The COVID-19 pandemic’s impact on sexually transmitted infections and the modifying role of public health funding: an interrupted time series study
Bibliographic record
Abstract
BACKGROUND: This study investigated whether the COVID-19 pandemic was associated with changes in sexually transmitted infection (STI) incidence and trajectories in Ontario, Canada and the modifying role of public health funding for infectious and communicable diseases (ICD). METHODS: A repeated cross-sectional study was conducted using population-level age and sex standardized incidence of chlamydia, gonorrhea, and infectious syphilis by public health unit (PHU) in Ontario from January 2015-October 2022. A negative binomial regression interrupted time series analysis estimated trends in STI incidence rates pre- and post-pandemic-onset and whether associations differed by public health ICD funding per capita. RESULTS: There was an immediate reduction post- compared to pre-pandemic onset in chlamydia (Rate Ratio (RR) = 0.27, 95% confidence interval (CI): 0.21-0.35), gonorrhea (RR = 0.15, 95% CI: 0.02-0.94), and syphilis (RR = 0.16, 95% CI: 0.02-1.18). Post-pandemic onset, a dollar increase in PHU's ICD funding per capita was associated with greater incidence of chlamydia (RR = 1.10, 95% CI: 1.08-1.12), gonorrhea (RR = 1.14, 95% CI: 0.99-1.32) and syphilis (RR = 1.11, 95% CI: 0.97-1.28). Following this initial drop, trends in STI incidence did not differ importantly by ICD funding. CONCLUSIONS: Findings are consistent with a role of public health funding in mitigating observed decreases in STIs. Results suggest that public health funding plays a role in maintaining STI detection services during a public health emergency.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".