Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era

Abstract

BackgroundWith the internet’s penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma—a common respiratory disease—is present.ObjectiveThis study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data.MethodsApplying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term “asthma,” forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined.ResultsOur analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years’ queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=–.82, P=.001) and current asthma (r=–.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=–.78, P=.003) and current asthma (r=–.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available.ConclusionsOnline behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma.

Winter peaks in web-based public inquiry into epistaxis

“…Although it is generally assumed (and even taught) at university level that the incidence of epistaxis increases during winter months, the literature itself remains contradictory. In this regard, Google Trends has become a valuable tool to expand existing knowledge and provide further information for hypothesis formulation [21,22]. Here, we showed for the first-time seasonal differences in Google search query frequency for epistaxis-related search terms in various countries, from both hemispheres, indicating that the search query frequency for epistaxis-related search terms follow a seasonal pattern.…”

“…A popular tool for big data mining is represented by Google Trends (GT), which allows users to explore oscillations in public interest, by selecting filters on geographical location and time [14]. Owing to these circumstances, it is not surprising that many health-related issues have already been associated with internet-search peaks in recent years to complement traditional data sources [15][16][17][18][19][20][21][22]. Noteworthy, previous studies on nose bleeding and GT suffered from two major methodological shortcomings: First, no systematical approach Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s0040 5-020-05915 -x) contains supplementary material, which is available to authorized users.…”

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

“…These data have the advantage of being real time, thus tackling the issue of long periods of delay from gathering data to analysis and forecasting. Over the past decade, the field of infodemiology has been shown to be highly valuable in assessing health topics, retrieving web-based data from, for example, Google [ 29 , 30 ], Twitter [ 31 - 34 ], social media [ 35 , 36 ], or combinations of ≥2 Web-based data sources [ 37 , 38 ].…”

“…Google Trends has also been valuable in examining seasonal trends in various diseases and health issues, such as Lyme disease [ 66 ], urinary tract infection [ 67 ], asthma [ 30 ], varicose vein treatment [ 68 ], and snoring and sleep apnea [ 69 ]. Furthermore, Deiner et al [ 70 ] showed that indeed there exists the same seasonality in Google Trends and clinical diagnoses.…”

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

“…Their results show a seasonality pattern in searching for cancer. Mavragani et al [4] measured the online seeking behaviour towards asthma in the United States from 2004 to 2015 with the objective of predict future trends regarding asthma. Their results show robust forecasting models for a five-year period.…”