Ophthalmic Epidemiology: Current Concepts to Digital Strategies

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
Contributors
Part I Current and Future Epidemiological Approaches to Ophthalmology
1 Use of Electronic Health Records, Disease Registries, and Health Insurance Databases in Ophthalmology
1.1 Background
1.2 Electronic Health Records and Disease Registries
1.2.1 What Are Electronic Health Records?
1.2.2 What Are Disease Registries?
1.2.3 Applications of EHRs and Disease Registries in Ophthalmology
1.2.4 Applications of EHR-Linked Biobanks in Genomics
1.2.5 Overall Applications of EHR-Linked Genomics Research in Medicine
1.2.6 Examples of EHR-Linked Genomics Research in Ophthalmology
1.3 Health Insurance Databases
1.3.1 What Are Health Insurance Databases?
1.3.2 Applications of Health Insurance Databases in Ophthalmology
1.3.2.1 USA
1.3.2.2 Taiwan
1.3.2.3 South Korea
1.3.2.4 France
1.4 Future Outlook and Challenges
1.5 Conclusion
References
2 Application of Mobile and Wearable Technology in Data Collection for Ophthalmology
2.1 Overview of General Use Cases of Remote Monitoring
2.2 Domains of Remote Measurements
2.2.1 Physiological Measures
2.2.2 Functional Measures
2.2.3 Metabolic/Structural Measures
2.2.4 Behavioral Measures
2.2.5 Patient-Centered Outcome Measures
2.3 Conclusion
References
3 Telemedicine in Ophthalmology
3.1 Telemedicine
3.1.1 Teleophthalmology
3.1.2 Teleophthalmology Scope and Delivery
3.1.2.1 Hybrid Teleophthalmology: Lions Outback Vision in Rural Western Australia
3.1.3 Teleophthalmology Challenges
3.2 Diabetic Retinopathy Background
3.2.1 Justification for Implementation
3.2.2 Examination Frequency and Referral Recommendations
3.2.3 Screening Modalities for Detecting DR
3.2.4 DR Screening Personnel
3.2.4.1 Imager
3.2.4.2 Image Grader
3.2.5 Validation of DR Teleophthalmology Programs
3.2.5.1 The Joslin Vision Network: A Category 3 Validated Teleophthalmology Program
3.2.6 Integration Into Routine Clinical Practice
3.2.7 Limitations
3.2.8 Future Directions
3.3 Retinopathy of Prematurity Background
3.3.1 Screening for ROP
3.3.2 Traditional ROP Screening
3.3.3 Wide-Field Digital Retinal Imaging
3.3.4 Teleophthalmology
3.3.5 Current Implementations and Efficacy
3.3.5.1 The Auckland Regional Telemedicine Retinopathy of Prematurity Screening Network
3.3.6 Limitations
3.3.7 Future Directions
3.4 Conclusion
References
4 Biochemical Markers in Ophthalmology
4.1 Introduction
4.2 Genomic Markers in Ophthalmology
4.2.1 Basic Notions, Disease Heritability, and Polygenicity of Phenotypes
4.2.2 Genomic Biomarkers and Age-Related Macular Degeneration
4.2.3 Genomic Biomarkers Associated With Primary Open-Angle Glaucoma and Its Related Endophenotypes
4.2.3.1 Linkage-Based Identification of Genomic Biomarkers for Glaucoma
4.2.3.2 Genome-Wide Association Studies for POAG Endophenotypes
4.2.3.3 Genome-Wide Association Studies of POAG
4.2.3.4 Learning About Mechanisms Underlying POAG From Genetic Analyses
4.2.3.5 Predicting POAG Using Genetic Biomarkers
4.2.4 Genomic Biomarkers in Complex Eye Disease: The Near Future
4.3 Epigenetic and Transcription Regulation Biomarkers and Ocular Disease Phenotypes
4.3.1 Epigenetic Control of Transcription and Phenotype Expression
4.3.1.1 DNA Methylation Biomarkers and Ocular Disease
4.3.1.2 Genetic Control of Transcriptional Activity
4.4 Small Molecules and Proteins in Ophthalmology
4.4.1 Introduction to Metabolomics
