Global Efforts to Generate Evidence for Vision 2020

He, Mingguang; Mathenge, Wanjiku; Lewallen, Susan; Courtright, Paul

doi:10.3109/09286586.2015.1058958

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Furthermore, these disorders mostly fall under avoidable blindness therefore, additional priority should be accorded in this area. This also aligns with the initiative of “VISION 2020: the Right to Sight”, which aims to eliminate avoidable blindness by the year 2020 [25]. Additionally, cataract was found to be the third leading cause of vision loss burden, and the dominating cause of blindness and SVL in Pakistan with a contribution of more than 50%.…”

Section: Discussionsupporting

confidence: 60%

A comprehensive study capturing vision loss burden in Pakistan (1990-2025): Findings from the Global Burden of Disease (GBD) 2017 study

Hassan

Ahmed

et al. 2019

PLoS ONE

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This study aims to provide estimates, trends and projections of vision loss burden in Pakistan from 1990 to 2025. Global Burden of Diseases, Injuries, and Risk Factors Study (GBD 2017) was used to observe the vision loss burden in terms of prevalence and Years Lived with Disability (YLDs). As of 2017, out of 207.7 million people in Pakistan, an estimated 1.12 million (95% Uncertainty Interval [UI] 1.07–1.19) were blind (Visual Acuity [VA] <3/60), 1.09 million [0.93–1.24] people had severe vision loss (3/60≤VA<6/60) and 6.79 million [6.00–7.74] people had moderate vision loss (6/60≤VA<6/18). Presbyopia was found to be the most common ocular condition that affected an estimated 12.64 million [11.94–13.41] people (crude prevalence 6.08% [5.75–6.45]; 61% female). In terms of age-standardized YLDs rate, Pakistan is ranked fourth among other South Asian countries and twenty-first among other 42 low-middle income countries (classified by World Bank), with 552.98 YLDs [392.98–752.95] per 100,000. Compared with 1990, all-age YLDs count of blindness and vision impairment increased by 55% in 2017, which is the tenth highest increase among major health loss causes (such as dietary iron deficiency, headache disorders, low back pain etc.) in Pakistan. Moreover, our statistics show an increase in vision loss burden by 2025 for which Pakistan needs to make more efforts to encounter the growing burden of eye diseases.

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Section: Discussionsupporting

confidence: 60%

A comprehensive study capturing vision loss burden in Pakistan (1990-2025): Findings from the Global Burden of Disease (GBD) 2017 study

Hassan

Ahmed

et al. 2019

PLoS ONE

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“…Myopia has become a global public health concern, and the World Health Organization (WHO) has incorporated myopia prevention and control measures into its global blindness prevention plan ( 8 ). In addition, the WHO has listed myopia as one of the five types of eye diseases that aims to improve and eliminate ( 9 ). It is, therefore, imperative to enhance educational reforms and develop effective and comprehensive prevention and control programs, as well as encourage the whole society to take action against visual impairment in children.…”

