Hasil untuk "Diseases of the respiratory system"

Tobias Allander, M. Tammi, Margareta Eriksson et al.

D. Yach, C. Hawkes, C. Gould et al.

Stijn J M Niessen, Robert Shiel, Astrid Wehner et al.

Simple Summary After having achieved international consensus over disease, diagnosis, classification, and monitoring concepts in the area of companion animal diabetes mellitus, Cushing’s syndrome, and hypoadrenocorticism, a group of 14 experts and one chair embarked on the third cycle of project “Agreeing Language in Veterinary Endocrinology” (ALIVE), this time focusing on thyroid disease terminology. This cycle’s methods followed, like previous ones, a modified Delphi-approach with small changes to improve efficiency and flexibility, including an off-site chair. For the first time, additionally, feedback on definitions of a previous cycle was incorporated, leading to an update of diabetes mellitus related definitions of ALIVE Cycle 1. This third cycle was completed successfully, accomplishing a majority-based consensus among panellists and international veterinary endocrinology society memberships over 78 thyroid related terminology and five updated diabetes mellitus definitions. As has been the case with the definitions created for other hormonal diseases, it is hoped this work will improve education, research, diagnosis, and treatment in cats and dogs with endocrine disease.

Xinyu Li, Jinyang Huang, Feng-Qi Cui et al.

Respiratory monitoring is an extremely important task in modern medical services. Due to its significant advantages, e.g., non-contact, radar-based respiratory monitoring has attracted widespread attention from both academia and industry. Unfortunately, though it can achieve high monitoring accuracy, consumer electronics-grade radar data inevitably contains User-sensitive Identity Information (USI), which may be maliciously used and further lead to privacy leakage. To track these challenges, by variational mode decomposition (VMD) and adversarial loss-based encryption, we propose a novel Trusted Respiratory Monitoring paradigm, Tru-RM, to perform automated respiratory monitoring through radio signals while effectively anonymizing USI. The key enablers of Tru-RM are Attribute Feature Decoupling (AFD), Flexible Perturbation Encryptor (FPE), and robust Perturbation Tolerable Network (PTN) used for attribute decomposition, identity encryption, and perturbed respiratory monitoring, respectively. Specifically, AFD is designed to decompose the raw radar signals into the universal respiratory component, the personal difference component, and other unrelated components. Then, by using large noise to drown out the other unrelated components, and the phase noise algorithm with a learning intensity parameter to eliminate USI in the personal difference component, FPE is designed to achieve complete user identity information encryption without affecting respiratory features. Finally, by designing the transferred generalized domain-independent network, PTN is employed to accurately detect respiration when waveforms change significantly. Extensive experiments based on various detection distances, respiratory patterns, and durations demonstrate the superior performance of Tru-RM on strong anonymity of USI, and high detection accuracy of perturbed respiratory waveforms.

Buzuneh Tasfa Marine, Dagne Tesfaye Mengistie

Abstract Introduction Pneumonia is one of the most common and deadly infectious diseases affecting under-five children, responsible for about 15% of all deaths in this age group worldwide. In Ethiopia, the prevalence ranges from 16% to 21%, contributing substantially to under-five mortality. Despite national child survival efforts, pneumonia-related deaths remain a major public health concern. Understanding the burden and identifying key risk factors are essential for effective prevention and timely intervention. This study aimed to estimate the time to death and identify its predictors among under-five children with acute pneumonia using Bayesian parametric survival analysis. Methods A retrospective study was conducted with 451 under-five children diagnosed with acute pneumonia. Three survival analysis models were applied: the Cox proportional hazards model, the parametric accelerated failure time (AFT) model, and the Bayesian parametric survival model. In the Bayesian model, Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling and the Metropolis-Hastings algorithm were employed to obtain samples from the posterior distributions of the parameters. Each model was evaluated using appropriate model selection criteria to identify the best-fitting approach. Results The Bayesian Lognormal AFT model identified several significant predictors of time to death among under-five children with acute pneumonia. All model parameters showed good convergence, with Monte Carlo errors under 5% of their standard deviations. Female children had shorter survival times compared to males (AF = 0.46; 95% CI: 0.36–0.97). Children aged 1–11 months had better survival outcomes (AF = 0.10; 95% CI: 0.05–0.21) than those aged 48–59 months. Rural residence (AF = 1.48; 95% CI: 1.03–2.09), diagnosis during spring (AF = 0.73; 95% CI: 0.52–0.92) and summer (AF = 0.66; 95% CI: 0.49–0.84), comorbidities (AF = 1.26; 95% CI: 1.03–1.65), severe acute malnutrition (AF = 0.26; 95% CI: 0.13–0.43), anemia (AF = 0.88; 95% CI: 0.73–0.93), low weight (AF = 0.72; 95% CI: 0.55–0.90), and home delivery (AF = 0.75; 95% CI: 0.59–0.95) were all associated with reduced survival times. Conclusion This study identified key predictors of mortality among under-five children with acute pneumonia using a Bayesian parametric survival model. Female, rural residence, severe acute malnutrition, comorbidity, anemia, and low weight were significantly associated with reduced survival times. Seasonal variation and place of delivery also influenced mortality, highlighting the impact of environmental and health system factors. These findings emphasize the need for targeted interventions focusing on early diagnosis, nutritional support, and tailored care for high-risk groups. Furthermore, the Federal Ministry of Health could enhance community awareness of early pneumonia detection and effective home management, particularly in rural areas where mortality risk is higher.

