Prevalence Of Blindness Research Articles

An Extensive-Form Game Paradigm for Visual Field Testing via Deep Reinforcement Learning.

Glaucoma is the leading cause of irreversible but preventable blindness worldwide, and visual field testing is an important tool for its diagnosis and monitoring. Testing using standard visual field thresholding procedures is time-consuming, and prolonged test duration leads to patient fatigue and decreased test reliability. Different visual field testing algorithms have been developed to shorten testing time while maintaining accuracy. However, the performance of these algorithms depends heavily on prior knowledge and manually crafted rules that determine the intensity of each light stimulus as well as the termination criteria, which is suboptimal. We leverage deep reinforcement learning to find improved decision strategies for visual field testing. In our proposed algorithms, multiple intelligent agents are employed to interact with the patient in an extensive-form game fashion, with each agent controlling the test on one of the testing locations in the patient's visual field. Through training, each agent learns an optimized policy that determines the intensities of light stimuli and the termination criteria, which minimizes the error in sensitivity estimation and test duration at the same time. In simulation experiments, we compare the performance of our algorithms against baseline visual field testing algorithms and show that our algorithms achieve a better trade-off between estimation accuracy and test duration. By retaining testing accuracy with reduced test duration, our algorithms improve test reliability, clinic efficiency, and patient satisfaction, and translationally affect clinical outcomes.

Application of the Support Vector Machine (SVM) Algorithm for the Diagnosis of Diabetic Retinopathy

Diabetic Retinopathy (DR) is a disease whose main cause is complications of diabetes mellitus. High levels of sugar in the blood (glucose) are caused by the pancreas' inability to produce insulin. Prevention of diabetic retinopathy and blindness by carrying out examinations at an early stage and doing them regularly. Currently, doctors still carry out examinations manually so they are prone to errors in examinations. This research aims to build an application to diagnose Diabetic Retinopathy in order to facilitate the work of the medical team and doctors at the eye clinic. In the application creation process, MATLAB is used, while feature extraction uses GLCM and for classification, SVM is used. The results of the research are that doctors and medical teams are helped in carrying out manual patient diagnoses and reduce the occurrence of human error.

Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM.

Hypertensive retinopathy (HR) results from the microvascular retinal changes triggered by hypertension, which is the most common leading cause of preventable blindness worldwide. Therefore, it is necessary to develop an automated system for HR detection and evaluation using retinal images. We aimed to propose an automated approach to identify and categorize the various degrees of HR severity. A new network called the spatial convolution module (SCM) combines cross-channel and spatial information, and the convolution operations extract helpful features. The present model is evaluated using publicly accessible datasets ODIR, INSPIREVR, and VICAVR. We applied the augmentation to artificially increase the dataset of 1200 fundus images. The different HR severity levels of normal, mild, moderate, severe, and malignant are finally classified with the reduced time when compared to the existing models because in the proposed model, convolutional layers run only once on the input fundus images, which leads to a speedup and reduces the processing time in detecting the abnormalities in the vascular structure. According to the findings, the improved SVM had the highest detection and classification accuracy rate in the vessel classification with an accuracy of 98.99% and completed the task in 160.4 s. The ten-fold classification achieved the highest accuracy of 98.99%, i.e., 0.27 higher than the five-fold classification accuracy and the improved KNN classifier achieved an accuracy of 98.72%. When computation efficiency is a priority, the proposed model's ability to quickly recognize different HR severity levels is significant.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Associations of antidiabetic drugs with diabetic retinopathy in people with type 2 diabetes: an umbrella review and meta-analysis.

