Background: An infant's presentation at delivery may be an early indicator of developmental differences. Non-vertex presentation (malpresentation) complicates delivery and often leads to caesarean section, which has been associated with neurodevelopmental delays, including autism spectrum disorder (ASD). However, malpresentation could be an early sign of an existing developmental problem that is also an upstream factor from caesarean delivery. Little research has been done to investigate the association between malpresentation and ASD. Objectives: We examine the association between malpresentation at delivery and ASD and whether this association differs by gestational age. Methods: We used data from the Study to Explore Early Development (SEED), a multi-site, case–control study of children with ASD compared to population controls. The foetal presentation was determined using medical records, birth records and maternal interviews. We defined malpresentation as a non-vertex presentation at delivery, then further categorised into breech and other malpresentation. We used multivariable logistic regression to estimate the adjusted odds ratio (aOR) for the association between malpresentation and ASD. Results: We included 4047 SEED participants, 1873 children with ASD and 2174 controls. At delivery, most infants presented vertex (n = 3760, 92.9%). Malpresentation was associated with higher odds of ASD (aOR 1.31, 95% confidence interval [CI] 1.02, 1.68) after adjustment for maternal age, poverty level, hypertensive disorder and smoking. The association was similar for breech and other types of malpresentation (aOR 1.28, 95% CI 0.97, 1.70 and aOR 1.40, 95% CI 0.87, 2.26, respectively) and did not differ markedly by gestational age. Conclusions: Malpresentation at delivery was modestly associated with ASD. Early monitoring of the neurodevelopment of children born with malpresentation could identify children with ASD sooner and enhance opportunities to provide support to optimise developmental outcomes. © 2024 John Wiley & Sons Ltd.

BACKGROUND: To control the spread of the coronavirus disease (COVID-19), many jurisdictions throughout the world enacted public health measures that had vast socio-economic implications. In emergency situations, families of children with developmental disabilities (DDs), including autism, may experience increased difficulty accessing therapies, economic hardship, and caregiver stress, with the potential to exacerbate autism symptoms. Yet, limited research exists on the economic impacts of the COVID-19 pandemic on families of children with autism or another DD compared to families of children from the general population. OBJECTIVES: To assess impact of the COVID-19 pandemic related to parental employment and economic difficulties in families of children with autism, another DD, and in the general population, considering potential modification by socioeconomic disadvantage before the pandemic and levels of child behavioral and emotional problems. METHODS: The Study to Explore Early Development (SEED) is a multi-site, multi-phase, case-control study of young children with autism or another DD as compared to a population comparison group (POP). During January-July 2021, a COVID-19 Impact Assessment Questionnaire was sent to eligible participants (n=1,789) who had enrolled in SEED Phase 3 from September 2017-March 2020. Parents completed a questionnaire on impacts of the pandemic in 2020 and completed the Child Behavior Checklist (CBCL) to measure behavioral and emotional health of their child during this time. Multiple logistic regression models were built for employment reduction, increased remote work, difficulty paying bills, or fear of losing their home. Covariates include group status (autism, DD, POP), household income at enrollment, child's race and ethnicity, and binary CBCL Total Problems T-score (<60 vs. ≥60). Unadjusted and adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated. RESULTS: The study included 274 children with autism, 368 children with another DD, and 385 POP children. The mean age of 6.1 years (standard deviation, 0.8) at the COVID-19 Impact Assessment did not differ between study groups. Parents of children with autism were less likely to transition to remote work (aOR [95% CI] = 0.6 [0.4, 1.0]) and more likely to report difficulty paying bills during the pandemic (1.8 [1.2, 2.9]) relative to parents of POP children. Lower income was associated with greater employment reduction, difficulty paying bills, and fear of losing their home, but inversely associated with transitioning to remote work. Parents of non-Hispanic (NH) Black children experienced greater employment reduction compared to parents of NH White children (1.9 [1.1, 3.0]). Parents from racial and ethnic minority groups were more likely to experience difficulty paying bills and fear losing their home, relative to NH White parents. Caregivers of children with CBCL scores in the clinical range were more likely to fear losing their home (2.1 [1.3, 3.4]). CONCLUSION: These findings suggest that families of children with autism, families of lower socio-economic status, and families of racial and ethnic minority groups experienced fewer work flexibilities and greater financial distress during the pandemic. Future research can be used to assess if these impacts are sustained over time.

Intellectual disability (ID) commonly co-occurs in children with autism. Although diagnostic criteria for ID require impairments in both cognitive and adaptive functioning, most population-based estimates of the frequency of co-occurring ID in children with autism-including studies of racial and ethnic disparities in co-occurring autism and ID-base the definition of ID solely on cognitive scores. The goal of this analysis was to examine the effect of including both cognitive and adaptive behavior criteria on estimates of co-occurring ID in a well-characterized sample of 2- to 5-year-old children with autism. Participants included 3264 children with research or community diagnoses of autism enrolled in the population-based Study to Explore Early Development (SEED) phases 1-3. Based only on Mullen Scales of Early Learning (MSEL) composite cognitive scores, 62.9% (95% confidence interval [CI]: 61.1, 64.7%) of children with autism were estimated to have co-occurring ID. After incorporating Vineland Adaptive Behavior Scales, Second Edition (VABS-II) composite or domains criteria, co-occurring ID estimates were reduced to 38.0% (95% CI: 36.2, 39.8%) and 45.0% (95% CI: 43.1, 46.9%), respectively. The increased odds of meeting ID criteria observed for non-Hispanic (NH) Black and Hispanic children relative to NH White children when only MSEL criteria were used were substantially reduced, though not eliminated, after incorporating VABS-II criteria and adjusting for selected socioeconomic variables. This study provides evidence for the importance of considering adaptive behavior as well as socioeconomic disadvantage when describing racial and ethnic disparities in co-occurring ID in epidemiologic studies of autism.

