Quantifying Mycobacterium tuberculosis transmission dynamics across global settings: a systematic analysis.

The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and the size distributions of putative TB transmission clusters were enumerated. We fit cluster size distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproductive number) and dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, nine studies met inclusion criteria ($n=5$ all TB; $n=4$ drug resistant TB) from eight global settings. Estimated $R$ values (range: 0.10, 0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range: 0.02, 0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range 2-31%) drive the majority (80%) of ongoing transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

Estimating severe acute respiratory coronavirus virus 2 (SARS-CoV-2) seroprevalence from residual clinical blood samples, January-March 2021.

We describe severe acute respiratory coronavirus virus 2 (SARS-CoV-2) IgG seroprevalence and antigenemia among patients at a medical center in January-March 2021 using residual clinical blood samples. The overall seroprevalences were 17% by infection and 16% by vaccination. Spent or residual samples are a feasible alternative for rapidly estimating seroprevalence or monitoring trends in infection and vaccination.

Use of surveillance data to elucidate household clustering of SARS-CoV-2 in Fulton County, Georgia a major metropolitan area.

BACKGROUND: Households are important for SARS-CoV-2 transmission due to high intensity exposure in enclosed spaces over prolonged durations. We quantified and characterized household clustering of COVID-19 cases in Fulton County, Georgia. METHODS: We used surveillance data to identify all confirmed COVID-19 cases in Fulton County. Household clustered cases were defined as cases with matching residential address. We described the proportion of COVID-19 cases that were clustered, stratified by age over time and explore trends in age of first diagnosed case within households and subsequent household cases. RESULTS: Between June 1, 2020 and October 31, 2021, 31,449(37%) of 106,233 cases were clustered in households. Children were the most likely to be in household clusters than any other age group. Initially, children were rarely (∼ 10%) the first cases diagnosed in the household but increased to almost 1 of 3 in later periods. DISCUSSION: One-third of COVID-19 cases in Fulton County were part of a household cluster. Increasingly children were the first diagnosed case, coinciding with temporal trends in vaccine roll-out among the elderly and the return to in-person schooling in Fall 2021. Limitations include restrictions to cases with a valid address and unit number and that the first diagnosed case may not be the infection source for the household.

A University-Led Contact Tracing Program Response to a COVID-19 Outbreak Among Students in Georgia, February-March 2021.

Few reports have described how university programs have controlled COVID-19 outbreaks. Emory University established a case investigation and contact tracing program in June 2020 to identify and mitigate transmission of SARS-CoV-2 in the Emory community. In February 2021, this program identified a surge in COVID-19 cases. In this case study, we present details of outbreak investigation, construction of transmission networks to assess clustering and identify groups for targeted testing, and program quality metrics demonstrating the efficiency of case investigation and contact tracing, which helped bring the surge under control. During February 10-March 5, 2021, Emory University identified 265 COVID-19 cases confirmed by nucleic acid testing in saliva or nasopharyngeal samples. Most students with COVID-19 were undergraduates (95%) and were affiliated with Greek life organizations (70%); 41% lived on campus. Network analysis identified 1 epidemiologically linked cluster of 198 people. Nearly all students diagnosed with COVID-19 (96%) were interviewed the same day as their positive test result. Of 340 close contacts, 90% were traced and 89% were tested. The median time from contact interview to first test was 2 days (interquartile range, 0-6 days); 43% received a positive test result during their quarantine. The surge was considered under control within 17 days, after which new cases were no longer epidemiologically linked. Early detection through systematic testing protocols and rapid and near-complete contact tracing, paired with isolation and quarantine measures, helped to contain the surge. Our approach emphasizes the importance of early preparation of adequate outbreak response infrastructure and staff to implement interventions appropriately and consistently during a pandemic.

Nucleocapsid Antigenemia Is a Marker of Acute SARS-CoV-2 Infection.

Detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is essential for diagnosis, treatment, and infection control. Polymerase chain reaction (PCR) fails to distinguish acute from resolved infections, as RNA is frequently detected after infectiousness. We hypothesized that nucleocapsid in blood marks acute infection with the potential to enhance isolation and treatment strategies. In a retrospective serosurvey of inpatient and outpatient encounters, we categorized samples along an infection timeline using timing of SARS-CoV-2 testing and symptomatology. Among 1860 specimens from 1607 patients, the highest levels and frequency of antigenemia were observed in samples from acute SARS-CoV-2 infection. Antigenemia was higher in seronegative individuals and in those with severe disease. In our analysis, antigenemia exhibited 85.8% sensitivity and 98.6% specificity as a biomarker for acute coronavirus disease 2019 (COVID-19). Thus, antigenemia sensitively and specifically marks acute SARS-CoV-2 infection. Further study is warranted to determine whether antigenemia may aid individualized assessment of active COVID-19.

