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.

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 Medical cannabis use in the United States: a retrospective database study

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
V. Kishan Mahabir ◽  
Jamil J. Merchant ◽  
Christopher Smith ◽  
Alisha Garibaldi
Keyword(s):  
United States ◽  
Medical Condition ◽  
Statistical Tests ◽  
The United States ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Medical Conditions ◽  
Comorbid Conditions ◽  
Medical Cannabis ◽  
Number Of Patients

Abstract Introduction Growing interest in the medicinal properties of cannabis has led to an increase in its use to treat medical conditions, and the establishment of state-specific medical cannabis programs. Despite medical cannabis being legal in 33 states and the District of Colombia, there remains a paucity of data characterizing the patients accessing medical cannabis programs. Methods We retrospectively reviewed a registry with data from 33 medical cannabis evaluation clinics in the United States, owned and operated by CB2 Insights. Data were collected primarily by face-to-face interviews for patients seeking medical cannabis certification between November 18, 2018 and March 18, 2020. Patients were removed from the analysis if they did not have a valid date of birth, were less than 18, or did not have a primary medical condition reported; a total of 61,379 patients were included in the analysis. Data were summarized using descriptive statistics expressed as a mean (standard deviation (SD)) or median (interquartile range (IQR)) as appropriate for continuous variables, and number (percent) for categorical variables. Statistical tests performed across groups included t-tests, chi-squared tests and regression. Results The average age of patients was 45.5, 54.8% were male and the majority were Caucasian (87.5%). Female patients were significantly older than males (47.0 compared to 44.6). Most patients reported cannabis experience prior to seeking medical certification (66.9%). The top three mutually exclusive primary medical conditions reported were unspecified chronic pain (38.8%), anxiety (13.5%) and post-traumatic stress disorder (PTSD) (8.4%). The average number of comorbid conditions reported was 2.7, of which anxiety was the most common (28.3%). Females reported significantly more comorbid conditions than males (3.1 compared to 2.3). Conclusion This retrospective study highlighted the range and number of conditions for which patients in the US seek medical cannabis. Rigorous clinical trials investigating the use of medical cannabis to treat pain conditions, anxiety, insomnia, depression and PTSD would benefit a large number of patients, many of whom use medical cannabis to treat multiple conditions.

scholarly journals Cross-National Outcomes of a Digital Weight Loss Intervention in the United States, Canada, United Kingdom and Ireland, and Australia and New Zealand: A Retrospective Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiuchen Yang ◽  
Ellen Siobhan Mitchell ◽  
Annabell S. Ho ◽  
Laura DeLuca ◽  
Heather Behr ◽  
...  
Keyword(s):  
United States ◽  
Weight Loss ◽  
United Kingdom ◽  
New Zealand ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
The United States ◽  
Weight Loss Intervention ◽  
The Us ◽  
Over Time

Mobile health (mHealth) interventions are ubiquitous and effective treatment options for obesity. There is a widespread assumption that the mHealth interventions will be equally effective in other locations. In an initial test of this assumption, this retrospective study assesses weight loss and engagement with an mHealth behavior change weight loss intervention developed in the United States (US) in four English-speaking regions: the US, Australia and New Zealand (AU/NZ), Canada (CA), and the United Kingdom and Ireland (UK/IE). Data for 18,459 participants were extracted from the database of Noom's Healthy Weight Program. Self-reported weight was collected every week until program end (week 16). Engagement was measured using user-logged and automatically recorded actions. Linear mixed models were used to evaluate change in weight over time, and ANOVAs evaluated differences in engagement. In all regions, 27.2–33.2% of participants achieved at least 5% weight loss by week 16, with an average of 3–3.7% weight loss. Linear mixed models revealed similar weight outcomes in each region compared to the US, with a few differences. Engagement, however, significantly differed across regions (P &lt; 0.001 on 5 of 6 factors). Depending on the level of engagement, the rate of weight loss over time differed for AU/NZ and UK/IE compared to the US. Our findings have important implications for the use and understanding of digital weight loss interventions worldwide. Future research should investigate the determinants of cross-country engagement differences and their long-term effects on intervention outcomes.

scholarly journals Increasing Utility of Google Trends in Monitoring Cardiovascular Disease

2021 ◽  
Author(s):  
Conor Senecal ◽  
Madeline Mahowald ◽  
Lilach Lerman ◽  
Francisco Lopez-Jimenez ◽  
Amir Lerman
Keyword(s):  
United States ◽  
Risk Factors ◽  
Cardiovascular Disease ◽  
Strong Correlation ◽  
The United States ◽  
Google Trends ◽  
Online Search ◽  
Health Related ◽  
Search Data ◽  
Over Time

