The Rapid Assessment of Avoidable Blindness survey: Review of the methodology and protocol for the seventh version (RAAB7).

The Rapid Assessment of Avoidable Blindness (RAAB) is a population-based cross-sectional survey methodology used to collect data on the prevalence of vision impairment and its causes and eye care service indicators among the population 50 years and older. RAAB has been used for over 20 years with modifications to the protocol over time reflected in changing version numbers; this paper describes the latest version of the methodology-RAAB7. RAAB7 is a collaborative project between the International Centre for Eye Health and Peek Vision with guidance from a steering group of global eye health stakeholders. We have fully digitised RAAB, allowing for fast, accurate and secure data collection. A bespoke Android mobile application automatically synchronises data to a secure Amazon Web Services virtual private cloud when devices are online so users can monitor data collection in real-time. Vision is screened using Peek Vision's digital visual acuity test for mobile devices and uncorrected, corrected and pinhole visual acuity are collected. An optional module on Disability is available. We have rebuilt the RAAB data repository as the end point of RAAB7's digital data workflow, including a front-end website to access the past 20 years of RAAB surveys worldwide. This website ( https://www.raab.world) hosts open access RAAB data to support the advocacy and research efforts of the global eye health community. Active research sub-projects are finalising three new components in 2024-2025: 1) Near vision screening to address data gaps on near vision impairment and effective refractive error coverage; 2) an optional Health Economics module to assess the affordability of eye care services and productivity losses associated with vision impairment; 3) an optional Health Systems data collection module to support RAAB's primary aim to inform eye health service planning by supporting users to integrate eye care facility data with population data.

In 2020 there were an estimated 1.1 billion people with vision impairment globally. Vision impairment negatively affects people's quality of life, social inclusion and productivity. The Rapid Assessment of Avoidable Blindness (RAAB) survey tool collects information about the vision and eye health of people aged 50 years and older in a defined population. It has been used worldwide for over 20 years to inform eye health service planning. This paper outlines the current survey methodology and summarises recent and upcoming developments. The RAAB project team has updated the survey to allow users to measure vision and collect other information on mobile devices (telephones or tablets) and send the findings directly to a central computer for automated analysis. The project team has built a new website to store this information and to allow anyone interested to find out more about the surveys done to date. The RAAB project continues to develop new features to make the information collected in surveys more useful for eye health service planning and eye health advocacy.

Near vision data and near correction requirements from community eye health programmes in nine countries.

BACKGROUND: Recent estimates of global prevalence of uncorrected presbyopia range from 510 to 826 million. There is a shortage of primary data regarding Near Visual Impairment (NVI) magnitude. METHODS: Near visual acuity (NVA) and NVI data was collected from over 388,000 people aged 35 or over across 9 countries, within Community Eye Health programmes between January 2022 and June 2023. In Kenya (n = 34,328), dioptric power of required near correction was also recorded, and any association with age, gender or level of NVA was assessed via linear regression model. RESULTS: 146,801 of 388,939 people failed initial near vision screening (37.74%, 95% CI 37.59-37.89%), with significantly higher prevalence of NVI in Sub-Saharan Africa than South Asia. Of those with distance acuity 6/12 or better, 27.97% failed (95% CI 27.81-28.13%) with evidence of difference between genders (p < 0.001): 30.77% of women vs. 24.47% of men. The most commonly required dioptric powers of correction were +2.00D, +2.50D and +3.00D, and required power correlated with age and NVA. CONCLUSIONS: NVI remains common among Community Eye Health programme participants aged 35 and over. Data from large scale programmes such as these provide an opportunity to contribute to more accurate epidemiological estimates, and to guide future research, resource planning and intervention, ideally with improved standardisation of testing in the future.

Improvement studies for equitable and evidence-based innovation: an overview of the 'IM-SEEN' model.

BACKGROUND: Health inequalities are ubiquitous, and as countries seek to expand service coverage, they are at risk of exacerbating existing inequalities unless they adopt equity-focused approaches to service delivery. MAIN TEXT: Our team has developed an equity-focused continuous improvement model that reconciles prioritisation of disadvantaged groups with the expansion of service coverage. Our new approach is based on the foundations of routinely collecting sociodemographic data; identifying left-behind groups; engaging with these service users to elicit barriers and potential solutions; and then rigorously testing these solutions with pragmatic, embedded trials. This paper presents the rationale for the model, a holistic overview of how the different elements fit together, and potential applications. Future work will present findings as the model is operationalised in eye-health programmes in Botswana, India, Kenya, and Nepal. CONCLUSION: There is a real paucity of approaches for operationalising equity. By bringing a series of steps together that force programme managers to focus on groups that are being left behind, we present a model that can be used in any service delivery setting to build equity into routine practice.