The Role of Staff in Transmission of SARS-CoV-2 in Long-term Care Facilities.

BACKGROUND: US long-term care facilities (LTCFs) have experienced a disproportionate burden of COVID-19 morbidity and mortality. METHODS: We examined SARS-CoV-2 transmission among residents and staff in 60 LTCFs in Fulton County, Georgia, from March 2020 to September 2021. Using the Wallinga-Teunis method to estimate the time-varying reproduction number, R(t), and linear-mixed regression models, we examined associations between case characteristics and R(t). RESULTS: Case counts, outbreak size and duration, and R(t) declined rapidly and remained low after vaccines were first distributed to LTCFs in December 2020, despite increases in community incidence in summer 2021. Staff cases were more infectious than resident cases (average individual reproduction number, R i = 0.6 [95% confidence intervals [CI] = 0.4, 0.7] and 0.1 [95% CI = 0.1, 0.2], respectively). Unvaccinated resident cases were more infectious than vaccinated resident cases (R i = 0.5 [95% CI = 0.4, 0.6] and 0.2 [95% CI = 0.0, 0.8], respectively), but estimates were imprecise. CONCLUSIONS: COVID-19 vaccines slowed transmission and contributed to reduced caseload in LTCFs. However, due to data limitations, we were unable to determine whether breakthrough vaccinated cases were less infectious than unvaccinated cases. Staff cases were six times more infectious than resident cases, consistent with the hypothesis that staff were the primary drivers of SARS-CoV-2 transmission in LTCFs.

Characteristics and Risk Factors for Mortality by Coronavirus Disease 2019 Pandemic Waves in Fulton County, Georgia: A Cohort Study March 2020-February 2021.

Background: We examined differences in mortality among coronavirus disease 2019 (COVID-19) cases in the first, second, and third waves of the COVID-19 pandemic. Methods: A retrospective cohort study of COVID-19 cases in Fulton County, Georgia, USA, reported to a public health surveillance from March 2020 through February 2021. We estimated case-fatality rates (CFR) by wave and used Cox proportional hazards random-effects models in each wave, with random effects at individual and long-term-care-facility level, to determine risk factors associated with rates of mortality. Results: Of 75 289 confirmed cases, 4490 (6%) were diagnosed in wave 1 (CFR 31 deaths/100 000 person days [pd]), 24 293 (32%) in wave 2 (CFR 7 deaths/100 000 pd), and 46 506 (62%) in wave 3 (CFR 9 deaths/100 000 pd). Compared with females, males were more likely to die in each wave: wave 1 (adjusted hazard ratio [aHR], 1.5; 95% confidence interval [CI], 1.2-1.8), wave 2 (aHR 1.5, 95% CI, 1.2-1.8), and wave 3 (aHR 1.7, 95% CI, 1.5-2.0). Compared with non-Hispanic whites, non-Hispanic blacks were more likely to die in each wave: wave 1 (aHR, 1.4; 95% CI, 1.1-1.8), wave 2 (aHR, 1.5; 95% CI, 1.2-1.9), and wave 3 (aHR, 1.7; 95% CI, 1.4-2.0). Cases with any disability, chronic renal disease, and cardiovascular disease were more likely to die in each wave compared with those without these comorbidities. Conclusions: Our study found gender and racial/ethnic disparities in COVID-19 mortality and certain comorbidities associated with COVID-19 mortality. These factors have persisted throughout the COVID-19 pandemic waves, despite improvements in diagnosis and treatment.

