A Review of Extracellular Vesicles in COVID-19 Diagnosis, Treatment, and Prevention.

The 2019 novel coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is ongoing, and has necessitated scientific efforts in disease diagnosis, treatment, and prevention. Interestingly, extracellular vesicles (EVs) have been crucial in these developments. EVs are a collection of various nanovesicles which are delimited by a lipid bilayer. They are enriched in proteins, nucleic acids, lipids, and metabolites, and naturally released from different cells. Their natural material transport properties, inherent long-term recycling ability, excellent biocompatibility, editable targeting, and inheritance of parental cell properties make EVs one of the most promising next-generation drug delivery nanocarriers and active biologics. During the COVID-19 pandemic, many efforts have been made to exploit the payload of natural EVs for the treatment of COVID-19. Furthermore, strategies that use engineered EVs to manufacture vaccines and neutralization traps have produced excellent efficacy in animal experiments and clinical trials. Here, the recent literature on the application of EVs in COVID-19 diagnosis, treatment, damage repair, and prevention is reviewed. And the therapeutic value, application strategies, safety, and biotoxicity in the production and clinical applications of EV agents for COVID-19 treatment, as well as inspiration for using EVs to block and eliminate novel viruses are discussed.

The safety of digestive tract cancer surgery during COVID-19: A living systematic review and meta-analysis.

Surgery is the primary curative treatment of solid cancers. However, its safety has been compromised by the outbreak of COVID-19. Therefore, it is necessary to evaluate the safety of digestive tract cancer surgery in the context of COVID-19. We used the Review Manager software (v.5.4) and Stata software (version 16.0) for meta-analysis and statistical analysis. Sixteen retrospective studies involving 17,077 patients met the inclusion criteria. The data indicates that performing digestive tract cancer surgery during the COVID-19 pandemic led to increased blood loss(MD = -11.31, 95%CI:-21.43 to -1.20, P = 0.03), but did not increase postoperative complications(OR = 1.03, 95%CI:0.78 to1.35, P = 0 0.86), anastomotic leakage (OR = 0.96, 95%CI:0.52 to1.77, P = 0 0.89), postoperative mortality (OR = 0.65, 95%CI:0.40 to1.07, P = 0 0.09), number of transfusions (OR = 0.74, 95%CI:0.30 to 1.80, P = 0.51), number of patients requiring ICU care(OR = 1.37, 95%CI:0.90 to 2.07, P = 0.14), postoperative 30-d readmission (OR = 0.94, 95%CI:0.82 to 1.07, P = 0 0.33), total hospital stay (MD = 0.11, 95%CI:-2.37 to 2.59, P = 0.93), preoperative waiting time(MD = - 0.78, 95%CI:-2.34 to 0.79, P = 0.33), postoperative hospital stay(MD = - 0.44, 95%CI:-1.61 to 0.74, P = 0.47), total operation time(MD = -12.99, 95%CI:-28.00 to 2.02, P = 0.09) and postoperative ICU stay (MD = - 0.02, 95%CI:-0.62 to 0.57, P = 0.94). Digestive tract cancer surgery can be safely performed during the COVID-19.

Decision Analysis in SHared decision making for Thromboprophylaxis during Pregnancy (DASH-TOP): a sequential explanatory mixed-methods pilot study.

OBJECTIVES: To gain insight into formal methods of integrating patient preferences and clinical evidence to inform treatment decisions, we explored patients' experience with a personalised decision analysis intervention, for prophylactic low-molecular-weight heparin (LMWH) in the antenatal period. DESIGN: Mixed-methods explanatory sequential pilot study. SETTING: Hospitals in Canada (n=1) and Spain (n=4 sites). Due to the COVID-19 pandemic, we conducted part of the study virtually. PARTICIPANTS: 15 individuals with a prior venous thromboembolism who were pregnant or planning pregnancy and had been referred for counselling regarding LMWH. INTERVENTION: A shared decision-making intervention that included three components: (1) direct choice exercise; (2) preference elicitation exercises and (3) personalised decision analysis. MAIN OUTCOME MEASURES: Participants completed a self-administered questionnaire to evaluate decision quality (decisional conflict, self-efficacy and satisfaction). Semistructured interviews were then conducted to explore their experience and perceptions of the decision-making process. RESULTS: Participants in the study appreciated the opportunity to use an evidence-based decision support tool that considered their personal values and preferences and reported feeling more prepared for their consultation. However, there were mixed reactions to the standard gamble and personalised treatment recommendation. Some participants could not understand how to complete the standard gamble exercises, and others highlighted the need for more informative ways of presenting results of the decision analysis. CONCLUSION: Our results highlight the challenges and opportunities for those who wish to incorporate decision analysis to support shared decision-making for clinical decisions.

