Characterizing the etiology of recurrent tuberculosis using whole genome sequencing-Alaska, USA, 2008-2020.

BACKGROUND: Understanding the etiology of recurrent tuberculosis (rTB) is important for effective TB control. Prior to the advent of whole genome sequencing (WGS), attributing rTB to relapse or reinfection using genetic information was complicated by the limited resolution of conventional genotyping methods. METHODS: We applied a systematic method of evaluating whole genome single nucleotide polymorphism (wgSNP) distances and results of phylogenetic analyses to characterize the etiology of rTB in American Indian and Alaska Native (AIAN) persons in Alaska during 2008-2020. We contextualized our findings through descriptive analyses of surveillance data and results of a literature search for investigations that characterized rTB etiology using WGS. RESULTS: The percentage of TB cases in AIAN persons in Alaska classified as recurrent episodes (11.8%) was three times the national percentage (3.9%). Of 38 recurrent episodes included in genetic analyses, we attributed 25 (65.8%) to reinfection based on wgSNP distances and phylogenetic analyses; this proportion was the highest among 16 published point estimates identified through the literature search. By comparison, we attributed 11 of 38 (28.9%) and 6 of 38 (15.8%) recurrent episodes to reinfection based on wgSNP distances alone and on conventional genotyping methods, respectively. CONCLUSIONS: WGS and attribution criteria involving genetic distances and patterns of relatedness can provide an effective means of elucidating rTB etiology. Our findings indicate that rTB occurs at high proportions among AIAN persons in Alaska and is frequently attributable to reinfection, reinforcing the importance of active surveillance and control measures to limit the spread of TB disease in Alaskan AIAN communities.

Tuberculosis Outbreaks in State Prisons, United States, 2011-2019.

Objectives. To understand the frequency, magnitude, geography, and characteristics of tuberculosis outbreaks in US state prisons. Methods. Using data from the National Tuberculosis Surveillance System, we identified all cases of tuberculosis during 2011 to 2019 that were reported as occurring among individuals incarcerated in a state prison at the time of diagnosis. We used whole-genome sequencing to define 3 or more cases within 2 single nucleotide polymorphisms within 3 years as clustered; we classified clusters with 6 or more cases during a 3-year period as tuberculosis outbreaks. Results. During 2011 to 2019, 566 tuberculosis cases occurred in 41 state prison systems (a median of 3 cases per state). A total of 19 tuberculosis genotype clusters comprising 134 cases were identified in 6 state prison systems; these clusters included a subset of 5 outbreaks in 2 states. Two Alabama outbreaks during 2011 to 2017 totaled 20 cases; 3 Texas outbreaks during 2014 to 2019 totaled 51 cases. Conclusions. Only Alabama and Texas reported outbreaks during the 9-year period; only Texas state prisons had ongoing transmission in 2019. Effective interventions are needed to stop tuberculosis outbreaks in Texas state prisons. (Am J Public Health. 2022;112(8):1170-1179. https://doi.org/10.2105/AJPH.2022.306864).

Coronavirus Disease 2019 Case Surveillance - United States, January 22-May 30, 2020.

