Spatial Analysis of Sexually Transmitted Infections in Ontario
Bibliographic record
Abstract
Introduction: In recent years, Ontario has witnessed a dramatic rise in the incidence of sexually transmitted infections (STIs); however, the spatial patterning and causal factors contributing to this increase are not fully understood. The objectives of this thesis were: 1) to map the rates of commonly diagnosed STIs at a fine level of spatial aggregation; 2) to characterize global and local trends in the spatial patterning of STIs to inform the identification of priority locations for STI initiatives; and 3) to explore the influence healthcare accessibility and other community-level socio-economic characteristics have on STI incidence. Methods: The residential location of Ontario chlamydia, gonorrhea, and syphilis case data reported to the integrated Public Health Information System from 2005 through 2016 were geocoded on a tailored geography of census tracts and census subdivisions. One global (Moran’s I) and three local tests (local indicators of spatial association, Kulldorff's circular spatial scan statistic, and Tango and Takahashi’s flexible scan statistic) for spatial patterning were applied to the case data. The association between healthcare accessibility and the 12-year cumulative incidence rate ratio of chlamydia diagnosis in Ontario was assessed through Poisson geographically weighted regression, and Besag-York-Mollie Bayesian hierarchical model. Results: Findings suggest that each STI exerts spatial patterning. Temporal autocorrelation trends for gonorrhoea and syphilis remained consistent over the study period, while the autocorrelation for chlamydia cases decreased by greater than two-fold. Both the geographically weighted regression and Besag-York-Mollie model identified an increase in healthcare accessibility to be positively associated with the incidence chlamydia diagnosis. Findings also identify locations experiencing a high incidence of chlamydia. Conclusions: Spatial findings suggest that mapping STI rates at a finer level of spatial resolution is feasible for epidemiologists and needed to better inform geographically-targeted interventions. Although there are localized clusters of chlamydia, gonorrhoea, and syphilis throughout Ontario, programming efforts should be directed towards addressing a college-aged demographic of young adults in the City of Toronto, and other high chlamydia incidence locations. With greater diagnosis in highly accessible areas, this research also supports the necessity for improved access to healthcare services, screening campaigns, and educational initiatives in rural Ontario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".