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BackgroundEvidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.MethodsConsented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to June 2021 at four diabetes clinics in Rwanda. Additionally, images were graded by a UK National Health System-certified retinal image grader. DR grades based on the International Classification of Diabetic Retinopathy with a grade of 2.0 or higher were considered referable. The AI system was designed to detect optic nerve and macular anomalies outside of DR. A vertical cup to disc ratio of 0.7 and higher and/or macular anomalies recognised at a cut-off of 60% and higher were also considered referable by AI.ResultsAmong 827 participants (59.6% women (n=493)) screened by AI, 33.2% (n=275) were referred for follow-up. Satisfaction with AI screening was high (99.5%, n=823), and 63.7% of participants (n=527) preferred AI over human grading. Compared with human grading, the sensitivity of the AI for referable DR was 92% (95% CI 0.863%, 0.968%), with a specificity of 85% (95% CI 0.751%, 0.882%). Of the participants referred by AI: 88 (32.0%) were for DR only, 109 (39.6%) for DR and an anomaly, 65 (23.6%) for an anomaly only and 13 (4.73%) for other reasons. Adherence to referrals was highest for those referred for DR at 53.4%.ConclusionDR screening using AI led to accurate referrals from diabetes clinics in Rwanda and high rates of participant satisfaction, suggesting AI screening for DR is practical and acceptable.

IMPORTANCE: Despite persistent inequalities in access to eye care services globally, guidance on a set of recommended, evidence-based eye care interventions to support country health care planning has not been available. To overcome this barrier, the World Health Organization (WHO) Package of Eye Care Interventions (PECI) has been developed. OBJECTIVE: To describe the key outcomes of the PECI development. EVIDENCE REVIEW: A standardized stepwise approach that included the following stages: (1) selection of priority eye conditions by an expert panel after reviewing epidemiological evidence and health facility data; (2) identification of interventions and related evidence for the selected eye conditions from a systematic review of clinical practice guidelines (CPGs); stage 2 included a systematic literature search, screening of title and abstracts (excluding articles that were not relevant CPGs), full-text review to assess disclosure of conflicts of interest and affiliations, quality appraisal, and data extraction; (3) expert review of the evidence extracted in stage 2, identification of missed interventions, and agreement on the inclusion of essential interventions suitable for implementation in low- and middle-income resource settings; and (4) peer review. FINDINGS: Fifteen priority eye conditions were chosen. The literature search identified 3601 articles. Of these, 469 passed title and abstract screening, 151 passed full-text screening, 98 passed quality appraisal, and 87 were selected for data extraction. Little evidence (≤1 CPG identified) was available for pterygium, keratoconus, congenital eyelid disorders, vision rehabilitation, myopic macular degeneration, ptosis, entropion, and ectropion. In stage 3, domain-specific expert groups voted to include 135 interventions (57%) of a potential 235 interventions collated from stage 2. After synthesis across all interventions and eye conditions, 64 interventions (13 health promotion and education, 6 screening and prevention, 38 treatment, and 7 rehabilitation) were included in the PECI. CONCLUSIONS AND RELEVANCE: This systematic review of CPGs for priority eye conditions, followed by an expert consensus procedure, identified 64 essential, evidence-based, eye care interventions that are required to achieve universal eye health coverage. The review identified some important gaps, including a paucity of high-quality, English-language CPGs, for several eye diseases and a dearth of evidence-based recommendations on eye health promotion and prevention within existing CPGs.

To develop age- and sex-specific centile reference curves for fat-free mass (FFM) and fat mass (FM) adjusted for height in an adult Kenyan population and to investigate the association between FM, FFM and blood pressure (BP). Measures of body composition from bioimpedance analyses and BP were collected in 1995 participants aged ≥50 years in Nakuru County, Kenya. Reference curves were produced using the LMS method. Multivariable linear regression models were used to test the cross-sectional association between body composition indexes and BP. The age- and sex-specific reference curves for body composition (FMI and FFMI) confirmed that FFMI is lower in both men and women with increasing age. FMI declines with age in women while among men the decline starts after 70 years. FFM was higher in men (47.4 ± 7.2 kg) than in women (38.8 ± 5.5 kg), while FM was lower in men (17.3 ± 8.1 kg) than in women (24.4 ± 10.2 kg). FMI, FFMI and BMI were all positively associated with systolic and diastolic BP, and after adjusting for body weight, FFMI remained positively associated with systolic BP and the FMI remained positively associated with diastolic BP. There was no evidence to suggest that FMI and FFMI were superior to measurement of BMI alone. These body composition reference curves provide normative data on body composition for older adults in Kenya. Further research should consider the prospective associations with health, including frailty-related outcomes.

