Automated, High-throughput Surveillance Systems for Public Health

  • Ross Lazarus
Chapter

Abstract

Modern public health is a relatively recent innovation, a mid-nineteenth century response to explosive epidemics of infectious diseases, including plague, influenza, and cholera, that periodically ravaged human settlements. Public health practice is distinguished from most other medical services by a focus on the prevention of illness at the level of whole communities or populations, motivated by the fact that timely and effective preventive or curative intervention, directed at susceptible individuals, can be an extremely cost-efficient way of improving health at the level of a population. Routine childhood vaccination against polio and diphtheria and targeted interventions to minimize the spread of tuberculosis are well-known examples. Ideally, public health service delivery is informed, evaluated, and improved using objective criteria and data provided by a variety of population-based routine surveillance systems. These collect and summarize information needed for intervention and evaluation, ranging from identifiable, clinical case details to aggregate summaries of new cases by time and location, for illnesses of potential public health importance. Statutory health care practitioner initiated reporting is the basis for most of these collections and often requires paper forms to be completed, submitted, and processed manually, with associated delay, missing data, and poor reliability. More recently, automated, high-throughput surveillance methods adding value to large collections of electronic health records (EHR) have been shown to improve the timeliness, completeness, and reliability of surveillance data, and are the main focus of this chapter. Some of the associated issues of privacy protection, governance, validation, and evaluation, together with practical technical options for constructing secure, automated, portable, and high-throughput systems, adding value to existing EHR or personally controlled health records, are reviewed. The importance of establishing external validity and public health utility is emphasized, because these measures are fundamental to evaluating, improving, and justifying ongoing investment in information systems, competing in a modern, resource-constrained public health service delivery environment.

Keywords

Hepatitis Europe Influenza Tuberculosis Marketing 

Notes

Acknowledgments

The author acknowledges substantial contributions from Michael Klompas, Richard Platt, other members of the DACP/Channing Public Health Informatics group, and our many external collaborators, to the material presented here. This work was supported by grants from the Agency for Healthcare Research and Quality (HS 17045) and the CDC.

References

  1. Calderwood M, Klompas M, Hou X et al (2007) Automated detection of tuberculosis using electronic medical record data. Adv Dis Surv 4:46Google Scholar
  2. Fienberg SE, Shmueli G (2005) Statistical issues and challenges associated with rapid detection of bio-terrorist attacks. Stat Med 24:513–529CrossRefPubMedGoogle Scholar
  3. German R, Lee L, Horan J et al (2001) Guidelines Working Group Centers for Disease Control and Prevention (CDC). Updated guidelines for evaluating public health surveillance systems: Recommendations from the Guidelines Working Group. MMWR Recomm Rep 50(RR-13):1–35PubMedGoogle Scholar
  4. Graham D, Campen D, Hui R et al (2005) Risk of acute myocardial infarction and sudden cardiac death in patients treated with cyclo-Oxygenase 2 selective and non-selective non-steroidal anti-inflammatory drugs: Nested case-control study. Lancet 365(9458):475–481PubMedGoogle Scholar
  5. Haas CN (2002) On the risk of mortality to primates exposed to anthrax spores. Risk Anal 22:189–193CrossRefPubMedGoogle Scholar
  6. Kleinman K, Lazarus R, Platt R (2004) A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. Am J Epidemiol 159:217–224CrossRefPubMedGoogle Scholar
  7. Klompas M, Lazarus R, Daniel J et al (2007) Electronic medical record support for public health (Esp): Automated detection and reporting of statutory notifiable diseases to public health authorities. Adv Dis Surv 3(3):1–5Google Scholar
  8. Klompas M, Haney G, Church D, Lazarus R, Hou X, Platt R (2008a) Automated identification of acute hepatitis B using electronic medical record data to facilitate public health surveillance. PLoS ONE 3(7):e2626CrossRefPubMedGoogle Scholar
  9. Klompas M, Lazarus R, Platt R et al (2008b) Automated detection and reporting of notifiable diseases using electronic medical records versus passive surveillance – Massachusetts, June 2006–July 2007. MMWR Morb Mortal Wkly Rep 57:373–376Google Scholar
  10. Konstantinidou K, Mantadakis E, Falagas M, Sardi T, Samonis G (2009) Venetian rule and control of plague epidemics on the Ionian Islands during 17th and 18th centuries. Emerg Infect Dis 15:39–43CrossRefPubMedGoogle Scholar
  11. MHR, Hartman J, Assunção RM, Mostashari F (2005) A space-time permutation scan statistic for the early detection of disease outbreaks. PLoS Med 2:216–224CrossRefGoogle Scholar
  12. Lazarus R, Kleinman KP, Dashevsky I, DeMaria A, Platt R (2001) Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): The example of lower respiratory infection. BMC Public Health 1:1–9CrossRefGoogle Scholar
  13. Lazarus R, Kleinman K, Dashevsky I et al (2002) Use of automated ambulatory-care encounter records for detection of acute illness clusters, including potential bioterrorism events. Emerg Infect Dis 8:753–60PubMedGoogle Scholar
  14. Lazarus R, Klompas M, Campion F et al (2009) Electronic support for public health: Validated case finding and reporting for notifiable diseases using electronic medical data. J Am Med Inform Assoc 16(1):18–24Google Scholar
  15. Paneth N (2004) Assessing the contributions of John Snow to epidemiology: 150 years after removal of the Broad Street pump handle. Epidemiol 15:514–6CrossRefGoogle Scholar
  16. Platt R, Bocchino C, Harmon R et al (2003) Syndromic surveillance using minimum transfer of identifiable data: The National Bioterrorism Syndromic Surveillance Demonstration Project. J Urban Health 80:25–31Google Scholar
  17. Reis BY, Kirby C, Hadden L et al (2007) Aegis: A robust and scalable real-time public health surveillance system. J Am Med Inform Assoc 14:581–588CrossRefPubMedGoogle Scholar
  18. Yih WK, Caldwell B, Harmon R et al (2004) National bioterrorism syndromic surveillance demonstration program. Morb Mortal Weekly Rep Suppl 53:S43–49Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ross Lazarus
    • 1
  1. 1.Department of Ambulatory Care and PreventionHarvard Medical SchoolBostonUSA

Personalised recommendations