BACKGROUND: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. OBJECTIVE: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. METHODS: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. RESULTS: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. CONCLUSIONS: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.

Definitive diagnosis of sporadic Creutzfeldt-Jakob disease (sCJD) relies on the examination of brain tissues for the pathological prion protein (PrP(Sc)). Our previous study revealed that PrP(Sc)-seeding activity (PrP(Sc)-SA) is detectable in skin of sCJD patients by an ultrasensitive PrP(Sc) seed amplification assay (PrP(Sc)-SAA) known as real-time quaking-induced conversion (RT-QuIC). A total of 875 skin samples were collected from 2 cohorts (1 and 2) at autopsy from 2-3 body areas of 339 cases with neuropathologically confirmed prion diseases and non-sCJD controls. The skin samples were analyzed for PrP(Sc)-SA by RT-QuIC assay. The results were compared with demographic information, clinical manifestations, cerebrospinal fluid (CSF) PrP(Sc)-SA, other laboratory tests, subtypes of prion diseases defined by the methionine (M) or valine (V) polymorphism at residue 129 of PrP, PrP(Sc) types (#1 or #2), and gene mutations in deceased patients. RT-QuIC assays of the cohort #1 by two independent laboratories gave 87.3% or 91.3% sensitivity and 94.7% or 100% specificity, respectively. The cohort #2 showed sensitivity of 89.4% and specificity of 95.5%. RT-QuIC of CSF available from 212 cases gave 89.7% sensitivity and 94.1% specificity. The sensitivity of skin RT-QuIC was subtype dependent, being highest in sCJDVV1-2 subtype, followed by VV2, MV1-2, MV1, MV2, MM1, MM1-2, MM2, and VV1. The skin area next to the ear gave highest sensitivity, followed by lower back and apex of the head. Although no difference in brain PrP(Sc)-SA was detected between the cases with false negative and true positive skin RT-QuIC results, the disease duration was significantly longer with the false negatives [12.0 ± 13.3 (months, SD) vs. 6.5 ± 6.4, p < 0.001]. Our study validates skin PrP(Sc)-SA as a biomarker for the detection of prion diseases, which is influenced by the PrP(Sc) types, PRNP 129 polymorphisms, dermatome sampled, and disease duration.

OBJECTIVES: To examine associations between pregnancy planning and autism spectrum disorder (ASD) in offspring. METHODS: The Study to Explore Early Development (SEED), a multi-site case-control study, enrolled preschool-aged children with ASD, other DDs, and from the general population (POP). Some children with DDs had ASD symptoms but did not meet the ASD case definition. We examined associations between mother's report of trying to get pregnant (pregnancy planning) and (1) ASD and (2) ASD symptomatology (ASD group, plus DD with ASD symptoms group combined) (each vs. POP group). We computed odds ratios adjusted for demographic, maternal, health, and perinatal health factors (aORs) via logistic regression. Due to differential associations by race-ethnicity, final analyses were stratified by race-ethnicity. RESULTS: Pregnancy planning was reported by 66.4%, 64.8%, and 76.6% of non-Hispanic White (NHW) mothers in the ASD, ASD symptomatology, and POP groups, respectively. Among NHW mother-child pairs, pregnancy planning was inversely associated with ASD (aOR = 0.71 [95% confidence interval 0.56-0.91]) and ASD symptomatology (aOR = 0.67 [0.54-0.84]). Pregnancy planning was much less common among non-Hispanic Black mothers (28-32% depending on study group) and Hispanic mothers (49-56%) and was not associated with ASD or ASD symptomatology in these two race-ethnicity groups. CONCLUSION: Pregnancy planning was inversely associated with ASD and ASD symptomatology in NHW mother-child pairs. The findings were not explained by several adverse maternal or perinatal health factors. The associations observed in NHW mother-child pairs did not extend to other race-ethnicity groups, for whom pregnancy planning was lower overall.

This study investigated the prevalence, and the developmental, behavior and emotional outcomes of 675 preschoolers with ASD with or without a history of regression, who participated in the Study to Explore Early Development (SEED). The SEED project is a cross-sectional case-control study that collected data between 2007 and 2011. Children’s history of regression, adaptive skills, and behavior problems were assessed using the Autism Diagnostic Interview-Revised (ADI-R), the Vineland Adaptive Behavior Scales-Second Edition (Vineland-2), and the Child Behavior Checklist (CBCL), respectively; and children’s developmental levels were assessed using the Mullen Scales of Learning (MSEL). Findings from this study indicated that 26% of children experienced social and language regression, and of those with regression, 76% had regained lost skills upon completion of the study. Compared to children without a history of regression, children with social regression demonstrated increased internalizing problems and decreased fine motor skills, and children with language regression demonstrated poorer language skills. Also, children with language and social regression displayed poorer adaptive communication skills than children without regression. Children who experienced regression in one area of development demonstrated better outcomes than those who experience regression in multiple areas. To conclude, children with regression are at risk for poorer outcomes during their preschool years. © 2023, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

