Chronic Medical Disorders during COVID-19: Differences in Morbidities, Work, Habits and Attitudes

Background: Chronic medical disorders (CMD) are highly prevalent and are often associated with multiple mental and physical comorbidities, which amplify health risks and pose significant challenges for long-term treatment, contributing to their substantial burden on individuals and healthcare systems. During the pandemic CMD were particularly affected. Portugal besides having a higher prevalence of CMD is among the countries which used more strict protective pandemic measures with subsequent negative impacts. Objectives: The paper aims to identify different dysfunctional patterns in 14 CMD: diabetes, hypertension, cardiovascular (CVD), fibromyalgia, rheumatologic, respiratory, allergic, autoimmune, gastrointestinal and dermatologic disorders, chronic pain, fatigue, tinnitus and dizziness. Methods: Online surveys were used for data collection gathering 5479 individuals, mean age 48.5 years, ranging from 18 to 90 years, 67.7% were females. Demographics, work before COVID, health status, confinement, attitudes and behaviors, mental health, sleep, physical activity, multimedia use, nutrition, toxics and additions. ANOVA and linear discriminant analysis (LDA) were used for data analysis and significance was set at p<0.05. Results: The most important features to differentiate CMD were comorbidities, work stress before COVID, Sleep, Mental Health and Attitudes. The disorders with higher canonical correlations with the discriminant function in LDA were: Chronic Pain, Fibromyalgia, Autoimmune, CVD, Fatigue, Diabetes, Dermatologic, Dizziness. Correct classification of the original group varied between 79.2 and 92.0%. Correct classification for “Not having” a specific CMD varied between 79.7 and 92.5%. Correct classification for “Having” a specific CMD provided lower values and varied between 60.0 and 82.4%. Conclusions: The CMD studied exhibited distinct characteristics both among themselves and compared to individuals without the disease. LDA achieved high accuracy in classifying each CMD. These findings provide valuable insights for guiding strategies to manage CMD’s during future public heath disasters.