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Primary immunodeficiencies (PIs) are a group of diseases that increase susceptibility to infectious diseases. Few studies have examined the relationship between PI and COVID-19 outcomes. In this study, we used Premier Healthcare Database, which contains information on inpatient discharges, to analyze COVID-19 outcomes among 853 adult PI and 1,197,430 non-PI patients who visited the emergency department. Hospitalization, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and death had higher odds in PI patients than in non-PI patients (hospitalization aOR: 2.36, 95% CI: 1.87–2.98; ICU admission aOR: 1.53, 95% CI: 1.19–1.96; IMV aOR: 1.41, 95% CI: 1.15–1.72; death aOR: 1.37, 95% CI: 1.08–1.74), and PI patients spent on average 1.91 more days in the hospital than non-PI patients when adjusted for age, sex, race/ethnicity, and chronic conditions associated with severe COVID-19. Of the largest four PI groups, selective deficiency of the immunoglobulin G subclass had the highest hospitalization frequency (75.2%). This large study of United States PI patients provides real-world evidence that PI is a risk factor for adverse COVID-19 outcomes.

BACKGROUND Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. OBJECTIVE Assessing social media posts from Twitter, YouTube, Reddit, and Drugs-Forum.com for the presence of health misinformation about treatment for OUD. METHODS We developed a multi-stage analytic pipeline to assess social media posts from Twitter, YouTube, Reddit, and Drugs-Forum.com for the presence of health misinformation about treatment for OUD. Our approach first utilized document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering and public health experts then reviewed results for misinformation. RESULTS We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multi-stage analytic pipeline identified seven main clusters or discussion themes. Among a high-yield dataset of posts (N=303) for further public health expert review, these included discussion about potential treatments for opioid use disorder (N=90, 29.8%), the nature of addiction (N=68, 22.5%), pharmacologic properties of substances (N=52, 16.88%), injection drug use (N=36, 11.9%), pain and opioids (N=28, 9.27%), physical dependence of medications (N=22, 7.2%), and tramadol use (N=7, 2.3%). Public health expert review of content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. CONCLUSIONS Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component to preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.