Disease Monitoring and Health Campaign Evaluation Using Google Search Activities for HIV and AIDS, Stroke, Colorectal Cancer, and Marijuana Use in Canada: A Retrospective Observational Study

Influence of Breast Cancer Awareness Month on Public Interest of Breast Cancer in High-Income Countries Between 2012 and 2022: Google Trends Analysis

BACKGROUND

Breast cancer is the most common cancer among women worldwide. High-income countries have a greater incidence and mortality rate of breast cancer than low-income countries. As a result, raising awareness about breast cancer is crucial in increasing the chances of early detection and treatment. Social media has evolved into an essential tool for Breast Cancer Awareness Month campaigns, allowing people to share their breast cancer stories and experiences while also providing a venue for education and support.

OBJECTIVE

The aim of this study was to assess the level of public interest in searches linked to breast cancer among a sample of high-income nations with a sizable internet user base from 2012 to 2022. We also sought to compare the proportional search volume for breast cancer during Breast Cancer Awareness Month with that during other months of the year.

METHODS

Google Trends was used to retrieve data on internet user search behaviors in the context of breast cancer from 2012 to 2022. Seven countries were evaluated in this study: Australia, Canada, Ireland, New Zealand, the United Kingdom, Saudi Arabia, and the United States, in addition to global data. Breast cancer relative search volume trends were analyzed annually, monthly, and weekly from 2012 to 2022. The annual percent change (APC) was calculated for each country and worldwide. Monthly and weekly data were used to identify potential trends.

RESULTS

A fluctuating pattern in APC rates was observed, with a notable increase in 2018 and a significant decrease in 2020, particularly in Saudi Arabia. Monthly analysis revealed a consistent peak in search volume during October (Breast Cancer Awareness Month) each year. Weekly trends over a 20-year period indicated significant decreases in Australia, Canada, New Zealand, and the United States, while increases were noted in Ireland. Heatmap analysis further highlighted a consistent elevation in median search volume during October across all countries.

CONCLUSIONS

These findings underscore the impact of Breast Cancer Awareness Month and suggest potential influences of governmental COVID-19 pandemic control measures in 2020 on internet search behavior.

Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review

PURPOSE OF REVIEW

The purpose of this scoping review was to summarize literature regarding the use of user-generated digital data collected for non-epidemiological purposes in human immunodeficiency virus (HIV) research.

RECENT FINDINGS

Thirty-nine papers were included in the final review. Four types of digital data were used: social media data, web search queries, mobile phone data, and data from global positioning system (GPS) devices. With these data, four HIV epidemiological objectives were pursued, including disease surveillance, behavioral surveillance, assessment of public attention to HIV, and characterization of risk contexts. Approximately one-third used machine learning for classification, prediction, or topic modeling. Less than a quarter discussed the ethics of using user-generated data for epidemiological purposes. User-generated digital data can be used to monitor, predict, and contextualize HIV risk and can help disrupt trajectories of risk closer to onset. However, more attention needs to be paid to digital ethics and the direction of the field in a post-Application Programming Interface (API) world.

Impact of cancer awareness campaigns in Peru: a 5-year Google Trends analysis

Background

The aim of this research was to characterise the interest on the most frequent cancers in Peru through Google Trends, its geographic and temporal relationship with massive awareness campaigns.

Methods

A temporal trends analysis for the last 5 years was carried out, comparing the Relative Search Volume (RSV) with the dates of mass cancer awareness campaigns in Peru. Google Trends application was used to evaluate the interest in the topics: breast, prostate, cervical stomach and colorectal cancer between 1 January 2016 and 31 December 2020, expressed in RSV. The annual RSV for each neoplasm was compared, as well as its annual variation using the Kruskal-Wallis test. The correlation between the RSV and the estimated incidence for each province was measured using the Spearman test.

Results

The topics with the highest RSV were breast (median: 20, range: 6-100) and prostate cancer (median: 28, range: 9-48). The topic 'breast cancer' showed a cyclical punctual increase in October, its awareness month. Searches for cervical, stomach and colorectal cancer were smaller and did not show peaks of interest. It was observed that the RSV was variable when compared with previous years (p < 0.05 for all the evaluated topics). Geographically, different provincial configurations of interest were observed according to neoplasia. When correlating the RSV with the incidence by province, a non-significant positive correlation (p > 0.05) was found for breast, cervical and colorectal cancer.

Conclusions

This study suggests a positive temporal correlation between RSV and awareness cancer campaigns in Peru specially to breast cancer and, to a lesser extent, prostate cancer. Significant variations of interest were demonstrated for each neoplasm among the evaluated years. No significant correlation was found between the incidence rate and the average RSV among Peruvian provinces.

Impact of the Food and Drug Administration enforcement policy on flavored e-cigarettes on the online popularity of disposable e-cigarettes: analyses of Google search query data

BACKGROUND

The impact of the U.S. Food and Drug Administration's (FDA) initial enforcement policy on flavored cartridge-based e-cigarettes and subsequent notice for the removal of flavored disposable electronic cigarettes (e-cigarettes) such as Puff Bar from the market has not been well evaluated. We, therefore, sought to examine the impact of the e-cigarette flavor-related policy changes on the online popularity of Puff Bar, a prototypic disposable e-cigarette.

METHODS

We tabulated the total weekly Google search queries originating from the U.S. for "Puff Bar" and "Puff Bars" from January 1, 2019, to December 31, 2021. We divided the three years into four (4) distinct periods using the dates of the initial announcement to ban non-tobacco flavored e-cigarettes (September 11, 2019), the finalization of the FDA enforcement policy on cartridge-based flavors (January 2, 2020), and the notice for the market withdrawal of flavored disposable e-cigarettes (July 20, 2020) as reference time points. Then, we used piecewise linear regression and autoregressive integrated moving average (ARIMA) to compare the trends in searches for Puff Bar for the four (4) periods.

RESULTS

Before the initial announcement to ban non-tobacco flavored e-cigarettes, online search queries (per 10 million Google searches) for Puff Bar were slowly rising at a rate of 0.58 queries per week (95%CI: -0.80 - 1.97). Following the announcement, searches for Puff Bar increased significantly at a rate of 16.61 queries per week (95%CI: 12.13 - 21.10). The rate of searches for Puff Bar then increased exponentially at 40.08 queries per week (95%CI: 27.32 - 52.84) following the FDA flavor ban, which excluded disposable e-cigarettes. Then, the rate of increase declined but remained relatively stable at 3.67 queries per week (95%CI: 0.69-6.65) until the FDA's notice to remove flavored Puff Bar products from the market. Following this notice, the rate of searches for Puff Bar significantly declined (rate: -4.97 queries per week; 95%CI: -5.40--4.54).

