Covid-19 vaccine responses in individuals with type 1 diabetes are comparable to control subjects without diabetes

Aims/Hypothesis: Covid-19 has been associated with poorer outcomes in individuals with type 1 diabetes. Most existing data relate to hospitalised patients with few data available on seroprevalence and the effects of Covid-19 on people with diabetes in the general population. We examined antibody responses to SARS Cov-2 infection and vaccination in people with and without diabetes. Method(s): From June 2020, capillary blood samples collected remotely from 1828 individuals (type 1 diabetes n = 267) were analysed for SARS-CoV- 2 antibodies to RBD (infection pre-Jan 2021/vaccination post -Jan 21) and N (infection post Jan 21) antigens using low serum volume luciferase-based assays developed "in house". Questionnaire data recording experiences of Covid-19 and vaccinations dates were collected simultaneously with the samples. Median antibody levels were compared using Kruskal-Wallis tests. Result(s): There was evidence of SARS CoV-2 infection in 317/1828 (17%) of individuals screened with no evidence of more severe self-reported Covid-19 symptoms in those with diabetes (no participants were hospitalised) and almost a quarter of those with type 1 diabetes were asymptomatic. Although samples were collected at variable time points from vaccination, robust antibody responses to vaccination were observed (Pfizer, AstraZeneca, and Moderna) after the second vaccination with no differences in antibody levels between those with and without diabetes (p = 0.3). Conclusion(s): Hospitalised individuals with Covid-19 and type 1 diabetes were at greater risk of complications but this study shows that among the non-hospitalised population, clinical symptoms, antibody responses to infection, and vaccination in those with type 1 diabetes was similar to control subjects.

Digital Relationality, Rights, Resilience: Conceptualising a Digital Social Ecology for Children's Birth Family Relationships When in Care or Adopted

The use of digital communication technology by children residing in out-of-home care or adopted from foster care has mainly been approached hesitantly and from a risk paradigm. The Covid-19 pandemic catalysed many digital and social work intersections, including practices used for birth family contact where in-person visits were supplemented or replaced with 'virtual' contact via digital devices. Whilst technology-mediated contact is characterised as 'virtual', the relationships it facilitates and emotions it generates are very real within children's social ecology. Digital ubiquity in social life and the rapid pace of technological change presents significant ethical and practical tensions. To help social workers navigate this complexity of 'contact-in-reality' and facilitate safe, ethical use of digital communication technology for birth family contact, we connect an understanding of the dynamics of birth family contact with literature on children's use of digital technology and ecological concepts of person-in-environment to offer a digital social ecology heuristic for social work practice. Three key aspects cut across all systems and levels, referred to here as the three Digital R's: digital relationality;digital rights;and digital resilience. Future research is needed to understand how these dynamics play out. Conversations about use of social media, mobile Internet and other digital communication technology by children in care or adopted from care often focus on risk. The Covid-19 pandemic meant that social workers had to rethink digital technology and consider how it might be used for birth family contact. When contact happens using technology, it is often thought of as 'virtual', however, these relationships and emotions are very real for children. Technological progress is happening quickly, and digital technology is everywhere. Social workers must think about how this will affect their practice. We bring together work on birth family contact, children's digital technology use and theoretical literature on the influence of environments on individuals. We offer a way of looking at these issues to help social workers consider how to use digital communication technology for birth family contact in safe and ethical ways. We propose consideration of the three Digital R's: digital relationality (digital technology and relationships);digital rights (legal protection of children's safety and opportunities whilst using digital technology);and digital resilience (digital skills and ability to cope with negative experiences). Future research is needed to strengthen empirical evidence on how these factors manifest and interact.

Using real-world data from a large, multi-specialty health care system to longitudinally phenotype multiple sclerosis

Background: Real-world data (RWD) are an important complement to randomized, controlled and registry datasets in defining a disease course longitudinally. There is growing interest in understanding the insidious progression in multiple sclerosis (MS) that can occur despite aggressive relapse prevention, as well as how diversity and comorbidities impact multiple sclerosis (MS) patients, particularly in the era of the coronavirus (COVID19) pandemic. Objectives: We aim to derive RWD from a diverse cohort of approximately 4,000 MS patients in Northern California to pair with biomarkers from the Sutter-wide Precision Medicine Biobank - a longitudinal biorepository with a healthy aging comparator cohort. This pilot of 34 patients evaluates the integration of several data sources to extract key information about disease course. From the EHR, we use a combination of text processing, automated data element extraction, manual chart curation, and patientand physician-targeted questionnaires to form a real-world dataset of interpretable outcome metrics. Methods: This is an ambidirectional cohort study of subjects at least 18 years old, with a defining diagnosis of MS from at least one hospitalization or two outpatient encounters. Data elements including demographics, medication orders and comorbidities were directly extracted from the EHR. MRI reports in text format were stored in an Epic Clarity database, and neurology notes were mined for terms indicating stability versus worsening. Manual curation was used to transform prose clinician notes into tabularformat outcome scores. Results: We curated 9930 total encounters, 136 brain MRI reports and 137 spine MRI reports. We found 7.5 years (+/- 3.3) of data per patient in this pilot of 34 patients. 79% of patients were female, 21% male;68% white, 26% black and 6% other/not disclosed. The most common disease-modifying therapies used were natalizumab, dimethyl fumarate and glatiramer acetate. 68% of patients had at least one comorbidity, 35% specifically had hypertension. Using automated and manual data methods, we were able to compile metrics of clinical and radiographic worsening versus stability from information in the EHR. Conclusions: Our methods may be used to generate interpretable data on a system-wide scale from the comprehensive, longitudinal data of an EHR. These RWD can be paired with biospecimens, research assessments, and other datasets to add to the diversity of data on MS natural history and medication response.