Google Trends can improve surveillance of Type 2 diabetes

Public interest in drug-related problems reflected in information search trends: an infodemiological study

BACKGROUND

The analysis of how people search and "navigate" the internet to obtain health-related information and how they communicate and share this information can provide valuable knowledge about the disease patterns behaviour and health habits of populations.

OBJECTIVE

To determine the population's interest in drug-related problems through information search trends.

METHOD

A descriptive ecological correlational study, based on obtaining Google Trends data.

VARIABLES STUDIED

relative search volume (RSV), evolution over time, milestones and seasonality.

RESULTS

The most searched topic was drug overdose, with mean RSV of 56.25 ± 0.65. The highest increase occurred in the contraindication topic (R2 = 0.87, p < 0.001). The main milestone was observed in the drug overdose topic in July 2018 (RSV = 100). A very close relationship was found between adverse drug reaction and contraindication (R = 0.89, p < 0.001). Slight seasonality was noted in the adverse drug reaction (augmented Dickey-Fuller test [ADF] = -1.96), contraindication (ADF = -2.66) and drug interaction (ADF = -1.67) topics, but did not show an epidemiological trend.

CONCLUSIONS

The greatest public interest was found in the drug overdose and contraindication topics, which showed a stronger upward trend, although the seasonality study did not show any very notable data or demonstrate epidemiological information search behaviour. The main milestone observed was due to media factors related to the consumption of narcotics. There was a clear difference in English-speaking countries in the use of the drug overdose topic. A correlation between the adverse drug reaction and contraindication topics was confirmed.

Mining Google Trends data for nowcasting and forecasting colorectal cancer (CRC) prevalence

Background

Colorectal cancer (CRC) is the third most prevalent and second most lethal form of cancer in the world. Consequently, CRC cancer prevalence projections are essential for assessing the future burden of the disease, planning resource allocation, and developing service delivery strategies, as well as for grasping the shifting environment of cancer risk factors. However, unlike cancer incidence and mortality rates, national and international agencies do not routinely issue projections for cancer prevalence. Moreover, the limited or even nonexistent cancer statistics for large portions of the world, along with the high heterogeneity among world nations, further complicate the task of producing timely and accurate CRC prevalence projections. In this situation, population interest, as shown by Internet searches, can be very important for improving cancer statistics and, in the long run, for helping cancer research.

Methods

This study aims to model, nowcast and forecast the CRC prevalence at the global level using a three-step framework that incorporates three well-established univariate statistical and machine-learning models. First, data mining is performed to evaluate the relevancy of Google Trends (GT) data as a surrogate for the number of CRC survivors. The results demonstrate that population web-search interest in the term "colonoscopy" is the most reliable indicator to nowcast CRC disease prevalence. Then, various statistical and machine-learning models, including ARIMA, ETS, and FNNAR, are trained and tested using relevant GT time series. Finally, the updated monthly query series spanning 2004-2022 and the best forecasting model in terms of out-of-sample forecasting ability (i.e., the neural network autoregression) are utilized to generate point forecasts up to 2025.

Results

Results show that the number of people with colorectal cancer will continue to rise over the next 24 months. This in turn emphasizes the urgency for public policies aimed at reducing the population's exposure to the principal modifiable risk factors, such as lifestyle and nutrition. In addition, given the major drop in population interest in CRC during the first wave of the COVID-19 pandemic, the findings suggest that public health authorities should implement measures to increase cancer screening rates during pandemics. This in turn would deliver positive externalities, including the mitigation of the global burden and the enhancement of the quality of official statistics.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis

