Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study

English tweets on allergy: Content analysis and association with surveillance data

BACKGROUND

Analysis of X (formerly Twitter) posts can inform on the interest/perceptions that social media users have on health subjects. In this study, we aimed to analyse tweets on allergic conditions, comparing them with surveillance data.

METHODS

We retrieved tweets from England on "allergy," "asthma," and "allergic rhinitis," published between 2016 and 2021. We estimated the correlation between the frequency of tweets on "asthma" and "allergic rhinitis" and English surveillance data on the incidence of asthma and allergic rhinitis medical visits. We performed sentiment analysis, computing a score informing on the emotional tone of assessed tweets. We applied a topic modelling approach to identify topics (clusters of words frequently occurring together) for tweets on each assessed condition.

RESULTS

We analysed a total of 13,605 tweets on "allergy," 7767 tweets on "asthma," and 11,974 tweets on "allergic rhinitis." Food-related words were preponderant on tweets on "allergy," while "eyes" was the most frequent meaningful word on "allergy rhinitis" tweets. We observed seasonal patterns for tweets on "allergic rhinitis," both in their frequency and sentiment - the incidence of allergic rhinitis medical visits was moderately to strongly correlated with the frequency (ρ = 0.866) and sentiment (ρ = -0.474) of tweets on "allergic rhinitis." For tweets on "asthma," no such patterns/correlations were observed. The average sentiment score was negative for all assessed conditions, ranging from -0.004 ("asthma") to -0.083 ("allergic rhinitis").

CONCLUSIONS

Tweets on "allergic rhinitis" displayed a seasonal pattern regarding their frequency and sentiment, which correlated with surveillance data. No such patterns were observed for "asthma."

MASK-air: An OECD (Organisation for Economic Co-operation and Development) Best Practice for Public Health on Integrated Care for Chronic Diseases

In the recent report of the Organisation for Economic Co-operation and Development (OECD) on Best Practices (BPs) for Integrating Care to Prevent and Manage Chronic Diseases, an app on rhinitis and asthma (MASK-air [Mobile Airways Sentinel networK for airway diseases]) has been listed. The OECD is a reliable source of evidence-based policy analysis and economic data largely used by governments. It has published several BPs on public health. On May 10, 2023, the OECD published 13 BPs for Integrating Care to Prevent and Manage Chronic Diseases in the European Union. The report did not cover all models of integrated care; rather, it "focuse(d) on those that are of key strategic interest to policy makers." New MASK-air studies (not published in the report) include equity, usability of the app in old-age adults, economic impact, quality of life, and allergen immunotherapy. MASK-air is freely available on iOS and Android in 30 countries and has been recently introduced in the United States. The MASK-air OECD BP represents a model of digitally enabled, patient-centered care for chronic diseases using a holistic approach of shared decision making.

Looking beyond vaccines: Cultural tightness-looseness moderates the relationship between immunization coverage and disease prevention vigilance

Advancements in vaccination technologies mitigate disease transmission risks but may inadvertently suppress the behavioral immune system, an evolved disease avoidance mechanism. Applying behavioral immune system theory and utilizing robust big data analytics, we examined associations between rising vaccination coverage and government policies, public mobility, and online information seeking regarding disease precautions. We tested whether cultural tightness-looseness moderates the relationship between mass immunization and disease prevention vigilance. Comprehensive time series analyses were conducted using American data (Study 1) and international data (Study 2), employing transfer function modeling, cross-correlation function analysis, and meta-regression analysis. Across both the US and global analyses, as vaccination rates rose over time, government COVID-19 restrictions significantly relaxed, community mobility increased, and online searches for prevention information declined. The relationship between higher vaccination rates and lower disease prevention vigilance was stronger in culturally looser contexts. Results provide initial evidence that mass immunization may be associated with attenuated sensitivity and enhanced flexibility of disease avoidance psychology and actions. However, cultural tightness-looseness significantly moderates this relationship, with tighter cultures displaying sustained vigilance amidst immunization upticks. These findings offer valuable perspectives to inform nuanced policymaking and public health strategies that balance prudent precautions against undue alarm when expanding vaccine coverage worldwide.

The COVID-19 pandemic and seeking information about condoms online: an infodemiology approach

Objectives: As condoms are effective tools for pathogen-avoidance in sexual intercourse, seeking information about condoms online may be a reactive response to the COVID-19 according to the behavioral immune system theory.Design: Taking an infodemiology perspective, this research employed multilevel analyses to examine how COVID-19 online query data (i.e., Google topic search terms Coronavirus and COVID-19) and coronavirus epidemiological data (i.e., COVID-19 cases per million and case fatality rate) would predict condom information seeking behavior online (i.e., Google topic search term Condom) throughout the pandemic across American states (Study 1) and 102 countries/territories (Study 2), after accounting for death-thought accessibility (i.e., illness-related searches), interest in birth control (i.e., birth-control-related searches), COVID-19 control policy, stay at home behavior, season, religious holidays, yearly trends, autocorrelation, and contextual variables such as HIV prevalence rate and socioeconomic development indicators (GINI index, urbanization, etc.).Results: When there were high levels of COVID-19 concerns in cyberspace in a given week, search volume for condoms increased from the previous week across American states and different countries/territories. By contrast, the effect of actual coronavirus threat was non-significant.Conclusion: Seeking information about condoms online could be a reactive response to high levels of COVID-19 concerns across different populations.

