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In the United States, tobacco control activities are organized primarily in state tobacco control programs. These programs are comprised of public and private agencies working together to reduce tobacco use. The human, financial, and informational resources that go into state tobacco control programs are documented, and the outcomes of these programs have been studied in terms of health and health behavior. However, little is known about the organizational infrastructure that transforms the human, financial, and informational resources into positive health outcomes. This study examined the inter-organizational relationships among key partner agencies in eight state tobacco control programs. The state programs varied in terms of funding level, funding stability, and region of the country. Using a network analytic approach we asked an average of 14 agencies in each state program about their contacts and partnerships with the other key tobacco control agencies in their state program. Using network visualization and statistics we determined that the state networks shared some common features such as a highly central lead agency, but also had differences in network structure in terms of density and centralization. Using blockmodeling we found that, despite differences in state and program characteristics, there was a common organizational structure among the eight state programs. Understanding the inter-organizational relationships and the common organizational structures of state programs can aid researchers and practitioners in enhancing program capacity and in developing strategies for organizing effective public health systems. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To evaluate the effectiveness of different strategies for disseminating evaluation results to program stakeholders. METHODS: The results from a process evaluation of eight states' tobacco control programs were disseminated to the state programs that were assigned to one of four dissemination conditions: print reports only, reports and web site, reports and workshop, or all three dissemination modes. Key measures included levels of usefulness of the evaluation results and satisfaction of participation by study participants. RESULTS: Although exposure to the web site and workshop individually did not provide a statistically higher degree of usefulness, a clear upward trend was observed in usefulness as the number of dissemination modes increased. Participants who engaged in all three dissemination modes found the results more useful (P < .05) for their work and the work of their agency than participants using one or two dissemination modes. Participants who engaged in the three dissemination modes also appeared to be more likely to share the results with their colleagues (P = .06). CONCLUSIONS: This study shows that disseminating evaluation results through multiple, active modes increased usefulness, satisfaction, and further dissemination of the results. Evaluators should consider implementing more than one mode of dissemination to share findings with stakeholders. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Effective response to large-scale public health threats requires well-coordinated efforts among individuals and agencies. While guidance is available to help states put emergency planning programs into place, little has been done to evaluate the human infrastructure that facilitates successful implementation of these programs. This study examined the human infrastructure of the Missouri public health emergency planning system in 2006. METHODS: The Center for Emergency Response and Terrorism (CERT) at the Missouri Department of Health and Senior Services has responsibility for planning, guiding, and funding statewide emergency response activities. Thirty-two public health emergency planners working primarily in county health departments contract with CERT to support statewide preparedness. We surveyed the planners to determine whom they communicate with, work with, seek expertise from, and exchange guidance with regarding emergency preparedness in Missouri. RESULTS: Most planners communicated regularly with planners in their region but seldom with planners outside their region. Planners also reported working with an average of 12 local entities (e.g., emergency management, hospitals/ clinics). Planners identified the following leaders in Missouri's public health emergency preparedness system: local public health emergency planners, state epidemiologists, the state vaccine and grant coordinator, regional public health emergency planners, State Emergency Management Agency area coordinators, the state Strategic National Stockpile coordinator, and Federal Bureau of Investigation Weapons of Mass Destruction coordinators. Generally, planners listed few federal-level or private-sector individuals in their emergency preparedness networks. CONCLUSIONS: While Missouri public health emergency planners maintain large and varied emergency preparedness networks, there are opportunities for strengthening existing ties and seeking additional connections. [ABSTRACT FROM AUTHOR]

BACKGROUND: Screening for colorectal cancer (CRC) has been shown to decrease mortality. OBJECTIVE: To examine determinants associated with having (1) a screening colonoscopy, (2) an appropriate indication for screening, and (3) a significant diagnosis at screening. DESIGN: Prospective observational study. SETTING: Twenty-one endoscopy centers from 11 countries. PATIENTS: Asymptomatic patients who underwent a colonoscopy for the purpose of detecting CRC and who did not have a history of polyps or CRC, a lesion observed at a recent barium enema or sigmoidoscopy, or a recent positive fecal occult blood test. INTERVENTION: Screening colonoscopy. MAIN OUTCOME MEASUREMENTS: Appropriateness according to the European Panel on the Appropriateness of Gastrointestinal Endoscopy (EPAGE) criteria and significant diagnoses (cancer, adenomatous polyps, new diagnoses of inflammatory bowel disease, angiodysplasia). RESULTS: Of 5069 colonoscopies, 561 (11%) were performed for screening purposes. Patients were more likely to have a screening colonoscopy if they were aged 45 to 54 years (odds ratio [OR] 2.53, 95% confidence interval [CI] 1.60-3.99). Screening colonoscopies were appropriate, uncertain, and inappropriate in 26%, 60%, and 14% of cases, respectively. Eighty-one significant diagnoses were made, including 4 cancers. Significant diagnoses were more often made for uncertain/appropriate indications (OR 3.20, 95% CI 1.12-9.17) than for inappropriate indications. LIMITATIONS: Although data completeness was asked of all centers, it is possible that not all consecutive patients were included. Participating centers were a convenience sample and thus may not be representative. CONCLUSIONS: About 1 of 10 colonoscopies were performed for screening, preferentially in middle-aged individuals. A higher diagnostic yield in uncertain/appropriate indications suggests that the use of appropriateness criteria may enhance the efficient use of colonoscopy for screening. [ABSTRACT FROM AUTHOR]

