Background: Diabetic retinopathy (DR) is the major cause of vision impairment or blindness in individuals who have diabetes. It has accounted for 2.6% of all cases of blindness, and 1.9% of all cases of vision impairments globally. There is a lack of data on the prevalence of diabetic retinopathy and its associated factors amongst diabetic rural populations. Hence, the current study aimed to determine factors associated with diabetic retinopathy (DR) among diabetes mellitus (DM) patients undergoing diabetic therapy.
Methods: The study was cross-sectional in design and the participants were selected using convenient sampling. STATA version 15 software was used for data analysis. Chi-square was used to compare proportions. Logistic regression was used to determine the relationship between DR and associated risk factors.
Results: The prevalence of DR was 35.3%, of which 32% were mild and 3.4% were moderate non-proliferative DR (NPDR). Females were more unemployed than males (32.1% versus 16.8%, Males were found to drink alcohol (21.8% versus 1.9%, ) and smoke cigarettes (4% versus 0.3%, p=0.0034) more than females. Being aged ≥ 55 years (OR: 2.7, 95% CI: 1.6-4.4), with matric qualification (OR: 0.6; 95% CI: 0.4-1.0); employed (OR: 1.4, 95% CI: 1.2-1.6); having high systolic blood pressure (OR=1.4, 95%CI=1.1-1.7) were the independent determinants of DR.
Conclusions: The prevalence of diabetic retinopathy was 34%. DR was determined by high systolic blood pressure, old age, and employment. Although not statistically significant, gender, hyperglycemic state, poor glycemic control, smoking, and increased body mass index (BMI) were associated with increased risk of developing DR.
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http://dx.doi.org/10.3389/fcdhc.2024.1319840 | DOI Listing |
Photodiagnosis Photodyn Ther
January 2025
Department of Ophthalmology, Tung Wah Eastern Hospital, Hong Kong. Electronic address:
Lancet Diabetes Endocrinol
January 2025
NIHR Moorfields Biomedical Research Centre, Medical Retina, Moorfields Eye Hospital, London, EC1V 2PD, UK. Electronic address:
J Diabetes Res
January 2025
First Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece.
To describe the demographic and clinical characteristics of patients with Charcot neuro-osteoarthropathy (CNO) and to examine for differences between participants with Type 1 diabetes mellitus (DM) (T1DM) and Type 2 diabetes mellitus (T2DM). Multicenter observational study in eight diabetic foot clinics in six countries between January 1, 1996, and December 31, 2022. Demographic, clinical, and laboratory parameters were obtained from the medical records.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
School of Nursing, University of Washington, Seattle, WA, USA.
Background: The prevalence of diabetes is escalating globally, underscoring the need for comprehensive evidence to inform health systems in effectively addressing this epidemic. The purpose of this study was to examine the patterns of countries' capacity to manage diabetes using latent class analysis (LCA) and to determine whether the patterns are associated with diabetes-related deaths and healthcare costs.
Methods: Eight indicators of country-level capacity were drawn from the World Health Organization Global Health Observatory dataset: the widespread availability of hemoglobin A1C (HbA1c) testing, existence of diabetes registry, national diabetes management guidelines, national strategy for diabetes care, blood glucose testing, diabetic retinopathy screening, sulfonylureas, and metformin in the public health sector.
Br J Ophthalmol
January 2025
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.
Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).
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