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The extent to which semi-quantitative antibody levels confer protection against SARS-CoV-2 infection in populations with heterogenous immune histories is unclear. Two nested case-control studies were designed within the multisite HEROES/RECOVER prospective cohort of frontline workers to study the relationship between antibody levels and protection against first-time post-vaccination infection and reinfection with SARS-CoV-2 from December 2021 to January 2023. All participants submitted weekly nasal swabs for rRT-PCR testing and blood samples quarterly and following infection or vaccination. Cases of first-time post-vaccination infection following a third dose of monovalent (origin strain WA-1) mRNA vaccine (n = 613) and reinfection (n = 350) were 1:1 matched to controls based on timing of blood draw and other potential confounders. Conditional logistic regression models were fit to estimate infection risk reductions associated with 3-fold increases in end titers for receptor binding domain (RBD). In first-time post-vaccination and reinfection study samples, most were female (67%, 57%), non-Hispanic (82%, 68%), and without chronic conditions (65%, 65%). The odds of first-time post-vaccination infection were reduced by 21% (aOR = 0.79, 95% CI = [0.66–0.96]) for each 3-fold increase in RBD end titers. The odds of reinfection associated with a 3-fold increase in RBD end titers were reduced by 23% (aOR = 0.77, 95% CI = [0.65–0.92] for unvaccinated individuals and 58% (aOR = 0.42, 95% CI = [0.22–0.84]) for individuals with three mRNA vaccine doses following their first infection. Frontline workers with higher antibody levels following a third dose of mRNA COVID-19 vaccine were at reduced risk of SARS-CoV-2 during Omicron predominance. Among those with previous infections, the point estimates of risk reduction associated with antibody levels was greater for those with three vaccine doses compared to those who were unvaccinated.

Repeat photography offers distinctive insights into ecological change, with ground‐based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end‐to‐end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high‐resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root‐mean‐square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas. Repeat photography offers distinctive insights into ecological change, with ground‐based oblique photographs often predating early aerial images by decades. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end‐to‐end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. We provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to produce a landcover map from oblique photographs for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by measuring the distance between features identified on a subset of georeferenced images and their real‐world locations, achieving a mean displacement of 3.7 m. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method.

Protection from Covid-19 after Third Vaccine DoseIn a cohort of frontline health care workers, a third dose of an mRNA vaccine provided 91% protection against SARS-CoV-2 infection with the delta variant and 60% against the omicron variant.

The BNT162b2 (Pfizer-BioNTech) mRNA COVID-19 vaccine was recommended by CDC's Advisory Committee on Immunization Practices for persons aged 12-15 years (referred to as adolescents in this report) on May 12, 2021, and for children aged 5-11 years on November 2, 2021 (1-4). Real-world data on vaccine effectiveness (VE) in these age groups are needed, especially because when the B.1.1.529 (Omicron) variant became predominant in the United States in December 2021, early investigations of VE demonstrated a decline in protection against symptomatic infection for adolescents aged 12-15 years and adults* (5). The PROTECT prospective cohort of 1,364 children and adolescents aged 5-15 years was tested weekly for SARS-CoV-2, irrespective of symptoms, and upon COVID-19-associated illness during July 25, 2021-February 12, 2022. Among unvaccinated participants (i.e., those who had received no COVID-19 vaccine doses) with any laboratory-confirmed SARS-CoV-2 infection, those with B.1.617.2 (Delta) variant infections were more likely to report COVID-19 symptoms (66%) than were those with Omicron infections (49%). Among fully vaccinated children aged 5-11 years, VE against any symptomatic and asymptomatic Omicron infection 14-82 days (the longest interval after dose 2 in this age group) after receipt of dose 2 of the Pfizer-BioNTech vaccine was 31% (95% CI = 9%-48%), adjusted for sociodemographic characteristics, health information, frequency of social contact, mask use, location, and local virus circulation. Among adolescents aged 12-15 years, adjusted VE 14-149 days after dose 2 was 87% (95% CI = 49%-97%) against symptomatic and asymptomatic Delta infection and 59% (95% CI = 22%-79%) against Omicron infection. Fully vaccinated participants with Omicron infection spent an average of one half day less sick in bed than did unvaccinated participants with Omicron infection. All eligible children and adolescents should remain up to date with recommended COVID-19 vaccinations.

Background We sought to evaluate the impact of changes in estimates of COVID‐19 vaccine effectiveness on the incidence of laboratory‐confirmed infection among frontline workers at high risk for SARS‐CoV‐2. Methods We analyzed data from a prospective frontline worker cohort to estimate the incidence of COVID‐19 by month as well as the association of COVID‐19 vaccination, occupation, demographics, physical distancing, and mask use with infection risk. Participants completed baseline and quarterly surveys, and each week self‐collected mid‐turbinate nasal swabs and reported symptoms. Results Among 1018 unvaccinated and 3531 fully vaccinated workers, the monthly incidence of laboratory‐confirmed SARS‐CoV‐2 infection in January 2021 was 13.9 (95% confidence interval [CI]: 10.4–17.4), declining to 0.5 (95% CI ‐0.4‐1.4) per 1000 person‐weeks in June. By September 2021, when the Delta variant predominated, incidence had once again risen to 13.6 (95% CI 7.8–19.4) per 1000 person‐weeks. In contrast, there was no reportable incidence among fully vaccinated participants at the end of January 2021, and incidence remained low until September 2021 when it rose modestly to 4.1 (95% CI 1.9–3.8) per 1000. Below average facemask use was associated with a higher risk of infection for unvaccinated participants during exposure to persons who may have COVID‐19 and vaccinated participants during hours in the community. Conclusions COVID‐19 vaccination was significantly associated with a lower risk of SARS‐CoV‐2 infection despite Delta variant predominance. Our data demonstrate the added protective benefit of facemask use among both unvaccinated and vaccinated frontline workers.

