IMPORTANCE: Increasing the understanding of vaccine effectiveness (VE) against levels of severe influenza in children could help increase uptake of influenza vaccination and strengthen vaccine policies globally. OBJECTIVE: To investigate VE in children by severity of influenza illness. DESIGN, SETTING, AND PARTICIPANTS: This case-control study with a test-negative design used data from 8 participating medical centers located in geographically different US states in the New Vaccine Surveillance Network from November 6, 2015, through April 8, 2020. Participants included children 6 months through 17 years of age who were hospitalized or presented to an emergency department (ED) with acute respiratory illness. EXPOSURES: Receipt of at least 1 dose of the current season's influenza vaccine. MAIN OUTCOMES AND MEASURES: Demographic and clinical characteristics of patients presenting to the hospital or ED with or without influenza were recorded and grouped by influenza vaccination status. Estimated VE against severe influenza illness was calculated using multiple measures to capture illness severity. Data were analyzed between June 1, 2022, and September 30, 2023. RESULTS: Among 15 728 children presenting for care with acute respiratory illness (8708 [55.4%] male; 13 450 [85.5%] 6 months to 8 years of age and 2278 [14.5%] 9-17 years of age), 2710 (17.2%) had positive influenza tests and 13 018 (82.8%) had negative influenza tests (controls). Of the influenza test-positive cases, 1676 children (61.8%) had an ED visit, 896 children (33.1%) required hospitalization for noncritical influenza, and 138 children (5.1%) required hospitalization for critical influenza. About half (7779 [49.5%]) of the children (both influenza test positive and test negative) were vaccinated. Receiving at least 1 influenza vaccine dose was estimated to have a VE of 55.7% (95% CI, 51.6%-59.6%) for preventing influenza-associated ED visits or hospitalizations among children of all ages. The estimated VE was similar across severity levels: 52.8% (95% CI, 46.6%-58.3%) for ED visits, 52.3% (95% CI, 44.8%-58.8%) for noncritical hospitalization, and 50.4% (95% CI, 29.7%-65.3%) for critical hospitalization. CONCLUSIONS AND RELEVANCE: Findings from this case-control study with a test-negative design involving children with a spectrum of influenza severity suggest that influenza vaccination protects children against all levels of severe influenza illness.

BACKGROUND: Previous investigations into clinical signs and symptoms associated with influenza types and subtypes have not definitively established differences in the clinical presentation or severity of influenza disease. METHODS: The study population included children 0 through 17 years old enrolled at 8 New Vaccine Surveillance Network sites between 2015 and 2020 who tested positive for influenza virus by molecular testing. Demographic and clinical data were collected for study participants via parent/guardian interview and medical chart review. Descriptive statistics were used to summarize demographic and clinical characteristics by influenza subtype. Multivariable logistic regression and Cox proportional hazard models were used to assess effects of age, sex, influenza subtype, and history of asthma on severity, including hospital admission, need for supplemental oxygen, and length of stay. RESULTS: Retractions, cyanosis, and need for supplemental oxygen were more frequently observed among patients with influenza A(H1N1)pdm09. Headaches and sore throat were more commonly reported among patients with influenza B. Children with influenza A(H1N1)pdm09 and children with asthma had significantly increased odds of hospital admission (adjusted odds ratio (AOR): 1.39, 95% CI: 1.14-1.69 and AOR: 2.14, 95% CI: 1.72-2.67, respectively). During admission, children with influenza A(H1N1)pdm09 had significantly increased use of supplemental oxygen compared to children with A(H3N2) (AOR: 0.60, 95% CI: 0.44-0.82) or B (AOR: 0.56, 95% CI: 0.41-0.76). CONCLUSIONS: Among children presenting to the emergency department and admitted to the hospital, influenza A(H1N1)pdm09 caused more severe disease compared to influenza A(H3N2) and influenza B. Asthma also contributed to severe influenza disease regardless of subtype.

Objectives. To compare the incidence, case-hospitalization rates, and vaccination rates of COVID-19 between people experiencing sheltered homelessness (PESH) and the broader community in Chicago, Illinois, and describe the impact of a whole community approach to disease mitigation during the public health emergency. Methods. Incidence of COVID-19 among PESH was compared with community-wide incidence using case-based surveillance data from March 1, 2020, to May 11, 2023. Seven-day rolling means of COVID-19 incidence were assessed for the overall study period and for each of 6 distinct waves of COVID-19 transmission. Results. A total of 774 009 cases of COVID-19 were detected: 2579 among PESH and 771 430 in the broader community. Incidence and hospitalization rates per 100 000 in PESH were more than 5 times higher (99.84 vs 13.94 and 16.88 vs 2.14) than the community at large in wave 1 (March 1, 2020-October 3, 2020). This difference decreased through wave 3 (March 7, 2021-June 26, 2021), with PESH having a lower incidence rate per 100 000 than the wider community (8.02 vs 13.03). Incidence and hospitalization of PESH rose again to rates higher than the broader community in waves 4 through 6 but never returned to wave 1 levels. Throughout the study period, COVID-19 incidence among PESH was 2.88 times higher than that of the community (70.90 vs 24.65), and hospitalization was 4.56 times higher among PESH (7.51 vs 1.65). Conclusions. Our findings suggest that whole-community approaches can minimize disparities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission between vulnerable populations and the broader community, and reinforce the benefits of a shared approach that include multiple partners when addressing public health emergencies in special populations. (Am J Public Health. Published online ahead of print August 28, 2024:e1-e9. https://doi.org/10.2105/AJPH.2024.307801).

