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Record W7024530623

Spatial Analysis of Sexually Transmitted Infections in Ontario

2020· dissertation· en· W7024530623 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2020
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpatial analysisPoisson regressionSpatial epidemiologyGeocodingChlamydiaGeospatial analysisCensusIncidence (geometry)Public health
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.005
GPT teacher head0.186
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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