Dataset Information

Social impact of brain fog and analysis of risk factors: Long COVID in Japanese population.

ABSTRACT:

Objective

To reveal the clinical features and assess risk factors linked to brain fog and its societal implications, including labor productivity, providing valuable insights for the future care of individuals who have experienced coronavirus disease 2019 (COVID-19).

Methods

We analyzed a comprehensive cohort dataset comprising 1,009 patients with COVID-19 admitted to Japanese hospitals. To assess brain fog, we analyzed patients who responded to a questionnaire indicating symptoms such as memory impairment and poor concentration.

Results

The prevalence of brain fog symptoms decreased 3 months posthospitalization but remained stable up to 12 months. Neurological symptoms such as taste and smell disorders and numbness at hospitalization correlated with a higher frequency of identifying brain fog as a long COVID manifestation. Our findings indicated that advanced age, female sex, a high body mass index, oxygen required during hospitalization, chronic obstructive pulmonary disease, asthma, and elevated C-reactive protein and elevated D-dimer levels were risk factors in patients exhibiting brain fog. Additionally, we demonstrated the negative impact of brain fog on labor productivity by presenteeism scores.

Interpretations

This study clarified the clinical characteristics of patients experiencing brain fog as a long COVID manifestation, specifically emphasizing neurological symptoms during hospitalization and their correlation with brain fog. Additionally, the study identified associated risk factors for its onset and revealed that the emergence of brain fog was linked to a decline in labor productivity.

SUBMITTER: Shigematsu L

PROVIDER: S-EPMC11330230 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Social impact of brain fog and analysis of risk factors: Long COVID in Japanese population.

Shigematsu Lisa L Kimura Ryusei R Terai Hideki H Mimura Yu Y Ito Daisuke D Bun Shogyoku S Namkoong Ho H Asakura Takanori T Chubachi Shotaro S Masaki Katsunori K Ohgino Keiko K Miyata Jun J Kawada Ichiro I Ishii Makoto M Takemura Ryo R Ueda Soichiro S Yoshiyama Takashi T Kokuto Hiroyuki H Kusumoto Tatsuya T Oashi Ayano A Miyawaki Masayoshi M Saito Fumitake F Tani Tetsuo T Ishioka Kota K Takahashi Saeko S Nakamura Morio M Sato Yasunori Y Fukunaga Koichi K

Annals of clinical and translational neurology 20240704 8

<h4>Objective</h4>To reveal the clinical features and assess risk factors linked to brain fog and its societal implications, including labor productivity, providing valuable insights for the future care of individuals who have experienced coronavirus disease 2019 (COVID-19).<h4>Methods</h4>We analyzed a comprehensive cohort dataset comprising 1,009 patients with COVID-19 admitted to Japanese hospitals. To assess brain fog, we analyzed patients who responded to a questionnaire indicating symptoms ...[more]

PMID: 38961833

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Project description:ObjectivesLimited research exists on COVID-19 associated brain fog, and on the long-term cognitive and psychiatric sequelae in racially and ethnically diverse patients. We characterize the neuropsychological sequelae of post-acute COVID-19 in a diverse cohort and investigate whether COVID-19 clinical severity remains associated with brain fog and cognitive deficits approximately 2 years post infection.MethodsA cross-sectional study of patients with a history of COVID-19 hospitalization (March-September 2020). COVID-19 clinical severity was indexed using the National Early Warning Score 2 and a comprehensive neuropsychological tele-battery was administered 2 years post discharge. Pearson's r correlations assessed association, while independent sample t-tests examined group differences. Significant outcomes were further analyzed using multiple regression and ANCOVAs, adjusting for key covariates.ResultsIn 41 adult patients (19 female, 30 Hispanic, 13 Black, mean age of 65 (SD = 15), COVID-19 level of severity was associated with greater number of endorsed brain fog symptoms (Pearson's r = .34, 95% CI [.04, .59]), worse overall cognitive functioning (global cognition: r = -.36, 95% CI [-.61, -.05]) and reduced performance on an attention and working memory task (digit span backwards: r = -.41, 95% CI [-.66, -.09]) at 2-year follow-up. Brain fog symptoms most associated with COVID-19 severity included difficulty focusing (r = .46, 95% CI [.18, .67]), detached (r = .41, 95% CI [.12, .64]) and feeling sleepy (r = .40, 95% CI [.11, .63]). Patients' cognitive performance was generally below average (global cognition z-score: M = -.96, SD = .66), with group differences based on sex and ethnicity evidenced on individual cognitive tests.DiscussionThis study emphasizes the importance of continued research on the long-term effects of COVID-19 infection on neuropsychological outcomes, particularly among underrepresented, health-disparate groups. Greater understanding of these associations could improve detection and treatment of those at increased risk of cognitive decline or impairment.