Nebulizers are commonly employed for inhaled drug delivery. As they deliver medication through aerosol generation, clarification is needed on what constitutes safe aerosol delivery in infectious respiratory disease settings. The coronavirus disease 2019 pandemic highlighted the importance of understanding the safety and potential risks of aerosol-generating procedures. However, evidence supporting the increased risk of disease transmission with nebulized treatments is inconclusive, and inconsistent guidelines and differing opinions have left uncertainty regarding their use. Many clinicians opt for alternative devices, but this practice could negatively impact outcomes, especially for patients who may not derive full treatment benefit from hand-held inhalers. Therefore, it is prudent to develop strategies that can be used during nebulized treatment to minimize the emission of fugitive aerosols, these comprising bioaerosols exhaled by infected individuals and medical aerosols generated by the device that may also be contaminated. This is particularly relevant for patient care in the context of a highly transmissible virus. The COPD Foundation Nebulizer Consortium (CNC) was formed in 2020 to address uncertainties surrounding administration of nebulized medication. The CNC is an international, multidisciplinary collaboration of patient advocates, pulmonary physicians, critical care physicians, respiratory therapists, clinical scientists, and pharmacists from research centers, medical centers, professional societies, industry, and government agencies. The CNC developed this Expert Guidance to inform the safe use of nebulized therapies for patients and providers and to answer key questions surrounding medication delivery with nebulizers during pandemics or when exposure to common respiratory pathogens is anticipated. CNC members reviewed literature and guidelines regarding nebulization and developed two sets of guidance statements: one for the health care setting, and one for the home environment. Future studies need to explore the risk of disease transmission with fugitive aerosols associated with different nebulizer types in real patient-care situations and to evaluate the effectiveness of mitigation strategies.

Culture-independent diagnostic tests (CIDTs) are increasingly used for clinical diagnosis of gastrointestinal diseases such as salmonellosis, Shiga toxin-producing E. coli disease, and shigellosis because of their speed, convenience, and generally high-performance characteristics. These tests are also used to screen potentially infectious asymptomatic persons during outbreak investigations in sensitive settings such as childcare, food service, and healthcare. However, only limited performance data are available for CIDTs used on specimens from asymptomatic persons. The Association of Public Health Laboratories (APHL) and Council of State and Territorial Epidemiologists (CSTE) convened a workgroup to examine the available scientific data to inform interim decision-making related to exclusion and readmission criteria for potentially infectious persons in sensitive settings, the risks and benefits of different testing strategies, and to identify knowledge gaps for further research. This is the report on the Workgroup findings.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

Concurrent separate human outbreaks of Salmonella enterica serotype Reading occurred in 2017-2019 in the United States and Canada, which were both linked to the consumption of raw turkey products. In this study, a comprehensive genomic investigation was conducted to reconstruct the evolutionary history of S. Reading from turkeys, and to determine the genomic context of outbreaks involving this rarely isolated Salmonella serotype. A total of 988 isolates of U.S. origin were examined using whole genome-based approaches, including current and historical isolates from humans, meat, and live food animals. Broadly, isolates clustered into three major clades, with one apparently highly adapted turkey clade. Within the turkey clade isolates clustered into three subclades, including an “emergent” clade that only contained isolates dated 2016 or later, including many of the isolates from these outbreaks. Genomic differences were identified between emergent and other turkey subclades suggesting that the apparent success of currently circulating subclades clade is, in part, attributable to plasmid acquisitions conferring antimicrobial resistance, gain of phage-like sequences with cargo virulence factors, and mutations in systems that may be involved in beta-glucuronidase activity and resistance towards colicins. U.S. and Canadian outbreak isolates were found interspersed throughout the emergent subclade and the other circulating subclade. The emergence of a novel S. Reading turkey subclade, coinciding temporally with expansion in commercial turkey production and with U.S. and Canadian human outbreaks, indicates that emergent strains with higher potential for niche success were likely vertically transferred and rapidly disseminated from a common source.Importance Increasingly, outbreak investigations involving foodborne pathogens are confounded by the inter-connectedness of food animal production and distribution, necessitating high-resolution genomic investigations to determine their basis. Fortunately, surveillance and whole genome sequencing, combined with the public availability of these data, enable comprehensive queries to determine underlying causes of such outbreaks. Utilizing this pipeline, it was determined that a novel clone of Salmonella Reading has emerged that coincides with increased abundance in raw turkey products and two outbreaks of human illness in North America. The rapid dissemination of this highly adapted and conserved clone indicates that it was likely obtained from a common source and rapidly disseminated across turkey production. Key genomic changes may have contributed to its apparent continued success in the barn environment, and ability to cause illness in humans.

