Characteristics of children with autism and unspecified intellectual developmental disorder (intellectual disability) presenting with severe self-injurious behaviours.

Objective: This study aims to delineate the characteristics of severe self-injurious behaviors (SIB) in a cohort of children with autism and unspecified intellectual developmental disorder (UIDD) (intellectual disability) and examine potential risk factors for developing SIB. Methods: A retrospective chart review studied characteristics of severe SIB in 30 children with autism spectrum disorder (ASD) and UIDD referred to a tertiary care center. Characteristics examined include genetic syndromes, brain MRI abnormalities, verbal ability, adaptive functioning, SIB frequency and severity, age of onset, number of psychopharmacological agents, irritability, hyperactivity, stereotypy, psychiatric and physical comorbidities, among others. Descriptive and bivariate analysis were applied to explore potential relationships between factors. Results: Children with severe SIB exhibit this behaviour with high frequency, inflicting moderate to severe injury. Most children in the study sample are non-verbal and have ASD (93.3%; n = 28) with psychiatric (96.7%; n = 29) and physical (90%; n = 27) comorbidities. Overall SIB improvement using the Clinical Global Impression, Improvement Score (CGI-I) was 3.0 (minimally improved). A minority were much or very much improved following appropriate intervention. Conclusions: The severity of SIB is much higher in this sample than previously noted in the literature. Severe SIB is associated with ADHD, early onset mood disorders, tics, avoidant restrictive food intake disorder and Obsessive-Compulsive Disorder.

Longitudinal peanut and Ara h 2 specific-IgE, -IgG4, and -IgG4/-IgE ratios are associated with the natural resolution of peanut allergy in childhood.

BACKGROUND: There are no studies of longitudinal immunoglobulin measurements in a population-based cohort alongside challenge-confirmed peanut allergy outcomes. Little is known about biomarkers for identifying naturally resolving peanut allergy during childhood. OBJECTIVES: To measure longitudinal trends in whole peanut and component Ara h 2 sIgE and sIgG4 in the first 10 years of life, in a population cohort of children with challenge-confirmed peanut allergy, and to determine whether peanut-specific immunoglobulin levels or trends are associated with peanut allergy persistence or resolution by 10 years of age. METHODS: One-year-old infants with challenge-confirmed peanut allergy (n = 156) from the HealthNuts study (n = 5276) were prospectively followed at ages 4, 6, and 10 years with questionnaires, skin prick tests, oral food challenges, and plasma total-IgE, sIgE and sIgG4 to peanut and Ara h 2. RESULTS: Peanut allergy resolved in 33.9% (95% CI = 25.3%, 43.3%) of children by 10 years old with most resolving (97.4%, 95% CI = 86.5%, 99.9%) by 6 years old. Decreasing Ara h 2 sIgE (p = .01) and increasing peanut sIgG4 (p < .001), Ara h 2 sIgG4 (p = .01), peanut sIgG4/sIgE (p < .001) and Ara h 2 sIgG4/sIgE (p < .001) from 1 to 10 years of age were associated with peanut allergy resolution. Peanut sIgE measured at 1 year old had the greatest prognostic value (AUC = 0.75 [95% CI = 0.66, 0.82]); however, no single threshold produced both high sensitivity and specificity. CONCLUSION: One third of infant peanut allergy resolved by 10 years of age. Decreasing sIgE and sIgG4 to peanut and Ara h 2 over time were associated with natural resolution of peanut allergy. However, biomarker levels at diagnosis were not strongly associated with the natural history of peanut allergy.

Mitigating Contaminant-Driven Risks for the Safe Expansion of the Agricultural-Sanitation Circular Economy in an Urbanizing World.

The widespread adoption of an agricultural circular economy requires the recovery of resources such as water, organic matter, and nutrients from livestock manure and sanitation. While this approach offers many benefits, we argue this is not without potential risks to human and environmental health that largely stem from the presence of contaminants in the recycled resources (e.g., pharmaceuticals, pathogens). We discuss context specific challenges and solutions across the three themes: (1) contaminant monitoring; (2) collection transport and treatment; and (3) regulation and policy. We advocate for the redesign of sanitary and agricultural management practices to enable safe resource reuse in a proportionate and effective way. In populous urban regions with access to sanitation provision, processes can be optimized using emergent technologies to maximize removal of contaminant from excreta prior to reuse. Comparatively, in regions with limited existing capacity for conveyance of excreta to centralized treatment facilities, we suggest efforts should focus on creation of collection facilities (e.g., pit latrines) and decentralized treatment options such as composting systems. Overall, circular economy approaches to sanitation and resource management offer a potential solution to a pressing challenge; however, to ensure this is done in a safe manner, contaminant risks must be mitigated.

