Go big or go home: impact of screening coverage on syphilis infection dynamics
Bibliographic record
Abstract
OBJECTIVES: Syphilis outbreaks in urban men who have sex with men (MSM) are an ongoing public health challenge in many high-income countries, despite intensification of efforts to screen and treat at-risk individuals. We sought to understand how population-level coverage of asymptomatic screening impacts the ability to control syphilis transmission. METHODS: We developed a risk-structured deterministic compartmental mathematical model of syphilis transmission in a population of sexually active MSM. We assumed a baseline level of treatment of syphilis cases due to seeking medical care in all scenarios. We evaluated the impact of sustained annual population-wide screening coverage ranging from 0% to 90% on syphilis incidence over the short term (20 years) and at endemic equilibrium. RESULTS: The relationship between screening coverage and equilibrium syphilis incidence displayed an inverted U-shape relationship, with peak equilibrium incidence occurring with 20-30% annual screening coverage. Annual screening of 62% of the population was required for local elimination (incidence <1 case per 100 000 population). Results were qualitatively similar in the face of differing programmatic, behavioural and natural history assumptions, although the screening thresholds for local elimination differed. With 6-monthly or 3-monthly screening, the population coverage required to achieve local elimination was reduced to 39% or 23%, respectively. CONCLUSIONS: Although screening has the potential to control syphilis outbreaks, suboptimal coverage may paradoxically lead to a higher equilibrium infection incidence than that observed in the absence of intervention. Suboptimal screening programme design should be considered as a possible contributor to unsuccessful syphilis control programmes in the context of the current epidemic.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".