Online Search Behavior Related to COVID-19 Vaccines: Infodemiology Study

Understanding caregivers' decision to vaccinate childhood cancer survivors against COVID-19

BACKGROUND

Vaccination against COVID-19 is recommended for childhood cancer survivors (CCS). This study aimed to identify antecedents contributing to caregivers' decisions to vaccinate CCS aged 5-17 years against COVID-19 by applying the Theory of Planned Behavior.

METHODS

Participants in this cross-sectional study completed an online survey assessing caregiver attitudes, subjective norms, perceived behavioral control, intention to vaccinate CCS, CCS vaccination status, COVID-19 health literacy, and frequency of COVID-19 information-seeking. Surveys were completed between May and June 2022 following approval for the emergency use of COVID-19 vaccines among children aged ≥5 years in the U.S. Data were analyzed using unadjusted linear regressions and structural equation modeling.

RESULTS

Participants were caregivers (n = 160, 87.5% biological mothers, 75.6% white/non-Hispanic) of CCS (n = 160, 44.4% female, mean (M) = 12.5 years old, M = 8.0 years off treatment). 70.0% (n = 112) of caregivers and 53.8% (n = 86) of CCS received a COVID-19 vaccine. Over one-third (37.5%) of caregivers reported disagreement or indecision about future COVID-19 vaccination for the CCS. Caregivers' intention (β = 0.962; standard error [S.E.] = 0.028; p < 0.001) was highly related to CCS vaccination status. Attitudes (β = 0.568; S.E. = 0.078; p < 0.001) and subjective norms (β = 0.322; S.E. = 0.062; p < 0.001) were associated with intention. Higher frequency of COVID-19 information-seeking (β = 0.313; S.E. = 0.063; p < 0.001) and COVID-19 health literacy (β = 0.234; S.E. = 0.059; p < 0.001) had a positive indirect effect on intention through attitudes and subjective norms.

CONCLUSIONS

Caregivers' vaccination intentions for minor CCS are highly related to vaccination behavior and shaped by attitudes, subjective norms, COVID-19 health literacy, and frequency of COVID-19 information-seeking. Promoting tailored communication with caregivers of CCS and encouraging them to review reputable sources of information can address their vaccine hesitancy.

Quality and readability of web-based information on dental caries in Arabic: an infodemiological study

BACKGROUND

Web-based information on dental caries in Arabic remains poorly understood. This study aimed to assess the quality and readability of web-based information about dental caries in Arabic.

METHODS

The first 100 websites in Arabic about dental caries were retrieved from Google and Bing using common terms. The websites were classified and evaluated for quality based on the Journal of the American Medical Association (JAMA) benchmark criteria, the DISCERN tool, and the presence of the Health on the Net Foundation Code of Conduct (HONcode). Readability was assessed using online readability indexes.

RESULTS

A total of 102 Arabic websites were included. The JAMA benchmark score was low (m = 0.36, SD = 0.56), with 67.7% failing to meet any of the JAMA criteria. The DISCERN total score mean was 37.68 (SD = 7.99), with a majority (67.65%) of moderate quality. None of the websites had the HONcode. Readability was generally good, with 52.94% of websites having a Flesch-Kincaid Grade Level (FKGL) < 7, 91.18% having a Simple Measure of Gobbledygook (SMOG) < 7, and 85.29% having a Flesch reading ease (FRE) score ≥ 80. There was a positive correlation between JAMA and DISCERN scores (p < 0.001). DISCERN scores were positively correlated with the number of words (p < 0.001) and sentences (p = 0.004) on the websites. However, JAMA or DISCERN scores were not correlated with FKGL, SMOG, or FRE scores (p > 0.05).

CONCLUSIONS

The quality of Arabic dental caries websites was found to be low, despite their readability. Efforts are needed to introduce more reliable sources for discussing dental caries and treatment options on sites aimed at Arabic populations.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

BACKGROUND

The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks.

OBJECTIVE

This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk.

METHODS

We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated.

