Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine.

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

Fostering Governance and Information Partnerships for Chronic Disease Surveillance: The Multi-State EHR-Based Network for Disease Surveillance.

CONTEXT: Electronic health records (EHRs) are an emerging chronic disease surveillance data source and facilitating this data sharing is complex. PROGRAM: Using the experience of the Multi-State EHR-Based Network for Disease Surveillance (MENDS), this article describes implementation of a governance framework that aligns technical, statutory, and organizational requirements to facilitate EHR data sharing for chronic disease surveillance. IMPLEMENTATION: MENDS governance was cocreated with data contributors and health departments representing Texas, New Orleans, Louisiana, Chicago, Washington, and Indiana through engagement from 2020 to 2022. MENDS convened a governance body, executed data-sharing agreements, and developed a master governance document to codify policies and procedures. RESULTS: The MENDS governance committee meets regularly to develop policies and procedures on data use and access, timeliness and quality, validation, representativeness, analytics, security, small cell suppression, software implementation and maintenance, and privacy. Resultant policies are codified in a master governance document. DISCUSSION: The MENDS governance approach resulted in a transparent governance framework that cultivates trust across the network. MENDS's experience highlights the time and resources needed by EHR-based public health surveillance networks to establish effective governance.

A marine biogenic source of atmospheric ice-nucleating particles.

The amount of ice present in clouds can affect cloud lifetime, precipitation and radiative properties. The formation of ice in clouds is facilitated by the presence of airborne ice-nucleating particles. Sea spray is one of the major global sources of atmospheric particles, but it is unclear to what extent these particles are capable of nucleating ice. Sea-spray aerosol contains large amounts of organic material that is ejected into the atmosphere during bubble bursting at the organically enriched sea-air interface or sea surface microlayer. Here we show that organic material in the sea surface microlayer nucleates ice under conditions relevant for mixed-phase cloud and high-altitude ice cloud formation. The ice-nucleating material is probably biogenic and less than approximately 0.2 micrometres in size. We find that exudates separated from cells of the marine diatom Thalassiosira pseudonana nucleate ice, and propose that organic material associated with phytoplankton cell exudates is a likely candidate for the observed ice-nucleating ability of the microlayer samples. Global model simulations of marine organic aerosol, in combination with our measurements, suggest that marine organic material may be an important source of ice-nucleating particles in remote marine environments such as the Southern Ocean, North Pacific Ocean and North Atlantic Ocean.

Resolution of anemia and improved quality of life following laparoscopic hiatal hernia repair.

BACKGROUND: Cameron lesions (CL) are common complications of large hiatal hernia (HH) disease and are known to result in chronic blood loss with resultant microcytic anemia. There is support in the literature that repair of HH may lead to resolution of CL and restore normal hemoglobin levels. This study aimed to determine the impact of elective HH repair on resolution of anemia and the quality of life (QOL) in patients with CL. METHOD: A single-institution, retrospective review analyzed all patients with history of CL or anemia (hemoglobin < 12.0 gm/dl in women, < 13.5 gm/dl in men) who underwent HH repair from January 2012 to May 2019. Four validated surveys were used to assess QOL: Reflux Symptom Index (RSI), gastroesophageal reflux disease health-related QOL (GERD-HRQL), laryngopharyngeal reflux health-related QOL (LPR-HRQL), and QOL and swallowing disorders (SWAL) survey. History of iron supplements and perioperative hemoglobin were also noted. RESULT: Ninety-six patients were included in this study. The mean age was 67.4 ± 10.8 years and 79% of patients were female. CL were endoscopically identified in 61.5% of patients preoperatively, and the rest of the patients experienced anemia of undiagnosed origin but had a high suspicion for CL. Mean follow-up after HH repair was 17.3 months (range, 1 month-5 years). Mean preoperative hemoglobin was 11.01 ± 2.9 gm/dl and 13.23 ± 1.6 gm/dl postoperatively (p < 0.01). Forty-two (73.7%) patients had resolution of anemia during follow-up and 94.5% stopped supplemental oral iron. Finally, QOL scores significantly improved after surgical intervention: RSI (63%), GERD-HRQL (77%), LPR-HRQOL (72%), and SWAL (13%). CONCLUSION: Elective HH repair in patients with chronic anemia secondary to CL may potentially resolve CL and anemia and contribute to significant QOL improvements. Future studies will prospectively assess the resolution of CL with biochemical and endoscopic follow-up to confirm the preliminary findings of our analysis.

