Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes.

OBJECTIVES: We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. MATERIALS AND METHODS: We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. RESULTS: We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. DISCUSSION: Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. CONCLUSION: We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.

Learning from an equitable, data-informed response to COVID-19: Translating knowledge into future action and preparation.

Introduction: The COVID-19 pandemic revealed numerous barriers to effectively managing public health crises, including difficulties in using publicly available, community-level data to create learning systems in support of local public health decision responses. Early in the COVID-19 pandemic, a group of health care partners began meeting to learn from their collective experiences. We identified key tools and processes for using data and learning system structures to drive equitable public health decision making throughout different phases of the pandemic. Methods: In fall of 2021, the team developed an initial theory of change directed at achieving herd immunity for COVID-19. The theoretical drivers were explored qualitatively through a series of nine 45-min telephonic interviews conducted with 16 public health and community leaders across the United States. Interview responses were analyzed into key themes to inform potential future practices, tools, and systems. In addition to the interviews, partners in Dallas and Cincinnati reflected on their own COVID-19 experiences. Results: Interview responses fell broadly into four themes that contribute to effective, community driven responses to COVID-19: real-time, accessible data that are mindful of the tension between community transparency and individual privacy; a continued fostering of public trust; adaptable infrastructures and systems; and creating cohesive community coalitions with shared alignment and goals. These themes and partner experiences helped us revise our preliminary theory of change around the importance of community collaboration and trust building and also helped refine the development of the Community Protection Dashboard tool. Conclusions: There was broad agreement amongst public health and community leaders about the key elements of the data and learning systems required to manage public health responses to COVID-19. These findings may be informative for guiding the use of data and learning in the management of future public health crises or population health initiatives.

Epidemiology and Severity of Illness of MIS-C and Kawasaki Disease During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Multisystem inflammatory syndrome in children (MIS-C) is a novel, severe condition following severe acute respiratory syndrome coronavirus 2 infection. Large epidemiologic studies comparing MIS-C to Kawasaki disease (KD) and evaluating the evolving epidemiology of MIS-C over time are lacking. We sought to understand the illness severity of MIS-C compared with KD and evaluate changes in MIS-C illness severity over time during the coronavirus disease 2019 pandemic compared with KD. METHODS: We included hospitalizations of children with MIS-C and KD from April 2020 to May 2022 from the Pediatric Health Information System administrative database. Our primary outcome measure was the presence of shock, defined as the use of vasoactive/inotropic cardiac support or extracorporeal membrane oxygenation. We examined the volume of MIS-C and KD hospitalizations and the proportion of hospitalizations with shock over time using 2-week intervals. We compared the proportion of hospitalizations with shock in MIS-C and KD patients over time using generalized estimating equations adjusting for hospital clustering and age, with time as a fixed effect. RESULTS: We identified 4868 hospitalizations for MIS-C and 2387 hospitalizations for KD. There was a higher proportion of hospitalizations with shock in MIS-C compared with KD (38.7% vs 5.1%). In our models with time as a fixed effect, we observed a significant decrease in the odds of shock over time in MIS-C patients (odds ratio 0.98, P < .001) but not in KD patients (odds ratio 1.00, P = .062). CONCLUSIONS: We provide further evidence that MIS-C is a distinct condition from KD. MIS-C was a source of lower morbidity as the pandemic progressed.

Neurodiversity, Networks, and Narratives: Exploring Intimacy and Expressive Freedom in the Time of Covid-19.

The Narratives of Neurodiversity Network (NNN) is a neurodivergent academic, creative, and educator collective that came together with allies during the Covid-19 pandemic to create a network centred around emerging narratives about neurodiversity and exploring new ways of learning and socialising. The network focuses on exploring the roles of written, spoken, and visual narratives across cultural locations about neuro-atypical experiences in generating improved agency and self-advocacy for those who have been subject to pathologization through neuro-normativity and intersecting oppression. During the last year, widening access to digital platforms has provided a space to explore these issues outside of traditional academic spaces. We run a monthly "Salon," our mixed-media "reading, listening, and watching" group, in an effort to find positive representation within contemporary culture. Discussions have moved beyond mimesis and into a consideration of how narrative and storyworlds can question the supposed naturalness of certain ways of being in and perceiving the world. This article interrogates the network's core principles of nonhierarchical co-production, including the roles of creativity, community, identity, and emancipatory research which were animated by the new techno-social context. We consider the cultural lives of neurodiversity in the West and beyond, including ethical and aesthetic dimensions. We share a faith in the power of storytelling to inform new social identities for neurodivergent people and to inform scientific understandings of atypical cognition. In exploring this, we speak through a porous first-person plural narrator, to unsettle the idea that there is a hegemonic "we" speaking on behalf of all neurodivergent people.

