Bariatric surgery interest around the world: What Google Trends can teach us

BackgroundBariatric surgery may prove an effective weight loss option for those struggling with severe obesity, but it is difficult to determine levels of interest in such procedures at the population level through traditional approaches. Analysis of Google Trend information may give providers and healthcare systems useful information regarding Internet users’ interest in bariatric procedures. The objective of this study was to gather Google Trend information on worldwide Internet searches for “bariatric surgery”, “gastric bypass”, “gastric sleeve”, “gastric plication”, and “lap band” from 2004–2012 and to explore temporal relationships with relevant media events, economic variations, and policy modifications.MethodsData were collected using Google Trends. Trend analyses were performed using Microsoft Excel Version 14.3.5 and Minitab V.16.0.ResultsTrend analyses showed that total search volume for the term “bariatric surgery” has declined roughly 25% since January 2004, although interest increased approximately 5% from 2011 to 2012. Interest in lap band procedures declined 30% over the past 5 years, while “gastric sleeve” has increased 15%. Spikes in search numbers show an association with events such as changing policy and insurance guidelines and media coverage for bariatric procedures.ConclusionThis report illustrates that variations in Internet search volume for terms related to bariatric surgery are multifactorial in origin. Although it is impossible to ascertain if reported Internet search volume is based on interest in potentially undergoing bariatric surgery or simply general interest, this analysis reveals that search volume appears to mirror real world events. Therefore, Google Trends could be a way to supplement understanding about interest in bariatric procedures.     

Performance of eHealth Data Sources in Local Influenza Surveillance: A 5-Year Open Cohort Study

Background

There is abundant global interest in using syndromic data from population-wide health information systems—referred to as eHealth resources—to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments.

Objective

The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity.

Methods

An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases.

Results

Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data.

Conclusions

Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice.     

A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

Background

Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter’s relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.

Objective

The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.

Methods

From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords “flu”, “influenza”, “gripe”, and “high fever”. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.

Results

Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay’s Center and the Atlantic Avenue Terminal.

Conclusions

While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter’s strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.     

Pick Up a Book or “Google It?” A Survey of Radiologist and Trainee-Preferred References and Resources

The purpose of this study was to investigate radiologist and trainee-preferred sources for solving imaging questions. The institutional review board determined this study to be exempt from informed consent requirements. Web-based surveys were distributed to radiology staff and trainees at 16 academic institutions. Surveys queried ownership and use of tablet computers and habits of utilization of various electronic and hardcopy resources for general reference. For investigating specific cases, respondents identified a single primary resource. Comparisons were performed using Fisher’s exact test. For staff, use of Google and online journals was nearly universal for general imaging questions (93 [103/111] and 94 % [104/111], respectively). For trainees, Google and resident-generated study materials were commonly utilized for such questions (82 [111/135] and 74 % [100/135], respectively). For specific imaging questions, online journals and PubMed were rarely chosen as a primary resource; the most common primary resources were STATdx for trainees and Google for staff (44 [55/126] and 52 % [51/99], respectively). Use of hard copy journals was nearly absent among trainees. Sixty percent of trainees (78/130) own a tablet computer versus 41 % of staff (46/111; p = 0.005), and 71 % (55/78) of those trainees reported at least weekly use of radiology-specific tablet applications, compared to 48 % (22/46) of staff (p < 0.001). Staff radiologists rely heavily on Google for both general and specific imaging queries, while residents utilize customized, radiology-focused products and apps. Interestingly, residents note continued use of hard copy books but have replaced hard copy journals with online resources.     

Know Your Market: Use of Online Query Tools to Quantify Trends in Patient Information-seeking Behavior for Varicose Vein Treatment

PurposeTo analyze Internet search data to characterize the temporal and geographic interest of Internet users in the United States in varicose vein treatment.Materials and MethodsFrom January 1, 2004, to September 1, 2012, the Google Trends tool was used to analyze query data for “varicose vein treatment” to identify individuals seeking treatment information for varicose veins. The term “varicose vein treatment” returned a search volume index (SVI), representing the search frequency relative to the total search volume during a specific time interval and region. Linear regression analysis and Kruskal-Wallis one-way analysis of variance were employed to characterize search results.ResultsSearch traffic for varicose vein treatment increased by 520% over the 104-month study period. There was an annual mean increase of 28% (range, −18%–100%; standard deviation [SD], 35%), with a statistically significant linear increase in average yearly SVI over time (R² = 0.94, P < .0001). All years showed positive growth in mean SVI except for 2008 (18% decrease). There were statistically significant differences in SVI by month (Kruskal-Wallis, P < .0001) with significantly higher mean SVI compared with other months in May (190% increase; range, 26%–670%; SD, 15%) and June (209% increase; range, 35%–700%; SD, 20%). The southern United States showed significantly higher search traffic than all other regions (Tukey-Kramer, P < .00001).ConclusionsThere have been significant increases in Internet search traffic related to varicose vein treatment in the past 8 years. Reflected in this trend is an annual peak in search traffic in the late spring months with an overall geographic bias toward southern states. Rigorous analysis of Internet search queries for medical procedures may prove useful to guide the efficient use of limited resources and marketing dollars.     

