Community-based vision health screening with on-site definitive exams: Design and outcomes

Brinks, Mitchell V.; Zaback, Tosha; Park, Dong-Wouk; Randall, Joan A.; Cramer, Stephanie; Chiang, Michael F.

doi:10.1080/2331205x.2018.1560641

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…In Portland, Oregon, Brinks et al showed that partnering with community organizations could provide resources to manage screenings, scheduling, provision of spectacles and referral to clinical eye care, among other tasks. 37 Community organizations could be a source of personnel to maintain the patient navigator role. In student-run clinics or screening programs at academic centers, the patient navigator role could be handled by interested medical or public health students.…”

Section: Discussionmentioning

confidence: 99%

Follow-Up Adherence After Community Health Vision Screening Programs: A Review of the Literature

Zeng,

LaMattina

2023

Journal of Academic Ophthalmology

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Objective Although the purpose of community eye screening programs is to reduce health care disparities, the effectiveness of these programs is limited by the follow-up adherence of their participants. The aim of this review is to investigate factors that may promote or hinder participants from attending follow-up ophthalmological exams after community eye screenings and identify interventions to increase follow-up rates. Methods For literature review, PubMed, Web of Science, Embase, Proquest/Global Health Library, and Google Scholar databases were searched to identify studies of community eye screenings published between January 2000 and May 2023. Data from these articles were analyzed to identify barriers and facilitators of follow-up adherence after community eye screenings in the United States and to examine strategies used to increase follow-up rates. Only published manuscripts were included. We excluded studies of school screenings and clinic-based screenings. Results A total of 28 articles were included. Follow-up rates ranged from 12.5 to 89%. Nineteen articles reviewed facilitators and barriers to follow-up. Eighteen articles were non interventional and seven (see Table 1 and 2, respectively) articles described interventions that were tested to improve follow-up rates after screening. Interventions included prescheduled appointments, transportation assistance, patient education, and patient navigators. Conclusion Several interventions are promising to increase follow-up adherence in community eye screenings, but more evidence is needed. Future research should focus on randomized trials of isolated interventions to improve follow-up adherence of disadvantaged populations, although this may be limited given ethical considerations and documented lack of follow-up after screening.

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Section: Discussionmentioning

confidence: 99%

Follow-Up Adherence After Community Health Vision Screening Programs: A Review of the Literature

Zeng,

LaMattina

2023

Journal of Academic Ophthalmology

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“…In addition to health education, public health officials created resources like the Manhattan Screening Project to promote vision screenings. Vision screenings in community settings can have a direct impact on the prevalence of vision loss and blindness as they allow patients and doctors to identify the early beginnings of diseases like glaucoma, diabetic retinopathy, and macular degeneration [ 9 ]. Therefore, the objective of this analysis is to understand and characterize the obstacles encountered by patients in accessing eye care services, as determined by existing literature.…”

Section: Introductionmentioning

confidence: 99%

Barriers to Eye Care for Adults in the United States and Solutions for It: A Literature Review

Green,

Miller,

Khanna

2024

Cureus

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Vision loss and blindness is a significant public health concern that has had a profound impact on various communities in the United States. Both anticipated and unforeseen barriers have been linked to the rising rates of vision loss and blindness in the country. Extensive research has identified numerous barriers that put many Americans at a disadvantage when trying to seek high-quality eye care services. Not only do the barriers to eye care services create problems for eye health, but also create a poor quality of life. Therefore, understanding and identifying barriers to eye healthcare services is incredibly important. In addition to understanding and identifying barriers, it is also important to identify solutions to the problems created by these barriers. A systematic review of articles characterizing the barriers to eye care was completed which resulted in the identification of the major barriers that affect Americans. The review of previous research was also used to identify available solutions for problems associated with the barriers to eye care services. The major barriers identified were cost, insurance, transport and accessibility, eye health care literacy, and communication. Because of the identification of the major barriers, solutions were also identified. Health education and increased vision screenings were found to be the most used forms of solutions by healthcare professionals promoting good eye health. Telemedicine has also been cited as a possible solution to the growing problem of visual impairment and blindness within the American population.

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“…Most traditional models of vision screening in the community rely on a visual acuity chart test with a pinhole to identify people who should be referred for disease-related visual impairment. [14][15][16] However, such models have inherent limitations. For instance, although the pinhole visual acuity test is commonly thought to be a quick measure of vision level and can be done by a nurse or layperson, it has poor reliability and repeatability, 17,18 with inconsis tencies in doc umentation of results among testers.…”

Section: Introductionmentioning

confidence: 99%

“…19 Importantly, sub optimal pinhole visual acuity results are only an estimate to indicate potential disease-related visual impairment but do not definitively confirm the presence of eye disease, thus often resulting in a high proportion of unnecessary false positive referrals. 18,20,21 A second model used in some countries is to add retinal photography 22,23 or other methods such as air-puff tonometry (for intraocular pressure measurement), 14 to identify eye disease. In particular, adding retinal photography offers the advantage of ascertaining the presence of disease by providing direct visualisation of the fundus.…”

Section: Introductionmentioning

confidence: 99%

“…However, assessment of retinal photographs requires highly trained personnel (ie, optometrists or graders), which restricts feasibility and sustainability of current screening prog rammes with retinal photography. 14,24,25 Taken together, pathways in current screening approaches to identify and refer disease-related visual impairment are not efficient. Furthermore, in community or national screening programmes, the pitfalls of over-referring patients with mild or early disease (especially those with normal vision), who might not require imme diate treatment in the short term, need to be avoided.…”

Section: Introductionmentioning

confidence: 99%

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Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study

Tham

Anees

Liang

et al. 2021

The Lancet Digital Health

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Background In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment. MethodsIn this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any diseaserelated visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC). Findings In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94•2% (95% CI 93•

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Community-based vision health screening with on-site definitive exams: Design and outcomes

Cited by 7 publications

References 10 publications

Follow-Up Adherence After Community Health Vision Screening Programs: A Review of the Literature

Follow-Up Adherence After Community Health Vision Screening Programs: A Review of the Literature

Barriers to Eye Care for Adults in the United States and Solutions for It: A Literature Review

Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study

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