Diabetic retinopathy (DR) is the most frequent complication of type 2 diabetes and remains the leading cause of preventable blindness. Current clinical decisions regarding the administration of antidiabetic drugs do not sufficiently incorporate the risk of DR due to the inconclusive evidence from preceding meta-analyses. This umbrella review aimed to systematically evaluate the effects of antidiabetic drugs on DR in people with type 2 diabetes. A systematic literature search was undertaken in Medline, Embase, and the Cochrane Library (from inception till 17th May 2022) without language restrictions to identify systematic reviews and meta-analyses of randomized controlled trials or longitudinal studies that examined the association between antidiabetic drugs and DR in people with type 2 diabetes. Two authors independently extracted data and assessed the quality of included studies using the AMSTAR-2 (A MeaSurement Tool to Assess Systematic Reviews) checklist, and evidence assessment was performed using the GRADE (Grading of recommendations, Assessment, Development and Evaluation). Random-effects models were applied to calculate relative risk (RR) or odds ratios (OR) with 95% confidence intervals (CI). This study was registered with PROSPERO (CRD42022332052). With trial evidence from 11 systematic reviews and meta-analyses, we found that the use of glucagon-like peptide-1 receptor agonists (GLP-1 RA), sodium-glucose cotransporter-2 inhibitors (SGLT-2i), or dipeptidyl peptidase-4 inhibitors (DPP-4i) was not statistically associated with the risk of DR, compared to either placebo (RR: GLP-1 RA, 0.98, 0.89-1.08; SGLT-2i, 1.00, 95% CI 0.79-1.27; DPP-4i, 1.17, 0.99-1.39) or other antidiabetic drugs. Compared to other antidiabetic drugs, meglitinides (0.34, 0.01-8.25), SGLT-2i (0.73, 0.10-5.16), thiazolidinediones (0.92, 0.67-1.26), metformin (1.15, 0.81-1.63), sulphonylureas (1.24, 0.93-1.65), and acarbose (4.21, 0.44-40.43) were not statistically associated with the risk of DR. With evidence from longitudinal studies only, insulin was found to have a higher risk of DR than other antidiabetic drugs (OR: 2.47, 95% CI: 2.04-2.99). Our results indicate that antidiabetic drugs are generally safe to prescribe regarding the risk of DR among people with type 2 diabetes. Further robust and large-scale trials investigating the effects of insulin, meglitinides, and acarbose on DR are warranted. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=332052, identifier CRD42022332052.

The second rapid assessment of avoidable blindness survey in Palestine: A national cross‐sectional study

Aim: To assess prevalence and main causes of blindness, vision impairment and cataract surgical coverage in Palestinians aged 50 years and older.Methods: The survey was part of a larger population‐based survey using the rapid assessment of avoidable blindness (RAAB) methodology to estimate prevalence of vision impairment, blindness and disability in individuals aged 50 years and older in Palestine. It was a nationally representative, cross‐sectional, population‐based survey in Gaza Strip and West Bank. A sample size of 4224 participants aged 50 years and older was determined in 69 clusters in the West Bank and 52 clusters in the Gaza Strip. Within each cluster, 35 people aged 50 years or older were examined in their homes.Results: The total examined was 3848. Adjusted prevalence of blindness was 2.6%. This is a non‐significant decrease from 3.4% in 2008. The prevalence of blindness in Gaza (4.0%) was higher than the prevalence of blindness in the West Bank (1.8%). The prevalence was higher among females than males.Adjusted prevalence of severe vision impairment, moderate vision impairment and mild vision impairment was 1.4%, 10.2% and 11.6%, respectively. Principal cause of blindness, severe and moderate vision impairment was untreated cataract followed by diabetic retinopathy. Refractive error was the principal cause of mild vision impairment. More than 80% of vision impairment was avoidable.Unmet need for cataract surgery varied from 0.9% among blind participants to 6.5% among participants with mild vision impairment. Effective cataract surgical coverage was low in general and varied from 42.4% among blind operated participants to 25.9% among participants with mild vision impairment.Conclusions: Around 80% of vision impairment was avoidable. Untreated cataract and diabetic retinopathy were the main causes of blindness, severe and moderate vision impairment. Refractive error was the principal cause of mild vision impairment. Effective cataract surgical coverage was low.

