title: Projections of hepatitis A virus infection associated with flood events by 2020 and 2030 in Anhui Province, China.
abstract: Assessing and responding to health risk of climate change is important because of its impact on the natural and societal ecosystems. More frequent and severe flood events will occur in China due to climate change. Given that population is projected to increase, more people will be vulnerable to flood events, which may lead to an increased incidence of HAV infection in the future. This population-based study is going to project the future health burden of HAV infection associated with flood events in Huai River Basin of China. The study area covered four cities of Anhui province in China, where flood events were frequent. Time-series adjusted Poisson regression model was developed to quantify the risks of flood events on HAV infection based on the number of daily cases during summer seasons from 2005 to 2010, controlling for other meteorological variables. Projections of HAV infection in 2020 and 2030 were estimated based on the scenarios of flood events and demographic data. Poisson regression model suggested that compared with the periods without flood events, the risks of severe flood events for HAV infection were significant (OR = 1.28, 95 % CI 1.05-1.55), while risks were not significant from moderate flood events (OR = 1.16, 95 % CI 0.72-1.87) and mild flood events (OR = 1.14, 95 % CI 0.87-1.48). Using the 2010 baseline data and the flood event scenarios (one severe flood event), increased incidence of HAV infection were estimated to be between 0.126/105and 0.127/105for 2020. Similarly, the increased HAV infection incidence for 2030 was projected to be between 0.382/105and 0.399/105. Our study has, for the first time, quantified the increased incidence of HAV infection that will result from flood events in Anhui, China, in 2020 and 2030. The results have implications for public health preparation for developing public health responses to reduce HAV infection during future flood events.
authors.affiliation: Department of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, 250012, People's Republic of China.
authors.forename: Lu
authors.initials: L
authors.lastname: Gao
authors.affiliation: Department of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, 250012, People's Republic of China.|School of Public Health, China Studies Centre, The University of Sydney, Sydney, NSW, Australia.|Center for Climate Change and Health, School of Public Health, Shandong University, Jinan City, Shandong Province, People's Republic of China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Occupational and Environmental Health, School of Public Health, Taishan Medical College, Taian City, Shandong Province, People's Republic of China.
authors.forename: Guoyong
authors.initials: G
authors.lastname: Ding
authors.affiliation: State Key Laboratory for Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, Beijing, People's Republic of China.
authors.forename: Qiyong
authors.initials: Q
authors.lastname: Liu
authors.affiliation: National Climate Center, China Meteorological Administration, Beijing, People's Republic of China.
authors.forename: Changke
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Epidemiology, School of Public Health, Shandong University, Jinan City, Shandong Province, 250012, People's Republic of China. bjiang2013@gmail.com.
authors.forename: Baofa
authors.initials: B
authors.lastname: Jiang
mesh_terms: D002681:China / Q000453:epidemiology; D057231:Climate Change; D055868:Floods; D006506:Hepatitis A / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D012044:Regression Analysis
keywords: Hepatitis A virus infection; Poisson regression; Projection; Scenario-based modeling; Time series