(1) Objective: to identify and review existing infodemiological studies on nutritional disorders applied to occupational health and to analyse the effect of the intervention on body mass index (BMI) or alternatively body weight (BW); (2) Methods: This study involved a critical analysis of articles retrieved from MEDLINE (via PubMed), Embase, Cochrane Library, PsycINFO, Scopus, Web of Science, Latin American, and Caribbean Health Sciences Literature (LILACS) and Medicina en Español (MEDES) using the descriptors "Nutrition Disorders, "Occupational Health" and "Infodemiology", applying the filters "Humans" and "Adult: 19+ years". The search was conducted on 29 May 2021; (3) Results: a total of 357 references were identified from the bibliographic database searches; after applying the inclusion and exclusion criteria, a total of 11 valid studies were obtained for the review. Interventions could be categorised into (1) interventions related to lifestyle, physical activity, and dietary changes through education programmes, (2) interventions associated with lifestyle, physical activity, and dietary changes through the use of telemonitoring systems or self-help applications, (3) interventions tied to lifestyle, physical activity, and dietary changes through control and/or social network support groups, and (4) interventions linked to changes in the work environment, including behavioural change training and work environment training tasks. The meta-analysis demonstrated that the heterogeneity present when analysing the results for BMI was 72% (p < 0.01), which decreased to 0% (p = 0.57) when analysing the outcomes for weight, in which case the null hypothesis of homogeneity could be accepted. In all instances, the final summary of the effect was on the decreasing side for both BMI and BW; (4) Conclusions: Despite the high heterogeneity of the results reported, the trend shown in all cases indicates that the intervention methodologies implemented by empowering individuals through Web 2.0 technologies are positive in terms of the problem of overweight. Further implementation of novel strategies to support individuals is needed to overcome obesity, and, at least in the early studies, these strategies seem to be making the necessary change.

Cardiovascular diseases display etiological and seasonal trend in human population: Evidence from seasonal cardiovascular comorbid diseases (SCCD) index

Seasonal changes in the human cardiovascular system are known to play an important role in the onset of many diseases. Confounding variables include behavioral and environmental factors; failing to address such variables makes measuring the true temporal impact of these diseases difficult. On the other hand, numerous clinical studies imply that only specific groups of people are more seasonal sensitive and that their maladaptation might contribute to various illnesses. As a result, it is critical to evaluate the etiological and seasonal sensitive patterns of cardiovascular diseases (CVD), which impact the majority of the human population. The hypothesis for this study formulated that cardiovascular and associated illnesses had substantial connections with seasonal and etiological variations. Thus in the present study, 4519 systematic screen-eligible studies were analyzed using data mining to uncover 852 disease association relationships between cardiovascular and associated disorders. A disease ontology-based semantic similarity network (DSN) analysis was performed to narrow down the identified CVDs. Further, topological analysis was used to predict the seven CVDs, including myocardial infarction (MI), in three clusters. Following that, Mann-Kendall and Cox-Stuart analyses were used to investigate the seasonal sensitivity and temporal relationship of these seven CVDs. Finally, temporal relationships were confirmed using LOESS and TBATS, as well as seasonal breakdown utilizing autocorrelation and fast Fourier transform results. The study provides indirect evidence of a severe etiological association among the three cardiovascular diseases, including MI, atrial fibrillation, and atherosclerosis, which are winter season sensitive in most of the world population. Hypertension has two seasonal falls and peaks due to its seasonal nature, that is, summer and winter hypertension. While, heart failure was also identified, with minor temporal trends. Hence, all five diseases could be classified as seasonal cardiovascular comorbid diseases (SCCD). Furthermore, these diseases could be studied for potential common risk factors such as biochemical, genetic, and physiological factors.

Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

The COVID-19 Pandemic and the Interest in Prayer and Spirituality in Poland According to Google Trends Data in the CONTEXT of the Mediatisation of Religion Processes

The research undertaken in this article uses the Google Trends tool to study the degree of interest in prayer and general spirituality during the initial phase of the COVID-19 pandemic in Poland and Europe. The authors assumed that for people interested in prayer during the COVID-19 pandemic, the Internet served as a virtual prayer book. The main research questions addressed the frequency of typed queries, referring not only to the word “prayer” but also to specific types of prayer. In addition, interest in prayer was compared with interest in the word “prophecy” to explore the relationship between religiosity and interest in the supernatural sphere in its broadest sense. The analysis shows that there is distinct recurrence regarding the terms searched, with some of them noticeably intensifying with the outbreak of the COVID-19 pandemic. The findings also show that keywords related to prophecies were searched more frequently at significant moments in Polish history (2005—the death of John Paul II, 2010—the plane crash in which the President of Poland died) than in the months of 2020 when the pandemic struck and escalated. At that time, searches related to religion were more frequent. It can also be concluded that the outbreak of the pandemic contributed to an increase in the religious activity of Poles. The article is interdisciplinary in nature, referring primarily to Religion Studies and Mass Media and Communication Studies.

