Barriers and facilitators to diabetic retinopathy screening within Australian primary care

Improving Access to Specialist Eye Care for Indigenous Australians via Telehealth: An Observational Cohort Study

Background: Indigenous people are often neglected in eye health research and service delivery programs, despite having a greater burden of vision loss, most of which is avoidable. The objective of this work was to improve access to specialist eye care for Indigenous Australians living in rural and remote areas, by providing direct access to expert diagnostic services based in metropolitan areas through a tele-ophthalmology system. Methods: Over a four-year study period, 13 remote communities in Queensland and the Northern Territory were identified that had limited or no access to eye screening services. Relationships with health service providers in the communities were established to codesign a sustainable model of service delivery and referral pathways to ensure that patients identified with eye issues received appropriate treatment. Results: Over the course of the study, screening records from 378 patients were uploaded to a web-based telehealth system and diagnosed by ophthalmologists. From these examinations, 64 new cases of diabetic retinopathy (DR) were identified (including 2 cases of proliferative DR and 4 cases of severe nonproliferative DR), and diabetic macular edema was noted in 18 patients. The majority of participants screened had no eye problems, which enables the removal of these patients from the queues of overwhelmed specialist lists, improving service efficiency. The study also demonstrates capacity building of healthcare workers to perform eye screening and improved patient health awareness where the retinal cameras were used as an educational tool. Conclusions: A valuable screening service has been established in the target areas, where access to ophthalmic services has been improved for residents of the study screening locations. Routine eye examination (instead of opportunistic eye examination) is feasible for early detection of some eye diseases for remote and rural patients.

Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis

Background

We aimed to evaluate the cost-effectiveness of an artificial intelligence-(AI) based diabetic retinopathy (DR) screening system in the primary care setting for both non-Indigenous and Indigenous people living with diabetes in Australia.

Methods

We performed a cost-effectiveness analysis between January 01, 2022 and August 01, 2023. A decision-analytic Markov model was constructed to simulate DR progression in a population of 1,197,818 non-Indigenous and 65,160 Indigenous Australians living with diabetes aged ≥20 years over 40 years. From a healthcare provider's perspective, we compared current practice to three primary care AI-based screening scenarios-(A) substitution of current manual grading, (B) scaling up to patient acceptance level, and (C) achieving universal screening. Study results were presented as incremental cost-effectiveness ratio (ICER), benefit-cost ratio (BCR), and net monetary benefits (NMB). A Willingness-to-pay (WTP) threshold of AU$50,000 per quality-adjusted life year (QALY) and a discount rate of 3.5% were adopted in this study.

Findings

With the status quo, the non-Indigenous diabetic population was projected to develop 96,269 blindness cases, resulting in AU$13,039.6 m spending on DR screening and treatment during 2020-2060. In comparison, all three intervention scenarios were effective and cost-saving. In particular, if a universal screening program was to be implemented (Scenario C), it would prevent 38,347 blindness cases, gain 172,090 QALYs and save AU$595.8 m, leading to a BCR of 3.96 and NMB of AU$9,200 m. Similar findings were also reported in the Indigenous population. With the status quo, 3,396 Indigenous individuals would develop blindness, which would cost the health system AU$796.0 m during 2020-2060. All three intervention scenarios were cost-saving for the Indigenous population. Notably, universal AI-based DR screening (Scenario C) would prevent 1,211 blindness cases and gain 9,800 QALYs in the Indigenous population, leading to a saving of AU$19.2 m with a BCR of 1.62 and NMB of AU$509 m.

Interpretation

Our findings suggest that implementing AI-based DR screening in primary care is highly effective and cost-saving in both Indigenous and non-Indigenous populations.

Funding

This project received grant funding from the Australian Government: the National Critical Research Infrastructure Initiative, Medical Research Future Fund (MRFAI00035) and the NHMRC Investigator Grant (APP1175405). The contents of the published material are solely the responsibility of the Administering Institution, a participating institution or individual authors and do not reflect the views of the NHMRC. This work was supported by the Global STEM Professorship Scheme (P0046113), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology, Project of Investigation on Health Status of Employees in Financial Industry in Guangzhou, China (Z012014075). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. W.H. is supported by the Melbourne Research Scholarship established by the University of Melbourne. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Undiagnosed diabetic retinopathy in Northeast China: prevalence and determinants

Objective

To report the prevalence and contributing factors of undiagnosed diabetic retinopathy (DR) in a population from Northeastern China.

Subjects/Methods

A total of 800 subjects from the Fushun Diabetic Retinopathy Cohort Study were enrolled. A questionnaire assessing incentives and barriers to diagnosis of DR was administered. Logistic regression was used to identify clinical and sociodemographic factors associated with undiagnosed DR. In a prespecified subgroup analysis, we divided patients into vision-threatening diabetic retinopathy (VTDR) and non-VTDR (NVTDR) subgroups.

