Online Search Behaviour of Seeking Support for Obsessive Compulsive Disorder and Its Impact on Unemployment Level of the United States: Evidence from Google Search Queries (Preprint)

2019 ◽  
Author(s):  
Chamil W Senarathne ◽  
Wei Jianguo
Keyword(s):  
United States ◽  
The United States ◽  
Aggregate Data ◽  
Online Search ◽  
Search Behaviour ◽  
Search Queries ◽  
Compulsive Disorder ◽  
Google Search ◽  
The Impact ◽  
Age Category

BACKGROUND People have access to a massive volume of up-to-date health information processed by various search engines. Before seeing a doctor, people are used to seek information about identification and support available (e.g. doctors, support centers. forum discussions etc.) for their disorder/s online. Researchers have shown that Internet search queries contain much valuable information about the disequilibrium dynamics of various economics activities (e.g. employment, consumption). OCD as a disorder steals much of the valuable time, energy and effort in day-to-day work life and scholars argues that patients diagnosed with OCD may have higher unemployment rates and lower average income. Except for a handful of work examining the relationship between various disorders (e.g. cancer) and online search volume data, the direct linkage between online search behaviour of seeking support for OCD and unemployment in the United States has been completely ignored in the literature. OBJECTIVE The objective of this paper is to examine the impact of online search behaviour of identifying and seeking support for OCD on unemployment level of the United States at aggregate data and age category level. METHODS This paper analyzes 50 closely related online search terms on identifying and seeking support for OCD from March 2006 to June 2019. Ordinary least squares technique is used to identify the significance of the impact of search behaviour on the unemployment levels of the United States. After screening for instrumentality, a reduced version of regression is derived after treating for multicollinearity among regression variables. In order to eliminate the effect of searches made by people other than employed people who have subsequently been unemployed, a diagnostic regression is run. RESULTS The findings show that online search behaviour of identifying and seeking support for OCD significantly impacts unemployment level of the United States at overall regression level (p<0.01, R^2=73%) and age category level regressions (p<0.01, average R^2=66%). Moreover, the diagnostic test confirms that the regression on aggregate data and age category level data properly explains the underlying relationship as hypothesized because the coefficient of Google search queries driven (the effect) by employed population is positive and highly significant in explaining the unemployment level of the United States (p<0.01, average R^2=90%). CONCLUSIONS The findings of this study are helpful for policymakers and regulators in providing useful inputs for designing and administering programms on prevention and counseling OCD diagnosed working population of the United States. In particular, this paper is helpful in identifying the age categories of male and female employed population who are searching and seeking support on OCD. The government institutions in the USA must utilize online search queries for effective analysis and identification of different age category of people who are in need of support. Since search query data are available at country-level and regional level, this could easily be done by IT rather than population surveys that are costly and time consuming.

Correlations in Trends of Sinusitis-Related Online Google Search Queries in the United States

2020 ◽  
Vol 34 (4) ◽  
pp. 482-486
Author(s):  
Dhruv Sharma ◽  
Morgan M. Sandelski ◽  
Jonathan Ting ◽  
Thomas S. Higgins
Keyword(s):  
United States ◽  
Pearson Correlation ◽  
Real Life ◽  
The United States ◽  
Online Search ◽  
Search Query ◽  
Search Queries ◽  
Disease States ◽  
Postnasal Drip ◽  
Google Search

Background Online search query trends have been shown to correlate with real-life epidemiologic phenomena. Objective The aim of this study was to analyze correlations in trends in Google online search volumes of sinusitis-related terms, including symptomatology and similar disease states. Methods Terms clinically associated with “sinusitis” were determined by consensus. Terms of symptomatology were derived from the validated 22-item sinonasal outcome test (SNOT-22) as well as terminology encountered with the authors’ clinical experience. Terms of disease states that could overlap in symptomatology with sinusitis were then chosen. Google Trends, an online tool for extracting relative frequencies from a public database of search queries, was used to query normalized monthly volumes in the United States from January 2004 to September 2017 of searches related to the topics decided upon by consensus. Bivariate Pearson correlation was used to compare the search queries. Results Online search volumes of “sinusitis” have a distinct seasonal variation, with consistent annual peaks and troughs. In terms of symptomatology, “postnasal drip,” “nasal congestion,” “cough,” “rhinorrhea,” and “sore throat” most highly correlated with “sinusitis” search volumes with statistical significance. “sinusitis” search query volume had a higher positive correlation with “common cold” and “acute sinusitis” than “chronic sinusitis” with regard to disease states. Conclusions Trends in Google online search volumes over time of “sinusitis” symptomatology mimic real-world clinical phenomena and provide insight into the issues affecting the general population.

