A multi-level analysis of risk factors for Schistosoma japonicum infection in China
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the risk factors of schistosomiasis japonica in China, using a hierarchical multi-level model with individuals nested within villages. METHODS: A cross-sectional survey of schistosomiasis japonica was conducted in 16 villages in the Chinese province of Hunan. A multi-level modeling technique (HLM version 6.04) was used to assess risk factors of schistosomiasis. The results from this multi-level model were compared with those from a conventional single-level logistic regression model. RESULTS: A total of 10,245 individuals were enrolled in this study, of whom about 4.1% were infected with Schistosoma japonicum. In the multi-level model analysis, individual level variables such as gender, age, and occupation, and village level variables such as type of S. japonicum endemic area, drinking water source, sewage treatment, June temperature, and April rainfall were associated with schistosomiasis japonica infection. Conventional single-level logistic regression analysis selected more independent variables, and had narrower confidence intervals around the corresponding regression coefficients. In particular, per capita income, precipitation in October, and density of infected snails were statistically significant in the conventional single-level logistic regression analysis but not in the multi-level model. CONCLUSIONS: Multi-level modeling is a useful tool in the analysis of risk factors of schistosomiasis japonica. Because the multi-level model captures the hierarchical structure of the data, it may be considered a more appropriate analytical tool for data of this type. This technique may also be useful in the analysis of other infectious diseases with a similar hierarchical structure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".