Evolution of Nutritional Habits Behaviour of Spanish Population Confined Through Social Media

Introduction: In Spain, on 14 March 2020, a state of alarm is declared to face the health emergency situation caused by the COVID-19 coronavirus, limiting the freedom of movement of people. The Spanish population is confined. Objective: With this situation, "NUTRITIONAL HEALTH IS NOT CONFINED" arises a research project that seeks to promote nutritional education based on the pattern of the Mediterranean diet (MD) using new computer technologies. It is about providing the population with the information of general interest about the promotion of a healthy diet through social networks and analysing the impact of its dissemination, in the form of a longitudinal intervention study of the Spanish nutritional evolution during confinement, with a daily survey format, and it is intended to assess food consumption during the period of confinement. Materials and methods: In total, 936 participants were asked every day. Short publications were published every day based on the scientific evidence (FAO, WHO, AECOSAN) through social media such as Instagram, accompanied by a questionnaire of 11 questions (yes/no) where it was intended to assess the evolution of daily consumption. Results and Discussion: The diffusion through social media has allowed to have a greater reach of the population. We observed that mood throughout confinement generally improves. There are certain eating habits from the MD that are well established in the daily diet of our population, such as the consumption of fruits, vegetables, legumes, dairy products, and eggs. It seems that enjoying good health is a growing concern in pandemic situations, which is why inappropriate behaviours such as "snacking" between meals or the consumption of processed foods such as snacks, industrial pastries, soft drinks, and sweets are avoided, increasing the amount of healthy food such as meat and fish. This study opens up future avenues of research promoting MD and implements new cohort nutritional databases, especially about young adult people, who are adept at navigating digital spaces and therefore using social media.

The Pearls and Perils of Google Trends: A Housing Market Application

This study aims at providing insights into the correct usage of Google search data, which are available through Google Trends. The focus is on the effect of sampling errors, which has not received the attention that it deserves. A housing market application is used to demonstrate the effects. For this purpose, the relationship between online search activity for mortgages and real housing market activity is investigated. A simple time series model, which explains transactions by an online mortgage search, is estimated. The results show that the effect of sampling errors is substantial. Thus, although the application of Google Trends data in research remains promising, far more attention should be given to the limitations of these data.

Trends and Seasonality of Information Searches Carried Out through Google on Nutrition and Healthy Diet in Relation to Occupational Health: Infodemiological Study

This study aimed to analyze and relate the population interest through information search trends on Nutrition and Healthy Diet (HD) with the Occupational Health (OH). Ecological and correlational study of the Relative Search Volume (RSV) obtained from Google Trends query, segmented in two searched periods concerning antiquity; date of query: 20 April 2021. The RSV trends for the analyzed three Topics were: Nutrition (R² = 0.02), HD (R² = 0.07) and OH (R² = -0.72). There was a good positive correlation between Nutrition and OH (R = 0.56, p < 0.001) and a moderate one between HD and OH (R = 0.32, p < 0.001). According to seasons, differences were verified between RSV means in the Topics HD (p < 0.01) and OH (p < 0.001). Temporal dependence was demonstrated on Nutrition searches (Augmented Dickey-Fuller = -2.35, p > 0.05). There was only a significant relationship between the RSV Topic HD (p < 0.05) for the Developing and Least Developed countries. The data on the analyzed RSV demonstrated diminishing interest in the search information on HD and OH as well as a clearly positive trend change in recent years for Nutrition. A good positive correlation was observed between the RSV of nutrition and OH whereas the correlation between HD and OH was moderate. There were no milestones found that may report a punctual event leading to the improvement of information searches. Temporal dependence was corroborated in the RSV on Nutrition, but not in the other two Topics. Strangely, only an association was found on HD searches between the Developing and Least Developed Countries. The study of information search trends may provide useful information on the population's interest in the disease data, as well as would gradually allow the analysis of differences in popularity, or interest even between different countries. Thus, this information might be used as a guide for public health approaches regarding nutrition and a healthy diet at work.

