Prevalence of Long COVID-19 in the Canary Islands

With more than 246 million people diagnosed with coronavirus 2019 (COVID-19) around the world, many of whom have required medical attention for past 23 months, greater emphasis is being placed on post-acute care of COVID-19 survivors. According to recent data, millions of patients who have recovered from acute COVID-19 are experiencing persistent symptoms, which lead to disability and impaired activities of daily living. Various terms have been used to describe the condition of patients who do not return to their initial health status to include post-acute sequelae of COVID-19, such as post-acute COVID syndrome (PACS) and Long COVID. The growing global burden of COVID-19 suggests that the potential effects of Long COVID in public health are vast even if Long COVID is experienced by a small proportion of patients recovering from acute infection. The ability to identify patients at high risk for Long COVID and forecasting medical resource requirements is of important clinical utility in the present. In view of the large number of people surviving after infection with COVID-19 and that should require follow-up, determine which patients are at risk of have Long COVID and who require close monitoring is crucial. In this study, therefore, we set out to analyze what type of patient profile with COVID-19 who, after 12 weeks of having suffered the infection, experiences the signs and symptoms described by current literature.

It is intended to develop a Cross-sectional Descriptive Observational Study. The investigation will be carried out following the guidelines of "Strengthening the Reporting of Observational Studies in Epidemiology" (STROBE). A registry will be carried out at a multicenter level, in which a physical-functional evaluation will be carried out. of the patient who has suffered from COVID-19 and whose signs and symptoms last 12 weeks after its detection. This assessment is carried out by physiotherapists members of the entire Canarian Autonomous Community. Some quizzes will be completed with the assistance of a specific evaluator (physiotherapist), while that others will be self-completion. In order to avoid bias and try to guarantee the highest methodological quality, prior to the recruitment, screening and evaluation of the participants, a specific training for all evaluators (physiotherapists). They will be addressed general theoretical content on COVID-persistent, justification and development logistics of the proposed study protocol, and implementation of the battery of evaluation tests. The study participants will be recruited through non-recruited sampling probabilistic convenience. The statistical analysis of the data will be carried out using the statistical software International Business Machines (IBM) Statistical Package for the Social Sciences (SPSS) 27. Categorical variables will be summarized using percentages and relative frequencies. The equality of proportions of the categories will be contrasted with the non-parametric tests Bi-nominal and Chi-Square (Goodness of fit). To test the possible association between categorical variables will use the Chi-square test or the exact test Fisher's. To analyze the possible association between ordinal categorical variables, we will use to contrast Spearman's correlation coefficient. Numeric variables are summarized with the mean and standard deviation or by means of the median and the interquartile range, depending on whether or not the normality condition of the data is met. To analyze the normality of the samples, the test of Kolmogorov-Smirnov or Shapiro-Wild. To compare the means of two independent samples, will use the Student's T test or the non-parametric U-Mann Whitney test, depending on whether or not the data normality condition. In the case of means of 3 or more independent samples, will use the one-way ANOVA procedure or the non-parametric Kruskal Wallis test, depending on whether or not the normality condition of the data is met. To analyze the possible association between the numerical variables, the contrast of the coefficient of linear Pearson correlation. According to the objectives and the associations that could be detected, it will be possible to use other statistical tools, or propose the construction of mathematical models predictive. The results will be considered statistically significant if p-value \<0.05.