The global prevalence of dry eye disease: A Bayesian view

Abstract

Purpose To provide estimates for the prevalence of dry eye disease globally and in sub‐groups defined by: diagnostic criterion, sex, geographic location and age, using a Bayesian approach. Methods Modelling prevalence as a Beta distribution, estimates were inferred from Bayesian posterior distributions obtained by combing an uninformed prior with likelihood functions generated from all relevant studies reporting dry eye prevalence between 1997 and 2021. Results Global prevalence of dry eye disease was estimated at 11.59% (standard deviation (SD) = 0.04). For symptomatic disease, the estimate was 9.12% (SD = 0.04), with women 9.5% (SD = 0.05) and men 6.8% (SD = 0.06); prevalence was lowest in North America, 4.6% (SD = 0.03) and highest in Africa, 47.9% (SD = 1.8). For signs, prevalence was 35.2% (SD = 0.3), with woman 34.7% (SD = 0.7) and men 37.6% (SD = 0.7); North America showed the lowest regional prevalence, 3.5%, (SD = 0.4) with Eastern Asia the highest, 42.8% (SD = 0.4). Using TFOS DEWS II diagnostic criteria resulted in a global prevalence of 29.5% (SD = 0.8), with women 28.1% (SD = 1.2) and men 24.9% (SD = 1.4). Prevalence was lowest during the fifth decade, increasing approximately linearly with age thereafter. Estimates for other categories are given in accompanying tables. Conclusion A simple, flexible, yet powerful means of combining data from multiple sources to yield prevalence estimates across a range of circumstances is described, that is compatible with published guidelines for conducting meta‐analysis. Estimates can be readily updated as new information emerges, or according to need. Understanding the specific characteristics of studies chosen for inclusion is critical to the validity of the outcome. Although dry eye disease is evidently common, affecting about one in 11 people world‐wide, data are sparse for the young and all geographical locations except Eastern Asia.

The Impact of Probiotics and Prebiotics on Dry Eye Disease Signs and Symptoms

“…Dry eye disease is considered one of the most common ocular surface diseases, with a global prevalence of 11.59%, depending on the chosen diagnostic criteria [ 1 ]. In 2017, the second TFOS DEWS report defined dry eye as a “…multifactorial disease of the ocular surface characterized by a loss of homeostasis of the tear film, and accompanied by ocular symptoms, in which tear film instability and hyperosmolarity, ocular surface inflammation and damage, and neurosensory abnormalities play etiological roles” [ 2 ].…”

Response of tear cytokines following intense pulsed light combined with meibomian gland expression for treating meibomian gland dysfunction-related dry eye

“…Dry eye disease (DED) has been recognized as a high incidence of ocular surface disease over the past few decades with its estimated prevalence of nearly 12% around the world, and the trend continuously increases rapidly ( 1 , 2 ). In clinical practice, DED can be divided into two types: aqueous-deficient due to low tear production and evaporative due to meibomian gland dysfunction (MGD).…”

Predicting demographics from meibography using deep learning

“…The Meibomian glands of the human eyelid secrete lipid-rich meibum that during blinking forms a thin film on the surface of the tears 1 , 2 that serves to inhibit evaporation of the tear aqueous and stabilize the tear film by reducing surface tension 3 , 4 . Dysfunction of the Meibomian glands leading to insufficient or poor quality lipids is a primary cause of dry eye (DE) 5 , 6 , a globally impactful and highly prevalent ocular surface disease 7 . Infrared meibography is the biomedical imaging of the Meibomian glands, viewed by everting the eyelids, using a thermographic camera.…”