Background: This study was undertaken to determine the association between poor developmental attainment (PDA) and biological, home environment and socio-demographic factors in a population-based sample of Canadian children.Methods: Cross-sectional data from two cycles (1994/95 and 1996/97) of the National Longitudinal Survey of Children and Youth were used. Children aged 1-5 years were included. PDA was defined as < or = 15th percentile for motor and social developmental skills (1-3 year olds) or Peabody Picture Vocabulary Test (4-5 year olds). Multiple logistic regression was used.Results: The proportion of children with PDA varies across Canada, between males and females, and by age. Among 1 year olds in Cycle I, having a low birthweight (OR=3.3; 95% CI: 2.1-5.2), being male (OR=1.6; 95% CI: 1.2-2.2) and having a mother who is an immigrant (OR=1.6; 95% CI: 1.1-2.2) increased the odds of PDA. Similar results were observed in Cycle II. Among children aged 4-5 years in Cycle II, having a mother who is an immigrant (OR=5.3; 95% CI: 4.1-6.9) and a mother with low educational attainment (OR=2.8; 95% CI: 2.1-3.9) increased the odds of PDA. Low income was a significant predictor of PDA across all age groups.Interpretation: The strong and consistent associations with living in a low-income household, having a mother with low educational attainment or a mother who is an immigrant highlight the need for targeting developmental assessments and services to this population. [ABSTRACT FROM AUTHOR]

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas ), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway., Competing Interests: The authors declare no conflict of interest.

Background: Progressive, obstructive lung disease resulting from chronic infection and inflammation is the leading cause of morbidity and mortality in persons with cystic fibrosis (PWCF). Metabolomics and next -generation sequencing (NGS) of airway secretions can allow for better understanding of cystic fibrosis (CF) pathophysiology. In this study, global metabolomic profiling on bronchoalveolar lavage fluid (BALF) obtained from pediatric PWCF and disease controls (DCs) was performed and compared to lower airway microbiota, inflammation, and lung function., Methods: BALF was collected from children undergoing flexible bronchoscopies for clinical indications. Metabolomic profiling was performed using a platform developed by Metabolon Inc. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR), and bacterial communities were characterized using 16S ribosomal RNA (rRNA) sequencing. Random Forest Analysis (RFA), principal component analysis (PCA), and hierarchical clustering analysis (HCA) were performed., Results: One hundred ninety-five BALF samples were analyzed, 142 (73 %) from PWCF. Most metabolites (425/665) and summed categories (7/9) were significantly increased in PWCF. PCA of the metabolomic data revealed CF BALF exhibited more dispersed clustering compared to DC BALF. Higher metabolite concentrations correlated with increased inflammation, increased abundance of Staphylococcus, and decreased lung function., Conclusions: The lower airway metabolome of PWCF was defined by a complex expansion of metabolomic activity. These findings could be attributed to heightened inflammation in PWCF and aspects of the CF airway polymicrobial ecology. CF-specific metabolomic features are associated with the unique underlying biology of the CF airway., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression., Competing Interests: Declaration of competing interest Dr. Sidney Kennedy has received funding for Consulting or Speaking engagements from Abbvie, Boehringer-Ingelheim, Janssen, Lundbeck, Lundbeck Institute, Merck, Otsuka Pfizer, Sunovion and Servier. Dr. Kennedy has received Research Support from Abbott, Brain Canada, CIHR (Canadian Institutes of Health Research), Janssen, Lundbeck, Ontario Brain Institute, Otsuka, Pfizer, SPOR (Canada's Strategy for Patient-Oriented Research); and has stock/stock options in Field Trip Health. No other disclosures or conflict of interests stated by authors of this work. SJR has received consulting or research funding from Allergan, Janssen, Neurocrine, and Pfizer Canada., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity ( awFC )). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC ) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach., (© 2024 The Authors.)

Host response to airway infections can vary widely. Cystic fibrosis (CF) pulmonary exacerbations provide an opportunity to better understand the interplay between respiratory microbes and the host. This study aimed to investigate the observed heterogeneity in airway infection recovery by analyzing microbiome and host response (i.e., blood proteome) data collected during the onset of 33 pulmonary infection events. We used sparse multiple canonical correlation network (SmCCNet) analysis to integrate these two types of -omics data along with a clinical measure of recovery. Four microbe-protein SmCCNet subnetworks at infection onset were identified that strongly correlate with recovery. Our findings support existing knowledge regarding CF airway infections. Additionally, we discovered novel microbe-protein subnetworks that are associated with recovery and merit further investigation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: Tobramycin inhalation solution (TIS) and chronic azithromycin (AZ) have known clinical benefits for children with CF, likely due to antimicrobial and anti-inflammatory activity. The effects of chronic AZ in combination with TIS on the airway microbiome have not been extensively investigated. Oropharyngeal swab samples were collected in the OPTIMIZE multicenter, randomized, placebo-controlled trial examining the addition of AZ to TIS in 198 children with CF and early P. aeruginosa infection. Bacterial small subunit rRNA gene community profiles were determined. The effects of TIS and AZ were assessed on oropharyngeal microbial diversity and composition to uncover whether effects on the bacterial community may be a mechanism of action related to the observed changes in clinical outcomes., Results: Substantial changes in bacterial communities (total bacterial load, diversity and relative abundance of specific taxa) were observed by week 3 of TIS treatment for both the AZ and placebo groups. On average, these shifts were due to changes in non-traditional CF taxa that were not sustained at the later study visits (weeks 13 and 26). Bacterial community measures did not differ between the AZ and placebo groups., Conclusions: This study provides further evidence that the mechanism for AZ's effect on clinical outcomes is not due solely to action on airway microbial composition., (© 2023. The Author(s).)