Results

Among 800 participants with DR, 712 (89.0%) were undiagnosed. Among 601 with NVTDR, 566 (94.2%) were undiagnosed. Among 199 with VTDR, 146 (73.4%) were undiagnosed. The risk factors affecting the timely diagnosis of NVTDR and VTDR exhibit significant disparities. In multivariate models, factors associated with undiagnosed VTDR were age over 60 years (OR = 2.966; 95% CI = 1.205-7.299; P = 0.018), duration of diabetes over 10 years (OR = 0.299; 95% CI = 0.118-0753; P = 0.010), visual impairment or blindness (OR = 0.310; 95% CI = 0.117-0.820; P = 0.018), receiving a reminder to schedule an eye examination (OR = 0.380; 95% CI = 0.163-0.883; P = 0.025), and the belief that "people with diabetes are unlikely to develop an eye disease" (OR = 4.691; 95% CI = 1.116-19.724; P = 0.035). However, none of the factors were associated with undiagnosed NVTDR (all P ≥ 0.145).

Conclusion

Our research has uncovered a disconcerting trend of underdiagnosis in cases of DR within our population. Addressing determinants of undiagnosed DR may facilitate early detection.

Implementation of Diabetic Retinopathy Screening in Adult Patients With Type 2 Diabetes in a Primary Care Setting

Diabetic retinopathy (DR) is a microvascular complication of type 2 diabetes and the leading cause of blindness globally. Although diabetes-related eye exams are widely recognized as an effective method for early detection of DR, which can help to prevent eventual vision loss, adherence to screening exams in the United States is suboptimal. This article describes a quality improvement project to increase DR screening rates and increase knowledge and awareness of DR in adults with type 2 diabetes in a primary care setting using mobile DR screening units. This project addressed gaps of care and demonstrated that primary care settings can increase access to DR screening through a patient-centered process and thereby help to prevent irreversible outcomes of DR and improve quality of life.

Knowledge, attitude, and practice pattern towards diabetic retinopathy screening among general practitioners in primary health centres in Jakarta, the capital of Indonesia

BACKGROUND

Diabetic retinopathy (DR) is an emerging cause of visual impairment and blindness and is often detected in the irreversible stage. General practitioners (GPs) play an essential role in the prevention of DR through diabetes control, early detection of retinal changes, and timely referral to ophthalmologists. This study aimed to determine the knowledge, attitude, and practice (KAP) towards DR screening among GPs in the district primary health centres (PHCs) in Jakarta, Indonesia.

METHODS

A cross-sectional study was conducted between April 2021 and February 2022 in 17 randomly selected district PHCs. A validated online questionnaire was then distributed. Good knowledge was defined when the correct response rate was > 75%, positive attitude was indicated when desired attitudes were found in more than half of the items (> 50%), and good practice was defined when more than half of the practice items (> 50%) were performed.

RESULTS

A total of 92 GPs, with a response rate of 60.1%, completed the questionnaire. Seventy-nine respondents (85.9%) were female with a median (range) age of 32 (24-58) years. Among the respondents, 82 (89.1%) had good knowledge and all showed positive attitude on DR screening. However, only four (4.3%) demonstrated good practices. We found a weak positive correlation (r_s = 0.298, p = 0.004) between attitude and practices.

CONCLUSION

GPs in Jakarta showed good knowledge and positive attitude on DR screening. However, they did not show good practice. There was a positive correlation between attitude and practice.

Single-Field Fundus Photography for Screening of Diabetic Retinopathy: The Prevalence and Associated Factors in a Population-Based Study

INTRODUCTION

We aimed to determine the prevalence and risk factors for diabetic retinopathy (DR) in a multi-primary healthcare facilities-based DR screening project by analyzing single-field fundus photographs among patients with diabetes in Rafsanjan City, Iran, based on the Rafsanjan Cohort Study, as a part of the prospective epidemiological research studies in IrAN (PERSIAN).

METHODS

Of all participants in the Rafsanjan Cohort Study (performed in four primary healthcare facilities across Rafsanjan City from August 2015 to December 2017), patients with diabetes were recruited in this study. All participants underwent a standardized interview and clinical and paraclinical examinations for demographic characteristics, and medical conditions according to the PERSIAN's protocols. In addition, digital fovea-centered and single-field fundus photography was performed for DR identification and grading. For assessment of agreement, a subgroup of participants underwent fundus examination, randomly. DR was graded as nonproliferative (NPDR) or proliferative (PDR).

RESULTS

Of 8414 screened participants, 1889 had diabetes. The total prevalence of DR was 6.93% [131 individuals including 110 (5.82%) with NPDR, and 21 (1.11%) with PDR] based on single-field fundus photographs, with almost perfect agreement with fundus examinations (κ = 0.82). On adjusted multivariate analysis, duration of diabetes (OR 1.16, 95% CI 1.13-1.19), positive family history for diabetes (OR 1.73, 95% CI 1.09-2.75), fasting plasma glucose (FPG) ≥ 126 mg/dL (OR 1.98, 95% CI 1.16-3.39), and serum creatinine level (OR 1.79, 95% CI 1.08-2.98) were associated with DR. Factors including age, education level, physical activity, body mass index, hypertension, and cardiovascular and renal diseases did not have association with DR on adjusted multivariate analysis.

CONCLUSIONS

Single-field fundus photography can be used for screening of DR in primary healthcare facilities. In individuals with diabetes, duration of diabetes, positive family history for diabetes, FPG ≥ 126 mg/dL, and serum creatinine level may be associated with DR.

Deep learning: applications in retinal and optic nerve diseases

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.