Tracking self-reported symptoms and medical conditions on social media during the COVID-19 pandemic (Preprint)

2021 ◽  
Author(s):  
Qinglan Ding ◽  
Daisy Massey ◽  
Chenxi Huang ◽  
Connor Grady ◽  
Yuan Lu ◽  
...  
Keyword(s):  
Mental Health ◽  
Infectious Disease ◽  
Social Media ◽  
Positive Predictive Value ◽  
Medical Condition ◽  
Medical Conditions ◽  
Health Related ◽  
The U.S ◽  
Over Time

BACKGROUND Harnessing health-related data posted on social media in real-time has the potential to offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. OBJECTIVE The aim of this study was to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the coronavirus disease 2019 (COVID-19) pandemic, and to determine how discussion of these symptoms and medical conditions on social media changed over time. METHODS We used natural language processing (NLP) algorithms to identify symptom and medical condition topics being discussed on social media between June 14 and December 13, 2020. The sample social media posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of the posts. We also assessed the frequency of different health-related discussions on social media over time during the study period, and compared the changes in the frequency of each symptom/medical condition discussion to the fluctuation of U.S. daily new COVID-19 cases during the study period. Additionally, we compared the trends of the 5 most commonly mentioned symptoms and medical conditions from June 14 to August 31 (when the U.S. passed 6 million COVID-19 cases) to the trends observed from September 1 to December 13, 2020. RESULTS Within a total of 9,807,813 posts (nearly 70% were sourced from the U.S.), we identified discussion of 120 symptom topics and 1,542 medical condition topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were: anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were: COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). The changes in the frequency of 2 medical conditions, COVID-19 and unspecified infectious disease, were similar to the fluctuation of daily new confirmed cases of COVID-19 in the U.S. CONCLUSIONS COVID-19 and symptoms of anxiety were the two most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

scholarly journals Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study

10.2196/29413 ◽  
2021 ◽  
Vol 7 (9) ◽  
pp. e29413
Author(s):  
Qinglan Ding ◽  
Daisy Massey ◽  
Chenxi Huang ◽  
Connor B Grady ◽  
Yuan Lu ◽  
...  
Keyword(s):  
Infectious Disease ◽  
United States ◽  
Social Media ◽  
Weight Loss ◽  
Medical Condition ◽  
The United States ◽  
Medical Conditions ◽  
Health Related ◽  
Over Time

Background Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. Objective This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. Methods We used natural language processing (NLP) algorithms to identify symptom- and medical condition–related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. Results Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition–related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). Conclusions COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population’s mental health status and enhance public health surveillance for infectious disease.

Do Materialistic Adolescents Ruminate More About Their Social Media Posts?

2020 ◽  
pp. 0044118X2098417
Author(s):  
Keeley Hynes ◽  
Daniel G. Lannin ◽  
Jeremy B. Kanter ◽  
Ani Yazedjian ◽  
Margaret M. Nauta
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Social Status ◽  
Mental Distress ◽  
External Validation ◽  
Value Orientation ◽  
Media Content ◽  
Over Time

Previous research suggests that ruminating on social media content is associated with greater mental distress (Yang et al., 2018). This study examined whether materialistic value orientation (MVO)—prioritizing values and goals related to consumerism, consumption, and social status—predicted social media rumination in a sample of diverse adolescents in a two-wave cross-lagged design. A cross-lagged analysis among 119 adolescents indicated that MVO at Wave 1 predicted greater social media rumination 4 months later at Wave 2, but social media rumination at Wave 1 did not predict MVO at Wave 2. Cross-lagged results suggested that MVO may lead to greater social media rumination over time for diverse adolescents. Adolescents with MVO could benefit from interventions to reduce the effects of their need for external validation and maladaptive rumination, as external validation and maladaptive rumination are linked to behaviors and thoughts that can be harmful to mental health.