CONCLUSIONS

The tracking of online search data demonstrates rapid public recognition of the FDA's announcements of tobacco regulatory actions.

Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis

BACKGROUND

Most common viral skin infections are not reportable conditions. Studying the population dynamics of these viral epidemics using traditional field methods is costly and time-consuming, especially over wide geographical areas.

OBJECTIVE

This study aimed to explore the evolution, seasonality, and distribution of vaccinable and nonvaccinable viral skin infections through an analysis of Google Trends.

METHODS

Worldwide search trends from January 2004 through May 2021 for viral skin infections were extracted from Google Trends, quantified, and analyzed.

RESULTS

Time series decomposition showed that the total search term volume for warts; zoster; roseola; measles; hand, foot, and mouth disease (HFMD); varicella; and rubella increased worldwide over the study period, whereas the interest for Pityriasis rosea and herpes simplex decreased. Internet searches for HFMD, varicella, and measles exhibited the highest seasonal patterns. The interest for measles and rubella was more pronounced in African countries, whereas the interest for HFMD and roseola was more pronounced in East Asia.

CONCLUSIONS

Harnessing data generated by web searches may increase the efficacy of traditional surveillance systems and strengthens the suspicion that the incidence of some vaccinable viral skin infections such as varicella, measles, and rubella may be globally increasing, whereas the incidence of common nonvaccinable skin infections remains stable.

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China

Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of “high in the east and low in the west.” Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.

Confounding Effect of Undergraduate Semester-Driven "Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration

Background

Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year-driven internet search patterns of health care students.

Objective

This study aimed to (1) demonstrate that artificial "academic cycling" of Google Trends' search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples.

Methods

We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook.

Results

Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term ["Staphylococcus"], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) ["Aeromonas" + "Plesiomonas"] (nosocomial infections that were searched for more frequently during the summer), (2) ["Ehrlichia"] (a tick-borne pathogen that was searched for more frequently during late spring), (3) ["Moraxella"] and ["Haemophilus"] (respiratory infections that were searched for more frequently during late winter), (4) ["Legionella"] (searched for more frequently during midsummer), and (5) ["Vibrio"] (which spiked for 2 months during midsummer). The terms ["myocardial infarction"] and ["hypertension"] lacked any obvious seasonal cycling after filtering, whereas ["depression"] maintained an annual cycling pattern.

Conclusions

Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends' internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present.

Systematic Review on Information Technology Approaches to Evaluate the Impact of Public Health Campaigns: Real Cases and Possible Directions

Evaluating the success of a public health campaign is critical. It helps policy makers to improve prevention strategies and close existing gaps. For instance, Brazil's “Syphilis No!” campaign reached many people, but how do we analyze its real impact on population awareness? Are epidemiologic variables sufficient? This study examined literature on using of information technology approaches to analyze the impact of public health campaigns. We began the systematic review with 276 papers and narrowed it down to 17, which analyzed campaigns. In addition to epidemiological variables, other types of variables of interest included: level of (i) access to the campaign website, (ii) subject knowledge and awareness, based on questionnaires, (iii) target population's interest, measured from both online search engine and engagement with Social Network Service, and (iv) campaign exposure through advertising, using data from television commercials. Furthermore, we evaluated the impact by considering several dimensions such as: communication, epidemiology, and policy enforcement. Our findings provide researchers with an overview of various dimensions, and variables-of-interest, for measuring public campaign impact, and examples of how and which campaigns have used them.

Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends

Background

AIDS, caused by HIV, is a leading cause of mortality in Africa. HIV/AIDS is among the greatest public health challenges confronting health authorities, with South Africa having the greatest prevalence of the disease in the world. There is little research into how Africans meet their health information needs on HIV/AIDS online, and this research gap impacts programming and educational responses to the HIV/AIDS pandemic.

Objective

This paper reports on how, in general, interest in the search terms “HIV” and “AIDS” mirrors the increase in people living with HIV and the decline in AIDS cases in South Africa.

Methods

Data on search trends for HIV and AIDS for South Africa were found using the search terms “HIV” and “AIDS” (categories: health, web search) on Google Trends. This was compared with data on estimated adults and children living with HIV, and AIDS-related deaths in South Africa, from the Joint United Nations Programme on HIV/AIDS, and also with search interest in the topics “HIV” and “AIDS” on Wikipedia Afrikaans, the most developed local language Wikipedia service in South Africa. Nonparametric statistical tests were conducted to support the trends and associations identified in the data.

Results

Google Trends shows a statistically significant decline (P<.001) in search interest for AIDS relative to HIV in South Africa. This trend mirrors progress on the ground in South Africa and is significantly associated (P<.001) with a decline in AIDS-related deaths and people living longer with HIV. This trend was also replicated on Wikipedia Afrikaans, where there was a greater interest in HIV than AIDS.

Conclusions

This statistically significant (P<.001) association between interest in the search terms “HIV” and “AIDS” in South Africa (2004-2019) and the number of people living with HIV and AIDS in the country (2004-2019) might be an indicator that multilateral efforts at combating HIV/AIDS—particularly through awareness raising and behavioral interventions in South Africa—are bearing fruit, and this is not only evident on the ground, but is also reflected in the online information seeking on the HIV/AIDS pandemic. We acknowledge the limitation that in studying the association between Google search interests on HIV/AIDS and cases/deaths, causal relationships should not be drawn due to the limitations of the data.

COVID-19 information received by the Peruvian population, during the first phase of the pandemic, and its association with developing psychological distress: Information about COVID-19 and distress in Peru

ABSTRACT

It is suspected that the information the population has about coronavirus disease 2019 (COVID-19) determines both its preventive measures and its effects on mental health. The internet and social media are the sources that have largely replaced the official and traditional channels of information. The objective of this study is to analyse the influence of the sources used by the population in Peru to obtain information on COVID-19 and its association with developing psychological distress (PD) and preventive measures against contagion.1699 questionnaires were analysed. A previously validated instrument adapted to Peru was used. Participants were questioned about the information received regarding COVID-19, its sources, time of exposition, assessment, or beliefs about it. Mental health was measured with the Goldberg General Health Questionnaire. Descriptive and bivariate analysis were performed, developing a classification and regression tree for PD based on beliefs and information about the pandemic.The most used source of information on COVID-19 in Peru was social media and this is associated with developing PD, both in the general population and among health professionals. The quality of the information about treatments for COVID-19 is associated with PD in the general population, whereas prognosis generates more distress among healthcare professionals. The biggest concern is transmitting the virus to family members, close persons, or patients, with more confidence in health professionals than in the health system.The health authorities should use the social media to transmit quality information about COVID-19 and, at the same time, to gather in real time the opinions on the implemented preventive measures. For all, this it is necessary to have higher credibility in the population to increase the confidence in the health system, looking at basic aspects for compliance with prevention measures and improvement of mental health.

