The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland

"How" web searches change under stress

To adjust to stressful environments, people seek information. Here, we show that in response to stressful public and private events the high-level features of information people seek online alter, reflecting their motives for seeking knowledge. We first show that when people want information to guide action they selectively ask "How" questions. Next, we reveal that "How" searches submitted to Google increased dramatically during the pandemic (controlling for search volume). Strikingly, the proportion of these searches predicted weekly self-reported stress of ~ 17K individuals. To rule out third factors we manipulate stress and find that "How" searches increase in response to stressful, personal, events. The findings suggest that under stress people ask questions to guide action, and mental state is reflected in features that tap into why people seek information rather than the topics they search for. Tracking such features may provide clues regrading population stress levels.

The association of internet searches and actual suicide in Spain

High suicide rates are a major issue in Spain, to the extent that they are the main non-natural cause of death in this country. The present study analyses the relationship between Internet searches and actual suicide rates in Spain. For this purpose, we employ data from actual suicide rates and Google® searches for Spain, differencing by the means used to commit suicide. Our results show that suicide ("suicidio") search term seems to be positively associated with higher total suicide rates, in addition to suicides using poison, suffocation and jumping. The suicide ("suicidio") topic presents similar results to the suicide search term, and suicide by car crash also seems to be associated with Internet searches of this topic.

Seasonal Trends in Suicide Attempts-Keywords Related Searches: A Google Trends Analysis

Suicide is a significant public health concern globally, with its varying rates influenced by numerous factors, including seasonal changes. Online search behaviors, particularly searches related to suicide and mental health, have been proposed as real-time indicators of suicidal ideation in populations. In this study, a cross-sectional time series analysis was conducted, utilizing data on suicide attempts from the Polish Police Headquarters and online search behavior from Google Trends over a decade. Suicide attempt data were analyzed alongside the frequency of Google searches for suicide-related keywords derived from the Polish Corpus of Suicide Notes. A total of 66 keywords were selected for analysis to identify seasonal trends and patterns in search behavior. The study employed linear regression, Seasonal Mann-Kendall tests, and TBATS models to analyze the data. Suicide rates show seasonal patterns, peaking in warmer months. However, keyword searches did not strongly correlate with peak suicide months. This study enhances our understanding of suicide-related search trends and their potential connection to suicide rates. It suggests avenues for more effective prevention efforts and the potential for future algorithms to predict suicide rates and identify at-risk groups.

Predicting state level suicide fatalities in the united states with realtime data and machine learning

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Estimating national and state-level suicide deaths using a novel online symptom search data source

BACKGROUND

Suicide mortality data are a critical source of information for understanding suicide-related trends in the United States. However, official suicide mortality data experience significant delays. The Google Symptom Search Dataset (SSD), a novel population-level data source derived from online search behavior, has not been evaluated for its utility in predicting suicide mortality trends.

METHODS

We identified five mental health related variables (suicidal ideation, self-harm, depression, major depressive disorder, and pain) from the SSD. Daily search trends for these symptoms were utilized to estimate national and state suicide counts in 2020, the most recent year for which data was available, via a linear regression model. We compared the performance of this model to a baseline autoregressive integrated moving average (ARIMA) model and a model including all 422 symptoms (All Symptoms) in the SSD.

RESULTS

Our Mental Health Model estimated the national number of suicide deaths with an error of -3.86 %, compared to an error of 7.17 % and 28.49 % for the ARIMA baseline and All Symptoms models. At the state level, 70 % (N = 35) of states had a prediction error of <10 % with the Mental Health Model, with accuracy generally favoring larger population states with higher number of suicide deaths.

CONCLUSION

The Google SSD is a new real-time data source that can be used to make accurate predictions of suicide mortality monthly trends at the national level. Additional research is needed to optimize state level predictions for states with low suicide counts.

Understanding mental health conditions and key coping strategies utilized during major lockdowns in the Caribbean based on Google trends searches

The COVID-19 pandemic has prompted countries to implement extended Shelter in Place Orders (SIPOs) to restrict population movement and mitigate community spread. While these lockdown measures may be effective in containing the virus, they can substantially impact the population's well-being, potentially undermining their overall welfare. This study investigates whether major lockdowns implemented in the Caribbean produced differential changes in mental health among key English-Speaking Caribbean countries. More importantly, unlike past studies, we examine key coping strategies persons utilize during major lockdowns. Finally, this paper utilizes a novel near real-time high-frequency data source in Google Trends data analytics to assess mental health patterns and coping strategies among major Caribbean countries. Based on the results of difference-in-difference and event study models, we find positive and significant increases in searches for fear, depression, and suicide during key lockdown periods, which suggest negative mental health effects. Regarding coping strategies, searches for Zoom, learning, books, exercise, prayer, religion, and meditation increased, together with searches for addiction and marijuana. These results indicate the types of programs health administrators and policymakers can implement during lockdown periods to help local mental health communities, particularly among island communities.

Covid-related surge in global wild bird feeding: Implications for biodiversity and human-nature interaction

The global extent of supplementary bird feeding is unknown but has consequences for bird conservation and human well-being. Using a measure of search intensity for words related to bird feeding from Google, we document a surge of interest in bird feeding that occurred around the world after Covid-19 led to lockdowns where people stayed home: 115 countries saw an increase in bird feeding search interest. We test whether the existence of interest in bird feeding is associated with greater species richness of bird species, our proxy for biodiversity, and find the relationship is highly significant. Covid-19 lockdowns may have persistent influences on global bird populations and humans' connection to nature.

