The Association Between New COVID-19 Cases and Google Searches for Mental Health
Patrick S. Tennant, Ph.D.*
PSTennant@Rice.edu
Project Manager
Rice University’s Baker Institute for Public Policy
Houston, Texas
United States of America
Jennifer Reingle Gonzalez, Ph.D.
jgonzalez@texasstateofmind.org
Meadows Mental Health Policy Institute
Melissa Rowan, MBA, MSW
mrowan@texasstateofmind.org
Meadows Mental Health Policy Institute
Catie Hilbelink, MPP
chilbelink@texasstateofmind.org
Meadows Mental Health Policy Institute
Quianta Moore, M.D, J.D.
Rice University’s Baker Institute for Public Policy
Quianta.Moore@Rice.edu
*Corresponding Author
Discipline: Public Health
Keywords: Mental health; COVID-19; Google search trends; population mental health; state-level analyses
Abstract: Mental health concerns related to the COVID-19 pandemic and the resulting economic and societal changes are an important matter of public health. We examined the state-level association between new cases of COVID-19 reported and mental health, as measured through Google search trends, on a daily basis. Our analyses indicate a significant positive association, such that increases in mental health Google searches should be expected on days when relatively more new cases of COVID-19 are announced. The overall effect and state-level variation were analyzed via a multi-level model and full results are included here. Implications and public policy suggestions are discussed.
No funding was received to support this work and the authors have no conflicts of interest to disclose.
The Centers for Disease Control and Prevention projected a substantial increase in COVID-19 mortality (CDC, 2020) across the United States in May 2020. New data suggest that many Americans are feeling distressed by the rising number of COVID-19 cases in their state (Panchal et al., 2020). This collective uncertainty is unlikely to decline given the spread of disease, expanded “shelter in place” orders in many jurisdictions, and the removal of social isolation restrictions in some localities that may lead to repeated waves of infection. The widespread impact of COVID-19 has led to job layoffs or furloughing in a variety of industries, which exacerbates stressors for families across the U.S. (Panchal et al., 2020).
We analyzed Google trends data in an attempt to detect changes in population mental health associated with an increase in COVID-19 cases. Our analyses revealed a positive association between the number of new COVID-19 cases reported and mental health related Google searches. That is, there were more mental health searches on Google in a given state on days when more new cases of COVID-19 were announced in that state. This finding suggests an association between mental health related thoughts (measured through Google searches) and reports of new cases.
Measuring mental health through Google searches is not equivalent to tallying clinical diagnoses, but search trends have been used to capture health care trends (Nuti et al., 2014), population mental health (Soreni et al., 2019), COVID-19 symptoms (Stephens-Davidowitz, 2020) and Zika epidemics (Teng et al., 2017), among other phenomena. Traditional population-based measures often have a multi-year lag time in releasing prevalence and treatment-seeking rates for critical health-related indicators, such as mental illness and substance use (both of which are expected to increase due to COVID-19 (Meadows Mental Health Policy Institute, 2020)). Therefore, high quality measures of the impact of COVID-19 on our mental health may not be fully clear for years. At this point, even a rough measure of population mental health is important to assess so that we can project where the need for mental health services may lie as the pandemic continues. We attempted to remedy this problem using some of the most contemporary data available to demonstrate how the increasing number of COVID-19 cases is associated with population mental health.
We used state-level data from the New York Times COVID Cases database (The New York Times, n.d.) and Google Search Trends (Google Trends , n.d.) to measure the number of new COVID-19 cases per day and Google searches for mental health key words, respectively. Our data represents daily new COVID-19 cases and Google searches at the state level from March 11 to April 11, 2020. We calculated the number of new COVID-19 cases per day (new cases ) as the number of total cases reported for each state minus the number of cases reported from the prior day. We then conducted a natural log transformation of new cases because the distribution was highly variable and commonly grew exponentially over time.
Google search trends (obtained from the gtrendsR package (Massicotte & Eddelbuettel, 2019)) were scaled to reflect the relative popularity of a given search term within the state over the queried time period (in this case, 30 days). Values represent a percentage of search volume relative to the highest search volume day, ranging from 100 (i.e., the day with the most searches) to possible low of 0. This score, called the Relative Search Value (or RSV), was calculated for a composite measure calledmental health searches , which included independent RSV scores for the search terms “depression”, “anxiety”, “suicide”, and “mental health” in each of the 50 states over the month. The composite of mental health searches was comprised of the average RSV for all search terms on a given state-day.
A multi-level regression model was fit to assess the magnitude of the within-state association between new cases and mental health searches after accounting for date and weekly fluctuations (because mental health searches occur less commonly on weekends). Our results identified a significant association between new casesand mental health searches , such that a 10% increase in the number of new cases in a state would, on average, result in a 0.17-point increase in RSV of mental health searches in that state. There was also meaningful variation in the effect between states (Figure 1 depicts this variation and full model results are provided in the Appendix).