Spatial-Temporal Epidemiology of the Syphilis Epidemic in Relation to Neighborhood-Level Structural Factors in British Columbia, 2005–2016
Bibliographic record
Abstract
BACKGROUND: Spatial clusters of syphilis have been observed within several jurisdictions globally; however, the degree to which they are predicted by the spatial distributions of gay, bisexual, and other men who have sex with men (GBM) and testing remains unknown. We sought to describe the spatial-temporal epidemiology of infectious syphilis and identify associations between neighborhood-level factors and rates of syphilis, in British Columbia, Canada. METHODS: We used ArcGIS to map infectious syphilis cases among men (2005 to 2016), SaTScan to detect areas with significantly elevated rates of syphilis, and spatial regression to identify associations between neighborhood-level factors and rates of syphilis. RESULTS: Five clusters were identified: a core in downtown Vancouver (incidence rate ratio [IRR], 18.0; 2007-2016), 2 clusters adjacent to the core (IRR, 3.3; 2012-2016; and IRR, 2.2; 2013-2016), 1 cluster east of Vancouver (IRR, 2.1; 2013-2016), and 1 cluster in Victoria (IRR, 4.3; 2015-2016). Epidemic curves were synchronized across cluster and noncluster regions. Neighborhood-level GBM population estimates and testing rates were both associated with syphilis rates; however, the spatial distribution of syphilis was not fully explained by either of these factors. CONCLUSIONS: We identified two novel ecologic correlates of the spatial distribution of infectious syphilis-density of GBM and rates of syphilis testing-and found that these factors partially, though not entirely, explained the spatial distribution of clusters. Residual spatial autocorrelation suggests that greater syphilis testing coverage may be needed and low-barrier GBM-affirming testing should be expanded to regions outside the core.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".