Korean adolescent suicide and search volume for "self-injury" on internet search engines

Introduction

Many adolescents with suicidal ideation receive support through the Internet. However, they also find ways to attempt suicide or strengthen their suicidal ideation through this medium. This study analyzed the association between the search volume of suicide-related terms and the number of suicides among Korean adolescents. We also analyzed the correlations between the search volumes of suicide-related terms.

Methods

We selected seven words (suicide, self-injury, depression, academic score, school violence, outcasts, and family trouble) related to adolescent suicide. A dataset was constructed by combining data from the most commonly used search engine in Korea (Naver Datalab) and the daily number of adolescent suicides in school settings (n = 347) from January 1, 2016 to December 31, 2018, collected from the Ministry of Education. Poisson regression and Pearson correlation analyses were performed.

Results

Significant associations were found between suicide attempts and search term volumes, which differed according to sex and time interval. Among the search terms, "self-injury" was most strongly associated with suicide, and this association was significant at all time intervals (daily, weekly, and monthly) in female adolescents and in the total population. Further, the association was strongest in the daily suicide data. More search term volumes were related to suicide in the daily and weekly data than in the monthly data. There were positive correlations between "suicide," "self-injury," and "depression" search volumes.

Conclusion

Further studies with larger sample sizes, more search terms, and analysis of time intervals between suicide-related term search and suicide death are required. These studies can contribute to the establishment of an online suicide prevention system to detect suicide risk in adolescents and provide interventions.

Association Between Internet Searches Related to Suicide/Self-Harm and Adolescent Suicide Death in South Korea, 2016–2020: Data Analysis Study (Preprint)

BACKGROUND

Previous studies have investigated the association between suicide and internet search volumes of terms related to suicide or self-harm. However, the results varied by people's age, period, and country, and no study has exclusively investigated suicide or self-harm rates among adolescents.

OBJECTIVE

This study aims to determine the association between the internet search volumes of terms related to suicide/self-harm and the number of suicides among South Korean adolescents. We investigated gender differences in this association and the time lag between the internet search volumes of the terms and the connected suicide deaths.

METHODS

We selected 26 search terms related to suicide and self-harm among South Korean adolescents, and the search volumes of these terms for adolescents aged 13-18 years were obtained from the leading internet search engine in South Korea (Naver Datalab). A data set was constructed by combining data from Naver Datalab and the number of suicide deaths of adolescents on a daily basis from January 1, 2016, to December 31, 2020. Spearman rank correlation and multivariate Poisson regression analyses were performed to identify the association between the search volumes of the terms and the suicide deaths during that period. The time lag between suicide death and the increasing trend in the search volumes of the related terms was estimated from the cross-correlation coefficients.

RESULTS

Significant correlations were observed within the search volumes of the 26 terms related to suicide/self-harm. The internet search volumes of several terms were associated with the number of suicide deaths among South Korean adolescents, and this association differed by gender. The search volume for "dropout" showed a statistically significant correlation with the number of suicides in all adolescent population groups. The correlation between the internet search volume for "dropout" and the connected suicide deaths was the strongest for a time lag of 0 days. In females, self-harm and academic score showed significant associations with suicide deaths, but academic score showed a negative correlation, and the time lags with the strongest correlations were 0 and -11 days, respectively. In the total population, self-harm and suicide method were associated with the number of suicides, and the time lags with the strongest correlations were +7 and 0 days, respectively.

CONCLUSIONS

This study identifies a correlation between suicides and internet search volumes related to suicide/self-harm among South Korean adolescents, but the relatively weak correlation (incidence rate ratio 0.990-1.068) should be interpreted with caution.

Anti-transgender rights legislation and internet searches pertaining to depression and suicide

The purpose of this study was to examine whether anti-transgender rights legislation among state legislators is associated with increased suicide- and depression-related Internet searches. Employing a quasi-experimental non-equivalent control group design, we focused on bills that were introduced to state legislatures from July 2019 to July 2020. As our panel is constructed of 51 states/territories over a 52-week time frame, our final dataset is composed of 2,652 observations. Results showed that states' passing of anti-transgender rights bills were linked with suicide- and depression-related Internet searches. Second, introducing or debating the bills did not have an association with Internet searches. Third, the defeat of anti-transgender bills was linked with fewer depression-related searches. Finally, the LGBT context in the state affected the results: anti-transgender legislation had a particularly strong association with suicide-related Internet searches when the state had a high LGBT population density.

An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends

Due to significant social and economic upheavals brought on by the COVID-19 pandemic, there is a great deal of psychological pain. Google Trends data have been seen as a corollary measure to assess population-wide trends via observing trends in search results. Judicious analysis of Google Trends data can have both analytical and predictive capacities. This study aimed to compare nation-wide and inter-state trends in mental health before and after the Malaysian Movement Control Order 3.0 (MCO 3.0) commencing 12 May 2021. This was through assessment of two terms, “stress” and “sleep” in both the Malay and English language. Google Trends daily data between March 6 and 31 May in both 2019 and 2021 was obtained, and both series were re-scaled to be comparable. Searches before and after MCO 3.0 in 2021 were compared to searches before and after the same date in 2019. This was carried out using the differences in difference (DiD) method. This ensured that seasonal variations between states were not the source of our findings. We found that DiD estimates, β_3 for “sleep” and “stress” were not significantly different from zero, implying that MCO 3.0 had no effect on psychological distress in all states. Johor was the only state where the DiD estimates β_3 were significantly different from zero for the search topic ‘Tidur’. For the topic ‘Tekanan’, there were two states with significant DiD estimates, β_3, namely Penang and Sarawak. This study hence demonstrates that there are particular state-level differences in Google Trend search terms, which gives an indicator as to states to prioritise interventions and increase surveillance for mental health. In conclusion, Google Trends is a powerful tool to examine larger population-based trends especially in monitoring public health parameters such as population-level psychological distress, which can facilitate interventions.