BACKGROUND In February 2021 Kazakhstan began offering COVID-19 vaccines to adults. Breakthrough SARS-CoV-2 infections raised concerns about real-world vaccine effectiveness. We aimed to evaluate effectiveness of four vaccines against SARS-CoV-2 infection. METHODS We conducted a retrospective cohort analysis among adults in Almaty using aggregated vaccination data and individual-level breakthrough COVID-19 cases (>=14 days from 2nd dose) using national surveillance data. We ran time-adjusted Cox-proportional-hazards model with sensitivity analysis accounting for varying entry into vaccinated cohort to assess vaccine effectiveness for each vaccine (measured as 1-adjusted hazard ratios) using the unvaccinated population as reference (N=565,390). We separately calculated daily cumulative hazards for COVID-19 breakthrough among vaccinated persons by age and vaccine month. RESULTS From February 22 to Sept 1, 2021 in Almaty, 747,558 (57%) adults were fully vaccinated (received 2 doses) and 108,324 COVID-19 cases (11,472 breakthrough) were registered. Vaccine effectiveness against infection was 78% (sensitivity estimates: 74-82%) for QazVac, 77% (72-81%) for Sputnik V, 71% (69-72%) for Hayat-Vax, and 69% (64-72%) for CoronaVac. Among vaccinated persons, the 90-day follow-up cumulative hazard for breakthrough infection was 2.2%. Cumulative hazard was 2.9% among people aged >=60 years versus 1.9% among persons aged 18-39 years (p<0.001), and 1.2% for people vaccinated in February-May versus 3.3% in June-August (p<0.001). CONCLUSION Our analysis demonstrates high effectiveness of COVID-19 vaccines against infection in Almaty similar to other observational studies. Higher cumulative hazard of breakthrough among people >60 years of age and during variant surges warrants targeted booster vaccination campaigns. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.

OBJECTIVES: Studies on excess deaths (ED) show that reported deaths from COVID-19 underestimate death. To understand mortality for improved pandemic preparedness, we estimated ED directly and indirectly attributable to COVID-19 and ED by age groups. DESIGN: Cross-sectional study using routinely reported individual deaths data. SETTINGS: The 21 health facilities in Bishkek that register all city deaths. PARTICIPANTS: Residents of Bishkek who died in the city from 2015 to 2020. OUTCOME MEASURE: We report weekly and cumulative ED by age, sex and causes of death for 2020. EDs are the difference between observed and expected deaths. Expected deaths were calculated using the historical average and the upper bound of the 95% CI from 2015 to 2019. We calculated the percentage of deaths above expected using the upper bound of the 95% CI of expected deaths. COVID-19 deaths were laboratory confirmed (U07.1) or probable (U07.2 or unspecified pneumonia). RESULTS: Of 4660 deaths in 2020, we estimated 840-1042 ED (79-98 ED per 100 000 people). Deaths were 22% greater than expected. EDs were greater for men (28%) than for women (20%). EDs were observed in all age groups, with the highest ED (43%) among people 65-74 years of age. Hospital deaths were 45% higher than expected. During peak mortality (1 July -21 July), weekly ED was 267% above expected, and ED by disease-specific cause of death were above expected: 193% for ischaemic heart diseases, 52% for cerebrovascular diseases and 421% for lower respiratory diseases. COVID-19 was directly attributable to 69% of ED. CONCLUSION: Deaths directly and indirectly associated with the COVID-19 pandemic were markedly higher than reported, especially for older populations, in hospital settings, and during peak weeks of SARS-CoV-2 transmission. These ED estimates can support efforts to prioritise support for persons at greatest risk of dying during surges.

BACKGROUND: In February 2021 Kazakhstan began offering COVID-19 vaccines to adults. Breakthrough SARS-CoV-2 infections raised concerns about real-world vaccine effectiveness. We aimed to evaluate effectiveness of four vaccines against SARS-CoV-2 infection. METHODS: We conducted a retrospective cohort analysis among adults in Almaty using aggregated vaccination data and individual-level breakthrough COVID-19 cases (≥14 days from 2nd dose) using national surveillance data. We ran time-adjusted Cox-proportional-hazards model with sensitivity analysis accounting for varying entry into vaccinated cohort to assess vaccine effectiveness for each vaccine (measured as 1-adjusted hazard ratios) using the unvaccinated population as reference (N = 565,390). We separately calculated daily cumulative hazards for COVID-19 breakthrough among vaccinated persons by age and vaccination month. RESULTS: From February 22 to September 1, 2021, in Almaty, 747,558 (57%) adults were fully vaccinated (received 2 doses), and 108,324 COVID-19 cases (11,472 breakthrough) were registered. Vaccine effectiveness against infection was 79% [sensitivity estimates (SE): 74%-82%] for QazVac, 77% (SE: 71%-81%) for Sputnik V, 71% (SE: 69%-72%) for Hayat-Vax, and 70% (SE: 65%-72%) for CoronaVac. Among vaccinated persons, the 90-day follow-up cumulative hazard for breakthrough infection was 2.2%. Cumulative hazard was 2.9% among people aged ≥60 years versus 1.9% among persons aged 18-39 years (p < 0.001), and 1.2% for people vaccinated in February-May versus 3.3% in June-August (p < 0.001). CONCLUSION: Our analysis demonstrates high effectiveness of COVID-19 vaccines against infection in Almaty similar to other observational studies. Higher cumulative hazard of breakthrough among people ≥60 years of age and during variant surges warrants targeted booster vaccination campaigns.

