Trends of infodemiology studies: a scoping review

Exploring home fall events among infants and toddlers using social media information: an infodemiology study in China

BACKGROUND

Practical interventions of fall prevention are challenging for infants and toddlers. This study aimed to explore specific details of falls that occurred at home for kids 0-3 years old using key information from social media platforms, which provided abundant data sources for fall events.

METHODS

We used internet-based search techniques to collect fall events information from 2013 to 2023. The search was restricted and implemented between 1 and 12 April 2023. Online platforms included Baidu, Weibo, WeChat, TikTok, Toutiao and Little Red Book. A qualitative descriptive approach was used to analyse the fall events and major factors, including the fall event time, child age, environmental factors and behavioural characteristics of children and caregivers.

RESULTS

We identified 1005 fall injury cases among infants and toddlers. Fall mechanisms included falls from household furniture (71.2%), falls from height (21.4%) and falls on the same level (7.4%). Environmental risk factors mainly consisted of not using or installing bed rails incorrectly, a gap between beds, unstable furniture, slippery ground and windows without guardrails. Behavioural factors included caregivers leaving a child alone, lapsed attention, turning around to retrieve something, misusing baby products, inadequately holding the child and falling asleep with children. Child behavioural factors included walking or running while holding an object in hand or mouth and underdeveloped walking skills.

CONCLUSION

Interventions for preventing falls should be designed specifically for Chinese families, especially considering family function in the context of Chinese culture. Social media reports could provide rich information for researchers.

Maximum generable interest: A universal standard for Google Trends search queries

The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different works is desirable to simplify the comparison. The coronavirus pandemic's enormous impact and media coverage have triggered an exceptionally high search interest. Consequently, the maximum generable interest (MGI) on coronavirus is proposed as a universal reference for objectifying and comparing relative search interest in the future. This search interest can be explored with search engine data such as Google Trends data. Additional standards for medium and low search volumes can also be used to reflect the search interest of topics at different levels. Size standards, such as reference to MGI, may help make research more comparable and better evaluate relative search volumes. This study presents a framework for this purpose using the example of stroke.

Population Interest in Information on Obesity, Nutrition, and Occupational Health and Its Relationship with the Prevalence of Obesity: An Infodemiological Study

OBJECTIVE

To identify and analyze population interest in obesity, nutrition, and occupational health and safety and its relationship with the worldwide prevalence of obesity through information search trends.

METHOD

In this ecological study, data were obtained through online access to Google Trends using the topics "obesity", "nutrition", and "occupational health and safety". Obesity data were obtained from the World Health Organization (WHO) website for crude adult prevalence and estimates by region. The variables studied were relative search volume (RSV), temporal evolution, milestone, trend, and seasonality. The temporal evolution of the search trends was examined by regression analysis (R2). To assess the relationship between quantitative variables, the Spearman correlation coefficient (Rho) was used. Seasonality was verified using the augmented Dickey-Fuller (ADF) test.

RESULTS

The RSV trends were as follows: obesity (R2 = 0.04, p = 0.004); nutrition (R2 = 0.42, p < 0.001); and occupational health and safety (R2 = 0.45, p < 0.001). The analysis of seasonality showed the absence of a temporal pattern (p < 0.05 for all terms). The associations between world obesity prevalence (WOP) and the different RSVs were as follows: WOP versus RSV obesity, Rho = -0.79, p = 0.003; WOP versus RSV nutrition, Rho = 0.57, p = 0.044; and WOP versus RSV occupational health and safety, Rho = -0.93, p = 0.001.

CONCLUSIONS

Population interest in obesity continues to be a trend in countries with the highest prevalence, although there are clear signs popularity loss in favor of searches focused on possible solutions and treatments, with a notable increase in searches related to nutrition and diet. Despite the fact that most people spend a large part of their time in the workplace and that interventions including various strategies have been shown to be useful in combating overweight and obesity, there has been a decrease in the population's interest in information related to obesity in the workplace. This information can be used as a guide for public health approaches to obesity and its relationship to nutrition and a healthy diet, approaches that are of equal utility and applicability in occupational health.

A Google Trends analysis revealed global public interest and awareness of nasal polyps

INTRODUCTION

Nasal polyps (NPs) is a common upper airway inflammatory disorder with a huge negative burden on both the quality of life and costs to patients. However, NPs patients remain undiagnosed and untreated in a timely, which may be due to a lack of disease-related awareness. Google Trends (GT) is an online and open tool, which can provide real-world data on health informatics worldwide.

OBJECTIVES

This study aimed to explore global public interest and awareness in nasal polyps (NPs) by performing a GT analysis.

METHODS

Data on relative search volume (RSV) for NPs globally were collected by the public website Google Trends from January 2007 to December 2021. Top-related topics, rising-related topics, and regions were extracted for further analysis. Seasonal variation analysis, the latitude difference analysis, and the rising-related topics between the developed countries and the developing countries were analyzed. A P value less than 0.05 was considered statistically significant.

RESULTS

The average searching strength showed an overall increasing trend, although with slight fluctuation. The public interest of NPs focuses on the symptoms and treatment for NPs and changes with time. For seasonal variation countries, the peak for the RSV occurred in winter and the bottom in summer. A region in higher latitudes may yield more RSV than that in lower latitudes. The rising-related topics in the recent 5 years reflected the significant differences in treatment and public interest of NPs between the developed and developing countries.

