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.

The report “Genomic Surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants — United States, June 2021–January 2022” contained several errors.

BACKGROUND: Early in the pandemic, cruise travel exacerbated the global spread of SARS-CoV-2. We report epidemiologic and molecular findings from an investigation of a cluster of travelers with confirmed COVID-19 returning to the U.S. from Nile River cruises in Egypt. METHODS: State health departments reported data on real-time reverse transcription-polymerase chain reaction-confirmed COVID-19 cases with a history of Nile River cruise travel during February-March 2020 to the Centers for Disease Control and Prevention (CDC). Demographic and epidemiologic data were collected through routine surveillance channels. Sequences were obtained from either state health departments or from the Global Initiative on Sharing Avian Flu Data (GISAID). We conducted descriptive analyses of epidemiologic data and explored phylogenetic relationships between sequences. RESULTS: We identified 149 Nile River cruise travelers with confirmed COVID-19 who returned to 67 different U.S. counties in 27 states: among those with complete data, 4.7% (6/128) died and 28.1% (38/135) were hospitalized. These individuals traveled on 20 different Nile River cruise voyages (12 unique vessels). Fifteen community transmission events were identified in four states, with 73.3% (11/15) of these occurring in Wisconsin (as the result of a more detailed contact investigation in that state). Phylogenetic analyses supported the hypothesis that travelers were most likely infected in Egypt, with most sequences in Nextstrain clade 20A 93% (87/94). We observed genetic clustering by Nile River cruise voyage and vessel. CONCLUSIONS: Nile River cruise travelers with COVID-19 introduced SARS-CoV-2 over a very large geographic range, facilitating transmission across the United States early in the pandemic. Travelers who participate in cruises, even on small river vessels as investigated in this study, are at increased risk of SARS-CoV-2 exposure. Therefore, history of river cruise travel should be considered in contact tracing and outbreak investigations.

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.

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.

Genomic surveillance is a critical tool for tracking emerging variants of SARS-CoV-2 (the virus that causes COVID-19), which can exhibit characteristics that potentially affect public health and clinical interventions, including increased transmissibility, illness severity, and capacity for immune escape. During June 2021-January 2022, CDC expanded genomic surveillance data sources to incorporate sequence data from public repositories to produce weighted estimates of variant proportions at the jurisdiction level and refined analytic methods to enhance the timeliness and accuracy of national and regional variant proportion estimates. These changes also allowed for more comprehensive variant proportion estimation at the jurisdictional level (i.e., U.S. state, district, territory, and freely associated state). The data in this report are a summary of findings of recent proportions of circulating variants that are updated weekly on CDC's COVID Data Tracker website to enable timely public health action.(†) The SARS-CoV-2 Delta (B.1.617.2 and AY sublineages) variant rose from 1% to >50% of viral lineages circulating nationally during 8 weeks, from May 1-June 26, 2021. Delta-associated infections remained predominant until being rapidly overtaken by infections associated with the Omicron (B.1.1.529 and BA sublineages) variant in December 2021, when Omicron increased from 1% to >50% of circulating viral lineages during a 2-week period. As of the week ending January 22, 2022, Omicron was estimated to account for 99.2% (95% CI = 99.0%-99.5%) of SARS-CoV-2 infections nationwide, and Delta for 0.7% (95% CI = 0.5%-1.0%). The dynamic landscape of SARS-CoV-2 variants in 2021, including Delta- and Omicron-driven resurgences of SARS-CoV-2 transmission across the United States, underscores the importance of robust genomic surveillance efforts to inform public health planning and practice.

Tuberculosis (TB) control programs use whole-genome sequencing (WGS) of Mycobacterium tuberculosis (Mtb) for detecting and investigating TB case clusters. Existence of few genomic differences between Mtb isolates might indicate TB cases are the result of recent transmission. However, the variable and sometimes long duration of latent infection, combined with uncertainty in the Mtb mutation rate during latency, can complicate interpretation of WGS results. To estimate the association between infection duration and single nucleotide polymorphism (SNP) accumulation in the Mtb genome, we first analyzed pairwise SNP differences among TB cases from Los Angeles County, California, with strong epidemiologic links. We found that SNP distance alone was insufficient for concluding that cases are linked through recent transmission. Second, we describe a well-characterized cluster of TB cases in California to illustrate the role of genomic data in conclusions regarding recent transmission. Longer presumed latent periods were inconsistently associated with larger SNP differences. Our analyses suggest that WGS alone cannot be used to definitively determine that a case is attributable to recent transmission. Methods for integrating clinical, epidemiologic, and genomic data can guide conclusions regarding the likelihood of recent transmission, providing local public health practitioners with better tools for monitoring and investigating TB transmission.

