scholarly journals What value can Google search data add to existing syndromic surveillance systems?

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Helen K. Green ◽  
Obaghe Edeghere ◽  
Alex Elliot ◽  
Ingemar Cox ◽  
Rachel McKendry ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Added Value ◽  
Surveillance Systems ◽  
Search Term ◽  
Public Health Importance ◽  
Health Events ◽  
Search Terms ◽  
Search Data ◽  
Google Search

ObjectiveTo carry out an observational study to explore what added value Google search data can provide to existing routine syndromic surveillance systems in England for a range of conditions of public health importance and summarise lessons learned for other countries.IntroductionGlobally, there have been various studies assessing trends in Google search terms in the context of public health surveillance1. However, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. A proposed advantage is improved timeliness relative to established surveillance systems. However, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. Google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services.MethodsWe sought to determine the added public health utility of Google search data alongside established syndromic surveillance systems in England2 for a range of conditions of public health importance, including allergic rhinitis, scarlet fever, bronchitis, pertussis, measles, rotavirus and the health impact of heatwaves. Google search term selection was based on diagnostic and clinical codes underlying the syndromic indicators, with Google Trends3 used to identify additional related internet search terms. Daily data was extracted from syndromic surveillance systems2 and from the Google Health Trends Application Programming Interface (API) from 2012 to 2017 and a retrospective daily analysis undertaken during pre-identified public health events to identify a) whether signals were detected during these events and b) assess the correlation with analogous syndromic surveillance indicators through calculation of Spearman correlation coefficients and lag assessment to determine timeliness.ResultsWe detected increases in Google search term frequency during public health events of interest. Good correlation was seen with comparable syndromic surveillance indicators on a daily timescale for several health outcomes, including the search terms hayfever, scarlet fever, bronchiolitis and heatstroke. Weaker correlation was seen for conditions which occur in small numbers and are vaccine preventable such as measles and pertussis. Lag analysis showed similar timeliness between daily syndromic and Google data, suggesting that, overall, Google data did not provide an earlier or delayed signal compared to syndromic surveillance indicators in England.ConclusionsTo the best of our knowledge this is the first time trends in Google search data have been compared against syndromic data for a range of public health conditions in England. These findings demonstrate the potential utility of internet search query data in conjunction with existing systems in England, with syndromic surveillance data found to be as timely as Google data. These findings also have important implications for countries where there are no such healthcare-based syndromic surveillance systems in place. Factors to consider with analyses of Google search trend data in the context of disease surveillance have been highlighted, including the choice of search terms and interpretation of the reasons behind searching the internet.References1Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI, Murugiah K. The use of google trends in health care research: a systematic review. PLoS One. 2014 Oct 22;9(10):e109583.2Public Health England. Syndromic surveillance: systems and analyses. 2017. Available online: https://www.gov.uk/government/collections/syndromic-surveillance-systems-and-analyses3Google. 2017. Google Trends. Available online:https://trends.google.com/trends/

scholarly journals Syndromic surveillance: two decades experience of sustainable systems – its people not just data!

2019 ◽  
Vol 147 ◽  
Author(s):  
Gillian E. Smith ◽  
Alex J. Elliot ◽  
Iain Lake ◽  
Obaghe Edeghere ◽  
Roger Morbey ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Clinical Signs ◽  
Added Value ◽  
Surveillance Systems ◽  
Public Health Action ◽  
Related Data ◽  
The Impact

AbstractSyndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of ‘big data’, but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services.

scholarly journals Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Christopher H. Arehart ◽  
Michael Z. David ◽  
Vanja Dukic
Keyword(s):  
Public Health ◽  
Linear Models ◽  
State Level ◽  
Individual State ◽  
Control And Prevention ◽  
Search Data ◽  
State Models ◽  
Uniform Manner ◽  
Google Search ◽  
The U.S

AbstractThe Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004–2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2 = 0.2369, p < 0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.

scholarly journals Infodemiology of systemic lupus erythematous using Google Trends

Lupus ◽  
2017 ◽  
Vol 26 (8) ◽  
pp. 886-889 ◽  
Author(s):  
M Radin ◽  
S Sciascia
Keyword(s):  
Big Data ◽  
Northern Hemisphere ◽  
Lupus Erythematosus ◽  
Kendall Test ◽  
Google Trends ◽  
Data Monitoring ◽  
Search Term ◽  
Systemic Lupus ◽  
Search Terms ◽  
Google Search

Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the Internet and search engines to look for terms related to their disease and its possible causes, symptoms and treatments. ‘Infodemiology’ and ‘infoveillance’ are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach. Methods We analysed the large amount of data generated by Google Trends, considering ‘lupus’, ‘relapse’ and ‘fatigue’ in a 10-year web-based research. Google Trends automatically normalized data for the overall number of searches, and presented them as relative search volumes, in order to compare variations of different search terms across regions and periods. The Menn–Kendall test was used to evaluate the overall seasonal trend of each search term and possible correlation between search terms. Results We observed a seasonality for Google search volumes for lupus-related terms. In the Northern hemisphere, relative search volumes for ‘lupus’ were correlated with ‘relapse’ (τ = 0.85; p = 0.019) and with fatigue (τ = 0.82; p = 0.003), whereas in the Southern hemisphere we observed a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.85; p = 0.018). Similarly, a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.70; p < 0.001) was seen also in the Northern hemisphere. Conclusion Despite the intrinsic limitations of this approach, Internet-acquired data might represent a real-time surveillance tool and an alert for healthcare systems in order to plan the most appropriate resources in specific moments with higher disease burden.