Benchmarking emergency department prediction models with machine learning and public electronic health records.

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care.

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department.

Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients' ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department.

Comparison of demographic features and laboratory parameters between COVID-19 deceased patients and surviving severe and critically ill cases.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60ºC, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction. The demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China with such climatic characteristics are still not clearly illustrated. AIM: To illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases. METHODS: COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups. RESULTS: Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Neutrophil percentage (NEUT%), platelet (PLT), C-reactive protein (CRP), creatine kinase isoenzyme (CK-MB), serum troponin I (TNI) and brain natriuretic peptides (BNP) showed significant differences among the groups (P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P < 0.001, respectively). The increase of CRP, D-dimer and NEUT% levels, as well as the decrease of lymphocyte count (LYMPH) and PLT counts, showed significant correlation with death of COVID-19 patients (P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively). CONCLUSION: Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.

An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency department (preprint)

Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients’ ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department

Decrease of atmospheric black carbon and CO2 concentrations due to COVID-19 lockdown at the Mt. Waliguan WMO/GAW baseline station in China.

The Coronavirus Disease 2019 (COVID-19) lockdown policy reduced anthropogenic emissions and impacted the atmospheric chemical characteristics in Chinese urban cities. However, rare studies were conducted at the high mountain site. In this work, in-situ measurements of light absorption by carbonaceous aerosols and carbon dioxide (CO2) concentrations were conducted at Waliguan (WLG) over the northeastern Tibetan Plateau of China from January 3 to March 30, 2020. The data was employed to explore the influence of the COVID-19 lockdown on atmospheric chemistry in the background-free troposphere. During the sampling period, the light absorption near-infrared (>470 nm) was mainly contributed by BC (>72%), however, BC and brown carbon (BrC) contributed equally to light absorption in the short wavelength (∼350 nm). The average BC concentrations in the pre-, during and post-lockdown were 0.28 ±â€¯0.25, 0.18 ±â€¯0.16, and 0.28 ±â€¯0.20 µg m-3, respectively, which decreased by approximately 35% during the lockdown period. Meanwhile, CO2 also showed slight decreases during the lockdown period. The declined BC was profoundly attributed to the reduced emissions (∼86%), especially for the combustion of fossil fuels. Moreover, the declined light absorption of BC, primary and secondary BrC decreased the solar energy absorbance by 35, 15, and 14%, respectively. The concentration weighted trajectories (CWT) analysis suggested that the decreased BC and CO2 at WLG were exclusively associated with the emission reduction in the eastern region of WLG. Our results highlighted that the reduced anthropogenic emissions attributed to the lockdown in the urban cities did impact the atmospheric chemistry in the free troposphere of the Tibetan Plateau.

Fighting COVID-19: a qualitative study into the lives of intensive care unit survivors in Wuhan, China.

OBJECTIVES: We aimed to provide an insight into the life of survivors of critical COVID-19 in China. METHODS: We conducted an online survey and qualitative interviews among intensive care unit survivors of critical COVID-19 between November and December 2020 in Wuhan, China. Eligible participants were asked to complete the EQ-5D-5L and the Short Form 36-Item Survey, and invited to participate in a semistructured face-to-face interview. Descriptive analyses and phenomenological approach were adopted to analyse quantitative and qualitative data, respectively. RESULTS: Of 10 survivors who completed the questionnaire, 8 participated in the interview. The mean scores±SD of EuroQol-5 Dimensions-5 Level utility and EuroQol-Visual Analogue Scale were 0.88±0.15 and 80.9±14.2, respectively. The qualitative interview identified four themes, namely poor physical health, post-traumatic stress, social stigma and family support. CONCLUSIONS: COVID-19 survivors continue fighting physical and psychological impacts. Despite strong family support, these patients are struggling with social stigma. It is a long, challenging journey to recovery for patients and society.

A look back at the first wave of COVID-19 in China: A systematic review and meta-analysis of mortality and health care resource use among severe or critical patients.

