Epidemiological Trends of Syphilis Infections in Japan and South Korea From 2011 to 2019
Bibliographic record
Abstract
Background: This study aimed to investigate and compare the epidemiological trends of syphilis infections in Japan and South Korea from 2011 to 2019 and examine the factors contributing to the disparities in the incidence of syphilis between the two countries. Methods: A retrospective analysis was conducted using syphilis data from the National Institute of Infectious Diseases in Japan and the Korea Disease Control and Prevention Agency. Incidence rates were calculated and analyzed by gender and age group, with a focus on annual trends. Statistical analysis was performed using EZR (Easy R), and appropriate tests were conducted to evaluate the statistical significance of the results. Results: Japan experienced a substantial rise in syphilis cases, with an eight-fold increase (from 827 cases in 2011 to 7,007 in 2018) primarily among women aged 20 - 29 years (P < 0.05). In contrast, South Korea underwent a more modest increase, with a 1.82-fold rise in cases (from 965 in 2011 to 1,753 in 2019) predominantly among men aged 20 - 39 years (P < 0.05). The incidence rate in Japan increased by 8.09 times, while South Korea saw a 1.76-fold increase over the same period. In addition, Japan experienced an increase in congenital syphilis cases, whereas South Korea saw a decline. Conclusions: The contrasting syphilis trends in Japan and South Korea highlight the need for country-specific public health strategies. Japan’s sharp increase in syphilis cases, particularly among young women, necessitates an urgent reassessment of current preventive measures. In contrast, the relatively stable trend in South Korea suggests more effective disease management, although further investigation is needed to identify the contributing factors. These findings underscore the importance of tailored public health interventions to address the unique epidemiological challenges in each country.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".