4.4.1.1 Available Technologies
4.4.2 Metabolomics in Ophthalmology
4.4.2.1 Glaucoma
4.4.2.2 Dry-Eye Disease
4.4.2.3 Age-Related Macular Degeneration and Retinal Disease
4.4.2.4 Diabetic Eye Disease
4.4.2.5 Retinal Detachment
4.5 Proteomics
4.5.1 Proteomics Techniques
4.5.2 Proteomics in Ophthalmology
4.5.2.1 Proteomics Application in Glaucoma Research
4.5.2.2 Proteomics of Dry-Eye Disease
4.5.2.3 Age-Related Macular Degeneration
4.5.2.4 Proteomics Application to Other Retinal Disorders
4.5.3 Proteomics in Ophthalmology: A Look Into the Future
4.6 Microbiome and Human Disease
4.6.1 Methods Utilized in Microbiome Analysis
4.6.2 Microbiome and Ophthalmology
4.6.2.1 Microbiome Research in AMD
4.6.2.2 Microbiome Research in Glaucoma
4.6.2.3 The Microbiome of the Ocular Surface
4.6.3 Microbiomes in Ophthalmology
References
5 Statistical Methods for Big Data
5.1 Introduction
5.1.1 What Are the Big Data in Healthcare?
5.1.2 Hypothesis-Driven Versus Data-Driven Analysis
5.2 Fundamental Notions of Statistical Modeling
5.2.1 Inference and Prediction
5.2.2 Classification Versus Regression
5.2.3 Model Evaluation
5.2.4 Variance–Bias Trade-Off
5.2.5 Training and Testing Performances
5.2.6 Resampling Techniques
5.2.7 Tuning Parameters
5.3 The Main Machine Learning Models
5.3.1 Hypothesis-Based Models
5.3.1.1 Logistic Model
5.3.1.2 Penalized Model
5.3.1.3 Linear Discriminant Analysis and (Orthogonal) Partial Least Square Discriminant Analysis
5.3.2 Data-Driven Models
5.3.2.1 K-Nearest Neighbors
5.3.2.2 Single-Layer Neural Networks
5.3.2.3 Multivariate Adaptive Regression Splines
5.3.2.4 Support Vector Machines
5.3.2.5 Tree-Based Methods
5.3.3 Deep Learning
5.3.3.1 DL for Images: CNNs and RNNs
5.3.3.2 DL for Text: RNNs and LSTMs
5.3.3.3 Calibration Issues
5.3.4 Summary of the Main Characteristics of the Models Reviewed
5.3.5 Interpretability
5.4 Examples of Statistical Methods in Big Data
5.4.1 Omics Data
5.4.2 Electronic Health Records
5.4.3 Retinal Fundus Images
5.4.4 Administrative Databases
5.5 Conclusion
References
6 Common Statistical Issues in Ophthalmic Research
6.1 Introduction
6.1.1 Is Misuse of Statistics Common in Ophthalmic Research?
6.2 What Is Statistics and Why Do We Use It?
6.3 Common Issues in Inferential Statistical Methods
6.3.1 Method Agreement
6.3.2 Significance Testing – Clinical Significance Is Not the Same as Statistical Significance
6.3.3 Absence of Evidence Does Not Mean Evidence of Absence
6.3.4 Multiplicity
6.3.5 Missingness
6.3.6 Unit of Analysis
6.3.7 Reliance Upon Statistical Testing to Establish Normality
6.4 Common Issues in Descriptive Statistical Methods
6.4.1 Use of the Mean With Skewed Data
6.4.2 Use of the Standard Error Instead of the Standard Deviation
6.4.3 Use of the Term ± When Reporting a Standard Deviation
6.4.4 Too Many Decimal Places
6.5 Summary
References
Part II Updates On Epidemiology of Eye Diseases
7 Refractive Errors, Myopia, and Presbyopia
7.1 Introduction
7.2 Myopia
7.2.1 Prevalence of Myopia
7.2.1.1 Prevalence of Myopia in Children
7.2.1.2 Prevalence of Myopia in Adults
7.2.1.3 Prevalence of High Myopia
7.2.1.4 Prevalence of Pathologic Myopia
7.2.2 Possible Causes for the Epidemic of Myopia
7.2.2.1 Myopia Genetics
7.2.2.2 Environmental and Lifestyle Factors
7.2.2.3 Parental Myopia
7.2.3 Prevention Strategies
7.2.3.1 Lifestyle Intervention: Increasing Outdoor Time and Lessening Near Work
7.2.3.2 Optical Methods: Orthokeratology, Defocus Spectacles, and Contact Lenses