Beyond the reminder: The next steps in pharmacist-driven, mHealth patient engagement.

OBJECTIVE: To describe opportunities for pharmacists to use mobile messaging and real-time monitoring to engage with patients taking long-term medications. SUMMARY: The proliferation of mobile phone use across the United States has been met with increased application of these devices by the medical community. However, beyond simple text messages and app-based functions, use of these devices by pharmacies and pharmacists has not been leveraged to improve patient outcomes, such as medication adherence. Resources now exist that can facilitate more advanced mobile communication between patients and pharmacists, which can be managed and informed by data available in most pharmacies. Such tailored messaging can be personalized further by being reactive to patient behavior using real-time medication use monitoring tools, facilitating low-cost, high-reach interventions for patients in need of ongoing guidance. CONCLUSION: Mechanisms now exist for pharmacies to engage patients more proactively with their prescribed therapy using mobile communication and devices. By facilitating such engagement, pharmacists can remain connected with patients throughout their care, better interpret their needs, navigate adherence-related issues, and more holistically counsel patients based on observed behaviors. Community pharmacy leadership should pursue the use of these advanced mobile messaging techniques as another tool in their arsenal to improve patient outcomes.

Tyrosine phosphorylation regulates the endocytosis and surface expression of GluN3A-containing NMDA receptors.

Selective control of receptor trafficking provides a mechanism for remodeling the receptor composition of excitatory synapses, and thus supports synaptic transmission, plasticity, and development. GluN3A (formerly NR3A) is a nonconventional member of the NMDA receptor (NMDAR) subunit family, which endows NMDAR channels with low calcium permeability and reduced magnesium sensitivity compared with NMDARs comprising only GluN1 and GluN2 subunits. Because of these special properties, GluN3A subunits act as a molecular brake to limit the plasticity and maturation of excitatory synapses, pointing toward GluN3A removal as a critical step in the development of neuronal circuitry. However, the molecular signals mediating GluN3A endocytic removal remain unclear. Here we define a novel endocytic motif (YWL), which is located within the cytoplasmic C-terminal tail of GluN3A and mediates its binding to the clathrin adaptor AP2. Alanine mutations within the GluN3A endocytic motif inhibited clathrin-dependent internalization and led to accumulation of GluN3A-containing NMDARs at the cell surface, whereas mimicking phosphorylation of the tyrosine residue promoted internalization and reduced cell-surface expression as shown by immunocytochemical and electrophysiological approaches in recombinant systems and rat neurons in primary culture. We further demonstrate that the tyrosine residue is phosphorylated by Src family kinases, and that Src-activation limits surface GluN3A expression in neurons. Together, our results identify a new molecular signal for GluN3A internalization that couples the functional surface expression of GluN3A-containing receptors to the phosphorylation state of GluN3A subunits, and provides a molecular framework for the regulation of NMDAR subunit composition with implications for synaptic plasticity and neurodevelopment.

MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.

Objectives: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Accessing and utilizing clinical and genomic data from an electronic health record data warehouse.

Electronic health records (EHRs) and linked biobanks have tremendous potential to advance biomedical research and ultimately improve the health of future generations. Repurposing EHR data for research is not without challenges, however. In this paper, we describe the processes and considerations necessary to successfully access and utilize a data warehouse for research. Although imperfect, data warehouses are a powerful tool for harnessing a large amount of data to phenotype disease. They will have increasing relevance and applications in clinical research with growing sophistication in processes for EHR data abstraction, biobank integration, and cross-institutional linkage.

MENDS-on-FHIR: Leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.

