Population trends in the 10-year incidence and prevalence of diabetic retinopathy in the UK: a cohort study in the Clinical Practice Research Datalink 2004–2014

Abstract

ObjectivesTo describe trends in the incidence and prevalence of diabetic retinopathy (DR) in the UK by diabetes type, age, sex, ethnicity, deprivation, region and calendar year.DesignCohort study using the Clinical Practice Research Datalink (CPRD).SettingUK primary care.Participants7.7 million patients ≥12 contributing to the CPRD from 2004 to 2014.Primary and secondary outcome measures Age-standardised prevalence and incidence of diabetes, DR and severe DR (requiring photocoagulation) by calendar year and population subgroup. Relative risk of developing DR and severe DR by population subgroup.ResultsThe prevalence of DR was 48.4% in the population type 1 diabetes mellitus (T1DM) (14 846/30 657) and 28.3% (95 807/338 390) in the population with type 2 diabetes mellitus (T2DM). Prevalence of DR remained stable in people with T2DM and decreased in people with T1DM. Screening for DR increased over time for patients with T2DM and remained static for patients with T1DM Incidence of DR increased in parallel with the incidence of T2DM in both diabetic populations. Among patients with T2DM, relative risk of DR varied significantly by region, was increased for older age groups and in men compared with women, with risk of severe DR increased in South Asian groups and more deprived groups. Relative risk of DR for patients with T1DM varied by age and region, but not by gender, ethnic group or deprivation.ConclusionsThis is the largest study to date examining the burden of DR in the UK. Regional disparities in incidence may relate to differences in screening delivery and disease ascertainment. Evidence that deprivation and ethnicity are associated with a higher risk of severe DR highlights a significant potential health inequality. Findings from this study will have implications for professionals working in the diabetes and sight loss sectors, particularly to inform approaches for diagnosis of retinopathy and campaigning to better tackle the disease for at risk groups.

Different Phenotypes of Mild Nonproliferative Diabetic Retinopathy with Different Risks for Development of Macular Edema (C-TRACER Study)

“…The identification of different retinopathy phenotypes with different risks of progression to CIME and their different levels of occurrence in different populations from different regions of the world suggest that other factors rather than metabolic ones may play a relevant role in retinopathy progression and the development of sightthreatening complications [21]. It also shows promise for a more precise and personalized management of DR.…”

Prevalence of Diabetic Retinopathy and Associated Factors among Type 2 Diabetes Patients at Tikur Anbessa Hospital, Ethiopia

“…Nevertheless, the prevalence of diabetic retinopathy in type 2 diabetes greatly varies from country to country. For instance, the reported prevalence was 28.5% in the United States [18], 9.6% in India [19], 36.2% in Armenia [20], 8.1% in Beijing, China [21], 14.9% in Spain [22], 28.3% in the United Kingdom [23], 23.2% in Japan [24], and 64.1% in Iran [25]. A systematic review reported that the prevalence of diabetic retinopathy in population-based studies range from 30.2 to 31.6% and the prevalence in clinic-based studies range from 7.0 to 62.4% in Africa [26].…”

“…Studies have indicated that longer diabetes duration [16][17][18][20][21][22], higher hemoglobin A 1 c [16][17][18][19]22], higher blood pressure [16][17][18]21,22], and higher fasting blood glucose [19,21] were associated with presence of diabetic retinopathy. Studies have also shown that higher prevalence of diabetic retinopathy was associated with increasing age [19,20,22,23], being under insulin treatment [18,20,22], body mass index and creatinine clearance rate [21], higher blood monocyte count [19], estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 m 3 [22], and male gender compared with female [18,23]. Furthermore, some studies have reported that lower serum cholesterol [16], black race compared to white [18], and lower socioeconomic status [24] were associated with increased risk of diabetic retinopathy.…”

Promises and pitfalls of electronic health record analysis

“…EHRs are widely used to enable contemporary estimation of disease incidence or prevalence [13][14][15], study prospective associations between risk factors and disease outcomes [16], establish trends over time [17] and model the best use of healthcare resources [18,19]. Importantly, many EHRs also provide high-quality data on medication prescribing.…”

“…Algorithms combining both diagnostic and supporting information (e.g. medication, laboratory results, age, BMI) have been developed to overcome these challenges [14,29].…”