Prevalence of visual impairment and its causes in adults aged 50 years and older: Estimates from the National Eye Surveys in Malaysia.

Population surveys are necessary to measure a community's eye care needs. We conducted simultaneous surveys in two regions in Malaysia in 2023 to estimate the prevalence of blindness and/or visual impairment (VI), identify its main causes, and compare the results with the survey in 2014. The surveys were simultaneously done in Eastern and Sarawak administrative regions using the Rapid Assessment of Avoidable Blindness (RAAB) technique. It involved a multistage cluster sampling method, each cluster comprising 50 residents aged 50 years and older. The prevalence of blindness and/or visual impairment (blindness, severe, moderate, and early) and its primary cause were determined through a visual acuity test and eye examination with a hand-held ophthalmoscope. Results were compared with the previous survey in 2014. A total of 10,184 subjects were enumerated, and 9,709 were examined (94.5% and 96.2% responses for Eastern and Sarawak, respectively). The prevalence of blindness and severe VI appeared lower than the previous survey. For blindness: Eastern 1.4%, 95%CI (0.9, 1.9) to 0.8%, 95%CI (0.5, 1.1) and Sarawak: 1.6% 95%CI (1.0, 2.1) to 0.6%, 95%CI (0.3, 0.9). For severe VI: Eastern 1.2%, 95%CI (0.8, 1.7) to 0.9%, 95%CI (0.6, 1.1) and Sarawak 1.1% 95%CI (0.6, 1.6) to 0.9%, 95% CI(0.6, 1.2). The main cause of blindness was untreated cataracts: 77.3% (Eastern) and 75.0% (Sarawak). Diabetic retinopathy was the 2nd main cause of blindness for Eastern at 9.1%, but it only caused early to severe VI in Sarawak. The prevalence of blindness and severe VI were lower than in the previous survey. It could have been attributed to a community cataract program implemented soon after the survey in 2014. However, more efforts are needed to address the high percentage of avoidable blindness within both regions.

THE GLAUCOMA RIDDLE – Why do Patients Still Go Blind from Glaucoma Despite All the Advances in its Management?

Glaucoma is the first leading cause of preventable irreversible blindness worldwide. Despite significant resources and treatments, a substantial portion of patients go blind. This editorial examines the complexities surrounding glaucoma's progression, emphasizing the urgent need for comprehensive approaches to diagnosis, treatment, and patient education. It underscores the imperative of early intervention, accurate risk assessment, and innovative strategies to mitigate the devastating impact of glaucoma-induced blindness on a global scale.

Batch Size Selection in Convolutional Neural Networks for Glaucoma Classification

Accurate and precise glaucoma screening is crucial for early assessment to prevent blindness, particularly in communities where access to ophthalmologists’ services is limited. It is in this context where convolutional neural networks (CNN) can be harnessed to help in the correct and dependable recognition of glaucoma. One of the neural network parameters that needed to be considered is the issue of batch size. There were very few studies exploring the impact of batch size in the diagnostic capability of neural networks but none so far for glaucoma classification. The purpose of the study is to probe the diagnostic capability of CNN in glaucoma classification as well as to investigate the suitable batch size generating the optimum classification performance of these models. Various CNN models (base CNN, VGG16, ResNet50, and DenseNet121) were applied to a Retinal Image Dataset. The models generated high values for the performance indicators with accuracy (79% - 88%), recall (80% - 93%), precision (79% - 86%) and F1-score (81% - 89%) indicating good and acceptable diagnostic capability for glaucoma recognition. DenseNet121 obtained the highest normalized Matthews Correlation Coefficient stipulating the best diagnostic capability for glaucoma classification. The batch sizes with the best diagnostic capability were 64 and 128 (base CNN model), 64 (VGG16), 16 (ResNet50), and 16 (DenseNet121). The results agreed with the recommendations of other published works of using lower batch size particularly for deeper neural network models as higher batch sizes did not generate superior performance as well posing hardware constraints. The deployment and incorporation of these CNN models can be useful complementary support tools aiding health professionals in the initial assessment of patients. This approach would allow health professionals to initiate referrals to experts for further management thereby preventing visual impairment to our patients. A working partnership of healthcare workers with data scientists is very ideal and helpful for quick and precise diagnosis of glaucoma leading to prevention of blindness in our patients.