Sodium Nitrite Intoxication and Death: Summarizing Evidence to Facilitate Diagnosis

BACKGROUND

Over the years, forensic pathology has registered the spread of new methods of suicide, such as the ingestion of sodium nitrite. Sodium nitrite causes increased methemoglobin, resulting in systemic hypoxia, metabolic acidosis, and cyanosis. Since sodium nitrite is a preservative, the ingestion of foods containing an excessive amount of this substance can also cause acute intoxication up to death. The present review is aimed at guiding health professionals in the identification and management of sodium-nitrite-related intoxications and deaths.

METHODS

A systematic literature search was carried out on PubMed by following the PRISMA statement's criteria. A total of 35 studies with 132 cases were enrolled, and the data were cataloged in Microsoft Excel. To establish the causal correlation between sodium nitrite ingestion and death, the Naranjo Adverse Drug Reaction Probability Scale was used.

RESULTS

In addition to the small number of cases that have currently been published, the study demonstrated that there was a general methodological discrepancy in the diagnostic process. However, some interesting results have emerged, especially in post-mortem diagnostics.

CONCLUSION

Sodium-nitrite-related deaths represent a challenge for forensic pathologists; therefore, it is important to promptly recognize the essential features and perform the necessary and unrepeatable examinations for the correct diagnosis of the cause of death.

An evaluation of Internet searches as a marker of trends in population mental health in the US

The absence of continuous, real-time mental health assessment has made it challenging to quantify the impacts of the COVID-19 pandemic on population mental health. We examined publicly available, anonymized, aggregated data on weekly trends in Google searches related to anxiety, depression, and suicidal ideation from 2018 to 2020 in the US. We correlated these trends with (1) emergency department (ED) visits for mental health problems and suicide attempts, and (2) surveys of self-reported symptoms of anxiety, depression, and mental health care use. Search queries related to anxiety, depression, and suicidal ideation decreased sharply around March 2020, returning to pre-pandemic levels by summer 2020. Searches related to depression were correlated with the proportion of individuals reporting receiving therapy (r = 0.73), taking medication (r = 0.62) and having unmet mental healthcare needs (r = 0.57) on US Census Household Pulse Survey and modestly correlated with rates of ED visits for mental health conditions. Results were similar when considering instead searches for anxiety. Searches for suicidal ideation did not correlate with external variables. These results suggest aggregated data on Internet searches can provide timely and continuous insights into population mental health and complement other existing tools in this domain.

Trends of Internet Search Volumes for Major Depressive Disorder Symptoms During the COVID-19 Pandemic in Korea: An Interrupted Time-Series Analysis

BACKGROUND

The effect of coronavirus disease 2019 (COVID-19) pandemic on public mental health has been increasing. Additionally, the underlying psychological stressors remain unexplored, and few studies have been conducted nationally on the social distancing measures. Therefore, the present study aimed to identify the psychological impacts of the implementation of social distancing measures by analyzing the Internet search trends of major depressive disorder (MDD) symptoms.

METHODS

Using Naver^® Trends' relative search volumes (RSVs), we analyzed the average search volumes and trend changes of 16 terms, adopted from the Diagnostic and Statistical Manual of Mental Disorders-5 criteria for diagnosing MDD. An interrupted time-series analysis was performed using the data from January 1, 2016 to December 31, 2020. Furthermore, changes in RSVs, according to the intensity of the social distancing measures implemented from January 1 to December 31, 2020, were determined using Wilcoxon rank sum tests.

RESULTS

Of the 16 terms, the search trends of 'feeling guilty' (P < 0.001) and 'wanting to die' (P = 0.002) showed a significant increase as of February 29, 2020, when the social distancing measures were officially implemented. Additionally, the average search volumes for 'hopelessness' (P = 0.003), 'sexual desire' (P < 0.001), 'insomnia' (P = 0.002), 'hypersomnia' (P < 0.001), 'restlessness' (P < 0.001), and 'feeling lethargic' (P < 0.001) increased significantly. Moreover, gender analysis showed that the average search volume of 'depressed mood' (P = 0.033) and the trend of 'impaired concentration' (P < 0.001) increased in males only. However, the average search volume of 'feeling lethargic' (P = 0.001) and the trend of 'feeling guilty' (P = 0.001) increased in females only. The average search volumes for 'depressed mood' (P < 0.001), 'impaired concentration' (P = 0.038), and 'indecisiveness' (P < 0.001) were significantly higher during the enforcement of level 2 or higher social distancing measures.

CONCLUSION

Our results reported the negative effect of COVID-19 preventive measures on public mental health in South Korea, especially for MDD symptoms. Moreover, the findings suggested the utility of Naver Trends as a feasible big data source to assess future mental health crises.

Leveraging data science to enhance suicide prevention research: a literature review

OBJECTIVE

The purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.

DESIGN

We conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.

METHODS

For the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.

RESULTS

Results showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.

CONCLUSION

Data science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.

Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model

BACKGROUND

The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures.

OBJECTIVE

This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword "suicide."

METHODS

This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with "suicide" on "Yahoo! JAPAN Search" from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword "suicide" and extracted and used for analyses: "abuse"; "work, don't want to go"; "company, want to quit"; "divorce"; and "no money." The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable.

RESULTS

In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: -9.24; max tau 3: -5.38) and women (minimum tau 3: -9.24; max tau 3: -5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were "divorce" for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and "no money" for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, "divorce" was significant for both men (F₁₀₄=3.29; P=.04) and women (F₁₀₄=3.23; P=.04).