Background: Changes in upper airway microbiota may impact early disease manifestations in infants with cystic fibrosis (CF). To investigate early airway microbiota, the microbiota present in the oropharynx of CF infants over the first year of life was assessed along with the relationships between microbiota and growth, antibiotic use and other clinical variables., Methods: Oropharyngeal (OP) swabs were collected longitudinally between 1 and 12 months of age from infants diagnosed with CF by newborn screen and enrolled in the Baby Observational and Nutrition Study (BONUS). DNA extraction was performed after enzymatic digestion of OP swabs. Total bacterial load was determined by qPCR and community composition assessed using 16S rRNA gene analysis (V1/V2 region). Changes in diversity with age were evaluated using mixed models with cubic B-splines. Associations between clinical variables and bacterial taxa were determined using a canonical correlation analysis., Results: 1,052 OP swabs collected from 205 infants with CF were analyzed. Most infants (77%) received at least one course of antibiotics during the study and 131 OP swabs were collected while the infant was prescribed an antibiotic. Alpha diversity increased with age and was only marginally impacted by antibiotic use. Community composition was most highly correlated with age and was only moderately correlated with antibiotic exposure, feeding method and weight z-scores. Relative abundance of Streptococcus decreased while Neisseria and other taxa increased over the first year., Conclusions: Age was more influential on the oropharyngeal microbiota of infants with CF than clinical variables including antibiotics in the first year of life., Competing Interests: Declaration of Competing Interest Dr. Harris reports grants from the Cystic Fibrosis Foundation during the conduct of the study Dr. Wagner has nothing to disclose Dr. Robertson has nothing to disclose Dr. Lingard has nothing to disclose Dr. Borowitz has nothing to disclose Dr. Leung reports other from Merck, grants and other from Gilead, grants from Abbvie, grants from CF Foundation, outside the submitted work Dr. Heltshe has nothing to disclose Dr. Ramsey has nothing to disclose Dr. Zemanick reports grants from Cystic Fibrosis Foundation, NIH/NCATS, NIH/NHLBI, Savara and Calithera Biosciences during the conduct of the study, grants and fees from Vertex Pharmaceuticals, Cystic Fibrosis Foundation outside the submitted work, (Copyright © 2023. Published by Elsevier B.V.)

Purpose: To evaluate the trend of harm perception for e-cigarettes and the trend of the association between harm perception for e-cigarettes and for cigarettes among US youth from 2014 to 2019., Design, Setting and Subjects: The National Youth Tobacco Survey is an annual, cross-sectional, school-based survey done among youth selected using three-stage probability sampling., Analysis: Data were drawn from the 2014 to 2019 Surveys. A Multinomial logistic regression model was used to assess the association between harm perception for e-cigarettes and harm perception for cigarettes for each year., Results: The percentage of youth who perceived e-cigarettes as harmless decreased from 2014 to 2019 (17.2% to 5.8%). From 2015 to 2018, the percentage of smokers who perceived e-cigarettes as a little harmful increased (33.6% to 41.2%). The positive association between harm perception for e-cigarettes and harm perception for cigarettes became stronger with time. In 2014, the odds of perceiving e-cigarettes as harmless relative to very harmful were 19.55 times greater for youth who perceived cigarettes as harmless, compared to those who perceived cigarettes as very harmful (OR = 19.55; 95% CI: 14.19-26.94). These odds increased to 77.65 times in 2019 (OR = 77.65; 95% CI: 41.48-107.85)., Conclusion: This study suggests a stronger relationship between perceived harm of cigarettes and e-cigarettes with time. Interventions to prevent smoking have the potential to change e-cigarette use.

Objective: The aim of the study was to determine the mucosal microbiota associated with eosinophilic esophagitis (EoE) and eosinophilic gastritis (EoG) in a geographically diverse cohort of patients compared to controls., Methods: We conducted a prospective study of individuals with eosinophilic gastrointestinal disease (EGID) in the Consortium of Eosinophilic Gastrointestinal Disease Researchers, including pediatric and adult tertiary care centers. Eligible individuals had clinical data, mucosal biopsies, and stool collected. Total bacterial load was determined from mucosal biopsy samples by quantitative polymerase chain reaction (PCR). Community composition was determined by small subunit rRNA gene amplicons., Results: One hundred thirty-nine mucosal biopsies were evaluated corresponding to 93 EoE, 17 EoG, and 29 control specimens (18 esophageal) from 10 sites across the United States. Dominant community members across disease activity differed significantly. When comparing EoE and EoG with controls, the dominant taxa in individuals with EGIDs was increased ( Streptococcus in esophagus; Prevotella in stomach). Specific taxa were associated with active disease for both EoE ( Streptococcus , Gemella ) and EoG ( Leptotrichia ), although highly individualized communities likely impacted statistical testing. Alpha diversity metrics were similar across groups, but with high variability among individuals. Stool analyses did not correlate with bacterial communities found in mucosal biopsy samples and was similar in patients and controls., Conclusions: Dominant community members ( Streptococcus for EoE, Prevotella for EoG) were different in the mucosal biopsies but not stool of individuals with EGIDs compared to controls; taxa associated with EGIDs were highly variable across individuals. Further study is needed to determine if therapeutic interventions contribute to the observed community differences., Competing Interests: G.T.F. Chief Medical Officer, EnteroTrack, received research funding from NIH, Arena, and Holoclara. S.S.A. is co-inventor, oral viscous budesonide, Takeda license, UCSD patent. She received consulting fees from Regeneron/Sanofi, AstraZeneca, and Bristol Meyers Squibb. She received funding from NIH/NIAID/NIDDK. M.C. received consultant fees from Regeneron, Allakos, Adare/Ellodi, Shire/Takeda, AstraZeneca, Sanofi, Bristol Myers Squibb, and Phathom. She received research funding from Regeneron, Allakos, Shire/Takeda, AstraZeneca, Adare/Ellodi, and Danone. S.K.G. received consultant fees from Abbott, Adare, Celgene, Gossamer Bio, QOL, Takeda, MedScape, ViaSkin, and UpToDate. He received research support from Allakos, Ellodi, and AstraZeneca. V.A.M. received consultant fees from Takeda, Allakos, and Sanofi. He is on the Adjudication Committee of Alladapt. The remaining authors report no conflicts of interest., (Copyright © 2022 by European Society for European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition.)