Review and analysis of the overlapping threats of carbapenem and polymyxin resistant E. coli and Klebsiella in Africa.

BACKGROUND: Carbapenem-resistant Enterobacterales are among the most serious antimicrobial resistance (AMR) threats. Emerging resistance to polymyxins raises the specter of untreatable infections. These resistant organisms have spread globally but, as indicated in WHO reports, the surveillance needed to identify and track them is insufficient, particularly in less resourced countries. This study employs comprehensive search strategies with data extraction, meta-analysis and mapping to help address gaps in the understanding of the risks of carbapenem and polymyxin resistance in the nations of Africa. METHODS: Three comprehensive Boolean searches were constructed and utilized to query scientific and medical databases as well as grey literature sources through the end of 2019. Search results were screened to exclude irrelevant results and remaining studies were examined for relevant information regarding carbapenem and/or polymyxin(s) susceptibility and/or resistance amongst E. coli and Klebsiella isolates from humans. Such data and study characteristics were extracted and coded, and the resulting data was analyzed and geographically mapped. RESULTS: Our analysis yielded 1341 reports documenting carbapenem resistance in 40 of 54 nations. Resistance among E. coli was estimated as high (> 5%) in 3, moderate (1-5%) in 8 and low (< 1%) in 14 nations with at least 100 representative isolates from 2010 to 2019, while present in 9 others with insufficient isolates to support estimates. Carbapenem resistance was generally higher among Klebsiella: high in 10 nations, moderate in 6, low in 6, and present in 11 with insufficient isolates for estimates. While much less information was available concerning polymyxins, we found 341 reports from 33 of 54 nations, documenting resistance in 23. Resistance among E. coli was high in 2 nations, moderate in 1 and low in 6, while present in 10 with insufficient isolates for estimates. Among Klebsiella, resistance was low in 8 nations and present in 8 with insufficient isolates for estimates. The most widespread associated genotypes were, for carbapenems, blaOXA-48, blaNDM-1 and blaOXA-181 and, for polymyxins, mcr-1, mgrB, and phoPQ/pmrAB. Overlapping carbapenem and polymyxin resistance was documented in 23 nations. CONCLUSIONS: While numerous data gaps remain, these data show that significant carbapenem resistance is widespread in Africa and polymyxin resistance is also widely distributed, indicating the need to support robust AMR surveillance, antimicrobial stewardship and infection control in a manner that also addresses broader animal and environmental health dimensions.

Identifying and Validating Pediatric Hospitalizations for MIS-C Through Administrative Data.

BACKGROUND: Individual children's hospitals care for a small number of patients with multisystem inflammatory syndrome in children (MIS-C). Administrative databases offer an opportunity to conduct generalizable research; however, identifying patients with MIS-C is challenging. METHODS: We developed and validated algorithms to identify MIS-C hospitalizations in administrative databases. We developed 10 approaches using diagnostic codes and medication billing data and applied them to the Pediatric Health Information System from January 2020 to August 2021. We reviewed medical records at 7 geographically diverse hospitals to compare potential cases of MIS-C identified by algorithms to each participating hospital's list of patients with MIS-C (used for public health reporting). RESULTS: The sites had 245 hospitalizations for MIS-C in 2020 and 358 additional MIS-C hospitalizations through August 2021. One algorithm for the identification of cases in 2020 had a sensitivity of 82%, a low false positive rate of 22%, and a positive predictive value (PPV) of 78%. For hospitalizations in 2021, the sensitivity of the MIS-C diagnosis code was 98% with 84% PPV. CONCLUSION: We developed high-sensitivity algorithms to use for epidemiologic research and high-PPV algorithms for comparative effectiveness research. Accurate algorithms to identify MIS-C hospitalizations can facilitate important research for understanding this novel entity as it evolves during new waves.

Genomic diagnosis and care co-ordination for monogenic inflammatory bowel disease in children and adults: consensus guideline on behalf of the British Society of Gastroenterology and British Society of Paediatric Gastroenterology, Hepatology and Nutrition.

