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Li Q, Zheng JX, Jia TW, Feng XY, Lv C, Zhang LJ, Yang GJ, Xu J, Zhou XN. Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling. Parasit Vectors 2023; 16:419. [PMID: 37968661 PMCID: PMC10652544 DOI: 10.1186/s13071-023-06001-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/06/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. METHODS We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. RESULTS The XGBoost model had the highest prediction accuracy with an R2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. CONCLUSIONS In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving.
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Affiliation(s)
- Qin Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Jin-Xin Zheng
- Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School, Shanghai, 200025, China
| | - Tie-Wu Jia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Xin-Yu Feng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Chao Lv
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University and The Edinburgh University, Shanghai, 200025, China
| | - Li-Juan Zhang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Guo-Jing Yang
- School of Tropical Medicine, Hainan Medical University, Haikou, 571199, China
| | - Jing Xu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, 200025, China.
- School of Global Health, Chinese Center for Tropical Diseases Research and Shanghai Jiao Tong University School of Medicine, One Health Center, Shanghai Jiao Tong University and The Edinburgh University, Shanghai, 200025, China.
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Alene KA, Gordon CA, Clements ACA, Williams GM, Gray DJ, Zhou XN, Li Y, Utzinger J, Kurscheid J, Forsyth S, Zhou J, Li Z, Li G, Lin D, Lou Z, Li S, Ge J, Xu J, Yu X, Hu F, Xie S, McManus DP. Spatial Analysis of Schistosomiasis in Hunan and Jiangxi Provinces in the People's Republic of China. Diseases 2022; 10:93. [PMID: 36278592 PMCID: PMC9590053 DOI: 10.3390/diseases10040093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/30/2022] [Accepted: 10/11/2022] [Indexed: 11/07/2023] Open
Abstract
Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People's Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, β = -1.158; 95% credible interval [CrI]: -2.104, -0.116) in Hunan and temperature (°C, β = -4.359; 95% CrI: -9.641, -0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.
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Affiliation(s)
| | - Catherine A. Gordon
- Infection and Inflammation Program, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
| | | | - Gail M. Williams
- School of Population Health, University of Queensland, Brisbane 4072, Australia
| | - Darren J. Gray
- Department of Global Health, Australian National University, Canberra 0200, Australia
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Yuesheng Li
- Infection and Inflammation Program, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
- Hunan Institute of Schistosomiasis Control, Yueyang 414000, China
| | - Jürg Utzinger
- Swiss Tropical and Public Health Institute, CH-4051 Allschwil, Switzerland
- University of Basel, CH-4003 Basel, Switzerland
| | - Johanna Kurscheid
- School of Population Health, University of Queensland, Brisbane 4072, Australia
- Swiss Tropical and Public Health Institute, CH-4051 Allschwil, Switzerland
| | - Simon Forsyth
- School of Population Health, University of Queensland, Brisbane 4072, Australia
| | - Jie Zhou
- Hunan Institute of Schistosomiasis Control, Yueyang 414000, China
| | - Zhaojun Li
- Jiangxi Institute of Parasitic Diseases, Nanchang 330096, China
| | - Guangpin Li
- Hunan Institute of Schistosomiasis Control, Yueyang 414000, China
| | - Dandan Lin
- Jiangxi Institute of Parasitic Diseases, Nanchang 330096, China
| | - Zhihong Lou
- Hunan Institute of Schistosomiasis Control, Yueyang 414000, China
| | - Shengming Li
- Hunan Institute of Schistosomiasis Control, Yueyang 414000, China
| | - Jun Ge
- Jiangxi Institute of Parasitic Diseases, Nanchang 330096, China
| | - Jing Xu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai 200025, China
| | - Xinling Yu
- Hunan Institute of Schistosomiasis Control, Yueyang 414000, China
| | - Fei Hu
- Jiangxi Institute of Parasitic Diseases, Nanchang 330096, China
| | - Shuying Xie
- Jiangxi Institute of Parasitic Diseases, Nanchang 330096, China
| | - Donald P. McManus
- Infection and Inflammation Program, QIMR Berghofer Medical Research Institute, Brisbane 4006, Australia
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