Electronic Health Records for Population Health Management: Comparison of Electronic Health Record-Derived Hypertension Prevalence Measures Against Established Survey Data.

BACKGROUND: Hypertension is the most prevalent risk factor for mortality globally. Uncontrolled hypertension is associated with excess morbidity and mortality, and nearly one-half of individuals with hypertension do not have the condition under control. Data from electronic health record (EHR) systems may be useful for community hypertension surveillance, filling a gap in local public health departments' community health assessments and supporting the public health data modernization initiatives currently underway. To identify patients with hypertension, computable phenotypes are required. These phenotypes leverage available data elements-such as vitals measurements and medications-to identify patients diagnosed with hypertension. However, there are multiple methodologies for creating a phenotype, and the identification of which method most accurately reflects real-world prevalence rates is needed to support data modernization initiatives. OBJECTIVE: This study sought to assess the comparability of 6 different EHR-based hypertension prevalence estimates with estimates from a national survey. Each of the prevalence estimates was created using a different computable phenotype. The overarching goal is to identify which phenotypes most closely align with nationally accepted estimations. METHODS: Using the 6 different EHR-based computable phenotypes, we calculated hypertension prevalence estimates for Marion County, Indiana, for the period from 2014 to 2015. We extracted hypertension rates from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period. We used the two 1-sided t test (TOST) to test equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race. RESULTS: Using both 80% and 90% CIs, the TOST analysis resulted in 2 computable phenotypes demonstrating rough equivalence to BRFSS estimates. Variation in performance was noted across phenotypes as well as demographics. TOST with 80% CIs demonstrated that the phenotypes had less variance compared to BRFSS estimates within subpopulations, particularly those related to racial categories. Overall, less variance occurred on phenotypes that included vitals measurements. CONCLUSIONS: This study demonstrates that certain EHR-derived prevalence estimates may serve as rough substitutes for population-based survey estimates. These outcomes demonstrate the importance of critically assessing which data elements to include in EHR-based computer phenotypes. Using comprehensive data sources, containing complete clinical data as well as data representative of the population, are crucial to producing robust estimates of chronic disease. As public health departments look toward data modernization activities, the EHR may serve to assist in more timely, locally representative estimates for chronic disease prevalence.

Treatment Rates for Chlamydia trachomatis and Neisseria gonorrhoeae in a Metropolitan Area: Observational Cohort Analysis.

BACKGROUND: Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are the 2 most common sexually transmitted infections (STIs) in the United States. The Centers for Disease Control and Prevention regularly publishes and updates STI Treatment Guidelines. The purpose of this study was to measure and compare treatment rates for CT and GC among public and private providers. METHODS: Data from multiple sources, including electronic health records and Medicaid claims, were linked and integrated. Cases observed during 2016-2020 were defined based on positive laboratory results. We calculated descriptive statistics and odd ratios based on characteristics of providers and patients, stratifying by public versus private providers. Univariate logistic regression models were used to examine the factors associated with recommended treatment. RESULTS: Overall, we found that 82.2% and 63.0% of initial CT and GC episodes, respectively, received Centers for Disease Control and Prevention-recommended treatment. The public STI clinic treated more than 90% of CT and GC cases consistently across the 5-year period. Private providers were significantly less likely to treat first episodes for CT (79.6%) and GC (53.3%; P < 0.01). Other factors associated with a higher likelihood of recommended treatment included being male, being HIV positive, and identifying as Black or multiracial. Among GC cases, 10.8% received nonrecommended treatment; all CT cases with treatment occurred per guidelines. CONCLUSIONS: Although these treatment rates are higher than previous studies, there remain significant gaps in STI treatment that require intervention from public health.

Clinical Epidemiology and Risk Factors for Critical Outcomes Among Vaccinated and Unvaccinated Adults Hospitalized With COVID-19-VISION Network, 10 States, June 2021-March 2023.