Development and Validation of a Digital (Peek) Near Visual Acuity Test for Clinical Practice, Community-Based Survey, and Research.

Purpose: Unaddressed near vision impairment (NVI) affects more than 500 million people. Testing near vision is necessary to identify those in need of services. To make such testing readily accessible, we have developed and validated a new smartphone-based near visual acuity (NVA) test: Peek Near Vision (PeekNV). Methods: Two forms of the PeekNV test were developed: (1) quantitative measurement of NVA, and (2) binary screening test for presence or absence of NVI. The validity study was carried out with 483 participants in Sagarmatha Choudhary Eye Hospital, Lahan, Nepal, using a conventional Tumbling "E" Near Point Vision Chart as the reference standard. Bland-Altman limits of agreement (LoA) were used to evaluate test agreement and test-retest repeatability. NVI screening was assessed using Cohen's kappa coefficient, sensitivity, and specificity. Results: The mean difference between PeekNV and chart NVA results was 0.008 logMAR units (95% confidence interval [CI], -0.005 to 0.021) in right eye data, and the 95% LoA between PeekNV and chart testing were within 0.235 and -0.218 logMAR. As a NVI screening tool, the overall agreement between tests was 92.9% (κ = 0.85). The positive predictive value of PeekNV was 93.2% (95% CI, 89.6% to 96.9%), and the negative predictive value 92.7% (95% CI, 88.9% to 96.4%). PeekNV had a faster NVI screening time (11.6 seconds; 95% CI, 10.5 to 12.6) than the chart (14.9 seconds; 95% CI, 13.5 to 16.2; P < 0.001). Conclusions: The PeekNV smartphone-based test produces rapid NVA test results, comparable to those of an accepted NV test. Translational Relevance: PeekNV is a validated, reliable option for NV testing for use with smartphones or digital devices.

Performance and Resource Requirements of In-Person, Voice Call, and Automated Telephone-Based Socioeconomic Data Collection Modalities for Community-Based Health Programs: A Systematic Review.

Importance: Gathering data on socioeconomic status (SES) is a prerequisite for health programs that aim to improve equity. There is a lack of evidence on which approaches offer the best combination of reliability, cost, and acceptability. Objective: To compare the performance of different approaches to gathering data on SES in community health programs. Data Sources: A search of the Cochrane Library, MEDLINE, Embase, Global Health, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform, and OpenGrey from 1999 to June 29, 2021, was conducted, with no language limits. Google Scholar was also searched and the reference lists of included articles were checked to identify further studies. The search was performed on June 29, 2021. Study Selection: Any empirical study design was eligible if it compared 2 or more modalities to elicit SES data from the following 3 categories: in-person, voice call, or automated telephone-based systems. Data Extraction and Synthesis: Two reviewers independently screened titles, abstracts, and full-text articles and extracted data. They also assessed the risk of bias using Cochrane tools and assessed the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation approach. Findings were synthesized thematically without meta-analysis. Main Outcomes and Measures: Response rate, equivalence, time, costs, and acceptability to patients and health care professionals. Results: The searches returned 3943 records. The 11 included studies reported data on 14 036 individuals from 7 countries, collecting data on 11 socioeconomic domains using 2 or more of the following modes: in-person surveys, computer-assisted telephone interviews (CATIs), and 2 types of automated data collection: interactive voice response calls (IVRs) and web surveys. Response rates were greater than 80% for all modes except IVRs. Equivalence was high across all modes (Cohen κ > 0.5). There were insufficient data to make robust time and cost comparisons. Patients reported high levels of acceptability providing data via IVRs, web surveys, and CATIs. Conclusions and Relevance: Selecting an appropriate and cost-effective modality to elicit SES data is an important first step toward advancing equitable effective service coverage. This systematic review did not identify evidence that remote and automated data collection modes differed from human-led and in-person approaches in terms of reliability, cost, or acceptability.

Improving equity, efficiency and adherence to referral in Pakistan's eye health programmes: Pre- and post-pandemic onset.

