Using statistical methods and genotyping to detect tuberculosis outbreaks

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.

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

“…Studies reporting the application of the spatial methods in the early identification of TB outbreak were uncommon. Space-time TB studies using retrospective surveillance data in the USA found that the spatial scan statistic and other methods could effectively detect outbreaks months before local public authorities became aware of the problem [ 25 , 28 ]. However, as space-time clusters of TB can be due to either ongoing transmission or reactivation, characterising the drivers that resulted in the spatial clustering is essential.…”

Validation of genotype cluster investigations for Mycobacterium tuberculosis: application results for 44 clusters from four heterogeneous United States jurisdictions

“…Clusters were eligible for random sampling selection if the cluster consisted of at least three TB patients residing in the same given public health jurisdiction, whose TB status were reported between January 1, 2006 and the time of cluster evaluation. Eligible clusters for each of the four sites (see above) were assigned to three priority groups (low, medium, and high priority) based on their calculated log-likelihood ratio (LLR: <1.00, 1.00-5.79, and ≥5.80, respectively) associated with the public health cluster priorities [ 20 , 21 ]. After reviewing the geospatial scores [ 21 ], initial expert panel rankings, and cluster investigation findings, the CDC statistician and the expert panel determined the log-likelihood ratio (LLR) cut-points that were associated with high-, medium-, and low-priority clusters in our surveillance data.…”

Completing treatment for latent tuberculosis: patient background matters

“…TB outbreaks among immigrants in the US were all associated with crowded environments and lack of access to medical care, whereas outbreaks involving mainly US-born persons were associated with substance abuse, homelessness and incarceration(250). A number of outbreaks with MDR strains have been described in foreign-born people in low prevalence countries(249,251,252).Statistical techniques, when applied retrospectively to routinely collected TB data, can successfully detect outbreaks, earlier than local public health authorities become aware of the problem(253). Routinely reported data may identify small clusters that are likely to become outbreaks and which are therefore candidates for intensified contact investigations.…”