ArticleInternational journal of environmental research and public health2017
Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China.
Article in International journal of environmental research and public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Green Space and Health in Mainland China: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Spatio-Temporal Dynamics of Bloody Diarrhea Syndrome in Iran: A County-Level Identification of Clusters, Hot Spots, Outliers, and Seasonal Trends (2015-2019).Medical journal of the Islamic Republic of Iran · 2026Article
- Exploring the multidimensional factors associated with the incidence of bacillary dysentery in China based on a panel data model.Frontiers in public health · 2026Article
- Shigellosis seasonality and transmission characteristics in different areas of China: A modelling study.Infectious Disease Modelling · 2022Article
- Effects of daily mean temperature and other meteorological variables on bacillary dysentery in Beijing-Tianjin-Hebei region, China.Environmental health and preventive medicine · 2022Article
- Relative transmissibility of shigellosis among male and female individuals: a modeling study in Hubei Province, China.Infectious diseases of poverty · 2020Article
- Brain diseases in changing climate.Environmental research · 2019Review
- Exploring the Relevance of Green Space and Epidemic Diseases Based on Panel Data in China from 2007 to 2016.International journal of environmental research and public health · 2019Article
- Spatiotemporal Characteristics of Bacillary Dysentery from 2005 to 2017 in Zhejiang Province, China.International journal of environmental research and public health · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.