The coronavirus disease 2019 (COVID-19) pandemic resulted in 5,817,385 reported cases and 362,705 deaths worldwide through May, 30, 2020,† including 1,761,503 aggregated reported cases and 103,700 deaths in the United States.§ Previous analyses during February-early April 2020 indicated that age ≥65 years and underlying health conditions were associated with a higher risk for severe outcomes, which were less common among children aged <18 years (1-3). This report describes demographic characteristics, underlying health conditions, symptoms, and outcomes among 1,320,488 laboratory-confirmed COVID-19 cases individually reported to CDC during January 22-May 30, 2020. Cumulative incidence, 403.6 cases per 100,000 persons,¶ was similar among males (401.1) and females (406.0) and highest among persons aged ≥80 years (902.0). Among 599,636 (45%) cases with known information, 33% of persons were Hispanic or Latino of any race (Hispanic), 22% were non-Hispanic black (black), and 1.3% were non-Hispanic American Indian or Alaska Native (AI/AN). Among 287,320 (22%) cases with sufficient data on underlying health conditions, the most common were cardiovascular disease (32%), diabetes (30%), and chronic lung disease (18%). Overall, 184,673 (14%) patients were hospitalized, 29,837 (2%) were admitted to an intensive care unit (ICU), and 71,116 (5%) died. Hospitalizations were six times higher among patients with a reported underlying condition (45.4%) than those without reported underlying conditions (7.6%). Deaths were 12 times higher among patients with reported underlying conditions (19.5%) compared with those without reported underlying conditions (1.6%). The COVID-19 pandemic continues to be severe, particularly in certain population groups. These preliminary findings underscore the need to build on current efforts to collect and analyze case data, especially among those with underlying health conditions. These data are used to monitor trends in COVID-19 illness, identify and respond to localized incidence increase, and inform policies and practices designed to reduce transmission in the United States.

The core seafloor microbiome in the Gulf of Mexico is remarkably consistent and shows evidence of recovery from disturbance caused by major oil spills.

The microbial ecology of oligotrophic deep ocean sediments is understudied relative to their shallow counterparts, and this lack of understanding hampers our ability to predict responses to current and future perturbations. The Gulf of Mexico has experienced two of the largest accidental marine oil spills, the 1979 Ixtoc-1 blowout and the 2010 Deepwater Horizon (DWH) discharge. Here, microbial communities were characterized for 29 sites across multiple years in > 700 samples. The composition of the seafloor microbiome was broadly consistent across the region and was well approximated by the overlying water depth and depth within the sediment column, while geographic distance played a limited role. Biogeographical distributions were employed to generate predictive models for over 4000 OTU that leverage easy-to-obtain geospatial variables which are linked to measured sedimentary oxygen profiles. Depth stratification and putative niche diversification are evidenced by the distribution of taxa that mediate the microbial nitrogen cycle. Furthermore, these results demonstrate that sediments impacted by the DWH spill had returned to near baseline conditions after 2 years. The distributions of benthic microorganisms in the Gulf can be constrained, and moreover, deviations from these predictions may pinpoint impacted sites and aid in future response efforts or long-term stability studies.

Simple Estimates for Local Prevalence of Latent Tuberculosis Infection, United States, 2011-2015.

We used tuberculosis genotyping results to derive estimates of prevalence of latent tuberculosis infection in the United States. We estimated <1% prevalence in 1,981 US counties, 1%-<3% in 785 counties, and >3% in 377 counties. This method for estimating prevalence could be applied in any jurisdiction with an established tuberculosis surveillance system.

Molecular surveillance for large outbreaks of tuberculosis in the United States, 2014-2018.

OBJECTIVE: This study describes characteristics of large tuberculosis (TB) outbreaks in the United States detected using novel molecular surveillance methods during 2014-2016 and followed for 2 years through 2018. METHODS: We developed 4 genotype-based detection algorithms to identify large TB outbreaks of ≥10 cases related by recent transmission during a 3-year period. We used whole-genome sequencing and epidemiologic data to assess evidence of recent transmission among cases. RESULTS: There were 24 large outbreaks involving 518 cases; patients were primarily U.S.-born (85.1%) racial/ethnic minorities (84.1%). Compared with all other TB patients, patients associated with large outbreaks were more likely to report substance use, homelessness, and having been diagnosed while incarcerated. Most large outbreaks primarily occurred within residences among families and nonfamilial social contacts. A source case with a prolonged infectious period and difficulties in eliciting contacts were commonly reported contributors to transmission. CONCLUSION: Large outbreak surveillance can inform targeted interventions to decrease outbreak-associated TB morbidity.

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2-69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation. Code available at: https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.