Impact of a health campaign on Chinese public awareness of stroke: evidence from internet search data

INTRODUCTION

Health campaigns have the potential to improve public awareness, but their impact can be difficult to assess. Internet search data provide information concerning online health information-seeking behaviour in the population and may serve as a proxy for public awareness to evaluate health campaigns. This study aimed to measure the impact of World Stroke Day (WSD) in China using Baidu search data.

METHODS

Daily search index values (SIV) for the term 'stroke' were collected from January 2011 to December 2019 using the Baidu Index platform. We examined the mean difference in SIV between the 4 weeks surrounding WSD (period of interest) and the rest of the year (control period) for each year by t-test analysis. The mean difference between the period of interest and the control period was also calculated. The joinpoint regression model was used to analyse the trends of internet search activity 30 days before and after WSD for each year (2011-2019). Finally, the top and rising queries related to stroke during the week of the campaign in 2020 were summarised.

RESULTS

A significant mean increase in SIV of 418.5 (95% CI: 298.8 to 538.2) for the period of interest surrounding WSD was observed, 36.2% greater than the SIV during the control period (2011-2019). Short-term joinpoint analysis showed a significant increase in SIV 3 days before WSD, a peak on WSD and a decrease to the precampaign level 3 days after WSD. The rising related queries suggested that the public had increasing concerns about stroke warning signs, stroke prevention and stroke recovery during the campaign.

CONCLUSIONS

The WSD campaign increased internet search activity. These research techniques can be applied to evaluation of other health campaigns. Advancing understanding of public demand will enable tailoring of the campaign and strengthen health management.

Association of COVID-19 with lifestyle behaviours and socio-economic variables in Turkey: An analysis of Google Trends

This study aims to examine the relationship between COVID‐19 cases/deaths and Google data on lifestyle behaviours and socio‐economic variables in Turkey. The data of the research are composed of Google Trends search volume for various words related to socio‐economic conditions, nutritional attitudes, indoor behaviour, outdoor activities and confirmed COVID‐19 case and death data from the Ministry of Health from 31 December 2019 to 31 January 2021. Spearman correlation analysis was conducted to evaluate the relationship between the Google search volumes of selected keywords and COVID‐19 case and deaths. In addition, repeated ANOVA and Bonferroni post‐hoc tests were performed to compare the differences in search volumes of selected keywords before and during the COVID‐19 outbreak. Correlation analysis showed that the strongest variables in each category were vitamin C, zinc, Zoom, online shopping, hotel, market, gym, unemployment and unemployment benefit. Compared to previous years, during the pandemic, there was a significant increase or decrease in the search volumes of almost all words. These results showed that the COVID‐19 significantly changed people's online interests regarding lifestyle behaviours and socio‐economic conditions. It is thought that the findings can guide health policies to be followed in reducing the effects of both behavioural changes and negative socio‐economic consequences.

Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis

Background

Common cutaneous infestations and arthropod bites are not reportable conditions in most countries. Their worldwide epidemiologic evolution and distribution are mostly unknown.

Objective

To explore the evolution and geographic distribution of common cutaneous infestations and arthropod bites through an analysis of Google Trends.

Methods

Search trends from 2004 through March 2021 for common cutaneous infestations and arthropod bites were extracted from Google Trends, quantified, and analyzed.

Results

Time series decomposition showed that total search term volume for pubic lice decreased worldwide over the study period, while the interest for ticks, pediculosis, insect bites, scabies, lice, and bed bugs increased (in increasing order). The interest for bed bugs was more pronounced in the former Union of Soviet Socialist Republics countries, interest for lice in Near East and Middle East countries, and interest for pubic lice in South American countries. Internet searches for bed bugs, insect bites, and ticks exhibited the highest seasonal patterns.

Limitations

Retrospective analysis limits interpretation.

Conclusion

Surveillance systems based on Google Trends may enhance the timeliness of traditional surveillance systems and suggest that, while most cutaneous infestations increase worldwide, pubic lice may be globally declining.

Using Infodemiology Metrics to Assess Public Interest in Liver Transplantation: Google Trends Analysis

Background

Liver transplantation (LT) is the only curative treatment for end-stage liver disease. Less than 10% of global transplantation needs are met worldwide, and the need for LT is still increasing. The death rates on the waiting list remain too high.

Objective

It is, therefore, critical to raise awareness among the public and health care providers and in turn increasingly acquire donors.

Methods

We performed a Google Trends search using the search terms liver transplantation and liver transplant on October 15, 2020. On the basis of the resulting monthly data, the annual average Google Trends indices were calculated for the years 2004 to 2018. We not only investigated the trend worldwide but also used data from the United Network for Organ Sharing (UNOS), Spain, and Eurotransplant. Using pairwise Spearman correlations, Google Trends indices were examined over time and compared with the total number of liver transplants retrieved from the respective official websites of UNOS, the Organización Nacional de Trasplantes, and Eurotransplant.

Results

From 2004 to 2018, there was a significant decrease in the worldwide Google Trends index from 78.2 in 2004 to 20.5 in 2018 (–71.2%). This trend was more evident in UNOS than in the Eurotransplant group. In the same period, the number of transplanted livers increased worldwide. The waiting list mortality rate was 31% for Eurotransplant and 29% for UNOS. However, in Spain, where there are excellent awareness programs, the Google Trends index remained stable over the years with comparable, increasing LT numbers but a significantly lower waiting list mortality (15%).

Conclusions

Public awareness in LT has decreased significantly over the past two decades. Therefore, novel awareness programs should be initialized.

Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India

OBJECTIVE

Digital surveillance has shown mixed results as a supplement to traditional surveillance. Google Trends™ (GT) (Google, Mountain View, CA, United States) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.

METHODS

Data was obtained on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behavior in India between January 1 and May 31, 2020 in the form of relative search volume (RSV). We also used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.

RESULTS

GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms "COVID 19," "COVID," "social distancing," "soap," and "lockdown" at the national level. In 5 high-burden states, high correlation was observed for these 5 terms along with "Corona." Peaks in RSV, both at the national level and in high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.

CONCLUSION

The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage-induced curiosity, or health-seeking curiosity.

Google search behavior for meningitis and its vaccines: an infodemiological study

Background

The internet has made significant contributions towards health education. Analyzing the pattern of online behavior regarding meningitis and vaccinations may be worthwhile. It is hypothesized that the online search patterns in meningitis are correlated with its number of cases and the search patterns of its related vaccines.

Methods

This was an infodemiological study that determined the relationship among online search interest in meningitis, its worldwide number of cases and its associated vaccines. Using Google Trends™ Search Volume Indices (SVIs), we evaluated the search queries “meningitis,” “pneumococcal vaccine,” “BCG vaccine,” “meningococcal vaccine” and “influenza vaccine” in January 2021, covering January 2008 to December 2020. Spearman rank correlation was used to determine correlations between these queries.

