Background: Autoimmune diseases have been associated with changes in the gut microbiome. In this study, the gut microbiome was evaluated in individuals with dry eye and bacterial compositions were correlated to dry eye (DE) measures. We prospectively included 13 individuals with who met full criteria for Sjögren’s (SDE) and 8 individuals with features of Sjögren’s but who did not meet full criteria (NDE) for a total of 21 cases as compared to 21 healthy controls. Stool was analyzed by 16S pyrosequencing, and associations between bacterial classes and DE symptoms and signs were examined. Results: Results showed that Firmicutes was the dominant phylum in the gut, comprising 40–60% of all phyla. On a phyla level, subjects with DE (SDE and NDE) had depletion of Firmicutes (1.1-fold) and an expansion of Proteobacteria (3.0-fold), Actinobacteria (1.7-fold), and Bacteroidetes (1.3-fold) compared to controls. Shannon’s diversity index showed no differences between groups with respect to the numbers of different operational taxonomic units (OTUs) encountered (diversity) and the instances these unique OTUs were sampled (evenness). On the other hand, Faith’s phylogenetic diversity showed increased diversity in cases vs controls, which reached significance when comparing SDE and controls (13.57 ± 0.89 and 10.96 ± 0.76, p = 0.02). Using Principle Co-ordinate Analysis, qualitative differences in microbial composition were noted with differential clustering of cases and controls. Dimensionality reduction and clustering of complex microbial data further showed differences between the three groups, with regard to microbial composition, association and clustering. Finally, differences in certain classes of bacteria were associated with DE symptoms and signs. Conclusions: In conclusion, individuals with DE had gut microbiome alterations as compared to healthy controls. Certain classes of bacteria were associated with DE measures.

Importance: The geographic distribution of pediatric ophthalmological care has not been reported on since 2007; understanding this distribution could shed light on potential avenues to increase access, which is a necessary first step in addressing the pediatric ophthalmological needs of underserved areas.Objective: To analyze the number and location (ie, geographic distribution) of pediatric ophthalmologists in relation to US population demographic characteristics.Design, Setting, and Participants: In this cross-sectional study, public databases from the American Academy of Ophthalmology and American Association for Pediatric Ophthalmology and Strabismus were used to identify pediatric ophthalmologists in the US as of March 2022.Main Outcomes and Measures: Geographic distribution of pediatric ophthalmologists listed in public databases and any association between pediatric ophthalmologist distribution and US population demographic characteristics. Addresses were geocoded using ArcGIS Pro (Esri).Results: A total of 1056 pediatric ophthalmologists (611 men [57.9%]) were identified. States with the most pediatric ophthalmologists were California (n = 116 [11.0%]), New York (n = 97 [9.2%]), Florida (n = 69 [6.5%]), and Texas (n = 62 [5.9%]), the 4 most populous states. A total of 2828 of 3142 counties (90.0%) and 4 of 50 states (8.0%) had 0 pediatric ophthalmologists. In 314 counties (10.0%) with 1 or more pediatric ophthalmologists, the mean (range) pediatric ophthalmologists per million persons was 7.7 (0.4-185.5). The range of practitioner to million persons has increased since 2007. Counties with 1 or more pediatric ophthalmologists had a higher median (SD) household income compared with counties with 0 pediatric ophthalmologists ($70 230.59 [$18 945.05] vs $53 263.62 [$12 786.07]; difference, −$16 966.97; 95% CI, −$18 544.57 to −$14 389.37; P < .001). Additionally, the proportion of families in each county without internet service (8.0% vs 4.7%; difference, 3.4%; 95% CI, 3.0%-3.7%; P < .001), the proportion of persons younger than 19 years without health insurance (5.7% vs 4.1%; difference, 1.6%; 95% CI, 1.1%-2.2%; P < .001), and the proportion of households without vehicle access (2.1% vs 1.8%; difference, 0.3%; 95% CI, 0.6%-5.2%; P = .001) were greater in counties with 0 compared with counties with 1 or more pediatric ophthalmologists.Conclusion and Relevance: This cross-sectional study found that disparities in access to pediatric ophthalmological care have increased over the past 15 years and are associated with lower socioeconomic status. As patients may rely on online sources to identify the nearest pediatric ophthalmologist, accurate publicly available databases are important.