Last data update: Apr 29, 2024. (Total: 46658 publications since 2009)
Records 1-6 (of 6 Records) |
Query Trace: Raz KM[original query] |
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Molecular surveillance for large outbreaks of tuberculosis in the United States, 2014-2018.
Raz KM , Talarico S , Althomsons SP , Kammerer JS , Cowan LS , Haddad MB , McDaniel CJ , Wortham JM , France AM , Powell KM , Posey JE , Silk BJ . Tuberculosis (Edinb) 2022 136 102232 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 Outbreaks in State Prisons, United States, 2011-2019.
Stewart RJ , Raz KM , Burns SP , Kammerer JS , Haddad MB , Silk BJ , Wortham JM . Am J Public Health 2022 112 (8) 1170-1179 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). |
Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.
Winglee K , McDaniel CJ , Linde L , Kammerer S , Cilnis M , Raz KM , Noboa W , Knorr J , Cowan L , Reynolds S , Posey J , Sullivan Meissner J , Poonja S , Shaw T , Talarico S , Silk BJ . Front Public Health 2021 9 667337 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. |
Coronavirus Disease 2019 Case Surveillance - United States, January 22-May 30, 2020.
Stokes EK , Zambrano LD , Anderson KN , Marder EP , Raz KM , El Burai Felix S , Tie Y , Fullerton KE . MMWR Morb Mortal Wkly Rep 2020 69 (24) 759-765 The coronavirus disease 2019 (COVID-19) pandemic resulted in 5,817,385 reported cases and 362,705 deaths worldwide through May, 30, 2020,(dagger) including 1,761,503 aggregated reported cases and 103,700 deaths in the United States.( section sign) 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,( paragraph sign) 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. |
Influence of county sampling on past estimates of latent tuberculosis infection prevalence
Haddad MB , Raz KM , Hill AN , Navin TR , Castro KG , Winston CA , Gandhi NR , Lash TL . Ann Am Thorac Soc 2019 16 (8) 1069-1071 The National Health and Nutrition Examination Survey (NHANES) has tested for Mycobacterium tuberculosis infection three times: in 1971–1972, 1999–2000, and 2011–2012. Based on tuberculin skin test results, the estimated national prevalence of latent tuberculosis infection (LTBI) among adults was 11–18% in 1971–1972 but has remained less than or equal to 6% in subsequent NHANES cycles (1–4). A single 2-year NHANES cycle is designed to produce accurate and stable estimates for conditions with at least 10% prevalence in the noninstitutionalized civilian U.S. population (5–7), suggesting that NHANES might no longer be as nationally representative for LTBI as it is for more common health conditions. Approximately 30 counties were selected for each 2-year cycle (5). We wished to examine whether persons in selected counties might have been systematically more or less likely to have a positive tuberculin skin test result than their counterparts in the approximately 3,100 counties that were not selected for NHANES participation. |
Simple estimates for local prevalence of latent tuberculosis infection, United States, 2011-2015
Haddad MB , Raz KM , Lash TL , Hill AN , Kammerer JS , Winston CA , Castro KG , Gandhi NR , Navin TR . Emerg Infect Dis 2018 24 (10) 1930-1933 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. |
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