CONCLUSIONS

The number of suicides can be predicted by search queries related to the keyword "suicide." Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on "no money" and "divorce" predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

Googling for Suicide-Content and Quality Analysis of Suicide-Related Websites: Thematic Analysis

Background

Suicide represents a public health concern, imposing a dramatic burden. Prosuicide websites are “virtual pathways” facilitating a rise in suicidal behaviors, especially among socially isolated, susceptible individuals.

Objective

The aim of this study is to characterize suicide-related webpages in the Italian language.

Methods

The first 5 most commonly used search engines in Italy (ie, Bing, Virgilio, Yahoo, Google, and Libero) were mined using the term “suicidio” (Italian for suicide). For each search, the first 100 webpages were considered. Websites resulting from each search were collected and duplicates deleted so that unique webpages could be analyzed and rated with the HONcode instrument

Results

A total of 65 webpages were included: 12.5% (8/64) were antisuicide and 6.3% (4/64) explicitly prosuicide. The majority of the included websites had a mixed or neutral attitude toward suicide (52/64, 81.2%) and had informative content and purpose (39/64, 60.9%). Most webpages targeted adolescents as an age group (38/64, 59.4%), contained a reference to other psychiatric disorders or comorbidities (42/64, 65.6%), included medical/professional supervision or guidance (45/64, 70.3%), lacked figures or pictures related to suicide (41/64, 64.1%), and did not contain any access restraint (62/64, 96.9%). The major shortcoming to this study is the small sample size of webpages analyzed and the search limited to the keyword “suicide.”

Conclusions

Specialized mental health professionals should try to improve their presence online by providing high-quality material.

Is Google Trends a useful tool for tracking mental and social distress during a public health emergency? A time-series analysis

BACKGROUND

Google Trends data are increasingly used by researchers as an indicator of population mental health, but few studies have investigated the validity of this approach during a public health emergency.

METHODS

Relative search volumes (RSV) for the topics depression, anxiety, self-harm, suicide, suicidal ideation, loneliness, and abuse were obtained from Google Trends. We used graphical and time-series approaches to compare daily trends in searches for these topics against population measures of these outcomes recorded using validated self-report scales (PHQ-9; GAD-7; UCLA-3) in a weekly survey (n = ~70,000) of the impact COVID-19 on psychological and social experiences in the UK population (21/03/2020 to 21/08/ 2020).

RESULTS

Self-reported levels of depression, anxiety, self-harm/suicidal ideation, self-harm, loneliness and abuse decreased during the period studied. There was no evidence of an association between self-reported anxiety, self-harm, abuse and RSV on Google Trends. Trends in Google topic RSV for depression and suicidal ideation were inversely associated with self-reports of these outcomes (p = 0.03 and p = 0.04, respectively). However, there was statistical and graphical evidence that self-report and Google searches for loneliness (p < 0.001) tracked one another.

LIMITATIONS

No age/sex breakdown of Google Trends data available. Survey respondents were not representative of the UK population and no pre-pandemic data were available.

CONCLUSION

Google Trends data do not appear to be a useful indicator of changing levels of population mental health during a public health emergency, but may have some value as an indicator of loneliness.

Exploring the experiences of mental health professionals engaged in the adoption of mobile health technology in Irish mental health services

BACKGROUND

The World Health Organization report that an estimated 793,000 people died by suicide in 2016 globally. The use of digital technology has been found to be beneficial in the delivery of Web-based suicide prevention interventions. Research on the integration of digital technology within mental health services has indicated that despite the proliferation of technology, engagement by patients and professionals in adopting such technology can be poor.

OBJECTIVES

The current study aims to explore the experiences of 15 mental health professionals involved in integrating mobile health technology into their practice. A secondary aim was to identify the drivers and barriers to the adoption of such technology by mental health professionals, and to consider what theoretical models could best account for the data.

METHODS

Semi-structured interviews, conducted from July to October 2019, were used to explore the experiences of mental health professionals engaged in the adoption of mobile health technology within mental health services. Mental Health professionals and clinician managers working in HSE Child and Adolescent Mental Health, Adult Mental Health, and Primary Care Psychology services were recruited for the study. Qualitative interview data was transcribed and analysed using NVivo. Thematic Analysis was used to identify themes.

RESULTS

Four major themes were identified: Accessibility, 'Transitional Object', Integration, and Trust. Within these 4 major themes, a total of 9 subthemes were identified: Service Accessibility, Immediate Access, Client Engagement, Adjunct-to-therapy, Therapeutic Relationship, Infrastructural Support, Enhancing Treatment, Trust in the Technology, Trust in the Organisation.

CONCLUSIONS

Overall, Diffusion of Innovation Theory provides a useful theoretical framework which is consistent with and can adequately account for many of the Major and Subthemes identified in the data. In addition, 'Transitional Objects', a key concept within Object Relations Theory, could offer a means of better understanding how patients and professionals engage with digital technology within mental health services particularly.

Oral Health and Teledentistry Interest during the COVID-19 Pandemic

BACKGROUND

The COVID-19 pandemic outbreak has significantly changed access to dental treatments.

METHODS

The data related to oral health and teledentistry topics were collected from the open database Google Trends. The analyzed material was collected from 19 June 2016 to 6 June 2021 among anonymous search engine users. The following expressions were analyzed: "dental care", "emergency dental care", "oral health", "periodontitis", "teledentistry", "is it safe to go to the dentist", and "COVID-19" and "PPE dentist".

RESULTS

During the first lockdown in 2020, a significant increase in "emergency dental care" phrase queries was detected, with a simultaneous decrease in regular "dental care" questions, as well as a peak in the queries for "periodontitis" preceded by lower interest in "oral health." The number of searches stated for "teledentistry" increased during the time of the pandemic 5 times and for and "PPE dentist" 30 times. The risk of visiting the dental studio was seen in almost 40 times increase in the query "is it safe to go to the dentist."