CONCLUSIONS

Google Trends analysis revealed global public interest and awareness of the evolution of trends and related topics in nasal polyps over time. Geographic distribution and seasonal variation may be potential trigger factors for NPs, and the public's interest in treatment especially biologics is rising.

Infodemiology for oral health and disease: A scoping review

INTRODUCTION

Increasing affordability, accessibility and penetration of internet services worldwide, have substantially changed the ways of gathering health-related information. This has led to the origin of concept infodemiology that allows the information to be collected and analysed in near real time. Globally, oral diseases affect nearly 3.5 billion people; thus, volume and profile of oral health searches would help in understanding specific community dental needs and formulation of pertinent oral health strategies.

AIM

To review the published literature on infodemiological aspects of oral health and disease.

METHODOLOGY

This scoping review was conducted in accordance with PRISMA-ScR guidelines. Electronic search engines (Google Scholar) and databases (PubMed, Web of science, Scopus) were searched from 2002 onwards.

RESULTS

Thirty-eight articles were included in this review. The infodemiological studies for oral health and disease were mainly used in two domains. Out of 38 articles, 24 accessed the quality of available online information and 15 studied online oral health-related information seeking behaviour.

CONCLUSION

The most commonly searched oral diseases were toothache, oral cancer, dental caries, periodontal disease, oral maxillofacial surgical procedures and paediatric oral diseases. Most of the studies belonged to developed countries and Google was the most researched search engine.

Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media

Purpose

The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.

Design/methodology/approach

This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.

Findings

In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.

Originality/value

The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media

BACKGROUND

Long COVID-a condition with persistent symptoms post COVID-19 infection-is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data.

OBJECTIVE

We aimed to identify and analyze Long Haulers' main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles.

METHODS

Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users' messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term's total was used to create user-level hierarchal clusters.

RESULTS

Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year.

CONCLUSIONS

Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers' quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers' voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID.

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.

Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout

BACKGROUND AND OBJECTIVE

Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID-19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia.

METHODS

We retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia.

RESULTS

Using a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government's proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71, P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001).

CONCLUSIONS

Our results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens' reactions and expression in social media, especially Twitter, using sentiment analysis.

Association of Online Search Trends With Vaccination in the United States: June 2020 Through May 2021

Stagnating COVID-19 vaccination rates and vaccine hesitancy remain a threat to public health. Improved strategies for real-time tracking and estimation of population-level behavior regarding vaccinations are needed. The aim of this study was to evaluate whether online search trends for COIVD-19 and influenza mirror vaccination rates. State-level weekly fraction of online searches for top vaccination-related search terms and CDC vaccination data were obtained from June 1, 2020, to May 31, 2021. Next, trends in online search and vaccination data for COVID-19 and influenza were analyzed for visual and quantitative correlation patterns using Spearman’s rank correlation analysis. Online searches in the US for COVID-19 vaccinations increased 2.71-fold (95% CI: 1.98-3.45) in the 4 weeks after the FDA emergency authorization compared to the precedent 4 weeks. In March-April 2021, US online searches reached a plateau that was followed by a decline of 83.3% (95% CI: 31.2%-135.3%) until May 31, 2021. The timing of peaks in online searches varied across US states. Online searches were strongly correlated with vaccination rates (r=0.71, 95% CI: 0.45 - 0.87), preceding actual reported vaccination rates in 44 of 51 states. Online search trends preceded vaccination trends by a median of 3.0 weeks (95% CI: 2.0-4.0 weeks) across all states. For influenza vaccination searches, seasonal peaks in September-October between 2016-2020 were noted. Influenza search trends highly correlated with the timing of actual vaccinations for the 2019-2020 (r=0.82, 95% CI: 0.64 – 0.93) and 2020-2021 season (r=0.91, 95% CI: 0.78 – 0.97). Search trends and real-world vaccination rates are highly correlated. Temporal alignment and correlation levels were higher for influenza vaccinations; however, only online searches for COVID-19 vaccination preceded vaccination trends. These findings indicate that US online search data can potentially guide public health efforts, including policy changes and identifying geographical areas to expand vaccination campaigns.

Inclusion of environmentally themed search terms improves Elastic net regression nowcasts of regional Lyme disease rates

Lyme disease is the most widely reported vector-borne disease in the United States. 95% of confirmed human cases are reported in the Northeast and upper Midwest (25,778 total confirmed cases from Northeast and upper Midwest / 27,203 total US confirmed cases). Human cases typically occur in the spring and summer months when an infected nymph Ixodid tick takes a blood meal. Current federal surveillance strategies report data on an annual basis, leading to nearly a year lag in national data reporting. These lags in reporting make it difficult for public health agencies to assess and plan for the current burden of Lyme disease. Implementation of a nowcasting model, using historical data to predict current trends, provides a means for public health agencies to evaluate current Lyme disease burden and make timely priority-based budgeting decisions. The objective of the study was to develop and compare the performance of nowcasting models using free data from Google Trends and Centers of Disease Control and Prevention surveillance reports. We developed two sets of elastic net models for five regions of the United States: 1. Using only monthly proportional hit data from the 21 disease symptoms and tick related terms, and 2. Using monthly proportional hit data from terms identified via Google correlate and the disease symptom and vector terms. Elastic net models using the full-term list were highly accurate (Root Mean Square Error: 0.74, Mean Absolute Error: 0.52, R2: 0.97) for four of the five regions of the United States and improved accuracy 1.33-fold while reducing error 0.5-fold compared to predictions from models using disease symptom and vector terms alone. Many of the terms included and found to be important for model performance were environmentally related. These models can be implemented to help local and state public health agencies accurately monitor Lyme disease burden during times of reporting lag from federal public health reporting agencies.

