Research team creates statisticalmodel to predict COVID-19 resistance PLOSONE
Contributed equally to this work with: Kai-Wen K. Yang, Chloé F. Paris, Kevin T. Gorman, Ilia Rattsev, Rebecca H.
Yoo, Yijia ChenConceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editingDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States of AmericaContributed equally to this work with: Kai-Wen K. Yang, Chloé F. Paris, Kevin T. Gorman, Ilia Rattsev, Rebecca H.
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Coding long COVID: characterizing a new disease through an ICD-10 lens - BMC MedicineBackground Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for “Post COVID-19 condition, unspecified.” Methods We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions This work offers insight into potential subtypes and current practice patterns around long COVID and spe
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Personality traits and hardiness as risk- and protective factors for mental distress during the COVID-19 pandemic: a Norwegian two-wave study - BMC PsychiatryBackground Several risk factors for anxious-depressive symptomatology during the COVID-19 pandemic have been established. However, few studies have examined the relationship between personality traits, hardiness, and such symptomatology during the pandemic. These constructs might serve as risk- and/or protective factors for such mental distress through the pandemic. Methods A sample of 5783 Norwegians responded to a survey at two time points within the first year of the pandemic. The first data collection was in April 2020 (T1) and the second in December 2020 (T2). Measures included the Ten-Item Personality-Inventory, the Revised Norwegian Dispositional Resilience Scale, and the Patient Health Questionnaire Anxiety and Depression Scale. Analyses were performed using Pearson’s correlations, multiple linear regression, and a moderation analysis. Results Anxious-depressive symptomatology in early phases (T1) of the pandemic was the strongest predictor for the presence of such symptomatology 9 months after the outbreak (T2). Personality and hardiness correlated significantly with mental distress at T1 and T2. Personality traits explained 5% variance in symptoms when controlling for age, gender, solitary living, negative economic impact, and mental distress at baseline. Higher neuroticism predicted higher mental distress, whereas higher conscientiousness and extraversion predicted less mental distress. Hardiness did not explain variance in outcome beyond personality traits. Hardiness did not significantly moderate the relationship between neuroticism and mental distress. Conclusion Individuals with high levels of neuroticism had greater difficulties adapting to the circumstances of the COVID-19 pandemic and were more prone to mental distress. Contrastingly, higher conscientiousness and extraversion may have served as protective factors for mental distress during the pandemic. The current findings might aid identification of vulnerable individuals and groups. Consequently
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