Environmental risk factor assessment for major respiratory disorders in metropolitan cities of India using VIIRS Suomi Aerosol data and Google Trends

This study has investigated the association between the amount of atmospheric aerosols and the occurrences of Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer in Delhi, Mumbai, Chennai, Kolkata and Bengaluru. Aerosol Optical Thickness (AOT) data of Visible Infrared Imaging Radiometer Suite (VIIRS) and Google Trends (GT) have been used to acquire information regarding the abundance of atmospheric aerosols and the occurrences of the respiratory diseases respectively. The result of Granger causality test between AOT and GT has shown that Delhi, Mumbai and Chennai were quite vulnerable to the three respiratory diseases whereas Bengaluru did not display so much vulnerability to these ailments. Kolkata was not so much vulnerable to Asthma but did exhibit susceptibility to the other two diseases. GT is validated by correlating with Annual Morbidity data of Delhi. The result of Granger causality test between Particulate Matter (diameter ≤ 10 μm) (PM10) data and GT validates the result of Granger causality between AOT and GT, and shows the trustworthiness of GT and AOT. Thus, this study also proves the usefulness of VIIRS AOT and GT as dependable sources of information on atmospheric aerosols and prevalence of the respiratory diseases respectively, and the effectiveness of Granger causality test as a tool of analysis in health and geographic information systems (GIS).

Will I die of coronavirus? Google Trends data reveal that politics determine virus fears

Is Google Trends (GT) useful to survey populations? Extant work has shown that certain search queries reflect the attitudes of hard-to-survey populations, but we do not know if this extends to the general population. In this article, we leverage abundant data from the Covid-19 pandemic to assess whether people's worries about the pandemic match epidemiological trends as well as political preferences. We use the string 'will I die from coronavirus' on GT as the measure for people's level of distress regarding Covid-19. We also test whether concern for coronavirus is a partisan issue by contrasting GT data and 2016 election results. We find strong evidence that (1) GT search volume close matches epidemiological data and (2) significant differences exist between states that supported Clinton or Trump in 2016.

Group-level human values estimated with web search data and archival data explain the geographic variation in COVID-19 severity in the United States

OBJECTIVES

Personal Focus values (PF = Openness to change values + Self-enhancement values) motivate self-centred behaviours, and Social Focus values (SF = Conservation values + Self-transcendence values) promote self-sacrificial counterparts. This research investigated how a state-level PF-SF value-continuum would explain the geographic variation in COVID-19 severity in the United States.

DESIGN

This research estimated state-level values by Google search data (from 2004 to 2019) on value-related words (e.g. family for conservation values) (Study 1a) and archival indicators (e.g. gun ownership rate for security values) (Study 1b). COVID-19 severity was measured by shorter time delay of first documented cases, shorter overall doubling times, higher reproductive ratio and higher case fatality ratio. Hierarchical and multilevel analyses examined how state-level values would predict COVID-19 severity across U.S. states (Studies 1a and 1b) and 3,135 counties (Study 2).

RESULTS

State-level analyses accounting for spatial autocorrelation and covariates (e.g. COVID-19 testing rate, airport traffic, personality, etc.) revealed that the PF-SF value-continuums measured with different methods positively and significantly predicted COVID-19 severity, and the effects of state-level values on county-level COVID-19 severity were significant when county- and state-level covariates were controlled.

CONCLUSION

Social focus values may mitigate the devastating effect of COVID-19 in the United States.

A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States Through Google Trends

PURPOSE

Our study fills the spatiotemporal gaps in dry eye disease (DED) epidemiology by using Google Trends as a novel epidemiological tool for geographically mapping DED in relation to environmental risk factors.

METHODS

We used Google Trends to extract DED-related queries estimating users' intent from 2004 to 2019 in the United States. We incorporated national climate data to generate heat maps comparing geographic, temporal, and environmental relationships of DED. Multivariable regression models were constructed to generate quadratic forecasts predicting DED and control searches.