Results

The worldwide search interest in meningitis from 2008 to 2020 showed an average SVI of 46 ± 8.8. The most searched topics were symptoms, vaccines, and infectious agents with SVIs of 100, 52, and 39, respectively. The top three countries with the highest search interest were Ghana, Kazakhstan, and Kenya. There were weak, but statistically significant correlations between meningitis and the BCG (ρ = 0.369, p < 0.001) and meningococcal (ρ = 0.183, p < 0.05) vaccines. There were no statistically significant associations between the number of cases, influenza vaccine, and pneumococcal vaccine.

Conclusion

The relationships among the Google SVIs for meningitis and its related vaccines and number of cases data were inconsistent and remained unclear. Future infodemiological studies may expand their scopes to social media, semantics, and big data for more robust conclusions.

Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers

BACKGROUND

In the face of the COVID-19 pandemic, the German government and the 16 German federal states implemented a variety of nonpharmaceutical interventions (NPIs) to decelerate the spread of the SARS-CoV-2 virus and thus prevent a collapse of the health care system. These measures comprised, among others, social distancing, the temporary closure of shops and schools, and a ban of large public gatherings and meetings with people not living in the same household.

OBJECTIVE

It is fair to assume that the issued NPIs have heavily affected social life and psychological functioning. We therefore aimed to examine possible effects of this lockdown in conjunction with daily new infections and the state of the national economy on people's interests, motives, and other psychological states.

METHODS

We derived 249 keywords from the Google Trends database, tapping into 27 empirically and rationally selected psychological domains. To overcome issues with reliability and specificity of individual indicator variables, broad factors were derived by means of time series factor analysis. All domains were subjected to a change point analysis and time series regression analysis with infection rates, NPIs, and the state of the economy as predictors. All keywords and analyses were preregistered prior to analysis.

RESULTS

With the pandemic arriving in Germany, significant increases in people's search interests were observed in virtually all domains. Although most of the changes were short-lasting, each had a distinguishable onset during the lockdown period. Regression analysis of the Google Trends data confirmed pronounced autoregressive effects for the investigated variables, while forecasting by means of the tested predictors (ie, daily new infections, NPIs, and the state of economy) was moderate at best.

CONCLUSIONS

Our findings indicate that people's interests, motives, and psychological states are heavily affected in times of crisis and lockdown. Specifically, disease- and virus-related domains (eg, pandemic disease, symptoms) peaked early, whereas personal health strategies (eg, masks, homeschooling) peaked later during the lockdown. Domains addressing social life and psychosocial functioning showed long-term increases in public interest. Renovation was the only domain to show a decrease in search interest with the onset of the lockdown. As changes in search behavior are consistent over multiple domains, a Google Trends analysis may provide information for policy makers on how to adapt and develop intervention, information, and prevention strategies, especially when NPIs are in effect.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data

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.

When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018

BACKGROUND

Social media has substantially changed how people confront health issues. However, a comprehensive understanding of how social media has altered the foci and methods in public health research remains lacking.

OBJECTIVE

This study aims to examine research themes, the role of social media, and research methods in social media-based public health research published from 2000 to 2018.

METHODS

A dataset of 3419 valid studies was developed by searching a list of relevant keywords in the Web of Science and PubMed databases. In addition, this study employs an unsupervised text-mining technique and topic modeling to extract research themes of the published studies. Moreover, the role of social media and research methods adopted in those studies were analyzed.

RESULTS

This study identifies 25 research themes, covering different diseases, various population groups, physical and mental health, and other significant issues. Social media assumes two major roles in public health research: produce substantial research interest for public health research and furnish a research context for public health research. Social media provides substantial research interest for public health research when used for health intervention, human-computer interaction, as a platform of social influence, and for disease surveillance, risk assessment, or prevention. Social media acts as a research context for public health research when it is mere reference, used as a platform to recruit participants, and as a platform for data collection. While both qualitative and quantitative methods are frequently used in this emerging area, cutting edge computational methods play a marginal role.

CONCLUSIONS

Social media enables scholars to study new phenomena and propose new research questions in public health research. Meanwhile, the methodological potential of social media in public health research needs to be further explored.

General Public's Information-Seeking Patterns of Topics Related to Obesity: Google Trends Analysis

BACKGROUND

Obesity is a major public health challenge, and recent literature sheds light on the concept of "normalization" of obesity.

OBJECTIVE

We aimed to study the worldwide pattern of web-based information seeking by public on obesity and on its related terms and topics using Google Trends.

METHODS

We compared the relative frequency of obesity-related search terms and topics between 2004 and 2019 on Google Trends. The mean relative interest scores for these terms over the 4-year quartiles were compared.

RESULTS

The mean relative interest score of the search term "obesity" consistently decreased with time in all four quartiles (2004-2019), whereas the relative interest scores of the search topics "weight loss" and "abdominal obesity" increased. The topic "weight loss" was popular during the month of January, and its median relative interest score for January was higher than that for other months for the entire study period (P<.001). The relative interest score for the search term "obese" decreased over time, whereas those scores for the terms "body positivity" and "self-love" increased after 2013.

CONCLUSIONS

Despite a worldwide increase in the prevalence of obesity, its popularity as an internet search term diminished over time. The reason for peaks in months should be explored and applied to the awareness campaigns for better effectiveness. These patterns suggest normalization of obesity in society and a rise of public curiosity about image-related obesity rather than its medical implications and harm.

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.

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.

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

Background

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

Objective

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

Methods

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Results

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

Conclusions

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

The Google search engine answers many health and medical information queries every day. People have become used to searching for this type of information. This paper presents a study which examined the visibility of health and medical information websites. The purpose of this study was to find out why Google is decreasing the visibility of such websites and how to measure this decrease. Since August 2018, Google has been more rigorously rating these websites, since they can potentially impact people’s health. The method of the study was to collect data about the visibility of health and medical information websites in sequential time snapshots. Visibility consists of combined data of unique keywords, positions, and URL results. The sample under study was made up of 21 websites selected from 10 European countries. The findings reveal that in sequential time snapshots, search visibility decreased. The decrease was not dependent on the country or the language. The main reason why Google is decreasing the visibility of such websites is that they do not meet high ranking criteria.

Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study

Background

As the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines.

Objective

The aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States.

Methods

For each of the target variables (diabetes, obesity, and exercise), prevalence rates were collected. After a rigorous keyword selection process, data from Google Trends were collected. These data were denormalized to form spatio-temporal indices. L1-regularized regression models were trained to predict prevalence rates from denormalized Google Trends indices. Models were tested on a held-out set and compared against baselines from the literature as well as a trivial last year equals this year baseline. A similar analysis was done using a multivariate spatio-temporal model where the previous year’s prevalence was included as a covariate. This model was modified to create a time-lagged regression analysis framework. Finally, a hierarchical time-lagged multivariate spatio-temporal model was created to account for subnational trends in the data. The model trained on US data was, then, applied in a transfer learning framework to Canada.