OBJECTIVES: To identify the most important reasons underlying decisions to stock or not stock adult vaccines. STUDY DESIGN: US physicians, nurses, pharmacists, and administrators of internal medicine, family medicine, obstetrics/gynecology, and multispecialty practices who were involved in vaccine stocking decisions (N = 125) completed a best-worst scaling survey online between February and April 2018. METHODS: Sixteen potential factors influencing stocking decisions were developed based on key informant interviews and focus groups. Respondents selected factors that were most and least important in vaccine stocking decisions. Relative importance scores for the best-worst scaling factors were calculated. Survey respondents described which vaccines their practice stocks and reasons for not stocking specific vaccines. Subgroup analyses were performed based on the respondent's involvement in vaccine decision making, role in the organization, specialty, and affiliation status, as well as practice characteristics such as practice size, insurance mix, and patient age mix. RESULTS: Relative importance scores for stocking vaccines were highest for "cost of purchasing vaccine stock," "expense of maintaining vaccine inventory," and "lack of adequate reimbursement for vaccine acquisition and administration." Most respondents (97%) stocked influenza vaccines, but stocking rates of other vaccines varied from 39% (meningococcal B) to 83% (tetanus-diphtheria-pertussis). Best-worst scaling results were consistent across respondent subgroups, although the range of vaccine types stocked differed by practice type. CONCLUSIONS: Economic factors associated with the purchase and maintenance of vaccine inventory and inadequate reimbursement for vaccination services were the most important to decision makers when considering whether to stock or not stock vaccines for adults.

OBJECTIVES: Autism spectrum disorder (autism) is a heterogeneous condition that poses challenges in describing the needs of individuals with autism and making prognoses about future outcomes. We applied a newly proposed definition of profound autism to surveillance data to estimate the percentage of children with autism who have profound autism and describe their sociodemographic and clinical characteristics. METHODS: We analyzed population-based surveillance data from the Autism and Developmental Disabilities Monitoring Network for 20 135 children aged 8 years with autism during 2000-2016. Children were classified as having profound autism if they were nonverbal, were minimally verbal, or had an intelligence quotient <50. RESULTS: The percentage of 8-year-old children with profound autism among those with autism was 26.7%. Compared with children with non-profound autism, children with profound autism were more likely to be female, from racial and ethnic minority groups, of low socioeconomic status, born preterm or with low birth weight; have self-injurious behaviors; have seizure disorders; and have lower adaptive scores. In 2016, the prevalence of profound autism was 4.6 per 1000 8-year-olds. The prevalence ratio (PR) of profound autism was higher among non-Hispanic Asian/Native Hawaiian/Other Pacific Islander (PR = 1.55; 95 CI, 1.38-1.73), non-Hispanic Black (PR = 1.76; 95% CI, 1.67-1.86), and Hispanic (PR = 1.50; 95% CI, 0.88-1.26) children than among non-Hispanic White children. CONCLUSIONS: As the population of children with autism continues to change, describing and quantifying the population with profound autism is important for planning. Policies and programs could consider the needs of people with profound autism across the life span to ensure their needs are met.

In newborn screening, false-negative results can be disastrous, leading to disability and death, while false-positive results contribute to parental anxiety and unnecessary follow-ups. Cutoffs are set conservatively to prevent missed cases for Pompe and MPS I, resulting in increased falsepositive results and lower positive predictive values. Harmonization has been proposed as a way to minimize false-negative and false-positive results and correct for method differences, so we harmonized enzyme activities for Pompe and MPS I across laboratories and testing methods (Tandem Mass Spectrometry (MS/MS) or Digital Microfluidics (DMF)). Participating states analyzed proofof- concept calibrators, blanks, and contrived specimens and reported enzyme activities, cutoffs, and other testing parameters to Tennessee. Regression and multiples of the median were used to harmonize the data. We observed varied cutoffs and results. Six of seven MS/MS labs reported enzyme activities for one specimen for MPS I marginally above their respective cutoffs with results classified as negative, whereas all DMF labs reported this specimen's enzyme activity below their respective cutoffs with results classified as positive. Reasonable agreement in enzyme activities and cutoffs was achieved with harmonization; however, harmonization does not change how a value would be reported as this is dependent on the placement of cutoffs.

