Using electronic medical record data to establish and monitor the distribution of refractive errors.

Using electronic medical record data to establish and monitor the distribution of refractive errors. Longwill, Seán; Moore, Michael; Flitcroft, Daniel Ian; Loughman, James Objective: To establish the baseline distribution of refractive errors and associated factors amongst a population that attended primary care optometry clinics. Design: Retrospective cross sectional cohort study of electronic medical records (EMR). Methods: Electronic medical record data was extracted from forty optometry clinics, representing a mix of urban and rural areas in Ireland. The analysis was confined to demographic and clinical data gathered over a sixty-month period between 2015 and 2019. Distribution rates were calculated using the absolute and relative frequencies of refractive error in the dataset, stratified for age and gender using the following definitions: high myopia ≤ -6.00 D, myopia ≤ -0.50 D, hyperopia ≥ +0.50 D, astigmatism ≤ -0.75 DC and anisometropia ≥ 1.00 D. Visual acuity data was used to explore vision impairment rates in the population. Further analysis was carried out on a gender and age-adjusted subset of the EMR data, to match the proportion of patients in each age grouping to the population distribution in the most recent (2016) Irish census. Results: 153,598 clinic records were eligible for analysis. Refractive errors ranged from -26.00 to +18.50 D. Myopia was present in 32.7%, of which high myopia represented 2.4%, hyperopia in 40.1%, astigmatism in 38.3% and anisometropia in 13.4% of participants. The clinic distribution of hyperopia, astigmatism and anisometropia peaked in older age groups, whilst the myopia burden was highest amongst people in their twenties. A higher proportion of females were myopic, whilst a higher proportion of males were hyperopic and astigmatic. Vision impairment (LogMAR > 0.3) was present in 2.4% of participants. In the gender and age- adjusted distribution model, myopia was the most common refractive state, affecting 38.8% of patients. Conclusion: Although EMR data is not representative of the population as a whole, it is likely to provide a reasonable representation of the distribution of clinically significant (symptomatic) refractive errors. In the absence of any ongoing traditional epidemiological studies of refractive error in Ireland, this study establishes, for the first time, the distribution of refractive errors observed in clinical practice settings. This will serve as a baseline for future temporal trend analysis of the changing pattern of the distribution of refractive error in EMR data. This methodology could be deployed as a useful epidemiological resource in similar settings where primary eyecare coverage for the management of refractive error is well established.