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Liu X, Sun Y, Yin Y, Dai X, Bergquist R, Gao F, Liu R, Liu J, Wang F, Lv X, Zhang Z. Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study. Infect Dis Poverty 2023; 12:108. [PMID: 38017569 PMCID: PMC10685489 DOI: 10.1186/s40249-023-01163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Urbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China. METHODS County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography. RESULTS The mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from - 0.205 to - 0.131. CONCLUSIONS The influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.
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Affiliation(s)
- Xin Liu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Yang Sun
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
- No. 8 Institute of Geology and Mineral Resources Exploration of Shandong Province, Rizhao, Shandong, China
| | - Yun Yin
- School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xiaofeng Dai
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
| | | | - Fenghua Gao
- Anhui Institute of Schistosomiasis Control, Hefei, Anhui, China
| | - Rui Liu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Jie Liu
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Fuju Wang
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Xiao Lv
- College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong, China
| | - Zhijie Zhang
- School of Public Health, Fudan University, Shanghai, China.
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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Grover EN, Allshouse WB, Lund AJ, Liu Y, Paull SH, James KA, Crooks JL, Carlton EJ. Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination. Int J Health Geogr 2023; 22:12. [PMID: 37268933 DOI: 10.1186/s12942-023-00331-w] [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: 01/24/2023] [Accepted: 04/26/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. METHODS In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. RESULTS The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. CONCLUSION Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.
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Affiliation(s)
- Elise N Grover
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - William B Allshouse
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Andrea J Lund
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Yang Liu
- Institute of Parasitic Diseases, Sichuan Center for Disease Control and Prevention, Chengdu, China.
| | - Sara H Paull
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Katherine A James
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - James L Crooks
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA
- Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, USA
| | - Elizabeth J Carlton
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, USA.
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Grover E, Allshouse W, Lund A, Liu Y, Paull S, James K, Crooks J, Carlton E. Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination. RESEARCH SQUARE 2023:rs.3.rs-2511279. [PMID: 36747768 PMCID: PMC9901017 DOI: 10.21203/rs.3.rs-2511279/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Background: Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. Methods: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. Results: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen’s kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (NDWI) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. Conclusion: Our results suggest that in low-transmission environments, investing in training geographic information systems professionals to leverage open-source environmental data could yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.
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Affiliation(s)
| | | | | | - Yang Liu
- Sichuan Center for Disease Control and Prevention
| | - Sara Paull
- National Ecological Observatory network (NEON)
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Hu Y, Bergquist R, Chen Y, Ke Y, Dai J, He Z, Zhang Z. Dynamic evolution of schistosomiasis distribution under different control strategies: Results from surveillance covering 1991-2014 in Guichi, China. PLoS Negl Trop Dis 2021; 15:e0008976. [PMID: 33406136 PMCID: PMC7787434 DOI: 10.1371/journal.pntd.0008976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 11/11/2020] [Indexed: 12/03/2022] Open
Abstract
Background Since the founding of the China, the Chinese government, depending on the changing epidemiological situations over time, adopted different strategies to continue the progress towards elimination of schistosomiasis in the country. Although the changing pattern of schistosomiasis distribution in both time and space is well known and has been confirmed by numerous studies, the problem of how these patterns evolve under different control strategies is far from being understood. The purpose of this study is, therefore, to investigate the spatio-temporal change of the distribution of schistosomiasis with special reference to how these patterns evolve under different control strategies. Methodology / Principal findings Parasitological data at the village level were obtained through access to repeated cross-sectional surveys carried out during 1991–2014 in Guichi, a rural district along the Yangtze River in Anhui Province, China. A hierarchical dynamic spatio-temporal model was used to evaluate the evolving pattern of schistosomiasis prevalence, which accounted for mechanism of dynamics of the disease. Descriptive analysis indicates that schistosomiasis prevalence displayed fluctuating high-risk foci during implementation of the chemotherapy-based strategy (1991–2005), while it took on a homogenous pattern of