Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.
TL;DR: In this article, the authors explore Google Trends as a proxy for what people are thinking, needing, and planning in real-time across the United States and find that the increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor's appointments, health insurance, Medicare, and Medicaid.
Abstract: Background: The COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States.
Objective: We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events?
Methods: We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states.
Results: The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others.
Conclusions: COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.
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TL;DR: In this article, the authors investigated behavioral patterns in online health-related searches and Cyberchondria (CYB) during the COVID-19 pandemic time, in order to explore socio-demographic and psychopathological factors related to CYB.
Abstract: Objectives: The Internet has become one of the most common sources people use to search for health-related information, a behavior rapidly increased during the novel Coronavirus disease 2019 (COVID-19). The present study aimed to investigate behavioral patterns in the online health-related searches and Cyberchondria (CYB) during the COVID-19 pandemic time, in order to explore socio-demographic and psychopathological factors related to CYB. Methods: During the third wave of the COVID-19 pandemic in Italy, a cross-sectional online survey collected the main socio-demographic variables and habits related to Internet use of 572 participants. CYB was measured by the Cyberchondria Severity Scale-Short Version and different psychopathological factors were measured by specific questionnaires: the Coronavirus Anxiety Scale, the Hospital Anxiety and Depression Scale, the Short Health Anxiety Inventory, the Meta-Cognitions about Health Questionnaire, the Internet Addiction Test, the Maudsley Obsessional-Compulsive Questionnaire-Short Version, the Rosenberg's Self-Esteem Scale, and the WHO Quality of Life-BREF. Descriptives, non-parametric ANOVAs, and Spearman correlations were performed. Results: In the present sample, the Internet was the main source participants used to search for health-related information and nearly one-third increased this habit during the pandemic. Higher expression of CYB emerged in females, in younger participants, in students, and in those suffering from a physical/psychiatric illness. CYB showed a positive correlation with different phenomenology of anxiety (i.e., anxiety about COVID-19, health anxiety, general anxiety, metacognitive believes about anxiety) and with depression, obsessive-compulsive symptoms, and problematic usage of the Internet. Conversely, quality of life and self-esteem showed a negative correlation with CYB. Conclusion: During the COVID-19 pandemic, the use of the Internet for health-related information and CYB contribute to the psychological stress affecting individuals and society. Delineating subjects more vulnerable to CYB and associated psychopathological factors will help to elaborate operational indications for prevention and psychological support.
18 citations
TL;DR: Wang et al. as mentioned in this paper used a qualitative research approach to conduct semi-structured interviews with 15 participants from 9 cities in mainland China about information needs and access behaviors during the COVID-19 outbreak.
Abstract: PurposeThe impact of COVID-19 has led to a surge in the public’s reliance on the Internet for pandemic information, and the policy of home isolation has exacerbated this. This study aimed to investigate public information needs and ways of accessing and disseminating information during COVID-19 in mainland China.Design/methodology/approachThis study used a qualitative research approach to conduct semi-structured interviews with 15 participants from 9 cities in mainland China about information needs and access behaviors during the COVID-19 outbreak. All interview recordings were converted into text and proofread, then coded and summarised in correspondence with the research questions using the grounded theory.FindingsThis study summarized the dynamics of public information needs during the 2.5-year pandemic and identified the difficulties in accessing certain information.Originality/valueAlthough information needs of public health emergencies have been a hot topic during COVID-19, scant studies focus on information needs in specific countries in Asia, especially in mainland China, the first country with a major outbreak and stringent lockdown mandates. Therefore, the current study is well enriched by focusing on information demand behavior in the context of COVID-19. Possible measures for improvement were also given to existing and potential problems, taking into account the participants’ views.
16 citations
TL;DR: In this article , the authors tested whether reporting COVID-related information seeking throughout the pandemic is associated with subsequently poorer mental health outcomes and found that higher levels of information seeking were associated with poorer mental Health outcomes, particularly clinically significant levels of anxiety.
Abstract: Information seeking has generally been seen as an adaptive response to the COVID-19 pandemic. However, it may also result in negative outcomes on mental health. The present study tests whether reporting COVID-related information seeking throughout the pandemic is associated with subsequently poorer mental health outcomes. A quota-based, non-probability-sampling methodology was used to recruit a nationally representative sample. COVID-related information seeking was assessed at six waves along with symptoms of depression, anxiety, mental wellbeing and loneliness (N = 1945). Hierarchical linear modelling was used to assess the relationship between COVID-related information seeking and mental health outcomes. Information seeking was found to reduce over time. Overall, women, older and higher socioeconomic group individuals reported higher levels of information seeking. At waves 1-4 (March-June 2020) the majority of participants reported that they sought information on Covid 1-5 times per day, this decreased to less than once per day in waves 5 and 6 (July-November 2020). Higher levels of information seeking were associated with poorer mental health outcomes, particularly clinically significant levels of anxiety. Use of a non-probability sampling method may have been a study limitation, nevertheless, reducing or managing information seeking behaviour may be one method to reduce anxiety during pandemics and other public health crises.
7 citations
TL;DR: In this paper , the authors investigate the changes that the students at an Italian high school went through in terms of use of technologies, loneliness, and sense of community, through a survey focusing on their retrospective perceptions.
