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Long-term sequelae in hospitalized Coronavirus Disease 2019 (COVID-19) patients may result in limited quality of life. The current study aimed to determine health-related quality of life (HRQoL) after COVID-19 hospitalization in non-intensive care unit (ICU) and ICU patients. This is a single-center study at the University Hospital of Wuerzburg, Germany. Patients eligible were hospitalized with COVID-19 between March 2020 and December 2020. Patients were interviewed 3 and 12 months after hospital discharge. Questionnaires included the European Quality of Life 5 Dimensions 5 Level (EQ-5D-5L), patient health questionnaire-9 (PHQ-9), the generalized anxiety disorder 7 scale (GAD-7), FACIT fatigue scale, perceived stress scale (PSS-10) and posttraumatic symptom scale 10 (PTSS-10). 85 patients were included in the study. The EQ5D-5L-Index significantly differed between non-ICU (0.78 ± 0.33 and 0.84 ± 0.23) and ICU (0.71 ± 0.27; 0.74 ± 0.2) patients after 3- and 12-months. Of non-ICU 87% and 80% of ICU survivors lived at home without support after 12 months. One-third of ICU and half of the non-ICU patients returned to work. A higher percentage of ICU patients was limited in their activities of daily living compared to non-ICU patients. Depression and fatigue were present in one fifth of the ICU patients. Stress levels remained high with only 24% of non-ICU and 3% of ICU patients (p = 0.0186) having low perceived stress. Posttraumatic symptoms were present in 5% of non-ICU and 10% of ICU patients. HRQoL is limited in COVID-19 ICU patients 3- and 12-months post COVID-19 hospitalization, with significantly less improvement at 12-months compared to non-ICU patients. Mental disorders were common highlighting the complexity of post-COVID-19 symptoms as well as the necessity to educate patients and primary care providers about monitoring mental well-being post COVID-19.
During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.