Spatial comparison of areas at risk for schistosomiasis in the hilly and mountainous regions in the People’s Republic of China: evaluation of the long-term effect of the 10-year World Bank Loan Project
Bibliographic record
Abstract
The long-term effectiveness of the mainly chemotherapy-based control strategy of the World Bank Loan Project (WBLP) for schistosomiasis control in Chinese hilly and mountainous regions was evaluated with a view to determine the best road forward. Based on the national database of schistosomiasis prevalence for the periods of 1999-2001 and 2007-2008 in the People's Republic of China, a Bayesian regression model was used for spatial comparison of schistosomiasis risk distribution between two periods taking account of all the potential risk factors simultaneously through two latent components of random effects: spatially correlated heterogeneities (CH) and spatially uncorrelated heterogeneities (UH). Four different types of endemic areas were investigated: those that remained endemic despite control efforts (17 or 37.8%), those that became non-endemic (9 or 20.0%), those that reverted back to endemicity (7 or 15.6%), and those with fluctuating endemicity (12 or 26.7%). The overall prevalence of schistosomiasis was lower in 2007-2008 compared with that in 1999-2001, but the spatial distribution of risk remained similar. Compared to 1999-2001, the magnitude and range of risk even tended to be greater in 2007-2008. UH showed a fluctuating pattern, while CH increased gradually doubling over the two periods. There was no evidence for long-term effectiveness of the WBLP chemotherapy-based control strategy in this region. Controlling the effect of UH is still the main aspect of current schistosomiasis control strategy for the hilly and mountainous regions, but innovative methods are urgently needed for effectively controlling UH.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".