线上问诊大数据视角下空气污染的健康效应:因果分析及成本估算
结题报告
批准号:
72004092
项目类别:
青年科学基金项目
资助金额:
24.0 万元
负责人:
朱明英
依托单位:
学科分类:
环境与生态管理
结题年份:
2023
批准年份:
2020
项目状态:
已结题
项目参与者:
朱明英
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中文摘要
随着公众生活走向全面“网络化”,病症搜索和线上问诊逐渐成为人们应对疾病的普遍方式,然而,在分析空气污染的健康效应时,常用的医疗病例数据并未纳入公众线上诊疗信息,据此开展的相关工作在研究范围、对象和手段上存在局限性。本课题以线上问诊大数据为视角,开展以下研究内容:(1)采集病症搜索指数表征各地实际健康状况,将研究范围由特定地区扩大到全国层面,同时运用工具变量法,将空气污染与健康的相关性分析递进到因果识别。(2)追溯至未被纳入医疗病例数据的早期症状终端,从纵向上补充现代公众利用互联网信息治疗的线上健康成本。(3)根据各省市健康效应的敏感性差异,调整各地暴露-反应关系系数,估算线下各终端的健康成本,同时结合线上成本,综合评估各省市健康经济损失。本课题通过对空气污染的健康效应和经济成本进行全面地分析,为各地方政府环境政策的制定提供更加准确完善的经济依据,有效提高其公共决策的质量和效率。
英文摘要
As public life becomes fully "networked", symptoms searches and online consultation have gradually become a common way for people to cope with their diseases. However, when analyzing the health effects of air pollution, medical data, which are commonly used in the previous study, do not include online diagnosis and treatment information. The relevant work carried out accordingly has several limitations in terms of research scope, objects and means. From the perspective of disease symptoms related big data, this project will carry out the following research contents: (1) We collect the disease search index to indicate the actual health status in various regions, expanding the research scope from specific regions to the national level. We adopt the Instrumental Variable method to study the causal link from air pollution to public health instead of correlation analysis. (2) We trace back to the early symptom terminal that is not included in the medical data, and vertically supplement with the online health cost when people using online treatment. (3) We adjust the exposure-response coefficients for various regions according to the sensitivity difference in their health effects, and estimate the health costs of offline terminals. We combine the online and offline costs to comprehensively estimate the health economic costs of each region. Understanding the full range of negative impacts of pollution is an essential prerequisite for welfare evaluation of policy interventions, which also helps to enhance the efficiency and effectiveness of the decision-making process.
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DOI:10.1016/j.eswa.2022.118054
发表时间:2022-07
期刊:Expert Syst. Appl.
影响因子:--
作者:Jie Xie;Mingying Zhu
通讯作者:Jie Xie;Mingying Zhu
DOI:10.1080/1331677x.2022.2094984
发表时间:2022
期刊:Economic Research-Ekonomska Istraživanja
影响因子:--
作者:Mingying Zhu;Yifan Kang;Juan Liu
通讯作者:Juan Liu
DOI:10.1016/j.eswa.2022.118707
发表时间:2022-09
期刊:Expert Syst. Appl.
影响因子:--
作者:Mingying Zhu;Jie Xie
通讯作者:Mingying Zhu;Jie Xie
Improved seabird image classification based on dual transfer learning framework and spatial pyramid pooling
基于双迁移学习框架和空间金字塔池化改进的海鸟图像分类
DOI:10.1016/j.ecoinf.2022.101832
发表时间:2022-09
期刊:Ecological Informatics
影响因子:5.1
作者:Jie Xie;Mingying Zhu;Kai Hu
通讯作者:Kai Hu
“One Belt One Road” and women development
“一带一路”与妇女发展
DOI:10.1002/ise3.50
发表时间:2023
期刊:International Studies of Economics
影响因子:--
作者:Mingying Zhu;Zhiren Hu
通讯作者:Zhiren Hu
国内基金
海外基金