Online searching trend on Covid-19 using Google trend: infodemiological study in Malaysia

Since January 2020, the emergence of Covid-19 has sparked a worldwide search for information about Covid-19. People frequently use the internet to search the information on the virus. However, the pandemic have triggered the information-seeking trends among public. As a result, large amount of information could lead to infodemic. It will create public concerned such as panic and paranoid because this information spread rapidly. The aim of this study is to analyze information about Covid-19 that has been searched in Malaysia. We investigated online search behavior related to the Covid-19 outbreak among public by using Google Trends to understand public searching behavior on Covid-19. The findings from this study can be used as a tool to monitor public searching activities on Covid-19, which could predict future action regarding the outbreak.

The application of artificial intelligence in hepatology: A systematic review

The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21^st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.

Interest in dentistry in early months of the COVID-19 global pandemic: A Google Trends approach

Background

In early the COVID‐19 pandemic, routine dental treatments have been delayed due to the risk of disease transmission. This delay may lead public to search for information on the Internet for a solution.

Objectives

This study aims to evaluate the public interest in dentistry in the early months of the COVID‐19 global pandemic in the selected countries.

Methods

The daily numbers of new COVID‐19 cases were recorded for China, South Korea, Italy, Germany, Russia, Ukraine and Turkey. For these countries, Internet search interest of the keyword ‘dentistry’, ‘coronavirus’, ‘COVID‐19’, ‘SARS‐CoV‐2’ and ‘pandemic’ in the early months of the COVID‐19 pandemic was evaluated by using Google Trends data.

Results

In most countries included the public Internet search interest in ‘dentistry+coronavirus+COVID‐19+SARS‐CoV‐2+pandemic’ peaked prior to the peak of new COVID‐19 cases. While a statistically significant positive correlation was observed between the number of new cases and Google Trends data in China, South Korea, Italy and Germany, a statistically significant negative correlation was observed in Turkey.

Conclusion

The peak public interest in dentistry has been prior to the peak of COVID‐19 new cases in most countries. The use of Internet data can provide useful information about pandemics and many other diseases.

Patterns of Google search behavior for epilepsy and seizures in the Philippines: An infodemiological study

BACKGROUND

The internet has been established as a rich source of health information that is well utilized by the public. Online health information-seeking behavior may reflect public awareness in particular health-related topics, including epilepsy and seizures.

OBJECTIVE

This study aimed to describe online search behavior for epilepsy and seizures in the Philippines through the examination of trends of Philippine-based Google search queries for epilepsy and seizure-related terms.

METHODS

The search volume indices (SVIs) for the keywords "epilepsy (disorder)" and "seizure (disease)" based on Philippine-based Google searches from January 2004 to July 2021 were generated using Google Trends. Descriptive analysis of search volume patterns, including related topics and queries, was performed.

RESULTS

There was a recent relative increase in Philippine-based searches related to the term "seizure" with a concurrent relative decline in searches related to the term "epilepsy" over time. The Philippines had the fifth-highest SVI for "seizure" based on worldwide trends. Most searches centered on the definition, symptomatology, etiologies, classification, and treatment of seizures and epilepsy. Queries containing seizure-related terms rose in volume by more than 5000% during the seventeen-year period. Search volume tended to peak during the National Epilepsy Awareness Week in September but slumped during holiday months.

CONCLUSIONS

Online search behavior in the Philippines for seizure-related terms appears to be on the rise and may reflect increasing public awareness in epilepsy. Factors including fears surrounding epileptic seizures, the stigma surrounding the term "epilepsy", and seasonal events including a regularly held epilepsy awareness week may influence online search behavior.

Predicting New Daily COVID-19 Cases and Deaths Utilizing Search Engine Query Data in South Korea from 2020 to 2021: Infodemiology Study

Background

Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction.

Objective

The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods.

Methods

We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics.

Results

GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models.

Conclusions

NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective

BACKGROUND

The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance.

OBJECTIVE

The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter.

METHODS

We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes.

RESULTS

An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis.

CONCLUSIONS

The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.

Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways

This study aimed to analyze population flow using global positioning system (GPS) location data and evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. Neural collective graphical models (NCGMs; Iwata and Shimizu 2019) were applied for 25 cell areas, each measuring 10 × 10 km², in Osaka, Kyoto, Nara, and Hyogo prefectures to estimate population flow. An NCGM uses a neural network to incorporate the spatiotemporal dependency issue and reduce the estimated parameters. The prescription peaks between several cells with high population flow showed a high correlation with a delay of one to two days or with a seven-day time-lag. It was observed that not much population flows from one cell to the outside area on weekdays. This observation may have been due to geographical features and undeveloped transportation networks. The number of prescriptions for anti-influenza drugs in that cell remained low during the observation period. The present results indicate that influenza did not spread to areas with undeveloped traffic networks, and the peak number of drug prescriptions arrived with a time lag of several days in areas with a high amount of area-to-area movement due to commuting.