Tagreed Al-Subhi, Muhammad Yasir, Samar A. Badreddine et al.

Background: The microbiome of the respiratory system functions as a gatekeeper of respiratory health and is influenced by respiratory diseases. The aim of this study was to identify changes in the respiratory bacterial community composition associated with respiratory infections and to explore their relationship with specific bacterial pathogens in the Saudi Arabian population. Methods: Nasopharyngeal samples were screened from 73 individuals, including 34 symptomatic respiratory tract infection patients, 10 asymptomatic participants, and 29 healthy controls. Respiratory pathogens were detected using real-time PCR, and the microbiota were characterized through 16S rRNA gene amplicon sequencing. Results: Alpha diversity analysis revealed a slight decrease in bacterial richness in patients and asymptomatic individuals compared to healthy controls. In beta diversity analysis, healthy controls clustered together, while most symptomatic patients clustered separately. Actinobacteria, known for maintaining microbial homeostasis and preventing pathogenic colonization, were abundant in asymptomatic and healthy controls (> 30 %) but were substantially reduced to < 20 % relative abundance in symptomatic patients. Several bacterial genera, including Abiotrophia, Capnocytophaga, Megasphaera, Campylobacter, Peptostreptococcus, Veillonella, Streptococcus, and Bulleidia, were positively correlated with respiratory infections. Corynebacterium, Dolosigranulum, and Lawsonella were more abundantly found in healthy and asymptomatic individuals. Patients harboring Streptococcus pneumoniae or methicillin-resistant Staphylococcus aureus (MRSA) exhibited distinct bacterial profiles. Genera such as Staphylococcus, Pseudomonas, and Peptoniphilus were correlated with MRSA infection, while samples positive for S. pneumoniae exhibited a relatively higher abundance of Neisseria and Prevotella. Notably, a substantial number of symptomatic patients tested negative for any of the screened pathogens by real-time PCR but still showed alterations in bacterial community composition. Conclusions: Specific bacterial taxa showed significant differences between healthy controls and symptomatically infected patients, suggesting that bacterial community structures and groups of taxa, rather than individual bacterial taxa, may play a role in regulating respiratory infections.

Manuela Daniela Danu, George Marica, Constantin Suciu et al.

The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.

Agnieszka Brojakowska, J. Narula, R. Shimony et al.

Severe acute respiratory-syndrome coronavirus-2 (SARS-CoV-2) host cell infection is mediated by binding to angiotensin-converting enzyme 2 (ACE2). Systemic dysregulation observed in SARS-CoV was previously postulated to be due to ACE2/angiotensin 1-7 (Ang1-7)/Mas axis downregulation; increased ACE2 activity was shown to mediate disease protection. Because angiotensin II receptor blockers, ACE inhibitors, and mineralocorticoid receptor antagonists increase ACE2 receptor expression, it has been tacitly believed that the use of these agents may facilitate viral disease; thus, they should not be used in high-risk patients with cardiovascular disease. Based on the anti-inflammatory benefits of the upregulation of the ACE2/Ang1-7/Mas axis and previously demonstrated benefits of lung function improvement in SARS-CoV infections, it has been hypothesized that the benefits of treatment with renin-angiotensin system inhibitors in SARS-CoV-2 may outweigh the risks and at the very least should not be withheld.

Tianqi Wei, Zhi Chen, Xin Yu

Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on query pictures or the text description of suspicious plants is in high demand for initiating treatment before potential diseases spread further. In this paper, we develop a multimodal plant disease image retrieval system to support disease search based on either image or text prompts. Specifically, we utilize the largest in-the-wild plant disease dataset PlantWild, which includes over 18,000 images across 89 categories, to provide a comprehensive view of potential diseases relating to the query. Furthermore, cross-modal retrieval is achieved in the developed system, facilitated by a novel CLIP-based vision-language model that encodes both disease descriptions and disease images into the same latent space. Built on top of the retriever, our retrieval system allows users to upload either plant disease images or disease descriptions to retrieve the corresponding images with similar characteristics from the disease dataset to suggest candidate diseases for end users' consideration.