BACKGROUND: The epidemiology of coronavirus disease 2019 (COVID-19) continues to develop with emerging variants, expanding population-level immunity, and advances in clinical care. We describe changes in the clinical epidemiology of COVID-19 hospitalizations and risk factors for critical outcomes over time. METHODS: We included adults aged ≥18 years from 10 states hospitalized with COVID-19 June 2021-March 2023. We evaluated changes in demographics, clinical characteristics, and critical outcomes (intensive care unit admission and/or death) and evaluated critical outcomes risk factors (risk ratios [RRs]), stratified by COVID-19 vaccination status. RESULTS: A total of 60 488 COVID-19-associated hospitalizations were included in the analysis. Among those hospitalized, median age increased from 60 to 75 years, proportion vaccinated increased from 18.2% to 70.1%, and critical outcomes declined from 24.8% to 19.4% (all P < .001) between the Delta (June-December, 2021) and post-BA.4/BA.5 (September 2022-March 2023) periods. Hospitalization events with critical outcomes had a higher proportion of ≥4 categories of medical condition categories assessed (32.8%) compared to all hospitalizations (23.0%). Critical outcome risk factors were similar for unvaccinated and vaccinated populations; presence of ≥4 medical condition categories was most strongly associated with risk of critical outcomes regardless of vaccine status (unvaccinated: adjusted RR, 2.27 [95% confidence interval {CI}, 2.14-2.41]; vaccinated: adjusted RR, 1.73 [95% CI, 1.56-1.92]) across periods. CONCLUSIONS: The proportion of adults hospitalized with COVID-19 who experienced critical outcomes decreased with time, and median patient age increased with time. Multimorbidity was most strongly associated with critical outcomes.

Navigating the Landscape: Barriers and Facilitators in Electronic Case Reporting Implementation Across Public Health Agencies.

OBJECTIVE: The objectives were to identify barriers and facilitators for electronic case reporting (eCR) implementation associated with "organizational" and "people"-based knowledge/processes and to identify patterns across implementation stages to guide best practices for eCR implementation at public health agencies. DESIGN: This qualitative study uses semistructured interviews with key stakeholders across 6 public health agencies. This study leveraged 2 conceptual frameworks for the development of the interview guide and initial codebook and the organization of the findings of thematic analysis. SETTING: Interviews were conducted virtually with informants from public health agencies at varying stages of eCR implementation. PARTICIPANTS: Investigators aimed to enroll 3 participants from each participating public health agency, including an eCR lead, a technical lead, and a leadership informant. MAIN OUTCOME MEASURES: Patterns associated with barriers and facilitators across the eCR implementation stage. RESULTS: Twenty-eight themes were identified throughout interviews with 16 informants representing 6 public health agencies at varying stages of implementation. While there was variation across these levels, 3 distinct patterns were identified, including themes that were described (1) solely as a barrier or facilitator for eCR implementation regardless of implementation stages, (2) as a barrier for those in the early stages but evolved into a facilitator for those in later stages, and (3) as facilitators that were unique to the late-stage implementation. CONCLUSION: This study elucidated critical national, organizational, and person-centric best practices for public health agencies. These included the importance of engagement with the national eCR team, integrated development teams, cross-pollination, and developing solutions with the broader public health mission in mind. While the implementation of eCR was the focus of this study, the findings are generalizable to the broader data modernization efforts within public health agencies.

Identification of Hypertension in Electronic Health Records Through Computable Phenotype Development and Validation for Use in Public Health Surveillance: Retrospective Study.

