The cost-effectiveness of school-based interventions for chronic diseases: a systematic review.

BACKGROUND: Chronic diseases, or non-communicable diseases (NCD), are conditions of long duration and often influenced and contributed by complex interactions of several variables, including genetic, physiological, environmental, and behavioral factors. These conditions contribute to death, disability, and subsequent health care costs. Primary and secondary school settings provide an opportunity to deliver relatively low cost and effective interventions to improve public health outcomes. However, there lacks systematic evidence on the cost-effectiveness of these interventions. METHODS: We systematically searched four databases (PubMed/Medline, Cochrane, Embase, and Web of Science) for published studies on the cost-effectiveness of chronic-disease interventions in school settings. Studies were eligible for inclusion if they assessed interventions of any chronic or non-communicable disease, were conducted in a school setting, undertook a full cost-effectiveness analysis and were available in English, Spanish, or French. RESULTS: Our review identified 1029 articles during our initial search of the databases, and after screening, 33 studies were included in our final analysis. The most used effectiveness outcome measures were summary effectiveness units such as quality-adjusted life years (QALYs) (22 articles; 67%) or disability-adjusted life years (DALYs) (4 articles; 12%). The most common health condition for which an intervention targets is overweight and obesity. Almost all school-based interventions were found to be cost-effective (30 articles; 81%). CONCLUSION: Our review found evidence to support a number of cost-effective school-based interventions targeting NCDs focused on vaccination, routine physical activity, and supplement delivery interventions. Conversely, many classroom-based cognitive behavioral therapy for mental health and certain multi-component interventions for obesity were not found to be cost-effective.

The Patients' Perspective for the Impact of Late Detection of Ocular Diseases on Quality of Life: A Cross-Sectional Study.

Background: Late detection of ocular diseases negatively affects patients' quality of life (QoL), encompassing health status, psychological, financial, and social aspects. However, the early detection of eye conditions leads to rapid intervention and avoiding complications, thus preserving the QoL. This study assessed the impact of ocular diseases late detection on patients' QoL at multi-eye clinics based on questionnaire responses. Methods: We developed an original Arabic-English questionnaire to assess the QoL of patients with ocular diseases referred from primary and secondary healthcare centers to tertiary hospitals. It covered preliminary data, patient perspectives on having lately detected ocular disease and treatment costs, and the impact of late detection on finances, social life, psychology, health status, and awareness of current initiatives. Logistic regression analysis was used to explore the associations between patient perspectives on having ocular diseases detected at a late stage and its impact on different domains. Multivariate logistic regression was applied with impact types of health status, psychological, financial, and social (dependent variables) and age, income levels, and hospital type (independent variables). Results: Three hundred and eighty-eight responded, with 50% experiencing psychological effects, 27% health issues, 23% social impacts, and 23% financial burdens. Two hundred seventeen patients (56%) reported having ocular condition detected in late stage. Logistic regression analysis showed positive association with health status, social well-being, and financial effects (p < 0.05). Multivariate analysis revealed pronounced effects in patients ≤ 50 years, with income \< 5000 SAR, and those visiting private clinics (p < 0.05). The social impact was greater in patients visiting private hospitals. Ninety percent of all patients emphasized the importance of increasing awareness for better QoL. Conclusion: Significant associations were found between the late detection of eye diseases and their impact on QoL. Therefore, early detection and increasing patients' awareness of ocular diseases and treatment are essential.

Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation - A Review.

Background: Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods: A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results: Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion: We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.

Peripapillary atrophy classification using CNN deep learning for glaucoma screening.

Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. The model was developed based on localization for the region of interest (ROI) using a mask region-based convolutional neural networks R-CNN and a classification network for the presence of PPA using CNN deep learning algorithms. A total of 2,472 images, obtained from five public sources and one Saudi-based resource (King Abdullah International Medical Research Center in Riyadh, Saudi Arabia), were used to train and test the model. First the images from public sources were analyzed, followed by those from local sources, and finally, images from both sources were analyzed together. In testing the classification model, the area under the curve's (AUC) scores of 0.83, 0.89, and 0.87 were obtained for the local, public, and combined sets, respectively. The developed model will assist in diagnosing glaucoma in screening programs; however, more research is needed on segmenting the PPA boundaries for more detailed PPA detection, which can be combined with optic disc and cup boundaries to calculate the cup-to-disc ratio.