RESULTS

Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores.

CONCLUSIONS

The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

The Impact of COVID-19 on Psychiatric Health in the Korean Population

Coronavirus disease 2019 (COVID-19) has multiple negative impacts on the psychiatric health of both those previously infected and not infected with severe acute respiratory syndrome coronavirus 2. Moreover, the negative impacts of COVID-19 are closely associated with geographical region, culture, medical system, and ethnic background. We summarized the evidence of the impact of COVID-19 on the psychiatric health of the Korean population. This narrative review included thirteen research articles, which investigated the impact of COVID-19 on the psychiatric health of Koreans. COVID-19 survivors were reported to have a 2.4 times greater risk of developing psychiatric disorders compared to members of a control group, and anxiety and stress-related disorders were the most common newly diagnosed psychiatric illnesses. Studies also reported that COVID-19 survivors had a 3.33-fold higher prevalence of insomnia, a 2.72-fold higher prevalence of mild cognitive impairment, and a 3.09-fold higher prevalence of dementia compared to the control group. In addition, more than four studies have highlighted that the medical staff members, including nurses and medical students, exhibit a greater negative psychiatric impact of COVID-19. However, none of the articles investigated the biological pathophysiology or mechanism linking COVID-19 and the risk of diverse psychiatric disorders. Moreover, none of the studies were actual prospective studies. Thus, longitudinal studies are needed to more clearly elucidate the effect of COVID-19 on the psychiatric health of the Korean population. Lastly, studies focusing on preventing and treating COVID-19-associated psychiatric problems are needed to provide a benefit in real clinical settings.

Transparent communication of evidence does not undermine public trust in evidence

Does clear and transparent communication of risks, benefits, and uncertainties increase or undermine public trust in scientific information that people use to guide their decision-making? We examined the impact of reframing messages written in traditional persuasive style to align instead with recent "evidence communication" principles, aiming to inform decision-making: communicating a balance of risks and benefits, disclosing uncertainties and evidence quality, and prebunking misperceptions. In two pre-registered experiments, UK participants read either a persuasive message or a balanced and informative message adhering to evidence communication recommendations about COVID-19 vaccines (Study 1) or nuclear power plants (Study 2). We find that balanced messages are either perceived as trustworthy as persuasive messages (Study 1), or more so (Study 2). However, we note a moderating role of prior beliefs such that balanced messages were consistently perceived as more trustworthy among those with negative or neutral prior beliefs about the message content. We furthermore note that participants who had read the persuasive message on nuclear power plants voiced significantly stronger support for nuclear power than those who had read the balanced message, despite rating the information as less trustworthy. There was no difference in vaccination intentions between groups reading the different vaccine messages.

Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study

Background

Coronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends.

Methods

HFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic “HFMD” from 2009 to 2021. Japanese tweets containing “HFMD” were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation.

Results

HFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic “HFMD” presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic.

Conclusions

The significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07790-9.

The Relationship between Searches for COVID-19 Vaccines and Dynamics of Vaccinated People in Poland: An Infodemiological Study

BACKGROUND

Google Trends has turned out to be an appropriate tool for evaluating correlations and prognostic modelling regarding infectious diseases. The possibility of selecting a vaccine against COVID-19 has increased social interest in particular vaccines. The objective of this study was to show dependencies between the frequency of searches for COVID-19 vaccinations and the number of vaccinated people in Poland, along with epidemiological data.

METHODS

Data were collected regarding Google searches for COVID-19 vaccines, the number of people in Poland vaccinated against COVID-19, the number of new cases, and the number of deaths due to COVID-19. Data were filtered from 27 December 2020 to 1 September 2021.

