Abstract
BackgroundEarly identification of outbreaks remains a key component in continuing to reduce the burden of infectious disease in the United States. Previous studies have applied statistical methods to detect unexpected cases of disease in space or time. The objectives of our study were to assess the ability and timeliness of three spatio-temporal methods to detect known outbreaks of tuberculosis.MethodsWe used routinely available molecular and surveillance data to retrospectively assess the effectiveness of three statistical methods in detecting tuberculosis outbreaks: county-based log-likelihood ratio, cumulative sums, and a spatial scan statistic.ResultsOur methods identified 8 of the 9 outbreaks, and 6 outbreaks would have been identified 1–52 months (median = 10 months) before local public health authorities identified them. Assuming no delays in data availability, 46 (59.7%) of the 77 patients in the 9 outbreaks were identified after our statistical methods would have detected the outbreak but before local public health authorities became aware of the problem.ConclusionsStatistical methods, when applied retrospectively to routinely collected tuberculosis data, can successfully detect known outbreaks, potentially months before local public health authorities become aware of the problem. The three methods showed similar results; no single method was clearly superior to the other two. Further study to elucidate the performance of these methods in detecting tuberculosis outbreaks will be done in a prospective analysis.
Highlights
Identification of outbreaks remains a key component in continuing to reduce the burden of infectious disease in the United States
Effectiveness and timeliness of statistical methods The retrospectively applied methods would have successfully identified six of the nine outbreaks before they were locally identified as a problem by the local public health authorities (Table 2)
Outbreak “D,” identified by local public health authorities 11 months after the time it was detected by cumulative sums (CUSUM), was confirmed to be a true outbreak but was not detected by either county-based likelihood ratio (LLR) or SaTScan; this outbreak’s genotype is the most commonly found genotype in the United States
Summary
Previous studies have applied statistical methods to detect unexpected cases of disease in space or time. The objectives of our study were to assess the ability and timeliness of three spatio-temporal methods to detect known outbreaks of tuberculosis. Statistical algorithms applied to disease surveillance data aim to identify which cases most likely represent outbreaks, before local public health authorities would otherwise become aware of them. The algorithms work by applying statistical techniques to reported cases of disease, laboratory data, or pharmacy data to identify unusual deviations from expected values; some techniques use historic data to detect deviations from temporal trends and others examine spatial or spatio-temporal differences in disease concentrations [1,2]. In the United States, routine genotyping of M. tuberculosis isolates from culture-positive TB cases started in 2004 through the Centers for Disease Control and Prevention’s (CDC) National Tuberculosis Genotyping Service [4].
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