Trends in College Student-Athlete Mental Health in the National College Health Assessment (NCHA), 2011–2019

2021 ◽  
Author(s):  
Brian Edwards ◽  
Andrew W. Froehle ◽  
Siobhan E. Fagan
Keyword(s):  
Mental Health ◽  
Student Athletes ◽  
Athletic Trainers ◽  
Health Assessment ◽  
Treatment Seeking ◽  
Student Athlete ◽  
National College Health Assessment ◽  
Health Related ◽  
College Health Assessment ◽  
Over Time

ABSTRACT Context: Recently the athletic training community has paid increased attention to college student-athlete mental health, treatment-seeking, and impacts on athletic and academic performance. Ongoing efforts to better-educate and equip athletic trainers to help student-athletes in this regard should result in improved mental health-related outcomes. Objective: Examine changes in student-athlete mental health over the past decade compared to non-athlete students. Design: Cross-sectional study. Setting: United States colleges and universities. Patients or Other Participants: Varsity athletes (n=54,479) and non-athlete students (n=448,301) who completed the National College Health Assessment (NCHA) between 2011 and 2019. Main Outcome Measures: Survey responses (self-report) to questions in five mental health-related domains: symptoms, diagnoses, treatment-seeking, institutional information distribution, and academic impacts. Results: Student-athletes consistently reported significantly lower symptom and diagnose rates than non-athletes, except for attempted suicide, substance abuse, and eating disorders. Diagnoses increased over time in both groups, but remained lower in athletes. Treatment-seeking and openness to future treatment increased over time in both groups, but remained lower in athletes. Student-athletes received more information on stress reduction, substance abuse, eating disorders, and handling distress/violence than non-athletes. Both groups received information more frequently over time. Athletes reported lower academic impacts, especially for depression and anxiety, but impacts grew over time in both groups. Impacts of injuries and extracurricular activities on academic performance were higher in athletes than in non-athletes. Conclusions: Athletes reported overall lower levels of symptoms, diagnoses, and academic impacts than non-athletes. While non-athlete rates climbed over the past decade, athletes' rates broadly remained flat or climbed less rapidly. Increasingly positive attitudes toward treatment are encouraging, but the deficit relative to non-athletes remains. Ongoing efforts of athletic trainers to educate athletes and guide them to mental health resources are needed in order to continue (or, better yet, accelerate) the observed positive trends in information dissemination and treatment-seeking.

scholarly journals Mining Social Media Data for Biomedical Signals and Health-Related Behavior

2020 ◽  
Vol 3 (1) ◽  
pp. 433-458 ◽  
Author(s):  
Rion Brattig Correia ◽  
Ian B. Wood ◽  
Johan Bollen ◽  
Luis M. Rocha
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Population Level ◽  
Data Access ◽  
Health Conditions ◽  
Social Phenomena ◽  
Medical Treatments ◽  
Social Media Data ◽  
Health Related ◽  
Media Data

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

scholarly journals Dissecting the mechanisms of a 5-year decline in antibiotic prescribing

2020 ◽  
Author(s):  
Stephen M. Kissler ◽  
R. Monina Klevens ◽  
Michael L. Barnett ◽  
Yonatan H. Grad
Keyword(s):  
Infectious Disease ◽  
Antibiotic Stewardship ◽  
Respiratory Infections ◽  
Medical Condition ◽  
Outpatient Visit ◽  
Antibiotic Prescribing ◽  
Medical Conditions ◽  
Outpatient Visits ◽  
Prescribing Trends ◽  
Tract Infections

AbstractImportanceThe mechanisms driving the recent decline in outpatient antibiotic prescribing are unknown.ObjectiveTo estimate the extent to which reductions in the number of antibiotic prescriptions filled per outpatient visit (stewardship) and reductions in the monthly rate of outpatient visits (observed disease) for infectious disease conditions each contributed to the decline in annual outpatient antibiotic prescribing rate in Massachusetts between 2011 and 2015.DesignOutpatient medical and pharmacy claims from the Massachusetts All-Payer Claims Database were used to estimate rates of antibiotic prescribing and outpatient visits for 20 medical conditions and their contributions to the overall decline in antibiotic prescribing. Trends were compared to those in the National Ambulatory Medical Care Survey (NAMCS).SettingOutpatient visits in Massachusetts between January 2011 and September 2015.Participants5,075,908 individuals with commercial health insurance or Medicaid in Massachusetts under the age of 65 and 495,515 patients included in NAMCS.Main outcomes and measuresThe number of antibiotic prescriptions avoided through reductions in observed disease and reductions in per-visit prescribing rate per medical condition.ResultsBetween 2011 and 2015, the January antibiotic prescribing rate per 1,000 individuals in Massachusetts declined by 18.9% and the July antibiotic prescribing rate declined by 13.6%. The mean prescribing rate for children under 5 declined by 42.8% (95% CI 21.7%, 59.4%), principally reflecting reduced wintertime prescribing. The monthly rate of outpatient visits per 1,000 individuals in Massachusetts declined (p < 0.05) for respiratory infections and urinary tract infections. Nationally, visits for medical conditions that merit an antibiotic prescription also declined between 2010 and 2015. Of the estimated 358 antibiotic prescriptions per 1,000 individuals avoided over the study period in Massachusetts, 59% (95% CI 54%, 63%) were attributable to reductions in observed disease and 41% (95% CI 37%, 46%) to reductions in prescribing per outpatient visit.Conclusions and relevanceThe decline in antibiotic prescribing in Massachusetts was driven by a decline in observed disease and improved antibiotic stewardship, with a contemporaneous reduction in visits for conditions prompting antibiotics observed nationally. A focus on infectious disease prevention should be considered alongside antibiotic stewardship as a means to reduce antibiotic prescribing.Key pointsQuestionHow did the separate mechanisms of improved stewardship and reductions in observed disease contribute to a 5-year decline in outpatient antibiotic prescribing in Massachusetts from 2011-2015?FindingsIn an observational analysis of insurance claims, reduced monthly rates of outpatient visits for infectious conditions and reduced probability of prescribing an antibiotic per outpatient visit both contributed to the decline in antibiotic prescribing. An estimated 358 antibiotic prescriptions per 1,000 individuals were avoided over the study period through the two mechanisms, 211 of which were attributable to reductions in outpatient visits and 147 to reduced antibiotic prescribing per visit.MeaningPreventing the need for outpatient visits should be considered alongside antibiotic stewardship as a means of reducing antibiotic prescribing.

