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
Volume 21, Number 2—February 2015
Refining Historical Limits Method to Improve Disease Cluster Detection, New York City, New York, USA
Detecting aberrant clusters of reportable infectious disease quickly and accurately enough for meaningful action is a central goal of public health institutions (1–3). 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 (2,3,5,9–12), and although these methods might successfully correct for biases, many lack the ease of implementation and interpretation desired by health departments.
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 (12,14–18). 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.
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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
1Current affiliation: RTI International, Research Triangle Park, North Carolina, USA
2Current affiliation: Colorado Department of Public Health and Environment, Denver, Colorado, USA
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