scholarly journals The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis (Preprint)

2020 ◽  
Author(s):  
Milad Asgari Mehrabadi ◽  
Nikil Dutt ◽  
Amir M. Rahmani
Keyword(s):  
United States ◽  
Public Interest ◽  
Pearson Correlation ◽  
The United States ◽  
Google Trends ◽  
Causality Test ◽  
Search Queries ◽  
The Us ◽  
Google Search ◽  
The Impact

BACKGROUND The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. OBJECTIVE The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. In particular, we were concerned with searches for dine-in restaurants and bars. Data were obtained from Google trends API and COVID tracking project. METHODS To test causation of one time series on another, we used Granger’s Causality Test. We considered the causation of two different search query trends, namely restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. RESULTS Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average. California, for example, had most searches for restaurants on June 7th, 2020, which affected the number of new cases within two weeks after the peak with the P-value of .004 for Granger’s causality test. CONCLUSIONS Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for regions with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes.

scholarly journals Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States (Preprint)

2020 ◽  
Author(s):  
Nicholas C Jacobson ◽  
Damien Lekkas ◽  
George Price ◽  
Michael V Heinz ◽  
Minkeun Song ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Suicidal Ideation ◽  
The United States ◽  
Mental Health Symptoms ◽  
Health Symptoms ◽  
Search Queries ◽  
Negative Thoughts ◽  
The Impact ◽  
At Home

BACKGROUND The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people’s everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. OBJECTIVE The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. METHODS In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. RESULTS Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. CONCLUSIONS These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.

scholarly journals Impact of the COVID-19 Pandemic on Online Obsessive-Compulsive Disorder Support Community Members: Survey Study

10.2196/26715 ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. e26715
Author(s):  
Benjamin Kaveladze ◽  
Katherine Chang ◽  
Jedidiah Siev ◽  
Stephen M Schueller
Keyword(s):  
United States ◽  
Obsessive Compulsive Disorder ◽  
The United States ◽  
Survey Study ◽  
Community Members ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Body Of Knowledge ◽  
Growing Body ◽  
The Impact

Background People with obsessive-compulsive disorder (OCD) have faced unique challenges during the COVID-19 pandemic. Research from the first two months of the pandemic suggests that a small proportion of people with OCD experienced worsening in their OCD symptoms since the pandemic began, whereas the rest experienced either no change or an improvement in their symptoms. However, as society-level factors relating to the pandemic have evolved, the effects of the pandemic on people with OCD have likely changed as well, in complex and population-specific ways. Therefore, this study contributes to a growing body of knowledge on the impact of the COVID-19 pandemic on people and demonstrates how differences across studies might emerge when studying specific populations at specific timepoints. Objective This study aimed to assess how members of online OCD support communities felt the COVID-19 pandemic had affected their OCD symptoms, around 3 months after the pandemic began. Methods We recruited participants from online OCD support communities for our brief survey. Participants indicated how much they felt their OCD symptoms had changed since the pandemic began and how much they felt that having OCD was making it harder to deal with the pandemic. Results We collected survey data from June through August 2020 and received a total of 196 responses, some of which were partial responses. Among the nonmissing data, 65.9% (108/164) of the participants were from the United States and 90.5% (152/168) had been subjected to a stay-at-home order. In all, 92.9% (182/196) of the participants said they experienced worsening of their OCD symptoms since the pandemic began, although the extent to which their symptoms worsened differed across dimensions of OCD; notably, symmetry and completeness symptoms were less likely to have worsened than others. Moreover, 95.5% (171/179) of the participants felt that having OCD made it difficult to deal with the pandemic. Conclusions Our study of online OCD support community members found a much higher rate of OCD symptom worsening than did other studies on people with OCD conducted during the current COVID-19 pandemic. Factors such as quarantine length, location, overlapping society-level challenges, and differing measurement and sampling choices may help to explain this difference across studies.

