Author ORCID Identifier
https://orcid.org/0000-0003-3919-444X
Document Type
Article
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
Ophthalmology, Epidemiology
Abstract
Purpose
To examine whether data sourced from electronic medical records (EMR) and a large industrial spectacle lens manufacturing database can estimate refractive error distribution within large populations as an alternative to typical population surveys of refractive error.
Subjects
A total of 555,528 patient visits from 28 Irish primary care optometry practices between the years 1980 and 2019 and 141,547,436 spectacle lens sales records from an international European lens manufacturer between the years 1998 and 2016.
Methods
Anonymized EMR data included demographic, refractive and visual acuity values. Anonymized spectacle lens data included refractive data. Spectacle lens data was separated into lenses containing an addition (ADD) and those without an addition (SV). The proportions of refractive errors from the EMR data and ADD lenses were compared to published results from the European Eye Epidemiology (E3) Consortium and the Gutenberg Health Study (GHS).
Results
Age and gender matched proportions of refractive error were comparable in the E3 data and the EMR data, with no significant difference in the overall refractive error distribution (χ2 = 527, p = 0.29, DoF = 510). EMR data provided a closer match to the E3 refractive error distribution by age than the ADD lens data. The ADD lens data, however, provided a closer approximation to the E3 data for total myopia prevalence than the GHS data, up to age 64.
Conclusions
The prevalence of refractive error within a population can be estimated using EMR data in the absence of population surveys. Industry derived sales data can also provide insights on the epidemiology of refractive errors in a population over certain age ranges. EMR and industrial data may therefore provide a fast and cost-effective surrogate measure of refractive error distribution that can be used for future health service planning purposes.
DOI
https://doi.org/10.1371/journal.pone.0250468
Recommended Citation
Moore M, Loughman J, Butler JS, Ohlendorf A, Wahl S, Flitcroft DI (2021) Application of big-data for epidemiological studies of refractive error. PLoS ONE 16(4): e0250468. DOI: 10.1371/journal.pone.0250468
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Open access
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0250468