School eye screening survey in the Gaza Strip/Palestine

Aim: To estimate prevalence of vision impairment and associated refractive error in basic school children in the Gaza Strip in the State of Palestine. The study will also provide an estimate of the use of corrective spectacles among children with refractive error. Associated factors like age, sex, type of school, residence, parents’ education, parents’ occupation, other indicators of socio‐economic status and vision problems in the child and the family will be studied.Methods: This is the first comprehensive study of school children vision impairment in the Gaza Strip.The method is an adaptation of WHO's Assessment of the prevalence of visual impairment attributable to refractive error or other causes in school children (RESC) methodology.Study population are students in the first and the nineth grades from public and United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) schools in the Gaza Strip.The sampling unit (cluster) was classes in the target grade levels and the strata were grade (grade 1 and grade 9), type of school (public and UNRWA) and school gender (all male, all female and co‐ed schools).Based on an expected prevalence of blindness of 20% among target population, 95% confidence level, 20% non‐response rate (refusals and absent students/total number of students), 3.4% margin of error and a design effect of 1.35 to account for clustering, a sample size 1100 students was determined. 28 classes were selected with probability proportionate to size from a list of classes of target grades along with number of children enrolled within each class in public and UNRWA schools in the Gaza Strip.Results: The actual number of students was 1094 in 15 UNRWA and 13 public schools (15 first grade classes and 13 ninth grade classes). The total non‐response rate was 12%.Data collection was completed on 18.5.2023. I did not have time to clean and analyse the data. Results will be presented at the conference.Conclusions: Conclusions will be presented at the conference.

Global, regional and national burden of retinopathy of prematurity among childhood and adolescent: a spatiotemporal analysis based on the Global Burden of Disease Study 2019

BackgroundThis study aimed to provide a comprehensive assessment of burden estimates and the secular trend of vision loss due to retinopathy of prematurity (ROP) among people younger than 20 years,...

Innovative Wearable Technology for Visually Impaired

In Malaysia, the prevalence of blindness for all ages is 0.2%, and low vision is 2.44% (based on the National Eye Survey, 1996). Vision impairment severely impacts the quality of their lives and can contribute to social isolation and difficulty walking. Therefore, the invention of Innovative Wearable Technology for Visually Impaired People is important for helping them navigate with speed and confidence by detecting nearby obstacles using ultrasonic waves and notifying them with the speaker, MP3 player, and vibration. The preceding technology encountered several technical challenges that hindered its optimal functionality. The primary issue revolved around the use of the Arduino Uno, which proved to be prohibitively expensive and impractical for integration into a wearable device due to its cumbersome size and restricted mobility. This rendered the previous solution less feasible for assisting visually impaired individuals who require a compact and efficient navigation aid. Another significant limitation pertained to the simultaneous employment of a buzzer and a motor as outputs, leading to potential confusion for users. The concurrent activation of these outputs might have caused difficulties in interpretation and user discomfort, undermining the device’s overall effectiveness in aiding visually impaired individuals during their navigation endeavors. Considering these challenges, the development of Innovative Wearable Technology for Visually Impaired People seeks to overcome these drawbacks by introducing a more cost-effective and user-friendly solution. Therefore, this paper proposes Innovative Wearable Technology for Visually Impaired People, which aims to address the shortcomings of existing aids and provide enhanced assistance to the visually impaired community.