Section: Introductionmentioning

confidence: 99%

Temporal and spatial characterization of myopia in China

Shi

Gao

Leng

et al. 2022

Front. Public Health

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PurposeThe aim of this study was to characterize the temporal and spatial distribution of myopia among students aged 7–18 years, by analyzing the aggregation area and providing the basis for the prevention and control of myopia in China.MethodsA database for the spatial analysis of myopia in China during 1995–2014 was established using ArcGIS10.0 software as a platform for data management and presentation. A spatial autocorrelation analysis of myopia was undertaken, and a temporal and spatial scan analysis was performed using SaTScan9.5 software.ResultsOur data demonstrated that the prevalence of myopia in China in 1995, 2000, 2005, 2010, and 2014 was 35.9, 41.5, 48.7, 57.3, and 57.1%, respectively, thus indicating a gradual upward trend. The prevalence of myopia was analyzed in various provinces (municipalities and autonomous regions), and the highest was found in Jiangsu Province, with an average Moran's I index of 0.244295 in China (P ≤ 0.05). According to the local Moran's I autocorrelation analysis, there was a spatial aggregation of myopia prevalence among students in the entire country, with Shandong, Jiangsu, Anhui, and Shanghai being classified as high–high aggregation areas, while Hainan and Guangxi were classified as low–low aggregation areas. In addition, the Getis-Ord General G results of the global hotspot analysis showed a countrywide myopia prevalence index of 0.035020 and a Z score of 1.7959 (P = 0.07251). Because the myopia prevalence correlation difference was not statistically significant, there were no “positive hotspots” or “negative hotspots.” The local hotspot analysis shows that Shandong and Jiangsu belong to high-value aggregation areas, while Hainan and Guizhou belong to low-value aggregation areas. Further analysis using time-space scanning showed 15 aggregation regions in five stages, with four aggregation regions having statistically significant differences (P ≤ 0.05). However, the aggregation range has changed over time. Overall, from 1995 to 2014, the aggregation areas for the myopia prevalence in Chinese students have shifted from the northwest, north, and northeast regions to the southeast regions.ConclusionOur data demonstrate that, from 1995 to 2014, the prevalence of myopia increased in students aged 7–18 years in China. In addition, the prevalence of myopia is randomly distributed in various provinces (municipalities and autonomous regions) and exhibits spatial aggregation. Also, the gathering area is gradually shifting to the southeast, with the existence of high-risk areas. It is, therefore, necessary to focus on this area and undertake targeted prevention and control measures.

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“…However, early detection and laser photocoagulation can prevent sudden blindness in most of the cases. Moreover, many retinal complications are often treatable and according to the initiative of “VISION 2020: The Right to Sight”, different measures are being taken to eradicate avoidable blindness by the year 2020 [7]. At the same time, it is equally important to equip ophthalmologists with state-of-the-art retinal computer aided diagnostic systems for efficient detection and grading of retinopathy.…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities

Hassan

et al. 2019

Sensors

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Macular edema (ME) is a retinal condition in which central vision of a patient is affected. ME leads to accumulation of fluid in the surrounding macular region resulting in a swollen macula. Optical coherence tomography (OCT) and the fundus photography are the two widely used retinal examination techniques that can effectively detect ME. Many researchers have utilized retinal fundus and OCT imaging for detecting ME. However, to the best of our knowledge, no work is found in the literature that fuses the findings from both retinal imaging modalities for the effective and more reliable diagnosis of ME. In this paper, we proposed an automated framework for the classification of ME and healthy eyes using retinal fundus and OCT scans. The proposed framework is based on deep ensemble learning where the input fundus and OCT scans are recognized through the deep convolutional neural network (CNN) and are processed accordingly. The processed scans are further passed to the second layer of the deep CNN model, which extracts the required feature descriptors from both images. The extracted descriptors are then concatenated together and are passed to the supervised hybrid classifier made through the ensemble of the artificial neural networks, support vector machines and naïve Bayes. The proposed framework has been trained on 73,791 retinal scans and is validated on 5100 scans of publicly available Zhang dataset and Rabbani dataset. The proposed framework achieved the accuracy of 94.33% for diagnosing ME and healthy subjects and achieved the mean dice coefficient of 0.9019 ± 0.04 for accurately extracting the retinal fluids, 0.7069 ± 0.11 for accurately extracting hard exudates and 0.8203 ± 0.03 for accurately extracting retinal blood vessels against the clinical markings.

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Global Efforts to Generate Evidence for Vision 2020

Cited by 4 publications

References 10 publications

A comprehensive study capturing vision loss burden in Pakistan (1990-2025): Findings from the Global Burden of Disease (GBD) 2017 study

A comprehensive study capturing vision loss burden in Pakistan (1990-2025): Findings from the Global Burden of Disease (GBD) 2017 study

Temporal and spatial characterization of myopia in China

Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities

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