scholarly journals Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders Are Associated With Alterations in Mental Health Search Behavior in the United States

10.2196/19347 ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. e19347 ◽  
Author(s):  
Nicholas C Jacobson ◽  
Damien Lekkas ◽  
George Price ◽  
Michael V Heinz ◽  
Minkeun Song ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Suicidal Ideation ◽  
The United States ◽  
Mental Health Symptoms ◽  
Health Symptoms ◽  
Search Queries ◽  
Negative Thoughts ◽  
The Impact ◽  
At Home

Background The coronavirus disease (COVID-19) has led to dramatic changes worldwide in people’s everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. Objective The goal of this study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries on a national scale. Methods In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, 11 of the 50 states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the United States allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. This paper examines the changes in mental health search queries on Google between March 16-23, 2020, across each state and Washington, DC. Specifically, this paper examines differential changes in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. The participants were all the people who searched mental health terms in Google between March 16-23. Between March 16-23, 11 states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. Results Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances, with the most prominent flattening associated with suicidal ideation and anxiety. Conclusions These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediate leveling off following the issuance of stay-at-home orders.

scholarly journals The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis

10.2196/22880 ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e22880
Author(s):  
Milad Asgari Mehrabadi ◽  
Nikil Dutt ◽  
Amir M Rahmani
Keyword(s):  
United States ◽  
Granger Causality ◽  
Public Interest ◽  
Granger Causality Test ◽  
The United States ◽  
Google Trends ◽  
Causality Test ◽  
Us States ◽  
Search Queries ◽  
Google Search

Background The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.

scholarly journals Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States

2020 ◽  
Vol 13 ◽  
pp. 117863372092835
Author(s):  
Rand Obeidat ◽  
Izzat Alsmadi ◽  
Qanita Bani Bakr ◽  
Laith Obeidat
Keyword(s):  
United States ◽  
Skin Diseases ◽  
The United States ◽  
The Internet ◽  
Search Term ◽  
Care Providers ◽  
Search Queries ◽  
Additional Tool ◽  
Search Terms ◽  
The Impact

Background: In health and medicine, people heavily use the Internet to search for information about symptoms, diseases, and treatments. As such, the Internet information can simulate expert medical doctors, pharmacists, and other health care providers. Aim: This article aims to evaluate a dataset of search terms to determine whether search queries and terms can be used to reliably predict skin disease breakouts. Furthermore, the authors propose and evaluate a model to decide when to declare a particular month as Epidemic at the US national level. Methods: A Model was designed to distinguish a breakout in skin diseases based on the number of monthly discovered cases. To apply this model, the authors correlated Google Trends of popular search terms with monthly reported Rubella and Measles cases from Centers for Disease Control and Prevention (CDC). Regressions and decision trees were used to determine the impact of different terms to trigger the occurrence of epidemic classes. Results: Results showed that the volume of search keywords for Rubella and Measles rises when the volume of those reported diseases rises. Results also implied that the overall process was successful and should be repeated with other diseases. Such process can trigger different actions or activities to be taken when a certain month is declared as “Epidemic.” Furthermore, this research has shown great interest for vaccination against Measles and Rubella. Conclusions: The findings suggest that the search queries and keyword trends can be truly reliable to be used for the prediction of disease outbreaks and some other related knowledge extraction applications. Also search-term surveillance can provide an additional tool for infectious disease surveillance. Future research needs to re-apply the model used in this article, and researchers need to question whether characterizing the epidemiology of Coronavirus Disease 2019 (COVID-19) pandemic waves in United States can be done through search queries and keyword trends.

scholarly journals Flattening the Mental Health Curve: COVID-19 Stay-at-Home Orders are Associated with Alterations in Mental Health Search Behavior in the United States