Clinical Profile, Prognostic Factors and Surgical Outcomes in Lens Induced Glaucoma: A Prospective Study from a Tertiary Care Centre in Western Maharashtra, India

Introduction: Cataracts are a major cause of preventable blindness in India. A considerable number of patients continue to present with advanced cataracts, which can result in complications such as Lens Induced Glaucoma (LIG). LIG is a secondary glaucoma that occurs due to mature or hypermature cataracts, potentially causing significant visual impairment. Aim: To determine the clinical presentation, prognostic factors, and surgical outcomes in patients with LIG. Materials and Methods: A prospective study involving 60 patients with LIG was conducted at a tertiary care centre in Western Maharashtra, India from September 2022 to June 2024. All patients underwent Small Incision Cataract Surgery (SICS) and were followed-up postoperatively on day 1, day 7, and week 6. Results: The mean age of the patients was 69.03 years, with 66.7% being females. Symptoms such as pain, redness, headache and diminished vision were present in all patients. The mean duration of symptoms was 4.08 days. All patients presented with poor visual acuity (<6/60), and the majority had shallow Anterior Chamber (AC) depth (83.3%). The highest number of cases exhibited elevated Intraocular Pressure (IOP), with a range of 41-50 mmHg. Postoperative visual outcomes improved significantly by week 6 in 51.7% of patients. Conclusion: Early diagnosis and management of cataracts are crucial in preventing LIG and improving visual outcomes. Community outreach programs for early detection and management are essential in a developing country like India.

Rituximab in the Treatment of Non-Infectious Uveitis: A Review.

Non-infectious uveitis (NIU) is an immune-mediated disorder manifesting as ocular pain, redness, floaters, and photophobia, and is a leading cause of preventable blindness. Managing NIU presents considerable challenges due to the condition's resistance to high-dose corticosteroids and various immunotherapies. This review assesses the efficacy and safety of rituximab (RTX) in the treatment of NIU, based on individual case reports and small-scale studies. A cohort of 78 patients (20 males, 58 females), with a mean onset age of 32.3 years (range 8-72), was analyzed. Juvenile idiopathic arthritis (JIA) was the most frequently associated comorbidity, affecting 28 patients, while anterior uveitis was the predominant subtype, observed in 26 of 47 cases. Prior to RTX therapy, patients had been treated with an average of 1.7 conventional immunosuppressive agents (range 0-5) and 1.1 biologics (range 0-4). RTX was introduced following the failure of high-dose corticosteroids, immunosuppressive drugs, and biologics to control the uveitis. The median time from diagnosis to RTX initiation was 7.7 years (range 0.25-21). Post-RTX, 44.2% of patients experienced improvement in visual acuity, 79.5% achieved resolution of ocular inflammation, and 8.9% showed partial improvement. Additionally, 81.1% were able to reduce their corticosteroid dosage. Overall, 88.6% (69 out of 78) demonstrated a positive response to RTX treatment. These findings indicate that RTX may serve as an effective therapeutic option for NIU unresponsive to steroids and multiple immunotherapies. It may also warrant consideration as a potential first-line treatment in certain cases.

Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading.

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. Deep learning has exhibited promising performance in the grading of DR. Certain deep learning strategies have facilitated convenient regular eye check-ups, which are crucial for managing DR and preventing severe visual impairment. However, the generalization performance on cross-center, cross-vendor, and cross-user test datasets is compromised due to domain shift. Furthermore, the presence of small lesions and the imbalanced grade distribution, resulting from the characteristics of DR grading (e.g., the progressive nature of DR disease and the design of grading standards), complicates image-level domain adaptation for DR grading. The general predictions of the models trained on grade-skewed source domains will be significantly biased toward the majority grades, which further increases the adaptation difficulty. We formulate this problem as a grade-skewed domain adaptation challenge. Under the grade-skewed domain adaptation problem, we propose a novel method for image-level supervised DR grading via Asymmetric Bi-Classifier Discrepancy Minimization (ABiD). First, we propose optimizing the feature extractor by minimizing the discrepancy between the predictions of the asymmetric bi-classifier based on two classification criteria to encourage the exploration of crucial features in adjacent grades and stretch the distribution of adjacent grades in the latent space. Moreover, the classifier difference is maximized by using the forward and inverse distribution compensation mechanism to locate easily confused instances, which avoids pseudolabel bias on the target domain. The experimental results on two public DR datasets and one private DR dataset demonstrate that our method outperforms state-of-the-art methods significantly.

