Patients with severe dry eye disease produce Schirmer test results which are unaffected by environmental humidity. However, patients with moderate Schirmer wetting lengths may be falsely diagnosed as having dry eye disease if their test is undertaken in a low-humidity environment. This phenomenon may be overcome with the use of plastic sheathing. Previous studies investigating the effects of environmental conditions on the tear film may have been affected by this phenomenon.

BACKGROUND: Socioeconomic deprivation is associated with higher odds of chronic diseases, with many individuals living with more than one illness. This study aimed to examine the relationship between deprivation and severity of glaucoma at diagnosis, an important risk factor for glaucoma blindness. METHODS: A retrospective study of 472 consecutive patients referred by community optometrists to the glaucoma clinic at a university hospital was performed. Glaucoma severity was determined by standard automated perimetry mean deviation (MD) in the worse eye. The Scottish Index of Multiple Deprivation (SIMD) was determined for each patient as a measure of deprivation based on postcode. Regression analyses were performed to determine the relationship between visual field MD and SIMD. RESULTS: There was a significant relationship between higher levels of deprivation (lower SIMD) and worse severity of glaucoma at diagnosis. 32 of 472 patients (6.8%) had a MD of ≤−6 dB and 11 (2.3%) ≤−12 dB in their better eye. MD in the worse eye was 0.04 dB (95% CI 0.014 to 0.062 dB, P = 0.002) worse for each 100-point decrease in SIMD, with lower SIMD indicating a higher level of deprivation. A higher proportion of patients living in most deprived areas had a MD ≤ −6 dB or ≤ −12 dB at presentation compared to those living in the least deprived areas (14.3% versus 6.8% for ≤ −6 dB and 4.8% versus 0.8% for ≤ −12 dB). CONCLUSIONS: Higher levels of deprivation were associated with worse glaucoma severity at presentation. The reasons for poorer outcomes in those from more deprived communities need further study so that inequalities can be addressed and the frequency of patients presenting with advanced glaucoma reduced.

Background A repository of retinal images for research is being established in Scotland. It will permit researchers to validate, tune, and refine artificial intelligence (AI) decision-support algorithms to accelerate safe deployment in Scottish optometry and beyond. Research demonstrates the potential of AI systems in optometry and ophthalmology, though they are not yet widely adopted. Objective In this study, 18 optometrists were interviewed to (1) identify their expectations and concerns about the national image research repository and their use of AI decision support and (2) gather their suggestions for improving eye health care. The goal was to clarify attitudes among optometrists delivering primary eye care with respect to contributing their patients’ images and to using AI assistance. These attitudes are less well studied in primary care contexts. Five ophthalmologists were interviewed to discover their interactions with optometrists. Methods Between March and August 2021, 23 semistructured interviews were conducted online lasting for 30-60 minutes. Transcribed and pseudonymized recordings were analyzed using thematic analysis. Results All optometrists supported contributing retinal images to form an extensive and long-running research repository. Our main findings are summarized as follows. Optometrists were willing to share images of their patients’ eyes but expressed concern about technical difficulties, lack of standardization, and the effort involved. Those interviewed thought that sharing digital images would improve collaboration between optometrists and ophthalmologists, for example, during referral to secondary health care. Optometrists welcomed an expanded primary care role in diagnosis and management of diseases by exploiting new technologies and anticipated significant health benefits. Optometrists welcomed AI assistance but insisted that it should not reduce their role and responsibilities. Conclusions Our investigation focusing on optometrists is novel because most similar studies on AI assistance were performed in hospital settings. Our findings are consistent with those of studies with professionals in ophthalmology and other medical disciplines: showing near universal willingness to use AI to improve health care, alongside concerns over training, costs, responsibilities, skill retention, data sharing, and disruptions to professional practices. Our study on optometrists’ willingness to contribute images to a research repository introduces a new aspect; they hope that a digital image sharing infrastructure will facilitate service integration.

Background: Community optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools. Methods: As the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices. Results: A sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records. Conclusions: The potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices, data linkage is possible using robust, almost entirely automated processes.