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity. Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected. Methods: RSVs of the query coronavirus + covid during February 1-December 4, 2020 (period 1), and February 20-May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distributionX i = X ( σ i , x ¯ i ) was used to represent the trend of daily variations ofx i j = R S V s i j . When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch's t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t < 1.5 . A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). The percentage increase Δ was used to quantify the difference between two values. Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets ( M a x   | Δ |   =   +   625 % for Italian regions, and M a x   | Δ | =   + 175 %   for countries worldwide). Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. Finally, the large amount of anomalies registered in Italian and international cities' RSVs made these datasets unusable for any kind of statistical inference. Conclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries' data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected. Further research is needed to evaluate the effectiveness of this method.

Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study

BACKGROUND

Lupus is a complex autoimmune disease that is difficult to diagnose and treat. It is estimated that at least 5 million Americans have lupus, with more than 16,000 new cases of lupus being reported annually in the United States. Social media provides a platform for patients to find rheumatologists and peers and build awareness of the condition. Researchers have suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues. However, there is a lack of research about the characteristics of lupus patients on Twitter and their attitudes toward using Twitter for engaging them with their health care.

OBJECTIVE

This study has two objectives: (1) to conduct a content analysis of Twitter data published by users (in English) in the United States between September 1, 2017 and October 31, 2018 to identify patients who publicly discuss their lupus condition and to assess their expressed health themes and (2) to conduct a cross-sectional survey among these lupus patients on Twitter to study their attitudes toward using Twitter for engaging them with their health care.

METHODS

This is a mixed methods study that analyzes retrospective Twitter data and conducts a cross-sectional survey among lupus patients on Twitter. We used Symplur Signals, a health care social media analytics platform, to access the Twitter data and analyze user-generated posts that include keywords related to lupus. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among lupus patients. We will further conduct self-report surveys via Twitter by inviting all identified lupus patients who discuss their lupus condition on Twitter. The goal of the survey is to collect data about the characteristics of lupus patients (eg, gender, race/ethnicity, educational level) and their attitudes toward using Twitter for engaging them with their health care.

RESULTS

This study has been funded by the National Center for Advancing Translational Science through a Clinical and Translational Science Award. The institutional review board at the University of Southern California (HS-19-00048) approved the study. Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to "lupus" from users in the United States published in English between September 1, 2017 and October 31, 2018. We included 40,885 posts in the analysis. Data analysis was completed in Fall 2020.

CONCLUSIONS

The data obtained in this pilot study will shed light on whether Twitter provides a promising data source for garnering health-related attitudes among lupus patients. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of lupus among patients and implementing related health education interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/15716.

Digital public health surveillance: a systematic scoping review

The ubiquitous and openly accessible information produced by the public on the Internet has sparked an increasing interest in developing digital public health surveillance (DPHS) systems. We conducted a systematic scoping review in accordance with the PRISMA extension for scoping reviews to consolidate and characterize the existing research on DPHS and identify areas for further research. We used Natural Language Processing and content analysis to define the search strings and searched Global Health, Web of Science, PubMed, and Google Scholar from 2005 to January 2020 for peer-reviewed articles on DPHS, with extensive hand searching. Seven hundred fifty-five articles were included in this review. The studies were from 54 countries and utilized 26 digital platforms to study 208 sub-categories of 49 categories associated with 16 public health surveillance (PHS) themes. Most studies were conducted by researchers from the United States (56%, 426) and dominated by communicable diseases-related topics (25%, 187), followed by behavioural risk factors (17%, 131). While this review discusses the potentials of using Internet-based data as an affordable and instantaneous resource for DPHS, it highlights the paucity of longitudinal studies and the methodological and inherent practical limitations underpinning the successful implementation of a DPHS system. Little work studied Internet users' demographics when developing DPHS systems, and 39% (291) of studies did not stratify their results by geographic region. A clear methodology by which the results of DPHS can be linked to public health action has yet to be established, as only six (0.8%) studies deployed their system into a PHS context.

COVID-19 pandemic impact on people with rheumatic and musculoskeletal diseases: Insights from patient-generated health data on social media

Objectives

During the COVID-19 pandemic, much communication occurred online, through social media. This study aimed to provide patient perspective data on how the COVID-19 pandemic impacted people with rheumatic and musculoskeletal diseases (RMDs), using Twitter-based patient-generated health data (PGHD).

Methods

A convenience sample of Twitter messages in English posted by people with RMDs was extracted between March 1, and July 12, 2020 and examined using thematic analysis. Included were Twitter messages that mentioned keywords and hashtags related to both COVID-19 (or SARS-CoV-2) and select RMDs. The RMDs monitored included inflammatory-driven (joint) conditions (Ankylosing Spondylitis, Rheumatoid Arthritis, Psoriatic Arthritis, Lupus/Systemic Lupus Erythematosus, and Gout).

