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.

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.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children is often asymptomatic or results in only mild disease.1 Data on the extent of transmission of SARS-CoV-2 from children and adolescents in the household setting, including transmission to older persons who are at increased risk for severe disease, are limited.2 After an outbreak of coronavirus disease 2019 (Covid-19) at an overnight camp,3 we conducted a retrospective cohort study involving camp attendees and their household contacts to assess secondary transmission and factors associated with household transmission (additional details are provided in the Methods section in the Supplementary Appendix, available with the full text of this letter at NEJM.org).

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.

OBJECTIVES: Persons experiencing homelessness (PEH) are disproportionately affected by tuberculosis (TB). We estimate area-specific rates of TB among PEH and characterize the extent to which available data support recent transmission as an explanation of high TB incidence. METHODS: We estimated TB incidence among PEH using National Tuberculosis Surveillance System data and population estimates for the US Department of Housing and Urban Development's Continuums of Care areas. For areas with TB incidence higher than the national average among PEH, we estimated recent transmission using genotyping and a plausible source-case method. For cases with ≥1 plausible source case, we assessed with TB program partners whether available whole-genome sequencing and local epidemiologic data were consistent with recent transmission. RESULTS: During 2011-2016, 3164 TB patients reported experiencing homelessness. National incidence was 36 cases/100,000 PEH. Incidence estimates varied among 21 areas with ≥10,000 PEH (9-150 cases/100,000 PEH); 9 areas had higher than average incidence. Of the 2349 cases with Mycobacterium tuberculosis genotyping results, 874 (37%) had ≥1 plausible source identified. In the 9 areas, 23%-82% of cases had ≥1 plausible source. Of cases with ≥1 plausible source, 63% were consistent and 7% were inconsistent with recent transmission; 29% were inconclusive. CONCLUSIONS: Disparities in TB incidence for PEH persist; estimates of TB incidence and recent transmission vary by area. With a better understanding of the TB risk among PEH in their jurisdictions and the role of recent transmission as a driver, programs can make more informed decisions about prioritizing TB prevention strategies.

INTRODUCTION: Pediatric tuberculosis (TB) cases are sentinel events for Mycobacterium tuberculosis transmission in communities because children, by definition, must have been infected relatively recently. However, these events are not consistently identified by genotype-dependent surveillance alerting methods because many pediatric TB cases are not culture-positive, a prerequisite for genotyping. METHODS: We developed 3 potential indicators of ongoing TB transmission based on identifying counties in the United States with relatively high pediatric (aged <15 years) TB incidence: (1) a case proportion indicator: an above-average proportion of pediatric TB cases among all TB cases; (2) a case rate indicator: an above-average pediatric TB case rate; and (3) a statistical model indicator: a statistical model based on a significant increase in pediatric TB cases from the previous 8-quarter moving average. RESULTS: Of the 249 US counties reporting ≥2 pediatric TB cases during 2009-2017, 240 and 249 counties were identified by the case proportion and case rate indicators, respectively. The statistical model indicator identified 40 counties with a significant increase in the number of pediatric TB cases. We compared results from the 3 indicators with an independently generated list of 91 likely transmission events involving ≥2 pediatric cases (ie, known TB outbreaks or case clusters with reported epidemiologic links). All counties with likely transmission events involving multiple pediatric cases were identified by ≥1 indicator; 23 were identified by all 3 indicators. PRACTICE IMPLICATIONS: This retrospective analysis demonstrates the feasibility of using routine TB surveillance data to identify counties where ongoing TB transmission might be occurring, even in the absence of available genotyping data.

