Tuberculosis Outbreak in a State Prison System - Washington, 2021-2022.

During 2014-2020, no tuberculosis (TB) cases were reported within the Washington state prison system. However, during July 2021-June 2022, 25 TB cases were reported among persons incarcerated or formerly incarcerated in two Washington state prisons. Phylogenetic analyses of whole genome sequencing data indicated that Mycobacterium tuberculosis isolates from all 11 patients with culture-confirmed TB were closely related, suggesting that these cases represented a single outbreak. The median infectious period for 12 patients who were considered likely contagious was 170 days. As of November 15, 2022, the Washington State Department of Corrections (WADOC) and Washington State Department of Health (WADOH), with technical assistance from CDC, had identified 3,075 contacts among incarcerated residents and staff members at five state prisons, and 244 contacts without a known TB history received a diagnosis of latent TB infection (LTBI). Persons who were evaluated for TB disease were isolated; those receiving a diagnosis of TB then initiated antituberculosis therapy. Persons with LTBI were offered treatment to prevent progression to TB disease. This ongoing TB outbreak is the largest in Washington in 20 years. Suspension of annual TB screening while limited resources were redirected toward the COVID-19 response resulted in delayed case detection that facilitated TB transmission. In addition, fear of isolation might discourage residents and staff members from reporting symptoms, which likely also leads to delayed TB diagnoses. Continued close collaboration between WADOC and WADOH is needed to end this outbreak and prevent future outbreaks.

Transmission of Mycobacterium tuberculosis to Healthcare Personnel Resulting From Contaminated Bone Graft Material, United States, June 2021-August 2022.

A nationwide tuberculosis outbreak linked to a viable bone allograft product contaminated with Mycobacterium tuberculosis was identified in June 2021. Our subsequent investigation identified 73 healthcare personnel with new latent tuberculosis infection following exposure to the contaminated product, product recipients, surgical instruments, or medical waste.

Nationwide tuberculosis outbreak in the USA linked to a bone graft product: an outbreak report.

BACKGROUND: Mycobacterium tuberculosis transmission through solid organ transplantation has been well described, but transmission through transplanted tissues is rare. We investigated a tuberculosis outbreak in the USA linked to a bone graft product containing live cells derived from a single deceased donor. METHODS: In this outbreak report, we describe the management and severity of the outbreak and identify opportunities to improve tissue transplant safety in the USA. During early June, 2021, the US Centers for Disease Control and Prevention (CDC) worked with state and local health departments and health-care facilities to locate and sequester unused units from the recalled lot and notify, evaluate, and treat all identified product recipients. Investigators from CDC and the US Food and Drug Administration (FDA) reviewed donor screening and tissue processing. Unused product units from the recalled and other donor lots were tested for the presence of M tuberculosis using real-time PCR (rt PCR) assays and culture. M tuberculosis isolates from unused product and recipients were compared using phylogenetic analysis. FINDINGS: The tissue donor (a man aged 80 years) had unrecognised risk factors, symptoms, and signs consistent with tuberculosis. Bone was procured from the deceased donor and processed into 154 units of bone allograft product containing live cells, which were distributed to 37 hospitals and ambulatory surgical centres in 20 US states between March 1 and April 2, 2021. From March 3 to June 1, 2021, 136 (88%) units were implanted into 113 recipients aged 24-87 years in 18 states (some individuals received multiple units). The remaining 18 units (12%) were located and sequestered. 87 (77%) of 113 identified product recipients had microbiological or imaging evidence of tuberculosis disease. Eight product recipients died 8-99 days after product implantation (three deaths were attributed to tuberculosis after recognition of the outbreak). All 105 living recipients started treatment for tuberculosis disease at a median of 69 days (IQR 56-81) after product implantation. M tuberculosis was detected in all eight sequestered unused units tested from the recalled donor lot, but not in lots from other donors. M tuberculosis isolates from unused product and recipients were more than 99·99% genetically identical. INTERPRETATION: Donor-derived transmission of M tuberculosis via bone allograft resulted in substantial morbidity and mortality. All prospective tissue and organ donors should be routinely assessed for tuberculosis risk factors and clinical findings. When these are present, laboratory testing for M tuberculosis should be strongly considered. FUNDING: None.

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.

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

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 data set that was generalizable to a validation data set. Our model showed 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 have 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.

Developing National Genotype-Independent Indicators for Recent Mycobacterium Tuberculosis Transmission Using Pediatric Cases-United States, 2011-2017.

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.

Disparities in COVID-19 Vaccination Coverage Among Health Care Personnel Working in Long-Term Care Facilities, by Job Category, National Healthcare Safety Network - United States, March 2021.

