Use of Interrupted Time Series Analysis in Understanding the Course of the Congenital Syphilis Epidemic in Brazil
Bibliographic record
Abstract
Background: To fight against the rising incidence of syphilis, the Brazilian Ministry of Health (MoH) launched the "Syphilis No!" Project (SNP), with specific resources funded by a parliamentary amendment. Then, in 2018, a national rapid response started to be implemented on the Brazilian Unified Health System (SUS, Sistema Único de Saúde) in two strategic lines (1) to reinforce SUS's universal actions and (2) to implement specific ones to 100 municipalities chosen by the MoH as priorities for syphilis congenital response. In 2015, such localities represented 6895% of congenital syphilis cases in Brazil. In this context, SNP has implemented actions to strengthen epidemiological surveillance of acquired syphilis and congenital syphilis by instituting an integrated and collaborative response through health services networks and reinforcing interstate relations. Methods: A quasi-experimental study using time series analysis was conducted to assess immediate impacts and changes to the trend in national congenital syphilis before and after the project, from September 2016 to December 2019. Data were assessed considering rates of congenital syphilis per 1,000 live births in all priority municipalities (n=100) covered by the project and in non-priority municipalities (n=5,470) from all five macro-regions of Brazil. Findings: =0·0216) in non-priority municipalities. Interpretation: The study using ITS provides important evidence on the direction, timing, and magnitude of the effects of interventions introduced as part of the SNP on congenital syphilis in Brazil. Our results suggest that the Syphilis No! Project influenced the trends of congenital syphilis in Brazil from 2018, with higher reductions achieved in the priority municipalities. Funding: The research is funded by a grant to the Syphilis No! Project from Brazilian Ministry of Health (Project Number: 54/2017). The funders had no role in study design, analysis, decision to publish, or preparation of the manuscript.
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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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".