BackgroundICD-10 codes provide limited information on the location of drowning deaths, particularly those occurring in natural water. Application of natural language processing (NLP) to literal text on death certificates can provide greater detail.MethodsRecords that include literal text were selected from 2011–2017 U.S. National Vital Statistics System mortality data using ICD-10 multiple cause codes W65-W74, V90, V92, X71, X92, Y21 and T75.1. SAS Contextual Analysis software was applied to literal text data from six fields on the U.S. standard death certificate to create a list of possible terms describing drowning locations. Terms were categorized as: Domestic (e.g., swimming pool, bathtub, spa), Non-Domestic Freshwater (e.g., lake, pond, river, reservoir, well), Saltwater (e.g., ocean), Other, and Unspecified. For Non-Domestic Freshwater, subcategories included Natural and Man-made.ResultsOf 26,638 drowning deaths, 32% were categorized to Domestic, 52% to Non-Domestic Freshwater, 10% to Saltwater, and 6% to Other or Unspecified. For Non-Domestic Freshwater, 90% occurred in natural settings and 10% involved a man-made source (e.g. well, reservoir, cistern, tank). Among the Natural Non-Domestic Freshwater drownings, about half (52%) occurred in still water (e.g., lake, pond, wetlands, marsh) and half (47%) occurred in moving water (e.g., river, creek, rapids, flooding).ConclusionApplication of NLP methodology can provide more detailed information on location of drowning deaths. This information can be used to target prevention efforts to high risk populations by location.Learning OutcomesTo learn about the application of NLP processing to death certificate literal text to identify and categorize locations of drowning deaths.

13-year-old [Namar] was born in Djibouti, Eritrea, to an unmarried French father and Somali mother. His father left when Namar was very young. Namar was brought by his mother to Hargeisa, Somalia, and placed in an orphanage shortly before her death from AIDS. He left the orphanage because of abuse and has spent most of his life living alone on the streets. Occasionally Namar achieves something of a self-- placement with a family, but all have ended in abuse. Because of his European looks, behaviours he has developed while on the street, and his illegitimate status his mother's clan has not welcomed him. I fact, their intolerance of him has resulted in occasional beatings. In order to survive. Namar acquires food, shelter, and safety through begging, selling a drug known as chat (qat), stealing, working the streets, and engaging in other activities that he is ashamed to relate.

Organizations value workplaces with positive environments that reward, challenge, and positive environments. Prior studies found that organizations desire employees who contribute beyond assigned duties and leaders who foster workplace collaboration. Previous quantitative research has not examined a correlation between leader Emotional Intelligence (EI), Organizational Citizenship Behaviors (OCB), and Organizational Commitment (OC). This quantitative correlational associative study aimed to determine if there is a correlation between leader EI, OCB, and OC in South Florida. The theoretical foundations included Goleman's (1995) EI Model, the OCB Model, and the Three-Component Models of Commitment (TCM), which includes Affective Commitment (AC), Continuance Commitment (CC), and Normative Commitment (NC). The study included four research questions to examine the correlation between leader EI and OCB, leader EI and AC, leader EI and CC, and leader EI and NC. Participants (N = 110) completed an online survey that included the Genos EI, the OCB Scale, and the TCM Survey. The findings from the Spearman correlation indicated no statistically significant correlation between leader EI and employee OCB (rs = .11, p = .24); a positive statistically significant correlation between leader EI and AC (rs = .48, p = .001); a negative statistically significant correlation between leader EI and CC (rs = -.24, p = .01); and a positive statistically significant correlation between leader EI and NC (rs = .25, p = .009).

In the United States, annual influenza vaccination is recommended for all persons aged ≥6 months. Using data from four vaccine effectiveness (VE) networks during the 2023-24 influenza season, interim influenza VE was estimated among patients aged ≥6 months with acute respiratory illness-associated medical encounters using a test-negative case-control study design. Among children and adolescents aged 6 months-17 years, VE against influenza-associated outpatient visits ranged from 59% to 67% and against influenza-associated hospitalization ranged from 52% to 61%. Among adults aged ≥18 years, VE against influenza-associated outpatient visits ranged from 33% to 49% and against hospitalization from 41% to 44%. VE against influenza A ranged from 46% to 59% for children and adolescents and from 27% to 46% for adults across settings. VE against influenza B ranged from 64% to 89% for pediatric patients in outpatient settings and from 60% to 78% for all adults across settings. These findings demonstrate that the 2023-24 seasonal influenza vaccine is effective at reducing the risk for medically attended influenza virus infection. CDC recommends that all persons aged ≥6 months who have not yet been vaccinated this season get vaccinated while influenza circulates locally.