Background: Over one billion people worldwide live with avoidable blindness or vision impairment. Eye Health Programmes tackle this by providing screening, primary eye care, refractive correction, and referral to hospital eye services. One point where patients can be lost in the treatment journey is adherence to hospital referral. Context: Peek Vision's software solutions have been used in Pakistan with the goal of increasing eye health programme coverage and effectiveness. This involved collaboration between health system stakeholders, international partners, local community leaders, social organizers and "Lady Health Workers". Results: From the beginning of the programmes in November 2018, to the end of December 2021, 393,759 people have been screened, 26% of whom (n = 101,236) needed refractive services or secondary eye care, and so were referred onwards to the triage centers or hospital services. Except for a short period affected heavily by COVID-19 pandemic, the programmes reached an increasing number of people over time: screening coverage improved from 774 people per month to over 28,300 people per month. Gathering and discussing data regularly with stakeholders and implementers has enabled continuous improvement to service delivery. The quality of screening and adherence to hospital visits, gender balance differences and waiting time to hospital visits were also improved. Overall attendance to hospital appointments improved in 2020 compared to 2019 from 45% (95% CI: 42-48%) to 78% (95% CI: 76-80%) in women, and from 48% (95% CI: 45-52%) to 70% (95% CI: 68-73%) in men. These patients also accessed treatment more quickly: 30-day hospital referral adherence improved from 12% in 2019 to 66% in 2020. This approach helped to utilize refractive services more efficiently, reducing false positive referrals to triage from 10.6 to 5.9%. Hospital-based services were also utilized more efficiently, as primary eye care services and refractive services were mainly delivered at the primary healthcare level. Discussion: Despite various challenges, we demonstrate how data-driven decisions can lead to health programme systems changes, including patient counseling and appointment reminders, which can effectively improve adherence to referral, allowing programmes to better meet their community's needs.

Protocol for an automated, pragmatic, embedded, adaptive randomised controlled trial: behavioural economics-informed mobile phone-based reminder messages to improve clinic attendance in a Botswanan school-based vision screening programme.

BACKGROUND: Clinic non-attendance rates are high across the African continent. Emerging evidence suggests that phone-based reminder messages could make a small but important contribution to reducing non-attendance. We will use behavioural economics principles to develop an SMS and voice reminder message to improve attendance rates in a school-based eye screening programme in Botswana. METHODS: We will test a new theory-informed SMS and voice reminder message in a national school-based eye screening programme in Botswana. The control will be the standard SMS message used to remind parents/guardians to bring their child for ophthalmic assessment. All messages will be sent twice. The primary outcome is attendance for ophthalmic assessment. We will use an automated adaptive approach, starting with a 1:1 allocation ratio. DISCUSSION: As far as we are aware, only one other study has used behavioural economics to inform the development of reminder messages to be deployed in an African healthcare setting. Our study will use an adaptive trial design, embedded in a national screening programme. Our approach can be used to trial other forms of reminder message in the future. TRIAL REGISTRATION: ISRCTN 96528723 . Registered on 5 January 2022.

Performance and resource requirements of in-person versus voice call versus automated telephone-based socioeconomic data collection modalities for community-based health programmes: a systematic review protocol.

INTRODUCTION: Gathering data on socioeconomic status (SES) is a prerequisite for any health programme that aims to assess and improve the equitable distribution of its outcomes. Many different modalities can be used to collect SES data, ranging from (1) face-to-face elicitation, to (2) telephone-administered questionnaires, to (3) automated text message-based systems. The relative costs and perceived benefits to patients and providers of these different data collection approaches is unknown. This protocol is for a systematic review that aims to compare the resource requirements, performance characteristics, and acceptability to participants and service providers of these three approaches to collect SES data from those enrolled in health programmes. METHODS AND ANALYSIS: An information specialist will conduct searches on the Cochrane Library, MEDLINE, Embase, Global Health, ClinicalTrials.gov, the WHO ICTRP and OpenGrey. All databases will be searched from 1999 to present with no language limits used. We will also search Google Scholar and check the reference lists of relevant articles for further potentially eligible studies. Any empirical study design will be eligible if it compares two or more modalities to elicit SES data from the following three; in-person, voice call, or automated phone-based systems. Two reviewers will independently screen titles, abstracts and full-text articles; and complete data extraction. For each study, we will extract data on the modality characteristics, primary outcomes (response rate and equivalence) and secondary outcomes (time, costs and acceptability to patients and providers). We will synthesise findings thematically without meta-analysis. ETHICS AND DISSEMINATION: Ethical approval is not required, as our review will include published and publicly accessible data. This review is part of a project to improve equitable access to eye care services in low-ioncome and middle-income countries. However, the findings will be useful to policy-makers and programme managers in a range of health settings and non-health settings. We will publish our findings in a peer-reviewed journal and develop an accessible summary of results for website posting and stakeholder meetings. PROSPERO REGISTRATION NUMBER: CRD42021251959.