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 &amp; 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. &amp; Charlene Zalesky, and Claudia &amp; Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill &amp; 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, &amp; 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&amp; 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. &amp; Charlene Zalesky, and Claudia &amp; 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 &amp; 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, &amp; 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

BackgroundSeveral reports have suggested a role for epigenetic mechanisms in ASD etiology. Epigenome-wide association studies (EWAS) in autism spectrum disorder (ASD) may shed light on particular biological mechanisms. However, studies of ASD cases versus controls have been limited by post-mortem timing and severely small sample sizes. Reports from in-life sampling of blood or saliva have also been very limited in sample size, and/or genomic coverage. We present the largest case-control EWAS for ASD to date, combining data from population-based case-control and case-sibling pair studies.MethodsDNA from 968 blood samples from children in the Study to Explore Early Development (SEED 1) was used to generate epigenome-wide array DNA methylation (DNAm) data at 485,512 CpG sites for 453 cases and 515 controls, using the Illumina 450K Beadchip. The Simons Simplex Collection (SSC) provided 450K array DNAm data on an additional 343 cases and their unaffected siblings. We performed EWAS meta-analysis across results from the two data sets, with adjustment for sex and surrogate variables that reflect major sources of biological variation and technical confounding such as cell type, batch, and ancestry. We compared top EWAS results to those from a previous brain-based analysis. We also tested for enrichment of ASD EWAS CpGs for being targets of meQTL associations using available SNP genotype data in the SEED sample.FindingsIn this meta-analysis of blood-based DNA from 796 cases and 858 controls, no single CpG met a Bonferroni discovery threshold of p &lt; 1.12×10−7. Seven CpGs showed differences at p &lt; 1×10−5 and 48 at 1×10−4. Of the top 7, 5 showed brain-based ASD associations as well, often with larger effect sizes, and the top 48 overall showed modest concordance (r = 0.31) in direction of effect with cerebellum samples. Finally, we observed suggestive evidence for enrichment of CpG sites controlled by SNPs (meQTL targets) among the EWAS CpGs hits, which was consistent across EWAS and meQTL discovery p-value thresholds.ConclusionsWe report the largest case-control EWAS study of ASD to date. No single CpG site showed a large enough DNAm difference between cases and controls to achieve epigenome-wide significance in this sample size. However, our results suggest the potential to observe disease associations from blood-based samples. Among the 7 sites achieving suggestive statistical significance, we observed consistent, and stronger, effects at the same sites among brain samples. Discovery-oriented EWAS for ASD using blood samples will likely need even larger samples and unified genetic data to further understand DNAm differences in ASD.

During the last decade, endemic swine H1 influenza A viruses (IAV) from six different genetic clades of the hemagglutinin gene caused zoonotic infections in humans. The majority of zoonotic events with swine IAV were restricted to a single case with no subsequent transmission. However, repeated introduction of human-seasonal H1N1, continual reassortment between endemic swine IAV, and subsequent drift in the swine host resulted in highly diverse swine IAV with human-origin genes that may become a risk to the human population. To prepare for the potential of a future swine-origin IAV pandemic in humans, public health laboratories selected candidate vaccine viruses (CVV) for use as vaccine seed strains. To assess the pandemic risk of contemporary US swine H1N1 or H1N2 strains, we quantified the genetic diversity of swine H1 HA genes, and identified representative strains from each circulating clade. We then characterized the representative swine IAV against human seasonal vaccine and CVV strains using ferret antisera in hemagglutination inhibition assays (HI). HI assays revealed that 1A.3.3.2 (pdm) and 1B.2.1 (delta-2) demonstrated strong cross reactivity to human seasonal vaccines or CVVs. However, swine IAV from three clades that represent more than 50% of the detected swine IAVs in the USA showed significant reduction in cross-reactivity compared to the closest CVV virus: 1A.1.1.3 (alpha-deletion), 1A.3.3.3-clade 3 (gamma), and 1B.2.2.1 (delta-1a). Representative viruses from these three clades were further characterized in a pig-to-ferret transmission model and shown to exhibit variable transmission efficiency. Our data prioritize specific genotypes of swine H1N1 and H1N2 to further investigate in the risk they pose to the human population. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv 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.

Juneteenth, a federal holiday officially recognized in 2021, is celebrated annually in the United States to honor the emancipation of enslaved Black Americans. As a symbol of racial justice, equality, and equity, Juneteenth represents an opportunity to pay tribute to the achievements of a wide range of Black chemistry students and the initiatives taken to promote the success of every student within an environment that has historically not been inclusive. | | While the U.S. STEM workforce has become more diverse in the past 10 years, Black people continue to be underrepresented in science, and in chemistry, in particular. The recent National Science Foundation report Diversity and STEM: Women, Minorities and Persons with Disabilities 2023 shows that only 9% of the STEM workforce identifies as Black, 3 percentage points (roughly 7.7M people (1)) lower than their overall representation in the adult U.S. population. (2) The U.S. Bureau of Labor Statistics paints a similar picture─in 2022 only 10% of the chemical manufacturing workforce was Black. (3) Chemical & Engineering News culled the chemistry data from the 2023 NSF report revealing even bleaker figures─in 2021, Black people comprised only 4.4% of employed chemists, (4) and were more likely to occupy lower paying STEM jobs that do not require a college degree. (2) | | Recent publications have emphasized the importance of scientists and scientific institutions welcoming and supporting the development of individuals from groups historically marginalized in fields such as chemistry, biology, mathematics, computer science, and physics. (5) Not only is such intentional support a moral imperative, but numerous reports have demonstrated that increasing diversity and inclusion in these fields leads to a more innovative and productive scientific community. (6) | | Marginalized groups often face systemic barriers to accessing education and career opportunities, resulting in a chemical workforce that lacks diversity. To address this, chemists and chemistry organizations have taken steps to actively support and mentor individuals from historically excluded groups, providing access to resources and opportunities, and promoting a culture of inclusivity in academia and the workplace. For instance, since 1965, the American Chemical Society (ACS) Project Seed program has provided summer research experiences for more than 11,000 high school students, while the ACS Scholars program has provided renewable scholarships for more than 3,500 undergraduates interested in chemistry-related careers.

