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信息流行病学研究进展

潘海峰 赵婵娜 叶冬青

潘海峰, 赵婵娜, 叶冬青. 信息流行病学研究进展[J]. 中华疾病控制杂志, 2019, 23(5): 497-500. doi: 10.16462/j.cnki.zhjbkz.2019.05.001
引用本文: 潘海峰, 赵婵娜, 叶冬青. 信息流行病学研究进展[J]. 中华疾病控制杂志, 2019, 23(5): 497-500. doi: 10.16462/j.cnki.zhjbkz.2019.05.001
PAN Hai-feng, ZHAO Chan-na, YE Dong-qing. Research progress in infodemiology study[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(5): 497-500. doi: 10.16462/j.cnki.zhjbkz.2019.05.001
Citation: PAN Hai-feng, ZHAO Chan-na, YE Dong-qing. Research progress in infodemiology study[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(5): 497-500. doi: 10.16462/j.cnki.zhjbkz.2019.05.001

信息流行病学研究进展

doi: 10.16462/j.cnki.zhjbkz.2019.05.001
基金项目: 

国家自然科学基金 81872693

国家自然科学基金 81872687

详细信息
    通讯作者:

    叶冬青, E-mail: ydq@ahmu.edu.cn

  • 中图分类号: R181

Research progress in infodemiology study

Funds: 

National Natural Science Foundations of China 81872693

National Natural Science Foundations of China 81872687

More Information
  • 摘要: 搜索引擎和社交媒体等网络技术的普及为实时管理用户生成数据提供了机会。通过对网络来源的数据分析,可以了解人们所关注的健康问题,用于传染病流行的预测和慢性非传染性疾病的监测等。信息流行病学(Infodemiology,也称Information Epidemiology)由此产生,旨在研究电子媒介中健康信息的发生、分布和影响因素,以提高人们对疾病和健康问题的认识,并为疾病防控策略的制定提供依据。本文针对信息流行病学的研究进展进行概述。
  • 表  1  信息流行病学的研究类型