BACKGROUND: To investigate the mortality and health care resource use among patients with severe or critical coronavirus disease of 2019 (COVID-19) in the first wave of pandemic in China. METHODS: We performed a systematic review and meta-analysis to investigate the mortality, discharge rate, length of hospital stay, and use of invasive ventilation in severe or critical COVID-19 cases in China. We searched electronic databases for studies from China with no restrictions on language or interventions patients received. We screened records, extracted data and assessed the quality of included studies in duplicate. We performed the meta-analysis using random-effect models through a Bayesian framework. Subgroup analyses were conducted to examine studies by disease severity, study location and patient enrolment start date. We also performed sensitivity analysis using various priors, and assessed between-study heterogeneity and publication bias for the primary outcomes. RESULTS: Out of 6,205 titles and abstracts screened, 500 were reviewed in full text. A total of 42 studies were included in the review, of which 95% were observational studies (n = 40). The pooled 28-day and 14-day mortalities among severe or critical patients were 20.48% (7,136 patients, 95% credible interval (CrI), 13.11 to 30.70) and 10.83% (95% CrI, 6.78 to 16.75), respectively. The mortality declined over time and was higher in patients with critical disease than severe cases (1,235 patients, 45.73%, 95% CrI, 22.79 to 73.52 vs. 3,969 patients, 14.90%, 95% CrI, 4.70 to 39.57) and patients in Hubei compared to those outside Hubei (6,719 patients, 26.62%, 95% CrI, 13.11 to 30.70 vs. 244 patients, 5.88%, 95% CrI 2.03 to 14.11). The length of hospital stay was estimated at 18.48 days (6,847 patients, 95% CrI, 17.59 to 21.21), the 28-day discharge rate was 50.48% (3,645 patients, 95% CrI, 26.47 to 79.53), and the use of invasive ventilation rate was 13.46% (4,108 patients, 95% CrI, 7.61 to 22.31). CONCLUSIONS: Our systematic review and meta-analysis found high mortality among severe and critical COVID-19 cases. Severe or critical COVID-19 cases consumed a large amount of hospital resources during the outbreak.

Engineering Extracellular Vesicles Enriched with Palmitoylated ACE2 as COVID-19 Therapy (Adv. Mater. 49/2021)

COVID-19 Therapy In their work reported in article number 2103471, Long Zhang, Fangfang Zhou, and co-workers fuse the S-palmitoylation-dependent plasma membrane (PM) targeting sequence with angiotensin converting enzyme 2 (ACE2) and engineer extracellular vesicles (EVs) on their surface enriched with palmitoylated ACE2 (PM-ACE2-EVs). The PM-ACE2-EVs can bind to the SARS-CoV-2 S-RBD with high affinity and block its interaction with cell-surface ACE2, thereby preventing SARS-CoV-2 from entering the host cell. This study provides a novel EV-based candidate for prophylactic and therapeutic treatment against COVID-19.

Engineering Extracellular Vesicles Enriched with Palmitoylated ACE2 as COVID-19 Therapy.

Angiotensin converting enzyme 2 (ACE2) is a key receptor present on cell surfaces that directly interacts with the viral spike (S) protein of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). It is proposed that inhibiting this interaction can be promising in treating COVID-19. Here, the presence of ACE2 in extracellular vesicles (EVs) is reported and the EV-ACE2 levels are determined by protein palmitoylation. The Cys141 and Cys498 residues on ACE2 are S-palmitoylated by zinc finger DHHC-Type Palmitoyltransferase 3 (ZDHHC3) and de-palmitoylated by acyl protein thioesterase 1 (LYPLA1), which is critical for the membrane-targeting of ACE2 and their EV secretion. Importantly, by fusing the S-palmitoylation-dependent plasma membrane (PM) targeting sequence with ACE2, EVs enriched with ACE2 on their surface (referred to as PM-ACE2-EVs) are engineered. It is shown that PM-ACE2-EVs can bind to the SARS-CoV-2 S-RBD with high affinity and block its interaction with cell surface ACE2 in vitro. PM-ACE2-EVs show neutralization potency against pseudotyped and authentic SARS-CoV-2 in human ACE2 (hACE2) transgenic mice, efficiently block viral load of authentic SARS-CoV-2, and thus protect host against SARS-CoV-2-induced lung inflammation. The study provides an efficient engineering protocol for constructing a promising, novel biomaterial for application in prophylactic and therapeutic treatments against COVID-19.

