Leveraging Big Data for Exploring Occupational Diseases-Related Interest at the Level of Scientific Community, Media Coverage and Novel Data Streams: The Example of Silicosis as a Pilot Study

Objective Silicosis is an untreatable but preventable occupational disease, caused by exposure to silica. It can progressively evolve to lung impairment, respiratory failure and death, even after exposure has ceased. However, little is known about occupational diseases-related interest at the level of scientific community, media coverage and web behavior. This article aims at filling in this gap of knowledge, taking the silicosis as a case study. Methods We investigated silicosis-related web-activities using Google Trends (GT) for capturing the Internet behavior worldwide in the years 2004–2015. GT-generated data were, then, compared with the silicosis-related scientific production (i.e., PubMed and Google Scholar), the media coverage (i.e., Google news), the Wikipedia traffic (i.e, Wikitrends) and the usage of new media (i.e., YouTube and Twitter). Results A peak in silicosis-related web searches was noticed in 2010–2011: interestingly, both scientific articles production and media coverage markedly increased after these years in a statistically significant way. The public interest and the level of the public engagement were witnessed by an increase in likes, comments, hashtags, and re-tweets. However, it was found that only a small fraction of the posted/uploaded material contained accurate scientific information. Conclusions GT could be useful to assess the reaction of the public and the level of public engagement both to novel risk-factors associated to occupational diseases, and possibly related changes in disease natural history, and to the effectiveness of preventive workplace practices and legislative measures adopted to improve occupational health. Further, occupational clinicians should become aware of the topics most frequently searched by patients and proactively address these concerns during the medical examination. Institutional bodies and organisms should be more present and active in digital tools and media to disseminate and communicate scientifically accurate information. This manuscript should be intended as preliminary, exploratory communication, paving the way for further studies.

[1]  J. Brownstein,et al.  Digital disease detection--harnessing the Web for public health surveillance. , 2009, The New England journal of medicine.

[2]  H. de Vries,et al.  A randomized controlled trial evaluating the effectiveness of a web-based, computer-tailored self-management intervention for people with or at risk for COPD , 2015, International journal of chronic obstructive pulmonary disease.

[3]  Hans Peter Peters,et al.  Gap between science and media revisited: Scientists as public communicators , 2013, Proceedings of the National Academy of Sciences.

[4]  Mark Dredze,et al.  Big Data Sensors of Organic Advocacy: The Case of Leonardo DiCaprio and Climate Change , 2016, PloS one.

[5]  A. Rodríguez-Morales,et al.  A bibliometric analysis of global Ebola research. , 2015, Travel medicine and infectious disease.

[6]  Cécile Viboud,et al.  Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales , 2013, PLoS Comput. Biol..

[7]  R. Harrison,et al.  Silicosis in a Countertop Fabricator — Texas, 2014 , 2015, MMWR. Morbidity and mortality weekly report.

[8]  Nicola Luigi Bragazzi,et al.  From P0 to P6 medicine, a model of highly participatory, narrative, interactive, and “augmented” medicine: some considerations on Salvatore Iaconesi’s clinical story , 2013, Patient preference and adherence.

[9]  Sally J. Singh,et al.  The evaluation of an interactive web-based Pulmonary Rehabilitation programme: protocol for the WEB SPACE for COPD feasibility study , 2015, BMJ Open.

[10]  P. Sartorelli,et al.  Silicosis in Workers Exposed to Artificial Quartz Conglomerates: Does It Differ From Chronic Simple Silicosis? , 2015, Archivos de bronconeumologia.

[11]  S. Nuti,et al.  The Use of Google Trends in Health Care Research: A Systematic Review , 2014, PloS one.

[12]  Elizabeth E Ward,et al.  Silica: A lung carcinogen , 2014, CA: a cancer journal for clinicians.

[13]  Mark Dredze,et al.  Population health concerns during the United States' Great Recession. , 2014, American journal of preventive medicine.

[14]  M. Santillana,et al.  What can digital disease detection learn from (an external revision to) Google Flu Trends? , 2014, American journal of preventive medicine.

[15]  S. Hendricks,et al.  Silicosis Mortality Trends and New Exposures to Respirable Crystalline Silica — United States, 2001–2010 , 2015, MMWR. Morbidity and mortality weekly report.

