Changing epidemiology of leptospirosis in China from 1955 to 2022
Bibliographic record
Abstract
BACKGROUND: Leptospirosis, a zoonotic disease caused by pathogenic species of the genus Leptospira, is an important public health concern globally. Leptospirosis has been notifiable under statute in China since 1955, and its epidemiological characteristics have evolved during near 70 years. This study aimed to describe the spatial and temporal patterns and demographic characteristics of leptospirosis from 1955 to 2022 in China, and explore the possible factors that influence leptospirosis transmission risk. METHODS: Wavelet time series analysis, global Moran's I coefficients, space-time scanning statistics, and so on were used to analyze temporal, seasonal, geographic, and demographic trends in leptospirosis using reported national surveillance data from Chinese mainland from 1955 to 2022. Additionally, a Bayesian spatiotemporal model was used in a preliminary analysis to explore potential factors associated with leptospirosis occurrence. RESULTS: Between 1955 and 2022, China reported 25,236,601 leptospirosis cases, with 91% occurring from July to October. The annual incidence rate peaked at 38.28/100,000 during outbreaks in the 1960s-1980s but stabilized at a low level (0.07/100,000) between 2005 and 2022, with over 99% of cases in southern China. Clustering increased over time, being greatest during the period 2015-2022 (Moran's I = 0.41, P < 0.01). Space-time cluster analysis indicated that the most likely clusters were in northern provincial-level administrative divisions (PLADs) from 1955 to 1984, in southern PLADs from 1985 to 2022. The main identified risk factors of leptospirosis occurrence were annual average precipitation (3.68, 95% CI: 2.50 to 5.12), GDP per capita (-3.70, 95% CI: - 5.97 to - 1.41), and the total power of agricultural machinery (- 2.51, 95% CI: - 3.85 to - 1.17). CONCLUSIONS: Over past 70 years, leptospirosis in China has occurred as significant outbreaks but has ultimately declined to stable, low levels of occurrence. However, a clear north-south disparity persists, with tropical and subtropical regions in southern China remaining high-risk areas. The nearly 70-year dataset underscores the complex interplay of climate and socioeconomic factors influencing the disease's occurrence. Targeted prevention and control measures are critical to prevent outbreaks, especially in regions prone to extreme climatic events like heavy rainfall and floods, which may signal the resurgence of leptospirosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".