Residents of long-term care facilities (LTCFs) and health care personnel (HCP) working in these facilities are at high risk for COVID-19-associated mortality. As of March 2021, deaths among LTCF residents and HCP have accounted for almost one third (approximately 182,000) of COVID-19-associated deaths in the United States (1). Accordingly, LTCF residents and HCP were prioritized for early receipt of COVID-19 vaccination and were targeted for on-site vaccination through the federal Pharmacy Partnership for Long-Term Care Program (2). In December 2020, CDC's National Healthcare Safety Network (NHSN) launched COVID-19 vaccination modules, which allow U.S. LTCFs to voluntarily submit weekly facility-level COVID-19 vaccination data.* CDC analyzed data submitted during March 1-April 4, 2021, to describe COVID-19 vaccination coverage among a convenience sample of HCP working in LTCFs, by job category, and compare HCP vaccination coverage rates with social vulnerability metrics of the surrounding community using zip code tabulation area (zip code area) estimates. Through April 4, 2021, a total of 300 LTCFs nationwide, representing approximately 1.8% of LTCFs enrolled in NHSN, reported that 22,825 (56.8%) of 40,212 HCP completed COVID-19 vaccination.† Vaccination coverage was highest among physicians and advanced practice providers (75.1%) and lowest among nurses (56.7%) and aides (45.6%). Among aides (including certified nursing assistants, nurse aides, medication aides, and medication assistants), coverage was lower in facilities located in zip code areas with higher social vulnerability (social and structural factors associated with adverse health outcomes), corresponding to vaccination disparities present in the wider community (3). Additional efforts are needed to improve LTCF immunization policies and practices, build confidence in COVID-19 vaccines, and promote COVID-19 vaccination. CDC and partners have prepared education and training resources to help educate HCP and promote COVID-19 vaccination coverage among LTCF staff members.§.

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

INTRODUCTION: In the U.S., universal genotyping of culture-confirmed tuberculosis cases facilitates cluster detection. Early recognition of the small clusters more likely to become outbreaks can help prioritize public health resources for immediate interventions. METHODS: This study used national surveillance data reported during 2009-2018 to describe incident clusters (≥3 tuberculosis cases with matching genotypes not previously reported in the same county); data were analyzed during 2020. Cox proportional hazards regression models were used to examine the patient characteristics associated with clusters doubling in size to ≥6 cases. RESULTS: During 2009-2018, a total of 1,516 incident clusters (comprising 6,577 cases) occurred in 47 U.S. states; 231 clusters had ≥6 cases. Clusters of ≥6 cases disproportionately included patients who used substances, who had recently experienced homelessness, who were incarcerated, who were U.S. born, or who self-identified as being of American Indian or Alaska Native race or of Black race. A median of 54 months elapsed between the first and the third cases in clusters that remained at 3-5 cases compared with a median of 9.5 months in clusters that grew to ≥6 cases. The longer time between the first and third cases and the presence of ≥1 patient aged ≥65 years among the first 3 cases predicted a lower hazard for accumulating ≥6 cases. CONCLUSIONS: Clusters accumulating ≥3 cases within a year should be prioritized for intervention. Effective response strategies should include plans for targeted outreach to U.S.-born individuals, incarcerated people, those experiencing homelessness, people using substances, and individuals self-identifying as being of American Indian or Alaska Native race or of Black race.

Characteristics of Persons Who Died with COVID-19 - United States, February 12-May 18, 2020.

During January 1, 2020-May 18, 2020, approximately 1.3 million cases of coronavirus disease 2019 (COVID-19) and 83,000 COVID-19-associated deaths were reported in the United States (1). Understanding the demographic and clinical characteristics of decedents could inform medical and public health interventions focused on preventing COVID-19-associated mortality. This report describes decedents with laboratory-confirmed infection with SARS-CoV-2, the virus that causes COVID-19, using data from 1) the standardized CDC case-report form (case-based surveillance) (https://www.cdc.gov/coronavirus/2019-ncov/php/reporting-pui.html) and 2) supplementary data (supplemental surveillance), such as underlying medical conditions and location of death, obtained through collaboration between CDC and 16 public health jurisdictions (15 states and New York City).

Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting.

We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated.

Risk factors associated with default from multi- and extensively drug-resistant tuberculosis treatment, Uzbekistan: a retrospective cohort analysis.

