Diabetic eye disease and screening attendance by ethnicity in New Zealand: A systematic review

Ramke, Jacqueline; Jordan, Vanessa; Vincent, Andrea L; Harwood, Matire; Murphy, Rinki; Ameratunga, Shanthi

doi:10.1111/ceo.13528

Cited by 21 publications

(34 citation statements)

References 28 publications

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“…Most studies were conducted in the USA (90%), attempting to improve access for Black (48%) and Latinx (29%) populations. Issues with inequitable access to eyecare have been documented for high-income countries outside the USA (eg, in the UK, 106 New Zealand 107 and Sweden, 108 ) yet, knowledge of the inequity and barriers does not appear to have translated to research about interventions to address them.…”

Section: Discussionmentioning

confidence: 99%

Interventions to promote access to eyecare for non-dominant ethnic groups in high-income countries: a scoping review

et al. 2021

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PurposePeople who are distinct from the dominant ethnic group within a country can experience a variety of barriers to accessing eyecare services. We conducted a scoping review to map published interventions aimed at improving access to eyecare for non-Indigenous, non-dominant ethnic groups residing in high-income countries.MethodsWe searched MEDLINE, Embase and Global Health for studies that described an intervention to promote access to eyecare for the target population. Two authors independently screened titles and abstracts followed by review of the full text of potentially relevant sources. For included studies, data extraction was carried out independently by two authors. Findings were summarised using a combination of descriptive statistics and thematic analysis.ResultsWe screened 5220 titles/abstracts, of which 82 reports describing 67 studies met the inclusion criteria. Most studies were conducted in the USA (90%), attempted to improve access for Black (48%) or Latinx (28%) communities at-risk for diabetic retinopathy (42%) and glaucoma (18%). Only 30% included the target population in the design of the intervention; those that did tended to be larger, collaborative initiatives, which addressed both patient and provider components of access. Forty-eight studies (72%) evaluated whether an intervention changed an outcome measure. Among these, attendance at a follow-up eye examination after screening was the most common (n=20/48, 42%), and directly supporting patients to overcome barriers to attendance was reported as the most effective approach. Building relationships between patients and providers, running coordinated, longitudinal initiatives and supporting reduction of root causes for inequity (education and economic) were key themes highlighted for success.ConclusionAlthough research evaluating interventions for non-dominant, non-Indigenous ethnic groups exist, key gaps remain. In particular, the paucity of relevant studies outside the USA needs to be addressed, and target communities need to be involved in the design and implementation of interventions more frequently.

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Section: Discussionmentioning

confidence: 99%

Interventions to promote access to eyecare for non-dominant ethnic groups in high-income countries: a scoping review

et al. 2021

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“…New Zealand has never had a population-based eye health survey. A systematic review has been conducted on diabetic retinopathy prevalence and services, 22 but synthesis of information on other causes of vision impairment has not been undertaken. This information would assist decision-makers to plan equitable eye health services.…”

Section: Introductionmentioning

confidence: 99%

Vision impairment and differential access to eye health services in Aotearoa New Zealand: protocol for a scoping review

et al. 2021

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IntroductionIn Aotearoa New Zealand, Māori and Pacific people experience worse health outcomes compared with other New Zealanders. No population-based eye health survey has been conducted, and eye health services do not generate routine monitoring reports, so the extent of eye health inequality is unknown. This information is required to plan equitable eye health services. Here we outline the protocol for a scoping review to report the nature and extent of the evidence reporting vision impairment, and the use of eye health services by ethnicity in New Zealand.Methods and analysisAn information specialist will conduct searches on MEDLINE and Embase, with no limit on publication dates or language. We will search the grey literature via websites of relevant government and service provider agencies. Reference lists of included articles will be screened. Observational studies will be included if they report the prevalence of vision impairment, or any of the main causes (cataract, uncorrected refractive error, macular degeneration, glaucoma or diabetic retinopathy) or report the use of eye health services in New Zealand among people of any age. Two authors will independently review titles, abstracts and full-text articles, and complete data extraction. Overall findings will be summarised using descriptive statistics and thematic analysis, with an emphasis on disaggregation by ethnicity where this information is available.Ethics and disseminationEthical approval has not been sought as our review will only include published and publicly accessible data. We will publish the review in an open access peer-reviewed journal. We anticipate the findings will be useful to organisations and providers in New Zealand responsible to plan and deliver eye care services, as well as stakeholders in other countries with differential access to eye care.Registration detailsThe protocol has been registered with Open Science Framework (https://osf.io/yw7xb).