Project description:BackgroundLong COVID induces a substantial global burden of disease. The pathogenesis, complications, and epidemiological and clinical characteristics of patients with COVID-19 in the acute phase have been evaluated, while few studies have characterized the epidemiology, symptomatology, and risk factors of long COVID symptoms. Its characteristics among patients with COVID-19 in the general population remain unaddressed.ObjectiveWe examined the prevalence of long COVID symptoms, its symptom patterns, and its risk factors in 4 major Chinese cities in order to fill the knowledge gap.MethodsWe performed a population-based, multicenter survey using a representative sampling strategy via the Qualtrics platform in Beijing, Shanghai, Guangzhou, and Hong Kong in June 2022. We included 2712 community-dwelling patients with COVID-19 and measured the prevalence of long COVID symptoms defined by the World Health Organization (WHO), and their risk factors. The primary outcomes were the symptoms of long COVID, with various levels of impact. A descriptive analysis of the prevalence and distribution of long COVID symptoms according to disease severity was conducted. A sensitivity analysis of increasing the number of long COVID symptoms was also conducted. Univariate and multivariate regression analyses were performed to examine the risk factors of severe long COVID symptoms, including age, gender, marital status, current occupation, educational level, living status, smoking habits, monthly household income, self-perceived health status, the presence of chronic diseases, the use of chronic medication, COVID-19 vaccination status, and the severity of COVID-19.ResultsThe response rate was 63.6% (n=2712). The prevalence of long COVID, moderate or severe long COVID, and severe long COVID was 90.4% (n=2452), 62.4% (n=1692), and 31.0% (n=841), respectively. Fatigue (n=914, 33.7%), cough (n=865, 31.9%), sore throat (n=841, 31.0%), difficulty in concentrating (n=828, 30.5%), feeling of anxiety (n=817, 30.2%), myalgia (n=811, 29.9%), and arthralgia (n=811, 29.9%) were the most common severe long COVID symptoms. From multivariate regression analysis, female gender (adjusted odds ratio [aOR]=1.49, 95% CI 1.13-1.95); engagement in transportation, logistics, or the discipline workforce (aOR=2.52, 95% CI 1.58-4.03); living with domestic workers (aOR=2.37, 95% CI 1.39-4.03); smoking (aOR=1.55, 95% CI 1.17-2.05); poor or very poor self-perceived health status (aOR=15.4, 95% CI 7.88-30.00); ≥3 chronic diseases (aOR=2.71, 95% CI 1.54-4.79); chronic medication use (aOR=4.38, 95% CI 1.66-11.53); and critical severity of COVID-19 (aOR=1.52, 95% CI 1.07-2.15) were associated with severe long COVID. Prior vaccination with ≥2 doses of COVID-19 vaccines was a protective factor (aOR=0.35-0.22, 95% CI 0.08-0.90).ConclusionsWe examined the prevalence of long COVID symptoms in 4 Chinese cities according to the severity of COVID-19. We also evaluated the pattern of long COVID symptoms and their risk factors. These findings may inform early identification of patients with COVID-19 at risk of long COVID and planning of rehabilitative services.

Project description:Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.

Dataset Information

Social impact of brain fog and analysis of risk factors: Long COVID in Japanese population.

Objective

Methods

Results

Interpretations

Publications

Social impact of brain fog and analysis of risk factors: Long COVID in Japanese population.

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