Jinzhi Shen, Ke Ma

In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. We reviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomes and mitigating the impact of respiratory diseases in the years to come.

Whenty Ariyanti, Kai-Chun Liu, Kuan-Yu Chen et al.

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.

Abdoljalil Addeh, G. Bruce Pike, M. Ethan MacDonald

Acquiring accurate external respiratory data during functional Magnetic Resonance Imaging (fMRI) is challenging, prompting the exploration of machine learning methods to estimate respiratory variation (RV) from fMRI data. Respiration induces head motion, including real and pseudo motion, which likely provides useful information about respiratory events. Recommended notch filters mitigate respiratory-induced motion artifacts, suggesting that a bandpass filter at the respiratory frequency band isolates respiratory-induced head motion. This study seeks to enhance the accuracy of RV estimation from resting-state BOLD-fMRI data by integrating estimated head motion parameters. Specifically, we aim to determine the impact of incorporating raw versus bandpass-filtered head motion parameters on RV reconstruction accuracy using one-dimensional convolutional neural networks (1D-CNNs). This approach addresses the limitations of traditional filtering techniques and leverages the potential of head motion data to provide a more robust estimation of respiratory-induced variations.

L. Ortega‐Paz, D. Capodanno, G. Montalescot et al.

Abstract Coronavirus disease 2019 (COVID‐19) is an infectious disease caused by severe acute respiratory syndrome coronavirus‐2, which has posed a significant threat to global health. Although the infection is frequently asymptomatic or associated with mild symptoms, in a small proportion of patients it can produce an intense inflammatory and prothrombotic state that can lead to acute respiratory distress syndrome, multiple organ failure, and death. Angiotensin‐converting enzyme 2, highly expressed in the respiratory system, has been identified as a functional receptor for severe acute respiratory syndrome coronavirus‐2. Notably, angiotensin‐converting enzyme 2 is also expressed in the cardiovascular system, and there are multiple cardiovascular implications of COVID‐19. Cardiovascular risk factors and cardiovascular disease have been associated with severe manifestations and poor prognosis in patients with COVID‐19. More important, patients with COVID‐19 may have thrombotic and coagulation abnormalities, promoting a hypercoagulable state and resulting in an increased rate of thrombotic and thromboembolic events. This review will describe the pathophysiological characteristics of the cardiovascular involvement following infection by severe acute respiratory syndrome coronavirus‐2, with a focus on thrombotic and thromboembolic manifestations and implications for antithrombotic management.

Requena G, Czira A, Banks V et al.