Effect of Corneal Collagen Crosslinking with Riboflavin in Patients of Progressive Keratoconus: A Prospective Cohort Study

Introduction: Keratoconus is a non inflammatory disease characterised by progressive corneal thinning that results in a cone-like ectasia, irregular astigmatism, and decreased vision. Advanced disease can result in corneal scarring and blindness, necessitating penetrating keratoplasty. The only treatment believed to have the ability to stop or decrease the progression of keratoconus is Collagen Cross-Linking (CXL). Aim: To evaluate the efficacy of corneal collagen cross-linking with riboflavin (C3R) in patients with keratoconus by studying the outcomes in terms of Uncorrected Visual Acuity (UCVA), Best Corrected Visual Acuity (BCVA), manifest refraction, and corneal topography. Materials and Methods: A hospital-based prospective cohort study was conducted at the Regional Institute of Ophthalmology, Government Medical College, Thiruvananthapuram, Kerala, India from June 2017 to June 2018. Fifteen eyes of 12 patients who underwent corneal collagen cross-linking for progressive keratoconus were included. Routine ophthalmological examinations was done for all patients. Patients under the age of 35 years with progressive keratoconus, an average K value not exceeding 60 D, and who had not undergone any other surgical modalities of treatment were included in the study. Patients with scarred cornea or hydrops, active ocular infection, or pachymetry <400 µm were excluded. Detailed informed consent was obtained from all patients before surgery. Preoperative UCVA, BCVA cylinder, Spherical Equivalent (SE), and topography values were obtained. All patients were followed-up at three and six months postoperative period to look for changes in UCVA, BCVA, manifest refraction, and corneal topography. Data were expressed as the mean±Standard Deviation (SD) pre- and postoperatively and analysed statistically using the paired t-test, with p<0.05 considered statistically significant. Analysis was conducted using Statistical Package for Social Sciences (SPSS) version 16.0. Results: On follow-up at six months post-treatment, UCVA (log MAR) improved from 0.87±0.40 to 0.65±0.35 (p-value=0.001), and BCVA (log MAR) changed from 0.37±0.21 to 0.17±0.22 (p-value=0.001). K max decreased from 54.90±6.05 to 52.67±5.02 (p-value=0.001), and K min decreased from 48.14±4.24 to 45.93±4.07 (p-value=0.001). Astigmatism and SE also showed statistically significant improvement post-procedure (p-value 0.001 and 0.002, respectively). Astigmatism reduced from -3.50±1.58 to -2.90±1.37. SE reduced from -4.93±3.92 to -4.28±3.48. No significant complications were noted in any of the patients. Conclusion: The CXL is an effective procedure for stabilising the progression of keratoconus, thus reducing the burden of preventable blindness and the need for keratoplasty.

Ocular microvascular complications in diabetic retinopathy: insights from machine learning

IntroductionDiabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in...

Preterm Retinopathy of Prematurity and risk factors from a tertiary health care unit: A descriptive study

Abstract Introduction: Retinopathy Of Prematurity (ROP), is the major preventable blindness in infants. A study was conducted to find the risk factors of ROP in premature infants with gestational age (GA) < 36 weeks. Methods: It was a descriptive study conducted in Kakatiya Medical College, Hanumakonda. Babies with major congenital, chromosomal anomalies were excluded. One hour before screening, a drop of Tropicamide was instilled for every 10 – 15 minutes for 4 times followed by a drop of Phenylephrine just before examination. ROP screening was carried out by a senior ophthalmologist in NICU. First, the anterior segment was examined. This was followed by sequential examination of all clock hours of the peripheral retina and findings were recorded. Association between variables was analysed by using Chi-Square test. P < 0.05 was considered to be statistically significant. Results: ROP was detected in 31.4%; statistically there was no significant difference between gender. Apnoea, hyperbilirubinemia, anaemia, respiratory distress were found to be the risk factors for ROP; statistically there was significant difference, respectively. Conclusion: Preventing prematurity is the best way to reduce ROP. Proper coordination between obstetrician, neonatologist and ophthalmologist can help to reduce the incidence.