During the last decade, endemic swine H1 influenza A viruses (IAV) from six different genetic clades of the hemagglutinin gene caused zoonotic infections in humans. The majority of zoonotic events with swine IAV were restricted to a single case with no subsequent transmission. However, repeated introduction of human-seasonal H1N1, continual reassortment between endemic swine IAV, and subsequent drift in the swine host resulted in highly diverse swine IAV with human-origin genes that may become a risk to the human population. To prepare for the potential of a future swine-origin IAV pandemic in humans, public health laboratories selected candidate vaccine viruses (CVV) for use as vaccine seed strains. To assess the pandemic risk of contemporary US swine H1N1 or H1N2 strains, we quantified the genetic diversity of swine H1 HA genes, and identified representative strains from each circulating clade. We then characterized the representative swine IAV against human seasonal vaccine and CVV strains using ferret antisera in hemagglutination inhibition assays (HI). HI assays revealed that 1A.3.3.2 (pdm09) and 1B.2.1 (delta-2) demonstrated strong cross reactivity to human seasonal vaccines or CVVs. However, swine IAV from three clades that represent more than 50% of the detected swine IAVs in the USA showed significant reduction in cross-reactivity compared to the closest CVV virus: 1A.1.1.3 (alpha-deletion), 1A.3.3.3-clade 3 (gamma), and 1B.2.2.1 (delta-1a). Representative viruses from these three clades were further characterized in a pig-to-ferret transmission model and shown to exhibit variable transmission efficiency. Our data prioritize specific genotypes of swine H1N1 and H1N2 to further investigate in the risk they pose to the human population.

BACKGROUND: Current knowledge about parental reasons for allowing child participation in research comes mainly from clinical trials. Fewer data exist on parents' motivations to enrol children in observational studies. OBJECTIVES: Describe reasons parents of preschoolers gave for participating in the Study to Explore Early Development (SEED), a US multi-site study of autism spectrum disorder (ASD) and other developmental delays or disorders (DD), and explore reasons given by child diagnostic and behavioural characteristics at enrolment. METHODS: We included families of children, age 2-5 years, participating in SEED (n = 5696) during 2007-2016. We assigned children to groups based on characteristics at enrolment: previously diagnosed ASD; suspected ASD; non-ASD DD; and population controls (POP). During a study interview, we asked parents their reasons for participating. Two coders independently coded responses and resolved discrepancies via consensus. We fit binary mixed-effects models to evaluate associations of each reason with group and demographics, using POP as reference. RESULTS: Participants gave 1-5 reasons for participation (mean = 1.7, SD = 0.7). Altruism (48.3%), ASD research interest (47.4%) and perceived personal benefit (26.9%) were most common. Two novel reasons were knowing someone outside the household with the study conditions (peripheral relationship; 14.1%) and desire to contribute to a specified result (1.4%). Odds of reporting interest in ASD research were higher among diagnosed ASD participants (odds ratio [OR] 2.89, 95% confidence interval [CI] 2.49-3.35). Perceived personal benefit had higher odds among diagnosed (OR 1.92, 95% CI 1.61-2.29) or suspected ASD (OR 3.67, 95% CI 2.99-4.50) and non-ASD DD (OR 1.80, 95% CI 1.50-2.16) participants. Peripheral relationship with ASD/DD had lower odds among all case groups. CONCLUSIONS: We identified meaningful differences between groups in parent-reported reasons for participation. Differences demonstrate an opportunity for future studies to tailor recruitment materials and increase the perceived benefit for specific prospective participants.

Mass gatherings create settings conducive to infectious disease transmission. Empirical data to model infectious disease transmission at mass gatherings are limited. Video-analysis technology could be used to generate data on social mixing patterns needed for simulating influenza transmission at mass gatherings. We analyzed short video recordings of persons attending the GameFest event at a university in Troy, New York, in April 2013 to demonstrate the feasibility of this approach. Attendees were identified and tracked during three randomly selected time periods using an object-tracking algorithm. Tracks were analyzed to calculate the number and duration of unique pairwise contacts. A contact occurred each time two attendees were within 2 m of each other. We built and tested an agent-based stochastic influenza simulation model assuming two scenarios of mixing patterns in a geospatially accurate representation of the event venue -one calibrated to the mean cumulative contact duration estimated from GameFest video recordings and the other using a uniform mixing pattern. We compared one-hour attack rates (i.e., becoming infected) generated from these two scenarios following the introduction of a single infectious seed. Across the video recordings, 278 attendees were identified and tracked, resulting in 1,247 unique pairwise contacts with a cumulative mean contact duration of 74.76 s (SD: 80.71). The one-hour simulated mean attack rates were 2.17 % (95 % CI:1.45 - 2.82) and 0.21 % (95 % CI: 0.14 - 0.28) in the calibrated and uniform mixing model scenarios, respectively. We simulated influenza transmission at the GameFest event using social mixing data objectively captured through video-analysis technology. Microlevel geospatially accurate simulations can be used to assess the layout of event venues on social mixing and disease transmission. Future work can expand on this demonstration project to larger spatial and temporal scenes in more diverse settings.

