Training of nonophthalmologists in diabetic retinopathy screening

Management of diabetic ocular complications: from cellular insights to community strategies

The editorial outlines an integrated approach to managing diabetic ocular complications, combining advanced scientific research with practical public health strategies to improve the prevention, diagnosis, and treatment of diabetic retinopathy and macular edema globally.

Knowledge of Diabetic Retinopathy among Primary Care Nurses Performing Fundus Photography and Agreement with Ophthalmologists on Screening

Diabetic retinopathy (DR) is one of the complications of diabetes mellitus (DM), with macular oedema being one of the leading causes of avoidable blindness among individuals with DM worldwide. Fundus screening is the only method for early detection and treatment. High-quality training programmes for professionals performing primary care screening are essential to produce high-quality images that facilitate accurate lesion identification. This is a two-phase observational, descriptive, and cross-sectional study. The first phase analysed DR knowledge in a sample of nurses. The second phase explored agreement on DR screening between referral ophthalmologists in image assessment (gold standard) and a small group of nurses involved in the previous phase. In phase 1, the agreement rate for screening results was 90%. In phase 2, the overall raw agreement on the screening of fundus photography results between nurses and ophthalmologists was 75% (Cohen's kappa = 0.477; p < 0.001). Agreement on screening with ophthalmologists was moderate, suggesting that implementing a specific training programme for nurse-led imaging screening would help develop this competence among nurses, ensuring a good level of agreement and patient safety and adding value for users, and also for the sustainability of the healthcare system. This study was not registered.

Examining the Role of Telemedicine in Diabetic Retinopathy

With the increasing prevalence of diabetic retinopathy (DR), screening is of the utmost importance to prevent vision loss for patients and reduce financial costs for the healthcare system. Unfortunately, it appears that the capacity of optometrists and ophthalmologists to adequately perform in-person screenings of DR will be insufficient within the coming years. Telemedicine offers the opportunity to expand access to screening while reducing the economic and temporal burden associated with current in-person protocols. The present literature review summarizes the latest developments in telemedicine for DR screening, considerations for stakeholders, barriers to implementation, and future directions in this area. As the role of telemedicine in DR screening continues to expand, further work will be necessary to continually optimize practices and improve long-term patient outcomes.

Association between inequalities in human resources for health and all cause and cause specific mortality in 172 countries and territories, 1990-2019: observational study

OBJECTIVE

To explore inequalities in human resources for health (HRH) in relation to all cause and cause specific mortality globally in 1990-2019.

DESIGN

Observational study.

SETTING

172 countries and territories.

DATA SOURCES

Databases of the Global Burden of Disease Study 2019, United Nations Statistics, and Our World in Data.

MAIN OUTCOME MEASURES

The main outcome was age standardized all cause mortality per 100 000 population in relation to HRH density per 10 000 population, and secondary outcome was age standardized cause specific mortality. The Lorenz curve and the concentration index (CCI) were used to assess trends and inequalities in HRH.

RESULTS

Globally, the total HRH density per 10 000 population increased, from 56.0 in 1990 to 142.5 in 2019, whereas age standardized all cause mortality per 100 000 population decreased, from 995.5 in 1990 to 743.8 in 2019. The Lorenz curve lay below the equality line and CCI was 0.43 (P<0.05), indicating that the health workforce was more concentrated among countries and territories ranked high on the human development index. The CCI for HRH was stable, at about 0.42-0.43 between 1990 and 2001 and continued to decline (narrowed inequality), from 0.43 in 2001 to 0.38 in 2019 (P<0.001). In the multivariable generalized estimating equation model, a negative association was found between total HRH level and all cause mortality, with the highest levels of HRH as reference (low: incidence risk ratio 1.15, 95% confidence interval 1.00 to 1.32; middle: 1.14, 1.01 to 1.29; high: 1.18, 1.08 to 1.28). A negative association between total HRH density and mortality rate was more pronounced for some types of cause specific mortality, including neglected tropical diseases and malaria, enteric infections, maternal and neonatal disorders, and diabetes and kidney diseases. The risk of death was more likely to be higher in people from countries and territories with a lower density of doctors, dentistry staff, pharmaceutical staff, aides and emergency medical workers, optometrists, psychologists, personal care workers, physiotherapists, and radiographers.

CONCLUSIONS

Inequalities in HRH have been decreasing over the past 30 years globally but persist. All cause mortality and most types of cause specific mortality were relatively higher in countries and territories with a limited health workforce, especially for several specific HRH types among priority diseases. The findings highlight the importance of strengthening political commitment to develop equity oriented health workforce policies, expanding health financing, and implementing targeted measures to reduce deaths related to inadequate HRH to achieve universal health coverage by 2030.

Diabetic retinopathy screening in the emerging era of artificial intelligence

Diabetic retinopathy is a frequent complication in diabetes and a leading cause of visual impairment. Regular eye screening is imperative to detect sight-threatening stages of diabetic retinopathy such as proliferative diabetic retinopathy and diabetic macular oedema in order to treat these before irreversible visual loss occurs. Screening is cost-effective and has been implemented in various countries in Europe and elsewhere. Along with optimised diabetes care, this has substantially reduced the risk of visual loss. Nevertheless, the growing number of patients with diabetes poses an increasing burden on healthcare systems and automated solutions are needed to alleviate the task of screening and improve diagnostic accuracy. Deep learning by convolutional neural networks is an optimised branch of artificial intelligence that is particularly well suited to automated image analysis. Pivotal studies have demonstrated high sensitivity and specificity for classifying advanced stages of diabetic retinopathy and identifying diabetic macular oedema in optical coherence tomography scans. Based on this, different algorithms have obtained regulatory approval for clinical use and have recently been implemented to some extent in a few countries. Handheld mobile devices are another promising option for self-monitoring, but so far they have not demonstrated comparable image quality to that of fundus photography using non-portable retinal cameras, which is the gold standard for diabetic retinopathy screening. Such technology has the potential to be integrated in telemedicine-based screening programmes, enabling self-captured retinal images to be transferred virtually to reading centres for analysis and planning of further steps. While emerging technologies have shown a lot of promise, clinical implementation has been sparse. Legal obstacles and difficulties in software integration may partly explain this, but it may also indicate that existing algorithms may not necessarily integrate well with national screening initiatives, which often differ substantially between countries.