miércoles, 14 de enero de 2015

Ahead of Print -Refining Historical Limits Method to Improve Disease Cluster Detection, New York City, New York, USA - Volume 21, Number 2—February 2015 - Emerging Infectious Disease journal - CDC

full-text ►

Ahead of Print -Refining Historical Limits Method to Improve Disease Cluster Detection, New York City, New York, USA - Volume 21, Number 2—February 2015 - Emerging Infectious Disease journal - CDC

CDC. Centers for Disease Control and Prevention. CDC 24/7: Saving Lives. Protecting People.

Volume 21, Number 2—February 2015


Refining Historical Limits Method to Improve Disease Cluster Detection, New York City, New York, USA

Alison Levin-Rector1Comments to Author , Elisha L. Wilson2, Annie D. Fine, and Sharon K. Greene
Author affiliations: New York City Department of Health and Mental Hygiene, Queens, New York, USA


Since the early 2000s, the Bureau of Communicable Disease of the New York City Department of Health and Mental Hygiene has analyzed reportable infectious disease data weekly by using the historical limits method to detect unusual clusters that could represent outbreaks. This method typically produced too many signals for each to be investigated with available resources while possibly failing to signal during true disease outbreaks. We made method refinements that improved the consistency of case inclusion criteria and accounted for data lags and trends and aberrations in historical data. During a 12-week period in 2013, we prospectively assessed these refinements using actual surveillance data. The refined method yielded 74 signals, a 45% decrease from what the original method would have produced. Fewer and less biased signals included a true citywide increase in legionellosis and a localized campylobacteriosis cluster subsequently linked to live-poultry markets. Future evaluations using simulated data could complement this descriptive assessment.
Detecting aberrant clusters of reportable infectious disease quickly and accurately enough for meaningful action is a central goal of public health institutions (13). Clinicians’ reports of suspected clusters of illness remain critical for surveillance (4), but the application of automated statistical techniques to detect possible outbreaks that might otherwise not be recognized has become more common (5). These techniques are particularly important in jurisdictions that serve large populations and receive a high volume of reports because manual review and investigation of all reports are not feasible.
Challenges such as lags in reporting and case classification and discontinuities in surveillance case definitions, reporting practices, and diagnostic methods are common across jurisdictions. These factors can impede the timely detection of disease clusters. Statistically and computationally simple methods, including historical limits (6), a log-linear regression model (7), and cumulative sums (8), each have strengths and weaknesses for prospective cluster detection, but none adequately address these common data challenges. As technology advances, statistically and computationally intensive methods have been developed (2,3,5,912), and although these methods might successfully correct for biases, many lack the ease of implementation and interpretation desired by health departments.
Thumbnail of Following Stroup et al. (21), a schematic of the periods included in analyses using the historical limits method.
Figure 1. Following Stroup et al. (21), a schematic of the periods included in analyses using the historical limits method.
Since 1989, the US Centers for Disease Control and Prevention has applied the historical limits method (HLM) to disease counts and displayed the results in Figure 1 of the Notifiable Diseases and Mortality Tables in the Morbidity and Mortality Weekly Report (13). Because the method relies on a straightforward comparison of the number of reported cases in the current 4-week period with comparable historical data from the preceding 5 years, its major strengths include simplicity, interpretability, and implicit accounting for seasonal disease patterns. These strengths make it a potentially very useful aberration-detection method for health departments (12,1418). The Bureau of Communicable Disease (BCD) of the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) implemented the HLM in the early 2000s (HLMoriginal) as a weekly analysis for all reportable diseases for which at least 5 years of historical data were available.

In HLMoriginal, 4 major causes of bias existed: 1) inconsistent case inclusion criteria between current and historical data; 2) lack of adjustment in historical data for gradual trends; 3) lack of adjustment in historical data for disease clusters or aberrations; and 4) no consideration of reporting delays and lags in data accrual. Our objectives were to develop refinements to the HLM (HLMrefined) that preserved the simplicity of the method’s output and improved its validity and to characterize the performance of the refined method using actual reportable disease surveillance data. Although we describe the specific process for refining BCD’s aberration-detection method, the issues presented are common across jurisdictions, and the principles and results are likely to be generalizable.
Ms. Levin-Rector is a public health analyst within the Center for Justice, Safety and Resilience at RTI International. Her primary research interests are developing or improving upon existing statistical methods for analyzing public health data.


We thank the members of the analytic team who work to detect disease clusters each week, including Ana Maria Fireteanu, Deborah Kapell, and Stanley Wang. We also thank Nimi Kadar who contributed substantially to the original SAS code for this method.
A.L.R., E.L.W., and S.K.G. were supported by the Public Health Emergency Preparedness Cooperative Agreement (grant 5U90TP221298-08) from the Centers for Disease Control and Prevention. A.D.F. was supported by New York City tax levy funds. The authors declare no conflict of interest.