Genomic medicine enables the identification of patients with rare or ultra-rare monogenic forms of inflammatory bowel disease (IBD) and supports clinical decision making. Patients with monogenic IBD frequently experience extremely early onset of treatment-refractory disease, with complex extraintestinal disease typical of immunodeficiency. Since more than 100 monogenic disorders can present with IBD, new genetic disorders and variants are being discovered every year, and as phenotypic expression of the gene defects is variable, adaptive genomic technologies are required. Monogenic IBD has become a key area to establish the concept of precision medicine. Clear guidance and standardised, affordable applications of genomic technologies are needed to implement exome or genome sequencing in clinical practice. This joint British Society of Gastroenterology and British Society of Paediatric Gastroenterology, Hepatology and Nutrition guideline aims to ensure that testing resources are appropriately applied to maximise the benefit to patients on a national scale, minimise health-care disparities in accessing genomic technologies, and optimise resource use. We set out the structural requirements for genomic medicine as part of a multidisciplinary team approach. Initiation of genomic diagnostics should be guided by diagnostic criteria for the individual patient, in particular the age of IBD onset and the patient's history, and potential implications for future therapies. We outline the diagnostic care pathway for paediatric and adult patients. This guideline considers how to handle clinically actionable findings in research studies and the impact of consumer-based genomics for monogenic IBD. This document was developed by multiple stakeholders, including UK paediatric and adult gastroenterology physicians, immunologists, transplant specialists, clinical geneticists, scientists, and research leads of UK genetic programmes, in partnership with patient representatives of several IBD and rare disease charities.

Impact of the COVID-19 pandemic on hospitalizations of children with neurologic impairment.

INTRODUCTION: Children with neurologic impairment (NI) are frequently hospitalized for infectious and noninfectious illnesses. The early period of the COVID-19 pandemic was associated with overall lower pediatric hospitalization rates, particularly for respiratory infections, but the effect on utilization for children with NI is unknown. METHOD: This multicenter retrospective cohort study included hospitalizations of children 1-18 years of age with NI diagnosis codes from 49 children's hospitals. We calculated the percent change in the median weekly hospitalization volumes and the hospitalization resource intensity score (H-RISK), comparing the early-COVID era (March 15, 2020 to December 31, 2020) with the pre-COVID era (same timeframe of 2017-2019). Percent change was calculated over the entire study period as well as within three seasonal time periods (spring, summer, and fall/winter). Differences between infectious and noninfectious admission diagnoses were also examined. RESULTS: Compared with the pre-COVID era, there was a 14.4% decrease (interquartile range [IQR]: -33.8, -11.7) in the weekly median number of hospitalizations in the early-COVID era; the weekly median H-RISK score was 11.7% greater (IQR: 8.9, 14.9). Hospitalizations decreased for both noninfectious (-11.6%, IQR: -30.0, -8.0) and infectious (-35.5%, IQR: -51.1, -31.3) illnesses in the early-COVID era. This decrease was the largest in spring 2020 and continued throughout 2020. CONCLUSIONS: For children with NI, there was a substantial and significant decrease in hospitalizations for infectious and noninfectious diagnoses but an increase in illness severity during the early-COVID era compared with the pre-COVID era. Our data suggest a need to reconsider current thresholds for hospitalization and identify opportunities to support and guide families through certain illnesses without hospitalization.

Secondary Impact of the Coronavirus Disease 19 Pandemic on Patients and the Cellular Therapy Healthcare Ecosystem.

The Coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has significantly impacted global health and healthcare delivery systems. To characterize the secondary effects of the COVID-19 pandemic and mitigation strategies used in the delivery of hematopoietic stem cell transplantation (HSCT) care, we performed a comprehensive literature search encompassing changes in specific donor collection, processing practices, patient outcomes, and patient-related concerns specific to HSCT and HSCT-related healthcare delivery. In this review, we summarize the available literature on the secondary impacts the COVID-19 pandemic on the fields of HSCT and cellular therapy. The COVID-19 pandemic has had numerous secondary impacts on patients undergoing HSCT and the healthcare delivery systems involved in providing complex care to HSCT recipients. Institutions must identify these influences on outcomes and adjust accordingly to maintain and improve outcomes for the transplantation and cellular therapy community.

Toward an ontology of collaborative learning healthcare systems.