scholarly journals Cross-Sectional Trend Analysis of the NCHA II Survey Data on Canadian Post-Secondary Student Mental Health and Wellbeing from 2013 to 2019

2021 ◽  
Author(s):  
Brooke Linden ◽  
Randall Boyes ◽  
Heather Stuart
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Secondary Students ◽  
Help Seeking ◽  
Female Students ◽  
Male Students ◽  
Post Secondary ◽  
Health Related ◽  
Over Time ◽  
Post Secondary Students

BACKGROUND: Canadian post-secondary students are considered to be at risk for chronic stress and languishing mental health, but there has been no longitudinal analysis of the available population-level data. The purpose of this study was to examine trends in the overall and sex-specific prevalence of self-reported stress, distress, mental illness, and help seeking behaviours among Canadian post-secondary students over the past several years. METHODS: Using the 2013, 2016, and 2019 iterations of the National College Health Assessment II Canadian Reference data, we conducted a trend analysis for each variable of interest, stratified by sex. The significance and magnitude of the changes were modelled using cumulative linked ordinal regression models and log binomial regression models.RESULTS: With few exceptions, we observed significant increases over time in the proportion of students reporting symptoms of psychological distress, mental illness diagnoses, and help seeking for mental health related challenges. Female students reported a higher level of stress than male students, with a statistically significant increase in the stress level reported by female students observed over time. In all cases, larger proportions of female students were observed compared to male students, with the proportion of female students who self-reported mental illness diagnoses nearly doubling that of males. CONCLUSIONS: Our analysis indicated that the proportion of students self-reporting mental health related challenges, including stress, psychological distress, and diagnosed mental illnesses increased between the 2013, 2016 and 2019 iterations of the NCHA II conducted among Canadian post-secondary students.

scholarly journals Have associations between mental health and health related behaviours changed between 2005 and 2015? A population based cross-cohort study

2020 ◽  
Author(s):  
Suzanne H. Gage ◽  
Praveetha Patalay
Keyword(s):  
Mental Health ◽  
Substance Use ◽  
Cohort Study ◽  
Mental Health Problems ◽  
Weight Perception ◽  
Population Based ◽  
Parents And Children ◽  
Health Related ◽  
Avon Longitudinal Study ◽  
Over Time

AbstractBackgroundPoor adolescent mental health is a growing concern over recent decades with evidence of increasing internalising mental health problems corresponding with decrease in anti-social, smoking and alcohol behaviours. However, understanding whether and how the associations between mental health and health-related behaviours such as substance use, anti-social behaviour and obesity have changed over time is less well-understood.ObjectivesWe investigate whether the associations between different health-related outcomes in adolescence are stable or changing over time in two recent cohorts of adolescents born ten years apart.MethodData from two UK birth cohort studies, the Avon Longitudinal Study of Parents and Children (ALSPAC, born 1991-92, N=5627, 50.7% female) and Millennium Cohort Study (MCS, born 2000-1, N=11318, 50.6% female) at age 14 sweeps are used. The health outcomes of focus are depressive symptom score, substance use (alcohol, smoking, cannabis and other drugs), antisocial behaviours (assault, graffiti, vandalism, shoplifting and rowdy behaviour), weight (BMI), weight perception (perceive self as overweight) and sexual activity (had sexual intercourse).ResultsOur results suggest although directions of associations between mental-health and health-related behaviours (eg smoking) are similar over time, their strength across the distribution has changed. While smoking and alcohol use behaviours are decreasing in adolescents, those that endorse these behaviours in 2015 are more likely to have co-occurring mental-health and other problems than those born in 2005. Similarly, higher body mass index is more strongly associated with depressive symptoms in 2015.ConclusionsOur findings suggest that associations between these factors has changed over time, which has implications for public health and our understanding of the mechanisms underlying their observed associations in the population.

