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{"title"=>"Spatio-temporal trends and risk factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China", "type"=>"journal", "authors"=>[{"first_name"=>"Fenyang", "last_name"=>"Tang", "scopus_author_id"=>"35976974400"}, {"first_name"=>"Yuejia", "last_name"=>"Cheng", "scopus_author_id"=>"53263383500"}, {"first_name"=>"Changjun", "last_name"=>"Bao", "scopus_author_id"=>"12777940700"}, {"first_name"=>"Jianli", "last_name"=>"Hu", "scopus_author_id"=>"55433149900"}, {"first_name"=>"Wendong", "last_name"=>"Liu", "scopus_author_id"=>"55754605400"}, {"first_name"=>"Qi", "last_name"=>"Liang", "scopus_author_id"=>"55754602800"}, {"first_name"=>"Ying", "last_name"=>"Wu", "scopus_author_id"=>"56416754300"}, {"first_name"=>"Jessie", "last_name"=>"Norris", "scopus_author_id"=>"55188767900"}, {"first_name"=>"Zhihang", "last_name"=>"Peng", "scopus_author_id"=>"36704134800"}, {"first_name"=>"Rongbin", "last_name"=>"Yu", "scopus_author_id"=>"35724829600"}, {"first_name"=>"Hongbing", "last_name"=>"Shen", "scopus_author_id"=>"7404522651"}, {"first_name"=>"Feng", "last_name"=>"Chen", "scopus_author_id"=>"56714193200"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84897425616", "doi"=>"10.1371/journal.pone.0083487", "pui"=>"372732213", "pmid"=>"24416167", "scopus"=>"2-s2.0-84897425616", "issn"=>"19326203"}, "id"=>"aa5205e7-93c1-3dc2-92e9-1e1657eb1e00", "abstract"=>"OBJECTIVE: This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission.\\n\\nMETHODS: County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions.\\n\\nRESULTS: The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR = 11674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR = 3.64) and not having access to a safe water source (OR = 2.04) as the main causes of Shigella in Jiangsu Province.\\n\\nCONCLUSION: Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time.", "link"=>"http://www.mendeley.com/research/spatiotemporal-trends-risk-factors-shigella-2001-2011-jiangsu-province-peoples-republic-china-1", "reader_count"=>18, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>8, "Student > Master"=>4, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>8, "Student > Master"=>4, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Environmental Science"=>1, "Nursing and Health Professions"=>2, "Medicine and Dentistry"=>4, "Social Sciences"=>3, "Computer Science"=>1, "Immunology and Microbiology"=>3, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Social Sciences"=>{"Social Sciences"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>3}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>1}}, "group_count"=>1}

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• http://doi.org/10.1038/s41598-017-05928-3
• http://doi.org/10.1371/journal.pone.0180763
• http://doi.org/10.2166/ws.2017.205
• http://doi.org/10.3390/ijerph13020164
• http://doi.org/10.1371/journal.pone.0124478
• http://doi.org/10.1038/srep25407
• http://doi.org/10.3390/ijerph15010047
• http://doi.org/10.1038/s41598-018-28610-8
• http://doi.org/10.1371/journal.pone.0144690
• http://doi.org/10.1371/journal.pntd.0003882
• http://doi.org/10.1097/MOG.0000000000000136
• http://doi.org/10.1038/srep15264
• http://doi.org/10.1016/j.ttbdis.2018.03.026
• http://doi.org/10.1097/MD.0000000000003929
• http://doi.org/10.1007/s10393-016-1152-6
• http://doi.org/10.1016/j.proenv.2016.09.013
• http://doi.org/10.1038/s41598-017-16084-z

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• {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
• {"unique-ip"=>"6", "full-text"=>"4", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
• {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
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Relative Metric

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F1000Prime