[16]  D. Lazer,et al.  The Parable of Google Flu: Traps in Big Data Analysis , 2014, Science.

[17]  G Eysenbach,et al.  Epidemiological data can be gathered with world wide web , 1998, BMJ.

[18]  P. Kostkova,et al.  Follow #eHealth2011: Measuring the Role and Effectiveness of Online and Social Media in Increasing the Outreach of a Scientific Conference , 2016, Journal of medical Internet research.

[19]  Eugen Trinka,et al.  Information-seeking behaviour for epilepsy: an infodemiological study of searches for Wikipedia articles. , 2015, Epileptic disorders : international epilepsy journal with videotape.

[20]  D. Hinkle,et al.  Applied statistics for the behavioral sciences , 1979 .

[21]  Andrea K Carvalho,et al.  Internet Use for Health-Care Information by Subjects With COPD , 2015, Respiratory Care.

[22]  K. Rosenman,et al.  Summary of Notifiable Noninfectious Conditions and Disease Outbreaks: Surveillance for Silicosis - Michigan and New Jersey, 2003-2010. , 2015, MMWR. Morbidity and mortality weekly report.

[23]  Daniela Amicizia,et al.  Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes , 2015 .

[24]  Mark Dredze,et al.  Leveraging Big Data to Improve Health Awareness Campaigns: A Novel Evaluation of the Great American Smokeout , 2016, JMIR public health and surveillance.

[25]  Lianne W L Simonse,et al.  Information and Communication Technology–Enabled Person-Centered Care for the “Big Five” Chronic Conditions: Scoping Review , 2015, Journal of Medical Internet Research.

[26]  J. Brownstein,et al.  Early detection of disease outbreaks using the Internet , 2009, Canadian Medical Association Journal.

[27]  Feng Yi,et al.  Tracing the scientific outputs in the field of Ebola research based on publications in the Web of Science , 2016, BMC Research Notes.

[28]  N. Magnavita,et al.  Evidence-based approach for continuous improvement of occupational health. , 2015, Epidemiologia e prevenzione.

[29]  Y. Strekalova Health Risk Information Engagement and Amplification on Social Media , 2017, Health education & behavior : the official publication of the Society for Public Health Education.

[30]  S. Curti,et al.  Interventions to increase the reporting of occupational diseases by physicians: a Cochrane systematic review , 2016, Occupational and Environmental Medicine.

[31]  E. Segev,et al.  Temporal patterns of scientific information-seeking on Google and Wikipedia , 2017, Public understanding of science.

[32]  J. Meulen Priorities for research on tropical viruses after the 2014 Ebola epidemic. , 2015 .

[33]  Jessica Fitts Willoughby,et al.  Using digital surveillance to examine the impact of public figure pancreatic cancer announcements on media and search query outcomes. , 2013, Journal of the National Cancer Institute. Monographs.

[34]  H. Hull,et al.  Mandatory reporting of occupational diseases by clinicians. , 1989, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[35]  Mark Dredze,et al.  News and Internet Searches About Human Immunodeficiency Virus After Charlie Sheen's Disclosure. , 2016, JAMA internal medicine.

[36]  Vural Özdemir,et al.  ‘Regular science’ is inherently political , 2013, EMBO reports.

[37]  M. Bayram,et al.  Silicosis in denim sandblasters. , 2011, Chest.

[38]  Nicola Luigi Bragazzi,et al.  Psychology Research and Behavior Management Dovepress a Google Trends-based Approach for Monitoring Nssi , 2022 .

[39]  Laurent Hébert-Dufresne,et al.  Enhancing disease surveillance with novel data streams: challenges and opportunities , 2015, EPJ Data Science.

[40]  T. Kelley,et al.  A Brief Review of Silicosis in the United States , 2010, Environmental health insights.

[41]  Bill Bynum,et al.  Lancet , 2015, The Lancet.

[42]  Mariusz Duplaga,et al.  The acceptance of e-health solutions among patients with chronic respiratory conditions. , 2013, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[43]  B. Girdler-brown,et al.  Three Decades of Silicosis: Disease Trends at Autopsy in South African Gold Miners , 2009, Environmental health perspectives.

[44]  W. Otte,et al.  Wikipedia and neurological disorders , 2015, Journal of Clinical Neuroscience.

[45]  Mark Dredze,et al.  Could behavioral medicine lead the web data revolution? , 2014, JAMA.