    Table  1.   Types of research in Infodemiology

    研究类型 搜索工具
    基于需求的研究 利用Web (1.0) 工具,如Google Trends和search engines queries
    基于供给的研究 利用Web (2.0) 工具,如Twitter、Blogs、维基百科和在线论坛
    需求+供给研究 同时使用Web (1.0)和Web (2.0) 工具
    其他 未使用Web工具的研究
    下载: 导出CSV
  • [1] Jacobs W, Amuta AO, Jeon KC. Health information seeking in the digital age: an analysis of health information seeking behavior among US adults[J]. Cogent Soc Sci, 2017, 3(1): 1302785. DOI: 10.1080/23311886.2017.1302785.
    [2] Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic[J]. PLoS One, 2011, 6(5): e19467. DOI: 10.1371/journal.pone.0019467.
    [3] Wang FY, Zeng D, Carley KM, et al. Social computing: from social informatics to social intelligence[J]. IEEE Intell Syst, 2007, 22(2): 79-83. DOI: 10.1109/Mis.2007.41.
    [4] Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance[J]. AMIA Annu Symp Proc, 2006: 244-248. http://www.tandfonline.com/servlet/linkout?suffix=cit0014&dbid=8&doi=10.1080%2F21645515.2017.1420448&key=17238340
    [5] Aslam AA, Tsou MH, Spitzberg BH, et al. The reliability of tweets as a supplementary method of seasonal influenza surveillance[J]. J Med Internet Res, 2014, 16(11): e250. DOI: 10.2196/jmir.3532.
    [6] Yang YT, Horneffer M, DiLisio N. Mining social media and web searches for disease detection[J]. J Public Health Res. 2013, 2(1): 17-21. DOI: 10.4081/jphr.2013.e4.
    [7] Davison K. The quality of dietary information on the world wide web[J]. Clin Perform Qual Health Care. 1997, 5(2): 64-66. http://www.bmj.com/lookup/external-ref?access_num=10167213&link_type=MED&atom=%2Fbmj%2F321%2F7275%2F1511.atom
    [8] Davison K, Guan SC. The quality of dietary information on the world wide web[J]. Journal of the Canadian Dietetic Association, 1996, 57(4): 137-141.
    [9] Impicciatore P, Pandolfini C, Casella N, et al. Reliability of health information for the public on the world wide web: systematic survey of advice on managing fever in children at home[J], BMJ. 1997, 314(7098): 1875-1879. DOI: 10.1136/bmj.314.7098.1875.
    [10] Eysenbach G, Powell J, Kuss O, et al. Empirical studies assessing the quality of health information for consumers on the world wide web: a systematic review[J]. JAMA, 2002, 287(20): 2691-2700. DOI: 10.1001/jama.287.20.2691.
    [11] Eysenbach G. Infodemiology: The epidemiology of (mis)information[J]. Am J Med, 2002, 113(9): 763-765. DOI: 10.1016/S0002934302014730.
    [12] Eysenbach G. Infodemiology and infoveillance tracking online health information and cyberbehavior for public health[J]. Am J Prev Med, 2011, 40(Suppl2): S154-158. DOI: 10.1016/j.amepre.2011.02.006.
    [13] Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review[J]. Health Info Libr J. 2018, 35(2): 91-120. DOI: 10.1111/hir.12216.
    [14] Alicino C, Bragazzi NL, Faccio V, et al. Assessing Ebola-related web search behaviour: insights and implications from an analytical study of google trends-based query volumes[J]. Infect Dis Poverty. 2015, 4: 54. DOI: 10.1186/s40249-015-0090-9.
    [15] Ginsberg J, Mohebbi MH, Patel RS, et al. Detecting influenza epidemics using search engine query data[J]. Nature. 2009, 457(7232): 1012-1014. DOI: 10.1038/nature07634.
    [16] Siri A, Khabbache H, Al-Jafar A, et al. Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004-2012[J]. Data Brief. 2016, 9: 679-684. DOI: 10.1016/j.dib.2016.09.032.
    [17] Bragazzi NL, Dini G, Toletone A, et al. Leveraging big data for exploring occupational diseases-related interest at the level of scientific community, media coverage and novel data streams: the example of silicosis as a pilot study[J]. PLoS One. 2016, 11(11): e0166051. DOI: 10.1371/journal.pone.0166051.
    [18] Bragazzi NL, Barberis I, Rosselli R, et al. How often people google for vaccination: qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends[J]. Hum Vaccin Immunother. 2017, 13(2): 464-469. DOI: 10.1080/21645515.2017.1264742.
    [19] Kardes S. Seasonal variation in the internet searches for gout: an ecological study[J]. Clin Rheumatol, 2019, 38(2): 769-775. DOI: 10.1007/s10067-018-4345-2.
    [20] Martinez-Arroyo G, Ramos-Gomez S, Rojero-Gil EK, et al. Potential uses of an infodemiology approach for health-care services for rheumatology[J]. Clin Rheumatol, 2019, 38(3): 869-876. DOI: 10.1007/s10067-018-4364-z.
    [21] Wilson K, Brownstein JS. Early detection of disease outbreaks using the Internet[J]. CMAJ. 2009, 180(8): 829-831. DOI: 10.1503/cmaj.090215.
    [22] Ireland ME, Schwartz HA, Chen Q, et al. Future-oriented tweets predict lower county-level HIV prevalence in the United States[J]. Health Psychol, 2015, 34S: 1252-1260. DOI: 10.1037/hea0000279.
    [23] Popkin BM. What can public health nutritionists do to curb the epidemic of nutrition-related noncommunicable disease?[J]. Nutr Rev, 2009, 67(Suppl1): S79-82. DOI: 10.1111/j.1753-4887.2009.00165.x.
    [24] Eichstaedt JC, Schwartz HA, Kern ML, et al. Psychological language on twitter predicts county-level heart disease mortality[J]. Psychol Sci. 2015, 26(2): 159-169. DOI: 10.1177/0956797614557867.
    [25] Brown NJL, Coyne JC. Does Twitter language reliably predict heart disease? a commentary on Eichstaedt et al. (2015a)[J]. PeerJ. 2018, 6: e5656. DOI: 10.7717/peerj.5656.
    [26] Patel JC, Khurana P, Sharma YK, et al. Chronic lifestyle diseases display seasonal sensitive comorbid trend in human population evidence from google trends[J]. PLoS One. 2018, 13(12): e0207359. DOI: 10.1371/journal.pone.0207359.
    [27] Jellison SS, Bibens M, Checketts J, et al. Using google trends to assess global public interest in osteoarthritis[J]. Rheumatol Int, 2018, 38(11): 2133-2136. DOI: 10.1007/s00296-018-4158-2.
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出版历程
  • 收稿日期:  2019-03-15
  • 刊出日期:  2019-05-10

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