decreasing magnitude in the following period when the integrated strategy was implemented (2006–2014). The dynamic model analysis showed that regularly global propagation of the disease was not present after the effect of proximity to river was taken into account but local pattern transition existed. Maps of predicted prevalence shows that relatively high prevalence (>4%) occasionally occurred before 2006 and prevalence presents a homogenous and decreasing trend over the study area afterwards. Conclusions Proximity to river is still an important determinant for schistosomiasis infection regardless of different types of implemented prevention and control strategies. Between the transition from the chemotherapy-based strategy to the integrated one, we noticed a decreased prevalence. However, schistosomiasis would remain an endemic challenge in these study areas. Further prevention and control countermeasures are warranted. Schistosomiasis japonica is one of the most serious parasitic diseases in China. The Chinese government has launched three different rounds of national schistosomiasis control programs since 1950s. The latest two are the World Bank Loan Project (WBLP) that ushered in chemotherapy as the main control approach, active from 1992 to 2001, and the integrated control strategy that took its place in 2005. In this study, we investigated changes in the dynamics of schistosomiasis transmission over space and time under these different control strategies. Based on spatio-temporal analyses of the schistosomiasis prevalence data at the village level during 1991–2014 in Guichi, Anhui Province, we built a dynamic model to evaluate the evolving pattern of prevalence. We found that the schistosomiasis prevalence generally showed a north-western shift over the study area during 1991–2005, while there was no such trend during 2006–2014. This global shifting trend disappeared after the effect of proximity to river was taken into account, but local change still existed which was possibly due to the transition between the two latest national control strategies. We conclude that proximity to River is still an important determinant for schistosomiasis prevalence and that although the integrated control strategy is more effective than the WBPL in reducing schistosomiasis prevalence, the disease would remain endemic for the long term without further improvements of the control program.
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Affiliation(s)
- Yi Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
| | | | - Yue Chen
- School of Epidemiology, Pubic Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Yongwen Ke
- Schistosomiasis Station of Prevention and Control in Guichi Distirct, Anhui Province, China
| | - Jianjun Dai
- Schistosomiasis Station of Prevention and Control in Guichi Distirct, Anhui Province, China
| | - Zonggui He
- Schistosomiasis Station of Prevention and Control in Guichi Distirct, Anhui Province, China
| | - Zhijie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
- * E-mail:
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Qiu J, Li R, Xiao Y, Xia J, Zhu H, Niu Y, Huang D, Shao Q, Cui Y, Wang Y. Spatiotemporal Heterogeneity in Human Schistosoma japonicum Infection at Village Level in Hubei Province, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E2198. [PMID: 31234380 PMCID: PMC6617067 DOI: 10.3390/ijerph16122198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 06/19/2019] [Accepted: 06/20/2019] [Indexed: 11/16/2022]
Abstract
The spatiotemporal dynamics of Schistosoma japonicum, combined with temporal heterogeneity among regions of different epidemic areal-types from a microscale viewpoint might capture the local change dynamics and thus aid in optimizing the combinations of precise schistosomiasis control measures. The prevalence data on schistosomiasis infection from 2007 to 2012 in the 30 most endemic counties of Hubei Province, Central China, were appended to the village-level administrative division polygon layer. Anselin local Moran's I, a retrospective space-time scan statistic and a multilevel-growth model analysis framework, was used to investigate the spatiotemporal pattern of schistosomiasis resident infection rate (RIR) at the village level and how natural geographical environment influence the schistosomiasis RIR over time. Two spatiotemporal high-risk clusters and continuous high-rate clusters were identified mainly in the embankment region across flooding areas of lakes connected with the Yangze and Hanjiang Rivers. Moreover, 12 other clusters and outlier evolution modes were detected to be scattered across the continuous high-rate clusters. Villages located in embankment region had the highest initial values and most rapidly reduced RIRs over time, followed by villages located in marshland-and-lake regions and finally by villages located in hilly region. Moreover, initial RIR values and rates of change did significantly vary (p < 0.001 and p < 0.001, respectively) irrespective of their epidemic areal-type. These local spatiotemporal heterogeneities could contribute to the formulation of distinct control strategies based on local transmission dynamics and be applied in other endemic areas of schistosomiasis.
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Affiliation(s)
- Juan Qiu
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China.
| | - Rendong Li
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China.
| | - Ying Xiao
- Hubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, China.
| | - Jing Xia
- Hubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, China.
| | - Hong Zhu
- Hubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, China.
| | - Yingnan Niu
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Duan Huang
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China.
