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).