Persons identified in early childhood as having autism spectrum disorder (autism) often have co-occurring health problems that extend into adolescence (1-3). Although only limited data exist on their health and use of health care services as they transition to adolescence, emerging data suggest that a minority of these persons receive recommended guidance* from their primary care providers (PCPs) starting at age 12 years to ensure a planned transition from pediatric to adult health care (4,5). To address this gap in data, researchers analyzed preliminary data from a follow-up survey of parents and guardians of adolescents aged 12-16 years who previously participated in the Study to Explore Early Development (https://www.cdc.gov/ncbddd/autism/seed.html). The adolescents were originally studied at ages 2-5 years and identified at that age as having autism (autism group) or as general population controls (control group). Adjusted prevalence ratios (aPRs) that accounted for differences in demographic characteristics were used to compare outcomes between groups. Adolescents in the autism group were more likely than were those in the control group to have physical difficulties (21.2% versus 1.6%; aPR = 11.6; 95% confidence interval [CI] = 4.2-31.9), and to have additional mental health or other conditions(†) (one or more condition: 63.0% versus 28.9%; aPR = 1.9; 95% CI = 1.5-2.5). Adolescents in the autism group were more likely to receive mental health services (41.8% versus 22.1%; aPR = 1.8, 95% CI = 1.3-2.6) but were also more likely to have an unmet medical or mental health service need(§) (11.0% versus 3.2%; aPR = 3.1; 95% CI = 1.1-8.8). In both groups, a small percentage of adolescents (autism, 7.5%; control, 14.1%) received recommended health care transition (transition) guidance. These findings are consistent with previous research (4,5) indicating that few adolescents receive the recommended transition guidance and suggest that adolescents identified with autism in early childhood are more likely than adolescents in the general population to have unmet health care service needs. Improved provider training on the heath care needs of adolescents with autism and coordination of comprehensive programs(¶) to meet their needs can improve delivery of services and adherence to recommended guidance for transitioning from pediatric to adult health care.

The goal of this preliminary investigation was to compare demographic and clinical characteristics in a sample of children with likely Social (Pragmatic) Communication Disorder (SCD) (N = 117) to those in children with possible (N = 118) and some (N = 126) SCD traits, other developmental delay (DD) (N = 91) and autism spectrum disorder (ASD) (N = 642). We used data from the Study to Explore Early Development (SEED), a multi-site case-control study. Items reflecting SCD DSM-5 criteria were selected from an autism diagnostic measure, with SCD categories identified by creating quartiles. Our results suggest that SCD may fall along a continuum involving elevated deficits (in comparison to DD with no SCD) in social communication and restricted and repetitive behavior that do not reach the clinical threshold for ASD.

The highly pathogenic avian influenza virus (HPAIV) H5N1 A/goose/Guangdong/1996 lineage (Gs/GD) is endemic in poultry across several countries in the world, and has caused lethal, sporadic infections in humans. Vaccines are important in HPAI control for both poultry and in pre-pandemic preparedness in humans. This study assessed inactivated pre-pandemic vaccine strains in a One Health framework, focusing on the genetic and antigenic diversity of field H5N1 Gs/GD viruses from the agricultural sector and assessing cross protection in a chicken challenge model. Nearly half (47.92%) of the forty-eight combinations of vaccine/challenge viruses examined had bird protection of 80% or above. Most vaccinated groups had prolonged mean death time (MDT) and the virus shedding titers were significantly lower compared to the sham group (p≤ 0.05). The antibody titers in the pre-challenge sera were not predictive of protection. Although vaccinated birds had higher titers of hemagglutination inhibiting (HI) antibodies against homologous vaccine antigen, most of them also had lower or no antibody titer against the challenge antigen. The comparison of all parameters, homologous or closely related vaccine and challenge viruses, gave the best prediction protection. Through additional analysis, we identified a pattern of epitopes substitutions in the hemagglutinin (HA) of each challenge virus that impacted protection, regardless of the vaccine used. These changes were situated in the antigenic sites and/or reported epitopes associated with virus escape from antibody neutralization. As a result, this study highlights virus diversity, immune response complexity, and the importance of strain selection for vaccine development to control H5N1 HPAIV in the agricultural sector and for human pre-pandemic preparedness. We suggest that the engineering of specific antigenic sites can improve the immunogenicity of H5 vaccines.ImportanceThe sustained circulation of highly pathogenic avian influenza virus (HPAIV) H5N1 A/goose/Guangdong/1996 lineage (Gs/GD) in the agricultural sector and some wild birds has led to the evolution and selection of distinct viral lineages involved in the escape from vaccine protection. Our results using inactivated vaccine candidates from the human pandemic preparedness program in a chicken challenge model identified critical antigenic conformational epitopes on the H5 hemagglutinin (HA) from different clades that were associated with antibody recognition and escape. Even though other investigators have reported epitope mapping in the H5 HA, much of this information pertains to epitopes reactive towards mouse antibodies. Our findings validate changes in antigenic epitopes of HA associated with virus escape from antibody neutralization in chickens, which has direct relevance to field protection and virus evolution. Therefore, the knowledge of these immunodominant regions is essential to proactively develop diagnostic tests, improve surveillance platforms to monitor AIV outbreaks, and design more efficient and broad-spectrum agricultural and human prepandemic vaccines.