Исследование распространенности сочетанного поражения органов зрения, слуха и нарушений когнитивной функции у населения Южного Урала в возрасте 85 лет и старше

Material and methods. This study was carried out as part of the research project Ural Eye Study of Very Old People. The study involved 1526 respondents aged 85 years and older. Results. This study was conducted on 731 (47.9%) respondents (mean age 88.1±2.7 years; median 87 years, range 85–98 years) with available data on measures of vision, hearing, and cognitive function. The prevalence of low vision and blindness, hearing impairment, combined disorders and the presence of dementia was 51.8% (95% CI 48.2 to 55.5%), 33.1% (95% CI 29.7 to 36.5%), 20.5% (95% CI 17.8 to 23.5%) and 48.2% (95% CI 44.5 to 51.8%), respectively. A lower cognitive function score was associated with lower visual acuity (p<0.001) and a higher degree of hearing loss (p=0.03), adjusted for older age (p=0.001), rural region of residence (p=0.003), lower level of education (p<0.001) and higher depression scores (p<0.001). Higher prevalence of dementia was associated with higher incidence of hearing impairment (OR 2.18, 95% CI 1.59 to 2.98; p<0.001), higher incidence of low vision/blindness (OR 2.09, 95% CI 1.55 to 2.81; p<0.001) and a higher prevalence of combined visual and hearing impairment (OR 2.80, 95% CI 1.92 to 4.07; p<0.001). Conclusion. This study showed a stronger association of combined visual and hearing impairment (prevalence 20.5%) (OR 2.80) with dementia compared with hearing impairment alone (OR 2.18) or visual impairment alone (OR 2.09). The findings show the importance of correcting sensory impairments for the prevention of dementia. Key words: very old population, population-based study, blindness, low vision, hearing loss, dementia

Harmful traditional eye practices in Nigeria and its burden on eye care: A review of literature

Background/Aim: The use of traditional eye medication (TEM) is an age-long practice whose negative effects on eye health have been well documented. TEM involves the application of different harmful preparations to the eye as a form of treatment. This article is aimed at highlighting the burden of harmful traditional practices on eye care in Nigeria. Methodology: A review of available literature was conducted to summarize the different types of harmful traditional eye practices in Nigeria, including the many different preparations used. Prevalence of harmful traditional practices, determinants, burden in Nigeria. Issues relating to determinants, prevalence, indications for TEM use, presentation of patients with a history of TEM use, ocular complications following TEM use, the pathway to blindness with TEM use, economic burden, and prevention were considered in the review. The prevalence of TEM use was 15.5%, and the common determinants included low socioeconomic status, rural residence, poor access to conventional eye care, and cultural beliefs. The indications for the use of these medications included blurring of vision, red eyes, ocular pain, and itching. Patients who used TEMs presented with various levels of visual impairment (14.5% and 13.1%, respectively, for moderate and severe visual impairment), and 51.3% were reported blind at presentation. The common complications of these agents were corneal scars (opacities), staphylomas, endophthalmitis and panophthalmitis, corneal ulcers and perforations, uveitis, band and bullous keratopathies, complicated cataracts, subluxated lens, and descemetoceles. Conclusion: The prevalence of TEM use, visual impairment, and blindness arising from it is high in Nigeria. The ocular morbidities that follow TEM use constitute a significant burden (economic and otherwise) on eye care delivery in the country. A multipronged approach by stakeholders is required to mitigate the enablers of TEM practice in Nigeria. Furthermore, where there are complications, prompt referral to an ophthalmologist is advocated as they are well-equipped to provide the most appropriate management.