RESULTS

The number of new vaccinations smoothed per million correlated most strongly with searches for the word 'Pfizer' in Google Trends (Kendall's tau = 0.46, p < 0.001). The number of new deaths correlated most strongly with the search phrase 'AstraZeneca' (Kendall's tau = 0.46, p < 0.001). The number of new cases per million correlated most strongly with searches for 'AstraZeneca' (Kendall's tau = 0.49, p < 0.001). The maximum daily number of searches ranged between 110 and 130. A significant interest in COVID-19 vaccines was observed from February to June 2021, i.e., in the period of a considerable increase in the number of new cases and new deaths due to COVID-19.

CONCLUSIONS

A significant increase in interest in COVID-19 vaccines was observed from February to June 2021, i.e., in the period of gradually extended access to vaccinations, as well as a considerable increase in the number of new cases and new deaths due to COVID-19. The use of Google Trends with relevant keywords and a comparison with the course of the COVID-19 pandemic facilitates evaluation of the relationship between the frequency and types of searches for COVID-19 vaccines and epidemiological data.

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.

A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study

Background

Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients’ eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations.

Objective

To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other.

Methods

We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google’s search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant.

Results

Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, “pink eye” showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, “dry eyes” had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning.

Conclusions

The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.

Monitoring Mentions of COVID-19 Vaccine Side Effects from Japanese and Indonesian Twitter: Infodemiological Study (Preprint)

Background

The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy.

Objective

This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia.

Methods

We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country.

Results

We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets.

Conclusions

We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed.

Acceptance and Factors Influencing Acceptance of COVID-19 Vaccine in a Romanian Population

COVID-19 vaccination has been recognized as one of the most effective ways to overcome the current SARS-CoV-2 pandemic. However, the success of this effort relies on national vaccination programmes. In May 2021, we surveyed 1552 people from Romania to determine acceptance rates and factors influencing acceptance of a COVID-19 vaccine. Of these, 39.2% of participants reported that they were vaccinated and 25.6% desired vaccination; nonetheless, 29.5% expressed opposition to vaccination. Concerning vaccination refusal, the top justification given by respondents is that the vaccine is insufficiently safe and there is a risk of serious side effects (84.4%). A higher rate of vaccination refusal was observed among female gender, younger age, and lower educational level. Refusal was also associated with unemployment, being in a relationship, and having a decrease in income during the pandemic. People who are constantly informed by specialized medical staff have a statistically significant higher vaccination rate, while people who choose to get information from friends, family, and co-workers have the strongest intention of avoiding the vaccine. Current levels of vaccine are insufficient to achieve herd immunity of 67%. It is mandatory to understand the aspects that define and establish confidence and to craft nationwide interventions appropriately.

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.

Evolutions and Disparities of Online Attitudes Towards COVID-19 Vaccines: A Yearlong Longitudinal and Cross-sectional Study

BACKGROUND

Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate the COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes towards the new vaccines have arisen among different population groups.

OBJECTIVE

This study aims to discern the evolutions and disparities of attitudes towards COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year.

METHODS

We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study and a cross-sectional study, to collectively explore attitudes of major population groups.

RESULTS

Our study derived three main findings. First, the whole population's attentiveness towards vaccines strongly correlated (Pearson's r=0.9512) with the official COVID-19 statistics, including confirmed cases, deaths in particular; such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of the large-scale vaccine inoculation, sentiments of all population groups came to stabilize, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities towards vaccines existed among population groups defined by eight different demographic characteristics. By crossing the two dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals with age ≥40 years old, while some had low attentiveness ratios, such as individuals with age ≤18 years old, occupations of the 3rd category, account age <5 years, and follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about the vaccines.

CONCLUSIONS

This study tracked yearlong evolutions of attitudes towards COVID-19 vaccines among various population groups defined by eight demographic characteristics, through which significant disparities of attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially to males, older people, and other individuals with low levels of education, low awareness of news, low income and light use of social media. Moreover, public health authorities may also consider cooperating with Twitter users carrying high levels of social influence to promote the acceptance of the COVID-19 vaccines among all population groups.

CLINICALTRIAL

United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study

Background

The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends.

Objective

We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States.

Methods

We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data.

Results

We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707).

Conclusions

Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.