BACKGROUND: The Médecins Sans Frontières project of Uzbekistan has provided multidrug-resistant tuberculosis treatment in the Karakalpakstan region since 2003. Rates of default from treatment have been high, despite psychosocial support, increasing particularly since programme scale-up in 2007. We aimed to determine factors associated with default in multi- and extensively drug-resistant tuberculosis patients who started treatment between 2003 and 2008 and thus had finished approximately 2 years of treatment by the end of 2010. METHODS: A retrospective cohort analysis of multi- and extensively drug-resistant tuberculosis patients enrolled in treatment between 2003 and 2008 compared baseline demographic characteristics and possible risk factors for default. Default was defined as missing ≥60 consecutive days of treatment (all drugs). Data were routinely collected during treatment and entered in a database. Potential risk factors for default were assessed in univariate analysis using chi-square test and in multivariate analysis with logistic regression. RESULTS: 20% (142/710) of patients defaulted after a median of 6 months treatment (IQR 2.6-9.9). Factors associated with default included severity of resistance patterns (pre-extensively drug-resistant/extensively drug-resistant tuberculosis adjusted odds ratio 0.52, 95%CI: 0.31-0.86), previous default (2.38, 1.09-5.24) and age >45 years (1.77, 1.10-2.87). The default rate was 14% (42/294) for patients enrolled 2003-2006 and 24% (100/416) for 2007-2008 enrolments (p = 0.001). CONCLUSIONS: Default from treatment was high and increased with programme scale-up. It is essential to ensure scale-up of treatment is accompanied with scale-up of staff and patient support. A successful first course of tuberculosis treatment is important; patients who had previously defaulted were at increased risk of default and death. The protective effect of severe resistance profiles suggests that understanding disease severity or fear may motivate against default. Targeted health education and support for at-risk patients after 5 months of treatment when many begin to feel better may decrease default.

Using statistical methods and genotyping to detect tuberculosis outbreaks.

BACKGROUND: Early identification of outbreaks remains a key component in continuing to reduce the burden of infectious disease in the United States. Previous studies have applied statistical methods to detect unexpected cases of disease in space or time. The objectives of our study were to assess the ability and timeliness of three spatio-temporal methods to detect known outbreaks of tuberculosis. METHODS: We used routinely available molecular and surveillance data to retrospectively assess the effectiveness of three statistical methods in detecting tuberculosis outbreaks: county-based log-likelihood ratio, cumulative sums, and a spatial scan statistic. RESULTS: Our methods identified 8 of the 9 outbreaks, and 6 outbreaks would have been identified 1-52 months (median=10 months) before local public health authorities identified them. Assuming no delays in data availability, 46 (59.7%) of the 77 patients in the 9 outbreaks were identified after our statistical methods would have detected the outbreak but before local public health authorities became aware of the problem. CONCLUSIONS: Statistical methods, when applied retrospectively to routinely collected tuberculosis data, can successfully detect known outbreaks, potentially months before local public health authorities become aware of the problem. The three methods showed similar results; no single method was clearly superior to the other two. Further study to elucidate the performance of these methods in detecting tuberculosis outbreaks will be done in a prospective analysis.

Using routinely reported tuberculosis genotyping and surveillance data to predict tuberculosis outbreaks.

We combined routinely reported tuberculosis (TB) patient characteristics with genotyping data and measures of geospatial concentration to predict which small clusters (i.e., consisting of only 3 TB patients) in the United States were most likely to become outbreaks of at least 6 TB cases. Of 146 clusters analyzed, 16 (11.0%) grew into outbreaks. Clusters most likely to become outbreaks were those in which at least 1 of the first 3 patients reported homelessness or excess alcohol or illicit drug use or was incarcerated at the time of TB diagnosis and in which the cluster grew rapidly (i.e., the third case was diagnosed within 5.3 months of the first case). Of 17 clusters with these characteristics and therefore considered high risk, 9 (53%) became outbreaks. This retrospective cohort analysis of clusters in the United States suggests that routinely reported data may identify small clusters that are likely to become outbreaks and which are therefore candidates for intensified contact investigations.

Increasing proportions of advanced pulmonary tuberculosis reported in the United States: are delays in diagnosis on the rise?

RATIONALE: Delays in the diagnosis of tuberculosis (TB) can result in progression to advanced disease. Patients with pulmonary TB and advanced disease are more likely to transmit disease and fail treatment. OBJECTIVES: To examine clinical, epidemiological, and geographic factors associated with advanced pulmonary TB to further understanding of delayed diagnosis and transmission. METHODS: Pulmonary tuberculosis cases in persons older than 15 years of age reported to the U.S. National Tuberculosis Surveillance System with advanced disease (cavitation on chest radiograph and acid-fast bacilli smear-positive sputum result) were compared with those without advanced disease using trend and binomial regression analysis. MEASUREMENTS AND MAIN RESULTS: There were 35,584 cases of advanced pulmonary tuberculosis (APT) and 125,077 cases of non-APT reported from 1993 through 2006. Proportions of pulmonary TB cases with APT increased from 18.5% in 1993 to 26.1% in 2006, and the increase in the proportion of APT was most notable for national TB rates below 6.6 per 100,000. At the county level, the association between APT and low TB incidence has grown incrementally since 2000. The proportion of APT increased greatest among whites (65.4%), the employed (63.3%), and the U.S. born (59.2%). The prevalence of APT was 44% greater among persons with multidrug-resistant TB compared with those without it. CONCLUSIONS: This study highlights the need for TB diagnosis at early stages of the disease to minimize APT and decrease the risk of transmission. Additional efforts should concentrate on reducing time to treatment initiation in low-incidence areas and among groups traditionally seen as being at low risk for TB disease.