Universal Genotyping as a Tool for Establishing Successful Partnerships for Tuberculosis Elimination

Universal Genotyping for Tuberculosis Prevention Programs: a 5-Year Comparison with On-Request Genotyping

Prospective universal genotyping of tuberculosis (TB) isolates is used by many laboratories to detect clusters of cases and inform contact investigations. Prior to universal genotyping, most TB prevention programs genotyped isolates on request only, relying on requests from public health professionals whose knowledge of a patient's clinical, demographic, and epidemiological characteristics suggested potential transmission. To justify the switch from on-request to universal genotyping—particularly in the public health domain, with its limited resources and competing priorities—it is important to demonstrate the additional benefit provided by a universal genotyping program. We compared the clustering patterns revealed by retrospective 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat genotyping of all culture-positive isolates over a 5-year period to the patterns previously established by our genotyping-on-request program in the low-incidence setting of British Columbia, Canada. We found that 23.8% of isolates were requested during the study period, and while requested isolates had increased odds of belonging to a genotype cluster (adjusted odds ratio, 2.3; 95% confidence interval, 1.5 to 3.3), only 54.6% clustered with the requested comparator strain. Universal genotyping revealed 94 clusters ranging in size from 2 to 53 isolates (mean = 5) and involving 432 individuals. On-request genotyping missed 54 (57.4%) of these clusters and 130 (30.1%) clustered individuals. Our results underscore that TB patient networks are complex, with unrecognized linkages between patients, and a prospective province-wide universal genotyping program provides an informative, bias-free tool to explore transmission to a degree not possible with on-request genotyping.

Mapping late testers for HIV in Texas

Understanding the spatial patterns of late testing for HIV infection is critically important for designing and evaluating intervention strategies to reduce the social and economic burdens of HIV/AIDS. Traditional mapping methods that rely on frequency counts or rates in predefined areal units are known to be problematic due to issues of small numbers and visual biases. Additionally, confidentiality requirements associated with health data further restrict the ability to produce cartographic representations at fine geographic scales. While kernel density estimation methods produce stable and geographically detailed patterns of the late testing burden, the resulting pattern depends critically on the definition of the at-risk population. Using three definitions of at risk groups, we examine the cartographic representation of HIV late testers in Texas and show that the resulting spatial patterns and the interpretation of disease burdens are different based on the choice of the at-risk population. Disease mappers should exercise considerable caution in selecting the denominator population for mapping.