Lessons Learned From 2 Large Community-based Glaucoma Screening Studies

Kolomeyer, Natasha Nayak; Katz, L. Jay; Hark, Lisa A; Wahl, Madison; Gajwani, Prateek; Aziz, Kashif; Myers, Jonathan S.; Friedman, David S.

doi:10.1097/ijg.0000000000001920

Cited by 18 publications

(6 citation statements)

References 14 publications

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“…At the local level, ophthalmologists can examine their own practices for opportunities to close the access gap, including providing income-adjusted payment for care of populations with low income or for telehealth visits (eg, teleretinal screening) for remote populations with limited health care access . Additional outreach interventions include education on ophthalmic health, for example, at schools; school-based, primary care– or pediatrician-conducted, and community-based screenings; and home or outreach visits for remote populations and those unable to travel to an ophthalmologist, such as nursing home residents or incarcerated individuals . On a larger scale, working alongside state and federal advocacy groups may help promote broader, lasting policy changes.…”

Section: Discussionmentioning

confidence: 99%

Association of Socioeconomic, Demographic, and Health Care Access Disparities With Severe Visual Impairment in the US

Besagar

Yonekawa

Sridhar³

et al. 2022

JAMA Ophthalmol

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ImportanceApproximately 13% of US adults are affected by visual disability, with disproportionately higher rates in groups impacted by certain social determinants of health (SDOH).ObjectiveTo evaluate SDOH associated with severe visual impairment (SVI) to ultimately guide targeted interventions to improve ophthalmic health.Design, Setting, and ParticipantsThis quality improvement study used cross-sectional data from a telephone survey from the Behavioral Risk Factor Surveillance System (BRFSS) that was conducted in the US from January 2019 to December 2020. Participants were noninstitutionalized adult civilians who were randomly selected and interviewed and self-identified as “blind or having serious difficulty seeing, even while wearing glasses.”ExposuresDemographic and health care access factors.Main Outcomes and MeasuresThe main outcome was risk of SVI associated with various factors as measured by odds ratios (ORs) and 95% CIs. Descriptive and logistic regression analyses were performed using the Web Enabled Analysis Tool in the BRFFS.ResultsDuring the study period, 820 226 people (53.07% female) participated in the BRFSS survey, of whom 42 412 (5.17%) self-identified as “blind or having serious difficulty seeing, even while wearing glasses.” Compared with White, non-Hispanic individuals, risk of SVI was increased among American Indian/Alaska Native (OR, 1.63; 95% CI, 1.38-1.91), Black/African American (OR, 1.50; 95% CI, 1.39-1.62), Hispanic (OR, 1.65; 95% CI, 1.53-1.79), and multiracial (OR, 1.33; 95% CI, 1.15-1.53) individuals. Lower annual household income and educational level (eg, not completing high school) were associated with greater risk of SVI. Individuals who were out of work for 1 year or longer (OR, 1.78; 95% CI, 1.54-2.07) or who reported being unable to work (OR, 2.90; 95% CI, 2.66-3.16) had higher odds of SVI compared with the other variables studied. Mental health diagnoses and 14 or more days per month with poor mental health were associated with increased risk of SVI (OR, 1.87; 95% CI, 1.73-2.02). Health care access factors associated with increased visual impairment risk included lack of health care coverage and inability to afford to see a physician.Conclusions and RelevanceIn this study, various SDOH were associated with SVI, including self-identification as being from a racial or ethnic minority group; low socioeconomic status and educational level; long-term unemployment and inability to work; divorced, separated, or widowed marital status; poor mental health; and lack of health care coverage. These disparities in care and barriers to health care access should guide targeted interventions.

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Section: Discussionmentioning

confidence: 99%

Association of Socioeconomic, Demographic, and Health Care Access Disparities With Severe Visual Impairment in the US

Besagar

Yonekawa

Sridhar³

et al. 2022

JAMA Ophthalmol

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“…Optic disc photography has proven to be very useful for diagnosing glaucoma, showing a classic glaucomatous appearance to expert graders. While convenient and inexpensive, the low prevalence of glaucoma and screening limitations make it challenging to conduct meaningful screenings 7 . Therefore, it is important to develop an automatic model to assist clinicians in screening for and diagnosing incident POAG with high accuracy from the optic disc photographs.…”

Section: Introductionmentioning

confidence: 99%

Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network

Lin

Liu²,

Gordon³

et al. 2022

Ophthalmology Science

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“…LAUCOMA disease is a leading cause of irreversible blindness worldwide [4]. Early detection is an effective approach to slow glaucoma and prevent subsequent vision loss [5]. However, detecting glaucoma requires extensive clinical examination and assessment of imaging and visual field tests.…”

Section: Introductionmentioning

confidence: 99%

“…Fundus photography is a convenient and non-expensive technique for recording the status of the optic nerve head structure. Although, detecting glaucoma from fundus images requires extensive skills and experience, this process is subjective and there is a considerable disagreement in diagnosis even among experienced glaucoma experts [5]. Therefore, developing highly accurate, dependable, and low-cost automated models to detect glaucoma from fundus images is critical in ophthalmology.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

Madadi¹,

Abu-Serhan²,

Yousefi³

2022

Preprint

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<p>Domain adaptation methods are designed to extract shared domain-invariant features by projecting data on a common subspace in order to align their domain distributions. However, these methods do not usually consider domain-specific features, and therefore their distributions may not be well aligned. To address this problem, we introduce a novel model that learns domain-invariant and domain-specific representations to extract both their general and specific features. We also propose progressive weighting to accurately transfer the source domain knowledge and to mitigate negative knowledge transfer from the source to the target domain and to employ low-rank coding for aligning the source and target distributions. We evaluated the model based on several real-world datasets and showed that our method significantly improved the accuracy for forecasting and diagnosis of glaucoma disease from fundus photographs.</p>

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Lessons Learned From 2 Large Community-based Glaucoma Screening Studies

Cited by 18 publications

References 14 publications

Association of Socioeconomic, Demographic, and Health Care Access Disparities With Severe Visual Impairment in the US

Association of Socioeconomic, Demographic, and Health Care Access Disparities With Severe Visual Impairment in the US

Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network

Domain Adaptation-Based Deep Learning Models for Forecasting and Diagnosis of Glaucoma Disease

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