CONCLUSIONS

The COVID-19 imprinted a stigma on oral health care. In this difficult epidemiological situation, teledentistry might become a helpful solution.

The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends

Background: The COVID-19 pandemic has globally overwhelmed all sectors of life. The fast development of vaccines against COVID-19 has had a significant impact on the course of the pandemic. Methods: Global data from Google Trends was analyzed for vaccines against flu, BCG, HPV, pneumococcal disease, polio, and COVID-19. The time frame includes the last five-year period starting from 17 April 2016. Multiple training of time series models with back testing, including Holt–Winters forecasting, Exponential Smoothing State Space, Linear model with trend and seasonal components (tlsm), and ARIMA was conducted. Forecasting according to the best fitting model was performed. Results: Correlation analysis did not reveal a decrease in interest in vaccines during the analyzed period. The prediction models provided a short-term forecast of the dynamics of interest for flu, HPV, pneumococcal and polio vaccines with 5–10% growth in interest for the first quarter of 2022 when compared to the same quarter of 2021. Conclusions: Despite the huge interest in the COVID-19 vaccine, there has not been a detectable decline in the overall interest in the five analyzed vaccines.

Use and interest of electronic nicotine delivery systems (ENDS): Assessing the validity of Google Trends

Background: Initial evidence suggests a role for Google Trends (GT) in monitoring use and interest in Electronic Nicotine Delivery Systems (ENDS).Objective: To examine the validity of GT data for assessing population behavior and interest in ENDS.Methods: GT search, done from the US and India to assess: (a) whether descriptive and statistical trends of specific search words depicting online interest in ENDS were similar to the published reports on actual use of these products in the US; (b) correlations between state-wise relative search volumes (RSV) of these words and prevalence of ENDS use; (c) the change in the online interest of ENDS after change in its regulatory policy.Results: Locally weighted scatter plot analysis showed "electronic cigarette," "vaping," and "cigarette" trends mirrored the use trend reported by the US-based National Youth Tobacco Survey (2019). Online interest in "Juul pods" followed the trends of its use. Geo-spatial RSVs of "electronic cigarette" (r=0.74, p<0.0001) and "vaping" (r=0.55, p<0.0001) correlated positively with state-wise prevalence of switch to ENDS in adults. Complete ENDS use prohibition in India was associated with a decrease in the online interest in "electronic cigarettes" (t=3.18, p=0.01) and "vaping" (t=2.3, p=0.04). Regulation of use in New Mexico (USA) was associated with a reduction in "electronic cigarettes" (t=4.09, p=0.0005) but not in "vaping."Conclusion: GT may be used to validate existing information and assess the potential effect of ENDS regulations. Its role can also be extended for monitoring interest and use of other drugs and alcohol.

Internet Interest in Colon Cancer Following the Death of Chadwick Boseman: Infoveillance Study

Background

Compared with White Americans, Black Americans have higher colon cancer mortality rates but lower up-to-date screening rates. Chadwick Boseman was a prominent Black American actor who died of colon cancer on August 28, 2020. As announcements of celebrity diagnoses often result in increased awareness, Boseman’s death may have resulted in greater interest in colon cancer on the internet, particularly among Black Americans.

Objective

This study aims to quantify the impact of Chadwick Boseman’s death on web-based search interest in colon cancer and determine whether there was an increase in interest in regions of the United States with a greater proportion of Black Americans.

Methods

We conducted an infoveillance study using Google Trends (GT) and Wikipedia pageview analysis. Using an autoregressive integrated moving average algorithm, we forecasted the weekly relative search volume (RSV) for GT search topics and terms related to colon cancer that would have been expected had his death not occurred and compared it with observed RSV data. This analysis was also conducted for the number of page views on the Wikipedia page for colorectal cancer. We then delineated GT RSV data for the term colon cancer for states and metropolitan areas in the United States and determined how the RSV values for these regions correlated with the percentage of Black Americans in that region. Differences in these correlations before and after Boseman’s death were compared to determine whether there was a shift in the racial demographics of the individuals conducting the searches.

Results

The observed RSVs for the topics colorectal cancer and colon cancer screening increased by 598% and 707%, respectively, and were on average 121% (95% CI 72%-193%) and 256% (95% CI 35%-814%) greater than expected during the first 3 months following Boseman’s death. Daily Wikipedia page view volume during the 2 months following Boseman’s death was on average 1979% (95% CI 1375%-2894%) greater than expected, and it was estimated that this represented 547,354 (95% CI 497,708-585,167) excess Wikipedia page views. Before Boseman’s death, there were negative correlations between the percentage of Black Americans living in a state or metropolitan area and the RSV for colon cancer in that area (r=−0.18 and r=−0.05, respectively). However, in the 2 weeks following his death, there were positive correlations between the RSV for colon cancer and the percentage of Black Americans per state and per metropolitan area (r=0.73 and r=0.33, respectively). These changes persisted for 4 months and were all statistically significant (P<.001).

Conclusions

There was a significant increase in web-based activity related to colon cancer following Chadwick Boseman’s death, particularly in areas with a higher proportion of Black Americans. This reflects a heightened public awareness that can be leveraged to further educate the public.