BACKGROUND: Electronic health record (EHR) systems are widely used in the United States to document care delivery and outcomes. Health information exchange (HIE) networks, which integrate EHR data from the various health care providers treating patients, are increasingly used to analyze population-level data. Existing methods for population health surveillance of essential hypertension by public health authorities may be complemented using EHR data from HIE networks to characterize disease burden at the community level. OBJECTIVE: We aimed to derive and validate computable phenotypes (CPs) to estimate hypertension prevalence for population-based surveillance using an HIE network. METHODS: Using existing data available from an HIE network, we developed 6 candidate CPs for essential (primary) hypertension in an adult population from a medium-sized Midwestern metropolitan area in the United States. A total of 2 independent clinician reviewers validated the phenotypes through a manual chart review of 150 randomly selected patient records. We assessed the precision of CPs by calculating sensitivity, specificity, positive predictive value (PPV), F1-score, and validity of chart reviews using prevalence-adjusted bias-adjusted κ. We further used the most balanced CP to estimate the prevalence of hypertension in the population. RESULTS: Among a cohort of 548,232 adults, 6 CPs produced PPVs ranging from 71% (95% CI 64.3%-76.9%) to 95.7% (95% CI 84.9%-98.9%). The F1-score ranged from 0.40 to 0.91. The prevalence-adjusted bias-adjusted κ revealed a high percentage agreement of 0.88 for hypertension. Similarly, interrater agreement for individual phenotype determination demonstrated substantial agreement (range 0.70-0.88) for all 6 phenotypes examined. A phenotype based solely on diagnostic codes possessed reasonable performance (F1-score=0.63; PPV=95.1%) but was imbalanced with low sensitivity (47.6%). The most balanced phenotype (F1-score=0.91; PPV=83.5%) included diagnosis, blood pressure measurements, and medications and identified 210,764 (38.4%) individuals with hypertension during the study period (2014-2015). CONCLUSIONS: We identified several high-performing phenotypes to identify essential hypertension prevalence for local public health surveillance using EHR data. Given the increasing availability of EHR systems in the United States and other nations, leveraging EHR data has the potential to enhance surveillance of chronic disease in health systems and communities. Yet given variability in performance, public health authorities will need to decide whether to seek optimal balance or declare a preference for algorithms that lean toward sensitivity or specificity to estimate population prevalence of disease.

Developing a national strategy of consumer and community involvement (CCI) for women's health research.

OBJECTIVE: To develop a consumer and community involvement (CCI) strategy for the Women's Health Research, Translation and Impact Network (WHRTN), an initiative of the Australian Health Research Alliance (AHRA). TYPE OF PROGRAM: A national network, comprising representatives from 14 nationally-accredited research translation centres that aims to embed CCI at a systems level, to improve equity and health outcomes across women's health. METHODS: A CCI Sub-Committee of WHRTN was established, chaired by a Consumer Advisor/Advocate. This committee invited both internal and external Consumer Advisor/Advocates to participate in a workshop, to guide the development of WHRTN's CCI Strategy in women's health research. RESULTS: A CCI Strategy document was written with input from workshop attendees and leading academics in women's health and has now been implemented into WHRTN, informing all aspect of the Network's programs and activities. DISCUSSION: Broad and early consumer involvement can facilitate meaningful partnerships between researchers and community, and enable genuine consumer contributions to research across strategy development, priority setting and undertaking research. Appropriate finances and time need to be allocated for CCI, with training in CCI a key enabler for its effective implementation.

Consumer and community involvement in research is increasingly recognised as an important component of high-quality research. It is now required by many research funders and organisations. However, researchers and organisations often struggle with how to initiate and implement consumer and community involvement at a systems level. In this paper, we outline the processes used to develop a national consumer and community involvement strategy for the Australian Health Research Alliance, Women's Health Research Translation and Impact Network. This provides a roadmap of how organisations can achieve a framework that supports consumer and community involvement across the research pathway. The strategy highlights the need for broad and early inclusion of consumers in decision making, financing consumer involvement, allowing time to build partnerships, and inclusion of training for researchers and consumers.

The Structure of Working Memory and Its Relationship with Intelligence in Japanese Children.

There is a host of research on the structure of working memory (WM) and its relationship with intelligence in adults, but only a few studies have involved children. In this paper, several different WM models were tested on 170 Japanese school children (from 7 years and 5 months to 11 years and 6 months). Results showed that a model distinguishing between modalities (i.e., verbal and spatial WM) fitted the data well and was therefore selected. Notably, a bi-factor model distinguishing between modalities, but also including a common WM factor, presented with a very good fit, but was less parsimonious. Subsequently, we tested the predictive power of the verbal and spatial WM factors on fluid and crystallized intelligence. Results indicated that the shared contribution of WM explained the largest portion of variance of fluid intelligence, with verbal and spatial WM independently explaining a residual portion of the variance. Concerning crystallized intelligence, however, verbal WM explained the largest portion of the variance, with the joint contribution of verbal and spatial WM explaining the residual part. The distinction between verbal and spatial WM could be important in clinical settings (e.g., children with atypical development might struggle selectively on some WM components) and in school settings (e.g., verbal and spatial WM might be differently implicated in mathematical achievement).

