The Global Epidemiology of Syphilis in the Past Century – A Systematic Review Based on Antenatal Syphilis Prevalence
Bibliographic record
Abstract
BACKGROUND: How can we explain the uneven decline of syphilis around the world following the introduction of penicillin? In this paper we use antenatal syphilis prevalence (ASP) to investigate how syphilis prevalence varied worldwide in the past century, and what risk factors correlate with this variance. METHODS: 1) A systematic review using PubMed and Google Scholar was conducted to identify countries with published data relating to ASP estimates from before 1952 until the present. Eleven countries were identified (Canada, Denmark, Finland, India, Japan, Norway, Singapore, South Africa, United States of America (USA), United Kingdom (UK) and Zimbabwe). The ASP epidemic curve for each population was depicted graphically. In South Africa and the USA, results are reported separately for the black and white populations. 2) National antenatal syphilis prevalence estimates for 1990 to 1999 and 2008 were taken from an Institute for Health Metrics and Evaluation database on the prevalence of syphilis in low risk populations compiled for the Global Burden of Diseases study and from a recent review paper respectively. National ASPs were depicted graphically and regional median ASPs were calculated for both time periods. 3) Linear regression was used to test for an association between ASP in 1990-1999 and 2008 and four risk factors (efficacy of syphilis screening/treatment, health expenditure, GDP per capita and circumcision prevalence). WHO world regions were included as potential explanatory variables. RESULTS: In most populations, ASP dropped to under 1% before 1960. In Zimbabwe and black South Africans, ASP was high in the pre-penicillin period, dropped in the post-penicillin period, but then plateaued at around 6% until the end of the 20th century when ASP dropped to just above 1%. In black Americans, ASP declined in the post penicillin period, but plateaued at 3-5% thereafter. ASP was statistically significantly higher in sub-Saharan Africa in 1990-1999 and 2008 than in the other world regions (P < 0.001). On multivariate analysis in both time periods, ASP was only associated with residence in sub-Saharan Africa. CONCLUSIONS: Further research is necessary to elucidate the reasons for the higher prevalence of syphilis in sub-Saharan Africa.
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 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| 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".