Thierry Claudien Uhawenimana, Stella M. Umuhoza, Jean Bosco Ndayambaje, Yvonne Delphine Nsaba Uwera · 15 authors
BACKGROUND: Visual impairment is a major global public health concern, disproportionately affecting populations in low- and middle-income countries (LMICs), where access to eye care services remains limited. In 2019, the Rwandan Ministry of Health integrated eye care services into the Performance-Based Financing (PBF) framework to improve accessibility, service quality, and utilization. However, service users' perspectives on this initiative remain underexplored. This study aimed to examine service users' perceptions of accessibility, quality, affordability, satisfaction, and challenges in accessing eye care services at primary healthcare facilities. METHODS: An exploratory qualitative study was conducted in six purposively selected health centers in Rwanda. A total of 72 participants were recruited using maximum variation sampling, including older adults, individuals with disabilities, and participants from different socioeconomic backgrounds. Data were collected through six focus group discussions (FGDs), each comprising 8-12 participants. Discussions explored perceptions of accessibility, quality, affordability, satisfaction, and recommendations for improvement. Audio recordings were transcribed, translated into English, and analyzed inductively using Dedoose software. Trustworthiness was ensured through prolonged engagement, member checking, peer debriefing, and triangulation. RESULTS: Participants reported that the decentralization of eye care services to health centers improved geographical access and reduced travel time. However, financial barriers remained a major constraint, particularly due to the cost of eyeglasses and certain medications not fully covered by Community-Based Health Insurance (CBHI). Users also described inconsistencies in service quality, including frequent stock-outs of essential supplies, limited equipment, and variability in the availability of eye care personnel. Despite these challenges, many participants expressed satisfaction with provider attitudes and interpersonal care, highlighting respectful and supportive interactions with health workers. At the same time, dissatisfaction was reported due to systemic inefficiencies such as limited service days, understaffing, long waiting times, and referral-related burdens. CONCLUSIONS: Findings indicate that decentralization may have contributed to improved geographic accessibility and perceived service responsiveness; however, these improvements are uneven and remain constrained by financial and system-level barriers. Addressing these barriers through expanded service packages, reliable supply chains, human resource strengthening, CBHI coverage, and community education is critical to optimize equitable eye-care delivery.
Laurent Schwartz, Jules I. Schwartz, M. Henry, Ashraf Bakkar
Age-related macular degeneration (AMD) is both a poorly understood and devastating disease. Here, we analyze the physico-chemical forces at stake, including osmolarity, redox shift, and pressure due to inflammation. Hyperosmolarity plays a key role in diseases of the anterior segment of the eye such as glaucoma, cataracts or dry eyes, and corneal ulceration. However, its role in macular degeneration has been largely overlooked. Hyperosmolarity is responsible for metabolic shifts such as aerobic glycolysis which increases lactate secretion by Muller cells. Increased osmolarity will also cause neoangiogenesis and cell death. Because of its unique energetic demands, the macula is very sensitive to metabolic shifts. As a proof of concept, subretinal injection of drugs increasing hyperosmolarity such as polyethylene glycol causes neoangiogenesis and drusen-like structures in rodents. The link between AMD and hyperosmolarity is reinforced by the fact that treatments aiming to restore mitochondrial activity, such as lipoic acid and/or methylene blue, have been experimentally shown to be effective. We suggest that metabolic shift, inflammation, and hyperosmolarity are hallmarks in the pathogenesis and treatment of AMD.
Open access
Retinal Diseases and Treatments
Neuroinflammation and Neurodegeneration Mechanisms
BACKGROUND: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability. METHODS: In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China. FINDINGS: The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959-0·977) or higher for myopic macular degeneration and 0·913 (0·906-0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957-0·994] for myopic macular degeneration and 0·973 [0·941-0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries. INTERPRETATION: Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine. FUNDING: None.