Results

The analysis included 569 tweets by 375 Twitter users with RMDs across several countries. Eight themes emerged regarding the impact of the COVID-19 pandemic on people with RMDs: (1) lack of understanding of SARS-CoV-2/COVID-19; (2) critical changes in health behaviour; (3) challenges in healthcare practice and communication with healthcare professionals; (4) difficulties with access to medical care; (5) negative impact on physical and mental health, coping strategies; (6) issues around work participation, (7) negative effects of the media; (8) awareness-raising.

Conclusion

The findings show that Twitter serves as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. The platform provided “early signals” of potentially critical health behaviour changes. Future epidemics might benefit from the real-time use of Twitter-based PGHD to identify emerging health needs, facilitate communication, and inform clinical practice decisions.

Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation

Background

The COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown.

Objective

We used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available.

Methods

We used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries’ telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries’ ICT infrastructure profiles were retrieved from the World Economic Forum Report.

Results

Across the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (≥70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure.

Conclusions

There is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic.

Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis

BACKGROUND

Psoriasis is an autoimmune disease estimated to affect more than 6 million adults in the United States. It poses a significant public health problem and contributes to rising health care costs, affecting people's quality of life and ability to work. Previous research showed that nontreatment and undertreatment of patients with psoriasis remain a significant problem. Perspectives of patients toward seeking psoriasis treatment are understudied. Social media offers a new data source of user-generated content. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues.

OBJECTIVE

The objective of this study is to conduct a content analysis of Twitter posts (in English) published by users in the United States between February 1, 2016, and October 31, 2018, to examine perspectives that potentially influence the treatment decision among patients with psoriasis.

METHODS

User-generated Twitter posts that include keywords related to psoriasis will be analyzed using text classifiers to identify themes related to the research questions. We will use Symplur Signals, a health care social media analytics platform, to access the Twitter data. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among people with psoriasis.

RESULTS

This study is supported by the National Center for Advancing Translational Science through a Clinical and Translational Science Award award. Study approval was obtained from the institutional review board at the University of Southern California. Data extraction and cleaning are complete. For the time period from February 1, 2016, to October 31, 2018, we obtained 95,040 Twitter posts containing terms related to "psoriasis" from users in the United States published in English. After removing duplicates, retweets, and non-English tweets, we found that 75.51% (52,301/69,264) of the psoriasis-related posts were sent by commercial or bot-like accounts, while 16,963 posts were noncommercial and will be included in the analysis to assess the patient perspective. Analysis was completed in Summer 2020.

CONCLUSIONS

This protocol paper provides a detailed description of a social media research project including the process of data extraction, cleaning, and analysis. It is our goal to contribute to the development of more transparent social media research efforts. Our findings will shed light on whether Twitter provides a promising data source for garnering patient perspective data about psoriasis treatment decisions. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of psoriasis and treatment options among patients and implementing related health interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/13731.

Qualitative analysis of Amazon customer reviews of penile clamps for male urinary incontinence

AIMS

Penile clamps offer an alternative to manage male urinary incontinence in patients who are unfit for surgery. Patient experience with penile clamps is poorly understood. Our study elucidates patient opinions on commercially available penile clamps and the factors that associate with favorable and unfavorable opinions.

METHODS

We collected Amazon reviews of all penile clamps marketed for male urinary incontinence from November 2011 to January 2020 and qualitatively assessed the overall sentiment towards penile clamps, key praises and key complaints. Covariates such as designated Amazon star rating were further explored for association with coding patterns.

RESULTS

Amazon reviews of penile clamps were found to be more positive (n = 425) in overall sentiment than negative (n = 294). The most frequent praise was effective incontinence control (n = 334) and the most frequent complaint was bad design or material (n = 166). The majority of reviews were for lower priced penile clamps, had higher Amazon star ratings, were written for Wiesner-produced clamps, and were written more recently (i.e., 2015-2020). Penile clamps with higher Amazon star ratings were more often coded positive and with a praise compared to lower rated penile clamps.

CONCLUSIONS

Penile clamps are seen favorably by users as an effective treatment modality for male urinary incontinence. User reviews suggest opportunities for improvement in penile clamp design. The most frequently reviewed clamp seen positively is the Wiesner Incontinence Clamp Penile Clamp whereas the most frequently reviewed clamp seen negatively is the Pacey Cuff Male Incontinence Device.

An evaluation of online discussion relating to nonmedical use of prescription opioids within the UK

AIM

To identify and describe the nature of online discussion relating to prescription opioids within the UK.

METHODS

We performed analysis of posts originating in the UK related to buprenorphine, hydrocodone, oxycodone and tramadol using Social Studio, a web-monitoring platform. The study included posts published between January 2014 and December 2016. The data were cleaned to produce a final dataset consisting only of substantive mentions, which were then categorised by defined themes.

RESULTS

The final dataset included a total of 17 361 substantive mentions (2936 buprenorphine, 2894 hydrocodone, 3826 oxycodone and 7705 tramadol). The most common theme for all 4 drugs was sharing experience or opinion comprising over 90% of mentions for each drug, while discussion related to polysubstance use was present in >1/4 of mentions across drug substances. Mentions related to diversion were more common for hydrocodone and oxycodone (8.1% [6.3-10.1 95% confidence interval] and 7.8% [6.5-9.2], respectively) than buprenorphine or tramadol (4.1 and 3.9% [3.5-4.3], respectively).

