Changing seroprevalence of schistosomiasis japonica in China from 1982 to 2020: A systematic review and spatial analysis
Bibliographic record
Abstract
BACKGROUND: Schistosomiasis is a global public health issue. In China, while the seroprevalence of Schistosomiasis japonica has currently reduced to a relatively low level, risk of infection still exists in certain areas. However, there has been a lack of comprehensive research on the long-term trends of national seroprevalence, changes across age groups, and characteristics in spatial distribution, which is crucial for effectively targeting interventions and achieving the goal of eliminating schistosomiasis by 2030. Our study aimed to address this gap by analyzing the long-term trends of Schistosomiasis japonica seroprevalence in China from 1982 to 2020 based on the data from diverse sources spanning a period of 39 years. METHODOLOGY: Seroprevalence data were collected from literature databases and national schistosomiasis surveillance system. Meta-analysis was conducted to estimate the seroprevalence. Joinpoint model was used to identify changing trend and inflection point. Inverse distance weighted interpolation was used to determine the spatial distribution of seroprevalence. PRINCIPAL FINDINGS: The seroprevalence decreased from 34.8% in 1982 to 2.4% in 2020 in China. Before 2006, the seroprevalence was higher in the middle age group, and a pattern of increasing with age was observed afterwards. The areas with high seroprevalence existed in Dongting Lake, Poyang Lake, Jianghan Plain, the Anhui branch of the Yangtze River and some localized mountainous regions in Sichuan and Yunnan provinces. CONCLUSIONS/SIGNIFICANCE: There was a significant decline in the seroprevalence of Schistosomiasis japonica from 1982 to 2020 in China. Nevertheless, schistosomiasis has not been eradicated; thus, implementing precise and personalized monitoring measures is crucial for the elimination of schistosomiasis, especially in endemic areas and with a particular focus on the elderly.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".