IMPORTANCE: The incidence of age-related macular degeneration (AMD) is unknown in Africa. OBJECTIVE: To estimate the 6-year cumulative incidence and progression of AMD in older adults (≥50 years old) in Nakuru, Kenya. DESIGN, SETTING, AND PARTICIPANTS: This study assessed a population-based cohort with 6-year follow-up of 4414 participants who had a complete assessment. Random cluster sampling with probability proportionate to size procedures was used to select a representative, cross-sectional sample of adults 50 years and older from January 26, 2007, through November 11, 2008. A 6-year follow-up was undertaken from January 7, 2013, through March 12, 2014. On both occasions, a comprehensive ophthalmic examination was performed that included logMAR visual acuity, digital retinal photography, and grading of images at Moorfields Eye Hospital Reading Centre. Data were collected on general health and risk factors. MAIN OUTCOMES AND MEASURES: Incident AMD in participants with no AMD at baseline and progression from early to late AMD. RESULTS: A total of 1453 of the 2900 individuals (50.1%) at risk for AMD were followed up after 6 years (mean [SD] age, 60.7 [8.2] years; 635 female [49.5%]; 799 Kikuyu [62.3%], 324 Kalenjin [25.3%], and 159 other [12.4%]); 1282 had data on AMD status at follow-up. Of these, 202 developed early AMD, and no participants developed late AMD. The 6-year weighted (for loss to follow-up) cumulative incidence of early AMD was 164.2 per 1000 persons (95% CI, 136.7-195.9 per 1000 persons). Two individuals with baseline early AMD from the 142 at risk had developed late AMD at follow-up, with a 6-year cumulative incidence of progression from early to late AMD of 24.5 per 1000 persons (95% CI, 5.0-111.7 per 1000 persons). Cumulative incidence of AMD increased with age (≥80 years old vs 50-59 years old: 1.8; 95% CI, 0.9-3.5) and was higher in women (female vs male: 1.6; 95% CI, 1.2-2.1) and persons with diabetes (diabetes vs no diabetes: 1.7; 95% CI, 1.0-2.8). CONCLUSIONS AND RELEVANCE: In Kenya, more than 100 000 estimated new cases of AMD, mainly early AMD, will develop every year in individuals 50 years or older, although a 50% loss to follow-up and wide CIs for progression to late AMD limit definitive conclusions from these findings.

We examined the extent to which the association between socioeconomic position (SEP) and later life prevalence of hypertension, diabetes and visual impairment in Nakuru, Kenya is mediated by health-related behaviour.We used data from a community survey of 4,314 participants sampled from urban and rural areas in Nakuru, Kenya. Structural equation modelling was employed to estimate the direct and indirect—via health-related behaviour—effects of SEP on the three health outcomes.The accumulation of material resources was positively associated with hypertension and diabetes, whereas both education and material resources had a negative association with the prevalence of visual impairment. However, the observed health inequalities were not due to variation between SEP groups in health-related behaviour.The pattern of associations between education, material resources and the three health outcomes varied, suggesting that in Kenya, unlike the observed pattern of inequalities in high income countries, different dimensions of SEP provide different aspects of protection as well as risk. Smoking and alcohol use did not appear to mediate the observed associations, in contrast with countries past the epidemiologic transition.

OBJECTIVE To estimate the incidence of vision-reducing cataract in sub-Saharan Africa and use these data to calculate cataract surgical rates (CSR) needed to eliminate blindness and visual impairment due to cataract. METHODS Using data from recent population-based, standardized, rapid-assessment surveys, we calculated the age-specific prevalence of cataract (including operated and unoperated eyes) from surveys in 7 “districts” across Africa. This was done at 3 levels of visual acuity. Then we used the age-specific prevalence data to develop a model to estimate age-specific incidence at different visual acuities, taking into account differences in mortality rates between those with cataract compared with those without. The model included development of opacity in the first eye and second eye of people older than 50 years. The incidence data were used to calculate target cataract surgical rates. RESULTS Incidence and CSR needs varied significantly in different sites and were lower in some than expected. Cataract surgical rates may depend on genetic, environmental, or cultural variations and will vary with population structure, which is not uniform across Africa. CONCLUSION Africa should not be viewed as homogeneous in terms of cataract incidence or CSR needed. These CSR calculations should be useful for more appropriate planning of human resources and service delivery on the continent. The methodology can be applied to other population-based data as they become available to determine appropriate CSR targets.Arch Ophthalmol. 2010;128(12):1584-1589--