CONCLUSION

This investigation shows that there is substantial online discussion relating to a variety of nonmedical use (NMU) behaviours of prescription opioids within the UK, including for hydrocodone, which is not medically available. Web monitoring provides useful data and merits future investigation; this could include expansion to other categories of drugs and a more in-depth analysis of motivations behind NMU, both of which could add timely evidence regarding the current situation in the UK and help inform public health interventions for NMU of prescription drugs.

Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis

Background

Systemic lupus erythematosus (SLE) is the most common form of lupus. It is a chronic autoimmune disease that predominantly affects women of reproductive age, impacting contraception, fertility, and pregnancy. Although clinic-based studies have contributed to an increased understanding of reproductive health care needs of patients with SLE, misinformation abounds and perspectives on reproductive health issues among patients with lupus remain poorly understood. Social networks such as Twitter may serve as a data source for exploring how lupus patients communicate about their health issues, thus adding a dimension to enrich our understanding of communication regarding reproductive health in this unique patient population.

Objective

The objective of this study is to conduct a content analysis of Twitter data published by users in English in the United States from September 1, 2017, to October 31, 2018, in order to examine people’s perspectives on reproductive health among patients with lupus.

Methods

This study will analyze user-generated posts that include keywords related to lupus and reproductive health from Twitter. To access public Twitter user data, we will use Symplur Signals, a health care social media analytics platform. Text classifiers will be used to identify topics in posts. Posts will be classified manually into the a priori and emergent categories. Based on the information available in a user’s Twitter profile (ie, username, description, and profile image), we will further attempt to characterize the user who generated the post. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among patients with lupus.

Results

This study has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program. The Institutional Review Board at the University of Southern California approved the study (HS-18-00912). Data extraction and cleaning are complete. We obtained 47,715 Twitter posts containing terms related to “lupus” from users in the United States, published in English between September 1, 2017, and October 31, 2018. We will include 40,885 posts in the analysis, which will be completed in fall 2020. This study was supported by funds from the has been funded by the National Center for Advancing Translational Science (NCATS) through their Clinical and Translational Science Awards program.

Conclusions

The findings from this study will provide pilot data on the use of Twitter among patients with lupus. Our findings will shed light on whether Twitter is a promising data source for learning about reproductive health issues expressed among patients with lupus. The data will also help to determine whether Twitter can serve as a potential outreach platform for raising awareness of lupus and reproductive health and for implementing relevant health interventions.

International Registered Report Identifier (IRRID)

DERR1-10.2196/15623

Public interest in Raynaud's phenomenon: A Google Trends analysis

Raynaud's phenomenon is a common disorder affecting body extremities and other vascular beds. As infodemiological methods have developed, online search tools could be used to explore the public interest of the disease. The study aimed to determine the annual trend, seasonal pattern, and associated topics of Raynaud's phenomenon. Google Trends was used to collect the data. "Raynaud syndrome" was selected as the search term. Data on monthly relative search volume (RSV) were collected from four selected countries (United States, United Kingdom, Australia, and New Zealand) and globally. Related topics were obtained, and annual-related topics were also collected for analysis. The maximum RSV appeared in January 2019, and the minimum value was observed in August 2011. The peak for RSV occurred in winter, and the bottom appeared in summer. In top related topics, "Maurice Raynaud" was the most related. In rising related topics, disease manifestations and autoimmune connective diseases were highly concerned. For annual-related topics, associated diseases were attracting more attention over time. The population is interested in related diseases, pathogenesis, and treatment. There was a peak in winter for searching and supported the importance of season variation on the impact of Raynaud's phenomenon.

COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study

BACKGROUND

Since the beginning of the novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication.

OBJECTIVE

We investigated online search behavior related to the COVID-19 outbreak and the attitudes of "infodemic monikers" (ie, erroneous information that gives rise to interpretative mistakes, fake news, episodes of racism, etc) circulating in Italy.

METHODS

By using Google Trends to explore the internet search activity related to COVID-19 from January to March 2020, article titles from the most read newspapers and government websites were mined to investigate the attitudes of infodemic monikers circulating across various regions and cities in Italy. Search volume values and average peak comparison (APC) values were used to analyze the results.

RESULTS

Keywords such as "novel coronavirus," "China coronavirus," "COVID-19," "2019-nCOV," and "SARS-COV-2" were the top infodemic and scientific COVID-19 terms trending in Italy. The top five searches related to health were "face masks," "amuchina" (disinfectant), "symptoms of the novel coronavirus," "health bulletin," and "vaccines for coronavirus." The regions of Umbria and Basilicata recorded a high number of infodemic monikers (APC weighted total >140). Misinformation was widely circulated in the Campania region, and racism-related information was widespread in Umbria and Basilicata. These monikers were frequently searched (APC weighted total >100) in more than 10 major cities in Italy, including Rome.

