Estimating the use of antibiotics for humans across China.
The present study aimed to propose a method to estimate the spatially resolved dataset for human-use antibiotics, which are highly needed in exposure models dealing with regions of various environmental characteristics. In this study, a regression model describing the relationship between the use of antibiotics and a set of socio-economic determinants was developed. It has been demonstrated that economic status (expressed using per capita gross domestic production) dominates the antibiotic use at least in China. Linear regression analysis was used to build the model, resulting in high goodness-of-fit, R(2) (>0.75). Internal and external validations along with residue plot indicated that the model was robust and predictive. The model was successfully applied to allocate the use of antibiotics in China in 2011 at national-, provincial-, prefectural-, and county-level, which are comparable to that back-calculated from the available data of wastewater analysis in some cities. Antibiotic uses were higher in East China than other regions and it was found that uses of total antibiotics vary among Chinese counties on four orders of magnitude (0.186-1645 t antibiotics per year per county). Also management practice could be worked out according to our exploration of the impact transition of social-economic factors on antibiotic uses. To our knowledge, this is the first endeavor to explore this economic dominated relationship for estimating spatially resolved use map of antibiotics in China.
School of Chemical & Environmental Engineering, China University of Mining & Technology-Beijing, Beijing 100083, PR China. Electronic address: qingwei.bu@cumtb.edu.cn.
Qingwei
Q
Bu
School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Tsinghua University, Beijing 100084, PR China. Electronic address: thuwb@tsinghua.edu.cn.
Bin
B
Wang
School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Tsinghua University, Beijing 100084, PR China. Electronic address: huangjun@tsinghua.edu.cn.
Jun
J
Huang
School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Tsinghua University, Beijing 100084, PR China. Electronic address: kail13@mails.tsinghua.edu.cn.
Kai
K
Liu
School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Tsinghua University, Beijing 100084, PR China. Electronic address: dengshubo@tsinghua.edu.cn.
Shubo
S
Deng
School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Tsinghua University, Beijing 100084, PR China. Electronic address: wangyujue@tsinghua.edu.cn.
Yujue
Y
Wang
School of Environment, State Key Joint Laboratory of Environmental Simulation and Pollution Control, Beijing Key Laboratory for Emerging Organic Contaminants Control, Tsinghua University, Beijing 100084, PR China. Electronic address: yg-den@tsinghua.edu.cn.
Gang
G
Yu
D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D002681:China; D002947:Cities; D005843:Geography; D006801:Humans; D008962:Models, Theoretical; D025341:Principal Component Analysis; D012044:Regression Analysis; D012959:Socioeconomic Factors
D000900:Anti-Bacterial Agents
Antibiotic use; Economic status; Regression model; Spatially resolved dataset