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Qihui Shao
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China.
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Ying Cui
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China.
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yong Wang
- State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
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Wang E, Zhao Z, Miao C, Wu Z. A Spatiotemporal Analysis of Schistosomiasis in Hunan Province, China. Asia Pac J Public Health 2018; 30:521-531. [PMID: 30324822 DOI: 10.1177/1010539518800365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Based on annual parasitological data recently collected at county and village levels, this article presents a multiscale spatiotemporal analysis of transmission risk of schistosomiasis japonica in Hunan Province during 2001 to 2015 in a geographic information system environment. The study shows that the incidence and prevalence rate of human Schistosoma japonicum infection in Hunan Province decreased after 2001. A spatial autocorrelation analysis reveals the existence of spatial clusters of human Schistosoma japonicum infection and a growing tendency of spatial clustering over time. The identification of high-risk areas (hot spots) helps find areas of priority for future implementation of control strategies. The research demonstrates the importance of spatial scale in public health studies.
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Affiliation(s)
- Enru Wang
- 1 University of North Dakota, Grand Forks, ND, USA
| | - Zhengyuan Zhao
- 2 Hunan Institute of Parasitic Diseases, WHO Collaborating Centre for Research and Control of Schistosomiasis on Lake Region, Yueyang, China
| | | | - Zhongcai Wu
- 4 Hunan Institute of Science and Technology, Yueyang, China
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Cao ZG, Li S, Zhao YE, Wang TP, Bergquist R, Huang YY, Gao FH, Hu Y, Zhang ZJ. Spatio-temporal pattern of schistosomiasis in Anhui Province, East China: Potential effect of the Yangtze River - Huaihe River Water Transfer Project. Parasitol Int 2018; 67:538-546. [PMID: 29753097 DOI: 10.1016/j.parint.2018.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 04/20/2018] [Accepted: 05/08/2018] [Indexed: 11/17/2022]
Abstract
Anhui Province has been one of typical epidemic areas of schistosomiasis in East China as a wide range of large lake and marshland regions provide an ideal environment for growth and reproduction of the intermediate snail host. With the completion of the Yangtze River-Huaihe River Water Transfer Project (YHWTP), launched by the end of 2016, the epidemic areas are expected to expand and controlling schistosomiasis remains a challenge. Based on annual surveillance data at the county level in Anhui for the period 2006-2015, spatial and temporal cluster analyses were conducted to assess the pattern of risk through spatial (Local Moran's I and flexible scan statistic) and space-time scan statistic (Kulldorff). It was found that schistosomiasis sero-prevalence was dramatically reduced and maintained at a low level. Cluster results showed that spatial extent of schistosomiasis contracted, but snail distribution remained geographically stable across the study area. Clusters, both for schistosomiasis and snail presence, were common along the Yangtze River. Considering the effect of the ongoing YHWTP on the potential spread of schistosomiasis, Zongyang County and Anqing, which will be transected by the new water-transfer route, should be given a priority for strengthened surveillance and control. Attention should also be paid to Guichi since it is close to one of the planned inlets of the YHWTP.
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Affiliation(s)
- Zhi-Guo Cao
- Department of Immunology and Pathogen Biology, Xi'an Jiaotong University Health Science Center, No.76 Yanta West Road, Xi'an, Shannxi Province 710061, China; Anhui Institute of Schistosomiasis Control, No. 377 Wuhu Road, Hefei, Anhui Province 230061, China
| | - Si Li
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
| | - Ya-E Zhao
- Department of Immunology and Pathogen Biology, Xi'an Jiaotong University Health Science Center, No.76 Yanta West Road, Xi'an, Shannxi Province 710061, China.
| | - Tian-Ping Wang
- Anhui Institute of Schistosomiasis Control, No. 377 Wuhu Road, Hefei, Anhui Province 230061, China
| | | | - Yin-Yin Huang
- Anhui Institute of Schistosomiasis Control, No. 377 Wuhu Road, Hefei, Anhui Province 230061, China
| | - Feng-Hua Gao
- Anhui Institute of Schistosomiasis Control, No. 377 Wuhu Road, Hefei, Anhui Province 230061, China
| | - Yi Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.
| | - Zhi-Jie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China.
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