Abstract
This chapter provides an overview of the opportunities and challenges that “big data” presents for the advancement of public health. It begins by defining core functions and concepts; moves on to examples to illustrate potentials of success and failure; explains unresolved conceptual issues; and concludes with presentation of resources and cautions.
This is source 1 of chapter four. They may want to cite this paper by Lazer et al., science 2014, if this extract is taken from here http://science.sciencemag.org/content/343/6176/1203.full
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
CDC (2017) National public health performance standards. Available at http://www.cdc.gov/nphpsp/essentialservices.html. Accessed on 21 Nov 2017
NBPHE (undated) CPH content outline. Available through https://www.nbphe.org/cph-content-outline/ at https://s3.amazonaws.com/nbphe-wp-production/app/uploads/2017/05/ContentOutlineMay-21-2019.pdf. Accessed on 21 Nov 2017
Brownson RC, Samet JM, Gilbert F, Chavez GF, Davies MM, Galea S, Hiatt RA, Hornung CA, Khoury MJ, Koo D, Mays VM, Remington P, Yarber L (2015) Charting a future for epidemiologic training. Ann Epidemiol 25:458–465. Available at http://www.annalsofepidemiology.org/article/S1047-2797(15)00086-1/fulltext. Accessed on 21 Nov 2017
Ontario Agency for Health Protection and Promotion, Provincial Infectious Diseases Advisory Committee (2012) Syndromic surveillance discussion paper. Queen’s Printer for Ontario, Toronto, ON. Available at https://www.publichealthontario.ca/en/eRepository/PIDAC_SyndromicSurveillance_DiscussionPaper_ENG_2013.pdf. Accessed on 21 Nov 2017
Choi J, Cho Y, Shim E, Woo H (2016) Web-based infectious disease surveillance systems and public health perspectives: a systematic review. BMC Public Health 16:1238. Available at https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-016-3893-0. Accessed on 21 Nov 2017
Wiedeman C, Shaffner J, Squires K, Leegon J, Murphree R, Petersen PE (2017) Monitoring out-of-state patients during a hurricane response using syndromic surveillance—Tennessee, 2017. Morb Mortal Wkly Rep 66(49):1364–1365. Accessed on 5 Jan 2018
Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of Google flu: traps in big data analysis. Science 343(6176):1203–1205. https://doi.org/10.1126/science.1248506. Accessed on 5 Jan 2018
Lenert, L, Sundwall DN (2012) Public health surveillance and meaningful use regulations: a crisis of opportunity. Am J Public Health 102(3):e1–e7. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3487683/. Accessed on 22 Nov 2017
CDC (2016) Public health agency readiness for meaningful use, 2015–2018: guidance and recommendations. Available at https://www.cdc.gov/ehrmeaningfuluse/docs/readiness_guide_v3-0-final-508.pdf. Accessed on 21 Nov 2017
CMS (2017) Centralized repository. Available at https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/CentralizedRepository-.html. Accessed on 21 Nov 2017
Office of the Auditor General of British Columbia (2015) An audit of the panorama public health system, Aug. Available at https://www.bcauditor.com/sites/default/files/publications/reports/OAGBC_PanoramaReport_FINAL.pdf. Accessed on 25 Nov 2017
Birnbaum D, Borycki E, Karras BT, Denham E, Lacroix P (2015) Addressing public health informatics patient privacy concerns. Clin Gov 20(2):91–100
Birnbaum D, Gretsinger K, Antonio MG, Loewen L, Lacroix P (2018) Revisiting public health informatics: patient privacy concerns. Int J Health Gov 23(2):149–159
Birnbaum D (2016) Have international trade agreements been good for your health? Int J Health Gov 21(2):47–50
Labonté R, Shram A, Ruckert A (2016) The trans-pacific partnership: is it everything we feared for health? Int J Health Policy Manage 5(8):487–495. Available through http://www.ijhpm.com/article_3186_0.html at http://www.ijhpm.com/article_3186_741c0738f19120039415d58aedff5602.pdf. Accessed on 21 Nov 2017
Greenhalgh T, Potts HWW, Wong G, Bark P, Swingelhurst D (2009) Tensions and paradoxes in electronic patient record research: a systematic literature review using the meta-narrative method. Milbank Q 87(4):729–788. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2888022/. Accessed on 21 Nov 2017
Cleveland WS (1993) Visualizing data. Hobart Press, Summit, NJ
Box GEP (1979) Robustness in the strategy of scientific model building. In: Launer RL, Wilkinson GN (eds) Robustness in statistics. Academic Press, pp 201–236
Schuh HB, Merritt MW, Igusa T, Lee BY, Peters DH (2017) Examining the structure and behavior of Afghanistan’s routine childhood immunization system using system dynamics modeling. Int J Health Gov 22(3):212–227
Mangel M, Samaniego FJ (1984) Abraham Wald’s work in aircraft survivability. J Am Stat Assoc 79:259–267
Cochran WG (1977) Sampling techniques. Wiley, New York
Otero P, Hersh W, Jai Ganesh AU (2014) Big data: are biomedical and health informatics training programs ready? Yearb Med Inform 9(1):177–181. Available at http://pubmedcentralcanada.ca/pmcc/articles/PMC4287071/. Accessed on 22 Nov 2017
Tenover FC, Arbeit RD, Goering RV et al (1997) How to select and interpret molecular strain typing methods for epidemiological studies of bacterial infections: a review for healthcare epidemiologists. Infect Control Hosp Epidemiol 18(6):426–439
Gardy JL, Loman NJ (2018) Towards a genomics-informed, real-time, global pathogen surveillance system. Nat Rev Genet 19(1):9–20
Wyber R, Vaillancourt S, Perry W, Mannava P, Folaranmi T, Celi L (2015) Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ 93:203–208. Available at http://www.who.int/bulletin/volumes/93/3/14-139022/en/. Accessed on 21 Nov 2017
Miotto R, Li L, Kidd BA, Dudley JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. Available at https://www.nature.com/articles/srep26094. Accessed on 14 June 2018
Parkhurst J (2016) The politics of evidence: from evidence-based policy to the good governance of evidence. Available at http://blogs.lshtm.ac.uk/griphealth/books/. Accessed on 13 June 2018
Birnbaum D, Morris R (1996) Artificial stupidity. Clin Perform Qual Health Care 4(4):195–197
Khoury MJ, Ioannides JPA (2014) Big data meets public health: human well-being could benefit from large-scale data if large-scale noise is minimized. Science 346(6213):1054–1055. Available at http://pubmedcentralcanada.ca/pmcc/articles/PMC4684636/. Accessed on 21 Nov 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Birnbaum, D. (2019). Big Data Challenges from a Public Health Informatics Perspective. In: Househ, M., Kushniruk, A., Borycki, E. (eds) Big Data, Big Challenges: A Healthcare Perspective. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-06109-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-06109-8_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06108-1
Online ISBN: 978-3-030-06109-8
eBook Packages: MedicineMedicine (R0)