Introduction: Airway infection and inflammation lead to the progression of obstructive lung disease in persons with cystic fibrosis (PWCF). However, cystic fibrosis (CF) fungal communities, known drivers of CF pathophysiology, remain poorly understood due to the shortcomings of traditional fungal culture. Our objective was to apply a novel small subunit rRNA gene (SSU-rRNA) sequencing approach to characterize the lower airway mycobiome in children with and without CF., Methods: Bronchoalveolar lavage fluid (BALF) samples and relevant clinical data were collected from pediatric PWCF and disease control (DC) subjects. Total fungal load (TFL) was measured using quantitative PCR, and SSU-rRNA sequencing was used for mycobiome characterization. Results were compared across groups, and Morisita-Horn clustering was performed., Results: 161 (84%) of the BALF samples collected had sufficient load for SSU-rRNA sequencing, with amplification being more common in PWCF. BALF from PWCF had increased TFL and increased neutrophilic inflammation compared to DC subjects. PWCF exhibited increased abundance of Aspergillus and Candida , while Malassezia , Cladosporium , and Pleosporales were prevalent in both groups. CF and DC samples showed no clear differences in clustering when compared to each other or to negative controls. SSU-rRNA sequencing was used to profile the mycobiome in pediatric PWCF and DC subjects. Notable differences were observed between the groups, including the abundance of Aspergillus and Candida ., Discussion: Fungal DNA detected in the airway could represent a combination of pathogenic fungi and environmental exposure (e.g., dust) to fungus indicative of a common background signature. Next steps will require comparisons to airway bacterial communities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 O’Connor, Wagner, Harris, Frank, Clabots and Laguna.)

Effective measurement of sex and gender identity is challenging. Questions measuring gender identity were added as an optional module of the Behavioral Risk Factor Surveillance System (BRFSS) in 2014 and were modified repeatedly in following years. This study examines BRFSS participant responses to gender identity and sex questions, 2014-2020. About 70% of participants who identified as transgender male-to-female reported male sex while 30% reported female sex. Participants who identified as transgender female-to-male reported female sex about 65% of the time and reported male sex 35% of the time. Despite changes in sex question wording, these percentages were consistent over time. Reported sex is used in computing health-related variables and in routing participants to questions about birth control, pregnancy, HPV vaccines, and cancer screenings. Without better measurement, it is challenging to assess health and health risks and inform guidelines, programs, and policies protecting the health of transgender people.

Introduction: Several evidence-informed consent practices (ECPs) have been shown to improve informed consent in clinical trials but are not routinely used. These include optimizing consent formatting, using plain language, using validated instruments to assess understanding, and involving legally authorized representatives when appropriate. We hypothesized that participants receiving an implementation science toolkit and a social media push would have increased adoption of ECPs and other outcomes., Methods: We conducted a 1-year trial with clinical research professionals in the USA ( n = 1284) who have trials open to older adults or focus on Alzheimer's disease. We randomized participants to receive information on ECPs via receiving a toolkit with a social media push (intervention) or receiving an online learning module (active control). Participants completed a baseline survey and a follow-up survey after 1 year. A subset of participants was interviewed ( n = 43)., Results: Participants who engaged more with the toolkit were more likely to have tried to implement an ECP during the trial than participants less engaged with the toolkit or the active control group. However, there were no significant differences in the adoption of ECPs, intention to adopt, or positive attitudes. Participants reported the toolkit and social media push were satisfactory, and participating increased their awareness of ECPs. However, they reported lacking the time needed to engage with the toolkit more fully., Conclusions: Using an implementation science approach to increase the use of ECPs was only modestly successful. Data suggest that having institutional review boards recommend or require ECPs may be an effective way to increase their use., (© The Author(s) 2022.)

Objectives: As of 2016, 1.4 million people in the United States were identified as transgender. Transgender people face health disparities and may be at high risk for cardiovascular disease (CVD). The objective of this study was to examine the odds of CVD for those who report a transgender identity compared with cisgender., Methods: We used logistic regression to examine whether transgender identity was associated with CVD after accounting for factors commonly associated with CVD. Data were obtained from the Behavioral Risk Factor Surveillance Survey for the years 2015 through 2019., Results: Of the participants, 93.1% reported no CVD and 6.9% reported CVD. For participants with female sex recorded at birth, those identifying as transgender had 2.66 times higher odds of CVD (95% confidence interval [CI]: 1.60-4.41) than those identifying as cisgender. For participants with male sex recorded at birth, the odds of CVD were not statistically significantly different for transgender identity compared with cisgender identity; however, those who were gender nonconforming had 2.21 times higher odds of CVD (95% CI: 1.04-4.70) compared with those with cisgender identity. Additional significant predictors of CVD were age, race, body mass index, alcohol consumption, exercise, and smoking status., Conclusion: Participants who were female sex recorded at birth had statistically significant increased odds of CVD for those reporting transgender identity compared with cisgender identity. However, transgender identity did not change the odds of CVD compared with cisgender identity for participants who were male sex recorded at birth., Competing Interests: No competing financial interests exist., (Copyright 2022, Mary Ann Liebert, Inc., publishers.)

Background: The primary care sector is uniquely positioned to lead the coordination of providers and organizations across health and social care sectors. This study explores whether intra organizational (professional) integration within a primary care team might be related to inter organizational integration between primary care and other community partners involved in caring for complex patients., Methods: Two care coordination initiatives (Health Links) were selected - one led by a primary care team with a high level of intraorganizational integration as assessed by the Collaborative Practice Assessment Tool (CPAT), and the other led by a primary care team with a low level of intraorganizational integration. A case study design involving a social network approach was used to assess interorganizational integration across six types of relationships including regular contact, perceived level of integration, referrals, information sharing, joint care planning, and shared resources., Results: Compared to the high-CPAT led case, the low-CPAT led case had higher density (more ties among organizations) in terms of regular contact, integration, and sharing of resources, whereas the opposite was true for the referral, information sharing, and joint care planning networks. Network centralization (extent to which network activity is influenced by one or a group of organizations) was higher for the high-CPAT case compared to the low-CPAT case in the integration, referrals, and joint care planning networks, while the low-CPAT case had higher centralization with regard to regular contact, information sharing, and shared resources., Conclusion: The interplay between intra and interorganizational integration remains unclear. We found no consistent differences in the patterns of ties across the six types of networks examined between the two cases. Assessing changes in network metrics for different organizations in each case over time, and supplementing network findings through in-depth interviews with network members are key next steps to consider., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2022 The Author(s).)

