Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-25T15:17:24.852Z Has data issue: false hasContentIssue false

Chapter 4 - Big Data

The New Epidemiology

Published online by Cambridge University Press:  23 November 2023

By
Lucy Stirland ,
Tom Russ  and
Tom Foley
Edited by
Rob Waller ,
Omer S. Moghraby  and
Mark Lovell
Rob Waller
Affiliation:
NHS Lothian
Omer S. Moghraby
Affiliation:
South London & Maudsley NHS Foundation Trust
Mark Lovell
Affiliation:
Esk and Wear Valleys NHS Foundation Trust
Get access

Summary

As the use of big data in psychiatry continues to expand, it is crucial to involve patients and the public in decisions about its development and application. Mental Health Data Science Scotland has co-produced a best practice checklist involving both researchers and people with lived experience. This guidance emphasises the need for data to be securely accessible and carefully anonymised and for processes and analyses to be transparent, with participants or patients prioritised throughout.

Keywords

Big DataData AnalysisData AnalyticsResearchDatabases
Type
Chapter
Information
Digital Mental Health
From Theory to Practice
 , pp. 50 - 59
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Schofield, P. Big data in mental health research: do the n s justify the means? Using large data-sets of electronic health records for mental health research. BJPsych. Bull. 2017;41: 129–32. https://doi.org/10.1192/pb.bp.116.055053.Google Scholar
De Mauro, A., Greco, M., Grimaldi, M. What is Big Data? A consensual definition and a review of key research topics. AIP Conf. Proc. 2015; 1644(1): 97–104. https://doi.org/10.13140/2.1.2341.5048.Google Scholar
Russ, T. C., Woelbert, E., Davis, K. A. S. et al. How data science can advance mental health research. Nat. Hum. Behav. 2019;3: 24–32. https://doi.org/10.1038/s41562-018-0470-9.Google Scholar
Eisner, E., Drake, R. J., Berry, N. et al. Development and long-term acceptability of ExPRESS, a mobile phone app to monitor basic symptoms and early signs of psychosis relapse. JMIR mHealth uHealth 2019;7: e11568. https://doi.org/10.2196/11568.CrossRefGoogle ScholarPubMed
Menni, C., Valdes, A. M., Freidin, M. B. et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 2020;26: 1037–40. https://doi.org/10.1038/s41591-020-0916-2.CrossRefGoogle ScholarPubMed
Ortiz, S. N., Forrest, L. N., Fisher, T. J. et al. Changes in internet suicide search volumes following celebrity suicides. Cyberpsychol. Behav. Soc. Netw. 2019;22: 373–80. https://doi.org/10.1089/cyber.2018.0488.Google Scholar
Vayena, E., Dzenowagis, J., Brownstein, J. S. et al. Policy implications of big data in the health sector. Bull. World Health Organ. 2018; 96(1): 66–8. https://doi.org/10.2471/BLT.17.197426.Google Scholar
Lyall, L. M., Penades, N., Smith, D. J. Changes in prescribing for bipolar disorder between 2009 and 2016: national-level data linkage study in Scotland. Br. J. Psychiatry 2019;215: 415–21. https://doi.org/10.1192/bjp.2019.16.CrossRefGoogle ScholarPubMed
Sheehan, R., Hassiotis, A., Walters, K. et al. Mental illness, challenging behaviour, and psychotropic drug prescribing in people with intellectual disability: UK population based cohort study. BMJ 2015;351: h4326. https://doi.org/10.1136/BMJ.H4326.CrossRefGoogle ScholarPubMed
Perera, G., Khondoker, M., Broadbent, M. et al. Factors associated with response to acetylcholinesterase inhibition in dementia: a cohort study from a secondary mental health care case register in London. PLoS One 2014;9: e109484. https://doi.org/10.1371/journal.pone.0109484.CrossRefGoogle Scholar
Perera, G., Broadbent, M., Callard, F. et al. Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource. BMJ Open 2016;6: 8721. https://doi.org/10.1136/bmjopen-2015.CrossRefGoogle ScholarPubMed
Sudlow, C., Gallacher, J., Allen, N. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12: e1001779. https://doi.org/10.1371/journal.pmed.1001779.Google Scholar
Arias De La Torre, J., Ronaldson, A., Valderas, J.M. et al. Diagnostic promiscuity: the use of real-world data to study multimorbidity in mental health. Br. J. Psychiatry 2021; 218(5): 237–9. https://doi.org/10.1192/bjp.2020.257.CrossRefGoogle ScholarPubMed
Platt, R., Carnahan, R. M., Brown, J. S. et al. The U.S. Food and Drug Administration’s Mini-Sentinel program: status and direction. Pharmacoepidemiol. Drug Saf. 2012;21: 1–8. https://doi.org/10.1002/pds.2343.Google ScholarPubMed
Data Protection Act. United Kingdom Act of Parliament. 2018. Available at: www.legislation.gov.uk/ukpga/2018/12/contents/enacted (accessed 16 June 2023).Google Scholar
Aitken, M., McAteer, G., Davidson, S. et al. Public preferences regarding data linkage for health research: a discrete choice experiment. Int. J. Popul. Data Sci. 2018;3(1): 429. https://doi.org/10.23889/ijpds.v3i1.429.Google ScholarPubMed
Sterne, J. A., Davey Smith, G., Smith, G. D. Sifting the evidence: what’s wrong with significance tests? BMJ 2001;322: 226–31. https://doi.org/10.1136/BMJ.322.7280.226.CrossRefGoogle ScholarPubMed
Greenhalgh, T., Wherton, J., Papoutsi, C. et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J. Med. Internet Res. 2017;19(11): e8775. https://doi.org/10.2196/jmir.8775.Google Scholar
Kirkham, E. J., Iveson, M., Beange, I. et al. A Stakeholder-Derived, Best Practice Checklist for Mental Health Data Science in the UK. 2020. Edinburgh: University of Edinburgh. Available at: https://mhdss.ac.uk/best-practice-mental-health-data-science (accessed 27 January 2021).Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×