Gema Requena,1 Alexandrosz Czira,1 Victoria Banks,2 Robert Wood,2 Theo Tritton,2 Catherine Castillo,2 Jie Yeap,2 Rosie Wild,2 Chris Compton,1 Kieran J Rothnie,1 Felix JF Herth,3 Jennifer K Quint,4 Afisi S Ismaila5,6 1GSK, R&D Global Medical, Brentford, Middlesex, UK; 2Real-World Evidence, Adelphi Real World, Bollington, Cheshire, UK; 3Department of Pneumology and Critical Care Medicine, Thoraxklinik, University of Heidelberg and Translational Lung Research Center Heidelberg, Heidelberg, Germany; 4National Heart and Lung Institute, Imperial College London, London, UK; 5Value Evidence and Outcomes, GSK, Collegeville, PA, USA; 6Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, CanadaCorrespondence: Gema Requena, Epidemiology, Value Evidence and Outcomes, R&D Global Medical, GSK, Brentford, Middlesex, UK, Tel +44 20 80476893, Email gema.x.requena@gsk.comPurpose: Chronic obstructive pulmonary disease (COPD) exacerbations are associated with significant morbidity and mortality and increased economic healthcare burden for patients with COPD. Long-acting muscarinic antagonist (LAMA)/long-acting β2-agonist (LABA) dual therapy is recommended for patients receiving mono-bronchodilator therapy who experience exacerbations or ongoing breathlessness. This study compared two single-inhaler LAMA/LABA dual therapies, umeclidinium/vilanterol (UMEC/VI) and indacaterol/glycopyrronium (IND/GLY), on moderate-to-severe exacerbation rates in patients with COPD in England.Patients and Methods: This retrospective cohort study used linked primary care electronic health record data (Clinical Practice Research Datalink-Aurum) and secondary care data (Hospital Episode Statistics) to assess outcomes for patients with COPD who had a first prescription for single-inhaler UMEC/VI or IND/GLY (index date) between 1 January 2015 and 30 September 2019 (indexing period). Analyses compared UMEC/VI and IND/GLY on moderate-to-severe, moderate, and severe exacerbations, healthcare resource utilization (HCRU), and direct costs at 6, 12, 18, and 24 months, and time-to-first on-treatment exacerbation up to 24 months post-index date. Following inverse probability of treatment weighting (IPTW), non-inferiority and superiority of UMEC/VI versus IND/GLY were assessed.Results: In total, 12,031 patients were included, of whom 8753 (72.8%) were prescribed UMEC/VI and 3278 (27.2%) IND/GLY. After IPTW, for moderate-to-severe exacerbations, weighted rate ratios were < 1 at 6, 12, and 18 months and equal to 1 at 24 months for UMEC/VI; around the null value for moderate exacerbations and < 1 at all timepoints for severe exacerbations. UMEC/VI showed lower HCRU incidence rates than IND/GLY for all-cause Accident and Emergency visits and COPD-related inpatient stays and associated all-cause costs at 6 months post-indexing. Time-to-triple therapy was similar for both treatments.Conclusion: UMEC/VI demonstrated non-inferiority to IND/GLY in moderate-to-severe exacerbation reduction at 6, 12 and 18 months. These results support previous findings demonstrating similarity between UMEC/VI and IND/GLY on reduction of moderate-to-severe exacerbations.Plain Language Summary: Sudden exacerbations, or flare-ups, of chronic obstructive pulmonary disease (COPD) are linked with worsening health and increased risk of death, as well as increased healthcare costs for people with COPD. Long-acting muscarinic antagonist (LAMA)/long-acting β2-agonist (LABA) dual therapy is recommended for patients with COPD who take LAMA or LABA monotherapy but continue to experience flare-ups or ongoing breathlessness. This study compared two single-inhaler LAMA/LABA dual therapies, umeclidinium/vilanterol (UMEC/VI) and indacaterol/glycopyrronium (IND/GLY), in terms of flare-ups in patients with COPD in England.We used two linked databases of de-identified medical records from general practitioners and hospitals for patients with COPD who had a first prescription for UMEC/VI or IND/GLY between 1 January 2015 and 30 September 2019. We compared the two treatments on COPD flare-ups, healthcare resource utilization and related costs, and changes in medication over the 2 years following starting treatment.We found that the treatments were comparable for moderate-to-severe flare-ups. Patients taking UMEC/VI had less Accident and Emergency (A&E) visits in total and less inpatient stays related to their COPD, and had a lower overall cost of healthcare for A&E visits and inpatient stays than patients taking IND/GLY. Changes to treatment and time before their first flare-up were similar for all patients, regardless of their prescribed treatment.This study showed that UMEC/VI is as effective as IND/GLY at preventing moderate-to-severe flare-ups. These results support previous findings demonstrating similarity between UMEC/VI and IND/GLY in reducing the rate of moderate-to-severe exacerbations after starting treatment.Keywords: COPD dual therapy, LABA/LAMA new users, healthcare resource utilization, exacerbations, comparative effectiveness, single-inhaler dual therapy

Masaaki Iwabayashi, Rika Hashimoto, Mariko Takada et al.

Abstract The typical clinical manifestation of acute eosinophilic pneumonia is acute onset of respiratory symptoms due to smoking or medication use, accompanied by bilateral ground‐glass opacity with consolidations on chest radiography. However, differential diagnosis with acute eosinophilic pneumonia should not be excluded in cases of unilateral pneumonia of postoperative lung.

June-Woo Kim, Chihyeon Yoon, Miika Toikkanen et al.

Deep generative models have emerged as a promising approach in the medical image domain to address data scarcity. However, their use for sequential data like respiratory sounds is less explored. In this work, we propose a straightforward approach to augment imbalanced respiratory sound data using an audio diffusion model as a conditional neural vocoder. We also demonstrate a simple yet effective adversarial fine-tuning method to align features between the synthetic and real respiratory sound samples to improve respiratory sound classification performance. Our experimental results on the ICBHI dataset demonstrate that the proposed adversarial fine-tuning is effective, while only using the conventional augmentation method shows performance degradation. Moreover, our method outperforms the baseline by 2.24% on the ICBHI Score and improves the accuracy of the minority classes up to 26.58%. For the supplementary material, we provide the code at https://github.com/kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound.

Wentao He, Yuchen Yan, Jianfeng Ren et al.

