Comorbidity and polypharmacy among women living with HIV in British Columbia
Bibliographic record
Abstract
OBJECTIVE: To characterize comorbid disease and medication burden among women living with HIV (WLWH) in British Columbia (BC), Canada. DESIGN: We examined baseline data from 267 WLWH and 276 HIV-negative women, aged at least 19 years, enrolled in the Children and Women: Antiretrovirals and Markers of Aging (CARMA) cohort. METHODS: Self-reported demographic, medical condition, medication, vitamin, and substance exposure data were collected at baseline CARMA study visits. We considered conditions with appropriate concomitant medications to be 'treated'. Wilcoxon rank-sum and Fisher's exact tests compared continuous and categorical variables between WLWH and HIV-negative women. Number of diagnoses, prescribed medications (excluding HIV/antiretrovirals), vitamins, and prevalence of depression/anxiety/panic disorder were compared using negative binomial and logistic regressions for continuous and binary variables, respectively. RESULTS: WLWH were younger [median, interquartile range (IQR) 39.9, 33.6-46.9 vs. 43.6, 31.8-54.6 years; P = 0.01], attained lower education (40.5 vs. 69.6% college/university; P < 0.001), and more often currently smoked tobacco (47.9 vs. 31.9%; P < 0.001) or had income less than $15 000/year (49.0 vs. 43.1%; P < 0.001). Although younger, and despite omitting HIV infection, WLWH had a greater number of diagnoses (incidence rate ratio, 95% confidence interval 1.58, 1.38-1.81; P < 0.001), and more depression/anxiety/panic disorder vs. controls (odds ratio, 95% CI 1.86, 1.22-2.83; P = 0.004). Our model predicts that with mean BMI (26.3), WLWH and HIV-negative peers would have two comorbid diagnoses by age 30 and 60, respectively. CONCLUSIONS: WLWH living in BC have more comorbid illness earlier in life than their HIV-negative peers, and have very high rates of depression/anxiety/panic disorder. Addressing mental health and comorbid conditions is essential to improving health outcomes among WLWH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".