Introduction: The jequirity bean (Abrus precatorius) seed contains abrin, a toxalbumin, that irreversibly binds the 60-s ribosomal subunit inhibiting protein synthesis. Neurologic manifestations of ingestions are rare. Case details: We present a case of a 20-year-old man with 24 h of vomiting, diarrhea and 2 h of hematemesis and hematochezia. He admitted to purchasing 1000 jequirity beans online, crushing and ingesting them 26 h prior to presentation in a suicide attempt. Over the next 2 days, he developed hallucinations, incomprehensible mumbling and grunting, disconjugate gaze with abnormal roving eye movements and a left gaze preference with his right eye deviated medially. There was a fine tremor of the upper extremities and he had brief episodes of choreoathetoid movements of his legs. A head CT was normal with no cerebral edema. He progressed to minimally responsive to noxious stimuli, and was unable to converse or follow commands and displayed increased choreoathetoid movements of his extremities. An electroencephalogram (EEG) showed only mild background slowing. Magnetic resonance imaging (MRI) was performed showing bilaterally symmetric signal abnormalities in the basal ganglia, brainstem, corpus callosum and corona radiata with diffuse leptomeningeal enhancement. The patient developed a tonic-clonic seizure followed by pulseless electrical activity, from which he was resuscitated. He was provided comfort care and died just under 5 days after his ingestion. Results: Urine analysis using liquid chromatography coupled to tandem mass spectrometry was positive for 8.84 ng/ml of l-abrine (4.96 ng l-abrine/mg creatinine) 61 h after admission to the hospital (approximately 87 h post-ingestion). Serum concentrations for l-abrine and ricinine were both below the limits of detection. Discussion: Ingestion of 1000 crushed jequirity beans purchased on the internet resulted in progressive encephalopathy and death.

Approximately 700 women die in the United States each year as a result of pregnancy or its complications, and significant racial/ethnic disparities in pregnancy-related mortality exist (1). Data from CDC's Pregnancy Mortality Surveillance System (PMSS) for 2007-2016 were analyzed. Pregnancy-related mortality ratios (PRMRs) (i.e., pregnancy-related deaths per 100,000 live births) were analyzed by demographic characteristics and state PRMR tertiles (i.e., states with lowest, middle, and highest PRMR); cause-specific proportionate mortality by race/ethnicity also was calculated. Over the period analyzed, the U.S. overall PRMR was 16.7 pregnancy-related deaths per 100,000 births. Non-Hispanic black (black) and non-Hispanic American Indian/Alaska Native (AI/AN) women experienced higher PRMRs (40.8 and 29.7, respectively) than did all other racial/ethnic groups. This disparity persisted over time and across age groups. The PRMR for black and AI/AN women aged >/=30 years was approximately four to five times that for their white counterparts. PRMRs for black and AI/AN women with at least some college education were higher than those for all other racial/ethnic groups with less than a high school diploma. Among state PRMR tertiles, the PRMRs for black and AI/AN women were 2.8-3.3 and 1.7-3.3 times as high, respectively, as those for non-Hispanic white (white) women. Significant differences in cause-specific proportionate mortality were observed among racial/ethnic populations. Strategies to address racial/ethnic disparities in pregnancy-related deaths, including improving women's health and access to quality care in the preconception, pregnancy, and postpartum periods, can be implemented through coordination at the community, health facility, patient, provider, and system levels.

Since licensing of the first poliovirus vaccine in 1955, multiple types of live attenuated oral poliovirus vaccines (OPVs) and inactivated poliovirus vaccines (IPVs) have been tested or licensed for routine childhood vaccination schedules. IPVs have been manufactured by inactivating the three serotypes of different poliovirus seed strains, either the wild or the Sabin polioviruses, the latter of which is used for manufacturing OPVs.1 IPVs have also been used with different routes of administration and doses, and have been given at different ages.

Maternal infection and fever during pregnancy have been implicated in the etiology of autism spectrum disorder (ASD); however, studies have not been able to separate the effects of fever itself from the impact of a specific infectious organism on the developing brain. We utilized data from the Study to Explore Early Development (SEED), a case-control study among 2- to 5-year-old children born between 2003 and 2006 in the United States, to explore a possible association between maternal infection and fever during pregnancy and risk of ASD and other developmental disorders (DDs). Three groups of children were included: children with ASD (N = 606) and children with DDs (N = 856), ascertained from clinical and educational sources, and children from the general population (N = 796), randomly sampled from state birth records. Information about infection and fever during pregnancy was obtained from a telephone interview with the mother shortly after study enrollment and maternal prenatal and labor/delivery medical records. ASD and DD status was determined by an in-person standardized developmental assessment of the child at 3-5 years of age. After adjustment for covariates, maternal infection anytime during pregnancy was not associated with ASD or DDs. However, second trimester infection accompanied by fever elevated risk for ASD approximately twofold (aOR = 2.19, 95% confidence interval 1.14-4.23). These findings of an association between maternal infection with fever in the second trimester and increased risk of ASD in the offspring suggest that the inflammatory response to the infectious agent may be etiologically relevant. Autism Res2019. (c) 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Using data from a large multisite study in the United States-the Study to Explore Early Development-we found that women who had an infection during the second trimester of pregnancy accompanied by a fever are more likely to have children with ASD. These findings suggest the possibility that only more severe infections accompanied by a robust inflammatory response increase the risk of ASD.