Characteristics and Risk Factors for Mortality by COVID-19 Pandemic Waves in Fulton County, Georgia: A Cohort Study March 2020–February 2021

Background We examined differences in mortality among COVID-19 cases in the first, second and third waves of the COVID-19 pandemic. Methods A retrospective cohort study of COVID-19 cases in Fulton County, Georgia, USA, reported to a public health surveillance from March 2020 through February 2021. We estimated case fatality rates (CFR) by wave and used Cox proportional hazards random effects models in each wave, with random effects at individual and long-term-care-facility level, to determine risk factors associated with rates of mortality. Results Of 75,289 confirmed cases, 4,490 (6%) were diagnosed in wave one (CFR 31 deaths/100,000 person days [pd]), 24,293 (32%) in wave two (CFR 7 deaths/100,000 pd), and 46,506 (62%) in wave three (CFR 9 deaths/100,000 pd). Compared to females, males were more likely to die in each wave: Wave one (adjusted hazard ratio [aHR] 1.5, 95% confidence interval [CI] 1.2–1.8), wave two (aHR 1.5, 95% CI 1.2–1.8), and wave three (aHR 1.7, 95% CI 1.5–2.0). Compared to non-Hispanic Whites, non-Hispanic Blacks were more likely to die in each wave: Wave one (aHR 1.4, 95% CI 1.1–1.8), wave two (aHR 1.5, 95% CI 1.2–1.9), and wave three (aHR 1.7, 95% CI 1.4–2.0). Cases with any disability, chronic renal disease, and cardiovascular disease were more likely to die in each wave compared to those without these comorbidities. Conclusions Our study found gender and racial/ethnic disparities in COVID-19 mortality, and certain comorbidities associated with COVID-19 mortality. These factors have persisted throughout the COVID-19 pandemic waves, despite improvements in diagnosis and treatment.

Retrospective cohort study of COVID-19 among children in Fulton County, Georgia, March 2020-June 2021.

OBJECTIVE: To describe case rates, testing rates and percent positivity of COVID-19 among children aged 0-18 years by school-age grouping. DESIGN: We abstracted data from Georgia's State Electronic Notifiable Disease Surveillance System on all 10 437 laboratory-confirmed COVID-19 cases among children aged 0-18 years during 30 March 2020 to 6 June 2021. We examined case rates, testing rates and percent positivity by school-aged groupings, namely: preschool (0-4 years), elementary school (5-10 years), middle school (11-13 years), and high school (14-18 years) and compared these data among school-aged children with those in the adult population (19 years and older). SETTING: Fulton County, Georgia. MAIN OUTCOME MEASURES: COVID-19 case rates, testing rates and percent positivity. RESULTS: Over time, the proportion of paediatric cases rose substantially from 1.1% (April 2020) to 21.6% (April 2021) of all cases in the county. Age-specific case rates and test rates were consistently highest among high-school aged children. Test positivity was similar across school-age groups, with periods of higher positivity among high-school aged children. CONCLUSIONS: Low COVID-19 testing rates among children, especially early in the pandemic, likely underestimated the true burden of disease in this age group. Despite children having lower measured incidence of COVID-19, we found when broader community incidence increased, incidence also increased among all paediatric age groups. As the COVID-19 pandemic continues to evolve, it remains critical to continue learning about the incidence and transmissibility of COVID-19 in children.

TCA cycle remodeling drives proinflammatory signaling in humans with pulmonary tuberculosis.

The metabolic signaling pathways that drive pathologic tissue inflammation and damage in humans with pulmonary tuberculosis (TB) are not well understood. Using combined methods in plasma high-resolution metabolomics, lipidomics and cytokine profiling from a multicohort study of humans with pulmonary TB disease, we discovered that IL-1ß-mediated inflammatory signaling was closely associated with TCA cycle remodeling, characterized by accumulation of the proinflammatory metabolite succinate and decreased concentrations of the anti-inflammatory metabolite itaconate. This inflammatory metabolic response was particularly active in persons with multidrug-resistant (MDR)-TB that received at least 2 months of ineffective treatment and was only reversed after 1 year of appropriate anti-TB chemotherapy. Both succinate and IL-1ß were significantly associated with proinflammatory lipid signaling, including increases in the products of phospholipase A2, increased arachidonic acid formation, and metabolism of arachidonic acid to proinflammatory eicosanoids. Together, these results indicate that decreased itaconate and accumulation of succinate and other TCA cycle intermediates is associated with IL-1ß-mediated proinflammatory eicosanoid signaling in pulmonary TB disease. These findings support host metabolic remodeling as a key driver of pathologic inflammation in human TB disease.

Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.

BACKGROUND: Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data. METHODS: We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification. RESULTS: The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons. CONCLUSIONS: These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.

Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data.

Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.