Background: Lower respiratory tract infections (LRTI) are a leading cause of critical illness and mortality in mechanically ventilated children; however, the pathogenic microbes frequently remain unknown. We combined traditional diagnostics with metagenomic next generation sequencing (mNGS) to evaluate the cause of LRTI in critically ill children., Methods: We conducted a prospective, multicentre cohort study of critically ill children aged 31 days to 17 years with respiratory failure requiring mechanical ventilation (>72 h) in the USA. By combining bacterial culture and upper respiratory viral PCR testing with mNGS of tracheal aspirate collected from all patients within 24 h of intubation, we determined the prevalence, age distribution, and seasonal variation of viral and bacterial respiratory pathogens detected by either method in children with or without LRTI., Findings: Between Feb 26, 2015, and Dec 31, 2017, of the 514 enrolled patients, 397 were eligible and included in the study (276 children with LRTI and 121 with no evidence of LRTI). A presumptive microbiological cause was identified in 255 (92%) children with LRTI, with respiratory syncytial virus (127 [46%]), Haemophilus influenzae (70 [25%]), and Moraxella catarrhalis (65 [24%]) being most prevalent. mNGS identified uncommon pathogens including Ureaplasma parvum and Bocavirus. Co-detection of viral and bacterial pathogens occurred in 144 (52%) patients. Incidental carriage of potentially pathogenic microbes occurred in 82 (68%) children without LRTI, with rhinovirus (30 [25%]) being most prevalent. Respiratory syncytial virus (p<0·0001), H influenzae (p=0·0006), and M catarrhalis (p=0·0002) were most common in children younger than 5 years. Viral and bacterial LRTI occurred predominantly during winter months., Interpretation: These findings demonstrate that respiratory syncytial virus, H influenzae, and M catarrhalis contribute disproportionately to severe paediatric LRTI, co-infections are common, and incidental carriage of potentially pathogenic microbes occurs frequently. Further, we provide a framework for future epidemiological and emerging pathogen surveillance studies, highlighting the potential for metagenomics to enhance clinical diagnosis., Funding: US National Institutes of Health and CZ Biohub., Competing Interests: Declaration of interests JK reports support from Genentech, outside the submitted work. LA reports funding from Pfizer, outside the submitted work. ABM reports grants from the Francis Family Foundation and the US National Institutes of Health (NIH) Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), outside the submitted work. EAFS reports grants from the US NIH during the conduct of the study. EAFS reports grants, personal fees, and non-financial support from AstraZeneca, Merck, Regeneron, Pfizer, and Roche; personal fees, non-financial support, and other support from AbbVie; personal fees from Alere; other support from GSK; grants from Johnson and Johnson; and grants and non-financial support from Novavax, outside the submitted work. MWH reports grants from NIH NICHD, during the conduct of the study; and personal fees from La Jolla Pharmaceuticals, outside the submitted work. AFZ received NICHD funding through the Collaborative Pediatric Critical Care Research Network during the conduct of the study. KLM reports grants from NIH, during the conduct of the study. AS reports grants from NIH, outside the submitted work. MMP reports grants from NIH, during the conduct of the study. PSM reports grants from NIH NICHD, during the conduct of the study. JMD reports grants from NIH, during the conduct of the study. MSZ reports grants from National Heart, Lung, and Blood Institute (NHLBI; K23HL146936), outside the submitted work. PMM reports grants from NIH, during the conduct of the study. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus , a group of nontraditional taxa, including Prevotella , and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 O’Connor, Mottlowitz, Kruk, Mickelson, Wagner, Harris, Wendt and Laguna.)