Google Scholar is not enough to be used alone for systematic reviews

Background: Google Scholar (GS) has been noted for its ability to search broadly for important references in the literature. Gehanno et al. recently examined GS in their study: ‘Is Google scholar enough to be used alone for systematic reviews?’ In this paper, we revisit this important question, and some of Gehanno et al.’s other findings in evaluating the academic search engine.Methods: The authors searched for a recent systematic review (SR) of comparable size to run search tests similar to those in Gehanno et al. We selected Chou et al. (2013) contacting the authors for a list of publications they found in their SR on social media in health. We queried GS for each of those 506 titles (in quotes "), one by one. When GS failed to retrieve a paper, or produced too many results, we used the allintitle: command to find papers with the same title.Results: Google Scholar produced records for ~95% of the papers cited by Chou et al. (n=476/506). A few of the 30 papers that were not in GS were later retrieved via PubMed and even regular Google Search. But due to its different structure, we could not run searches in GS that were originally performed by Chou et al. in PubMed, Web of Science, Scopus and PsycINFO®. Identifying 506 papers in GS was an inefficient process, especially for papers using similar search terms.Conclusions: Has Google Scholar improved enough to be used alone in searching for systematic reviews? No. GS’ constantly-changing content, algorithms and database structure make it a poor choice for systematic reviews. Looking for papers when you know their titles is a far different issue from discovering them initially. Further research is needed to determine when and how (and for what purposes) GS can be used alone. Google should provide details about GS’ database coverage and improve its interface (e.g., with semantic search filters, stored searching, etc.). Perhaps then it will be an appropriate choice for systematic reviews.     

Using Google Dengue Trends to Estimate Climate Effects in Mexico

Objective

To evaluate the association between Dengue Fever (DF) and climate in Mexico with real-time data from Google Dengue Trends (GDT) and climate data from NASA Earth observing systems.

Introduction

The incidence of dengue fever (DF) has increased 30 fold between 1960 and 2010 [1]. The literature suggests that temperature plays a major role in the life cycle of the mosquito vector and in turn, the timing of DF outbreaks [2]. We use real-time data from GDT and real-time temperature estimates from NASA Earth observing systems to examine the relationship between dengue and climate in 17 Mexican states from 2003–2011. For the majority of states, we predict that a warming climate will increase the number of days the minimum temperature is within the risk range for dengue.

Methods

The GDT estimates are derived from internet search queries and use similar methods as those developed for Google Flu Trends [3]. To validate GDT data, we ran a correlation between GDT and dengue data from the Mexican Secretariat of Health (2003–2010). To analyze the relationship between GDT and varying lags of temperature, we constructed a time series meta-analysis. The mean, max and min of temperature were tested at lags 0 –12 weeks using data from the Modern Era Retrospective-Analysis for Research and Applications. Finally, we built a binomial model to identify the minimum 5° C temperature range associated with a 50% or higher Dengue activity threshold as predicted by GDT.

Results

The time series plot of GDT data and the Mexican Secretariat of Health data (2003– 2010) (Figure 1) produced a correlation coefficient of 0.87. The time series meta-analysis results for 17 states showed an increase in minimum temperature at lag week 8 had the greatest odds of dengue incidence, 1.12 Odds Ratio (1.09–1.16, 95% Confidence Interval). The comparison of dengue activity above 50% in each state to the minimum temperature at lag week 8 showed 14/17 states had an association with warmest 5 degrees of the minimum temperature range. The state of Sonora was the only state to show an association between dengue and the coldest 5 degrees of the minimum temperature range.Open in a separate windowFigure 1Time Series Correlation: Google Dengue Trends vs. Secretariat of Health, Mexico 2003–2010

Conclusions

Overall, the incidence data from the Mexican Secretariat of Health showed a close correlation with the GDT data. The meta-analysis indicates that an increase in the minimum temperature at lag week 8 is associated with an increased dengue risk. This is consistent with the Colon-Gonzales et al. Mexico study which also found a strong association with the 8 week lag of increasing minimum temperature [4]. The results from this binomial regression show, for the majority of states, the warmest 5 degree range for the minimum temperature had the greatest association with dengue activity 8 weeks later. Inevitably, several other factors contribute to dengue risk which we are unable to include in this model [5]. IPCC climate change predictions suggest a 4° C increase in Mexico. Under such scenario, we predict an increase in the number of days the minimum temperature falls within the range associated with DF risk.     

基于云计算平台的个人健康管理系统

个人健康管理系统作为现代医院健康管理系统的一个重要分支,在整个医疗体系中起着重要作用。论述了通过Google Health公共云计算平台构建一种个人健康信息管理的方法,设计实现一套基于云计算的健康管理系统。云计算作为一种新兴的网络技术,与医疗信息化结合,可提供安全可靠的医疗数据存储中心,轻松实现医疗数据与应用共享;同时,云计算系统对用户端的设备要求低,使用方便。有别于传统的健康管理系统,本系统通过云计算技术对医疗数据进行在线存储管理和实时共享,减少用户终端的资源,实现对患者健康信息最实时、最准确的管理。