Reconciling Statistical and Clinicians' Predictions of Suicide Risk

Statistical models, including those based on electronic health records, can accurately identify patients at high risk for a suicide attempt or death, leading to implementation of risk prediction models for population-based suicide prevention in health systems. However, some have questioned whether statistical predictions can really inform clinical decisions. Appropriately reconciling statistical algorithms with traditional clinician assessment depends on whether predictions from these two methods are competing, complementary, or merely duplicative. In June 2019, the National Institute of Mental Health convened a meeting, "Identifying Research Priorities for Risk Algorithms Applications in Healthcare Settings to Improve Suicide Prevention." Here, participants of this meeting summarize key issues regarding the potential clinical application of suicide prediction models. The authors attempt to clarify the key conceptual and technical differences between traditional risk prediction by clinicians and predictions from statistical models, review the limited evidence regarding both the accuracy of and the concordance between these alternative methods of prediction, present a conceptual framework for understanding agreement and disagreement between statistical and clinician predictions, identify priorities for improving data regarding suicide risk, and propose priority questions for future research. Future suicide risk assessment will likely combine statistical prediction with traditional clinician assessment, but research is needed to determine the optimal combination of these two methods.

Suicide, self-harm and thoughts of suicide or self-harm in infectious disease epidemics: a systematic review and meta-analysis

AIMS

Suicide accounts for 2.2% of all years of life lost worldwide. We aimed to establish whether infectious epidemics are associated with any changes in the incidence of suicide or the period prevalence of self-harm, or thoughts of suicide or self-harm, with a secondary objective of establishing the frequency of these outcomes.

METHODS

In this systematic review and meta-analysis, MEDLINE, Embase, PsycINFO and AMED were searched from inception to 9 September 2020. Studies of infectious epidemics reporting outcomes of (a) death by suicide, (b) self-harm or (c) thoughts of suicide or self-harm were identified. A random-effects model meta-analysis for the period prevalence of thoughts of suicide or self-harm was conducted.

RESULTS

In total, 1354 studies were screened with 57 meeting eligibility criteria, of which 7 described death by suicide, 9 by self-harm, and 45 thoughts of suicide or self-harm. The observation period ranged from 1910 to 2020 and included epidemics of Spanish Flu, severe acute respiratory syndrome, human monkeypox, Ebola virus disease and coronavirus disease 2019 (COVID-19). Regarding death by suicide, data with a clear longitudinal comparison group were available for only two epidemics: SARS in Hong Kong, finding an increase in suicides among the elderly, and COVID-19 in Japan, finding no change in suicides among children and adolescents. In terms of self-harm, five studies examined emergency department attendances in epidemic and non-epidemic periods, of which four found no difference and one showed a reduction during the epidemic. In studies of thoughts of suicide or self-harm, one large survey showed a substantial increase in period prevalence compared to non-epidemic periods, but smaller studies showed no difference. As a secondary objective, a meta-analysis of thoughts of suicide and self-harm found that the pooled prevalence was 8.0% overall (95% confidence interval (CI) 5.2-12.0%; 14 820 of 99 238 cases in 24 studies) over a time period of between seven days and six months. The quality assessment found 42 studies were of high quality, nine of moderate quality and six of high quality.

CONCLUSIONS

There is little robust evidence on the association of infectious epidemics with suicide, self-harm and thoughts of suicide or self-harm. There was an increase in suicides among the elderly in Hong Kong during SARS and no change in suicides among young people in Japan during COVID-19, but it is unclear how far these findings may be generalised. The development of up-to-date self-harm and suicide statistics to monitor the effect of the current pandemic is an urgent priority.

Managing awareness can avoid hysteresis in disease spread: an application to coronavirus Covid-19

A SEIR-type model is investigated to evaluate the effects of awareness campaigns in the presence of factors that can induce overexposure to disease. We find that high levels of overexposure can drive system dynamics towards a backward phenomenology and that increasing people awareness through balanced and aware information can be crucial to avoid dangerous dynamical transitions as hysteresis or transient oscillations before disease eradication. Investigations in the time dependent regimes are provided to support the results. Google Trends data in the context of Covid19 are also used to stress how low levels of awareness, combined with high overexposure, can be related to recent episodes of epidemic resurgence in Europe. Our results suggest that the interplay between overexposure and awareness is a point that should not be underestimated both in the current and future management of the Covid19 emergency.

Suicide-Related Internet Searches During the Early Stages of the COVID-19 Pandemic in the US

This cross-sectional study assesses trends in suicide-related internet search rates in the early stages of the COVID-19 pandemic in the US.

Time Trends of the Public's Attention Toward Suicide During the COVID-19 Pandemic: Retrospective, Longitudinal Time-Series Study

Background

The COVID-19 pandemic has overwhelmed health care systems around the world. Emerging evidence has suggested that substantially few patients seek help for suicidality at clinical settings during the COVID-19 pandemic, which has elicited concerns of an imminent mental health crisis as the course of the pandemic continues to unfold. Clarifying the relationship between the public’s attention to knowledge about suicide and the public’s attention to knowledge about the COVID-19 pandemic may provide insight into developing prevention strategies for a putative surge of suicide in relation to the impact of the COVID-19 pandemic.

Objective

The goal of this retrospective, longitudinal time-series study is to understand the relationship between temporal trends of interest for the search term “suicide” and those of COVID-19–related terms, such as “social distancing,” “school closure,” and “lockdown.”

Methods

We used the Google Trends platform to collect data on daily interest levels for search terms related to suicide, several other mental health-related issues, and COVID-19 over the period between February 14, 2020 and May 13, 2020. A correlational analysis was performed to determine the association between the search term ‘‘suicide’’ and COVID-19–related search terms in 16 countries. The Mann-Kendall test was used to examine significant differences between interest levels for the search term “suicide” before and after school closure.

