Influence of the definition of rurality on geographic differences in HIV outcomes in British Columbia: a retrospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: Improving rural health is often identified as a priority area for research and policy in Canada. We examined how findings on HIV outcomes (virologic suppression) can vary depending on the definition of rurality used. METHODS: We performed retrospective cohort analyses using the Comparative Outcomes and Service Utilization Trends study population-based cohort of adults (age ≥ 19 yr) living with HIV in British Columbia between Apr. 1, 2012, and Mar. 31, 2013. We performed univariate logistic regression analyses using the following geographic variables to predict HIV virologic suppression: rurality defined by forward sortation area, by Statistical Area Classification and by health authority. We mapped suppression using geographic information systems. RESULTS: Virologic suppression was observed in 5605 (65.2%) of 8598 participants. In univariate analysis, rurality defined by Statistical Area Classification (odds ratio [OR] 0.73, 95% confidence interval [CI] 0.65-0.82), but not by forward sortation area, was associated with lower odds of suppression. When we examined suppression by health authority, Northern Health had the lowest odds of suppression (OR 0.46, 95% CI 0.36-0.58 compared to Vancouver Coastal Health). Geographic information systems mapping showed poorer suppression in northern areas. INTERPRETATION: Health outcome findings can vary depending on the definition of the geographic variable. When including geographic variables, researchers should carefully consider variable definitions and whether other classification systems, such as north-south, are more appropriate than rurality for their analysis.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".