Visual impairment and its predictors among people living with type 2 diabetes mellitus at Dessie town hospitals, Northeast Ethiopia: institution-based cross-sectional study

Visual impairment among diabetes patients in Ethiopia: A systematic review and meta-analysis

INTRODUCTION

The increased prevalence of visual impairment among diabetes patients has become a major global public health problem. Although numerous primary studies have been conducted to determine the prevalence of visual impairment and its associated factors among diabetes patients in Ethiopia, these studies presented inconsistent findings. Therefore, this review aimed to determine the pooled prevalence of visual impairment and identify associated factors among diabetes patients.

METHODS

An extensive search of literature was done on PubMed, Google Scholar, and Web of Sciences databases. A manual search of the reference lists of included studies was performed. A weighted inverse-variance random-effects model was used to calculate the pooled prevalence of visual impairment.

RESULTS

A total of 34 eligible primary studies with a sample size of 11,884 participants were included in the final meta-analysis. The pooled prevalence of visual impairment was 21.73% (95% CI: 18.15, 25.30; I2 = 96.47%; P<0.001). Diabetes mellitus with a duration of diagnosis ≥10 years [AOR = 3.18, 95% CI: 1.85, 5.49], presence of co-morbid hypertension [AOR = 3.26, 95% CI: 1.93, 5.50], poor glycemic control [AOR = 4.30, 95% CI: 3.04, 6.06], age ≥56 years [AOR = 4.13, 95% CI: 2.27, 7.52], family history of diabetes mellitus [AOR = 4.18 (95% CI: 2.61, 6.69], obesity [AOR = 4.77, 95% CI: 3.00, 7.59], poor physical activity [AOR = 2.46, 95% CI: 1.75, 3.46], presence of visual symptoms [AOR = 4.28, 95% CI: 2.73, 6.69] and no history of eye exam [AOR = 2.30, 95% CI: 1.47, 3.57] were significantly associated with visual impairment.

CONCLUSIONS

The pooled prevalence of visual impairment was high in Ethiopia. Diabetes mellitus with a duration of diagnosis ≥10 years, presence of co-morbid hypertension, poor glycemic control, age ≥56 years, and family history of diabetes mellitus, obesity, poor physical activity, presence of visual symptoms, and no history of eye exam were independent predictors. Therefore, diabetic patients with these identified risks should be screened, and managed early to reduce the occurrence of visual impairment related to diabetes. Moreover, public health policy with educational programs and regular promotion of sight screening for all diabetes patients are needed.

Prevalence and associated factors of overweight and obesity among persons with type 2 diabetes in Africa: a systematic review and meta-analysis

BACKGROUND

Type 2 diabetes and obesity are serious public health concerns globally and a growing burden in Africa. Both conditions have serious repercussions on health when they co-occur, yet the extent of their co-occurrence in Africa remains unknown. Therefore, this review aimed to identify the prevalence and associated factors of overweight and obesity among persons with type 2 diabetes in Africa.

METHOD

A systematic search was conducted on PubMed, MEDLINE, Embase, African Index Medicus (AIM), and African Journals Online (AJOL) for observational studies that reported the prevalence of overweight and/or obesity among type 2 diabetes patients in Africa. The prevalence data from individual studies were aggregated through a random-effects meta-analysis. The I2 statistic was used to evaluate between-studies heterogeneity, while subgroup analysis and mixed-effects meta-regression were performed to identify sources of heterogeneity. We assessed publication bias using funnel plots and Egger's test. This review adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.

RESULTS

Of 1753 records retrieved, 80 articles were eligible for this review, with 74 cross-sectional studies included in the meta-analysis. The pooled prevalence of overweight and obesity was 35.6% and 25.6% respectively, while the overall prevalence of both overweight and obesity was 61.4%. Also, the pooled prevalence of both overweight and obesity across the five geographical areas in Africa ranged from 56.9% in East Africa to 88.5% in Southern Africa. Nineteen factors were significantly associated with overweight and obesity among patients with type 2 diabetes.