  1. Hutwagner LThompson WSeeman GMTreadwell TThe bioterrorism preparedness and response Early Aberration Reporting System (EARS). J Urban Health2003;80(Suppl 1):i8996 .PubMed
  2. Farrington PAndrews N. Outbreak detection: application to infectious disease surveillance. In: Brookmeyer R, Stroup DF, editors. Monitoring the health of populations. New York: Oxford University Press; 2004. p. 203–31.
  3. Choi BYKim HGo UYJeong J-HLee JWComparison of various statistical methods for detecting disease outbreaks. Comput Stat.2010;25:60317DOI
  4. Schuman SH. When the community is the “patient”: clusters of illness. environmental epidemiology for the busy clinician. London: Taylor & Francis;1997.
  5. Unkel SFarrington CPGarthwaite PHStatistical methods for the prospective detection of infectious disease outbreaks: a review. J R Stat Soc Ser A Stat Soc2012;175:4982DOI
  6. Stroup DFWilliamson GDHerndon JLKaron JMDetection of aberrations in the occurrence of notifiable diseases surveillance data. Stat Med.1989;8:3239DOIPubMed
  7. Farrington CPAndrews NJBeale DCatchpole MAA statistical algorithm for the early detection of outbreaks of infectious disease. J R Stat Soc Ser A Stat Soc1996;159:54763DOI
  8. Hutwagner LCMaloney EKBean NHSlutsker LMartin SMUsing laboratory-based surveillance data for prevention: an algorithm for detectingSalmonella outbreaks. Emerg Infect Dis1997;3:395400 . DOIPubMed
  9. Strat YL. Overview of temporal surveillance. In: Lawson AB, Kleinman K, editors. Spatial and syndromic surveillance for public health. Chichester (UK): John Wiley & Sons; 2005. p. 13–29.
  10. Serfling REMethods for current statistical analysis of excess pneumonia-influenza deaths. Public Health Rep1963;78:494506DOIPubMed
  11. Noufaily AEnki DGFarrington PGarthwaite PAndrews NCharlett AAn improved algorithm for outbreak detection in multiple surveillance systems. Stat Med2013;32:120622DOIPubMed
  12. Wharton MPrice WHoesly FWoolard DWhite KGreene CEvaluation of a method for detecting outbreaks of diseases in six states. Am J Prev Med1993;9:459 .PubMed
  13. Centers for Disease Control and PreventionProposed changes in format for presentation of notifiable disease report data. MMWR Morb Mortal Wkly Rep1989;38:8059 .PubMed
  14. Centers for Disease Control and PreventionNotes from the field: Yersinia enterocolitica infections associated with pasteurized milk—southwestern Pennsylvania, March–August, 2011. MMWR Morb Mortal Wkly Rep2011;60:1428 .PubMed
  15. Rigau-Pérez JGMillard PSWalker DRDeseda CCCasta-Velez AA deviation bar chart for detecting dengue outbreaks in Puerto Rico. Am J Public Health1999;89:3748DOIPubMed
  16. Pervaiz FPervaiz MAbdur Rehman NSaif UFluBreaks: early epidemic detection from Google flu trends. J Med Internet Res2012;14:e125.DOIPubMed
  17. Winscott MBetancourt AEreth RThe use of historical limits method of outbreak surveillance to retrospectively detect a syphilis outbreak among American Indians in Arizona. Sex Transm Infect2011;87:A165DOI
  18. Hutwagner LBrowne TSeeman GMFleischauer ATComparing aberration detection methods with simulated data. Emerg Infect Dis.2005;11:3146DOIPubMed
  19. New York City Department of Health and Mental Hygiene. Communicable disease surveillance data [cited 2013 Nov 15].http://www.nyc.gov/html/doh/html/data/cd-epiquery.shtml
  20. Nguyen TQThorpe LMakki HAMostashari FBenefits and barriers to electronic laboratory results reporting for notifiable diseases: the New York City Department of Health and Mental Hygiene experience. Am J Public Health2007;97(Suppl 1):S1425DOIPubMed
  21. Stroup DFWharton MKafadar KDean AGEvaluation of a method for detecting aberrations in public health surveillance data. Am J Epidemiol.1993;137:37380 .PubMed
  22. United Hospital Fund. Neighborhoods. New York City community health atlas: sources, methods and definitions. New York: United Hospital Fund;2002. p. 2–3.
  23. Centers for Disease Control and PreventionComparison of provisional with final notifiable disease case counts—National Notifiable Diseases Surveillance System, 2009. MMWR Morb Mortal Wkly Rep2013;62:74751 .PubMed
  24. Lotze TShmueli GYahav I. Simulating multivariate syndromic time series and outbreak signatures [cited 2014 Dec 3].http://papers.ssrn.com/sol3/papers.cfm?abstract_id=990020
  25. Centers for Disease Control and Prevention. Simulation data sets for comparison of aberration detection methods. 2004 April 16, 2004 [cited 2013 Aug 30]. http://www.bt.cdc.gov/surveillance/ears/datasets.asp
  26. Layton M. Increase in reported legionellosis cases. 2013 [cited 2013 Sep 18]. https://a816-health29ssl.nyc.gov/sites/NYCHAN/Lists/AlertUpdateAdvisoryDocuments/2013-07-03%20HAN_Legionella%20final2.pdf
  27. Kulldorff MHeffernan RHartman JAssuncao RMostashari FA space–time permutation scan statistic for disease outbreak detection. PLoS Med.2005;2:e59DOIPubMed



Technical Appendix

Suggested citation for this article: Levin-Rector A, Wilson EL, Fine AD, Greene SK. Refining historical limits method to improve disease cluster detection, New York City, New York, USA. Emerg Infect Dis [Internet]. 2015 Feb [date cited]. http://dx.doi.org/10.3201/eid2102.140098
DOI: 10.3201/eid2102.140098
1Current affiliation: RTI International, Research Triangle Park, North Carolina, USA
2Current affiliation: Colorado Department of Public Health and Environment, Denver, Colorado, USA

No hay comentarios:

Publicar un comentario