Sputum-culture confirmation guides tuberculosis (TB) diagnosis and patient management but has previously been reported to be low in the US-Affiliated Pacific Islands (USAPI). We evaluated factors associated with positive sputum-culture results by analyzing TB case surveillance and laboratory data, including sputa quality and quantity for diagnostic specimens from the USAPI. A lower proportion of sputum specimens were reported as culture positive from the USAPI (42%), compared with Hawaii (58%) and the United States (55%). Few (3%) sputa collected from TB patients in the USAPI had both optimal quality and quantity; 40% had optimal quality (mucoid), and 7% had optimal quantity (>5 mL). Suboptimal sputum specimen quality and quantity contributed to fewer sputum-culture positive results in the USAPI. Improving sputum collection and handling might lead to more culture positive results and ultimately improve patient care and TB control in USAPI.

Data from observational studies demonstrate that variants of SARS-CoV-2, the virus that causes COVID-19, have evolved rapidly across many countries (1,2). The SARS-CoV-2 B.1.617.2 (Delta) variant of concern is more transmissible than previously identified variants,* and as of September 2021, is the predominant variant in the United States.(†) Studies characterizing the distribution and severity of illness caused by SARS-CoV-2 variants, particularly the Delta variant, are limited in the United States (3), and are subject to limitations related to study setting, specimen collection, study population, or study period (4-7). This study used whole genome sequencing (WGS) data on SARS-CoV-2-positive specimens collected across Kaiser Permanente Southern California (KPSC), a large integrated health care system, to describe the distribution and risk of hospitalization associated with SARS-CoV-2 variants during March 4-July 21, 2021, by patient vaccination status. Among 13,039 SARS-CoV-2-positive specimens identified from KPSC patients during this period, 6,798 (52%) were sequenced and included in this report. Of these, 5,994 (88%) were collected from unvaccinated persons, 648 (10%) from fully vaccinated persons, and 156 (2%) from partially vaccinated persons. Among all sequenced specimens, the weekly percentage of B.1.1.7 (Alpha) variant infections increased from 20% to 67% during March 4-May 19, 2021. During April 15-July 21, 2021, the weekly percentage of Delta variant infections increased from 0% to 95%. During March 4-July 21, 2021, the weekly percentage of variants was similar among fully vaccinated and unvaccinated persons, but the Delta variant was more commonly identified among vaccinated persons then unvaccinated persons overall, relative to other variants. The Delta variant was more prevalent among younger persons, with the highest percentage (55%) identified among persons aged 18-44 years. Infections attributed to the Delta variant were also more commonly identified among non-Hispanic Black persons, relative to other variants. These findings reinforce the importance of continued monitoring of SARS-CoV-2 variants and implementing multiple COVID-19 prevention strategies, particularly during the current period in which Delta is the predominant variant circulating in the United States.

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.

CONTEXT: In 2010, California experienced its largest pertussis epidemic in more than 60 years; a substantial burden of disease was noted in the 7- to 10-year-old age group despite high diphtheria, tetanus, and acellular pertussis vaccine (DTaP) coverage, indicating the possibility of waning protection. OBJECTIVE: To evaluate the association between pertussis and receipt of 5 DTaP doses by time since fifth DTaP dose. DESIGN, SETTING, AND PARTICIPANTS: Case-control evaluation conducted in 15 California counties. Cases (n = 682) were all suspected, probable, and confirmed pertussis cases among children aged 4 to 10 years reported from January through December 14, 2010; controls (n = 2016) were children in the same age group who received care from the clinicians reporting the cases. Three controls were selected per case. Vaccination histories were obtained from medical records and immunization registries. MAIN OUTCOME MEASURES: Primary outcomes were (1) odds ratios (ORs) for the association between pertussis and receipt of the 5-dose DTaP series and (2) ORs for the association between pertussis and time since completion (<12, 12-23, 24-35, 36-47, 48-59, or ≥60 months) of the 5-dose DTaP series. Logistic regression was used to calculate ORs, accounting for clustering by county and clinician, and vaccine effectiveness (VE) was estimated as (1 - OR) x 100%. RESULTS: Among cases and controls, 53 (7.8%) and 19 (0.9%) had not received any pertussis-containing vaccines, respectively. Compared with controls, children with pertussis had a lower odds of having received all 5 doses of DTaP (OR, 0.11; 95% CI, 0.06-0.21 [estimated VE, 88.7%; 95% CI, 79.4%-93.8%]). When children were categorized by time since completion of the DTaP series, using an unvaccinated reference group, children with pertussis compared with controls were less likely to have received their fifth dose within the prior 12 months (19 [2.8%] vs 354 [17.6%], respectively; OR, 0.02; 95% CI, 0.01-0.04 [estimated VE, 98.1%; 95% CI, 96.1%-99.1%]). This association was evident with longer time since vaccination, with ORs increasing with time since the fifth dose. At 60 months or longer (n = 231 cases [33.9%] and n = 288 controls [14.3%]), the OR was 0.29 (95% CI, 0.15-0.54 [estimated VE, 71.2%; 95% CI, 45.8%-84.8%]). Accordingly, the estimated VE declined each year after receipt of the fifth dose of DTaP. CONCLUSION: Among children in 15 California counties, children with pertussis, compared with controls, had lower odds of having received the 5-dose DTaP series; as time since last DTaP dose increased, the odds increased, which is consistent with a progressive decrease in estimated vaccine effectiveness each year after the final dose of pertussis vaccine.