Understanding the neural underpinnings of major depressive disorder (MDD) and its treatment could improve treatment outcomes. So far, findings are variable and large sample replications scarce. We aimed to replicate and extend altered functional connectivity associated with MDD and pharmacotherapy outcomes in a large, multisite sample. Resting-state fMRI data were collected from 129 patients and 99 controls through the Canadian Biomarker Integration Network in Depression. Symptoms were assessed with the Montgomery-Åsberg Depression Rating Scale (MADRS). Connectivity was measured as correlations between four seeds (anterior and posterior cingulate cortex, insula and dorsolateral prefrontal cortex) and all other brain voxels. Partial least squares was used to compare connectivity prior to treatment between patients and controls, and between patients reaching remission (MADRS ≤ 10) early (within 8 weeks), late (within 16 weeks), or not at all. We replicated previous findings of altered connectivity in patients. In addition, baseline connectivity of the anterior/posterior cingulate and insula seeds differentiated patients with different treatment outcomes. The stability of these differences was established in the largest single-site subsample. Our replication and extension of altered connectivity highlighted previously reported and new differences between patients and controls, and revealed features that might predict remission prior to pharmacotherapy. Trial registration:ClinicalTrials.gov: NCT01655706., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: St Louis City has been demolishing vacant buildings at an increasing rate. Demolition can cause lead dust spread, and childhood lead exposure can have negative effects on cognition, growth, and development. Previous studies show an association between exposure to multiple demolitions and elevated blood lead levels (EBLLs) in children, but St Louis City does not monitor the effects of demolitions on children's blood lead levels., Objectives: The purpose of this study was to measure the association between exposure to demolitions and EBLLs in children younger than 6 years in St Louis City from 2017 to 2020., Design/setting/participants: We analyzed blood lead testing data for children 0 to 72 months of age (n = 22 192) and proximity to demolitions. Exposure was the presence of demolitions within 400 ft of a child's address in the 33 days before their first lead test., Main Outcome Measure: We used logistic regression to test the association between proximity to demolition and EBLLs (≥5 µg/dL)., Results: The percentage of children living in proximity to 1 or more demolitions was slightly higher among those with EBLLs (n = 21; 1.3%) than among those without EBLLs (n = 250; 1.2%). However, after adjusting for age, sex, year home was built, season, neighborhood socioeconomic percentile, and neighborhood racial composition, the odds of EBLLs were not significantly different for children exposed to 1 or more demolitions (OR = 0.82; 95% CI, 0.5-1.25) compared with exposed to zero demolitions., Conclusions: Although this study found no association between exposure to demolitions and EBLLs, results should be interpreted with caution, given numerous limitations. Given the consequences of childhood lead exposure, it is recommended that St Louis City conduct a similar analysis on demolitions conducted after 2020 using systematically collected demolition dates. Targeted testing or soil and air monitoring could also be informative., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zero b -value (i.e. single-shell) and diffusion weighting of b = 1000 s mm -2 . To produce microstructural parameter maps, the tensor model is usually used, despite known limitations. Although compartment models have demonstrated improved fits in multi-shell dMRI data, they are rarely used for single-shell parameter maps, where their effectiveness is unclear from the literature. Here, various compartment models combining isotropic balls and symmetric tensors were fitted to single-shell dMRI data to investigate model fitting optimization and extract the most information possible. Full testing was performed in 5 subjects, and 3 subjects with multi-shell data were included for comparison. The results were tested and confirmed in a further 50 subjects. The Markov chain Monte Carlo (MCMC) model fitting technique outperformed non-linear least squares. Using MCMC, the 2-fibre-orientation mono-exponential ball and stick model (BSME2) provided artifact-free, stable results, in little processing time. The analogous ball and zeppelin model (BZ2) also produced stable, low-noise parameter maps, though it required much greater computing resources (50 000 burn-in steps). In single-shell data, the gamma-distributed diffusivity ball and stick model (BSGD2) underperformed relative to other models, despite being an often-used software default. It produced artifacts in the diffusivity maps even with extremely long processing times. Neither increased diffusion weighting nor a greater number of gradient orientations improvedBSGD2fits. In white matter (WM), the tensor produced the best fit as measured by Bayesian information criterion. This result contrasts with studies using multi-shell data. However, in crossing fibre regions the tensor confounded geometric effects with fractional anisotropy (FA): the planar/linear WM FA ratio was 49%, whileBZ2andBSME2retained 76% and 83% of restricted fraction, respectively. As a result, theBZ2andBSME2models are strong candidates to optimize information extraction from single-shell dMRI studies., (© 2022 Institute of Physics and Engineering in Medicine.)

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Despite growing emphasis on adopting team-based models of primary care to facilitate patient access to a diverse range of care providers, our understanding of team functioning within primary care teams remains limited. This study examined interprofessional teamwork within primary care practices (Family Health Teams [FHT] and Community Health Centers - [CHC]) in Ontario and explored team-level and organizational factors associated with interprofessional teamwork. Interprofessional teamwork was measured using the Collaborative Practice Assessment Tool (CPAT), which was completed by providers in each participating team. The CPAT responses of 988 providers representing on average 12 professions (sd = 2.1) across 66 teams (44 FHTs and 22 CHCs) were included in the analysis. The average CPAT score was 46.6 (sd = 2.5). CHCs had significantly higher CPAT scores than FHTs (m diff  = 1.7, p = .02). Using diverse communication mechanisms to share information, increasing quality improvement capacities, and age of practice, had a statistically significant positive association with CPAT scores. Increasing team size, using centralized administrative processes, a high level of information exchange, and having a mixed governance board were significantly negatively associated with CPAT score. Findings illustrate factors associated with interprofessional teamwork and offer insight into the comparative performance of two team-based primary care models in Ontario.

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The public health system faces major challenges in 2020, including an aging workforce, reductions in funding, and 2 simultaneous major threats to public health-the coronavirus pandemic and racial injustice. To effectively harness promising new technologies and address these and other public health challenges ahead, public health professionals must be trained on evidence-based practices for protecting and improving public health. This project sought to understand the network of health departments and organizations that provide training in order to inform strategic efforts to fill training gaps and improve access to training for local health departments (LHDs), thereby strengthening the public health system., Design/setting/participants: We conducted a Web-based survey of 501 LHDs in the 6 states constituting Region V of the Department of Health & Human Services. The survey focused on the training relationship between LHDs and state and national organizations that provide public health training, allowing for a social network analysis. We used data visualization and descriptive statistics to examine the network., Results: Of 290 participating health departments (58% response rate), 248 had monthly or more frequent contact with at least 1 organization for the purpose of training. Altogether, the 248 LHDs were connected to 47 state-level organizations and 10 national-level organizations. In 5 of 6 states, more LHDs were connected to the state health department for training than to any other organization type. Universities, national nonprofits, and national membership organizations provided training to the fewest LHDs. Local health department characteristics did not have a clear relationship with its number of training connections., Conclusions: State health departments may benefit from the support of universities, national nonprofits, and national membership organizations by partnering to offer training or by recommending training from these organizations to LHDs. Additional qualitative information from local and state health departments would be useful to determine the best strategies for universities, national nonprofits, and national membership organizations to participate in training local practitioners to improve LHD capacity., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus . Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis., Competing Interests: MPi reports personal fees from Mérieux Université, grants from Abacus Diagnostica, outside the submitted work. AM reports grants and personal fees from Janssen, personal fees from Merck, personal fees from Sanofi-Pasteur, personal fees from Roche, outside the submitted work. EZ reports grants and personal fees from Cystic Fibrosis Foundation, outside the submitted work. OS is an employee of Nestlé Research – Societé des Produits Nestlé S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor.)