We conducted an outbreak investigation from June 3 to 15th in a rural village in northern Kazakhstan, after surveillance showed an increase in gastroenteritis. Cases were residents who presented for medical treatment for diarrhea, fever (>37.5 degrees C), vomiting, or weakness from May 14 to June 15, 2021. Controls were residents matched by age +or-2 years at a ratio of two controls for every case. Cases and controls were interviewed using structured questionnaires. We abstracted clinical data from medical records. We mapped cases and assessed risk for disease using conditional multivariable logistic regression. We identified 154 cases of acute gastroenteritis (attack rate of ~26 per 1,000 inhabitants). Symptoms were diarrhea, fever, vomiting, weakness, and decreased appetite. Among cases that participated (n = 107), 74% reported having drank unboiled tap water vs 18% of controls (n = 219). This was the only risk factor associated with disease (adjusted odds ratio: 18; 95% CI 9-35). Drinking water from a dispenser or carbonated drinks was protective. The city has two water supply networks; cases were clustered (107 cases in 79 households) in one. The investigation found that monitoring of quality and safety of water according to national regulations had not been conducted since 2018. No fatalities occurred, and no associated cases were reported after our investigation. Results suggest that untreated tap water was the probable source of the outbreak. The water supply had been cleaned and disinfected twice by the facility 2 days before our investigation began. Recommendations were made for regular monitoring of water supply facilities with rapid public notification when issues are detected to reduce likelihood of future drinking water associated outbreaks.

BACKGROUND: Adult studies have demonstrated within-season declines in influenza vaccine effectiveness (VE); data in children are limited. METHODS: We conducted a prospective, test-negative study of children 6 months-17 years hospitalized with acute respiratory illness at 7 pediatric medical centers during the 2015-2016 through 2019-2020 influenza seasons. Case-patients were children with an influenza-positive molecular test matched by illness onset to influenza-negative control-patients. We estimated VE [100% x (1 - odds ratio)] by comparing the odds of receipt of ≥1 dose of influenza vaccine ≥14 days before illness onset among influenza-positive children to influenza-negative children. Changes in VE over time between vaccination date and illness onset date were estimated using multivariable logistic regression. RESULTS: Of 8,430 children, 4,653 (55%) received ≥1 dose of influenza vaccine. On average, 48% were vaccinated through October and 85% through December each season. Influenza vaccine receipt was lower in case-patients than control-patients (39% vs. 57%, p < 0.001); overall VE against hospitalization was 53% (95% CI: 46%-60%). Pooling data across 5 seasons, the odds of influenza-associated hospitalization increased 4.2% (-3.2%-12.2%) per month since vaccination, with an average VE decrease of 1.9% per month (n = 4,000, p = 0.275). Odds of hospitalization increased 2.9% (95% CI: -5.4%-11.8%) and 9.6% (95% CI: -7.0%-29.1%) per month in children ≤8 years (n = 3,084) and 9-17 years (n = 916), respectively. These findings were not statistically significant. CONCLUSIONS: We observed minimal, not statistically significant within-season declines in VE. Vaccination following current ACIP guidelines for timing of vaccine receipt remains the best strategy for preventing influenza-associated hospitalizations in children.

Crimean-Congo hemorrhagic fever (CCHF) is a highly fatal zoonotic disease endemic to Kazakhstan. Previous work estimated the seroprevalence of CCHF virus (CCHFV) among livestock owners in the Zhambyl region of southern Kazakhstan at 1.2%. To estimate CCHFV seroprevalence among cattle and sheep, we selected 15 villages with known history of CCHFV circulation (endemic) and 15 villages without known circulation (nonendemic) by cluster sampling with probability proportional to livestock population size. We collected whole blood samples from 521 sheep and 454 cattle from randomly selected households within each village and collected ticks found on the animals. We tested livestock blood for CCHFV-specific IgG antibodies by ELISA; ticks were screened for CCHFV RNA by real-time reverse transcription polymerase chain reaction and CCHFV antigen by antigen-capture ELISA. We administered questionnaires covering animal demographics and livestock herd characteristics to an adult in each selected household. Overall weighted seroprevalence was 5.7% (95% CI: 3.1, 10.3) among sheep and 22.5% (95% CI: 15.8, 31.2) among cattle. CCHFV-positive tick pools were found on two sheep (2.4%, 95% CI: 0.6, 9.5) and three cattle (3.8%, 95% CI: 1.2, 11.5); three CCHFV-positive tick pools were found in nonendemic villages. Endemic villages reported higher seroprevalence among sheep (15.5% versus 2.8%, P < 0.001) but not cattle (25.9% versus 20.1%, P = 0.42). Findings suggest that the current village classification scheme may not reflect the geographic distribution of CCHFV in Zhambyl and underscore that public health measures must address the risk of CCHF even in areas without a known history of circulation.

From March to May 2020, 1306 oilfield workers in Kazakhstan tested positive for SARS-CoV-2. We conducted a case-control study to assess factors associated with SARS-CoV-2 transmission. The cases were PCR-positive for SARS-CoV-2 during June-September 2020. Controls lived at the same camp and were randomly selected from the workers who were PCR-negative for SARS-CoV-2. Data was collected telephonically by interviewing the oil workers. The study had 296 cases and 536 controls with 627 (75%) men, and 527 (63%) were below 40 years of age. Individual factors were the main drivers of transmission, with little contribution by environmental factors. Of the twenty individual factors, rare hand sanitizer use, travel before shift work, and social interactions outside of work increased SARS-CoV-2 transmission. Of the twenty-two environmental factors, only working in air-conditioned spaces was associated with SARS-CoV-2 transmission. Communication messages may enhance workers' individual responsibility and responsibility for the safety of others to reduce SARS-CoV-2 transmission.

