and 1:5 age-sex-matched persons without HIV. All participants had ≥5 years of historic data pre-baseline and ≥1 year(s) of follow-up. DM was identified using the BC Ministry of Health's definitions applied to hospitalisation, physician billing and drug dispensation datasets. Incident DM was identified using a 5-year run-in period. In addition to unadjusted incidence rates (IRs), we estimated adjusted incidence rate ratios (IRR) using Poisson regression and assessed annual trends in DM IRs per 1000 person years (PYs) between 2001 and 2013. RESULTS: A total of 129 PLHIV and 636 individuals without HIV developed DM over 17 529 PYs and 88,672 PYs, respectively. The unadjusted IRs of DM per 1000 PYs were 7.4 (95% CI 6.2 to 8.8) among PLHIV and 7.2 (95% CI 6.6 to 7.8) for individuals without HIV. After adjustment for confounding, HIV serostatus was not associated with DM incidence (adjusted IRR: 1.03, 95% CI 0.83 to 1.27). DM incidence did not increase over time among PLHIV (Kendall trend test: p=0.9369), but it increased among persons without HIV between 2001 and 2013 (p=0.0136). CONCLUSIONS: After adjustment, HIV serostatus was not associated with incidence of DM, between 2001 and 2013. Future studies should investigate the impact of ART on mitigating the potential risk of DM among PLHIV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".