RESULTS

Our results illustrated the upward trend, seasonal pattern, environmental influence, and spatial relationship of DED search volume across the US geography. Localized patches of DED interest were visualized in urban areas. There was no significant difference in DED queries across the US census regions (P = 0.3543). Regression model 1 predicted DED queries per state (R2 = 0.61), with the significant predictor being urban population [r = 0.56, adjusted (adj.) P < 0.001, n = 50]; model 2 predicted DED searches over time (R2 = 0.97), with significant predictors being control queries (r = 0.85, adj. P = 0.0169, n = 190), time (r = 0.96, adj. P < 0.001, n = 190), time2 (r = 0.97, adj. P < 0.001, n = 190), and seasonality (winter r = -0.04, adj. P = 0.0196, n = 190; spring r = 0.10, adj. P < 0.001, n = 190).

CONCLUSIONS

Our study used Google Trends as a novel epidemiologic approach to geographically mapping the US DED. Importantly, urban population and seasonality were stronger risk factors of DED searches than temperature, humidity, sunshine, pollution, or region. Our work paves the way for future exploration of geographic information systems for locating DED and other diseases through online population metrics.

Poor Medication Adherence in African Americans Is a Matter of Trust

The purpose of this paper was to explore the lack of medication adherence in the African American patient community. The paper will address myths that many African American patients believe related to type 2 diabetes, hypertension treatment, and natural remedies. Research has shown that the disparities in the acceptance of healthcare provider advice and prescriptions are a significant concern particularly in African American patients. The acceptance of a provider's diagnosis and subsequent intervention can vary based on several issues including healthcare access, patient preferences, trust of a provider, and treatment recommendations. Patient influences can range from their ability to trust the provider (and what he/she says) to following through with their advice. Several studies have looked at the beliefs and misconceptions some African Americans hold regarding the treatment of hypertension and diabetes with some showing a preference for "natural remedies." This paper will leverage a literature review to seek out myths from African American patients to understand why they are dealing with challenges related to adherence with medication. The searches identified 58 research papers. The study applied an inductive content analysis approach to assess the research papers and identify themes. The barriers identified in this study include disbelief of the diagnosis, distrust for medication, mistrust for physicians and healthcare system, belief in alternative medicine, cultural/generation norms, and access/affordability of care. One of the most prominent factors that crossed all barriers was medical mistrust.

Forecasting tuberculosis using diabetes-related google trends data

Online activity-based data can be used to aid infectious disease forecasting. Our aim was to exploit the converging nature of the tuberculosis (TB) and diabetes epidemics to forecast TB case numbers. Thus, we extended TB prediction models based on traditional data with diabetes-related Google searches. We obtained data on the weekly case numbers of TB in Germany from June 8th, 2014, to May 5th, 2019. Internet search data were obtained from a Google Trends (GTD) search for 'diabetes' to the corresponding interval. A seasonal autoregressive moving average (SARIMA) model (0,1,1) (1,0,0) [52] was selected to describe the weekly TB case numbers with and without GTD as an external regressor. We cross-validated the SARIMA models to obtain the root mean squared errors (RMSE). We repeated this procedure with autoregressive feed-forward neural network (NNAR) models using 5-fold cross-validation. To simulate a data-poor surveillance setting, we also tested traditional and GTD-extended models against a hold-out dataset using a decreased 52-week-long period with missing values for training. Cross-validation resulted in an RMSE of 20.83 for the traditional model and 18.56 for the GTD-extended model. Cross-validation of the NNAR models showed a mean RMSE of 19.49 for the traditional model and 18.99 for the GTD-extended model. When we tested the models trained on a decreased dataset with missing values, the GTD-extended models achieved significantly better prediction than the traditional models (p < 0.001). The GTD-extended models outperformed the traditional models in all assessed model evaluation parameters. Using online activity-based data regarding diabetes can improve TB forecasting, but further validation is warranted.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Google trend analysis of climatic zone based Indian severe seasonal sensitive population

Background

Our earlier Google Trend (GT) Analytics study reported that the worldwide human population severely subject to four seasonal (sensitive) comorbid lifestyle diseases (SCLD) such as asthma, obesity, hypertension and fibrosis. The human population subject to seasonal variability in these four diseases activity referred as “severe seasonal sensitive population”. In India, the estimated burden of these four seasonal diseases is more than 350 million as on the year 2018. It is a growing crisis for India with a projected disease burden of 500 million in the year 2025. This study was aimed to decipher the genuine SCLD seasonal trends in the entire Indian population using GT and validate these trends in Indian climatic zones.

