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Abstract

Background: Scholarly productivity is a key marker of achievement in academic ophthalmology, influencing promotion, research support, and leadership opportunities. Prior studies have reported variation by gender, subspecialty, and rank, but few applied regression methods to identify independent predictors.

Purpose: To evaluate demographic, academic, and subspecialty factors associated with research productivity among U.S. academic ophthalmologists and to identify independent predictors through multivariable regression.

Methods: This cross-sectional study included 4,028 ophthalmologists across 126 accredited U.S. residency programs using the U.S. Academic Ophthalmology Faculty Database (US-AOFD). Faculty demographics and academic information were obtained from institutional websites, and bibliometric data (publications, citations, H-index) from Scopus. Productivity was compared across rank, gender, region, institution type, and fellowship subspecialty. Stepwise multiple linear regression identified independent associations with H-index.

Results: Scopus profiles were identified for 88.2% of faculty, more often among core than affiliate faculty (92.9% vs. 79.0% [p < 0.001]). Core faculty demonstrated higher productivity across all measures (median H-index, 11 vs. 5 [p < 0.001]). Full professors had greater output than associates or assistants (27 vs. 11 vs. 5 [p < 0.001]). Subspecialties with the highest median H-indices included ophthalmic pathology (20), medical retina (17), ocular oncology (13), and uveitis (12). In multivariable analysis, independent predictors of higher H-index included years since residency (β = 0.664, p < 0.001), an additional PhD degree (β = 7.231, p < 0.001), male gender (β = 2.999, p < 0.001), and training in ocular oncology, retina, pathology, glaucoma, and neuroophthalmology. Oculoplastic surgery was negatively associated, and faculty in the Northeast and South had lower H-indices than the Midwest.

Conclusions: Research productivity varies across gender, rank, subspecialty, and geography. Adjusted models confirm male gender, PhD training, and select subspecialties as independent predictors, highlighting areas for targeted mentorship, equity initiatives, and academic development.

Received Date

24/08/2025

Revised Date

31/10/2025

Accepted Date

18/11/2025

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