Two-year trajectories of COVID-19 symptoms and their association with illness perception: A prospective cohort study in Amsterdam, the Netherlands 6 177 illness perceptions of long COVID may have evolved over time, we defined two calendar time periods related to the timing of SARS-CoV-2 infection: the first COVID-19 wave in the Netherlands (up to 1 June 2020) and all subsequent waves (on and after 1 June 2020)[13]. Statistical analysis Among participants who completed at least one symptom survey, characteristics were presented, stratifying participants by their initial COVID-19 severity. To assess selection bias, characteristics of participants who did and did not complete at least one symptom survey were compared. Continuous variables were presented as median (IQR) and compared using the Kruskal-Wallis test, whilst categorical variables were displayed as n (%) and compared using Pearson’s Χ2 test. We used group-based trajectory modelling (GBTM) to identify long COVID trajectories over the 24 month period since illness onset. Briefly, trajectories of the mean total number of long COVID symptoms reported (range: 0-20) were estimated using a finitemixture model with a censored normal distribution (thus, fully asymptomatic individuals contributed to 0 symptoms). We chose to model a priori at least three trajectories, assuming that one trajectory would represent participants without long COVID, and aimed to identify ≥2 further trajectories to differentiate participants with long COVID. The bestfitting model (either 3 or 4 trajectories with a linear, quadratic or cubic function of time) was identified by comparing the Bayesian Information Criterion (BIC)[14], conditional on entropy (i.e., measurement of how accurately the model classifies participants into different trajectories) of at least 0.6. Models containing a marginal probability of any one group at <5% were not considered further. We then modelled trajectories of the proportion of participants reporting fatigue, myalgia, loss of smell/taste and dyspnoea (the four most frequently-reported long COVID symptoms in our cohort[9]) using a binary logistic distribution. To study determinants of belonging to a given trajectory, the a posteriori probability of belonging to each group was calculated for each participant from the final GBTM based on the mean total number of symptoms. Study participants were then assigned to the trajectory group for which they had the highest probability of group membership. Participant characteristics were compared between trajectory groups using KruskalWallis test for continuous variables and Pearson’s Χ2 test for categorical variables. Considering that group membership is based on a finite-mixture distribution (i.e., there is misclassification of group membership), determinants of belonging to a group were
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