BACKGROUND: Antiviral treatment is recommended for all hospitalized children with suspected or confirmed influenza, regardless of their risk profile. Few data exist on adherence to these recommendations, so we sought to determine factors associated with influenza testing and antiviral treatment in children. METHODS: Hospitalized children <18 years of age with acute respiratory illness (ARI) were enrolled through active surveillance at pediatric medical centers in seven cities between 11/1/2015 and 6/30/2016; clinical information was obtained from parent interview and chart review. We used generalized linear mixed-effects models to identify factors associated with influenza testing and antiviral treatment. RESULTS: Of the 2299 hospitalized children with ARI enrolled during one influenza season, 51% (n = 1183) were tested clinically for influenza. Clinicians provided antiviral treatment for 61 of 117 (52%) patients with a positive influenza test versus 66 of 1066 (6%) with a negative or unknown test result. In multivariable analyses, factors associated with testing included neuromuscular disease (aOR = 5.35, 95% CI [3.58-8.01]), immunocompromised status (aOR = 2.88, 95% CI [1.66-5.01]), age (aOR = 0.93, 95% CI [0.91-0.96]), private only versus public only insurance (aOR = 0.78, 95% CI [0.63-0.98]), and chronic lung disease (aOR = 0.64, 95% CI [0.51-0.81]). Factors associated with antiviral treatment included neuromuscular disease (aOR = 1.86, 95% CI [1.04, 3.31]), immunocompromised state (aOR = 2.63, 95% CI [1.38, 4.99]), duration of illness (aOR = 0.92, 95% CI [0.84, 0.99]), and chronic lung disease (aOR = 0.60, 95% CI [0.38, 0.95]). CONCLUSION: Approximately half of children hospitalized with influenza during the 2015-2016 influenza season were treated with antivirals. Because antiviral treatment for influenza is associated with better health outcomes, further studies of subsequent seasons would help evaluate current use of antivirals among children and better understand barriers for treatment.

At nine US hospitals that enrolled children hospitalized with acute respiratory illness (ARI) during 2015-2016 through 2017-2018 influenza seasons, 50% of children with ARI received clinician-initiated testing for influenza and 35% of cases went undiagnosed due to lack of clinician-initiated testing. Marked heterogeneity in testing practice was observed across sites.

The article focuses on the Environmental Health and Land Reuse (EHLR) Certificate Program, an initiative that aims to increase knowledge about the danger of brownfield sites. Topics include the partnership of Agency for Toxic Substances and Disease Registry (ATSDR) with stakeholders throughout the Navajo Nation, the environmental health and land reuse training under the Summer Internship Program (SIP), and the challenges brought by COVID-19 pandemic in implementing the virtual SIP.

Encephalitis and meningitis (EM) are severe infections of the central nervous system associated with high morbidity and mortality. The etiology of EM in Kazakhstan is not clearly defined, so from February 1, 2017 to January 31, 2018 we conducted hospital-based syndromic surveillance for EM at the Shymkent City Hospital, in the South Kazakhstan region. All consenting inpatients meeting a standard case definition were enrolled. Blood and cerebrospinal fluid (CSF) samples were collected for bacterial culture, and CSF samples were additionally tested by PCR for four bacterial species and three viruses using a cascading algorithm. We enrolled 556 patients. Of these, 494 were of viral etiology (including 4 probable rabies cases), 37 were of bacterial etiology, 19 were of unknown etiology and 6 were not tested. The most commonly identified pathogens included enterovirus (73%, n = 406 cases), herpes simplex virus (12.8%, n = 71), and Neisseria meningitidis (3.8%, n = 21). The incidence rates (IRs) for enteroviral and meningococcal EM were found to be 14.5 and 0.7 per 100,000 persons, respectively. The IR for bacterial EM using both PCR and culture results was 3-5 times higher compared to culture-only results. Antibacterial medicines were used to treat 97.2% (480/494) of virus-associated EM. Incorporation of PCR into routine laboratory diagnostics of EM improves diagnosis, pathogen identification, ensures IRs are not underestimated, and can help avoid unnecessary antibacterial treatment.

BACKGROUND: For HIV-exposed infants in Malawi, there are missed opportunities at each step of the testing and treatment cascade. OBJECTIVE: This study assessed factors associated with HIV positivity among infants in Malawi and turn-around times for infant HIV testing. METHODS: HIV testing data for infants aged 0-18 months from 2012 to 2015 were extracted from the Malawi HIV laboratory information management system and analysed using logistic regression. Turn-around time was defined as time between collection of samples to results dispatch from the laboratory. RESULTS: A total of 106 997 tests were included in the analyses. A subset of 76 006 observations with complete dates were included in the turn-around time analysis. Overall positivity was 4.2%. Factors associated with positivity were increasing age (infants aged 3-6 months: adjusted odds ratio [aOR] = 2.24; infants aged 6-9 months: aOR = 3.42; infants aged > 9 months: aOR = 4.24), female sex (aOR = 1.08) and whether the mother was alive and not on antiretroviral therapy at time of the infant's test (aOR = 1.57). Provision of HIV prophylaxis to the infant after birth (aOR = 0.38) was found to be protective against HIV positivity. The median turn-around time was 24 days (increased from 19 to 34 days between 2012 and 2015). CONCLUSION: Infant HIV positivity has decreased in Malawi, whereas turn-around time has increased. Factors associated with positivity include increasing age, female sex, and whether the mother was alive and not on antiretroviral therapy at the time of the infant's test.

