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Qi YX, Huang MR, Sun HY, Wu XY, Liu ZT, Lu DB. Prevalence of depressive symptoms in patients with advanced schistosomiasis in China: A systematic review and meta-analysis. PLoS Negl Trop Dis 2024; 18:e0012003. [PMID: 38452104 PMCID: PMC10950241 DOI: 10.1371/journal.pntd.0012003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/19/2024] [Accepted: 02/16/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Advanced schistosomiasis is the most serious outcome of infection and has a negative impact on both physical fitness and mental health of patients, the latter of which has long been overlooked. Therefore, we performed this systematic review and meta-analysis to estimate the overall prevalence of depressive symptoms, one of the most common mental problems, in patients with advanced schistosomiasis in China. METHODS Six electronic databases were searched for studies reporting the prevalence of depressive symptoms in the targeted patients. Assessments were pooled using a fixed- or random-effects model based on heterogeneity test. Subgroup analyses were further performed and differences between/among groups were examined using the chi-squared test. The protocol had previously been registered in PROSPERO (CRD42023406708). RESULTS A total of 11 studies with 1,673 participants were included. The pooled prevalence of depressive symptoms in advanced schistosomiasis in China was 62.01% (95% CI: 51.30% - 72.72%), with a significant heterogeneity among studies. Depressive symptoms were more prevalent in patients with complications and more than half of the patients suffered a mild- or moderate-level of depression. No publication bias was found, and sensitivity analysis showed a stable result. CONCLUSIONS The overall prevalence of depressive symptoms in advanced schistosomiasis in China was high enough to warrant psychotherapeutic interventions, especially for patients with complications. This would greatly prevent or/and reduce depression and improve their quality of life.
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Affiliation(s)
- Yu-Xin Qi
- Department of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China
| | - Meng-Rui Huang
- Department of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China
| | - Hui-Ying Sun
- Department of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China
| | - Xiao-Yan Wu
- Department of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China
| | - Ze-Ting Liu
- Department of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China
| | - Da-Bing Lu
- Department of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, People’s Republic of China
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Gong Y, Tong Y, Jiang H, Xu N, Yin J, Wang J, Huang J, Chen Y, Jiang Q, Li S, Zhou Y. Three Gorges Dam: the changing trend of snail density in the Yangtze River basin between 1990 and 2019. Infect Dis Poverty 2023; 12:45. [PMID: 37118831 PMCID: PMC10142781 DOI: 10.1186/s40249-023-01095-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND The area of Oncomelania hupensis snail remains around 3.6 billion m2, with newly emerging and reemergent habitats continuing to appear in recent years. This study aimed to explore the long-term dynamics of snail density before and after the operation of Three Gorges Dam (TGD). METHODS Data of snail survey between 1990 and 2019 were collected from electronic databases and national schistosomiasis surveillance. Meta-analysis was conducted to estimate the snail density. Joinpoint model was used to identify the changing trend and inflection point. Inverse distance weighted interpolation (IDW) was used to determine the spatial distribution of recent snail density. RESULTS A total of 3777 snail survey sites with a precise location of village or beach were identified. For the downstream area, snail density peaked in 1998 (1.635/0.11 m2, 95% CI: 1.220, 2.189) and fluctuated at a relatively high level before 2003, then declined steadily from 2003 to 2012. Snail density maintained lower than 0.150/0.11 m2 between 2012 and 2019. Joinpoint model identified the inflection of 2003, and a significant decreasing trend from 2003 to 2012 with an annual percentage change (APC) being - 20.56% (95% CI: - 24.15, - 16.80). For the upstream area, snail density peaked in 2005 (0.760/0.11 m2, 95% CI: 0.479, 1.207) and was generally greater than 0.300/0.11 m2 before 2005. Snail density was generally lower than 0.150/0.11 m2 after 2011. Snail density showed a significant decreasing trend from 1990 to 2019 with an APC being - 6.05% (95% CI: - 7.97, - 7.09), and no inflection was identified. IDW showed the areas with a high snail density existed in Poyang Lake, Dongting Lake, Jianghan Plain, and the Anhui branch of the Yangtze River between 2015 and 2019. CONCLUSIONS Snail density exhibited a fluctuating downward trend in the Yangtze River basin. In the downstream area, the operation of TGD accelerated the decline of snail density during the first decade period, then snail density fluctuated at a relatively low level. There still exists local areas with a high snail density. Long-term control and monitoring of snails need to be insisted on and strengthened.
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Affiliation(s)
- Yanfeng Gong
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Yixin Tong
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Honglin Jiang
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Ning Xu
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Jiangfan Yin
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Jiamin Wang
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Junhui Huang
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Yue Chen
- School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, ON, K1G 5Z3, Canada
| | - Qingwu Jiang
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China
| | - Shizhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025, China.