Results

We found that interest levels for the search term “suicide” statistically significantly inversely correlated with interest levels for the search terms “COVID-19” or “coronavirus” in nearly all countries between February 14, 2020 and May 13, 2020. Additionally, search interest for the term ‘‘suicide’’ significantly and negatively correlated with that of many COVID-19–related search terms, and search interest varied between countries. The Mann-Kendall test was used to examine significant differences between search interest levels for the term “suicide” before and after school closure. The Netherlands (P=.19), New Zealand (P=.003), the United Kingdom (P=.006), and the United States (P=.049) showed significant negative trends in interest levels for suicide in the 2-week period preceding school closures. In contrast, interest levels for suicide had a significant positive trend in Canada (P<.001) and the United States (P=.002) after school closures.

Conclusions

The public’s attention to suicide might inversely correlate with the public’s attention to COVID-19–related issues. Additionally, several anticontagion policies, such as school closure, might have led to a turning point for mental health crises, because the attention to suicidality increased after restrictions were implemented. Our results suggest that an increased risk of suicidal ideation may ensue due to the ongoing anticontagion policies. Timely intervention strategies for suicides should therefore be an integral part of efforts to flatten the epidemic curve.

Can google trends search inform us about the population response and public health impact of abrupt change in alcohol policy? A case study from India during the covid-19 pandemic

Background

Sudden alcohol prohibition in India during the COVID-19 pandemic presented an opportunity to test whether Google Trends data could indicate population responses and the public health impact of alcohol policy. We hypothesized, following prohibition: there would be a significant change in the relative search volumes (RSV) of alcohol-related queries; that temporal analysis of the trends would reflect a public response to policy changes; and that geospatial analysis of RSV would correlate with the prevalence of alcohol use.

Methods

Three different search periods were used to test the hypotheses. The search inputs were based on potential public response to alcohol prohibition, as evidenced by the literature, newspaper articles, and consensus. We used RSV as the unit of analysis. Mean RSV of search queries, pre-post implementation of prohibition, were compared. Smoothing of scatter plots examined the temporal association of trends with policy measures. Multiple linear regression tested the relationship of state-wise RSV and alcohol use prevalence.

Results

Post-implementation of prohibition, a significant increase in the RSV was observed for searches related to alcohol withdrawal (p<0.001), how to extract alcohol from sanitizer (p = 0.002), alcohol home delivery online (p<0.001), alcohol home delivery (p<0.001), and sleeping pills (p = 0.006). The trends suggested a decrease in general interest in alcohol but increased demand, and a possible connection with changes in policy measures. State-level RSV and alcohol use prevalence did not reveal a significant relationship.

Conclusion

Google trend is a potential source of rapid feedback to policymakers about population responses to an abrupt change in alcohol policies.

Tracking online searches for emotional wellbeing concerns and coping strategies in the UK during the COVID-19 pandemic: a Google Trends analysis

Background: The coronavirus disease 2019 (COVID-19) pandemic is the largest acute public health emergency of this century. Government intervention to contain the virus focuses on non-pharmacological approaches such as physical distancing/lockdown (stay-at-home orders). As the situation develops, the impact of these measures on mental health and coping strategies in individuals and the population is unknown. Methods: We used Google Trends data (01 Jan 2020 to 09 Jun 2020) to explore the changing pattern of public concern in the UK to government measures as indexed by changes in search frequency for topics related to mental distress as well as coping and resilience. We explored the changes of specific topics in relation to key dates during the pandemic. In addition, we examined terms whose search frequency increased most. Results: Following lockdown, public concerns - as indexed by relative search trends - were directly related to COVID-19 and practicalities such as ‘furlough’ (paid leave scheme for people in employment) in response to the pandemic. Over time, searches with the most substantial growth were no longer directly or indirectly related to COVID-19. In contrast to relatively stable rates of searches related to mental distress, the topics that demonstrated a sustained increase were those associated with coping and resilience such as exercise and learning new skills. Conclusions: Google Trends is an expansive dataset which enables the investigation of population-level search activity as a proxy for public concerns. It has potential to enable policy makers to respond in real time to promote adaptive behaviours and deliver appropriate support.

The association between internet searches and suicide in Spain

Apart from disease, suicide has been the first cause of death in Spain since it overcame the victims of traffic accidents in the year 2007. Nevertheless, in spite of the high number of people affected by it every year, it is an almost silenced issue in this country. In the present research work we make use of Internet suicide-related search terms and official suicide rates for Spain to analyze whether these searches are related to actual suicides between 2004 and 2017. We find that the search terms "commit suicide", "commit suicide without pain" and "suicide" (as search term and as topic) are positively related to actual suicide rates. Thus, the Internet seems to be a consult tool for potential suicide victims in Spain which, joined to an increasing trend in the use of the Internet by the Spanish population, opens the gate for interventions through the results obtained in these Internet searches.

Google searches for suicide and suicide risk factors in the early stages of the COVID-19 pandemic

A novel coronavirus (SARS-CoV-2), which causes the COVID-19 respiratory illness, emerged in December of 2019 and has since spread globally. The dramatic lifestyle changes and stressors associated with this pandemic pose a threat to mental health and have the potential to exacerbate risk factors for suicide. We used autoregressive integrated moving average (ARIMA) models to assess Google Trends data representing searches in the United States for 18 terms related to suicide and known suicide risk factors following the emergence of COVID-19. Although the relative proportion of Google searches for suicide-related queries was lower than predicted during the early pandemic period, searches for the following queries representative of financial difficulty were dramatically elevated: "I lost my job" (226%; 95%CI, 120%-333%), "laid off" (1164%; 95%CI, 395%-1932%), "unemployment" (1238%; 95%CI, 560%-1915%), and "furlough" (5717%; 95%CI, 2769%-8665%). Searches for the Disaster Distress Helpline, which was promoted as a source of help for those impacted by COVID-19, were also remarkably elevated (3021%; 95%CI, 873%-5169%). Google searches for other queries representative of help-seeking and general mental health concerns were moderately elevated. It appears that some indices of suicidality have fallen in the United States in this early stage of the pandemic, but that COVID-19 may have caused an increase in suicide risk factors that could yield long-term increases in suicidality and suicide rates.