Background: Evidence about injury management and outcomes in children with autism spectrum disorder (ASD) is limited. Method(s): Cross-sectional analyses included children aged 30-68 months with at least one medically attended injury. Standardized diagnostic instruments determined ASD cases. Parent-reported injury treatments and outcomes were examined in ASD cases (n = 224) versus developmental delays/disorders (DD) (n = 188) and population (POP) (n = 267) controls, adjusting for child and family characteristics using logistic regression. Result(s): Injury characteristics were similar between groups. Most children (82.5%) had emergency care (EC) or hospitalization after injury. Nearly half (46.4%) ever received a medication or injection, mostly analgesics (53.4%) and local anesthetics (23.8%), while 9.4% ever received surgery, most often for open wound (47.0%) or fracture (16.7%). ASD group children were less likely than DD group children to receive medication/injection (41.1% vs. 53.2%, adjusted odds ratio [aOR] = 0.60 [0.40, 0.90]); receipt of EC/hospitalization and surgery were comparable. Children with ASD more often had surgery than POP children (14.3% vs. 4.9%, aOR = 2.62 [1.31, 5.25]); receipt of EC/hospitalization and medication/injection were similar. Loss of consciousness was uncommon (ASD = 6.3%, DD = 5.3%, POP = 3.4%), as was long-term or significant behavior change (ASD = 5.4%, DD = 3.2%, POP = 3.2%); differences were not significant. Conclusion(s): Injured children with ASD received fewer medications/injections than children with non-ASD developmental delays/disorders and more surgical treatments than general population children. Injury management was otherwise similar between groups. Understanding whether these results reflect child or injury characteristics or provider perceptions about behaviors and pain thresholds of children with ASD, and how these may influence care, requires further study.

BACKGROUND: Approximately 700 women die from pregnancy-related complications in the United States every year. METHODS: Data from CDC's national Pregnancy Mortality Surveillance System (PMSS) for 2011-2015 were analyzed. Pregnancy-related mortality ratios (pregnancy-related deaths per 100,000 live births; PRMRs) were calculated overall and by sociodemographic characteristics. The distribution of pregnancy-related deaths by timing relative to the end of pregnancy and leading causes of death were calculated. Detailed data on pregnancy-related deaths during 2013-2017 from 13 state maternal mortality review committees (MMRCs) were analyzed for preventability, factors that contributed to pregnancy-related deaths, and MMRC-identified prevention strategies to address contributing factors. RESULTS: For 2011-2015, the national PRMR was 17.2 per 100,000 live births. Non-Hispanic black (black) women and American Indian/Alaska Native women had the highest PRMRs (42.8 and 32.5, respectively), 3.3 and 2.5 times as high, respectively, as the PRMR for non-Hispanic white (white) women (13.0). Timing of death was known for 87.7% (2,990) of pregnancy-related deaths. Among these deaths, 31.3% occurred during pregnancy, 16.9% on the day of delivery, 18.6% 1-6 days postpartum, 21.4% 7-42 days postpartum, and 11.7% 43-365 days postpartum. Leading causes of death included cardiovascular conditions, infection, and hemorrhage, and varied by timing. Approximately sixty percent of pregnancy-related deaths from state MMRCs were determined to be preventable and did not differ significantly by race/ethnicity or timing of death. MMRC data indicated that multiple factors contributed to pregnancy-related deaths. Contributing factors and prevention strategies can be categorized at the community, health facility, patient, provider, and system levels and include improving access to, and coordination and delivery of, quality care. CONCLUSIONS: Pregnancy-related deaths occurred during pregnancy, around the time of delivery, and up to 1 year postpartum; leading causes varied by timing of death. Approximately three in five pregnancy-related deaths were preventable. IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Strategies to address contributing factors to pregnancy-related deaths can be enacted at the community, health facility, patient, provider, and system levels.

BACKGROUND: Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. METHODS: Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. RESULTS: On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. CONCLUSIONS: Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.

Previous studies have shown complications of pregnancy, often examined in aggregate, to be associated with autism spectrum disorder (ASD). Results for specific complications, such as maternal diabetes and hypertension, have not been uniformly consistent and should be investigated independently in relation to ASD in a large community-based sample. The Study to Explore Early Development (SEED), a US multisite case-control study, enrolled children born in 2003-2006 at 2-5 years of age. Children were classified into three groups based on confirmation of ASD (n = 698), non-ASD developmental delay (DD; n = 887), or controls drawn from the general population (POP; n = 979). Diagnoses of any diabetes or hypertensive disorder during pregnancy were identified from prenatal medical records and maternal self-report. Logistic regression models estimated adjusted odds ratios (aOR) and confidence intervals (CI) adjusting for maternal age, race/ethnicity, education, smoking during pregnancy, and study site. Models for hypertension were additionally adjusted for parity and plurality. Among 2,564 mothers, we identified 246 (9.6%) with any diabetes and 386 (15.1%) with any hypertension in pregnancy. After adjustment for covariates, any diabetes during pregnancy was not associated with ASD (aOR = 1.10 [95% CI 0.77, 1.56]), but any hypertension was associated with ASD (aOR = 1.69 [95% CI 1.26, 2.26]). Results were similar for DD, and any diabetes (aOR = 1.29 [95% CI 0.94, 1.78]) or any hypertension (aOR = 1.71 [95% CI 1.30, 2.25]). Some pregnancy complications, such as hypertension, may play a role in autism etiology and can possibly serve as a prompt for more vigilant ASD screening efforts. Autism Res 2019. (c) 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We studied if common complications in pregnancy are associated with autism spectrum disorder (ASD) in a large sample of mothers and children. Our results show an association between conditions marked by high blood pressure and ASD, but no association with conditions marked by high blood sugar and ASD. Associations were similar for children who had a developmental disorder that was not ASD, suggesting that this relationship may not be specific to ASD.