- Chinese Center for Tropical Diseases Research, NHC Key Laboratory of Parasite and Vector Biology, Shanghai, 200025, China.
| | - Yibiao Zhou
- Fudan University School of Public Health, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China.
- Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China.
- Fudan University Center for Tropical Disease Research, Building 8, 130 Dong'an Road, Xuhui District, Shanghai, 200032, China.
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Zheng JX, Xia S, Lv S, Zhang Y, Bergquist R, Zhou XN. Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach. Infect Dis Poverty 2021; 10:74. [PMID: 34011383 PMCID: PMC8135174 DOI: 10.1186/s40249-021-00852-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 04/23/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Oncomelania hupensis is only intermediate snail host of Schistosoma japonicum, and distribution of O. hupensis is an important indicator for the surveillance of schistosomiasis. This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China, with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy. METHODS The snail presence and absence records were collected from Anhui, Hunan, Hubei, Jiangxi and Jiangsu provinces in 2018. A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the O. hupensis intermediated snail host of S. japonicum. Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites. The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve (AUC). RESULTS The highest accuracy (AUC = 0.889 and Kappa = 0.618) was achieved at the 5 km distance weight. The five factors with the strongest correlation to O. hupensis infestation probability were: (1) distance to lake (48.9%), (2) distance to river (36.6%), (3) isothermality (29.5%), (4) mean daily difference in temperature (28.1%), and (5) altitude (26.0%). The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River, with the highest probability in the dividing, slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui, followed by areas near the shores of China's two main lakes, the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi. CONCLUSIONS Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability, an approach that could improve the sensitivity of the Chinese schistosome surveillance system. Redesign of the snail surveillance system by spatial bias correction of O. hupensis infestation in the Yangtze River Basin to reduce the number of sites required to investigate from 2369 to 1747.
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Affiliation(s)
- Jin-Xin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; NHC Key Laboratory of Parasite and Vector Biology, Shanghai, 200025, China
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; NHC Key Laboratory of Parasite and Vector Biology, Shanghai, 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine; One Health Center, The University of Edinburgh, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Shan Lv
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; NHC Key Laboratory of Parasite and Vector Biology, Shanghai, 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine; One Health Center, The University of Edinburgh, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Yi Zhang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; NHC Key Laboratory of Parasite and Vector Biology, Shanghai, 200025, China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine; One Health Center, The University of Edinburgh, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Robert Bergquist
- Ingerod, Brastad, Sweden/formerly with the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), World Health Organization, Geneva, Switzerland
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; NHC Key Laboratory of Parasite and Vector Biology, Shanghai, 200025, China.
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine; One Health Center, The University of Edinburgh, Shanghai Jiao Tong University, Shanghai, 200025, China.
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A New Method to Test Molluscicides against the Philippine Schistosomiasis Snail Vectors. J Parasitol Res 2020; 2020:3827125. [PMID: 32411420 PMCID: PMC7204092 DOI: 10.1155/2020/3827125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 08/29/2019] [Accepted: 10/04/2019] [Indexed: 11/17/2022] Open
Abstract
To expedite the discovery of novel molluscicides in the laboratory, this study aimed to evaluate the performance of a new molluscicidal assay. This assay is based on Oncomelania hupensis quadrasi snails and is called miniaturized plate test or mpt. To perform this assay, a 12-well plate, 3 snails per well, and 24-h exposure period were used. The performance of mpt was evaluated using niclosamide and Ardisia plant extract (tagpo extract) as test substances while WHO's guidelines for a conventional plate test (cpt) served as standard. One cpt and four mpt independent trials were performed for niclosamide and tagpo extract. Probit analysis of dose–response data was run in R to generate lethal concentrations (LC50 and LC90), while lethal ratio test was performed to detect significant difference between paired LC50s (or LC90s). Using niclosamide, the calculated LC50 values were 0.104, 0.127, 0.136, 0.139, and 0.140 g/m2 for cpt, mpt 1, mpt 2, mpt 3, and mpt 4, respectively, while the LC90 values were 0.266, 0.268, 0.244, 0.251, and 0.261 g/m2, using the same sequence, respectively. For tagpo extract, the LC50 values were 1.467, 1.547, 1.659, 1.797, and 1.659 g/m2, for cpt, mpt 1, mpt 2, mpt 3, and mpt 4, respectively, and the LC90s were 2.188, 2.195, 2.501, 2.358, and 2.501 g/m2, respectively. The lethal ratio test revealed that a significant difference exists between the LC50s of cpt and mpt 1 when using niclosamide with a lethal ratio and confidence limits of 0.820 (0.663, 0.977, p < 0.05) and another significant difference between LC50s of mpt 1 and mpt 3 using tagpo extract with computed lethal ratio and confidence limits of 0.861 (0.782, 0.939, p < 0.05). Taken together, the results point out that mpt generates accurate and reproducible lethal concentration values. Hence, mpt may be used as an alternative method to screen molluscicides that are active against schistosome snail vectors.
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