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.

BACKGROUND: Parent-reported influenza vaccination history may be valuable clinically and in influenza vaccine effectiveness (VE) studies. Few studies have assessed the validity of parental report among hospitalized children. METHODS: Parents of 2597 hospitalized children 6 months-17 years old were interviewed from November 1, 2015 to June 30, 2016, regarding their child's sociodemographic and influenza vaccination history. Parent-reported 2015-2016 influenza vaccination history was compared with documented vaccination records (considered the gold standard for analysis) obtained from medical records, immunization information systems, and providers. Multivariable logistic regression analyses were conducted to determine potential factors associated with discordance between the 2 sources of vaccination history. Using a test-negative design, we estimated VE using vaccination history obtained through parental report and documented records. RESULTS: According to parental report, 1718 (66%) children received the 2015-2016 influenza vaccine, and of those, 1432 (83%) had documentation of vaccine receipt. Percent agreement was 87%, with a sensitivity of 96% (95% confidence interval [CI], 95%-97%) and a specificity of 74% (95% CI, 72%-77%). In the multivariable logistic regression, study site and child's age 5-8 years were significant predictors of discordance. Adjusted VE among children who received ≥1 dose of the 2015-2016 influenza vaccine per parental report was 61% (95% CI, 43%-74%), whereas VE using documented records was 55% (95% CI, 33%-69%). CONCLUSIONS: Parental report of influenza vaccination was sensitive but not as specific compared with documented records. However, VE against influenza-associated hospitalizations using either source of vaccination history did not differ substantially. Parental report is valuable for timely influenza VE studies.

BACKGROUND: Respiratory syncytial virus (RSV) is a major cause of hospitalized acute respiratory illness (ARI) among young children. With RSV vaccines and immunoprophylaxis agents in clinical development, we sought to update estimates of US pediatric RSV hospitalization burden. METHODS: Children <5 years old hospitalized for ARI were enrolled through active, prospective, population-based surveillance from November 1, 2015, to June 30, 2016, at 7 US pediatric hospital sites. Clinical information was obtained from parent interviews and medical records. Midturbinate nasal and throat flocked swabs were collected and tested for RSV by using molecular diagnostic assays at each site. We conducted descriptive analyses and calculated population-based rates of RSV-associated hospitalizations. RESULTS: Among 2969 hospitalized children included in analyses, 1043 (35%) tested RSV-positive; 903 (87%) children who were RSV-positive were <2 years old, and 526 (50%) were <6 months old. RSV-associated hospitalization rates were 2.9 per 1000 children <5 years old and 14.7 per 1000 children <6 months old; the highest age-specific rate was observed in 1-month-old infants (25.1 per 1000). Most children who were infected with RSV (67%) had no underlying comorbid conditions and no history of preterm birth. CONCLUSIONS: During the 2015-2016 season, RSV infection was associated with one-third of ARI hospitalizations in our study population of young children. Hospitalization rates were highest in infants <6 months. Most children who were RSV-positive had no history of prematurity or underlying medical conditions, suggesting that all young children could benefit from targeted interventions against RSV.

Two separate human outbreaks of Salmonella enterica serotype Reading occurred between 2017 and 2019 in the United States and Canada, and both outbreaks were linked to the consumption of raw turkey products. In this study, a comprehensive genomic investigation was conducted to reconstruct the evolutionary history of S. Reading from turkeys and to determine the genomic context of outbreaks involving this infrequently isolated Salmonella serotype. A total of 988 isolates of U.S. origin were examined using whole-genome-based approaches, including current and historical isolates from humans, meat, and live food animals. Broadly, isolates clustered into three major clades, with one apparently highly adapted turkey clade. Within the turkey clade, isolates clustered into three subclades, including an "emergent" clade that contained only isolates dated 2016 or later, with many of the isolates from these outbreaks. Genomic differences were identified between emergent and other turkey subclades, suggesting that the apparent success of currently circulating subclades is, in part, attributable to plasmid acquisitions conferring antimicrobial resistance, gain of phage-like sequences with cargo virulence factors, and mutations in systems that may be involved in beta-glucuronidase activity and resistance towards colicins. U.S. and Canadian outbreak isolates were found interspersed throughout the emergent subclade and the other circulating subclade. The emergence of a novel S Reading turkey subclade, coinciding temporally with expansion in commercial turkey production and with U.S. and Canadian human outbreaks, indicates that emergent strains with higher potential for niche success were likely vertically transferred and rapidly disseminated from a common source.IMPORTANCE Increasingly, outbreak investigations involving foodborne pathogens are difficult due to the interconnectedness of food animal production and distribution, and homogeneous nature of industry integration, necessitating high-resolution genomic investigations to determine their basis. Fortunately, surveillance and whole-genome sequencing, combined with the public availability of these data, enable comprehensive queries to determine underlying causes of such outbreaks. Utilizing this pipeline, it was determined that a novel clone of Salmonella Reading has emerged that coincided with increased abundance in raw turkey products and two outbreaks of human illness in North America. The rapid dissemination of this highly adapted and conserved clone indicates that it was likely obtained from a common source and rapidly disseminated across turkey production. Key genomic changes may have contributed to its apparent continued success in commercial turkeys and ability to cause illness in humans.