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“…We are actively working towards implementation of our DR classifying AI, within a long-established diabetic eye-screening program in New Zealand(37, 38). This program has multiple facets, including community-based and clinic-based screening phases.…”

Section: Introductionmentioning

confidence: 99%

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

Xie

Yang

Squirrell

et al. 2019

Preprint

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25Convolutional Neural Networks (CNN)s have become a prominent method of AI 26 implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been 27 at the forefront of the development of AI for ophthalmology. However, major obstacles remain 28 in the generalization of these CNN's onto real-world DR screening programs. We believe these 29 difficulties are due to use of 1) small training datasets (<5,000 images), 2) private and 'curated' 30 repositories, 3) offline CNN implementation methods, while 4) relying on accuracy measured 31 as area under the curve (AUC) as the sole measure of CNN performance. 32 To address these issues, the public EyePACS Kaggle Diabetic Retinopathy dataset was 33 uploaded onto Microsoft Azure™ cloud platform. Two CNNs were trained as a "Quality 34 Assurance", and a "Classifier". The "Classifier" CNN performance was then tested both on 35 'un-curated' as well as the 'curated' test set created by the "Quality Assessment" CNN. Finally, 36 the sensitivity of the "Classifier" CNNs was boosted post-training using two post-training 37 techniques. 38Our "Classifier" CNN proved to be robust, as its performance was similar on 'curated' and 'un-39 curated' sets. The implementation of 'cascading thresholds' and 'max margin' techniques led 40 to significant improvements in the "Classifier" CNN's sensitivity, while also enhancing the 41 specificity of other grades. 42 43 4 44 45It is estimated that by 2040, nearly 600 million people will have diabetes worldwide(1). 46Diabetic retinopathy (DR) is a common diabetes-related microvascular complication, and is 47 the leading cause of preventable blindness in people of working age worldwide(2, 3). It has 48 been estimated that the overall prevalence of non-vision-threatening DR, vision-threatening 49 DR and the blinding diabetic eye disease were 34·6%, 10·2%, and 6·8% respectively (3-6). 50Clinical trials have shown that the risk of DR progression can be significantly reduced by 51 controlling major risk factors such as hyperglycaemia and hypertension (7-9). It is further 52 estimated that screening, appropriate referral and treatment can reduce the vision loss from DR 53 by 50% (10-12). However, DR screening programs are expensive to set up and administrate. It 54 is estimated that even in developed countries, these programs do not reach up to 30% of the 55 diabetic population (13, 14). 56 Artificial intelligence (AI) and its subcategory of deep learning have gained popularity in 57 medical screening programs, including DR screening. In deep learning, a convolutional neural 58 network (CNN) is designed and trained based on large datasets of ground truth data and labels. 59The CNN algorithm adjusts its weights and discovers which features to extract from medical 60 data (e.g. fundus photos) to achieve the best classification accuracy, when compared to human 61 performance (15-20). CNNs use layers with convolutions, which are defined as mathematical 62 functions that use filters to extract features from an image (21-23). The out...

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Diabetic eye disease and screening attendance by ethnicity in New Zealand: A systematic review

Cited by 21 publications

References 28 publications

Interventions to promote access to eyecare for non-dominant ethnic groups in high-income countries: a scoping review

Interventions to promote access to eyecare for non-dominant ethnic groups in high-income countries: a scoping review

Vision impairment and differential access to eye health services in Aotearoa New Zealand: protocol for a scoping review

Towards implementation of AI in New Zealand national screening program: Cloud-based, Robust, and Bespoke

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