Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
New York University, New York, NY, USA.
Transl Vis Sci Technol. 2024 Aug 1;13(8):25. doi: 10.1167/tvst.13.8.25.
To elucidate the impact of demographics, including gender, race, ethnicity, and preferred language, on regional visual field (VF) loss and progression in glaucoma.
Multivariable linear mixed regressions were performed to determine the impact of race, ethnicity, and preferred language on regional VF loss with adjustment for age and gender. Regional VF loss was defined by pointwise total deviation values and VF loss patterns quantified by an unsupervised machine learning method termed archetypal analysis. All cross-sectional and longitudinal analyses were performed both without and with adjustment for VF mean deviation, which represented overall VF loss severity. P values were corrected for multiple comparisons.
All results mentioned had corrected P values less than 0.05. Asian and Black patients showed worse pointwise VF loss than White patients with superior hemifield more affected. Patients with a preferred language other than English demonstrated worse pointwise VF loss than patients with English as their preferred language. Longitudinal analyses revealed Black patients showed worse VF loss/year compared to White patients. Patients with a preferred language other than English demonstrated worse VF loss/year compared to patients preferring English.
Blacks and non-English speakers have more severe VF loss, with superior hemifield being more affected and faster VF worsening.
This study furthered our understanding of racial, ethnic, and socioeconomic disparities in glaucoma outcomes. Understanding the VF loss burden in different racial, ethnic, and socioeconomic groups may guide more effective glaucoma screening and community outreach efforts. This research could help reduce vision loss and improve quality of life in disproportionately affected populations by guiding public health efforts to promote glaucoma awareness and access to care.
阐明人口统计学因素（包括性别、种族、民族和首选语言）对青光眼区域性视野（VF）丧失和进展的影响。
采用多变量线性混合回归分析，确定种族、民族和首选语言对 VF 区域丧失的影响，同时调整年龄和性别因素。区域 VF 丧失通过逐点总偏差值定义，VF 丧失模式通过称为原型分析的无监督机器学习方法进行量化。所有横断面和纵向分析均在不调整和调整 VF 平均偏差（代表整体 VF 丧失严重程度）的情况下进行。校正了多个比较的 P 值。
所有提到的结果校正 P 值均小于 0.05。亚洲和黑人患者的逐点 VF 丧失比白人患者更差，优势半视野受影响更严重。首选语言不是英语的患者比首选英语的患者表现出更差的逐点 VF 丧失。纵向分析显示，黑人患者的 VF 丧失/年比白人患者更差。首选语言不是英语的患者比首选英语的患者 VF 丧失/年更差。
黑人患者和非英语使用者的 VF 丧失更严重，优势半视野受影响更严重，VF 恶化速度更快。
本研究进一步了解了青光眼结局中的种族、民族和社会经济差异。了解不同种族、民族和社会经济群体的 VF 丧失负担可能有助于指导更有效的青光眼筛查和社区外展工作。通过指导公共卫生努力提高青光眼意识和获得护理，本研究可以帮助减少 disproportionately affected 人群的视力丧失和提高生活质量。
