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Informatics in Disease Prevention and Epidemiology

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Public Health Informatics and Information Systems

Part of the book series: Health Informatics ((HI))

Abstract

Technology changes continually, but the principles underlying informatics, epidemiology, and disease control are persistent. This chapter explores these principles and illustrates the varied information systems that support epidemiology. Concepts including reportable/notifiable diseases, passive/active surveillance, and the common components of public health prevention programs are discussed.

Public health information systems support certain common functions. Four of these functions and their informatics implications are examined: public health surveillance; outbreak or cluster recognition and response; acquisition of laboratory information; and field investigation. The chapter provides an understanding of public health interoperability and integration challenges, solutions, and future goals.

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References

  1. Kite-Powell A, Hamilton J, Wojcik R, Loschen W, Hopkins R. Florida’s Essence system: from syndromic surveillance to routine epidemiologic analysis across syndromic and non-syndromic data sources (Abstract). Emerg Health Threats J. 2011;4(supplement):44–5.

    Google Scholar 

  2. Public Health Informatics Institute. Redesigning public health surveillance in an eHealth World. Decatur: Public Health Informatics Institut; 2012.

    Google Scholar 

  3. Thacker SB, Berkelman RL. History of public health surveillance. In: Halperin W, Baker EL, editors. Public health surveillance. New York: Van Nostrand Reinhold; 1992.

    Google Scholar 

  4. NY State Department of Health. Electronic Clinical Laboratory Reporting System. 2011. https://www.health.ny.gov/professionals/reportable_diseases/eclrs/. Accessed 16 Oct 2019.

  5. CDC. National Outbreak Reporting System (NORS). 2019. https://www.cdc.gov/healthywater/surveillance/nors.html. Accessed 16 Oct 2019.

  6. CDC. About the Behavioral Risk Factor Surveillance System (BRFSS). 2014. https://www.cdc.gov/brfss/about/about_brfss.htm. Accessed 16 Oct 2019.

  7. European Centre for Disease Prevention and Control. Sentinel surveillance. 2019. https://www.ecdc.europa.eu/en/seasonal-influenza/surveillance-and-disease-data/facts-sentinel-surveillance. Accessed 16 Oct 2019.

  8. CDC. National Syndromic Surveillance Program (NSSP), BioSense Platform. 2017. https://www.cdc.gov/nssp/biosense/index.html. Accessed 16 Oct 2019.

  9. CDC. Metropolitan Atlanta Congenital Defects Program (MACDP). 2018. https://www.cdc.gov/ncbddd/birthdefects/macdp.html. Accessed 16 Oct 2019.

  10. AHRQ. Healthcare Cost and Utilization Project (HCUP). 2019. https://www.ahrq.gov/data/hcup/index.html. Accessed 16 Oct 2019.

  11. HealthyPeople.gov. National Poison Data System. 2019. https://www.healthypeople.gov/2020/data-source/national-poison-data-system. Accessed 16 Oct 2019.

  12. CDC. National Vital Statistics System. 2019. https://www.cdc.gov/nchs/nvss/index.htm. Accessed 16 Oct 2019.

  13. Centers for Disease Control and Prevention. National Notifiable Diseases Surveillance System (NNDSS). 2019. https://wwwn.cdc.gov/nndss/. Accessed 20 Aug 2019.

  14. Jajosky RA, Groseclose SL. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4:29. https://doi.org/10.1186/1471-2458-4-29.

    Article  PubMed  PubMed Central  Google Scholar 

  15. CDC. NBS Fact Sheet. 2019. https://www.cdc.gov/nbs/documents/nbs-fact-sheet-508.pdf. Accessed 16 Oct 2019.

  16. Overhage JM, Grannis S, McDonald CJ. A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. Am J Public Health. 2008;98(2):344–50.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hopkins RS, Eisenstein L. Prioritizing investigations of reported cases of selected enteric infections. Paper presented at Council of State and Territorial Epidemiologists, Omaha NE, June 6 2012.

    Google Scholar 

  18. CDC. Surveillance Case Definitions. 2017. https://wwwn.cdc.gov/nndss/case-definitions.html. Accessed 16 Oct 2019.

  19. McFarlane TD, Dixon BE, Gibson PJ. Using electronic health records for public health hypertension surveillance. Online J Public Health Inf. 2018;10(1):e193.

    Google Scholar 

  20. Mcfarlane TD. Towards estimating childhood obesity prevalence using electronic health records. Online J Public Health Inf. 2019;11(1):e356.

    Google Scholar 

  21. CDC Guidelines Working Group. Updated guidelines for evaluating public health surveillance systems. Recommendations from the Guidelines Working Group. MMWR. 2001;50(13):1–35.

    Google Scholar 

  22. Hopkins RS. Design and operation of local and state infectious disease surveillance systems. J Public Health Manag Pract. 2005;11(3):184–90.

    Article  PubMed  Google Scholar 

  23. CDC. U.S. Influenza surveillance system: purpose and methods. 2019. https://www.cdc.gov/flu/weekly/overview.htm. Accessed 16 Oct 2019.

  24. National Syndromic Surveillance Program Knowledge Repository. Final recommendation: core processes and EHR requirements for public health syndromic surveillance. 2011. https://www.surveillancerepository.org/final-recommendation-core-processes-and-ehr-requirements-public-health-syndromic-surveillance. Accessed 16 Oct 2019.