Background and Methods: Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs., Results: The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network., Conclusions: Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Simulation training is central in preparing practitioners for code management that is free from patient harm. 1 The purpose of this study was to determine if using a high-fidelity simulator in pediatric code education would improve trainee confidence and competency compared with the use of a traditional mannequin in the same setting., Methods: Fifty-third-year medical students participated in Pediatric Advanced Life Support code training, including a mock code scenario. Students were randomized into two groups and assigned to a simulator group: high-fidelity simulator (Group 1) or traditional mannequin (Group 2). To assess competency, trainees were evaluated using a checklist of required verbalized items or performed during the mock code scenario. To assess confidence, trainees completed pre- and postintervention confidence surveys, which were collected and compared., Results: Both Group 1 and Group 2 reported increased overall confidence in code management upon completion of their training. Although confidence increased universally, Group 1 reported increased confidence over that of Group 2 in three specific areas: ability to treat respiratory arrest, ability to run a code, and knowledge of the Pediatric Advanced Life Support algorithm. Group 1 also demonstrated increased competency in code management compared with Group 2 in four key code components: checking airway, checking breathing, checking pulses, and checking capillary refill., Conclusions: Trainee confidence increases after completion of Pediatric Advanced Life Support code training, regardless of simulator type utilized. However, trainees were more competent in code management when trained using a high-fidelity simulator compared with a traditional mannequin., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Family medicine has traditionally prioritised patient care over research. However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. The model produces ORs, which suggest increased, decreased or no change in odds of being in one category of the outcome with an increase in the value of the predictor. Model significance quantifies whether the model is better than the baseline value (ie, the percentage of people with the outcome) at explaining or predicting whether the observed cases in the data set have the outcome. One model fit measure is the count- [Formula: see text], which is the percentage of observations where the model correctly predicted the outcome variable value. Related to the count- [Formula: see text] are model sensitivity-the percentage of those with the outcome who were correctly predicted to have the outcome-and specificity-the percentage of those without the outcome who were correctly predicted to not have the outcome. Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT-awFC). The novel FATCAT-awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN-BIND-1) study. Large-scale resting-state networks were assessed. We found statistically significant anatomically-weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region-pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Rationale: Chronic airway infection and inflammation resulting in progressive, obstructive lung disease is the leading cause of morbidity and mortality in cystic fibrosis. Understanding the lower airway microbiota across the ages can provide valuable insight and potential therapeutic targets., Objectives: To characterize and compare the lower airway microbiota in cystic fibrosis and disease control subjects across the pediatric age spectrum., Methods: Bronchoalveolar lavage fluid samples from 191 subjects (63 with cystic fibrosis) aged 0 to 21 years were collected along with relevant clinical data. We measured total bacterial load using quantitative polymerase chain reaction and performed 16S rRNA gene sequencing to characterize bacterial communities with species-level sensitivity for select genera. Clinical comparisons were investigated., Measurements and Main Results: Cystic fibrosis samples had higher total bacterial load and lower microbial diversity, with a divergence from disease controls around 2-5 years of age, as well as higher neutrophilic inflammation relative to bacterial burden. Cystic fibrosis samples had increased abundance of traditional cystic fibrosis pathogens and decreased abundance of the Streptococcus mitis species group in older subjects. Interestingly, increased diversity in the heterogeneous disease controls was independent of diagnosis and indication. Sequencing was more sensitive than culture, and antibiotic exposure was more common in disease controls, which showed a negative relationship with load and neutrophilic inflammation., Conclusions: Analysis of lower airway samples from people with cystic fibrosis and disease controls across the ages revealed key differences in airway microbiota and inflammation. The divergence in subjects during early childhood may represent a window of opportunity for intervention and additional study., Competing Interests: The authors have declared that no competing interests exist.

Purpose of Review: To highlight the various uses of social media by public health practitioners and organizations, with special emphasis on how social media has been successfully applied and where applications have struggled to achieve the desired effects., Recent Findings: Social media has been used effectively in improving the timeliness and accuracy of public health surveillance. Social media has also been used to communicate information between public health organizations and reinforce consistent messaging about enduring threats to public health. It has been applied with some success to coordinate of disaster response and for keeping the public informed during other emergency situations. However, social media has also been weaponized against the public health community to spread disinformation and misinformation, and the public health community has yet to devise a successful strategy to mitigate this destructive use of social media., Summary: Social media can be an effective tool for public health practitioners and organizations who seek to disseminate information on a daily basis, rapidly convey information in emergent situations, and battle misinformation. Social media has been uniquely valuable and distinctly destructive when it comes to protecting and improving public health., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Background: The objective of this project was to increase the sensitivity of sequence-based bacterial community determination without impacting community composition or interfering with cluster formation during sequencing. Two PCR protocols (standard and modified) were examined in airway samples where we observed a large range in bacterial load (3.1-6.2 log 10 16S rRNA gene copies/reaction). Tracheal aspirate (TA) samples (n = 99) were collected from sixteen children requiring mechanical ventilation at a single center. DNA was extracted, and total bacterial load (TBL) was assessed using qPCR. Amplification of 16S rRNA was attempted with both protocols in all samples., Results: PCR product was observed using both protocols in 52 samples and in 24 additional samples only with the modified protocol. TBL, diversity metrics, and prominent taxa were compared for samples in three groups based on success of the two protocols (successful with both, success with modified only, unsuccessful for both). TBL differed significantly across the three groups (p<0.001). Specifically, the modified protocol allowed amplification from samples with intermediate TBL. Shannon diversity was similar between the two protocols, and Morisita-Horn beta diversity index showed high agreement between the two protocols within samples (median value 0.9997, range 0.9947 to 1). We show that both protocols identify similar communities, and the technical variability of both protocols was very low. The use of limited PCR cycles was a key feature to limit impact of background by exclusion of 24% of samples with no evidence of bacterial DNA present in the sample., Conclusion: The modified amplification protocol represents a viable approach that increased sensitivity of bacterial community analysis, which is important for study of the human airway microbiome where bacterial load is highly variable. Video abstract.