Objective: To establish a basis for a domain ontology - a formal, explicit specification of a shared conceptualization - of collaborative learning healthcare systems (CLHSs) in order to facilitate measurement, explanation, and improvement. Methods: We adapted the "Methontology" approach to begin building an ontology of CLHSs. We specified the purpose of an ontology, acquired domain knowledge via literature review, conceptualized a common framework of CLHSs using a grounded approach, refined these concepts based on expert panel input, and illustrated concept application via four cases. Results: The set of concepts identified as important to include in an ontology includes goals, values, structure, actors, environment, and products. To establish this set of concepts, we gathered input from content experts in two ways. First, expert panel methods were used to elicit feedback on these concepts and to test the elicitation of terms for the vocabulary of the Values concept. Second, from these discussions we developed a mapping exercise to test the intuitiveness of the concepts, requesting that network leaders from four CLHSs complete a mapping exercise to associate characteristics of their networks with the high-level concepts, building the vocabulary for each concept in a grounded fashion. We also solicited feedback from these participants on the experience of completing the mapping exercise, finding that the exercise is acceptable and could aid in CLHS development and collaboration. Respondents identified opportunities to improve the operational definitions of each concept to ensure that corresponding vocabularies are distinct and non-overlapping. Discussion: Our results provide a foundation for developing a formal, explicit shared conceptualization of CLHSs. Once developed, such a tool can be useful for measurement, explanation, and improvement. Further work, including alignment to a top-level ontology, expanding the vocabulary, and defining relations between vocabulary is required to formally build out an ontology for these uses.

Quality Improvement in Hematopoietic Stem Cell Transplant and Cellular Therapy: Using the Model for Improvement to impact Outcomes.

Quality improvement and quality assurance form a complementary and independent relationship. Quality assurance measures compliance against industry standards using audits, whereas quality improvement is a continuous process focused on processes and systems that can improve care. The Model for Improvement is a robust quality improvement tool that transplant and cellular therapy teams can use to redesign healthcare processes. The Model for Improvement uses several components addressed in sequence to organize and critically evaluate improvement activities. Unlike other health sciences clinical research, quality improvement projects, and research are based on dynamic hypotheses that develop into observable, serial tests of change with continuous collection and feedback of performance data to stakeholders.

Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites.

Introduction: Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D. Methods: In two PCORnet networks, PEDSnet and OneFlorida, the study assessed measures of glycemic control, diabetic ketoacidosis admissions, and clinic visits in 2016-2018 among youth 0-20 years of age. The study team developed measure EHR-based specifications, identified institution-specific rates using data stored in the CDM, and assessed agreement with manual chart review. Results: Among 9,740 youth with T1D across 12 institutions, one quarter (26%) had two or more measures of A1c greater than 9% annually (min 5%, max 47%). The median A1c was 8.5% (min site 7.9, max site 10.2). Overall, 4% were hospitalized for diabetic ketoacidosis (min 2%, max 8%). The predictive value of the PCORnet CDM was >75% for all measures and >90% for three measures. Conclusions: Using EHR-derived data to assess comparative quality for T1D is a valid, efficient, and reliable data collection tool for measuring T1D care and outcomes. Wide variations across institutions were observed, and even the best-performing institutions often failed to achieve the American Diabetes Association HbA1C goals (<7.5%).

Testing the Use of Data Drawn from the Electronic Health Record to Compare Quality.

INTRODUCTION: Health systems spend $1.5 billion annually reporting data on quality, but efficacy and utility for benchmarking are limited due, in part, to limitations of data sources. Our objective was to implement and evaluate measures of pediatric quality for three conditions using electronic health record (EHR)-derived data. METHODS: PCORnet networks standardized EHR-derived data to a common data model. In 13 health systems from 2 networks for 2015, we implemented the National Quality Forum measures: % children with sickle cell anemia who received a transcranial Doppler; % children on antipsychotics who had metabolic screening; and % pediatric acute otitis media with amoxicillin prescribed. Manual chart review assessed measure accuracy. RESULTS: Only 39% (N = 2,923) of 7,278 children on antipsychotics received metabolic screening (range: 20%-54%). If the measure indicated screening was performed, the chart agreed 88% of the time [95% confidence interval (CI): 81%-94%]; if it indicated screening was not done, the chart agreed 86% (95% CI: 78%-93%). Only 69% (N = 793) of 1,144 children received transcranial Doppler screening (range across sites: 49%-88%). If the measure indicated screening was performed, the chart agreed 98% of the time (95% CI: 94%-100%); if it indicated screening was not performed, the chart agreed 89% (95% CI: 82%-95%). For acute otitis media, chart review identified many qualifying cases missed by the National Quality Forum measure, which excluded a common diagnostic code. CONCLUSIONS: Measures of healthcare quality developed using EHR-derived data were valid and identified wide variation among network sites. This data can facilitate the identification and spread of best practices.