OBJECTIVES: In late June 2020, a large outbreak of coronavirus disease 2019 (COVID-19) occurred at a sleep-away youth camp in Georgia, affecting primarily persons </=21 years. We conducted a retrospective cohort study among campers and staff (attendees) to determine the extent of the outbreak and assess factors contributing to transmission. METHODS: Attendees were interviewed to ascertain demographic characteristics, known exposures to COVID-19 and community exposures, and mitigation measures before, during, and after attending camp. COVID-19 case status was determined for all camp attendees on the basis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results and reported symptoms. We calculated attack rates and instantaneous reproduction numbers and sequenced SARS-CoV-2 viral genomes from the outbreak. RESULTS: Among 627 attendees, the median age was 15 years (interquartile range: 12-16 years); 56% (351 of 627) of attendees were female. The attack rate was 56% (351 of 627) among all attendees. On the basis of date of illness onset or first positive test result on a specimen collected, 12 case patients were infected before arriving at camp and 339 case patients were camp associated. Among 288 case patients with available symptom information, 45 (16%) were asymptomatic. Despite cohorting, 50% of attendees reported direct contact with people outside their cabin cohort. On the first day of camp session, the instantaneous reproduction number was 10. Viral genomic diversity was low. CONCLUSIONS: Few introductions of SARS-CoV-2 into a youth congregate setting resulted in a large outbreak. Testing strategies should be combined with prearrival quarantine, routine symptom monitoring with appropriate isolation and quarantine, cohorting, social distancing, mask wearing, and enhanced disinfection and hand hygiene. Promotion of mitigation measures among younger populations is needed.

Limited data are available about transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), among youths. During June 17-20, an overnight camp in Georgia (camp A) held orientation for 138 trainees and 120 staff members; staff members remained for the first camp session, scheduled during June 21-27, and were joined by 363 campers and three senior staff members on June 21. Camp A adhered to the measures in Georgia's Executive Order* that allowed overnight camps to operate beginning on May 31, including requiring all trainees, staff members, and campers to provide documentation of a negative viral SARS-CoV-2 test ≤12 days before arriving. Camp A adopted most(†) components of CDC's Suggestions for Youth and Summer Camps(§) to minimize the risk for SARS-CoV-2 introduction and transmission. Measures not implemented were cloth masks for campers and opening windows and doors for increased ventilation in buildings. Cloth masks were required for staff members. Camp attendees were cohorted by cabin and engaged in a variety of indoor and outdoor activities, including daily vigorous singing and cheering. On June 23, a teenage staff member left camp A after developing chills the previous evening. The staff member was tested and reported a positive test result for SARS-CoV-2 the following day (June 24). Camp A officials began sending campers home on June 24 and closed the camp on June 27. On June 25, the Georgia Department of Public Health (DPH) was notified and initiated an investigation. DPH recommended that all attendees be tested and self-quarantine, and isolate if they had a positive test result.

BACKGROUND: Incarcerated persons are disproportionately diagnosed with tuberculosis (TB). California has the second highest inmate population in the United States, but reports the highest number of cases. OBJECTIVE: To describe the TB epidemiology among incarcerated patients in California. METHODS : Trends in incidence were assessed using Poisson regression, and trends in percentage were assessed using weighted linear regression. Demographic and clinical characteristics were compared using v2 or Mann-Whitney U tests. RESULTS: During 1993-2013, of the 64 090 TB cases reported, 2323 (4%) were correctional facility residents. Incidence in correctional facilities decreased until 2006 (annual per cent change [APC] -12.3%, 95%CI -14.4 to -10.1), but has since stabilized (APC 4.4%, 95%CI -2.1 to 11.4). Compared with state prisoners, federal prisoners were more likely to be male (98%, P<0.03), persons arriving in the United States within 5 years of diagnosis (62%, P< 0.001), and born in Mexico (88%, P=0.02), whereas local jail inmates were more likely to have a history of substance use (75%, P<0.001) and homelessness (35%, P< 0.001). CONCLUSIONS: TB incidence in correctional facilities had steadily declined over the last two decades, but has recently leveled out. To promote further reduction in incidence among diverse incarcerated populations, health departments and correctional facilities should strengthen collaboration by conducting TB risk-based assessments.