Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 19, No. 3, March 2013 493 Prioritizing Tuberculosis Clusters by Genotype for Public Health Action, Washington, USA Scott Lindquist, Sheanne Allen, Kim Field, Smita Ghosh, Maryam B. Haddad, Masahiro Narita, and Eyal Oren Groups of tuberculosis cases with indistinguishable Mycobacterium tuberculosis genotypes (clusters) might represent recent transmission. We compared geospatial concentration of genotype clusters with independent priority rankings determined by local public health oficials; indings were highly correlated. Routine use of geospatial statis- tics could help health departments identify recent disease transmission. M ycobacterium tuberculosis genotyping has been ap- plied to tuberculosis (TB) control activities for >2 decades, and epidemiologic or genotyping data can con- irm or disprove outbreaks (1–4). Investigation of genotype clusters can identify unrecognized transmission and lead to interventions that interrupt further transmission (5,6). How- ever, cluster investigations are complex, requiring patient interviews and ield observations. Focusing resources on clusters that most likely represent recent TB transmission could reduce the number of unnecessary investigations. Geospatial statistics can identify higher-than-expected concentrations of TB cases with indistinguishable geno- types (7). We describe a comparison of a quantitative geo- spatial statistic analysis with qualitative expert opinion for prioritizing TB cluster investigations in Washington, USA, a state with moderate TB incidence (3.5 cases/100,000 per- sons) (8). The comparison was performed for initial and follow-up 3-year periods, 2005–2007 (period 1) and 2008– 2010 (period 2). The Study TB genotype clusters were deined as groups of >3 TB case-patients whose isolates had matching spoligotyping and 12-locus mycobacterial interspersed repetitive unit–variable number tandem repeat (MIRU-VNTR) (9) genotyping results that were reported in the same county within Washington. A log-likelihood ratio (LLR) was calculated for each genotype cluster identiied during each of the two 3-year periods (Fig- ure). The larger the LLR, the greater the possibility the cluster represented geographically concentrated TB cases, a proxy for recent TB transmission. The cutoff point for the LLR was set to 5.0, based on the value used by the national TB Genotyping Information Management System (10). Qualitative analysis came from a 5-member expert panel of TB public health oficials in Washington. In 2008, the panel participated in a discussion of all county-level TB clusters, ranking each as high or low priority for addition- al investigation. Priority was determined on the basis of a review of patient characteristics, epidemiologic links from ield investigations, and maps of genotype distributions. The panel also had information from enhanced contact investi- gations from local public health investigation teams that in- cluded the ability to order IS6110 restriction fragment-length polymorphism (IS6110 RFLP) and 24-locus MIRU-VNTR testing for clusters of concern, but results from these tests were not universally available. The ranking exercise with the same 5-member panel was repeated after period 1 for clus- ters from period 1. The expert panel was blinded to the LLR. Author afiliations: Kitsap County Health District, Bremerton, Washington, USA (S. Lindquist); Washington State Department of Health, Olympia, Washington, USA (S. Lindquist, S. Allen, K. Field); Centers for Disease Control and Prevention, Atlanta, Georgia, USA (S. Ghosh, M.B. Haddad); and Public Health–Seattle & King Coun- ty Tuberculosis Control Program, Seattle, Washington, USA (M. Narita, E. Oren). DOI: http://dx.doi.org/10.3201/eid1903.121453 Figure. Formula used to calculate geospatial statistic (a modiied log-likelihood ratio [LLR]) on the basis of geographic distribution of Mycobacterium tuberculosis genotype clusters, Washington, USA. Variables are classiied as follows: a = number of tuberculosis (TB) cases with the genotype of interest in the selected county; b = number of cases with the genotype of interest in the United States; c = number of cases without the genotype of interest in the selected county; d = number of cases without the genotype of interest in the United States; N = total number of TB cases.