Background Long-COVID refers to a variety of symptoms affecting different organs reported by people following Coronavirus Disease 2019 (COVID-19) infection. To date, there have been no robust estimates of the incidence and co-occurrence of long-COVID features, their relationship to age, sex, or severity of infection, and the extent to which they are specific to COVID-19. The aim of this study is to address these issues. Methods and findings We conducted a retrospective cohort study based on linked electronic health records (EHRs) data from 81 million patients including 273,618 COVID-19 survivors. The incidence and co-occurrence within 6 months and in the 3 to 6 months after COVID-19 diagnosis were calculated for 9 core features of long-COVID (breathing difficulties/breathlessness, fatigue/malaise, chest/throat pain, headache, abdominal symptoms, myalgia, other pain, cognitive symptoms, and anxiety/depression). Their co-occurrence network was also analyzed. Comparison with a propensity score–matched cohort of patients diagnosed with influenza during the same time period was achieved using Kaplan–Meier analysis and the Cox proportional hazard model. The incidence of atopic dermatitis was used as a negative control. Among COVID-19 survivors (mean [SD] age: 46.3 [19.8], 55.6% female), 57.00% had one or more long-COVID feature recorded during the whole 6-month period (i.e., including the acute phase), and 36.55% between 3 and 6 months. The incidence of each feature was: abnormal breathing (18.71% in the 1- to 180-day period; 7.94% in the 90- to180-day period), fatigue/malaise (12.82%; 5.87%), chest/throat pain (12.60%; 5.71%), headache (8.67%; 4.63%), other pain (11.60%; 7.19%), abdominal symptoms (15.58%; 8.29%), myalgia (3.24%; 1.54%), cognitive symptoms (7.88%; 3.95%), and anxiety/depression (22.82%; 15.49%). All 9 features were more frequently reported after COVID-19 than after influenza (with an overall excess incidence of 16.60% and hazard ratios between 1.44 and 2.04, all p < 0.001), co-occurred more commonly, and formed a more interconnected network. Significant differences in incidence and co-occurrence were associated with sex, age, and illness severity. Besides the limitations inherent to EHR data, limitations of this study include that (i) the findings do not generalize to patients who have had COVID-19 but were not diagnosed, nor to patients who do not seek or receive medical attention when experiencing symptoms of long-COVID; (ii) the findings say nothing about the persistence of the clinical features; and (iii) the difference between cohorts might be affected by one cohort seeking or receiving more medical attention for their symptoms. Conclusions Long-COVID clinical features occurred and co-occurred frequently and showed some specificity to COVID-19, though they were also observed after influenza. Different long-COVID clinical profiles were observed based on demographics and illness severity.

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Background. Vaccination has proven effective against infection with SARS-CoV-2, as well as death and hospitalisation following COVID-19 illness. However, little is known about the effect of vaccination on other acute and post-acute outcomes of COVID-19. Methods. Data were obtained from the TriNetX electronic health records network (over 81 million patients mostly in the USA). Using a retrospective cohort study and time-to-event analysis, we compared the incidences of COVID-19 outcomes between individuals who received a COVID-19 vaccine (approved for use in the USA) at least 2 weeks before SARS-CoV-2 infection and propensity score-matched individuals unvaccinated for COVID-19 but who had received an influenza vaccine. Outcomes were ICD-10 codes representing documented COVID-19 sequelae in the 6 months after a confirmed SARS-CoV-2 infection (recorded between January 1 and August 31, 2021). Associations with the number of vaccine doses (1 vs. 2) and age (< 60 vs. ≥ 60 years-old) were assessed. Findings. Among 10,024 vaccinated individuals with SARS-CoV-2 infection, 9479 were matched to unvaccinated controls. Receiving at least one COVID-19 vaccine dose was associated with a significantly lower risk of respiratory failure, ICU admission, intubation/ventilation, hypoxaemia, oxygen requirement, hypercoagulopathy/venous thromboembolism, seizures, psychotic disorder, and hair loss (each as composite endpoints with death to account for competing risks; HR 0.70-0.83, Bonferroni-corrected p<.05), but not other outcomes, including long-COVID features, renal disease, mood, anxiety, and sleep disorders. Receiving 2 vaccine doses was associated with lower risks for most outcomes. Associations between prior vaccination and outcomes of SARS-CoV-2 infection were marked in those < 60 years-old, whereas no robust associations were observed in those ≥ 60 years-old. Interpretation. COVID-19 vaccination is associated with lower risk of several, but not all, COVID-19 sequelae in those with breakthrough SARS-CoV-2 infection. These benefits of vaccination were clear in younger people but not in the over-60s. The findings may inform service planning, contribute to forecasting public health impacts of vaccination programmes, and highlight the need to identify additional interventions for COVID-19 sequelae. Funding. National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre.

Background Grip strength is an indicator of physical function with potential predictive value for health in ageing populations. We assessed whether trends in grip strength from midlife predicted later-life brain health and cognition. Methods 446 participants in an ongoing British birth cohort study, the National Survey of Health and Development (NSHD), had their maximum grip strength measured at ages 53, 60–64, and 69, and subsequently underwent neuroimaging as part of a neuroscience sub-study, referred to as “Insight 46”, at age 69–71. A group-based trajectory model identified latent groups of individuals in the whole NSHD cohort with below- or above-average grip strength over time, plus a reference group. Group assignment, plus standardised grip strength levels and change from midlife were each related to measures of whole-brain volume (WBV) and white matter hyperintensity volume (WMHV), plus several cognitive tests. Models were adjusted for sex, body size, head size (where appropriate), sociodemographics, and behavioural and vascular risk factors. Results Lower grip strength from midlife was associated with smaller WBV and lower matrix reasoning scores at age 69–71, with findings consistent between analysis of individual time points and analysis of trajectory groups. There was little evidence of an association between grip strength and other cognitive test scores. Although greater declines in grip strength showed a weak association with higher WMHV at age 69–71, trends in the opposite direction were seen at individual time points with higher grip strength at ages 60–64, and 69 associated with higher WMHV. Conclusions This study provides preliminary evidence that maximum grip strength may have value in predicting brain health. Future work should assess to what extent age-related declines in grip strength from midlife reflect concurrent changes in brain structure.

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Cognitive distancing is a therapeutic technique commonly used in psychological treatment of various mental health disorders, but its computational mechanisms remain unknown. To determine the effects of cognitive distancing on computational learning mechanisms, we use an online reward decision-making task, combined with reinforcement learning modelling in 935 participants, 49.1% of whom were trained to regulate their emotional response to task performance feedback. Those participants practicing cognitive distancing showed heightened learning from negative events as well as an increased integration of previous choice values. These differences seemed to represent an evolving shift in strategy by the distancing participants during the task, from exploiting optimal choices earlier in the task (as indicated by greater inverse temperature parameters), to a late-stage increase in learning from negative outcomes (represented as higher loss learning rates). Our findings suggest adaptive changes in computational learning mechanisms underpin the clinical utility of cognitive distancing in psychological therapy.

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