Physicians’ patient base composition and mortality among people living with HIV who initiated antiretroviral therapy in a universal care setting
Bibliographic record
Abstract
Objectives To assess the impact of physicians’ patient base composition on all-cause mortality among people living with HIV (PLHIV) who initiated highly active antiretroviral therapy (HAART) in British Columbia (BC), Canada. Design Observational cohort study from 1 January 2000 to 31 December 2013. Setting BC Centre for Excellence in HIV/AIDS’ (BC-CfE) Drug Treatment Program, where HAART is available at no cost. Participants PLHIV aged ≥ 19 who initiated HAART in BC in the HAART Observational Medical Evaluation and Research (HOMER) Study. Outcome measures All-cause mortality as determined through monthly linkages to the BC Vital Statistics Agency. Statistical analysis We examined the relationships between patient characteristics, physicians’ patient base composition, the location of the practice, and physicians’ experience with PLHIV and all-cause mortality using unadjusted and adjusted Cox proportional hazards models. Results A total of 4 445 PLHIV (median age = 42, Q1, Q3 = 34–49; 80% male) were eligible for our study. Patients were seen by 683 prescribing physicians with a median experience of 77 previously treated PLHIV in the past 2 years (Q1, Q3 = 23–170). A multivariable Cox model indicated that the following factors were associated with all-cause mortality: age (aHR = 1.05 per 1-year increase, 95% CI = 1.04 to 1.06), year of HAART initiation (2004–2007: aHR = 0.65, 95% CI = 0.53 to 0.81, 2008-2011: aHR = 0.46, 95% CI = 0.35 to 0.61, Ref: 2000–2003), CD4 cell count at baseline (aHR = 0.88 per 100-unit increase in cells/mm3, 95% CI = 0.82 to 0.94), and < 95% adherence in first year on HAART (aHR = 2.28, 95% CI = 1.88 to 2.76). In addition, physicians’ patient base composition, specifically, the proportion of patients who have a history of injection drug use (aHR = 1.11 per 10% increase in the proportion of patients, 95% CI = 1.07 to 1.15) or Indigenous ancestry (aHR = 1.07 per 10% increase , 95% CI = 1.03–1.11) and being a patient of a physician who primarily serves individuals outside of the Vancouver Coastal Health Authority region (aHR = 1.22, 95% CI = 1.01 to 1.47) were associated with mortality. Conclusions Our findings suggest that physicians with a higher proportion of individuals who face potential barriers to care may need additional supports to decrease mortality among their patients. Future research is required to examine these relationships in other settings and to determine strategies that may mitigate the associations between the composition of physicians’ patient bases and survival.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".