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CONTEXTE: De nombreuses etudes sur les complications de la maladie a coronavirus 2019 (COVID-19) ont repose sur des series de cas et de petites cohortes qui ne permettaient pas d’etablir un lien causal avec la COVID-19 ni d’estimer les risques dans les differents milieux de soins. Nous avons voulu etudier toutes les complications possibles de la COVID-19 afin de confirmer les complications precedemment declarees et d’identifier de potentielles complications encore inconnues. METHODES: A partir des donnees sur les demandes de remboursement de frais medicaux aux Etats-Unis, nous avons compare la frequence de tous les codes de diagnostic de la Classification internationale des maladies, 10e revision, modification clinique (CIM-10-MC) enregistres avant et apres le declenchement de la pandemie de COVID-19 dans un modele d’auto-appariement pre- et post-exposition. Nous avons inclus les patients ayant recu un diagnostic de COVID-19 entre le 1er mars 2020 et le 30 avril 2020, et calcule les estimations de risque et les rapports de cotes (RC) pour le lien avec la COVID-19 de chaque code de diagnostic de la CIM-10-MC. RESULTATS: Sur les 1724 codes de diagnostic de la CIM-10-MC attribues a 70 288 patients atteints de COVID-19, 69 etaient significativement lies a la COVID-19. Les diagnostics etroitement lies a la COVID-19 et comportant un risque absolu eleve comprenaient la pneumonie virale (RC 177,63; intervalle de confiance [IC] a 95 % 147,19–214,37; risque absolu 27,6 %), l’insuffisance respiratoire (RC 11,36; IC a 95 % 10,74–12,02; risque absolu 22,6 %), l’insuffisance renale aigue (RC 3,50; IC a 95 % 3,34–3,68; risque absolu 11,8 %) et la sepsie (RC 4,23; IC a 95 % 4,01–4,46; risque absolu 10,4 %). Les diagnostics etroitement lies a la COVID-19, mais comportant un risque absolu faible comprenaient la myocardite (RC 8,17; IC a 95 % 3,58–18,62; risque absolu 0,1 %), la coagulation intravasculaire disseminee (RC 11,83; IC a 95 % 5,26–26,62; risque absolu 0,1 %) et le pneumothorax (RC 3,38; IC a 95 % 2,68–4,26; risque absolu 0,4 %). INTERPRETATION: Nous avons confirme et etabli les estimations du risque de plusieurs complications de la COVID-19. Ces resultats pourraient orienter le pronostic, les decisions therapeutiques et les conseils aux patients.

STRUCTURED ABSTRACTImportanceAlgorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data (RWD).ObjectiveTo explore use of electronic health record (EHR) data to inform an administrative data algorithm for classification of supplemental oxygen or noninvasive ventilation (O2/NIV) and invasive mechanical ventilation (IMV) to assess disease severity in hospitalized COVID-19 patients.DesignIn this retrospective cohort study, we developed an initial procedure-based algorithm to identify O2/NIV, IMV, and NEITHER O2/NIV nor IMV in two inpatient RWD sources. We then expanded the algorithm to explore the impact of adding diagnoses indicative of clinical need for O2/NIV (hypoxia, hypoxemia) or IMV (acute respiratory distress syndrome) and O2-related patient vitals available in the EHR. Observed changes in severity categorization were used to augment the administrative algorithm.SettingOptum de-identified COVID-19 EHR data and HealthVerity claims and chargemaster data (March – August 2020).ParticipantsAmong patients hospitalized with COVID-19 in each RWD source, our motivating example selected dexamethasone (DEX+) initiators and a random selection of patients who were non-initiators of a corticosteroid of interest (CSI-) matched on date of DEX initiation, age, sex, baseline comorbidity score, days since admission, and COVID-19 severity level (NEITHER, O2/NIV, IMV) on treatment index.Main Outcome and MeasuresInpatient COVID-19 severity was defined using the algorithms developed to classify respiratory support requirements among hospitalized COVID-19 patients (NEITHER, O2/NIV, IMV). Measures were reported as the treatment-specific distributions of patients in each severity level, and as observed changes in severity categorization between the initial procedure-based and expanded algorithms.ResultsIn the administrative data cohort with 5,524 DEX+ and CSI- patient pairs matched using the initial procedure-based algorithm, 30% were categorized as O2/NIV, 5% as IMV, and 65% as NEITHER. Among patients assigned NEITHER via the initial algorithm, use of an expanded algorithm informed by the EHR-based algorithm shifted 54% DEX+ and 28% CSI- to O2/NIV, and 2% DEX+ and 1% CSI- to IMV. Among patients initially assigned O2/NIV, 7% DEX+ and 3% CSI- shifted to IMV.Conclusions and RelevanceApplication of learnings from an EHR-based exploration to our administrative algorithm minimized treatment-differential misclassification of COVID-19 severity.