Abstract Introduction: Cardiovascular disease is the most common cause of morbidity and mortality in the United States and in the world. Patients are increasingly using internet search to find health-related information, including searches for cardiovascular diseases and risk factors. We sought to evaluate the change in the state by state correlation of cardiovascular disease and risk factors with Google Trends search volumes. Methods: Data on cardiovascular disease hospitalizations and risk factor prevalence were obtained from the publically available CDC website from 2006-2018. Google Trends data were obtained for matching conditions and time periods. Simple linear regression was performed to evaluate for an increase in correlation over time. Results: Hospitalizations for six separate cardiovascular disease conditions showed moderate to strong correlation with online search data in the last period studied (heart failure (0.58, P<0.001), atrial fibrillation (0.57, P<0.001), coronary heart disease (0.58, P<0.001), myocardial infarction (0.70, P<0.001), stroke (0.62, P<0.001), cardiac dysrhythmia (0.46, P<0.001)). All diseases studied showed a positive increase in correlation throughout the time period studied (P<0.05). All five of the cardiovascular risk factors studied showed strong correlation with online search data; diabetes (R=0.78, P<0.001), cigarette use (R=0.79, P<0.001), hypertension (R=0.81, P<0.001), high cholesterol (R=0.59, P<0.001), obesity (R=0.80, P<0.001). Three of the five showed an increasing correlation over time. Conclusion: The prevalence of and hospitalizations for cardiovascular conditions in the United States strongly correlate with online search volumes nationwide and when analyzed by state. This relationship has progressively strengthened or been strong and stable over recent years for these conditions. Google Trends represents an increasingly valuable tool for evaluating the burden of cardiovascular disease and risk factors in the United States.

scholarly journals Social by Definition: How Users Define Social Platforms and Why It Matters

2021 ◽  
Author(s):  
Lisa Rhee ◽  
Joseph Bayer ◽  
David Lee ◽  
Ozan Kuru
Keyword(s):  
United States ◽  
Social Media ◽  
Social Interaction ◽  
The United States ◽  
Social Resources ◽  
Self Report ◽  
Social Affordances ◽  
Social Media Platforms ◽  
Central Purpose ◽  
Over Time

Social media platforms are characterized by diverse features and functions, and these facets remain in constant flux over time. This research examines how users define the central purpose of four major platforms in the United States (Facebook, Twitter, Instagram, Snapchat), and how such lay definitions relate to key outcomes previously associated with social media use. In Study 1, we validated self-report measures using a comparative scaling approach to capture what users view as the most defining categories of the four platforms. In Study 2, we investigated whether lay definitions of platforms relate to perceptions of social affordances and social resources. Overall, results provided evidence that defining platforms as social interaction (vs. other categories) is associated with amplified social affordances and resources. Together, the studies contribute to our understanding of how users navigate a dynamic online ecosystem, as well as how lay definitions may anchor the experiences and effects of social media.

scholarly journals Assessment of the Frequency of Online Searches for Symptoms Before Diagnosis: Analysis of Archival Data

10.2196/15065 ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. e15065 ◽  
Author(s):  
Irit Hochberg ◽  
Raviv Allon ◽  
Elad Yom-Tov
Keyword(s):  
United States ◽  
Medical Diagnosis ◽  
Medical Condition ◽  
The United States ◽  
The Internet ◽  
Medical Conditions ◽  
Large Variability ◽  
People Search ◽  
Search For Information ◽  
Medical Symptoms

Background Surveys suggest that a large proportion of people use the internet to search for information on medical symptoms they experience and that around one-third of the people in the United States self-diagnose using online information. However, surveys are known to be biased, and the true rates at which people search for information on their medical symptoms before receiving a formal medical diagnosis are unknown. Objective This study aimed to estimate the rate at which people search for information on their medical symptoms before receiving a formal medical diagnosis by a health professional. Methods We collected queries made on a general-purpose internet search engine by people in the United States who self-identified their diagnosis from 1 of 20 medical conditions. We focused on conditions that have evident symptoms and are neither screened systematically nor a part of usual medical care. Thus, they are generally diagnosed after the investigation of specific symptoms. We evaluated how many of these people queried for symptoms associated with their medical condition before their formal diagnosis. In addition, we used a survey questionnaire to assess the familiarity of laypeople with the symptoms associated with these conditions. Results On average, 15.49% (1792/12,367, SD 8.4%) of people queried about symptoms associated with their medical condition before receiving a medical diagnosis. A longer duration between the first query for a symptom and the corresponding diagnosis was correlated with an increased likelihood of people querying about those symptoms (rho=0.6; P=.005); similarly, unfamiliarity with the association between a condition and its symptom was correlated with an increased likelihood of people querying about those symptoms (rho=−0.47; P=.08). In addition, worrying symptoms were 14% more likely to be queried about. Conclusions Our results indicate that there is large variability in the percentage of people who query the internet for their symptoms before a formal medical diagnosis is made. This finding has important implications for systems that attempt to screen for medical conditions.

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