Artificial intelligence in ovarian cancer histopathology: a systematic review.

This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.

Generalizability and portability of natural language processing system to extract individual social risk factors.

OBJECTIVE: The objective of this study is to validate and report on portability and generalizability of a Natural Language Processing (NLP) method to extract individual social factors from clinical notes, which was originally developed at a different institution. MATERIALS AND METHODS: A rule-based deterministic state machine NLP model was developed to extract financial insecurity and housing instability using notes from one institution and was applied on all notes written during 6 months at another institution. 10% of positively-classified notes by NLP and the same number of negatively-classified notes were manually annotated. The NLP model was adjusted to accommodate notes at the new site. Accuracy, positive predictive value, sensitivity, and specificity were calculated. RESULTS: More than 6 million notes were processed at the receiving site by the NLP model, which resulted in about 13,000 and 19,000 classified as positive for financial insecurity and housing instability, respectively. The NLP model showed excellent performance on the validation dataset with all measures over 0.87 for both social factors. DISCUSSION: Our study illustrated the need to accommodate institution-specific note-writing templates as well as clinical terminology of emergent diseases when applying NLP model for social factors. A state machine is relatively simple to port effectively across institutions. Our study. showed superior performance to similar generalizability studies for extracting social factors. CONCLUSION: Rule-based NLP model to extract social factors from clinical notes showed strong portability and generalizability across organizationally and geographically distinct institutions. With only relatively simple modifications, we obtained promising performance from an NLP-based model.

Factors associated with return of home oral fluid kits by suspected cases of measles, cohort study, London and South East of England, 2016-2018.

A testing rate for measles above 80% is required by the WHO European Region Measles Elimination strategy to verify elimination. To comply with this rate, we explored factors associated with the return of oral fluid kits (OFK) by suspected measles cases. We described the cases and conducted a mixed-effects analysis to assess the relationship between socio-demographic and public health management characteristics and the likelihood of returning an OFK to the reference laboratory. Of 3,929 cases who were sent a postal OFK, 2,513 (67%) returned the kit. Adjusting for confounding, registration with a general practitioner (GP) (aOR:1.48, 95%CI:1.23-1.76) and living in a less deprived area (aOR:1.35, 95%CI:1.04-1.74) were associated with an increased likelihood of returning the OFK. The odds of returning the OFK also increased if the HPT contacted the parents/guardians of all cases prior to sending the kit and confirmed their address (aOR:2.01, 95%CI:1.17-3.42). Cases notified by a hospital (aOR:1.94, 95%CI:1.31-2.87) or GP (aOR:1.52; 95%CI:1.06-2.16) also had higher odds of returning the OFK. HPTs may want to consider these factors when managing suspected cases of measles since this may help in increasing the testing rates to the WHO-recommended level.

Strengths, weaknesses, opportunities, and threats for the nation's public health information systems infrastructure: synthesis of discussions from the 2022 ACMI Symposium.

OBJECTIVE: The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance. MATERIALS AND METHODS: The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach. RESULTS: A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources. DISCUSSION: The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies. CONCLUSION: Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future.

Natural language processing-driven state machines to extract social factors from unstructured clinical documentation.

Objective: This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability. Materials and Methods: Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Results: PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Discussion: We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics. Conclusion: The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution.

TreEAT trial: Protocol for a randomized controlled trial investigating the efficacy and safety of early introduction of tree nuts for the prevention of tree nut allergy in infants with peanut allergy.