scholarly journals Case ascertainment in active paediatric surveillance systems: a report from the British Paediatric Surveillance Unit Ascertainment Group

2019 ◽  
Vol 105 (1) ◽  
pp. 62-68
Author(s):  
Richard M Lynn ◽  
Richard Reading
Keyword(s):  
Public Health ◽  
Active Surveillance ◽  
Surveillance System ◽  
Primary Outcome ◽  
Case Finding ◽  
Surveillance Systems ◽  
Public Health Importance ◽  
Surveillance Studies ◽  
Study Team ◽  
Surveillance Unit

The British Paediatric Surveillance Unit (BPSU) conducts surveillance of rare paediatric conditions using active, or prospective, case finding. The reliability of estimates of incidence, which is the primary outcome of public health importance, depends on ascertainment being as near complete as possible. This paper reviews evidence of the completeness of ascertainment in recent surveillance studies run through the BPSU. Ascertainment varied between 49% and 94% depending on the study. These are upper estimates. This was the basis of a discussion on barriers and facilitators of ascertainment which we have separated into factors related to the condition, factors related to the study methods, factors related to the study team and factors related to the surveillance system infrastructure. This leads to a series of recommendations to ensure continuing high levels of ascertainment in active surveillance studies.

scholarly journals Developing Syndromic Surveillance to Monitor and Respond to Adverse Health Events Related to Psychoactive Substance Use: Methods and Applications

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 65S-72S ◽  
Author(s):  
Michelle L. Nolan ◽  
Hillary V. Kunins ◽  
Ramona Lall ◽  
Denise Paone
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
York City ◽  
Syndromic Surveillance ◽  
Psychoactive Drug ◽  
Surveillance Systems ◽  
Chief Complaints ◽  
Level Information ◽  
Ed Visits

Introduction: Recent increases in drug overdose deaths, both in New York City and nationally, highlight the need for timely data on psychoactive drug-related morbidity. We developed drug syndrome definitions for syndromic surveillance to monitor drug-related emergency department (ED) visits in real time. Materials and Methods: We used 2012 archived syndromic surveillance data from New York City hospitals to develop definitions for psychoactive drug-related syndromes. The dataset contained ED visit-level information that included patients’ chief complaints, dates of visits, ZIP codes of residence, discharge diagnoses, and dispositions. After manually reviewing chief complaints, we developed a classification scheme comprising 3 categories (overdose, drug mention, and drug abuse/misuse), which we used to define 25 psychoactive drug syndromes. From July 2013 through December 2015, the New York City Department of Health and Mental Hygiene performed daily syndromic surveillance of psychoactive drug-related ED visits using the 25 syndrome definitions. Results: Syndromic surveillance triggered 4 public health investigations, supported 8 other public health investigations that had been triggered by other mechanisms, and resulted in the identification of 5 psychoactive drug-related outbreaks. Syndromic surveillance also identified a substantial increase in synthetic cannabinoid-related visits (from an average of 3 per week in January 2014 to >300 per week in July 2015) and an increase in heroin overdose visits (from 80 to 171 in the first 3 quarters of 2012 and 2014, respectively) in a single neighborhood. Practice Implications: Syndromic surveillance using these novel definitions enabled monitoring of trends in psychoactive drug-related morbidity, initiation and support of public health investigations, and targeting of interventions. Health departments can refine these definitions for their jurisdictions using the described methods and integrate them into existing syndromic surveillance systems.

scholarly journals Commercially available glutenases: a potential hazard in coeliac disease

2017 ◽  
Vol 10 (6) ◽  
pp. 473-481 ◽  
Author(s):  
Suneeta Krishnareddy ◽  
Kenneth Stier ◽  
Maya Recanati ◽  
Benjamin Lebwohl ◽  
Peter HR Green
Keyword(s):  
Carica Papaya ◽  
Gastrointestinal Symptoms ◽  
Scientific Basis ◽  
Potential Hazard ◽  
Gluten Free ◽  
Herbal Products ◽  
Search Terms ◽  
The Us ◽  
Search Data ◽  
Google Search

Background: The only treatment for celiac disease (CD) is a gluten-free diet (GFD). However, there is interest among patients in a medical therapy to replace or help with a GFD. Therapies include gluten-degrading enzymes (glutenases). There are glutenases available marketed as dietary supplements that have not been demonstrated to digest the toxic epitopes of gluten. Methods: We investigated the contents, claims, and disclaimers of glutenase products and assessed patient interest using Google AdWords to obtain Google search frequencies. Results: Among 14 glutenase product, all contained proteases, eight contained X-prolyl exopeptidase dipeptidyl peptidase IV, two did not state the protease contents, and eight failed to specify the name or origin of all proteases. Eleven contained carbohydrases and lipases and three probiotics. One declared wheat and milk as allergens, two contained herbal products (type not stated) and one Carica papaya. Thirteen claimed to degrade immunogenic gluten fragments, four claimed to help alleviate gastrointestinal symptoms associated with eating gluten. Disclaimers included not being evaluated by the US Food and Drug Administration and products not intended to diagnose, treat, cure, or prevent any disease. On Google AdWords, the search frequency for the product names and the search terms was 3173 searches per month. Conclusions: The names of these products make implicit claims that appear to be supported by the claims on the labels and websites for which there is no scientific basis. Google search data suggest great interest and therefore possible use by patients with CD. There needs to be greater oversight of these ‘drugs’.

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