Background: To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission. Methods: We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission. Results: Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes. Conclusions: We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.
Abstract Background In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available. Methods We developed a mathematical model of TB epidemiology linked to a web-based user interface — Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool’s functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.–born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029. Results Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.–born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective. Conclusions Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.
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Abstract In the United States (US), tuberculosis elimination strategies include scaling up latent tuberculosis infection (LTBI) diagnosis and treatment for persons at risk of progression to tuberculosis disease. The Massachusetts Department of Public Health partnered with Lynn Community Health Center to provide care to patients with LTBI who were born outside the US. The electronic health record was modified to facilitate collection of data elements for public health assessment of the LTBI care cascade. Among health center patients born outside the US, testing for tuberculosis infection increased by over 190%. From October 1, 2016 to March 21, 2019, 8827 patients were screened and 1368 (15.5%) were diagnosed with LTBI. Using the electronic health record, we documented treatment completion for 645/1368 (47.1%) patients. The greatest drop-offs occurred between testing for TB infection and clinical evaluation after a positive test (24.3%) and between the recommendation for LTBI treatment and completion of a treatment course (22.8%). Tuberculosis care delivery was embedded in the primary care medical home, bringing patient-centered care to those at high risk for loss to follow up. The partnership between public health and the community health center promoted quality improvement.
Massachusetts is one of five states that mandate the reporting of latent tuberculous infection (LTBI). We assessed 2006-2008 Massachusetts surveillance data for LTBI to describe the system and examine the characteristics of persons with LTBI. Over 3 years, 15 301 LTBI cases were reported (4742-5398/year). Among those with known country of birth (n = 11 655), 9983 (85.7%) were foreign-born. Substantial under-ascertainment and/or under-reporting appear likely; mandatory reporting does not appear sufficient for LTBI detection. Enhanced targeted testing, active LTBI surveillance, or laboratory-based surveillance may be needed to eliminate tuberculosis disease in the United States. Le Massachusetts est l'un des cinq états qui exige la déclaration de l'infection tuberculeuse latente (LTBI). Nous avons évalué les données de surveillance de la LTBI au Massachusetts de 2006 à 2008 afin de décrire le système et d'étudier les caractéristiques des patients. En trois ans, 15 301 cas ont été rapportés (4742 à 5398 par an). Parmi les 11 655 patients dont le pays d'origine était connu, 9983 (85,7%) étaient nés à l'étranger. Il est probable que ce système de déclaration et de surveillance est déficient, car la déclaration obligatoire ne parait pas suffire à la détection de la LTBI. L'élimination de la tuberculose aux Etats-Unis pourrait nécessiter de mettre l'accent sur le dépistage ciblé, la surveillance active de la LTBI ou une surveillance basée sur les examens de laboratoire.Massachusetts es uno de los cinco estados en los cuales la notificación de la infección tuberculosa latente (LTBI) es obligatoria. En el presente estudio se evaluaron los datos de la vigilancia de esta afección entre el 2006 y el 2008, con el objeto de describir el sistema de vigilancia y examinar las características de las personas con diagnóstico de LTBI en Massachusetts. Durante el período de 3 años del estudio se notificaron 15 301 casos (de 474 a 5398 por año). De los casos en los cuales se conocía el país de origen (n = 11 655), 9983 personas habían nacido en el extranjero (85,7%). Es muy probable que exista una considerable deficiencia en la verificación y la notificación; la declaración obligatoria no parece una medida suficiente para detectar la LTBI. Se precisa una intensificación de las pruebas diagnósticas dirigidas, una vigilancia activa o una vigilancia de laboratorio de esta afección, con el propósito de eliminar la enfermedad tuberculosa en los Estados Unidos de América.
We examined Massachusetts tuberculosis surveillance data from to 2009 to 2018. Of 1533 culture-confirmed cases, 190 (12.4%) demonstrated resistance to isoniazid including 32 (2.1%) with rifampin resistance. In multivariable analysis, isoniazid resistance increased significantly over time (per-year odds ratio = 1.07, 95% confidence interval = 1.01-1.13, P = .018) and was associated with younger age, foreign birth, and prior tuberculosis treatment.