CONCLUSIONS

We identified a growing regional and population-level interest in COVID-19 in Italy. The majority of searches were related to amuchina, face masks, health bulletins, and COVID-19 symptoms. Since a large number of infodemic monikers were observed across Italy, we recommend that health agencies use Google Trends to predict human behavior as well as to manage misinformation circulation in Italy.

Harnessing the power of social media: how can it help in axial spondyloarthritis research?

PURPOSE OF REVIEW

Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease that is relatively unknown among the general public. Most patients with axSpA are young or middle-aged adults and more likely to use some social media. This review highlights trends in the application of social media and different ways in which these tools do already or may benefit clinical research, delivery of care, and education in rheumatology, particularly in the field of axSpA.

RECENT FINDINGS

This article discusses four areas in the biomedical field that social media has infused with novel ideas: (i) the use of patient-generated health data from social media to learn about their disease experience, (ii) delivering health education and interventions, (iii) recruiting study participants, and (iv) reform, transfer, and disseminate medical education. We conclude with promising studies in rheumatology that have incorporated social media and suggestions for future directions.

SUMMARY

Rheumatologists now have the opportunity to use social media and innovate on many aspects of their practice. We propose further exploration of multiple ways in which social media might help with the identification, diagnosis, education, and research study enrollment of axSpA patients. However, standardization in study design, reporting, and managing ethical and regulatory aspects will be required to take full advantage of this opportunity.

Wither health science librarianship? Many questions, many options

The 2019 virtual issue of the Health Information and Libraries Journal (HILJ) is published to link to the 2019 EAHIL Workshop taking place in Basel, Switzerland on 17-20 June 2019. The workshop is structured around six topics: (i) Roadmap of our Profession; (ii) Technology Uptake; (iii) Ecology of Scholarly Communications; (iv) Impact + Assessment; (v) Benchmarking + Advocacy; (vi) Evidence-Based Practice. These themes have been used to compile this virtual issue, which contains published articles selected from HILJ from the March 2019 issue through to June 2017. The virtual issue mirrors the format of a regular issue of HILJ, namely a review article, five original articles and articles from our three regular features: 'Dissertations into Practice', 'International Perspectives and Initiatives' and 'Teaching and Learning in Action'. The authors come from the UK, Canada, Australia, Italy, Iran and Belgium. All articles included in this virtual issue are available free online.

Rhythmicity of health information behaviour

Purpose

This paper brings focus and attention to the aspect of time within health information behaviour. The purpose of this paper is to critically assess and present strengths and weaknesses of utilising the infodemiology approach and metrics as a novel way to examine temporal variations and patterns of online health information behaviour. The approach is shortly exemplified by presenting empirical evidence for temporal patterns of health information behaviour on different time-scales.

Design/methodology/approach

A short review of online health information behaviour is presented and methodological barriers to studying the temporal nature of this behaviour are emphasised. To exemplify how the infodemiology approach and metrics can be utilised to examine temporal patterns, and to test the hypothesis of existing rhythmicity of health information behaviour, a brief analysis of longitudinal data from a large discussion forum is analysed.

Findings

Clear evidence of robust temporal patterns and variations of online health information behaviour are shown. The paper highlights that focussing on time and the question of when people engage in health information behaviour can have significant consequences.

Practical implications

Studying temporal patterns and trends for health information behaviour can help in creating optimal interventions and health promotion campaigns at optimal times. This can be highly beneficial for positive health outcomes.

Originality/value

A new methodological approach to study online health information behaviour from a temporal perspective, a phenomenon that has previously been neglected, is presented. Providing evidence for rhythmicity can complement existing epidemiological data for a more holistic picture of health and diseases, and their behavioural aspects.

Environment and health: Risk perception and its determinants among Italian university students

Among the determinants of environmental health risk perception, health literacy and social media messages have been generally neglected. This study details the environmental health risk perception and its determinants in Italian university students, including a measure of functional health literacy and an analysis of newspapers and social media. A cross sectional survey was carried out among students from 15 Italian universities and different disciplines (grouped into Scientific-Health and Humanistic-Legal-Social sectors) using a self-administered anonymous questionnaire, divided into six sections: socio-demographic characteristics, information on health and environment, environmental health risk perception, trust, attitudes and behaviors and functional health literacy. Local newspapers and tweets in the same areas and period were analyzed in relation to quantity and topics. The study population included 4778 students (65.1% female) aged 21 ± 4.3 years, and functional health literacy was low (below the cutoff value) for 44.4% of students. A new outcome of the survey is that the detected association between high functional health literacy a higher global health risk perception and trust in institutions both as sources of information and as actors for protection against environmental risks. The internet and social networks were the most frequently consulted sources of information (77.7%), which was predictive of a higher risk perception. The possible relation between environmental health risk perception and tweet communication was highlighted by a comparison between the risk perception in the city with the highest number of tweets (Modena) and another one similar for socio-demographic characteristics (Pisa). In conclusion, the results of our study may be of help to strengthen information and education programs: functional health literacy should be taken into account in school programs, to produce a basic knowledge for a better understanding of health and environment. Moreover, mass and social media should be included in planning communication intervention and in verifying their results.