Transforming challenges into opportunities: conducting health preference research during the COVID-19 pandemic and beyond.

The disruptions to health research during the COVID-19 pandemic are being recognized globally, and there is a growing need for understanding the pandemic's impact on the health and health preferences of patients, caregivers, and the general public. Ongoing and planned health preference research (HPR) has been affected due to problems associated with recruitment, data collection, and data interpretation. While there are no "one size fits all" solutions, this commentary summarizes the key challenges in HPR within the context of the pandemic and offers pragmatic solutions and directions for future research. We recommend recruitment of a diverse, typically under-represented population in HPR using online, quota-based crowdsourcing platforms, and community partnerships. We foresee emerging evidence on remote, and telephone-based HPR modes of administration, with further studies on the shifts in preferences related to health and healthcare services as a result of the pandemic. We believe that the recalibration of HPR, due to what one would hope is an impermanent change, will permanently change how we conduct HPR in the future.

Quantifying the impacts of human mobility restriction on the spread of coronavirus disease 2019: an empirical analysis from 344 cities of China.

BACKGROUND: Since the outbreak of coronavirus disease 2019 (COVID-19), human mobility restriction measures have raised controversies, partly because of the inconsistent findings. An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction. The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19. METHODS: Our study applied the difference-in-difference (DID) model to assess the declines of population mobility at the city level, and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders. RESULTS: The DID model showed that a continual expansion of the relative declines over time in 2020. After 4 weeks, population mobility declined by -54.81% (interquartile range, -65.50% to -43.56%). The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks (ie, 1% decline of population mobility was associated with 0.72% [95% CI: 0.50%-0.93%] reduction of cumulative cases for 1 week, 1.42% 2 weeks, 1.69% 3 weeks, 1.72% 4 weeks, 1.64% 5 weeks, and 1.52% 6 weeks). The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter. The effects on cumulative cases differed by cities of different population sizes, with greater effects seen in larger cities. CONCLUSIONS: Persistent population mobility restrictions are well deserved. Implementation of mobility restrictions in major cities with large population sizes may be even more important.

A three-arm, multicenter, open-label randomized controlled trial of hydroxychloroquine and low-dose prednisone to treat recurrent pregnancy loss in women with undifferentiated connective tissue diseases: protocol for the Immunosuppressant regimens for LIving FEtuses (ILIFE) trial.

BACKGROUND: Undifferentiated connective tissue disease (UCTD) is known to induce adverse pregnancy outcomes and even recurrent spontaneous abortion (RSA) by placental vascular damage and inflammation activation. Anticoagulation can prevent pregnancy morbidities. However, it is unknown whether the addition of immune suppressants to anticoagulation can prevent spontaneous pregnancy loss in UCTD patients. The purpose of this study is to evaluate the efficacy of hydroxychloroquine (HCQ) and low-dose prednisone on recurrent pregnancy loss for women with UCTD. METHODS: The Immunosuppressant for Living Fetuses (ILIFE) Trial is a three-arm, multicenter, open-label randomized controlled trial with the primary objective of comparing hydroxychloroquine combined with low-dose prednisone and anticoagulation with anticoagulation alone in treating UCTD women with recurrent spontaneous abortion. The third arm of using hydroxychloroquine combined with anticoagulant for secondary comparison. A total of 426 eligible patients will be randomly assigned to each of the three arms with a 1:1:1 allocation ratio. The primary outcome is the rate of live births. Secondary outcomes include adverse pregnancy outcomes and progression of UCTD. DISCUSSION: This is the first multi-center, open-label, randomized controlled trial which evaluates the efficacy of immunosuppressant regimens on pregnancy outcomes and UCTD progression. It will provide evidence on whether the immunosuppressant ameliorates the pregnancy prognosis in UCTD patients with RSA and the progression into defined connective tissue disease. TRIAL REGISTRATION: ClinicalTrials.gov NCT03671174 . Registered on 14 September 2018.

A predictive score for progression of COVID-19 in hospitalized persons: a cohort study.

