June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Characteristics of large publicly available databases used in big data research in ophthalmology
Author Affiliations & Notes
  • John C Lin
    Brown University, Providence, Rhode Island, United States
  • Matthew Lee
    Brown University, Providence, Rhode Island, United States
  • Sophia Ghauri
    Brown University, Providence, Rhode Island, United States
  • Ingrid U Scott
    Penn State College of Medicine, Hershey, Pennsylvania, United States
  • Paul B Greenberg
    Brown University, Providence, Rhode Island, United States
  • Footnotes
    Commercial Relationships   John Lin None; Matthew Lee None; Sophia Ghauri None; Ingrid Scott None; Paul Greenberg None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 727 – F0455. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      John C Lin, Matthew Lee, Sophia Ghauri, Ingrid U Scott, Paul B Greenberg; Characteristics of large publicly available databases used in big data research in ophthalmology. Invest. Ophthalmol. Vis. Sci. 2022;63(7):727 – F0455.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : To comprehensively describe large publicly available health databases used in published big data research in ophthalmology.

Methods : To identify studies using large publicly available health databases for ophthalmic research, a literature search was formulated by a health sciences librarian and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using the PubMed and Embase digital literature databases from inception (1809 and 1947, respectively) through August 1, 2021. Databases were identified from full-text articles. Study selection and data extraction were performed independently by two investigators, and a third investigator arbitrated any disagreements.

Results : In total, 204 databases were identified in the included studies; most (52%; 107/204) were available online. Most were provided by government agencies (52%; 55/106) or academic institutions (30%; 32/106) and were from high-income (87%; 92/106) or English-speaking (68%; 72/106) countries; no publicly available databases were identified from Latin America and the Caribbean, the Middle East and North Africa, or Sub-Saharan Africa. Half were comprised of administrative data (50%; 53/106). Few published details about their sampling methods (23%; 24/106) or study populations (41%; 43/106). Some common requirements to access data included an application requirement (29%; 31/106), training requirement (10%; 11/106), purchase requirement (8%; 9/106), or other requirements such as citizenship or employment (8%; 8/106). One-third were freely available (33%; 35/106).

Conclusions : Big data research in ophthalmology was concentrated on databases based in high-income countries. Access to many publicly available databases was restricted by application, training, purchase, and other requirements. Infrequent disclosure of sampling methods and study populations may impact the quality, transparency, and generalizability of big data research in ophhtalmology.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×