Methods

GT is used to study the temporal trends in web search using weekly Relative Search Volume (RSV) for the period 2004 to 2017. The relative search volume (RSV) of the four-severe seasonal comorbid diseases namely Asthma, Hypertension, Obesity and Fibrosis were collected with and without obesity as the reference. The RSV were collected using the GT selection options as (i) Whole India (ii) Jammu and Kashmir (Cold zone) (iii) Rajasthan (Hot and Dry zone) (iii) West Bengal (Hot and Humid zone) and (iv) Uttar Pradesh state (Composite zone). The time series analysis was carried out to find seasonal patterns, comorbidity, trends and periodicity in the entire India and four of its states (zones).

Results

Our analysis of entire India (2004–2017) revealed high significant seasonal patterns and comorbidity in all the four diseases of SCLD. The positive tau values indicated strong positive seasonal trends in the SCLD throughout the period (Table). The auto correlation analysis revealed that these diseases were subjected to 3, 4 and 6 months period seasonal variations. Similar seasonal patterns and trends were also observed in all the four Indian temperature zones. Overall study indicated that SCLD seasonal search patterns and trends are highly conserved in India even in drastic Indian climatic zones.

Conclusions

The clinical outcome arise out of these observations could be of immense significance in handling the major chronic life style diseases asthma, hypertension, obesity and fibrosis. The possible strong comorbid relationship among asthma, hypertension, obesity and fibrosis may be useful to segregate Indian seasonal sensitive population. In disease activity-based chronotherapy, the search interest of segment of the population with access to Internet may be used as an indicator for public health sectors in the early detection of SCLD from a specific country or a region. As this disease population could be highly subject to the adverse effect of seasons in addition to life style and other environmental factors. Our study necessitates that these Indian populations need special attention from the Indian health care sectors.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Emerging Approaches in Surveillance of Type 1 Diabetes

PURPOSE OF REVIEW

Surveillance of type 1 diabetes provides an opportunity to address public health needs, inform etiological research, and plan health care services. We present issues in type 1 diabetes surveillance, review previous and current methods, and present new initiatives.

RECENT FINDINGS

Few diabetes surveillance systems distinguish between type 1 and type 2 diabetes. Most worldwide efforts have focused on registries and ages < 15 years, resulting in limited information among adults. Recently, surveillance includes use of electronic health information and national health surveys. However, distinguishing by diabetes type remains a challenge. Enhancing and improving surveillance of type 1 diabetes across all age groups could include validating questions for use in national health surveys. In addition, validated algorithms for classifying diabetes type in electronic health records could further improve surveillance efforts and close current gaps in our understanding of the epidemiology of type 1 diabetes.

From Diabetes Care to Diabetes Cure-The Integration of Systems Biology, eHealth, and Behavioral Change

From a biological view, most of the processes involved in insulin resistance, which drives the pathobiology of type 2 diabetes, are reversible. This theoretically makes the disease reversible and curable by changing dietary habits and physical activity, particularly when adopted early in the disease process. Yet, this is not fully implemented and exploited in health care due to numerous obstacles. This article reviews the state of the art in all areas involved in a diabetes cure-focused therapy and discusses the scientific and technological advancements that need to be integrated into a systems approach sustainable lifestyle-based healthcare system and economy. The implementation of lifestyle as cure necessitates personalized and sustained lifestyle adaptations, which can only be established by a systems approach, including all relevant aspects (personalized diagnosis and diet, physical activity and stress management, self-empowerment, motivation, participation and health literacy, all facilitated by blended care and ehealth). Introduction of such a systems approach in type 2 diabetes therapy not only requires a concerted action of many stakeholders but also a change in healthcare economy, with new winners and losers. A "call for action" is put forward to actually initiate this transition. The solution provided for type 2 diabetes is translatable to other lifestyle-related disorders.