Background: Acid blockade is commonly prescribed in patients with cystic fibrosis (CF). Growing concerns, however, exist about its possible role in the pathophysiology of pulmonary infections. We aimed to investigate if acid blockade alters esophageal and respiratory microbiota leading to dysbiosis and inflammation., Methods: We performed a cross sectional study of children with CF who were either prescribed acid blockade or not. Samples from the gastrointestinal and respiratory tracts were obtained and microbiome analyzed. Mixed effect models were used to compare outcomes between cohorts and across sampling sites. A random subject intercept was included to account for the multiple sampling sites per individual., Results: A cohort of 25 individuals, 44% girls with median age of 13.8 years [IQR 11.2--14.8] were enrolled. Alpha diversity, total bacterial load, and beta diversity were similar across anatomic compartments, across the upper gastrointestinal tract, and in respiratory samples. Similar alpha diversity, total bacterial load, and beta diversity results were also observed when comparing individuals on versus those off acid blockade. IL-8 was elevated in the distal versus proximal esophagus in the whole cohort (P < 0.01). IL-8 concentrations were similar in the distal esophagus in patients on and off acid blockade, but significantly greater in the proximal esophagus of subjects on treatment (P < 0.01)., Conclusions: On the basis of these data, acid blockade use does not appear to influence the microbiome of the aerodigestive tract in children with cystic fibrosis suggesting a complex interplay between these medications and the bacterial composition of the esophagus and lung., Competing Interests: Conflict of interest statement: G.F. is the Chief Medical Officer-EnteroTrack, the company that produces the esophageal string test. The other authors have no other conflicts of interest to report., (Copyright © 2020 by European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition.)

We sought to determine whether temporal changes in the lower airway microbiome are associated with ventilator-associated pneumonia (VAP) in children.Using a multicentre prospective study of children aged 31 days to 18 years requiring mechanical ventilation support for >72 h, daily tracheal aspirates were collected and analysed by sequencing of the 16S rRNA gene. VAP was assessed using 2008 Centers for Disease Control and Prevention paediatric criteria. The association between microbial factors and VAP was evaluated using joint longitudinal time-to-event modelling, matched case-control comparisons and unsupervised clustering.Out of 366 eligible subjects, 66 (15%) developed VAP at a median of 5 (interquartile range 3-5) days post intubation. At intubation, there was no difference in total bacterial load (TBL), but Shannon diversity and the relative abundance of Streptococcus , Lactobacillales and Prevotella were lower for VAP subjects versus non-VAP subjects. However, higher TBL on each sequential day was associated with a lower hazard (hazard ratio 0.39, 95% CI 0.23-0.64) for developing VAP, but sequential values of diversity were not associated with VAP. Similar findings were observed from the matched analysis and unsupervised clustering. The most common dominant VAP pathogens included Prevotella species (19%), Pseudomonas aeruginosa (14%) and Streptococcus mitis / pneumoniae (10%). Mycoplasma and Ureaplasma were also identified as dominant organisms in several subjects.In mechanically ventilated children, changes over time in microbial factors were marginally associated with VAP risk, although these changes were not suitable for predicting VAP in individual patients. These findings suggest that focusing exclusively on pathogen burden may not adequately inform VAP diagnosis., Competing Interests: Conflict of interest: P.M. Mourani reports grants from NIH NHLBI and NIH NICHD, during the conduct of the study. Conflict of interest: M.K. Sontag reports grants from NIH NHLBI, during the conduct of the study. Conflict of interest: K.M. Williamson has nothing to disclose. Conflict of interest: J.K. Harris has nothing to disclose. Conflict of interest: R. Reeder has nothing to disclose. Conflict of interest: C. Locandro has nothing to disclose. Conflict of interest: T.C. Carpenter reports grants from NIH NHLBI and NIH NICHD, during the conduct of the study. Conflict of interest: A.B. Maddux reports a grant from Parker B. Francis Foundation (Fellowship Award) and NIH/NICHD K23HD096018, outside the submitted work. Conflict of interest: K. Ziegler reports grants from NIH NHLBI during the conduct of the study. Conflict of interest: E.A.F. Simões reports grants from NIH NHLBI, during the conduct of the study. Conflict of interest: C.M. Osborne has nothing to disclose. Conflict of interest: L. Ambroggio has nothing to disclose. Conflict of interest: M.K. Leroue has nothing to disclose. Conflict of interest: C.E. Robertson has nothing to disclose. Conflict of interest: C. Langelier has nothing to disclose. Conflict of interest: J.L. DeRisi reports grants from NIH NHLBI, during the conduct of the study. Conflict of interest: J. Kamm has nothing to disclose. Conflict of interest: M.W. Hall reports grants from NIH NICHD, during the conduct of the study. Conflict of interest: A.F. Zuppa has nothing to disclose. Conflict of interest: J. Carcillo has nothing to disclose. Conflict of interest: K. Meert reports grants from NIH, during the conduct of the study. Conflict of interest: A. Sapru reports grants from NIH NICHD, during the conduct of the study. Conflict of interest: M.M. Pollack reports grants from NIH, during the conduct of the study. Conflict of interest: P. McQuillen reports grants from NIH NICHD, during the conduct of the study. Conflict of interest: D.A. Notterman has nothing to disclose. Conflict of interest: J.M. Dean reports grants from NIH, during the conduct of the study. Conflict of interest: B.D. Wagner reports grants from NIH NHLBI, during the conduct of the study., (Copyright ©ERS 2021.)