Mapping population mental health concerns related to COVID-19 and the consequences of physical distancing: a Google trends analysis

Background: The 2020 Coronavirus pandemic is a major international public health challenge.  Governments have taken public health protection measures to reduce the spread of the virus through non-pharmalogical measures. The impact of the pandemic and the public health response on individual and population mental health is unknown.  Methods: We used Google Trends data (1 Jan 2020 -  30 Mar 2020) to investigate the impact of the pandemic and government measures to curb it on people's concerns, as indexed by changes in search frequency for topics indicating mental distress, social and economic stressors and mental health treatment-seeking. We explored the changes of key topics in Google trends in Italy, Spain, USA, UK, and Worldwide in relation to sentinel events during the pandemic. Results: Globally there appears to be significant concerns over the financial and work-related consequences of the pandemic, with some evidence that levels of fear are rising. Conversely relative searching for topics related to depression and suicide fell after the pandemic was announced, with some evidence that searches for the latter have risen recently. Concerns over education and access to medication appear to be particular social stressors. Whilst searches for face-to-face treatments have declined, those for self-care have risen. Conclusions: Monitoring Google trends shows promise as a means of tracking changing public concerns. In weeks to come it may enable policy makers to assess the impact of their interventions including those aiming to limit negative consequences, such as government funded financial safety nets.

Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis

OBJECTIVES

To assess the association of public interest in coronavirus infections with the actual number of infected cases for selected countries across the globe.

METHODS

We performed a Google Trends^TM search for "Coronavirus" and compared Relative Search Volumes (RSV) indices to the number of reported COVID-19 cases by the European Center for Disease Control (ECDC) using time-lag correlation analysis.

RESULTS

Worldwide public interest in Coronavirus reached its first peak end of January when numbers of newly infected patients started to increase exponentially in China. The worldwide Google Trends^TM index reached its peak on the 12th of March 2020 at a time when numbers of infected patients started to increase in Europe and COVID-19 was declared a pandemic. At this time the general interest in China but also the Republic of Korea has already been significantly decreased as compared to end of January. Correlations between RSV indices and number of new COVID-19 cases were observed across all investigated countries with highest correlations observed with a time lag of -11.5 days, i.e. highest interest in coronavirus observed 11.5 days before the peak of newly infected cases. This pattern was very consistent across European countries but also holds true for the US. In Brazil and Australia, highest correlations were observed with a time lag of -7 days. In Egypt the highest correlation is given with a time lag of 0, potentially indicating that in this country, numbers of newly infected patients will increase exponentially within the course of April.

CONCLUSIONS

Public interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic. Public interest is on average highest 11.5 days before the peak of newly infected cases.

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

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.

Mapping population mental health concerns related to COVID-19 and the consequences of physical distancing: a Google trends analysis

Background: The 2020 Coronavirus pandemic is a major international public health challenge.  Governments have taken public health protection measures to reduce the spread of the virus through non-pharmalogical measures. The impact of the pandemic and the public health response on individual and population mental health is unknown.  Methods: We used Google Trends data (1 Jan 2020 - 1 Apr 2020) to investigate the impact of the pandemic and government measures to curb it on people's concerns, as indexed by changes in search frequency for topics indicating mental distress, social and economic stressors and mental health treatment-seeking. We explored the changes of key topics in Google trends in Italy, Spain, USA, UK, and Worldwide in relation to sentinel events during the pandemic. Results: Globally there appears to be significant concerns over the financial and work-related consequences of the pandemic, with some evidence that levels of fear are rising. Conversely searching for topics related to depression and suicide fell after the pandemic was announced, with some evidence that searches for the latter have risen recently. Concerns over education and access to medication appear to be particular social stressors. Whilst searches for face-to-face treatments have declined, those for self-care have risen. Conclusions: Monitoring Google trends shows promise as a means of tracking changing public concerns. In weeks to come it may enable policy makers to assess the impact of their interventions including those aiming to limit negative consequences, such as government funded financial safety nets.

Association of the COVID-19 pandemic with Internet Search Volumes: A Google TrendsTM Analysis

OBJECTIVES

To assess the association of public interest in coronavirus infections with the actual number of infected cases for selected countries across the globe.

METHODS

We performed a Google Trends^TM search for "Coronavirus" and compared Relative Search Volumes (RSV) indices to the number of reported COVID-19 cases by the European Center for Disease Control (ECDC) using time-lag correlation analysis.

RESULTS

Worldwide public interest in Coronavirus reached its first peak end of January when numbers of newly infected patients started to increase exponentially in China. The worldwide Google Trends^TM index reached its peak on the 12th of March 2020 at a time when numbers of infected patients started to increase in Europe and COVID-19 was declared a pandemic. At this time the general interest in China but also the Republic of Korea has already been significantly decreased as compared to end of January. Correlations between RSV indices and number of new COVID-19 cases were observed across all investigated countries with highest correlations observed with a time lag of -11.5 days, i.e. highest interest in coronavirus observed 11.5 days before the peak of newly infected cases. This pattern was very consistent across European countries but also holds true for the US. In Brazil and Australia, highest correlations were observed with a time lag of -7 days. In Egypt the highest correlation is given with a time lag of 0, potentially indicating that in this country, numbers of newly infected patients will increase exponentially within the course of April.

CONCLUSIONS

Public interest indicated by RSV indices can help to monitor the progression of an outbreak such as the current COVID-19 pandemic. Public interest is on average highest 11.5 days before the peak of newly infected cases.