INTRODUCTION: Children with peanut allergy are at increased risk of developing tree nut allergies, which can be severe and for most lifelong. Introduction of peanut in the first year of life can reduce the risk of peanut allergy; however, prevention strategies for tree nut allergies have not been established. We aimed to test the efficacy and safety of a novel strategy, a supervised multi-nut oral food challenge (OFC) compared with standard care for tree nut allergy prevention in infants at high risk of developing tree nut allergy, TreEAT. METHODS AND ANALYSIS: TreEAT is a 2-armed, open-label, randomized, controlled trial (RCT). Infants (n = 212) aged 4-11 months with peanut allergy will be randomized 1:1 at peanut allergy diagnosis to either a hospital-based multi-tree nut (almond, cashew, hazelnut, and walnut) OFC using multi-nut butter or standard care (home introduction of individual tree nuts). All infants will be assessed at age 18 months, with questionnaires and SPT to peanut and tree nuts. Peanut and tree nut OFCs will be performed as required to determine the allergy status for each nut. The primary outcome is tree nut allergy at age 18 months. Secondary outcomes include peanut allergy resolution, proportion, and severity of adverse events related to tree nut ingestion, number and frequency of tree nuts ingested, quality of life and parental anxiety, and allergy-related healthcare visits from randomization to 18 months of age. Analyses will be performed on an intention-to-treat basis. ETHICS AND DISSEMINATION: TreEAT was approved by the Royal Children's Hospital Human Research Ethics Committee (#70489). Outcomes will be presented at scientific conferences and disseminated through publication. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov ID: NCT04801823.

Enhancing the nation's public health information infrastructure: a report from the ACMI symposium.

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.

Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA.

Objective: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. Materials and Methods: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Results: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Discussion: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. Conclusion: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.

Relationships Between Social Vulnerability and Coronavirus Disease 2019 Vaccination Coverage and Vaccine Effectiveness.

BACKGROUND: Coronavirus disease 2019 (COVID-19) vaccination coverage remains lower in communities with higher social vulnerability. Factors such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure risk and access to healthcare are often correlated with social vulnerability and may therefore contribute to a relationship between vulnerability and observed vaccine effectiveness (VE). Understanding whether these factors impact VE could contribute to our understanding of real-world VE. METHODS: We used electronic health record data from 7 health systems to assess vaccination coverage among patients with medically attended COVID-19-like illness. We then used a test-negative design to assess VE for 2- and 3-dose messenger RNA (mRNA) adult (≥18 years) vaccine recipients across Social Vulnerability Index (SVI) quartiles. SVI rankings were determined by geocoding patient addresses to census tracts; rankings were grouped into quartiles for analysis. RESULTS: In July 2021, primary series vaccination coverage was higher in the least vulnerable quartile than in the most vulnerable quartile (56% vs 36%, respectively). In February 2022, booster dose coverage among persons who had completed a primary series was higher in the least vulnerable quartile than in the most vulnerable quartile (43% vs 30%). VE among 2-dose and 3-dose recipients during the Delta and Omicron BA.1 periods of predominance was similar across SVI quartiles. CONCLUSIONS: COVID-19 vaccination coverage varied substantially by SVI. Differences in VE estimates by SVI were minimal across groups after adjusting for baseline patient factors. However, lower vaccination coverage among more socially vulnerable groups means that the burden of illness is still disproportionately borne by the most socially vulnerable populations.

Findings From a Scoping Review: Presumptive Treatment for Chlamydiatrachomatis and Neisseria gonorrhoeae in the United States, 2006-2021.

ABSTRACT: Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are the 2 most common reported sexually transmitted infections in the United States. Current recommendations are to presumptively treat CT and/or GC in persons with symptoms or known contact. This review characterizes the literature around studies with presumptive treatment, including identifying rates of presumptive treatment and overtreatment and undertreatment rates. Of the 18 articles that met our inclusion criteria, 6 pertained to outpatient settings. In the outpatient setting, presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 12% to 100%, and the percent positive of those presumptively treated ranged from 25% to 46%. Three studies also reported data on positive results in patients not presumptively treated, which ranged from 2% to 9%. Two studies reported median follow-up time for untreated, which was roughly 9 days. The remaining 12 articles pertained to the emergency setting where presumptive treatment rates, for both asymptomatic and symptomic patients, varied from 16% to 91%, the percent positive following presumptive treatment ranged from 14% to 59%. Positive results without presumptive treatment ranged from 4% to 52%. Two studies reported the percent positive without any treatment (6% and 32%, respectively) and one reported follow-up time for untreated infections (median, 4.8 days). Rates of presumptive treatment, as well as rates of overtreatment or undertreatment vary widely across studies and within care settings. Given the large variability in presumptive treatment, the focus on urban settings, and minimal focus on social determinants of health, additional studies are needed to guide treatment practices for CT and GC in outpatient and emergency settings.