Can Big Data Be Used to Monitor the Mental Health Consequences of COVID-19?

Effects of risk exposure on emotional distress among Chinese adults during the COVID-19 pandemic: The moderating role of disruption of life and perceived controllability

Background

COVID-19 affects not only the physical health of individuals but also their mental health and different types of risk exposures are believed to have different effects on individual emotional distress.

Objective

This study explores the relationships between risk exposure, disruption of life, perceived controllability, and emotional distress among Chinese adults during the COVID-19 outbreak.

Methods

This study is based on an online survey conducted during the COVID-19 pandemic, from 1 to 10 February 2020, with a total of 2,993 Chinese respondents recruited through convenience and snowball sampling. Multiple linear regression analysis were used to examine the relationships among risk exposure, disruption of life, perceived controllability, and emotional distress.

Results

This study found that all types of risk exposures were significantly associated with emotional distress. Individuals with neighborhood infection, family member infection/close contact, and self-infection/close contact had higher levels of emotional distress (B = 0.551, 95% CI: -0.019, 1.121; B = 2.161, 95% CI: 1.067, 3.255; B = 3.240, 95% CI: 2.351, 4.129) than those without exposure. The highest levels of emotional distress occurred among individuals experiencing self-infection/close contact, while the lowest levels of emotional distress occurred among individuals experiencing neighborhood infection and the moderate levels of emotional distress occurred among individuals experiencing family member infection (Beta = 0.137; Beta = 0.073; Beta = 0.036). Notably, the disruption of life aggravated the effect of self-infection/close contact on emotional distress and family member infection/close contact on emotional distress (B = 0.217, 95% CI: 0.036, 0.398; B = 0.205, 95% CI: 0.017, 0.393). More importantly, perceived controllability lowered the strength of the association between self-infection/close contact and emotional distress, as well as family member infection/close contact and emotional distress (B = -0.180, 95% CI: -0.362, 0.002; B = -0.187, 95% CI: -0.404, 0.030).

Conclusion

These findings shed light on mental health interventions for people exposed to or infected with COVID-19 near the beginning of the pandemic, particularly those who themselves had COVID or had family members with COVID-19 risk exposure, including being infected/having close contact with an infected person. We call for appropriate measures to screen out individuals or families whose lives were, or remain, more severely affected by COVID-19. We advocate providing individuals with material support and online mindfulness-based interventions to help them cope with the after-effects of COVID-19. It is also essential to enhance the public's perception of controllability with the help of online psychological intervention strategies, such as mindfulness-based stress reduction programs and mindfulness-oriented meditation training programs.

Experiences of giving birth during the COVID-19 pandemic: a qualitative analysis of social media comments through the lens of birth integrity

BACKGROUND

Social media offer women a space to discuss birth-related fears and experiences. This is particularly the case during the COVID-19 pandemic when measures to contain the spread of the virus and high rates of infection have had an impact on the delivery of care, potentially restricting women's rights and increasing the risk of experiencing different forms of mistreatment or violence. Through the lens of birth integrity, we focused on the experiences of women giving birth in Germany as shared on social media, and on what may have sheltered or violated their integrity during birth.

METHODS

Using thematic analysis, we identified key themes in 127 comments and associated reactions (i.e. "likes", emojis) posted on a Facebook public page in response to the dissemination of a research survey on maternity care in the first year of the COVID-19 pandemic.

RESULTS

Women contributing to the dataset gave birth during March and December 2020. They were most negatively affected by own mask-wearing -especially during the active phase of labour, not being allowed a birth companion of choice, lack of supportive care, and exclusion of their partner from the hospital. Those topics generated the most reactions, revealing compassion from other women and mixed feelings about health measures, from acceptation to anger. Many women explicitly formulated how inhumane or disrespectful the care was. While some women felt restricted by the tight visiting rules, those were seen as positive by others, who benefited from the relative quiet of maternity wards and opportunities for postpartum healing and bonding.

CONCLUSION

Exceptional pandemic circumstances have introduced new parameters in maternity care, some of which appear acceptable, necessary, or beneficial to women, and some of which can be considered violations of birth integrity. Our research calls for the investigation of the long-term impact of those violations and the reassessment of the optimal conditions of the delivery of respectful maternity during the pandemic and beyond.

A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19

The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people's lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review

BACKGROUND

With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making.

OBJECTIVE

This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications.

METHODS

A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data.

RESULTS

Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked.

CONCLUSIONS

The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters.

Detecting Suicide Ideation in the Era of Social Media: The Population Neuroscience Perspective

Social media platforms are increasingly used across many population groups not only to communicate and consume information, but also to express symptoms of psychological distress and suicidal thoughts. The detection of suicidal ideation (SI) can contribute to suicide prevention. Twitter data suggesting SI have been associated with negative emotions (e.g., shame, sadness) and a number of geographical and ecological variables (e.g., geographic location, environmental stress). Other important research contributions on SI come from studies in neuroscience. To date, very few research studies have been conducted that combine different disciplines (epidemiology, health geography, neurosciences, psychology, and social media big data science), to build innovative research directions on this topic. This article aims to offer a new interdisciplinary perspective, that is, a Population Neuroscience perspective on SI in order to highlight new ways in which multiple scientific fields interact to successfully investigate emotions and stress in social media to detect SI in the population. We argue that a Population Neuroscience perspective may help to better understand the mechanisms underpinning SI and to promote more effective strategies to prevent suicide timely and at scale.

First-Time Psychotic Symptoms in a Patient After COVID-19 Infection-A Case Report

A 39-year-old, previously healthy, white male with no personal or family history of mental illness presented with new, first-time psychotic symptoms. The new psychotic symptoms appeared on patient admission to the hospital, occurring during a diagnosis of symptomatic SARS-CoV-2 infection. On the first day of hospitalization for worsening psychotic symptoms and the appearance of aggression toward the staff, the patient was transferred to the psychiatric hospital. After the initial treatment with antipsychotics and benzodiazepines, his mental condition improved. The patient was then transferred for further treatment of his somatic condition in the internal medicine ward, with a recommendation to continue treatment in the psychiatric ward once his somatic condition was stabilized. This is one of the few reported cases of COVID-19-related psychosis in a patient without a personal or family history; moreover, this description contains important data regarding elevated IL-6, which may prove to be a key factor in the induction of new psychotic symptoms. It indicates the important need for careful monitoring of neuropsychiatric symptoms among COVID-19 patients.