CONCLUSION

The high prevalence of overweight and obesity among patients with type 2 diabetes is a significant public health concern that transcends geographical boundaries within Africa. The findings from this review highlight the need for innovative weight management interventions that are tailored to the cultural context of the African setting.KEY MESSAGESThere was a high prevalence of overweight and obesity among the type 2 diabetes patients.Nineteen factors were identified to be significantly associated with overweight and obesity among type 2 diabetes patients.Only 12 out of the 80 included studies primarily focused on the prevalence of overweight and/or obesity which reflects a dearth of interest in this topic.

Prevalence and associated factors of visual impairment among adult diabetic patients visiting Adare General Hospital, Hawassa, South Ethiopia, 2022

BACKGROUND

The increased prevalence of visual impairment among diabetic patients has become a major public health problem. However, there was limited information on the extent of visual impairment among diabetic patients in our country, and there was no study in the study area. Providing updated data regarding this area is critical for the prevention of visual impairment among diabetic patients.

PURPOSE

The study aimed to assess the prevalence and associated factors of visual impairment among adult diabetic patients visiting Adare General Hospital, Hawassa, South Ethiopia, 2022.

METHODS

A hospital-based cross-sectional study was conducted on adult diabetic patients from May 30 to July 15, 2022, at Adare General Hospital, Hawassa, South Ethiopia. A systematic random sampling method was used to select 398 study participants. Data was collected through a face-to-face interview, a medical chart review, and an ocular examination. A binary logistic regression was performed to identify potential risk factors for visual impairment and their strength of association was expressed using an adjusted odds ratio with a 95% confidence interval. Variables with a P-value of < 0.05 were considered statistically significant.

RESULT

In this study, a total of 391 participants were involved, with a response rate of 98.2%. The prevalence of visual impairment was 28.6% (95% CI: 24.6-33.0). Age ≥ 60 years (AOR = 4.03, 95% CI: 1.72, 10.71), poor physical exercise (AOR = 3.26, 95% CI: 1.62, 6.53), poor glycemic control (AOR = 4.34, 95% CI: 2.26, 8.34), history of eye examination (AOR = 2.94, 95% CI: 1.50, 5.76), duration of diabetes ≥ 9 years (AOR = 4.78, 95% CI: 2.11, 10.83) and diabetic peripheral neuropathy (AOR = 3.01, 95% CI: 1.21, 7.50) were positively associated with visual impairment.

CONCLUSION

The study found a high prevalence of visual impairment among adult diabetic patients. Older age, longer duration of diabetes, poor physical exercise, poor glycemic control, history of eye examination, and diabetic peripheral neuropathy were significantly associated with visual impairment. Thus, regular physical activity, good control of glucose levels, and regular eye exams were recommended for all diabetic patients.

Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms

Diabetic Retinopathy is a vision impairment caused by blood vessel degeneration in the retina. It is becoming more widespread as it is linked to diabetes. Diabetic retinopathy can lead to blindness. Early detection of diabetic retinopathy by an ophthalmologist can help avoid vision loss and other complications. Diabetic retinopathy is currently diagnosed by visually recognizing irregularities on fundus pictures. This procedure, however, necessitates the use of ophthalmic imaging technologies to acquire fundus images as well as a detailed visual analysis of the stored photos, resulting in a costly and time-consuming diagnosis. The fundamental goal of this project is to create an easy-to-use machine learning model tool that can accurately predict diabetic retinopathy using pre-recorded digital fundus images. To create the suggested classifier model, we gathered annotated fundus images from publicly accessible data repositories and used two machine learning methods, support vector machine (SVM) and deep neural network (DNN). On test data, the proposed SVM model had a mean area under the receiver operating characteristic curve (AUC) of 97.11%, whereas the DNN model had a mean AUC of 99.15%.