Objective: The Multi-State EHR-Based Network for Disease Surveillance (MENDS) is a population-based chronic disease surveillance distributed data network that uses institution-specific extraction-transformation-load (ETL) routines. MENDS-on-FHIR examined using Health Language Seven's Fast Healthcare Interoperability Resources (HL7® FHIR®) and US Core Implementation Guide (US Core IG) compliant resources derived from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to create a standards-based ETL pipeline. Materials and Methods: The input data source was a research data warehouse containing clinical and administrative data in OMOP CDM Version 5.3 format. OMOP-to-FHIR transformations, using a unique JavaScript Object Notation (JSON)-to-JSON transformation language called Whistle, created FHIR R4 V4.0.1/US Core IG V4.0.0 conformant resources that were stored in a local FHIR server. A REST-based Bulk FHIR $export request extracted FHIR resources to populate a local MENDS database. Results: Eleven OMOP tables were used to create 10 FHIR/US Core compliant resource types. A total of 1.13 trillion resources were extracted and inserted into the MENDS repository. A very low rate of non-compliant resources was observed. Discussion: OMOP-to-FHIR transformation results passed validation with less than a 1% non-compliance rate. These standards-compliant FHIR resources provided standardized data elements required by the MENDS surveillance use case. The Bulk FHIR application programming interface (API) enabled population-level data exchange using interoperable FHIR resources. The OMOP-to-FHIR transformation pipeline creates a FHIR interface for accessing OMOP data. Conclusion: MENDS-on-FHIR successfully replaced custom ETL with standards-based interoperable FHIR resources using Bulk FHIR. The OMOP-to-FHIR transformations provide an alternative mechanism for sharing OMOP data.

Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC.

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern, following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products.

Ca2+/calmodulin-dependent protein kinase II inhibitors disrupt AKAP79-dependent PKC signaling to GluA1 AMPA receptors.

GluA1 (formerly GluR1) AMPA receptor subunit phosphorylation at Ser-831 is an early biochemical marker for long-term potentiation and learning. This site is a substrate for Ca(2+)/calmodulin (CaM)-dependent protein kinase II (CaMKII) and protein kinase C (PKC). By directing PKC to GluA1, A-kinase anchoring protein 79 (AKAP79) facilitates Ser-831 phosphorylation and makes PKC a more potent regulator of GluA1 than CaMKII. PKC and CaM bind to residues 31-52 of AKAP79 in a competitive manner. Here, we demonstrate that common CaMKII inhibitors alter PKC and CaM interactions with AKAP79(31-52). Most notably, the classical CaMKII inhibitors KN-93 and KN-62 potently enhanced the association of CaM to AKAP79(31-52) in the absence (apoCaM) but not the presence of Ca(2+). In contrast, apoCaM association to AKAP79(31-52) was unaffected by the control compound KN-92 or a mechanistically distinct CaMKII inhibitor (CaMKIINtide). In vitro studies demonstrated that KN-62 and KN-93, but not the other compounds, led to apoCaM-dependent displacement of PKC from AKAP79(31-52). In the absence of CaMKII activation, complementary cellular studies revealed that KN-62 and KN-93, but not KN-92 or CaMKIINtide, inhibited PKC-mediated phosphorylation of GluA1 in hippocampal neurons as well as AKAP79-dependent PKC-mediated augmentation of recombinant GluA1 currents. Buffering cellular CaM attenuated the ability of KN-62 and KN-93 to inhibit AKAP79-anchored PKC regulation of GluA1. Therefore, by favoring apoCaM binding to AKAP79, KN-62 and KN-93 derail the ability of AKAP79 to efficiently recruit PKC for regulation of GluA1. Thus, AKAP79 endows PKC with a pharmacological profile that overlaps with CaMKII.

Migrating a research data warehouse to a public cloud: challenges and opportunities.

OBJECTIVE: Clinical research data warehouses (RDWs) linked to genomic pipelines and open data archives are being created to support innovative, complex data-driven discoveries. The computing and storage needs of these research environments may quickly exceed the capacity of on-premises systems. New RDWs are migrating to cloud platforms for the scalability and flexibility needed to meet these challenges. We describe our experience in migrating a multi-institutional RDW to a public cloud. MATERIALS AND METHODS: This study is descriptive. Primary materials included internal and public presentations before and after the transition, analysis documents, and actual billing records. Findings were aggregated into topical categories. RESULTS: Eight categories of migration issues were identified. Unanticipated challenges included legacy system limitations; network, computing, and storage architectures that realize performance and cost benefits in the face of hyper-innovation, complex security reviews and approvals, and limited cloud consulting expertise. DISCUSSION: Cloud architectures enable previously unavailable capabilities, but numerous pitfalls can impede realizing the full benefits of a cloud environment. Rapid changes in cloud capabilities can quickly obsolete existing architectures and associated institutional policies. Touchpoints with on-premise networks and systems can add unforeseen complexity. Governance, resource management, and cost oversight are critical to allow rapid innovation while minimizing wasted resources and unnecessary costs. CONCLUSIONS: Migrating our RDW to the cloud has enabled capabilities and innovations that would not have been possible with an on-premises environment. Notwithstanding the challenges of managing cloud resources, the resulting RDW capabilities have been highly positive to our institution, research community, and partners.