Sepsis information-seeking behaviors via Wikipedia between 2015 and 2018: A mixed methods retrospective observational study

Raising public awareness of sepsis, a potentially life-threatening dysregulated host response to infection, to hasten its recognition has become a major focus of physicians, investigators, and both non-governmental and governmental agencies. While the internet is a common means by which to seek out healthcare information, little is understood about patterns and drivers of these behaviors. We sought to examine traffic to Wikipedia, a popular and publicly available online encyclopedia, to better understand how, when, and why users access information about sepsis. Utilizing pageview traffic data for all available language localizations of the sepsis and septic shock pages between July 1, 2015 and June 30, 2018, significantly outlying daily pageview totals were identified using a seasonal hybrid extreme studentized deviate approach. Consecutive outlying days were aggregated, and a qualitative analysis was undertaken of print and online news media coverage to identify potential correlates. Traffic patterns were further characterized using paired referrer to resource (i.e. clickstream) data, which were available for a temporal subset of the pageviews. Of the 20,557,055 pageviews across 65 linguistic localizations, 47 of the 1,096 total daily pageview counts were identified as upward outliers. After aggregating sequential outlying days, 25 epochs were examined. Qualitative analysis identified at least one major news media correlate for each, which were typically related to high-profile deaths from sepsis and, less commonly, awareness promotion efforts. Clickstream analysis suggests that most sepsis and septic shock Wikipedia pageviews originate from external referrals, namely search engines. Owing to its granular and publicly available traffic data, Wikipedia holds promise as a means by which to better understand global drivers of online sepsis information seeking. Further characterization of user engagement with this information may help to elucidate means by which to optimize the visibility, content, and delivery of awareness promotion efforts.

Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases

Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.

Benefits and Shortcomings of Direct-to-Consumer Hearing Devices: Analysis of Large Secondary Data Generated From Amazon Customer Reviews

Purpose The current study was aimed at understanding the benefits and shortcomings of direct-to-consumer hearing devices (DCHDs) by analyzing the large text corpus of secondary data generated from Amazon customer reviews. Method Secondary data were generated manually by gathering user feedback for 62 different DCHDs (cost range: $9.95-$635) on the Amazon.com website, which included 11,258 unique Amazon-verified customer reviews. The data were analyzed using both quantitative and qualitative analyses methods. Results The cluster analysis of large data corpus resulted in 7 unique clusters, which were labeled as (a) Issues related to fit and comfort (15%), (b) Friends and family recommendations (11.8%), (c) Issues related to sound quality (11.9%), (d) Listening and conversation (16.1%), (e) Positive customer service (12.1%), (f) General usage and customer service (14.7%), and (g) Cost and affordability (17.3%). Exploratory analysis also revealed an association between customer ratings and cost in relation to these clusters (i.e., customer reviews). For example, customer reviews about cheaper DCHDs are related to issues about sound quality, whereas reviews about expensive DCHDs are related to cost and affordability of the device. The qualitative content analysis resulted in 8 main themes, which include (a) intrinsic factors, (b) extrinsic factors, (c) supplemental items, (d) ease of use, (e) interaction with support services, (f) reasons for purchase, (g) experiences, and (h) general information. Conclusions The study using the text mining techniques highlights the benefits and shortcomings of DCHDs that are currently available in the U.S. market. Our findings relate well to the published study results of electroacoustic analysis on similar products, which provide clinicians with knowledge related to DCHDs that they can convey to consumers during clinical consultations. The findings may also be of interest to the hearing instrument industry from the perspective of developing products based on user feedback.

Potential uses of an infodemiology approach for health-care services for rheumatology

OBJECTIVES

This is a demand-based infodemiology study using the Google Trends and AdWords tools to illustrate infodemiology's potential use in rheumatology. The study investigates three questions in North American countries: (1) What terms associated with "rheumatology" and "arthritis" do people search for on Google? (2) What is the search volume for disease-modifying antirheumatic drugs (DMARDs)? and (3) What is the search volume for the term "arthritis" compared with for "hepatitis C" and "breast cancer"?

METHODS

We conducted independent searches by country and search term for 2015-2017. Seventeen DMARDs were searched for 2015 through May 2018, with the turmeric remedy included for comparison. Data were exported to Excel for further analysis, adjusted by country population, and expressed as searches per 100,000 inhabitants (SpTh).

RESULTS

There were approximately 550 associated terms for "arthritis" in each country, and 5679 SpTh for DMARDs across the three countries. Searches for turmeric numbered slightly lower than for all DMARDs together in Canada and the USA, but were 70% higher in Mexico. Turmeric was also searched four times more than the most-searched biological DMARD in Canada and the USA, and 60 times more in Mexico. Arthritis was more commonly searched for in Canada than hepatitis C and breast cancer, but hepatitis C was highest in the USA and breast cancer in Mexico. Monthly trends did not show expected peaks associated with arthritis awareness campaigns.

CONCLUSION

Infodemiology provides preliminary information that could help in generating hypotheses, assessing health-care interventions, or even in providing patient-centered care.

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.

Epistemic lenses and virtues, beyond evidence-based medicine

This editorial is based on the keynote by Dr Mark Murphy, Department of General Practice, Royal College of Surgeons, Ireland, at the Health Libraries Group conference, Keele University on 13-15 June 2018. https://bit.ly/2rubsIR#HLG2018.