7.2.3.3 Pharmacologic Treatment: Low-Concentration Atropine Eye Drops
7.3 Astigmatism
7.3.1 Prevalence of Astigmatism
7.3.2 Genetics of Astigmatism
7.3.3 Relationship Between Astigmatism and Myopia
7.4 Hyperopia
7.4.1 Prevalence of Hyperopia
7.4.2 Risk Factors for Hyperopia
7.5 Presbyopia
7.5.1 Prevalence of Presbyopia
7.5.2 Impact of Presbyopia
7.5.3 Correction of Presbyopia
7.6 Conclusion
Acknowledgments
References
8 Corneal Disorders
8.1 Introduction
8.2 Epidemiology of Corneal Blindness
8.2.1 Trachoma
8.2.2 Infectious Keratitis
8.2.3 Onchocerciasis
8.2.4 Pseudophakic Bullous Keratopathy
8.2.5 Xerophthalmia
8.2.6 Ophthalmia Neonatorum
8.3 Big Data for Corneal Diseases
8.3.1 Infectious Keratitis Studies and Registries
8.3.2 Corneal Transplant Registries
8.3.3 Corneal Genetic Studies
8.3.4 Other Large-Scale and Electronic Health Record-Based Registries
8.4 Future Technologies for Corneal Diseases
8.4.1 Role of Artificial Intelligence
8.4.1.1 Keratoconus
8.4.1.2 Refractive Surgery
8.4.1.3 Infectious Keratitis
8.4.2 Role of Telemedicine
8.5 Conclusion
References
9 Dry-Eye Disease
9.1 Introduction
9.2 Burden, Cost, and Impact of Dry-Eye Disease
9.2.1 Burden of Dry-Eye Disease
9.2.2 Cost and Impact of Dry-Eye Disease
9.2.3 Quality of Life
9.2.4 Work Productivity
9.3 Risk Factors for Dry-Eye Disease
9.4 Conclusion
References
10 Cataract and Cataract Surgical Coverage
10.1 Introduction
10.2 Age-Related Cataracts
10.2.1 Classification and Aetiology of Age-Related Cataracts
10.2.1.1 Secondary Cataracts
10.2.2 Epidemiology and Prevalence
10.2.3 Risk Factors for Age-Related Cataract
10.2.3.1 Personal and Individual Factors
10.2.3.2 Lifestyle Factors
10.2.3.3 Environmental Factors
10.2.3.4 Disease-Related Risk Factors
10.3 Congenital Cataract
10.3.1 Incidence and Risk Factors
10.3.2 Genetic Mutations in Cataractogenesis
10.4 Cataract-Grading Methods
10.4.1 Oxford Clinical Grading System
10.4.2 WHO Simplified Cataract-Grading System
10.4.3 Lens Opacities Classification System
10.4.4 Other Cataract-Grading Methods
10.4.5 Newer Technologies to Grade Cataract
10.5 Cataract Surgical Coverage and Its Measurement
10.5.1 Brief Review of Cataract Surgical Rate By Regions
10.5.2 Gender and Access to Cataract Surgery
10.5.3 Alternative Measures of Coverage
10.6 Community Intervention Programs to Increase Cataract Surgical Coverage
10.6.1 Examples From India
10.6.1.1 Involving the Community
10.6.1.2 Eye Health Education
10.6.1.3 Eye Camps
10.6.1.4 Key Informants Or Volunteers to Strengthen Referral Systems
10.7 Conclusion
References
11 Glaucoma
11.1 Introduction
11.2 Glaucoma Classification
11.2.1 Clinic Classification
11.2.2 Epidemiological Classification
11.3 Global Prevalence of Glaucoma
11.3.1 Prevalence of Primary Glaucoma
11.3.2 Prevalence of Secondary Glaucoma
11.3.3 Projected Number of People With Glaucoma
11.4 Incidence of Primary Glaucoma
11.5 Undetected Glaucoma
11.5.1 Magnitude of Undiagnosed Glaucoma
11.5.2 Reasons for Undetected Glaucoma
11.5.2.1 Lack of Disease Knowledge
11.5.2.2 Lack of Service Utilization
11.5.2.3 Lack of Access to Services
11.5.2.4 Lack of Accurate Diagnosis
11.5.2.5 Lack of Adequate Resources
11.5.2.6 Lack of Participatory Effort
11.5.2.7 Lack of Effective Screening Tools
11.6 Impact of Glaucoma
11.6.1 Visual Impairment
11.6.2 Quality of Life and Burden of Disease
11.6.3 Socio-Economic Impact
11.7 Risk Factors of Primary Glaucoma