Deployment to a combat zone and other risk factors for mental health-related disability discharge from the U.S. Army: 1994-2007

2011 ◽  
Vol 24 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Nicole S. Bell ◽  
Phillip R.Hunt ◽  
Thomas C. Harford ◽  
Ashley Kay
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Health Related ◽  
The U.S

scholarly journals Association of Social Media Use With Social Well-Being, Positive Mental Health, and Self-Rated Health: Disentangling Routine Use From Emotional Connection to Use

2019 ◽  
Vol 46 (2_suppl) ◽  
pp. 69S-80S ◽  
Author(s):  
Mesfin A. Bekalu ◽  
Rachel F. McCloud ◽  
K. Viswanath
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Health Outcomes ◽  
Media Use ◽  
Well Being ◽  
Dose Effect ◽  
Social Media Use ◽  
Emotional Connection ◽  
Self Rated Health ◽  
Health Related

Most studies addressing social media use as a normal social behavior with positive or negative effects on health-related outcomes have conceptualized and measured social media use and its effects in terms of dose–effect relations. These studies focus on measuring frequency and duration of use, and have seldom considered users’ emotional connections to social media use and the effects associated with such connections. By using a scale with two dimensions capturing users’ integration of social media use into their social routines and their emotional connection to the sites’ use, the present study has brought preliminary evidence that may help map where social media use, as a normal social behavior, may be considered beneficial or harmful. Data from a nationally representative sample ( n = 1,027) of American adults showed that while routine use is associated with positive health outcomes, emotional connection to social media use is associated with negative health outcomes. These associations have been consistent across three health-related outcomes: social well-being, positive mental health, and self-rated health. The data also showed that the strength of the positive and negative associations of routine use and emotional connection with the health outcomes varies across socioeconomic and racial/ethnic population subgroups. Our findings suggest that the link between social media use and health may not only be captured by and explained in terms of conventional dose–effect approaches but may also require a more sophisticated conceptualization and measurement of the social media use behavior.

scholarly journals Mapping gender transition sentiment patterns via social media data: toward decreasing transgender mental health disparities

2019 ◽  
Vol 26 (8-9) ◽  
pp. 749-758 ◽  
Author(s):  
Oliver L Haimson
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Health Disparities ◽  
Mental Health Disparities ◽  
Gender Transition ◽  
Transgender People ◽  
Transgender Identity ◽  
Difficult Time ◽  
Media Data ◽  
Over Time

Abstract Objective Transgender people face substantial mental health disparities, and this population’s emotional well-being can be particularly volatile during gender transition. Understanding gender transition sentiment patterns can positively impact transgender people by enabling them to anticipate, and put support in place for, particularly difficult time periods. Yet, tracking sentiment over time throughout gender transition is challenging using traditional research methods. This study’s objective was to use social media data to understand average gender transition sentiment patterns. Materials and Methods Computational sentiment analysis and statistics were used to analyze 41 066 posts from 240 Tumblr transition blogs (online spaces where transgender people document gender transitions) to understand sentiment patterns over time and quantify relationships between transgender identity disclosures, sentiment, and social support. Results Findings suggest that sentiment increases over time on average throughout gender transition, particularly when people receive supportive responses to transgender identity disclosures. However, after disclosures to family members, people experienced temporary increased negative sentiment, followed by increased positive sentiment in the long term. After transgender identity disclosures on Facebook, an important means of mass disclosure, those with supportive networks experienced increased positive sentiment. Conclusions With foreknowledge of sentiment patterns likely to occur during gender transition, transgender people and their mental healthcare professionals can prepare with proper support in place throughout the gender transition process. Social media are a novel data source for understanding transgender people’s sentiment patterns, which can help reduce mental health disparities for this marginalized population during a particularly difficult time.

Sign in / Sign up

Export Citation Format

Share Document