Sociodemographic characteristics of community eye screening participants: protocol for cross-sectional equity analyses in Botswana, India, Kenya, and Nepal.

Background: Attendance rates for eye clinics are low across low- and middle-income countries (LMICs) and exhibit marked sociodemographic inequalities. We aimed to quantify the association between a range of sociodemographic domains and attendance rates from vision screening in programmes launching in Botswana, India, Kenya and Nepal. Methods: We performed a literature review of international guidance on sociodemographic data collection. Once we had identified 13 core candidate domains (age, gender, place of residence, language, ethnicity/tribe/caste, religion, marital status, parent/guardian status, place of birth, education, occupation, income, wealth) we held workshops with researchers, academics, programme implementers, and programme designers in each country to tailor the domains and response options to the national context, basing our survey development on the USAID Demographic and Health Survey model questionnaire and the RAAB7 eye health survey methodology. The draft surveys were reviewed by health economists and piloted with laypeople before being finalised, translated, and back-translated for use in Botswana, Kenya, India, and Nepal. These surveys will be used to assess the distribution of eye disease among different sociodemographic groups, and to track attendance rates between groups in four major eye screening programmes. We gather data from 3,850 people in each country and use logistic regression to identify the groups that experience the worst access to community-based eye care services in each setting. We will use a secure, password protected android-based app to gather sociodemographic information. These data will be stored using state-of-the art security measures, complying with each country's data management legislation and UK law. Discussion: This low-risk, embedded, pragmatic, observational data collection will enable eye screening programme managers to accurately identify which sociodemographic groups are facing the highest systematic barriers to accessing care at any point in time. This information will be used to inform the development of service improvements to improve equity.

The Contribution of Artificial Intelligence in Achieving the Sustainable Development Goals (SDGs): What Can Eye Health Can Learn From Commercial Industry and Early Lessons From the Application of Machine Learning in Eye Health Programmes.

Achieving The United Nations sustainable developments goals by 2030 will be a challenge. Researchers around the world are working toward this aim across the breadth of healthcare. Technology, and more especially artificial intelligence, has the ability to propel us forwards and support these goals but requires careful application. Artificial intelligence shows promise within healthcare and there has been fast development in ophthalmology, cardiology, diabetes, and oncology. Healthcare is starting to learn from commercial industry leaders who utilize fast and continuous testing algorithms to gain efficiency and find the optimum solutions. This article provides examples of how commercial industry is benefitting from utilizing AI and improving service delivery. The article then provides a specific example in eye health on how machine learning algorithms can be purposed to drive service delivery in a resource-limited setting by utilizing the novel study designs in response adaptive randomization. We then aim to provide six key considerations for researchers who wish to begin working with AI technology which include collaboration, adopting a fast-fail culture and developing a capacity in ethics and data science.

Development and Validation of a Smartphone-based Contrast Sensitivity Test.

PURPOSE: Contrast sensitivity (CS) testing is an important measure of visual function reflecting variations in everyday visual experience in different conditions and helps to identify more subtle vision loss. However, it is only infrequently used. To make this more accessible, we have developed and validated a smartphone-based CS test. METHODS: A new tumbling-E smartphone-based CS test was developed, Peek Contrast Sensitivity (PeekCS). This was field tested and refined through several iterations. Reference standard was a tumbling-E Pelli-Robson CS test (PRCS). The validation study was conducted in community clinics in Ethiopia. Test-retest variability was measured for both PRCS and PeekCS. PRCS and PeekCS were then compared. Correlation coefficients and 95% confidence intervals (CIs) were calculated; 95% limits of agreement were calculated and displayed on Bland-Altman plots. RESULTS: PeekCS showed strong repeatability (correlation coefficient: 0.93; 95% CI: 0.91-0.95), which was comparable with PRCS (correlation coefficient: 0.96; 95% CI: 0.95-0.97). The 95% limit of agreement for test-retest variability of PRCS and PeekCS were -0.20 to 0.21 and -0.31 to 0.29, respectively. PRCS and PeekCS were highly correlated: 0.94 (95% CI: 0.93-0.95); 95% limits of agreement -0.27 to 0.29; and mean difference 0.010 (95% CI: -0.001 to 0.022). PeekCS had a faster testing time (44.6 seconds) than PRCS (48.6 seconds): mean difference -3.98 (95% CI: -5.38 to -2.58); P < 0.001. CONCLUSIONS: The smartphone-based PeekCS is a repeatable and rapid test, providing results that are highly comparable with the commonly used PRCS test. TRANSLATIONAL RELEVANCE: PeekCS provides an accessible and easy to perform alternative for CS testing, particularly in the community setting.