Deep neural networks have been applied to audio spectrograms for respiratory sound classification. Existing models often treat the spectrogram as a synthetic image while overlooking its physical characteristics. In this paper, a Multi-View Spectrogram Transformer (MVST) is proposed to embed different views of time-frequency characteristics into the vision transformer. Specifically, the proposed MVST splits the mel-spectrogram into different sized patches, representing the multi-view acoustic elements of a respiratory sound. These patches and positional embeddings are then fed into transformer encoders to extract the attentional information among patches through a self-attention mechanism. Finally, a gated fusion scheme is designed to automatically weigh the multi-view features to highlight the best one in a specific scenario. Experimental results on the ICBHI dataset demonstrate that the proposed MVST significantly outperforms state-of-the-art methods for classifying respiratory sounds.

Yusuke Akamatsu, Terumi Umematsu, Hitoshi Imaoka

Video-based heart and respiratory rate measurements using facial videos are more useful and user-friendly than traditional contact-based sensors. However, most of the current deep learning approaches require ground-truth pulse and respiratory waves for model training, which are expensive to collect. In this paper, we propose CalibrationPhys, a self-supervised video-based heart and respiratory rate measurement method that calibrates between multiple cameras. CalibrationPhys trains deep learning models without supervised labels by using facial videos captured simultaneously by multiple cameras. Contrastive learning is performed so that the pulse and respiratory waves predicted from the synchronized videos using multiple cameras are positive and those from different videos are negative. CalibrationPhys also improves the robustness of the models by means of a data augmentation technique and successfully leverages a pre-trained model for a particular camera. Experimental results utilizing two datasets demonstrate that CalibrationPhys outperforms state-of-the-art heart and respiratory rate measurement methods. Since we optimize camera-specific models using only videos from multiple cameras, our approach makes it easy to use arbitrary cameras for heart and respiratory rate measurements.

Muhammad Zubair Khan, Oleg E. Peil, Apoorva Sharma et al.

In the rapidly expanding field of two-dimensional materials, magnetic monolayers show great promise for the future applications in nanoelectronics, data storage, and sensing. The research in intrinsically magnetic two-dimensional materials mainly focuses on synthetic iodide and telluride based compounds, which inherently suffer from the lack of ambient stability. So far, naturally occurring layered magnetic materials have been vastly overlooked. These minerals offer a unique opportunity to explore air-stable complex layered systems with high concentration of local moment bearing ions. We demonstrate magnetic ordering in iron-rich two-dimensional phyllosilicates, focusing on mineral species of minnesotaite, annite, and biotite. These are naturally occurring van der Waals magnetic materials which integrate local moment baring ions of iron via magnesium/aluminium substitution in their octahedral sites. Due to self-inherent capping by silicate/aluminate tetrahedral groups, ultra-thin layers are air-stable. Chemical characterization, quantitative elemental analysis, and iron oxidation states were determined via Raman spectroscopy, wavelength disperse X-ray spectroscopy, X-ray absorption spectroscopy, and X-ray photoelectron spectroscopy. Superconducting quantum interference device magnetometry measurements were performed to examine the magnetic ordering. These layered materials exhibit paramagnetic or superparamagnetic characteristics at room temperature. At low temperature ferrimagnetic or antiferromagnetic ordering occurs, with the critical ordering temperature of 38.7 K for minnesotaite, 36.1 K for annite, and 4.9 K for biotite. In-field magnetic force microscopy on iron bearing phyllosilicates confirmed the paramagnetic response at room temperature, present down to monolayers.

J. Metzl, Aletha Maybank, Fernando De Maio

The coronavirus disease 2019 (COVID-19) pandemic has exposed the consequences of inequality in the US. Even though all US residents are likely equally susceptible to infection with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus that causes COVID-19 disease, the resulting illness and the distribution of deaths reinforces systems of discriminatory housing, education, employment, earnings, health care, and criminal justice.1,2 The patterns of COVID-19 illuminate centuries of support systems that the US did not build and investments it did not make. Each stage of the pandemic, from containment, to mitigation, to reopening, highlights the extent to which certain populations were rendered vulnerable long before the virus arrived. As a result, marginalized, minoritized, and communities of low wealth have been at highest risk, with disproportionate death rates among African American, Latinx, and Native American populations across the US.3,4 Sociodemographic differences in COVID-19 morbidity and mortality highlight an unavoidable reality facing the US health care system as it strives to fulfill its mission to promote health and well-being, and to treat disease. At its core, the practice of medicine is based on individual-level interactions among clinicians, patients, and families. Yet the pandemic highlights the extent to which illness for many people results from larger structures, systems, and economies.1,2