Results

In the US context, our proposed models beat the performances of the prior work, as well as the trivial baselines. In terms of the mean absolute error (MAE), the best of our proposed models yields 24% improvement (0.72-0.55; P<.001) for diabetes; 18% improvement (1.20-0.99; P=.001) for obesity, and 34% improvement (2.89-1.95; P<.001) for exercise. Our proposed across-country transfer learning framework also shows promising results with an average Spearman and Pearson correlation of 0.70 for diabetes and 0.90 and 0.91 for obesity, respectively.

Conclusions

Although our proposed models beat the baselines, we find the modeling of lifestyle diseases to be a challenging problem, one that requires an abundance of data as well as creative modeling strategies. In doing so, this study shows a low-to-moderate validity of Google Trends in the context of lifestyle disease surveillance, even when applying novel corrective approaches, including a proposed denormalization scheme. We envision qualitative analyses to be a more practical use of Google Trends in the context of lifestyle disease surveillance. For the quantitative analyses, the highest utility of using Google Trends is in the context of transfer learning where low-resource countries could benefit from high-resource countries by using proxy models.

Big Data, Computational Social Science, and Health Communication: A Review and Agenda for Advancing Theory

Contemporary research on health communication has been marked by the presence of big data and computational social science (CSS) techniques. The relative novelty of these approaches makes it worthwhile to consider their status and potential for advancing health communication scholarship. This essay offers an introduction focusing on how big data and CSS techniques are being employed to study health communication and their utility for theory development. Key trends in this body of research are summarized, including the use of big data and CSS for examining public perceptions of health conditions or events, investigating network-related dimensions of health phenomena, and illness monitoring. The implications of big data and CSS for health communication theory are also evaluated. Opportunities presented by big data and CSS to help extend existing theories and build new communication theories are discussed.

Access to Primary Care and Internet Searches for Walk-In Clinics and Emergency Departments in Canada: Observational Study Using Google Trends and Population Health Survey Data

Background

Access to primary care is a challenge for many Canadians. Models of primary care vary widely among provinces, including arrangements for same-day and after-hours access. Use of walk-in clinics and emergency departments (EDs) may also vary, but data sources that allow comparison are limited.

Objective

We used Google Trends to examine the relative frequency of searches for walk-in clinics and EDs across provinces and over time in Canada. We correlated provincial relative search frequencies from Google Trends with survey responses about primary care access from the Commonwealth Fund’s 2016 International Health Policy Survey of Adults in 11 Countries and the 2016 Canadian Community Health Survey.

Methods

We developed search strategies to capture the range of terms used for walk-in clinics (eg, urgent care clinic and after-hours clinic) and EDs (eg, emergency room) across Canadian provinces. We used Google Trends to determine the frequencies of these terms relative to total search volume within each province from January 2011 to December 2018. We calculated correlation coefficients and 95% CIs between provincial Google Trends relative search frequencies and survey responses.

Results

Relative search frequency of walk-in clinic searches increased steadily, doubling in most provinces between 2011 and 2018. Relative frequency of walk-in clinic searches was highest in the western provinces of British Columbia, Alberta, Saskatchewan, and Manitoba. At the provincial level, higher walk-in clinic relative search frequency was strongly positively correlated with the percentage of survey respondents who reported being able to get same- or next-day appointments to see a doctor or a nurse and inversely correlated with the percentage of respondents who reported going to ED for a condition that they thought could have been treated by providers at usual place of care. Relative search frequency for walk-in clinics was also inversely correlated with the percentage of respondents who reported having a regular medical provider. ED relative search frequencies were more stable over time, and we did not observe statistically significant correlation with survey data.

Conclusions

Higher relative search frequency for walk-in clinics was positively correlated with the ability to get a same- or next-day appointment and inversely correlated with ED use for conditions treatable in the patient’s regular place of care and also with having a regular medical provider. Findings suggest that patient use of Web-based tools to search for more convenient or accessible care through walk-in clinics is increasing over time. Further research is needed to validate Google Trends data with administrative information on service use.

Developing an early warning system of suicide using Google Trends and media reporting

BACKGROUND

Conventional surveillance systems for suicides typically suffer from a substantial time lag of six months to two years. This study aims to develop an early warning system of possible suicide outbreaks in Hong Kong using Google Trends and suicide-related media reporting.

METHODS

Data on 3,534 suicides from 2011 to 2015 were obtained from Hong Kong Census and Statistics Department, and the Coroner's Court. Using data from Google Trends and features extracted from media reporting on suicide news, we fitted Poisson regression models to predict the number and estimate the intensity of suicides on a weekly basis, for six subgroups, defined by gender and age. We adopted the cumulative sum (CUSUM) control chart-based method to identify outbreaks of suicide.

RESULTS

The proposed model was able to predict the number of suicides with reasonably low normalized root mean squared errors, ranging from 15.6% for young females to 24.16% for old females. The suicide intensity curves were well captured by the proposed models for young males and females, but not for other groups. The Sensitivity, Precision and F1 Score of the CUSUM-based method were 50%, 100% and 67% for young females, and 93%, 54% and 68% for young males.

LIMITATIONS

This study focused only on predicting the number of suicides in the current week, not in the future weeks. The model did not include social media, socioeconomic and climate data.

CONCLUSIONS

Our results indicate that Google Trends search terms and media reporting data may be valuable data sources for predicting possible outbreak of suicides in Hong Kong. The proposed system could support effective and targeted interventions.

Outlining Stroke Infodemiology

e-Health is becoming increasingly relevant in stroke awareness campaigns and clinical management. In our statement, we focus on a basic strategy to trace the global information flow generated on the internet about stroke, and how to better understand the interest surrounding stroke in the community as well as the effects of warning campaigns.

Influence of Climate on Google Internet Searches for Pruritus Across 16 German Cities: Retrospective Analysis

BACKGROUND

The burden of pruritus is high, especially among patients with dermatologic diseases. Identifying trends in pruritus burden and people's medical needs is challenging, since not all affected people consult a physician.

OBJECTIVE

The purpose of this study was to investigate pruritus search behavior trends in Germany and identify associations with weather factors.

METHODS

Google AdWords Keyword Planner was used to quantify pruritus-related search queries in 16 German cities from August 2014 to July 2018. All identified keywords were qualitatively categorized and pruritus-related terms were descriptively analyzed. The number of search queries per 100,000 inhabitants of each city was compared to environmental factors such as temperature, humidity, particulate matter 10 micrometers or less in diameter (PM10), and sunshine duration to investigate potential correlations.