BACKGROUND: Annual United States (US) estimates of influenza vaccine effectiveness (VE) in children typically measure protection against outpatient medically attended influenza illness, with limited data evaluating VE against influenza hospitalizations. We estimated VE for preventing laboratory-confirmed influenza hospitalization among US children. METHODS: We included children aged 6 months-17 years with acute respiratory illness enrolled in the New Vaccine Surveillance Network during the 2015-2016 influenza season. Documented influenza vaccination status was obtained from state immunization information systems, the electronic medical record, and/or provider records. Midturbinate nasal and throat swabs were tested for influenza using molecular assays. We estimated VE as 100% x (1 - odds ratio), comparing the odds of vaccination among subjects testing influenza positive with subjects testing negative, using multivariable logistic regression. RESULTS: Of 1653 participants, 36 of 707 (5%) of those fully vaccinated, 18 of 226 (8%) of those partially vaccinated, and 85 of 720 (12%) of unvaccinated children tested positive for influenza. Of those vaccinated, almost 90% were documented to have received inactivated vaccine. The majority (81%) of influenza cases were in children </= 8 years of age. Of the 139 influenza-positive cases, 42% were A(H1N1)pdm09, 42% were B viruses, and 14% were A(H3N2). Overall, adjusted VE for fully vaccinated children was 56% (95% confidence interval [CI], 34%-71%) against any influenza-associated hospitalization, 68% (95% CI, 36%-84%) for A(H1N1)pdm09, and 44% (95% CI, -1% to 69%) for B viruses. CONCLUSIONS: These findings demonstrate the importance of annual influenza vaccination in prevention of severe influenza disease and of reducing the number of children who remain unvaccinated or partially vaccinated against influenza.

From Rudolf Virchow's groundbreaking investigation of typhus among coal miners in 1848 (Brown and Fee 2006) through the World Health Organization's adoption of the social determinants of health (WHO 2008) paradigm in 2008, the relationship between work and health has been fundamental to the development of a social approach to health and well-being found in anthropology today (Abrams 2001; Brown and Fee 2006; Farmer et al, 2006; Singer et al. 1992). Concurrently, the working conditions of im/migrants figured prominently in the early studies and events establishing occupational health as a field in the United States (Abrams 2001). A historic turning point in the effort to securing safer working conditions in the United States occured in New York City in 1911 when im/migrant women made up the majority of workers killed in the fire Triangle Shirtwaist Company. In the same time period, Dr. Alice Hamilton, the founder of occupational medicine in the United States, spent much of her early career studying and treating work-related diseases and conditions of im/migrant workers she met through her association with Jane Addams at the Hull-House in Chicago (Abrams 2001).

Antibiotic resistance is a global concern, although it has been studied most extensively in developed countries. We studied Escherichia coli and class 1 integrons in western Uganda by analyzing 1,685 isolates from people, domestic animals, and wild non-human primates near two national parks. Overall, 499 isolates (29.6%) were resistant to at least one of 11 antibiotic tested. The frequency of resistance reached 20.3% of isolates for trimethoprim/sulfamethoxazole but was nearly zero for the less commonly available antibiotics ciprofloxacin (0.4%), gentamicin (0.2%) and ceftiofur (0.1%). The frequency of resistance was 57.4% in isolates from people, 19.5% in isolates from domestic animals, and 16.3% in isolates from wild non-human primates. Isolates of livestock and primate origin displayed multidrug resistance patterns identical to those of human-origin isolates. The percentage of resistant isolates in people was higher near Kibale National Park (64.3%) than near Bwindi Impenetrable National Park (34.6%), perhaps reflecting local socioeconomic or ecological conditions. Across antibiotics, resistance correlated negatively with the local price of the antibiotic, with the most expensive antibiotics (nalidixic acid and ciprofloxacin) showing near-zero resistance. Among phenotypically resistant isolates, 33.2% harbored class 1 integrons containing 11 common resistance genes arranged into nine distinct gene cassettes, five of which were present in isolates from multiple host species. Overall, these results show that phenotypic resistance and class 1 integrons are distributed broadly among E. coli isolates from different host species in this region, where local socioeconomic and ecological conditions may facilitate widespread diffusion of bacteria or resistance-conferring genetic elements.Importance Antibiotic resistance is a global problem. This study, conducted in rural western Uganda, describes antibiotic resistance patterns in E. coli bacteria near two forested national parks. Resistance was present not only in people, but also in their livestock and in wild non-human primates nearby. Multidrug resistance and class 1 integrons containing genes that confer resistance were common and similar in people and animals. The percent of resistant isolates decreased with increasing local price of the antibiotic. Antibiotic resistance in this setting likely reflects environmental diffusion of bacteria or their genes, perhaps facilitated by local ecological and socioeconomic conditions.