Tuberculosis Genotype Clusters and Transmission in the U.S., 2009–2018

Wortham, Jonathan M.; Li, Rongxia; Althomsons, Sandy; Kammerer, Steve; Haddad, Maryam B.; Powell, Krista M.

doi:10.1016/j.amepre.2021.02.006

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…18 Large genotypic clusters of TB cases gathered from data from 2009 through 2018 disproportionately included PEH, suggesting recent TB transmission. 19…”

Section: Resultsmentioning

confidence: 99%

“…18 Large genotypic clusters of TB cases gathered from data from 2009 through 2018 disproportionately included PEH, suggesting recent TB transmission. 19 TB Diagnoses Among PEH Rapid diagnosis of pulmonary TB, isolation, and treatment can reduce transmission in crowded congregate settings such as shelters, hospitals, and correctional facilities. The diagnostic process starts by determining whether TB disease symptoms are present, performing chest radiographs and, if results are abnormal, conducting additional tests.…”

Section: Tb Among Pehmentioning

confidence: 99%

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Diagnosis, Treatment, and Prevention of Tuberculosis Among People Experiencing Homelessness in the United States: Current Recommendations

et al. 2023

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Objective: Tuberculosis (TB) is a public health problem, especially among people experiencing homelessness (PEH). The Advisory Council for the Elimination of Tuberculosis issued recommendations in 1992 for TB prevention and control among PEH. Our goal was to provide current guidelines and information in one place to inform medical and public health providers and TB programs on TB incidence, diagnosis, and treatment among PEH. Methods: We reviewed and synthesized diagnostic and treatment recommendations for TB disease and latent TB infection (LTBI) as of 2022 and information after 1992 on the magnitude of homelessness in the United States, the incidence of TB among PEH, the role of public health departments in TB case management among PEH, and recently published evidence. Results: In 2018, there were 1.45 million estimated PEH in the United States. During the past 2 decades, the incidence of TB was >10 times higher and the prevalence of LTBI was 7 to 20 times higher among PEH than among people not experiencing homelessness. TB outbreaks were common in overnight shelters. Permanent housing for PEH and the use of rapid TB diagnostic tests, along with isolation and treatment, reduced TB exposure among PEH. The use of direct observation enhanced treatment adherence among PEH, as did involvement of social workers to help secure shelter, food, safety, and treatment for comorbidities, especially HIV and substance use disorders. Testing and treatment for LTBI prevented progression to TB disease, and shorter LTBI regimens helped improve adherence. Federal agencies and the National Health Care for the Homeless Council have helpful resources. Conclusion: Improvements in TB diagnosis, treatment, and prevention among PEH are possible by following existing recommendations and using client-centered approaches.

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“…18 Large genotypic clusters of TB cases gathered from data from 2009 through 2018 disproportionately included PEH, suggesting recent TB transmission. 19…”

Section: Resultsmentioning

confidence: 99%

Section: Tb Among Pehmentioning

confidence: 99%

Diagnosis, Treatment, and Prevention of Tuberculosis Among People Experiencing Homelessness in the United States: Current Recommendations

et al. 2023

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“…In recent years, molecular genotyping of Mycobacterium tuberculosis (Mtb) has emerged as a valuable tool for TB control and surveillance, particularly in low-incidence and high-income settings [3], [4]. Conventional genotyping methods such as spoligotyping and mycobacterial interspersed repetitive unitvariable number tandem repeat (MIRU-VNTR) typing and, more recently, whole genome sequencing (WGS) have been instrumental in investigating TB outbreaks and transmission events [5],[6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Genotyped Cluster Investigations versus Standard Contact Tracing: Comparative Impact on Latent Tuberculosis Infection Cascade of Care in a Low-Incidence Region

Asare-Baah,

Séraphin,

Salmon-Trejo

et al. 2024

Preprint

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Background Cluster and contact investigations aim to identify and treat individuals with tuberculosis (TB) and latent TB infection (LTBI). Although genotyped cluster investigations may be superior to contact investigations in generating additional epidemiological links, this may not necessarily translate into reducing infections. Here, we investigated the impact of genotyped cluster investigations compared to standard contact investigations on the LTBI care cascade in a low incidence setting. Methods A matched case-control study nested within a cohort of 6,921 TB cases from Florida (2009–2023) was conducted. Cases (n = 670) underwent genotyped cluster investigations, while controls (n = 670) received standard contact investigations and were matched 1:1 by age. The LTBI care cascade outcomes were compared using Pearson’s chi-square tests. Results Among 1,340 TB cases in our study population, 866 were investigated, and 5,767 contacts were identified. Of these contacts, 4,800 (83.2%) were evaluated, with 73 (1.5%) diagnosed with active TB and 1,005 (20.9%) with LTBI. Among LTBI-diagnosed contacts, 948 (94.3%) initiated TB preventive therapy (TPT), and 623 (65.7%) completed treatment. A higher proportion of contacts were evaluated in the control group (85.5%) than in the case group (81.5%, p < 0.001). While the proportion of evaluated contacts diagnosed with LTBI did not significantly differ between groups (case: 20.4%, control: 21.5%, p = 0.088), a higher percentage of LTBI-diagnosed contacts initiated TPT in the control group (95.9%) than the case group (92.9%, p = 0.029). TPT completion rates were similar, with 65.2% in the case group and 66.3% in the control group completing treatment (p = 0.055). Conclusion Genotyped cluster investigations identified more contacts, with no significant difference in contact diagnosed with LTBI, but were less effective than standard contact investigations in evaluating contacts, initiating LTBI treatment, and ensuring completion.

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Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters

Althomsons¹,

Winglee²,

Heilig³

et al. 2022

American Journal of Epidemiology

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The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009–2018) from the United States, statistically derived definitions of unexpected growth, and machine learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing dataset that was generalizable to a validation dataset. Our model shows that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.

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Tuberculosis Genotype Clusters and Transmission in the U.S., 2009–2018

Cited by 4 publications

References 15 publications

Diagnosis, Treatment, and Prevention of Tuberculosis Among People Experiencing Homelessness in the United States: Current Recommendations

Diagnosis, Treatment, and Prevention of Tuberculosis Among People Experiencing Homelessness in the United States: Current Recommendations

Genotyped Cluster Investigations versus Standard Contact Tracing: Comparative Impact on Latent Tuberculosis Infection Cascade of Care in a Low-Incidence Region

Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters

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