Factors influencing treatment status of syphilis among pregnant women: a retrospective cohort study in Guangzhou, China
Bibliographic record
Abstract
BACKGROUND: Many syphilis infected pregnant women do not receive treatment, representing a major missed opportunity to reduce the risk of syphilis-related adverse pregnancy outcomes. This study explored correlates of treatment among pregnant women with syphilis in Guangzhou, China. METHODS: Pregnant women with a diagnosis of syphilis in Guangzhou between January 2014 and December 2016 were included. Information of syphilis treatment and correlates were extracted from a comprehensive national case-reporting system. Multivariate logistic regression was used to identify the correlations between information on the demographic characteristics, previous history, clinical characteristics about current syphilis, information of diagnosing hospital, and receiving no treatment or inadequate treatment among syphilis-seropositive pregnant women. A causal mediation analysis was used to explore the potential mediating role of the timing of syphilis diagnosis in the correlates. RESULTS: Among 1248 syphilis-seropositive pregnant women, 379 (30.4%) women received no treatment or inadequate treatment. Migrant pregnant women (adjusted OR = 1.83, 95% CI: 1.25-2.73), multiparous participants (adjusted OR = 3.68, 95% CI: 2.51-5.50), unmarried participants (adjusted OR = 3.21, 95% CI: 1.97-5.28) and unemployed participants (adjusted OR = 2.43, 95% CI: 1.41-4.39) were more likely to receive no treatment or inadequate treatment. Participants who with history of syphilis infection (adjusted OR = 0.59, 95% CI: 0.42-0.82) and with high school and higher education participants (adjusted OR = 0.69, 95% CI: 0.49-0.97) were less likely to receive untreated or inadequately treatment. And that the impact of all these factors (except for the migrants) on treatment status are fully mediated through the syphilis diagnosis time, with the direct effect of migrants that would have resulted in a higher rate of no or inadequate treatment (OR = 2.34, 95% CI: 1.08-5.32) was partially cancelled out by the syphilis diagnosis time. CONCLUSIONS: Pregnant women who were migrant without local residence and women with syphilis diagnosed at a later gestational age were more likely to slip through the cracks of the existing antenatal care system. More programs should focus on eliminating these gaps of residence-related health inequalities. This research highlights actionable elements for health services interventions that could increase syphilis treatment rates among pregnant women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".