Identifying who has long COVID in the USA: a machine learning approach using N3C data.

BACKGROUND: Post-acute sequelae of SARS-CoV-2 infection, known as long COVID, have severely affected recovery from the COVID-19 pandemic for patients and society alike. Long COVID is characterised by evolving, heterogeneous symptoms, making it challenging to derive an unambiguous definition. Studies of electronic health records are a crucial element of the US National Institutes of Health's RECOVER Initiative, which is addressing the urgent need to understand long COVID, identify treatments, and accurately identify who has it-the latter is the aim of this study. METHODS: Using the National COVID Cohort Collaborative's (N3C) electronic health record repository, we developed XGBoost machine learning models to identify potential patients with long COVID. We defined our base population (n=1 793 604) as any non-deceased adult patient (age ≥18 years) with either an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) from an inpatient or emergency visit, or a positive SARS-CoV-2 PCR or antigen test, and for whom at least 90 days have passed since COVID-19 index date. We examined demographics, health-care utilisation, diagnoses, and medications for 97 995 adults with COVID-19. We used data on these features and 597 patients from a long COVID clinic to train three machine learning models to identify potential long COVID among all patients with COVID-19, patients hospitalised with COVID-19, and patients who had COVID-19 but were not hospitalised. Feature importance was determined via Shapley values. We further validated the models on data from a fourth site. FINDINGS: Our models identified, with high accuracy, patients who potentially have long COVID, achieving areas under the receiver operator characteristic curve of 0·92 (all patients), 0·90 (hospitalised), and 0·85 (non-hospitalised). Important features, as defined by Shapley values, include rate of health-care utilisation, patient age, dyspnoea, and other diagnosis and medication information available within the electronic health record. INTERPRETATION: Patients identified by our models as potentially having long COVID can be interpreted as patients warranting care at a specialty clinic for long COVID, which is an essential proxy for long COVID diagnosis as its definition continues to evolve. We also achieve the urgent goal of identifying potential long COVID in patients for clinical trials. As more data sources are identified, our models can be retrained and tuned based on the needs of individual studies. FUNDING: US National Institutes of Health and National Center for Advancing Translational Sciences through the RECOVER Initiative.

Highly Active Ice-Nucleating Particles at the Summer North Pole.

The amount of ice versus supercooled water in clouds is important for their radiative properties and role in climate feedbacks. Hence, knowledge of the concentration of ice-nucleating particles (INPs) is needed. Generally, the concentrations of INPs are found to be very low in remote marine locations allowing cloud water to persist in a supercooled state. We had expected the concentrations of INPs at the North Pole to be very low given the distance from open ocean and terrestrial sources coupled with effective wet scavenging processes. Here we show that during summer 2018 (August and September) high concentrations of biological INPs (active at >-20°C) were sporadically present at the North Pole. In fact, INP concentrations were sometimes as high as those recorded at mid-latitude locations strongly impacted by highly active biological INPs, in strong contrast to the Southern Ocean. Furthermore, using a balloon borne sampler we demonstrated that INP concentrations were often different at the surface versus higher in the boundary layer where clouds form. Back trajectory analysis suggests strong sources of INPs near the Russian coast, possibly associated with wind-driven sea spray production, whereas the pack ice, open leads, and the marginal ice zone were not sources of highly active INPs. These findings suggest that primary ice production, and therefore Arctic climate, is sensitive to transport from locations such as the Russian coast that are already experiencing marked climate change.

Physical and Chemical Properties of Cloud Droplet Residuals and Aerosol Particles During the Arctic Ocean 2018 Expedition.

Detailed knowledge of the physical and chemical properties and sources of particles that form clouds is especially important in pristine areas like the Arctic, where particle concentrations are often low and observations are sparse. Here, we present in situ cloud and aerosol measurements from the central Arctic Ocean in August-September 2018 combined with air parcel source analysis. We provide direct experimental evidence that Aitken mode particles (particles with diameters ≲70 nm) significantly contribute to cloud condensation nuclei (CCN) or cloud droplet residuals, especially after the freeze-up of the sea ice in the transition toward fall. These Aitken mode particles were associated with air that spent more time over the pack ice, while size distributions dominated by accumulation mode particles (particles with diameters ≳70 nm) showed a stronger contribution of oceanic air and slightly different source regions. This was accompanied by changes in the average chemical composition of the accumulation mode aerosol with an increased relative contribution of organic material toward fall. Addition of aerosol mass due to aqueous-phase chemistry during in-cloud processing was probably small over the pack ice given the fact that we observed very similar particle size distributions in both the whole-air and cloud droplet residual data. These aerosol-cloud interaction observations provide valuable insight into the origin and physical and chemical properties of CCN over the pristine central Arctic Ocean.