Accurate prediction of the risk of progression of coronavirus disease (COVID-19) is needed at the time of hospitalization. Logistic regression analyses are used to interrogate clinical and laboratory co-variates from every hospital admission from an area of 2 million people with sporadic cases. From a total of 98 subjects, 3 were severe COVID-19 on admission. From the remaining subjects, 24 developed severe/critical symptoms. The predictive model includes four co-variates: age (>60 years; odds ratio [OR] = 12 [2.3, 62]); blood oxygen saturation (<97%; OR = 10.4 [2.04, 53]); C-reactive protein (>5.75 mg/L; OR = 9.3 [1.5, 58]); and prothrombin time (>12.3 s; OR = 6.7 [1.1, 41]). Cutoff value is two factors, and the sensitivity and specificity are 96% and 78% respectively. The area under the receiver-operator characteristic curve is 0.937. This model is suitable in predicting which unselected newly hospitalized persons are at-risk to develop severe/critical COVID-19.

DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images.

PURPOSE: The global health crisis caused by coronavirus disease 2019 (COVID-19) is a common threat facing all humankind. In the process of diagnosing COVID-19 and treating patients, automatic COVID-19 lesion segmentation from computed tomography images helps doctors and patients intuitively understand lung infection. To effectively quantify lung infections, a convolutional neural network for automatic lung infection segmentation based on deep learning is proposed. METHOD: This new type of COVID-19 lesion segmentation network is based on a U-Net backbone. First, a coarse segmentation network is constructed to extract the lung areas. Second, in the encoding and decoding process of the fine segmentation network, a new soft attention mechanism, namely the dilated convolutional attention (DCA) mechanism, is introduced to enable the network to focus on better quantitative information to strengthen the network's segmentation ability in the subtle areas of the lesions. RESULTS: The experimental results show that the average Dice similarity coefficient (DSC), sensitivity (SEN), specificity (SPE) and area under the curve of DUDA-Net are 87.06%, 90.85%, 99.59% and 0.965, respectively. In addition, the introduction of a cascade U-shaped network scheme and DCA mechanism can improve the DSC by 24.46% and 14.33%, respectively. CONCLUSION: The proposed DUDA-Net approach can automatically segment COVID-19 lesions with excellent performance, which indicates that the proposed method is of great clinical significance. In addition, the introduction of a coarse segmentation network and DCA mechanism can improve the COVID-19 segmentation performance.

A US Population Health Survey on the Impact of COVID-19 Using the EQ-5D-5L.

BACKGROUND: The COVID-19 pandemic has resulted in negative impacts on the economy, population health, and health-related quality-of-life (HRQoL). OBJECTIVE: To assess the impact of COVID-19 on US population HRQoL using the EQ-5D-5L. DESIGN: We surveyed respondents on physical and mental health, demographics, socioeconomics, brief medical history, current COVID-19 status, sleep, dietary, financial, and spending changes. Results were compared to online and face-to-face US population norms. Predictors of EQ-5D-5L utility were analyzed using both standard and post-lasso OLS regressions. Robustness of regression coefficients against unmeasured confounding was analyzed using the E-Value sensitivity analysis. SUBJECTS: Amazon MTurk workers (n=2776) in the USA. MAIN MEASURES: EQ-5D-5L utility and VAS scores by age group. KEY RESULTS: We received n=2746 responses. Subjects 18-24 years reported lower mean (SD) health utility (0.752 (0.281)) compared with both online (0.844 (0.184), p=0.001) and face-to-face norms (0.919 (0.127), p<0.001). Among ages 25-34, utility was worse compared to face-to-face norms only (0.825 (0.235) vs. 0.911 (0.111), p<0.001). For ages 35-64, utility was better during pandemic compared to online norms (0.845 (0.195) vs. 0.794 (0.247), p<0.001). At age 65+, utility values (0.827 (0.213)) were similar across all samples. VAS scores were worse for all age groups (p<0.005) except ages 45-54. Increasing age and income were correlated with increased utility, while being Asian, American Indian or Alaska Native, Hispanic, married, living alone, having history of chronic illness or self-reported depression, experiencing COVID-19-like symptoms, having a family member diagnosed with COVID-19, fear of COVID-19, being underweight, and living in California were associated with worse utility scores. Results were robust to unmeasured confounding. CONCLUSIONS: HRQoL decreased during the pandemic compared to US population norms, especially for ages 18-24. The mental health impact of COVID-19 is significant and falls primarily on younger adults whose health outcomes may have been overlooked based on policy initiatives to date.