Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Rochelle E Watkins,Aileen J Plant,Graeme Wright,Bert Veenendaal,Serryn Eagleson

doi:10.1186/1472-6947-8-37

Abstract

BackgroundThe automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.MethodsRRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.ResultsWe found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.ConclusionOur retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.

Highlights

The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised
The performance of the Early Aberration Reporting System (EARS) C1, C2 and C3 algorithms [6], and a negative-binomial cusum were compared with the occurrence of events deemed by two experienced epidemiologists to be of potential public health importance using historical daily River virus (RRv) disease notification data
We found reasonable agreement between negative binomial cusum performance and retrospective epidemiological opinion at low false alarm rates

Summary

Introduction

The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. Few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. Automated monitoring methods are being applied to routinely collected population health data to facilitate the recognition of significant changes in the health indicators under surveillance. Statistical process control methods such as cumulative sums (cusums) are among the most commonly used methods to monitor population health surveillance data. Using cusums to monitor disease data traditionally assumes that the parameters which adequately describe the observed outcome of the disease processes when it is at an ideal level can be specified [5]. Changes in disease surveillance methods can produce apparent changes in disease incidence when no real change has occurred

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