Clinical Validation of a Smartphone-Based Adapter for Optic Disc Imaging in Kenya.

IMPORTANCE: Visualization and interpretation of the optic nerve and retina are essential parts of most physical examinations. OBJECTIVE: To design and validate a smartphone-based retinal adapter enabling image capture and remote grading of the retina. DESIGN, SETTING, AND PARTICIPANTS: This validation study compared the grading of optic nerves from smartphone images with those of a digital retinal camera. Both image sets were independently graded at Moorfields Eye Hospital Reading Centre. Nested within the 6-year follow-up (January 7, 2013, to March 12, 2014) of the Nakuru Eye Disease Cohort in Kenya, 1460 adults (2920 eyes) 55 years and older were recruited consecutively from the study. A subset of 100 optic disc images from both methods were further used to validate a grading app for the optic nerves. Data analysis was performed April 7 to April 12, 2015. MAIN OUTCOMES AND MEASURES: Vertical cup-disc ratio for each test was compared in terms of agreement (Bland-Altman and weighted κ) and test-retest variability. RESULTS: A total of 2152 optic nerve images were available from both methods (also 371 from the reference camera but not the smartphone, 170 from the smartphone but not the reference camera, and 227 from neither the reference camera nor the smartphone). Bland-Altman analysis revealed a mean difference of 0.02 (95% CI, -0.21 to 0.17) and a weighted κ coefficient of 0.69 (excellent agreement). The grades of an experienced retinal photographer were compared with those of a lay photographer (no health care experience before the study), and no observable difference in image acquisition quality was found. CONCLUSIONS AND RELEVANCE: Nonclinical photographers using the low-cost smartphone adapter were able to acquire optic nerve images at a standard that enabled independent remote grading of the images comparable to those acquired using a desktop retinal camera operated by an ophthalmic assistant. The potential for task shifting and the detection of avoidable causes of blindness in the most at-risk communities makes this an attractive public health intervention.

The Diabetic Retinopathy Screening Workflow: Potential for Smartphone Imaging.

Complications of diabetes mellitus, namely diabetic retinopathy and diabetic maculopathy, are the leading cause of blindness in working aged people. Sufferers can avoid blindness if identified early via retinal imaging. Systematic screening of the diabetic population has been shown to greatly reduce the prevalence and incidence of blindness within the population. Many national screening programs have digital fundus photography as their basis. In the past 5 years several techniques and adapters have been developed that allow digital fundus photography to be performed using smartphones. We review recent progress in smartphone-based fundus imaging and discuss its potential for integration into national systematic diabetic retinopathy screening programs. Some systems have produced promising initial results with respect to their agreement with reference standards. However further multisite trialling of such systems' use within implementable screening workflows is required if an evidence base strong enough to affect policy change is to be established. If this were to occur national diabetic retinopathy screening would, for the first time, become possible in low- and middle-income settings where cost and availability of trained eye care personnel are currently key barriers to implementation. As diabetes prevalence and incidence is increasing sharply in these settings, the impact on global blindness could be profound.

Towards a workflow driven design for mHealth devices within temporary eye clinics in low-income settings.

Only a small minority of mobile healthcare technologies that have been successful in pilot studies have subsequently been integrated into healthcare systems. Understanding the reasons behind this discrepancy is crucial if such technologies are to be adopted. We believe that the mismatch is due to a breakdown in the relation between technical soundness of the original mobile health (mHealth) device design, and integration into healthcare provision workflows. Quantitative workflow modelling provides an opportunity to test this hypothesis. In this paper we present our current progress in developing a clinical workflow model for mobile eye assessment in low-income settings. We test the model for determining the appropriateness of design parameters of a mHealth device within this workflow, by assessing their impact on the entire clinical workflow performance.

A smartphone based ophthalmoscope.

A low-cost alternative to the direct ophthalmoscope, a simple optical adapter for a smartphone, is described. It can overcome many of the technical challenges of fundoscopy, providing a high-resolution view of the retina through an un-dilated pupil. This can be used in locations with limited diagnostic resources to detect conditions such as glaucomatous optic neuropathy. Comparison of optic nerve images from commercial retinal screening cameras with the smartphone adapter demonstrates strong evidence for no difference in performance in glaucomatous disc grading (p=0.98, paired student t test, n=300).