A collaborative learning health system agent-based model: Computational and face validity.

INTRODUCTION: Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) - network organizations that allow all healthcare stakeholders to collaborate at scale - are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent-based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity. METHODS: CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know-how for improvement. We build up a CLHS ABM from a population of patient- and doctor-agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter - the degree to which patients influence other patients - and trace its effects on patient engagement, shared knowledge, and outcomes. RESULTS: The CLHS ABM, developed in Python and using the open-source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes. CONCLUSIONS: We present the first theoretically-derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well-developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.

Getting what is needed, when it's needed: Sharing information, knowledge, and know-how in a Collaborative Learning Health System.

BACKGROUND: Collaborative Learning Health Systems (CLHS) improve outcomes in part by facilitating collaboration among all stakeholders. One way to facilitate collaboration is by creating conditions for the production and sharing of medical and non-medical resources (information, knowledge, and knowhow [IKK]) so anybody can get "what is needed, when it's needed" (WINWIN) to act in ways that improve health and healthcare. Matching resources to needs can facilitate accurate diagnosis, appropriate prescribing, answered questions, provision of emotional and social support, and uptake of innovations. OBJECTIVES: We describe efforts in ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease (IBD), to increase the number of patients and families creating and accessing IKK, and the challenges faced in that process. METHODS: We applied tactics such as outreach through trusted messengers, community organizing, and digital outreach such as sharing resources on our website, via social media, and email to increase the number of people producing, able to access, and accessing IKK. We applied an existing measurement system to track our progress and supplemented this with community feedback. RESULTS: In August of 2017 we identified and began measuring specific actions to track community growth. The number of patients and families producing IKK has increased by a factor of 2.7, using resources has increased by a factor of 4.1 and aware of resources as increased by a factor of 4.0. We identified challenges to measurement and scaling. CONCLUSIONS: It is possible to intentionally increase the number of patients and caregivers engaged with a CHLS to produce and share resources to improve their health.

A science of collaborative learning health systems.

INTRODUCTION: Improving the U.S. healthcare system and health outcomes is one of the most pressing public health challenges of our time. Previously described Collaborative Learning Health Systems (CLHSs) are a promising approach to outcomes improvement. In order to fully realize this promise, a deeper understanding of this phenomenon is necessary. METHODS: We drew on our experience over the past decade with CLHSs as well as qualitative literature review to answer three questions: What kind of phenomena are CLHSs? and what is an appropriate scientific approach? How might we frame CLHSs conceptually? What are potential mechanisms of action? RESULTS: CLHSs are complex adaptive systems in which all stakeholders are able to collaborate, at scale, to create and share resources to satisfy a variety of needs. This is accomplished by providing infrastructure and services that enable stakeholders to act on their inherent motivations. This framing has implications for both research and practice. CONCLUSION: Articulating this framework and potential mechanisms of action should facilitate research to test and refine hypotheses as well as guide practice to develop and optimize this promising approach to improving healthcare systems.

Measuring engagement in a collaborative learning health system: The case of ImproveCareNow.

BACKGROUND: Collaborative learning health systems have demonstrated improved outcomes for a range of different chronic conditions. Patient and healthcare provider engagement in these systems is thought to be associated with improved outcomes. We have adapted an observational framework to measure, and track over time, engagement in ImproveCareNow, a collaborative learning health system for children with inflammatory bowel disease. INTRODUCTION: We developed a categorical classification scheme for engagement in ImproveCareNow. Each tier is defined in terms of observable individual behaviors. When an individual completes one or more qualifying behavior, s/he is classified as engaged at that tier. Individuals are entered into a database, which is accessible to care centers throughout the ImproveCareNow network. Database records include fields for individual name, behavior type, time, place, and level of engagement. RESULTS: The resulting system is employed at 79 ImproveCareNow care centers in the United States. The system recognizes four levels of engagement. Behaviors are recorded in a managed vocabulary and recorded in an online database. The database is queried weekly for individual engagement behaviors, which are tracked longitudinally. Center- and network-level statistics are generated and disseminated to stakeholders. CONCLUSION: It is possible to monitor longitudinal engagement in a collaborative learning health system, thereby charting progress toward engagement goals and enabling quantitative evaluation of interventions aimed at increasing engagement.