11.7.1 Primary Open-Angle Glaucoma
11.7.1.1 Demographic Factors
11.7.1.2 Ocular Factors
11.7.1.3 Family History and Genetic Factors
11.7.1.4 Systemic Factors
11.7.2 Primary Angle Closure Glaucoma
11.7.2.1 Demographic Factors
11.7.2.2 Ocular Factors
11.7.2.3 Family History and Genetic Factors
11.8 Glaucoma Screening
11.9 Future Directions
11.10 Conclusion
References
12 Age-Related Macular Degeneration
12.1 Introduction
12.2 Prevalence of AMD
12.3 Visual Impairment and Blindness Caused By AMD
12.4 Risk Factors of AMD
12.4.1 Gender
12.4.2 Ocular Factors
12.4.3 Systemic Factors
12.4.4 Diet
12.4.5 Smoking
12.4.6 Others
12.5 Limitations of Current Population-Based Studies and Future Works
12.6 Conclusions
References
13 Polypoidal Choroidal Vasculopathy
13.1 Introduction
13.2 Definition of PCV
13.3 Pathogenesis of PCV
13.4 Epidemiology of PCV
13.4.1 Population-Based Studies
13.4.2 Hospital-Based Studies
13.4.3 Incidence
13.4.4 Risk Factors
13.5 Genetics of PCV
13.6 Randomized Controlled Trials and Real-World Data
13.6.1 Randomized Controlled Studies of PCV
13.6.2 Disease Registries and Real-World Data of PCV
13.7 New Technologies in Epidemiological Research of PCV
References
14 Diabetic Retinopathy
14.1 Introduction
14.2 Classification of DR
14.3 Epidemiology and Impact of DR
14.3.1 Blindness Due to DR
14.3.2 Prevalence of DR and DME
14.3.3 Incidence and Progression of DR
14.3.4 Awareness of DR
14.3.5 Risk Factors of DR
14.3.6 Impact of DR On Quality of Life
14.4 Prevention of DR
14.4.1 Improve Awareness
14.4.2 Lifestyle Changes and Behavioral Modifications
14.4.3 Control of Systemic Risk Factors: Evidence From Meta-Analyses of Randomized Controlled Trials and Cohort Studies
14.5 DR Screening for Detecting Onset and Monitoring Progression of DR
14.6 Conclusion
References
15 Non-DR Retinal Vascular Diseases
15.1 Introduction
15.2 Retinal Vein Occlusion
15.2.1 Epidemiology of Retinal Vein Occlusion
15.2.2 Pathogenesis and Risk Factors of Retinal Vein Occlusion
15.3 Hypertensive Retinopathy
15.3.1 Epidemiology, Pathogenesis, and Risk Factors
15.4 Retinal Arterial Occlusion
15.4.1 Types of Retinal Arterial Occlusion
15.4.1.1 Central Retinal Artery Occlusion
15.4.1.2 Branch Retinal Artery Occlusion
15.4.1.3 Cilioretinal Artery Occlusion
15.4.2 Epidemiology of Retinal Arterial Occlusion
15.4.3 Pathogenesis of Retinal Arterial Occlusion
15.4.4 Risk Factors for Retinal Arterial Occlusion
15.4.4.1 Comorbidities Like Diabetes Mellitus and Systemic Hypertension
15.5 Ocular Ischemic Syndrome
15.5.1 Epidemiology of Ocular Ischemic Syndrome, Pathogenesis, and Risk Factors
15.5.2 Pathogenesis and Risk Factors of Ocular Ischemic Syndrome
References
16 Uveitis
16.1 Introduction
16.1.1 Visual Impairment Due to Uveitis
16.1.2 Clinical Diagnosis of Uveitis
16.1.3 Etiologies of Uveitis
16.1.4 Clinical Presentation of Uveitis
16.2 Epidemiology of Uveitis
16.2.1 Patterns and Distributions of Uveitis Etiologies
16.2.2 Incidence and Prevalence of Uveitis in Africa
16.2.3 Incidence and Prevalence of Uveitis in North America
16.2.4 Incidence and Prevalence of Uveitis in Asia
16.2.5 Incidence and Prevalence of Uveitis in Europe
16.2.6 Incidence and Prevalence of Uveitis in Oceania
16.3 Conditions in Specific Disease Entities
16.3.1 Behçet’s Disease
16.3.2 Sarcoidosis
16.3.3 Vogt–Koyanagi–Harada Disease
16.4 Conclusion
References
17 Ocular Tumors
17.1 Introduction
17.2 Retinoblastoma
17.2.1 Genetic Types