We analyzed CBCL/1(1/2)-5 Pervasive Developmental Problems (DSM-PDP) scores in 3- to 5-year-olds from the Study to Explore Early Development (SEED), a multi-site case control study, with the objective to discriminate children with ASD (N = 656) from children with Developmental Delay (DD) (N = 646), children with Developmental Delay (DD) plus ASD features (DD-AF) (N = 284), and population controls (POP) (N = 827). ASD diagnosis was confirmed with the ADOS and ADI-R. With a cut-point of T >/= 65, sensitivity was 80% for ASD, with specificity varying across groups: POP (0.93), DD-noAF (0.85), and DD-AF (0.50). One-way ANOVA yielded a large group effect (eta(2) = 0.50). Our results support the CBCL/1(1/2)-5's as a time-efficient ASD screener for identifying preschoolers needing further evaluation.

Immune system abnormalities have been widely reported among children with autism spectrum disorder (ASD), which may increase the risk of childhood infections. The Study to Explore Early Development (SEED) is a multisite case-control study of children aged 30-69 months, born in 2003-2006. Cases are children previously diagnosed and newly identified with ASD enrolled from education and clinical settings. Children with a previously diagnosed non-ASD developmental condition were included in the developmental delay/disorder (DD) control group. The population (POP) control group included children randomly sampled from birth certificates. Clinical illness from infection during the first 28 days ("neonatal," from medical records) and first three years of life (caregiver report) in cases was compared to DD and POP controls; and between cases with and without regression. Children with ASD had greater odds of neonatal (OR = 1.8; 95%CI: 1.1, 2.9) and early childhood infection (OR = 1.7; 95%CI: 1.5, 1.9) compared to POP children, and greater odds of neonatal infection (OR = 1.5; 95%CI: 1.1, 2.0) compared to DD children. Cases with regression had 1.6 times the odds (95%CI: 1.1, 2.3) of caregiver-reported infection during the first year of life compared to cases without regression, but neonatal infection risk and overall early childhood infection risk did not differ. Our results support the hypothesis that children with ASD are more likely to have infection early in life compared to the general population and to children with other developmental conditions. Future studies should examine the contributions of different causes, timing, frequency, and severity of infection to ASD risk. Autism Res 2018. (c) 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We looked at infections during early childhood in relation to autism spectrum disorder (ASD). We found that children with ASD were more likely to have an infection in the first 28 days of life and before age three compared to children with typical development. Children with ASD were also more likely than children with other developmental delays or disorders to have an infection in the first 28 days of life.

Telephone nurse triage lines, such as the Centers for Disease Control and Prevention's (CDC) Flu on Call((R)), a national nurse triage line, may help reduce the surge in demand for health care during an influenza pandemic by triaging callers, providing advice about clinical care and information about the pandemic, and providing access to prescription antiviral medication. We developed a Call Volume Projection Tool to estimate national call volume to Flu on Call((R)) during an influenza pandemic. The tool incorporates 2 influenza clinical attack rates (20% and 30%), 4 different levels of pandemic severity, and different initial "seed numbers" of cases (10 or 100), and it allows variation in which week the nurse triage line opens. The tool calculates call volume by using call-to-hospitalization ratios based on pandemic severity. We derived data on nurse triage line calls and call-to-hospitalization ratios from experience with the 2009 Minnesota FluLine nurse triage line. Assuming a 20% clinical attack rate and a case hospitalization rate of 0.8% to 1.5% (1968-like pandemic severity), we estimated the nationwide number of calls during the peak week of the pandemic to range from 1,551,882 to 3,523,902. Assuming a more severe 1957-like pandemic (case hospitalization rate = 1.5% to 3.0%), the national number of calls during the peak week of the pandemic ranged from 2,909,778 to 7,047,804. These results will aid in planning and developing nurse triage lines at both the national and state levels for use during a future influenza pandemic.

The 2017-2018 seasonal influenza epidemics were severe in the US and Australia where the A(H3N2) subtype viruses predominated. Although circulating A(H3N2) viruses did not differ antigenically from that recommended by the WHO for vaccine production, overall interim vaccine effectiveness estimates were below historic averages (33%) for A(H3N2) viruses. The majority (US) or all (Australian) vaccine doses contained multiple amino-acid changes in the hemagglutinin protein, resulting from the necessary adaptation of the virus to embryonated hen's eggs used for most vaccine manufacturing. Previous reports have suggested a potential negative impact of egg-driven substitutions on vaccine performance. With BARDA support, two vaccines licensed in the US are produced in cell culture: recombinant influenza vaccine (RIV, Flublok) manufactured in insect cells and inactivated mammalian cell-grown vaccine (ccIIV, Flucelvax). Quadrivalent ccIIV (ccIIV4) vaccine for the 2017-2018 influenza season was produced using an A(H3N2) seed virus propagated exclusively in cell culture and therefore lacking egg adaptative changes. Sufficient ccIIV doses were distributed (but not RIV doses) to enable preliminary estimates of its higher effectiveness relative to the traditional egg-based vaccines, with study details pending. The increased availability of comparative product-specific vaccine effectiveness estimates for cell-based and egg-based vaccines may provide critical clues to inform vaccine product improvements moving forward.