ImportanceVaccination against the SARS-CoV-2 virus is critical to control the pandemic. Randomized trials demonstrated efficacy of the single-dose Ad26.COV2.S COVID vaccine but data on longer-term protection in clinical practice and effectiveness against variants are needed.ObjectiveTo assess the effectiveness of Ad26.COV2.S in preventing COVID infections and COVID-related hospitalizations in clinical practice, the longer-term stability of its protective effect and effectiveness against Delta variants.DesignCohort study of newly Ad26.COV2.S-vaccinated and unvaccinated individuals.SettingU.S. insurance claims data through July 2021.ParticipantsIndividuals 18 years and older newly vaccinated with Ad26.COV2.S and up to 10 unvaccinated individuals matched exactly by age, sex, date, location, comorbidity index plus 17 COVID-19 risk factors via propensity score (PS) matching.InterventionVaccination with Ad26.COV2.S versus no vaccination.Main outcomesWe estimated vaccine effectiveness (VE) for observed COVID-19 infection and COVID-19-related hospitalization, nationwide and stratified by age, immunocompromised status, calendar time, and states with high incidence of the Delta variant. We corrected VE estimates for under-recording of vaccinations in insurance data.ResultsAmong 390,517 vaccinated and 1,524,153 matched unvaccinated individuals, VE was 79% (95% CI, 77% to 80%) for COVID-19 and 81% (79% to 84%) for COVID-19-related hospitalizations. VE was stable over calendar time. Among states with high Delta variant incidence, VE during June/July 2021 was 78% (73% to 82%) for infections and 85% (73% to 91%) for hospitalizations. VE for COVID-19 was higher in individuals ConclusionsThese non-randomized data across U.S. clinical practices show high and stable vaccine effectiveness of Ad26.COV2.S over time before the Delta variant emerged to when the Delta variant was dominant.

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

BACKGROUND: Many studies reporting coronavirus disease 2019 (COVID-19) complications have involved case series or small cohorts that could not establish a causal association with COVID-19 or provide risk estimates in different care settings. We sought to study all possible complications of COVID-19 to confirm previously reported complications and to identify potential complications not yet known. METHODS: Using United States health claims data, we compared the frequency of all International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes occurring before and after the onset of the COVID-19 pandemic in an exposure-crossover design. We included patients who received a diagnosis of COVID-19 between Mar. 1, 2020, and Apr. 30, 2020, and computed risk estimates and odds ratios (ORs) of association with COVID-19 for every ICD-10-CM diagnosis code. RESULTS: Among 70 288 patients with COVID-19, 69 of 1724 analyzed ICD-10-CM diagnosis codes were significantly associated with COVID-19. Disorders showing both strong association with COVID-19 and high absolute risk included viral pneumonia (OR 177.63, 95% confidence interval [CI] 147.19–214.37, absolute risk 27.6%), respiratory failure (OR 11.36, 95% CI 10.74–12.02, absolute risk 22.6%), acute kidney failure (OR 3.50, 95% CI 3.34–3.68, absolute risk 11.8%) and sepsis (OR 4.23, 95% CI 4.01–4.46, absolute risk 10.4%). Disorders showing strong associations with COVID-19 but low absolute risk included myocarditis (OR 8.17, 95% CI 3.58–18.62, absolute risk 0.1%), disseminated intravascular coagulation (OR 11.83, 95% CI 5.26–26.62, absolute risk 0.1%) and pneumothorax (OR 3.38, 95% CI 2.68–4.26, absolute risk 0.4%). INTERPRETATION: We confirmed and provided risk estimates for numerous complications of COVID-19. These results may guide prognosis, treatment decisions and patient counselling.

ObjectivesThere is lack of clarity regarding the role of angiotensin receptor blockers (ARB) or angiotensin converting enzyme inhibitors (ACEi) in interfering with the SARS-COV-2 binding on human cells and the resulting change in disease severity. We sought to assess the risk of hospitalization for COVID-19 and serious complications in current users of ARB or ACEi compared to users of dihydropyridine calcium channel blockers (dhpCCB).DesignCohort studySettingThe analysis used de-identified, patient level data from HealthVerity, linking longitudinal data from US medical and pharmacy claims, which contain information on inpatient or outpatient diagnoses, procedures and medication dispensing.ParticipantsWe identified patients aged 40+ and free of chronic kidney disease (CKD) who were newly diagnosed COVID-19, between March 1, 2020 and May 30, 2020, and adherent to ACEi, ARB, or dhpCCB therapy.InterventionsCurrent use of an ACEi, ARB, or dhpCCB.Main outcome measuresWe compared the 30-day risk of hospitalization for COVID-19 and serious complications.ResultsOf 24,708 patients identified, 7,571 were current users of an ARB, 8,484 of an ACEi, and 8,653 of a dhpCCB. The unadjusted 30-day risk of hospitalization for COVID-19 was 2.66% among ARB users, and 2.90% among ACEi users and 3.68% in dhpCCB users. In the PS-matched cohort, the risk of hospitalization among ARB users was 17% lower as compared to dhpCCB (RR=0.83; 0.68-1.00), and the risk among ACE users was 10% lower as compared to dhpCCB (RR=0.90; 0.76-1.07). When including patients with pre-existing CKD, the protective effect of ARB (RR= 0.74; 0.62-0.88) and ACEi (RR=0.84; 0.71-0.99) was more pronounced.ConclusionsThis cohort study showed that neither ARB nor ACEi use increase the risk of severe COVID-19 disease among those infected, and instead suggests that current use of ARB may offer a protective effect. This study found no evidence to support the discontinuation of ARB/ACEi therapy.

This paper describes a reflective mobile application to help young people with ADD and ADHD better understand their engagement levels during their daily tasks. The mobile application, paired with an electroencephalographic (EEG) device, collects data about user-specific task engagement and pairs it with geographic and temporal data to provide insights into the degree to which a user is engaged in tasks based on time and location. The paper describes two mobile prototypes developed to investigate the problem space of contextual visualizations of engagement information, and it presents future directions for development and study.