Abstract: Highlights The pandemic has massively exacerbated the sense of loneliness of high-school students, especially of young women. The pandemic has changed the use of technology by high-school students for social, information, leisure, and educational purposes. Young women changed their use of technology more than young men to stay in touch with their family and for information seeking and study purposes. High-school students’ sense of community has undergone modest variations due to the pandemic. Abstract The COVID-19 pandemic has brought important changes to how we engage in relationships of any kind. To combat the spread of the virus, schools resorted to remote-learning, and teenagers had to rely on various technologies to meet many of the needs that they used to satisfy offline (e.g., social, informational, and recreational/leisure purposes). This article was written to investigate the changes that the students at an Italian high school went through in terms of use of technologies, loneliness, and sense of community, through a survey focusing on their retrospective perceptions. The study was carried out on 917 students. In general, we have found that the COVID-19 pandemic has greatly increased the perception of loneliness in teenagers (especially in female respondents), as well as their use of technologies for social, informational, and leisure purposes. However, maybe thanks to the opportunities provided by ICTs and remote learning, the sense of community in Italian teenagers was only marginally impacted.
7 citations
TL;DR: Nonseekers were characterized by a lower socioeconomic status, lower affective risk responses, lower perceived information-related self-efficacy, and lower trust in information sources, which provide indications for strategic health approaches and can guide initiatives to address adequate use of health information.
Abstract: During health crises like the coronavirus disease 2019 (Covid-19) pandemic, it is crucial that individuals are able and willing to adequately respond to information. Individuals who deliberately seek information have an enhanced capacity to act on it and are capable of informed assessments of risks and self-protective behaviors. In contrast, overexposure to Covid-19 news as well as non-seeking can constitute information-related inequalities and hamper individuals? coping with the health crisis. Having this global health communication challenge in mind, our research aims to understand what characterizes non-, medium, and frequent seekers, considering sociodemographic and socioeconomic factors, health status, affective risk responses, efficacy assessments, trust in information sources, and satisfaction with information. This study is based on data of the second wave of the Health Information National Trends Survey (HINTS) Germany. Among 2602 participants, analysis revealed that 23.3% of the respondents did not actively seek information about Covid-19, while 34.3% of them intensively monitored information. Nonseekers, compared to medium and frequent seekers, were characterized by a lower socioeconomic status, lower affective risk responses, lower perceived information-related self-efficacy, and lower trust in information sources. These findings provide indications for strategic health approaches and can guide initiatives to address adequate use of health information.
6 citations
References
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TL;DR: A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented.
Abstract: This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3,4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
3,984 citations
TL;DR: This paper revisits the emerging fields of infodemiology and infoveillance and proposes an expanded framework, introducing some basic metrics such as information prevalence, concept occurrence ratios, and information incidence.
Abstract: Infodemiology can be defined as the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include: the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza); monitoring peoples' status updates on microblogs such as Twitter for syndromic surveillance; detecting and quantifying disparities in health information availability; identifying and monitoring of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports); automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations. Seven years after the infodemiology concept was first introduced, this paper revisits the emerging fields of infodemiology and infoveillance and proposes an expanded framework, introducing some basic metrics such as information prevalence, concept occurrence ratios, and information incidence. The framework distinguishes supply-based applications (analyzing what is being published on the Internet, eg. on Web sites, newsgroups, blogs, microblogs and social media) from demand-based methods (search and navigation behavior), and further distinguishes passive from active infoveillance methods. Infodemiology metrics follow population health relevant events or predict them. Thus, these metrics and methods are potentially useful for public health practice and research, and should be further developed and standardized. [J Med Internet Res 2009;11(1):e11]
969 citations
TL;DR: Google Trends is being used to study health phenomena in a variety of topic domains in myriad ways and poor documentation of methods precludes the reproducibility of the findings, but greater transparency can improve its reliability as a research tool.
Abstract: Google Trends is a novel, freely accessible tool that allows users to interact with Internet search data, which may provide deep insights into population behavior and health-related phenomena. However, there is limited knowledge about its potential uses and limitations. We therefore systematically reviewed health care literature using Google Trends to classify articles by topic and study aim; evaluate the methodology and validation of the tool; and address limitations for its use in research. PRISMA guidelines were followed. Two independent reviewers systematically identified studies utilizing Google Trends for health care research from MEDLINE and PubMed. Seventy studies met our inclusion criteria. Google Trends publications increased seven-fold from 2009 to 2013. Studies were classified into four topic domains: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Among surveillance studies, 92% were validated against a reference standard data source, and 80% of studies using correlation had a correlation statistic ≥0.70. Overall, 67% of articles provided a rationale for their search input. However, only 7% of articles were reproducible based on complete documentation of search strategy. We present a checklist to facilitate appropriate methodological documentation for future studies. A limitation of the study is the challenge of classifying heterogeneous studies utilizing a novel data source. Google Trends is being used to study health phenomena in a variety of topic domains in myriad ways. However, poor documentation of methods precludes the reproducibility of the findings. Such documentation would enable other researchers to determine the consistency of results provided by Google Trends for a well-specified query over time. Furthermore, greater transparency can improve its reliability as a research tool.
720 citations
TL;DR: Examination of factors associated with heath information seeking from the internet, traditional media, and health care professionals among a diverse population of US adults indicated that there is a possibility that while the Web is an easily available source of health information, it could also create inequalities in health information accessibility.
Abstract: We live in a digital age and this has changed the landscape of health information. With the changing US demographic, otherwise acute diseases morphing into chronic diseases as a result of treatment...
366 citations
TL;DR: This article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.
Abstract: Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.
245 citations