Barriers in accessing healthcare during the COVID-19 pandemic: analysis of the Virus Watch community cohort study (preprint)

Background: Differential barriers to accessing healthcare contribute to inequitable health outcomes. This study aims to describe the characteristics of individuals who experienced barriers, and what those barriers were, during the COVID-19 pandemic. Methods: We analysed data from Virus Watch: an online survey-based community study of households in England and Wales. The primary outcome was reported difficulty accessing healthcare in the previous year. Results: Minority ethnic participants reported difficulty accessing healthcare more than White British participants (41.6% vs 37%), while for migrants this was at broadly similar levels to non-migrants. Those living in the most deprived areas reported difficulty more than those living in the least deprived quintile (45.5% vs. 35.5%). The most frequently reported barrier was cancellation/disruption of services due to the COVID-19 pandemic (72.0%) followed by problems with digital or telephone access (21.8%). Ethnic minority participants, migrants, and those from deprived areas more commonly described 'insufficient flexibility of appointments' and 'not enough time to explain complex needs' as barriers. Conclusions: Minority ethnic individuals and those living in deprived areas were more likely to experience barriers to healthcare during the COVID-19 pandemic, and it is essential they are addressed as services seek to manage backlogs of care.

Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation (preprint)

Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection and detect the infection given the current audio sample, but do not monitor disease progression in COVID-19. Limited exploration has been put forward to continuously monitor COVID-19 progression, especially recovery, through longitudinal audio data. Tracking disease progression characteristics could lead to more timely treatment. The primary objective of this study is to explore the potential of longitudinal audio samples over time for COVID-19 progression prediction and, especially, recovery trend prediction using sequential deep learning techniques. Crowdsourced respiratory audio data, including breathing, cough, and voice samples, from 212 individuals over 5-385 days were analyzed. We developed a deep learning-enabled tracking tool using gated recurrent units (GRUs) to detect COVID-19 progression by exploring the audio dynamics of the individuals' historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19 detection in terms of positive and negative (healthy) tests, and (2) longitudinal disease progression prediction over time in terms of probability of positive tests. The strong performance for COVID-19 detection, yielding an AUROC of 0.79, a sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of the approach compared to methods that do not leverage longitudinal dynamics. We further examined the predicted disease progression trajectory, displaying high consistency with test results with a correlation of 0.75 in the test cohort and 0.86 in a subset of the test cohort who reported recovery. Our findings suggest that monitoring COVID-19 evolution via longitudinal audio data has potential in the tracking of individuals' disease progression and recovery.