2020 ◽  
Author(s):  
Nicholas C. Jacobson ◽  
Damien Lekkas ◽  
George Price ◽  
Michael V. Heinz ◽  
Minkeun Song ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Suicidal Ideation ◽  
The United States ◽  
Mental Health Symptoms ◽  
Health Symptoms ◽  
Search Queries ◽  
Negative Thoughts ◽  
The Impact ◽  
At Home

Background: COVID-19 has led to dramatic changes globally in persons’ everyday lives. To combat the pandemic, many governments have implemented social distancing, quarantine, and stay-at-home orders. There is limited research on the impact of such extreme measures on mental health. Objective: The goal of the present study was to examine whether stay-at-home orders produced differential changes in mental health symptoms using internet search queries at a national scale. Methods: In the United States, individual states vary in their adoption of measures to reduce the spread of COVID-19; as of March 23, 2020, eleven of the fifty states had issued stay-at-home orders. The staggered rollout of stay-at-home measures across the U.S. allows us to investigate whether these measures impact mental health by exploring variations in mental health search queries across the states. The current manuscript examines the changes in mental health search queries on Google between March 16-23, 2020 across each state and Washington D.C. Specifically, the current manuscript examines differential change in mental health searches based on patterns of search activity following issuance of stay-at-home orders in these states compared to all other states. Participants included all persons who searched mental health terms in Google between March 16-23. Between March 16-23, eleven states underwent stay-at-home orders to prevent the transmission of COVID-19. Outcomes included search terms measuring anxiety, depression, obsessive-compulsive, negative thoughts, irritability, fatigue, anhedonia, concentration, insomnia, and suicidal ideation. Results: Analyzing over 10 million search queries using generalized additive mixed models, the results suggested that the implementation of stay-at-home orders are associated with a significant flattening of the curve for searches for suicidal ideation, anxiety, negative thoughts, and sleep disturbances with the most prominent flattening associated with suicidal ideation and anxiety. Conclusions: These results suggest that, despite decreased social contact, mental health search queries increased rapidly prior to the issuance of stay-at-home orders, and these changes dissipated following the announcement and enactment of these orders. Although more research is needed to examine sustained effects, these results suggest mental health symptoms were associated with an immediately leveling off following the issuance of stay-at-home orders.

Proportion of Individuals With Past-Year Suicidal Ideation Who Attempted Suicide Over the Past 10 Years in the United States, and the Influence of Age and Sex

Crisis ◽  
2020 ◽  
pp. 1-5
Author(s):  
Shannon Lange ◽  
Courtney Bagge ◽  
Charlotte Probst ◽  
Jürgen Rehm
Keyword(s):  
United States ◽  
Suicidal Ideation ◽  
Random Effects ◽  
Attempted Suicide ◽  
Suicide Attempts ◽  
The United States ◽  
Individual Level ◽  
The Past ◽  
Meta Analyses ◽  
Age Category

Abstract. Background: In recent years, the rate of death by suicide has been increasing disproportionately among females and young adults in the United States. Presumably this trend has been mirrored by the proportion of individuals with suicidal ideation who attempted suicide. Aim: We aimed to investigate whether the proportion of individuals in the United States with suicidal ideation who attempted suicide differed by age and/or sex, and whether this proportion has increased over time. Method: Individual-level data from the National Survey on Drug Use and Health (NSDUH), 2008–2017, were used to estimate the year-, age category-, and sex-specific proportion of individuals with past-year suicidal ideation who attempted suicide. We then determined whether this proportion differed by age category, sex, and across years using random-effects meta-regression. Overall, age category- and sex-specific proportions across survey years were estimated using random-effects meta-analyses. Results: Although the proportion was found to be significantly higher among females and those aged 18–25 years, it had not significantly increased over the past 10 years. Limitations: Data were self-reported and restricted to past-year suicidal ideation and suicide attempts. Conclusion: The increase in the death by suicide rate in the United States over the past 10 years was not mirrored by the proportion of individuals with past-year suicidal ideation who attempted suicide during this period.

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