RESULTS

We included 1150 pruritus-related keywords, which resulted in 2,851,290 queries. "Pruritus" (n=115,680) and "anal pruritus" (n=102,390) were the most-searched-for keywords. Nearly half of all queries were related to the category localization, with Berlin and Munich having a comparatively high proportion of people that searched for pruritus in the genital and anal areas. People searched more frequently for information on chronic compared to acute pruritus. The most populated cities had the lowest number of queries per 100,000 inhabitants (Berlin, n=13,641; Hamburg, n=18,303; and Munich, n=21,363), while smaller cities (Kiel, n=35,027; and Freiburg, n=39,501) had the highest. Temperature had a greater effect on search query number (beta -7.94, 95% CI -10.74 to -5.15) than did PM10 (beta -5.13, 95% CI -7.04 to -3.22), humidity (beta 4.73, 95% CI 2.70 to 6.75), or sunshine duration (beta 0.66, 95% CI 0.36 to 0.97). The highest relative number of search queries occurred during the winter (ie, December to February).

CONCLUSIONS

By taking into account the study results, Google data analysis helps to examine people's search frequency, behavior, and interest across cities and regions. The results indicated a general increase in search queries during the winter as well as differences across cities located in the same region; for example, there was a decline in search volume in Saarbrucken, while there were increases in Cologne, Frankfurt, and Dortmund. In addition, the detected correlation between search volume and weather data seems to be valuable in predicting an increase in pruritus burden, since a significant association with rising humidity and sunshine duration, as well as declining temperature and PM10, was found. Accordingly, this is an unconventional and inexpensive method to identify search behavior trends and respective inhabitants' needs.

Artificial intelligence (AI) and cancer prevention: the potential application of AI in cancer control programming needs to be explored in population laboratories such as COMPASS

Understanding the risk factors that initiate cancer is essential for reducing the future cancer burden. Much of our current cancer control insight is from cohort studies and newer large-scale population laboratories designed to advance the science around precision oncology. Despite their promise for improving diagnosis and treatment outcomes, their current reductionist focus will likely have little impact shifting the cancer burden. However, it is possible that these big data assets can be adapted to have more impact on the future cancer burden through more focus on primary prevention efforts that incorporate artificial intelligence (AI) and machine learning (ML). ML automatically learns patterns and can devise complex models and algorithms that lend themselves to prediction in big data, revealing new unexpected relationships and pathways in a reliable and replicable fashion that otherwise would remain hidden given the complexities of big data. While AI has made big strides in several domains, the potential application in cancer prevention is lacking. As such, this commentary suggests that it may be time to consider the potential of AI within our existing cancer control population laboratories, and provides justification for why some small targeted investments to explore their impact on modelling existing real-time cancer prevention data may be a strategic cancer control opportunity.

Association Between Cancer Incidence and Mortality in Web-Based Data in China: Infodemiology Study

Background

Cancer poses a serious threat to the health of Chinese people, resulting in a major challenge for public health work. Today, people can obtain relevant information from not only medical workers in hospitals, but also the internet in any place in real-time. Search behaviors can reflect a population’s awareness of cancer from a completely new perspective, which could be driven by the underlying cancer epidemiology. However, such Web-retrieved data are not yet well validated or understood.

Objective

This study aimed to explore whether a correlation exists between the incidence and mortality of cancers and normalized internet search volumes on the big data platform, Baidu. We also assessed whether the distribution of people who searched for specific types of cancer differed by gender. Finally, we determined whether there were regional disparities among people who searched the Web for cancer-related information.

Methods

Standard Boolean operators were used to choose search terms for each type of cancer. Spearman’s correlation analysis was used to explore correlations among monthly search index values for each cancer type and their monthly incidence and mortality rates. We conducted cointegration analysis between search index data and incidence rates to examine whether a stable equilibrium existed between them. We also conducted cointegration analysis between search index data and mortality data.

Results

The monthly Baidu index was significantly correlated with cancer incidence rates for 26 of 28 cancers in China (lung cancer: r=.80, P<.001; liver cancer: r=.28, P=.016; stomach cancer: r=.50, P<.001; esophageal cancer: r=.50, P<.001; colorectal cancer: r=.81, P<.001; pancreatic cancer: r=.86, P<.001; breast cancer: r=.56, P<.001; brain and nervous system cancer: r=.63, P<.001; leukemia: r=.75, P<.001; Non-Hodgkin lymphoma: r=.88, P<.001; Hodgkin lymphoma: r=.91, P<.001; cervical cancer: r=.64, P<.001; prostate cancer: r=.67, P<.001; bladder cancer: r=.62, P<.001; gallbladder and biliary tract cancer: r=.88, P<.001; lip and oral cavity cancer: r=.88, P<.001; ovarian cancer: r=.58, P<.001; larynx cancer: r=.82, P<.001; kidney cancer: r=.73, P<.001; squamous cell carcinoma: r=.94, P<.001; multiple myeloma: r=.84, P<.001; thyroid cancer: r=.77, P<.001; malignant skin melanoma: r=.55, P<.001; mesothelioma: r=.79, P<.001; testicular cancer: r=.57, P<.001; basal cell carcinoma: r=.83, P<.001). The monthly Baidu index was significantly correlated with cancer mortality rates for 24 of 27 cancers. In terms of the whole population, the number of women who searched for cancer-related information has slowly risen over time. People aged 30-39 years were most likely to use search engines to retrieve cancer-related knowledge. East China had the highest Web search volumes for cancer.

Conclusions

Search behaviors indeed reflect public awareness of cancer from a different angle. Research on internet search behaviors could present an innovative and timely way to monitor and estimate cancer incidence and mortality rates, especially for cancers not included in national registries.

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Background

In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life.

Objective

This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject.

Methods

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling.

Results

All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data.

Conclusions

The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.

Using internet search data to predict new HIV diagnoses in China: a modelling study

OBJECTIVES

Internet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.

DESIGN

We identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).

RESULTS

Search query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.

CONCLUSIONS

Baidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention.

Monitoring public interest toward pertussis outbreaks: an extensive Google Trends-based analysis

OBJECTIVES

Pertussis is a vaccine-preventable disease. Despite this, it remains a major health problem among children in developing countries and in recent years, has re-emerged and has led to considerable outbreaks. Pertussis surveillance is of paramount importance; however, classical monitoring approaches are plagued by some shortcomings, such as considerable time delay and potential underestimation/underreporting of cases.

STUDY DESIGN

This study aims at investigating the possibility of using Google Trends (GT) as an instrument for tracking pertussis outbreaks to see if infodemiology and infoveillance approaches could overcome the previously mentioned issues because they are based on real-time monitoring and tracking of web-related activities.

