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1.
Preprint in English | medRxiv | ID: ppmedrxiv-22274890

ABSTRACT

BackgroundChanges in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDD) and Multiple Sclerosis (MS). MethodsData were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse - Central Nervous System) programme. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. ResultsParticipants with MDD (N=255) and MS (N=214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. Lower mean HR and HR variation were observed between pre and during lockdown during the day for MDD and during the night for MS. HR variation during rest periods also decreased between pre-and post-lockdown in both clinical conditions. We observed a reduction of physical activity for MDD and MS upon the introduction of lockdowns. The group with MDD exhibited a net increase in social interaction via social network apps over the three periods. ConclusionsBehavioral response to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDD and MS.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-21261638

ABSTRACT

BackgroundCOVID-19 antibody testing allows population studies to classify participants by previous SARS-CoV-2 infection status. Home lateral flow immune-antibody testing devices offer a very convenient way of doing this, but relatively little is known about how measurement and antibody variability will affect consistency in results over time. We examined consistency by looking at the outcome of two tests three months apart while COVID-19 infection rates were low (summer 2020 in the UK). MethodsThe KCL-Coronavirus Health and Experiences in Colleagues at Kings is an occupational cohort of staff and postgraduate research students. Lateral flow immune-antibody testing kits were sent to participants homes in late June 2020 and late September 2020. Participants also completed regular surveys that included asking about COVID-19 symptoms and whether they thought they had been infected. ResultsWe studied 1489 participants returned valid results in both June and September (59% of those sent kits). Lateral flow immune-antibody test was positive for 7.2% in June and 5.9% in September, with 3.9% positive in both. Being more symptomatic or suspecting infection increased the probability of ever being positive. Of those positive in June, 46% (49/107) were negative in September (seroreversion), and this was similar regardless of symptom characteristics, suspicion, and timing of possible infection. A possible outlier was those aged over 55 years, where only 3 of 13 (23%) had seroreversion. DiscussionThese results do not follow the pattern reported from studies specifically designed to monitor seropositivity, which have found greater consistency over time and the influence of presence, timing and severity of symptoms on seroreversion. We suggest several factors that may have contributed to this difference: our low bar in defining initial seropositivity (single test); a non-quantitative test known to have relatively low sensitivity; participants carrying out testing. We would encourage other studies to use these real-world performance characteristics alongside those from laboratory studies to plan and analyse any antibody testing.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-20245183

ABSTRACT

Background Definitive diagnosis of COVID-19 requires resources frequently restricted to the severely ill. Cohort studies must rely on surrogate indicators to define cases of COVID-19 in the community. We describe the prevalence and overlap of potential indicators including self-reported symptoms, suspicion, and routine test results, plus home antibody testing. Methods An occupational cohort of 2807 staff and postgraduate students at a large London university. Repeated surveys covering March to June 2020. Antibody test results from 'lateral flow' IgG/IgM cassettes in June 2020. Results 1882 participants had valid antibody test results, and 124 (7%) were positive. Core symptoms of COVID-19 were common (770 participants positive, 41%), although fewer met criteria on a symptom algorithm (n=297, 16%). Suspicion of COVID-19 (n=509, 27%) was much higher than positive external tests (n=39, 2%). Positive antibody tests were rare in people who had no suspicion (n=4, 1%) or no core symptoms (n=10, 2%). In those who reported external antibody tests, 15% were positive on the study antibody test, compared with 24% on earlier external antibody tests. Discussion Our results demonstrate the agreement between different COVID indicators. Antibody testing using lateral flow devices at home can detect asymptomatic cases and provide greater certainty to self-report; but due to weak and waning antibody responses to mild infection, may under-ascertain. Multiple indicators used in combination can provide a more complete story than one used alone. Cohort studies need to consider how they deal with different, sometimes conflicting, indicators of COVID-19 illness to understand its long-term outcomes.

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