  25. National Syndromic Surveillance Program Knowledge Repository. Electronic syndromic surveillance using hospital inpatient and ambulatory clinical care electronic health record data: recommendations from the ISDS Meaningful Use Workgroup. 2012. https://www.surveillancerepository.org/electronic-syndromic-surveillance-using-hospital-inpatient-and-ambulatory-clinical-care-electronic. Accessed 16 Oct 2019.

  26. CDC. National Program of Cancer Registries (NPCR). 2019. https://www.cdc.gov/cancer/npcr/. Accessed 16 Oct 2019.

  27. CDC. About the Coverdell Program. 2017. https://www.cdc.gov/dhdsp/programs/about_pcnasp.htm. Accessed 16 Oct 2019.

  28. CDC. Birth Defects. 2018. https://www.cdc.gov/ncbddd/birthdefects/research.html. Accessed 16 Oct 2019.

  29. NAACCR. Implementation Guidelines. 2018. https://www.naaccr.org/implementation-guidelines/. Accessed 16 Oct 2019.

  30. Florida Department of Health. Report on birth defects in Florida 1998–2007. 2011. http://www.fbdr.org/pdf/FBDR_report_May2011.pdf. Accessed 16 Oct 2019.

  31. Salemi JL, Tanner JP, Kennedy S, Block S, Bailey M, Correia JA, Watkins SM, Kirby RS. A comparison of two surveillance strategies for selected birth defects in Florida. Public Health Rep. 2012;127:391–400.

    Article  PubMed  PubMed Central  Google Scholar 

  32. El Paso County, Colorado. Online foodborne illness complaint form. 2019. https://epcpublichealth.elpasoco.com/surveys/?s=F4WTAR8K9Y. Accessed 16 Oct 2019.

  33. CDC. Epi Infoâ„¢. 2018. https://www.cdc.gov/epiinfo/index.html. Accessed 16 Oct 2019.

  34. CDC. Foodborne Illness Complaint Form. https://www.cdc.gov/nceh/ehs/ehsnet/docs/EHS-Net_Foodborne_Illness_Complaint_Form.pdf. Accessed 16 Oct 2019.

  35. CDC. National Notifiable Diseases Surveillance System (NNDSS). 2019. https://wwwn.cdc.gov/nndss/. Accessed 16 Oct 2019.

  36. Effler P, Ching-Lee M, Bogard A, Ieong M, Nekomoto T, Jernigan D. Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. JAMA. 1999;282:1845–50.

    Article  CAS  PubMed  Google Scholar 

  37. Overhage JM, Grannis S, McDonald CJ. A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. AJPH. 2008;98(2):344–35.

    Article  Google Scholar 

  38. CDC. Potential effects of electronic laboratory reporting on improving timeliness of infectious disease notification --- Florida, 2002–2006. MMWR. 2008;57(49):1325–8.

    Google Scholar 

  39. HL7® International. 2019. http://www.hl7.org. Accessed 16 Oct 2019.

  40. LOINC® from Regenstrief. Logical observation identifiers names and codes (LOINC®). http://loinc.org/. Accessed 6 Oct 2019.

  41. SNOMED International Health Terminology Standards Development Organisation. http://www.snomed.org. Accessed 6 Oct 2019.

  42. Hamilton, JJ, Hopkins, RS. Using technologies for data collection and management. In: The CDC field epidemiology manual. CDC. 2019. https://www.cdc.gov/eis/field-epi-manual/chapters/data-collection-management.html. Accessed 10 Nov 2019.

  43. CDC. Public health emergency preparedness cooperative agreement, budget period 1, at-a-glance: requirements and recommendations for strengthening public health emergency preparedness programs, pp. 10–11. https://www.cdc.gov/cpr/readiness/00_docs/PHEPSupplementalGuidanceAtAGlance_FINAL3419.pdf. Accessed September 24, 2019.

  44. Digital Bridge. 2019. https://digitalbridge.us/infoex/. Accessed 16 Oct 2019.

  45. CDC. Surveillance Data Platform (SDP) Program. 2019. https://www.cdc.gov/sdp/index.html. Accessed 16 Oct 2019.

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Author information

Authors and Affiliations

  1. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA

    J. A. Magnuson

  2. Council of State and Territorial Epidemiologists, Tallahassee, FL, USA

    Richard Hopkins

  3. Department of Epidemiology, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA

    Timothy D. McFarlane

Authors
  1. J. A. Magnuson
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  2. Richard Hopkins
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  3. Timothy D. McFarlane
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Corresponding author

Correspondence to J. A. Magnuson .

Editor information

Editors and Affiliations

  1. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA

    J.A. Magnuson

  2. Department of Epidemiology, Indiana University, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA

    Brian E. Dixon

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© 2020 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply

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Magnuson, J.A., Hopkins, R., McFarlane, T.D. (2020). Informatics in Disease Prevention and Epidemiology. In: Magnuson, J., Dixon, B. (eds) Public Health Informatics and Information Systems . Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-41215-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-41215-9_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41214-2

  • Online ISBN: 978-3-030-41215-9

  • eBook Packages: MedicineMedicine (R0)

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