17.2.1.1 Heritable Familial Retinoblastoma
17.2.1.2 Heritable New-Onset Germ Line Retinoblastoma
17.2.1.3 Nonheritable Sporadic Retinoblastoma
17.2.2 Incidence
17.2.2.1 Incidence in the United States
17.2.2.2 Incidence Globally
17.2.2.3 Age and Sex-Specific Incidence
17.2.3 Environmental and Host Risk Factors
17.2.3.1 Heritable Retinoblastoma
17.2.3.2 Sporadic Retinoblastoma
17.2.4 Conclusions
17.3 Uveal Melanoma
17.3.1 Incidence
17.3.1.1 Age and Sex-Specific Incidence
17.3.2 Host Factors
17.3.2.1 Skin Color and Race
17.3.2.2 Iris Color
17.3.2.3 Cutaneous Nevi
17.3.2.4 Uveal Nevi
17.3.2.5 Oculodermal Melanocytosis
17.3.2.6 BAP1 Tumor Predisposition Syndrome
17.3.3 Environmental Factors
17.3.3.1 Ultraviolet Light
17.3.3.2 Occupational Exposure
17.3.4 Conclusion
17.4 Conjunctival Squamous Cell Carcinoma
17.4.1 Etiology and Risk Factors
17.4.1.1 Sunlight Exposure
17.4.1.2 Human Papillomavirus
17.4.1.3 Acquired Immunodeficiency Syndrome
17.4.1.4 Post-Transplantation and Immunosuppression
17.4.1.5 Chronic Inflammation
17.4.1.6 Xeroderma Pigmentosum and Other DNA Repair Disorders
References
Part III Epidemiological Methods for Evaluation and Intervention
18 Systematic Review and Meta-Analysis
18.1 Introduction
18.2 Steps in Completing a Systematic Review of Interventions
18.3 Systematic Reviews of Other Types of Research Questions
18.3.1 Reviews of Diagnostic Test Accuracy Studies
18.3.2 Prognostic Reviews
18.3.3 Rapid, Living and Scoping Reviews
18.4 A Database of Systematic Reviews
18.5 Conclusions
References
19 Assessment of Vision-Related Quality of Life
19.1 Introduction
19.2 Measurement of Patient-Reported Outcomes
19.2.1 What Are Patient-Reported Outcomes?
19.2.2 Why Should PROMs Be Measured?
19.2.3 Vision-Related Quality of Life
19.3 Systematic Review of the VRQoL Impact of VI and Ocular Pathologies at a Population-Based Level
19.3.1 Visual Impairment
19.3.2 Age-Related Macular Degeneration
19.3.3 Diabetic Retinopathy
19.3.4 Glaucoma
19.3.5 Cataract and Refractive Error
19.4 What Are the Best VRQoL PROMs to Use in Ocular Epidemiology?
19.4.1 Determine the Trait to Be Measured
19.4.2 Assess the Developmental Stages of the Selected PROM
19.4.3 Ascertain the Psychometric Evaluation and Validation Stages of the Selected PROM
19.4.4 Cultural and Linguistic Validation of the Selected PROM
19.4.5 Other Important Considerations in PROM Selection
19.5 Conclusions and Future Research
References
20 Screening Programs
20.1 Introduction
20.2 Conceptualizing Screening
20.3 Screening Programs in Ophthalmology
20.3.1 Pediatric Eye Screening
20.3.1.1 Screening for Retinopathy of Prematurity
20.3.1.2 Detection of Visual Loss and Refractive Errors in Pre-School Children
20.3.2 Screening for Diabetic Retinopathy
20.3.2.1 Rationale and Implementation
20.3.2.2 Clinical Impact of National Screening Programs
20.3.2.3 Methodological Considerations for Diabetic Eye Screening
20.4 Future Advancements in Ophthalmic Screening
20.4.1 Screening By Machine Learning
20.4.2 Deep Learning
20.4.3 Handheld Screening Devices
20.5 Conclusions
References
21 Community Intervention Trials in Eye Health
21.1 Introduction
21.2 Randomized Controlled Trials
21.3 Community Intervention Trials
21.3.1 Relevance of Community Trials to Global Eye Health
21.3.2 Challenges in Conducting Community Intervention Trials
21.3.3 Implication of Evidence Collected From Community Intervention Trials
21.4 Conclusions
References
Index