METHODS

In the present study, GT was mined from inception (01 January 2004) to 31 December 2015 in the different European countries. Pertussis was searched using the 'search topic' strategy. Pertussis-related GT figures were correlated with the number of pertussis cases and deaths retrieved from the European Centre for Disease prevention and Control database.

RESULTS

At the European countries level, correlation between pertussis cases and GT-based search volumes was very large (ranging from 0.94 to 0.97) from 2004 to 2015. When examining each country, however, only a few reached the threshold of statistical significance.

CONCLUSIONS

GT could be particularly useful in pertussis surveillance and control, provided that the algorithm is better adjusted and refined at the country level.

Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data

BACKGROUND

Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals' overall mood state in relation to depression-like symptoms.

OBJECTIVE

The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking.

METHODS

Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day.

RESULTS

Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term "Depression," 33.5 pp for "Masennustesti," 30.6 pp for "Masennus," 12.8 pp for "Depression test," 12.0 pp for "Masennus testi," and 11.8 pp for "Masennus oireet." The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms "Masennus oireet" (P<.001), "Masennus" (P=.001), "Depression" (P=.005), and "Depression test" (P=.004). Higher search volumes for the terms "Masennus" (P=.14), "Masennustesti" (P=.07), and "Depression test" (P=.10) were present between the second and third time blocks.

CONCLUSIONS

Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes.

Capturing public interest toward new tools for controlling human immunodeficiency virus (HIV) infection exploiting data from Google Trends

Human immunodeficiency virus vaccination and pre-exposure prophylaxis represent two different emerging preventive tools. Google Trends was used to assess the public interest toward these tools in terms of digital activities. Worldwide web searches concerning the human immunodeficiency virus vaccine represented 0.34 percent, 0.03 percent, and 46.97 percent of human immunodeficiency virus, acquired immune deficiency syndrome, and human immunodeficiency virus/acquired immune deficiency syndrome treatment-related Google Trends queries, respectively. Concerning temporal trends, digital activities were shown to increase from 0 percent as of 1 January 2004 percent to 46 percent as of 8 October 2017 with two spikes observed in May and July 2012, coinciding with the US Food and Drug Administration approval. Bursts in search number and volume were recorded as human immunodeficiency virus vaccine trials emerged. This search topic has decreased in the past decade in parallel to the increase in Truvada-related topics. Concentrated searches were noticed among African countries with high human immunodeficiency virus/acquired immune deficiency syndrome prevalence. Stakeholders should take advantage of public interest especially in preventive medicine in high disease burden countries.

What, where and when? Using Google Trends and Google to investigate patient needs and inform pharmacy practice

Objectives

The aim was to provide a comprehensive overview (using pertinent examples) of the various ways that Google Trends and Google data could inform pharmacy practice. The objectives were to: examine what type of information people search for in relation to a common class of medicines; ascertain where people are directed to (websites) following an initial search for a medicine or medical condition; and establish information about when they search.

Methods

The methodology differed depending on whether Google Trends or Google was being interrogated, but the search domain was always limited to the United Kingdom. Google Trends was queried, typically for a 5-year time frame, and data downloaded for many search inputs relating to medical conditions (self-treatable and non-self-treatable) and medicines (bought over-the-counter and prescribed). Google was queried and data collected for searches related to ‘antibiotics’.

Key findings

Google Trends revealed a previously unknown seasonality pattern for irritable bowel syndrome. Related searches for ‘antibiotics’ revealed a high level of interest in the appropriateness of concomitant alcohol consumption and queries about what antibiotics are. Largely, people were being directed to reputable websites following their initial search input about a prescription-only medicine. However, searches for over-the-counter medicines were more likely to lead to commercial domains.

Conclusions

This is one of the first studies to investigate use of Google Trends and Google in a pharmacy-specific context. It is relevant for practice as it could inform marketing strategies, public health policy and help tailor patient advice and counselling.

Cancer and Social Media: A Comparison of Traffic about Breast Cancer, Prostate Cancer, and Other Reproductive Cancers on Twitter and Instagram

Social media are often heralded as offering cancer campaigns new opportunities to reach the public. However, these campaigns may not be equally successful, depending on the nature of the campaign itself, the type of cancer being addressed, and the social media platform being examined. This study is the first to compare social media activity on Twitter and Instagram across three time periods: #WorldCancerDay in February, the annual month-long campaigns of National Breast Cancer Awareness Month (NBCAM) in October and Movember in November, and during the full year outside of these campaigns. Our results suggest that women's reproductive cancers - especially breast cancer - tend to outperform men's reproductive cancer - especially prostate cancer - across campaigns and social media platforms. Twitter overall generates substantially more activity than Instagram for both cancer campaigns, suggesting Instagram may be an untapped resource. However, the messaging for both campaigns tends to focus on awareness and support rather than on concrete actions and behaviors. We suggest health communication efforts need to focus on effective messaging and building engaged communities for cancer communication across social media platforms.

A Human-Centered Platform for HIV Infection Reduction in New York: Development and Usage Analysis of the Ending the Epidemic (ETE) Dashboard

BACKGROUND

Dashboards have been increasingly used in clinic-based interventions, such as clinical performance improvement and monitoring risk of hospital readmissions, and are now gaining traction in population-based interventions, especially in disease assessment.

OBJECTIVE

We describe the design, development, and usage analysis of a geovisualization dashboard, the Ending the Epidemic (ETE) Dashboard. The ETE dashboard is a tool developed to track New York's progress towards achieving the goal of its ETE Initiative, to reduce new HIV infections from 3000 per year to 750 per year by the end of 2020.

METHODS

The ETE dashboard was adapted from an existing human-centered geovisualization platform, SanaViz, an Internet-enabled, interactive app incorporating principles of human-centered design and cognitive fit theory to enhance visual exploration of population health data. Usage evaluation of the ETE geovisualization dashboard was conducted using Google Analytics over a 4-week period from March 19 to April 18, 2016. The aim was to monitor user activity and analyze traffic on the ETE dashboard using evidence-based metrics that can provide adequate feedback to enhance its utilization. Usage was characterized based on three metrics: (1) number of unique visits to each page, (2) average time on each page in seconds, and (3) page bounce rate (ie, percentage of visits where user left the site immediately after viewing just a single page). Further analysis was also conducted by cross-tabulating specific usage metrics.

RESULTS

Of 860 sessions, 324 sessions were initiated by unique users (37.7%). The most common acquisition channels included direct source (353/860, 41.0%); followed by referral traffic (340/860, 39.5%) and organic search (134/860, 15.5%). Usage statistics indicate that for the 860 sessions initiated by both new and returning users, the average viewing time was 8 minutes, 51 seconds, and the bounce rate was 46%. These statistics reflect positive results given that prior literature estimates an average session duration of 10-20 seconds and a bounce rate of 40-60% for most websites. Key findings from our study showed that the highest bounce rates were seen for the "About" page of the ETE website (65%), which describes the ETE initiative. The lowest bounce rates were seen for the ETE blog page (17%), consistent with prior research on website usage metrics that indicate that blog pages are often the most frequently viewed pages on Web portals.