Shipborne eddy covariance observations of methane fluxes constrain Arctic sea emissions.

We demonstrate direct eddy covariance (EC) observations of methane (CH4) fluxes between the sea and atmosphere from an icebreaker in the eastern Arctic Ocean. EC-derived CH4 emissions averaged 4.58, 1.74, and 0.14 mg m-2 day-1 in the Laptev, East Siberian, and Chukchi seas, respectively, corresponding to annual sea-wide fluxes of 0.83, 0.62, and 0.03 Tg year-1. These EC results answer concerns that previous diffusive emission estimates, which excluded bubbling, may underestimate total emissions. We assert that bubbling dominates sea-air CH4 fluxes in only small constrained areas: A ~100-m2 area of the East Siberian Sea showed sea-air CH4 fluxes exceeding 600 mg m-2 day-1; in a similarly sized area of the Laptev Sea, peak CH4 fluxes were ~170 mg m-2 day-1. Calculating additional emissions below the noise level of our EC system suggests total ESAS CH4 emissions of 3.02 Tg year-1, closely matching an earlier diffusive emission estimate of 2.9 Tg year-1.

Frequent new particle formation over the high Arctic pack ice by enhanced iodine emissions.

In the central Arctic Ocean the formation of clouds and their properties are sensitive to the availability of cloud condensation nuclei (CCN). The vapors responsible for new particle formation (NPF), potentially leading to CCN, have remained unidentified since the first aerosol measurements in 1991. Here, we report that all the observed NPF events from the Arctic Ocean 2018 expedition are driven by iodic acid with little contribution from sulfuric acid. Iodic acid largely explains the growth of ultrafine particles (UFP) in most events. The iodic acid concentration increases significantly from summer towards autumn, possibly linked to the ocean freeze-up and a seasonal rise in ozone. This leads to a one order of magnitude higher UFP concentration in autumn. Measurements of cloud residuals suggest that particles smaller than 30 nm in diameter can activate as CCN. Therefore, iodine NPF has the potential to influence cloud properties over the Arctic Ocean.

A mid-South chronic disease registry and practice-based research network to address disparities.

OBJECTIVES: To describe an innovative health information technology (HIT) model for supporting community-wide health improvement through multiprovider collaboration in a regional population health registry and practice-based research network (PBRN). STUDY DESIGN: Case study. METHODS: We describe the HIT data structure and governance of the Diabetes Wellness and Prevention Coalition (DWPC) Registry and PBRN based in Memphis, Tennessee. The population served and their characteristics were assessed for all adult patients with at least 1 encounter in a participating health care delivery system from January 1, 2013, to March 31, 2019. Disparities in access and health care utilization were assessed by residential zip code. RESULTS: The DWPC Registry is a chronic disease and population health data warehouse designed to facilitate chronic disease surveillance and tracking of processes and outcomes of care in medically underserved areas of the mid-South. The Registry primarily focuses on obesity-associated chronic conditions such as diabetes, hypertension, hyperlipidemia, and chronic kidney disease. It combines patient data from 7 regional health systems, which include 6 adult hospitals and more than 50 outpatient practices, covering 462,223 adults with 2,032,425 clinic visits and 602,679 hospitalizations and/or emergency department visits from January 1, 2013, to March 31, 2019. The most prevalent chronic conditions include obesity (37.2%), hypertension (34.4%), overweight (26.4%), hyperlipidemia (18.0%), and type 2 diabetes (14.0%). The Registry provides quarterly practice improvement reports to participating clinics, facilitates surveillance of and outreach to patients with unmet health needs, and supports a pragmatic clinical trial and multiple cohort studies. CONCLUSIONS: Regional registries and PBRNs are powerful tools that can support real-world quality improvement and population health efforts to reduce disparities and improve equity in chronic disease care in medically underserved communities across the United States.