Impact of asthma in Europe: A comparison of web search data in 21 European countries

Background

Asthma is a chronic inflammatory disorder of the airways and one of the most important non-communicable diseases worldwide. Analyzing crowdsourced data can help understand public interest and unmet needs as well as potential factors influencing search behavior.

Objective

The study aimed to investigate asthma-related web search data in Europe to identify possible regional and seasonal variations and to assess public interest.

Methods

Google Ads Keyword Planner was used to measure search volume for search terms related to asthma, allergic asthma, and bronchial asthma in 21 European countries between January 2018 and December 2021. The top 10 keywords of each country were categorized qualitatively. Search volume per 100 000 inhabitants was descriptively assessed in terms of regional and seasonal trends. Spearman correlations between search volume and pollen concentration as well as coronavirus disease (COVID-19) cases were investigated.

Results

The median search volume per 100 000 inhabitants for asthma and allergic asthma was highest in Northern and Western Europe, while the highest search volume for bronchial asthma was observed in Western and Eastern regions. A seasonal trend was identified for all search terms and in all regions. Correlations were found between search frequency and pollen load and search behavior and COVID-19 cases. Overall, Europeans were most interested in the diseases in general, their treatment options, and symptoms.

Conclusion

These results highlighted the need for reliable and region-specific information about the disease and for public campaigns to improve asthma control. The study also emphasizes the importance of using crowdsourced data for a more encompassing overview beyond conventional healthcare data.

The association between air pollutants and hospitalizations for asthma: an evidence from Chengdu, China

Background

Exposure to air pollutants has been linked to the exacerbation of asthma, and it has become a risk to human welfare and health throughout the globe. Previous works did not achieve a systematic study by considering interactive effects of age, gender, and meteorological factors on the exposure-effect relationship between air pollutants and asthma. We aimed to quantitatively investigate the effects of air pollutants on hospitalizations for asthma in Chengdu, China.

Methods

This is a retrospective and population-based study. Data of asthma hospitalization records for residents, the average daily concentrations of air pollutants including SO₂, CO, NO₂, O₃, PM10, and PM2.5, and meteorological variables from 1 January 2014 to 31 December 2014 in Chengdu, China, were obtained from several government departments. A generalized additive model (GAM) was used to analyze the exposure-effect relationship between air pollutants and daily asthma hospitalizations after controlling the long-time seasonal trend, "day-of-week (DOW)" effect, holiday effect, and confounding meteorological factors.

Results

A total of 7,503 hospitalizations were assessed. Significant associations between hospitalizations and air pollutants were found. The relative risk (RR) for hospitalizations for every 10 µg/m³ increase in PM2.5 and PM10 for the male group were 1.0121 [95% confidence interval (CI): 1.0012-1.0232] and 1.0075 (95% CI: 1.0001-1.015), respectively. The elderly (≥65 years old) tended to have a higher RR (1.0022; 95% CI: 1.0001-1.0043) for each 10 mg/m³ increase in CO than the other age groups. All pollutants had slightly protective effects on the younger age group (≤14 years old). O₃ had more significant effects in cold season, whereas SO₂ impacted more significantly in warm seasons, particularly for females and adults (14-65 years old).

Conclusions

Adverse effects of ambient concentrations of air pollutants on hospitalizations for asthma are evident, especially in specific population groups. Male patients were more susceptible to PM2.5 and PM10, and the elderly were more sensitive to CO. The effects of O₃ in China were significant in the cold season, whereas SO₂ impacted more significantly in the warm season particularly on females and adults. The study would be meaningful for asthma intervention and corresponding healthcare resource management.

Digitally-enabled, patient-centred care in rhinitis and asthma multimorbidity: The ARIA-MASK-air® approach

MASK-air^® , a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air^® is a Good Practice of DG Santé on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air^® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air^® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air^® results should lead to change management in rhinitis and asthma.

Infodemiology of RSV in Italy (2017-2022): An Alternative Option for the Surveillance of Incident Cases in Pediatric Age?