CONCLUSIONS

Our findings reveal the potential of Google Analytics as a tool to enhance user traffic and performance of the ETE geovisualization platform by using feedback from regular monitoring of key parameters including page bounce rates and average time on page. It also identifies the need for a follow-up usability assessment of the system.

News trends and web search query of HIV/AIDS in Hong Kong

BACKGROUND

The HIV epidemic in Hong Kong has worsened in recent years, with major contributions from high-risk subgroup of men who have sex with men (MSM). Internet use is prevalent among the majority of the local population, where they sought health information online. This study examines the impacts of HIV/AIDS and MSM news coverage on web search query in Hong Kong.

METHODS

Relevant news coverage about HIV/AIDS and MSM from January 1st, 2004 to December 31st, 2014 was obtained from the WiseNews databse. News trends were created by computing the number of relevant articles by type, topic, place of origin and sub-populations. We then obtained relevant search volumes from Google and analysed causality between news trends and Google Trends using Granger Causality test and orthogonal impulse function.

RESULTS

We found that editorial news has an impact on "HIV" Google searches on HIV, with the search term popularity peaking at an average of two weeks after the news are published. Similarly, editorial news has an impact on the frequency of "AIDS" searches two weeks after. MSM-related news trends have a more fluctuating impact on "MSM" Google searches, although the time lag varies anywhere from one week later to ten weeks later.

CONCLUSIONS

This infodemiological study shows that there is a positive impact of news trends on the online search behavior of HIV/AIDS or MSM-related issues for up to ten weeks after. Health promotional professionals could make use of this brief time window to tailor the timing of HIV awareness campaigns and public health interventions to maximise its reach and effectiveness.

Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts

BACKGROUND

Data concerning patients originates from a variety of sources on social media.

OBJECTIVE

The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients' perspectives on a given medical prescription.

METHODS

To shed light on patients' behavior and concerns, we focused on Crohn's disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn's disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen's kappa coefficient method.

RESULTS

The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen's kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts' opinion and the software tool.

CONCLUSIONS

We show how data posted on Facebook by Crohn's disease patients are a useful dataset to understand the patient's perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients' opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients' sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients' perspective on a specific medical therapy.

Using Google Trends Data to Study Public Interest in Breast Cancer Screening in Brazil: Why Not a Pink February?

Background

One of the major challenges of the Brazilian Ministry of Health is to foster interest in breast cancer screening (BCS), especially among women at high risk. Strategies have been developed to promote the early identification of breast cancer mainly by Pink October campaigns. The massive number of queries conducted through Google creates traffic data that can be analyzed to show unrevealed interest cycles and their seasonalities.

Objectives

Using Google Trends, we studied cycles of public interest in queries toward mammography and breast cancer along the last 5 years. We hypothesize that these data may be correlated with collective interest cycles leveraged by national BCS campaigns such as Pink October.

Methods

Google Trends was employed to normalize traffic data on a scale from 0 (<1% of the peak volume) to 100 (peak of traffic) presented as weekly relative search volume (RSV) concerning mammography and breast cancer as search terms. A time series covered the last 261 weeks (November 2011 to October 2016), and RSV of both terms were compared with their respective annual means. Polynomial trendlines (second order) were employed to estimate overall trends.

Results

We found an upward trend for both terms over the 5 years, with almost parallel trendlines. Remarkable peaks were found along Pink October months— mammography and breast cancer searches were leveraged up reaching, respectively, 119.1% (2016) and 196.8% (2015) above annual means. Short downward RSVs along December-January months were also noteworthy along all the studied period. These trends traced an N-shaped pattern with higher peaks in Pink October months and sharp falls along subsequent December and January.

Conclusions

Considering these findings, it would be reasonable to bring Pink October to the beginning of each year, thereby extending the beneficial effect of the campaigns. It would be more appropriate to start screening campaigns at the beginning of the year, when new resolutions are taken and new projects are added to everyday routines. Our work raises attention to the study of traffic data to encourage health campaign analysts to undertake better analysis based on marketing practices.

Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context

Background

In many Western countries, after a motor vehicle collision, those involved seek health care for the assessment of injuries and for insurance documentation purposes. In contrast, in many less wealthy countries, there may be limited access to care and no insurance or compensation system.

Objective

The purpose of this infodemiology study was to investigate the global pattern of evolving Internet usage in countries with and without insurance and the corresponding compensation systems for whiplash injury.

Methods

We used the Internet search engine analytics via Google Trends to study the health information-seeking behavior concerning whiplash injury at national population levels in Europe.

Results

We found that the search for “whiplash” is strikingly and consistently often associated with the search for “compensation” in countries or cultures with a tort system. Frequent or traumatic painful injuries; diseases or disorders such as arthritis, headache, radius, and hip fracture; depressive disorders; and fibromyalgia were not associated similarly with searches on “compensation.”

Conclusions

In this study, we present evidence from the evolving viewpoint of naturalistic Internet search engine analytics that the expectations for receiving compensation may influence Internet search behavior in relation to whiplash injury.

Using Web-Based Search Data to Study the Public's Reactions to Societal Events: The Case of the Sandy Hook Shooting

Background

Internet search is the most common activity on the World Wide Web and generates a vast amount of user-reported data regarding their information-seeking preferences and behavior. Although this data has been successfully used to examine outbreaks, health care utilization, and outcomes related to quality of care, its value in informing public health policy remains unclear.

Objective

The aim of this study was to evaluate the role of Internet search query data in health policy development. To do so, we studied the public’s reaction to a major societal event in the context of the 2012 Sandy Hook School shooting incident.

Methods

Query data from the Yahoo! search engine regarding firearm-related searches was analyzed to examine changes in user-selected search terms and subsequent websites visited for a period of 14 days before and after the shooting incident.

Results

A total of 5,653,588 firearm-related search queries were analyzed. In the after period, queries increased for search terms related to “guns” (+50.06%), “shooting incident” (+333.71%), “ammunition” (+155.14%), and “gun-related laws” (+535.47%). The highest increase (+1054.37%) in Web traffic was seen by news websites following “shooting incident” queries whereas searches for “guns” (+61.02%) and “ammunition” (+173.15%) resulted in notable increases in visits to retail websites. Firearm-related queries generally returned to baseline levels after approximately 10 days.

Conclusions

Search engine queries present a viable infodemiology metric on public reactions and subsequent behaviors to major societal events and could be used by policymakers to inform policy development.