The aim of this study was to evaluate whether or not online queries for Respiratory Syncytial Virus (RSV) retrieved by means of Google Trends™ and the Italian Wikipedia analysis program mirror the occurrence of influenza-like illnesses (ILI), as reported by the Italian Influenza Surveillance network (InfluNet). Estimated rates for ILI in the general population and in the age groups 0−4 years and 5−14 years were obtained for the influenza seasons 2017−2018 to 2020−2021. Similarly, a weekly fraction of online searches was retrieved for a series of terms associated with Respiratory Syncytial Virus. Next, trends for daily visualization of Italian Wikipedia Pages for Human Respiratory Syncytial Virus, Pneumonia, Bronchiolitis, Influenza, and Respiratory Failure were similarly retrieved. The correlation of all search terms with ILI was analyzed by means of Spearman’s rank correlation analysis. Among search terms associated with the clinical diagnosis of Respiratory Syncytial Virus infections, the occurrence of ILI was highly correlated only with Bronchiolitis in the age group 0−4 years (β 0.210, p = 0.028), while more generic search terms, such as Bronchitis, fever, influenza, and Pneumonia, were identified as effective predictors of ILI, in general and by age groups. In a regression analysis modeled with ILIs as the outcome variable, daily visualizations for the Wikipedia pages on Bronchiolitis were identified as negative predictors for ILI in general (β = −0.152, p = 0.032), ILI in age group 0−4 years (β = −0.264, p = 0.001) and 5−14 years (β = −0.202, p = 0.006), while Influenza was characterized as a positive effector for ILIs in the age group 5−14 years (β = 0.245, p = 0.001). Interestingly, not only were the search terms extensively correlated with one another, but all of them were also characterized by autocorrelation through a Durbin-Watson test (all estimates DW < 2.0) In summary, our study identified a complicated pattern of data visualization as no clear association between rates of ILI in pediatric age group 0−4 and 5 to 14 years was actually found. Finally, our data stress that the infodemiology option may be quite problematic for assessing the time trend of RSV infections in Italy until more appropriate reporting will be made available, by sharing estimates of Lower Respiratory Tract Infections, and through a more accurate characterization of younger age groups.

Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a "sampling-feature-hyperparameter-weight" synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R² of BSEE2 fused by the "nonlinear + linear" method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R² of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.

Heightened religiosity proactively and reactively responds to the COVID-19 pandemic across the globe: Novel insights from the parasite-stress theory of sociality and the behavioral immune system theory

According to the parasite-stress theory of sociality and the behavioral immune system theory, heightened religiosity serves an anti-pathogen function by promoting in-group assortative sociality. Thus, highly religious countries/territories could have better control of the COVID-19 (proactively avoids disease-threat), and heightened COVID-19 threat could increase religiosity (reactively responds to disease-threat). As expected, country-level religiosity (religion-related online searches (Allah, Buddhism, Jesus, etc.) and number of total religions/ethnoreligions) negatively and significantly predicted COVID-19 severity (a composite index of COVID-19 susceptibility, reproductive rate, morbidity, and mortality rates) (Study 1a), after accounting for covariates (e.g., socioeconomic factors, ecological factors, collectivism index, cultural tightness-looseness index, COVID-19 policy response, test-to-case ratio). Moreover, multilevel analysis accounting for daily- (e.g., time-trend effect, season) and macro-level (same as in Study 1a) covariates showed that country-level religious searches, compared with the number of total religions/ethnoreligions, were more robust in negatively and significantly predicting daily-level COVID-19 severity during early pandemic stages (Study 1b). At weekly level, perceived coronavirus threat measured with coronavirus-related searches (corona, covid, covid-19, etc.), compared with actual COVID-19 threat measured with epidemiological data, showed larger effects in positively predicting religious searches (Study 2), after accounting for weekly- (e.g., autocorrelation, time-trend effect, season, religious holidays, major-illness-related searches) and macro-level (e.g., Christian-majority country/territory and all country-level variables in Study 1) covariates. Accordingly, heightened religiosity could proactively and reactively respond to the COVID-19 pandemic across the globe.

Usage patterns of oral H1-antihistamines in 10 European countries: A study using MASK-air® and Google Trends real-world data

Real-world data represent an increasingly important source of knowledge in health care. However, assessing their representativeness can be challenging. We compared (i) real-world data from a mobile app for allergic rhinitis (MASK-air®) on the usage of oral H1-antihistamines from 2016 to 2020 in 10 European countries with (ii) Google Trends data on the relative volume of searches for such antihistamines. For each country, we sorted 5 different oral H1-antihistamines by their frequency of use and volume of searches. We found perfect agreement on the order of antihistamine use in MASK-air® and in Google Trends searches in 4 countries (France, Germany, Sweden, and the United Kingdom). Different levels of agreement were observed in the remaining countries (kappa coefficient from −0.50 to 0.75). Oral H1-antihistamine data from Google Trends and MASK-air® were consistent with nationwide medication sales data from France, Germany, and the United Kingdom. These results suggest that MASK-air® data may be consistent with other sources of real-world data, although assessing the representativeness of their users may require further studies.

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.