Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study.

doi:10.21203/rs.3.rs-4620020/v1

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Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study.

https://doi.org/10.21203/rs.3.rs-4620020/v1

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Background

Diabetic retinopathy (DR) is emerging as a global public health issue that may result in visual impairment. It has become the leading cause of blindness among working-age adults globally, despite established treatments that can reduce the risk by 60%. Disease progression, as indicated by longitudinal HbA1c measurements, may affect the time of interest (retinopathy). Hence, poor glycemic control increases and hastens the risk of retinopathy.

Objective

This study aimed to determine the incidence of diabetic retinopathy and its predictors among adult diabetic patients in public hospitals in Central and Southern Ethiopia.

Methods

A retrospective follow-up study was conducted at selected hospitals in Central and Southern Ethiopia among newly diagnosed patients with adult diabetes between January 1, 2015, and June 30, 2022. A systematic random sampling technique was applied. The data were collected and entered into Epi-data version 4.6.0.2 and exported to STATA version 14 for analysis. Descriptive statistics of the variables were obtained. The Cox proportional hazard assumption was checked. The Cox regression model was used to quantify the effects of covariates on the time to diabetic retinopathy. A p value less than 0.25 was the cut-off point for selecting variables for the bivariable analysis and candidates for the final analysis. In the multivariable analysis, variables with a p value less than 0.05 and a corresponding 95% confidence interval in the final model were used. Model adequacy was checked.

Results

A total of 376 adult diabetic patients were followed for 45752 person-months. Overall, 96 (25.5%) patients developed diabetic retinopathy, with an incidence rate of 11.7 per 1000 person-months of observation. Positive proteinuria (AHR = 2.19: 95% CI: 1.18, 4.08), hypertension (Yes) (AHR = 2.23: 95% CI: 1.39, 3.55) and type II DM (AHR = 2.89: 95% CI: 1.19, 7.05) were identified as significant predictors of diabetic retinopathy.

Conclusion

The incidence rate of diabetic retinopathy was high. Hypertension, proteinuria and type of diabetes were identified as predictors of diabetic retinopathy. Aggressive management should be implemented, and DM patients with hypertension and positive proteinuria should be followed to optimize positive outcomes.

incidence

diabetic retinopathy

predictors

adult diabetic patients

Diabetes retinopathy (DR) is the most prevalent cause of acquired blindness in adults and is one of the most life-threatening conditions (1). One of the microcirculatory lesions of the general diabetic illness, diabetic retinopathy, is an ocular consequence of diabetes that can result in blindness (2). It can be caused by changes in platelet aggregation, blood flow variations, or lesions in the artery walls (3). The three main stages of diabetic macular edema are proliferative DR, early and severe non-proliferative DR, and diabetic macular edema (3). Retinal changes occur in diabetic patients as a result of the development of retinal capillary micro aneurysms, excessive vascular permeability, vascular occlusion, proliferation of new blood vessels and accompanying fibrous tissue on the surface of the retina and optic disc, and contraction of these fibro-vascular proliferations and the vitreous (4, 5). The first visible lesions of diabetic retinopathy are retinal capillary micro aneurysm, although they can also arise in patients with other retinal vascular disorders, particularly those linked with vascular occlusion (6).

Diabetes is a dangerous, long-term condition in which the body cannot create enough or any insulin or cannot utilize the insulin it produces adequately. Diabetes mellitus is divided into two types: type 1 and type 2. Type 1 diabetes mellitus and type 2 diabetes mellitus commonly occur in childhood and adulthood, respectively (7). Although genetics is thought to be the cause of type 1 diabetes mellitus, environmental factors such as viruses may play a role in disease onset. Several variables, including lifestyle choices and genetics, contribute to type 2 diabetes mellitus (8). According to the International Diabetes Federation (IDF) 2021, the global prevalence of diabetes among individuals aged 20 to 79 years is 537 million (9.3% of all persons in this age range), with 79.4% of cases occurring in low- and middle-income countries (LMICs). By 2030, it is anticipated to reach 643 million, and by 2045, it will reach 782 million. It is also estimated that more than 6.7 million people aged 20–79 will die from diabetes-related causes in 2021. Type 2 diabetes accounts for the great majority of diabetes cases worldwide (almost 90%). There is evidence that type 2 diabetes can be prevented or delayed, and there is growing evidence that type 2 diabetes remission is sometimes feasible (9). It has a pooled prevalence of 4.99% in Ethiopia (10).

Globally, the number of diabetic patients increased from 108 million in 1980 to 422 million in 2014 (11). It is estimated to reach 9.3% by 2019 and is expected to increase to 10.2% by 2030 and 10.9% by 2045. Urban areas have a greater prevalence (10.8%) than rural areas (7.2%), while high-income countries have a greater prevalence (10.4%) than low-income countries (4.0%) (12). However, low- and middle-income countries are experiencing a large share of the rapid increase in the prevalence of this disease. An estimated 1.5 million deaths were directly caused by diabetes in 2019, and between 2000 and 2016, diabetes caused a 5% increase in premature mortality (13).

Type 2 diabetes is a chronic disease that has a significant global impact (14). The presence of type 2 diabetes has been associated with an increased risk of depression and vice versa (15–18). Patients with type 2 diabetes are at increased risk of both microvascular and macrovascular disease (15–17, 19, 20). Type 2 diabetes often occurs concomitantly and increases the risk of vascular diseases independently. However, their coexistence dramatically increases the risk (21) and is thus associated with retinopathy (22–26).

Diabetic retinopathy (DR) is emerging as a global public health issue that may result in visual impairment. It has become the leading cause of blindness among working-age adults globally, despite established treatments that can reduce the risk by 60% (27). The incidence of diabetic retinopathy in patients with type 2 diabetes is twofold greater than that in patients without this disease (28). Type II diabetes is often devastating and remains one of the leading causes of blindness (29). DR is a common cause of blindness and visual impairment in the working-age population (30). It is a major cause of blindness(13) and causes 2.6% of blindness(31) and 4.8% of visual impairment(32) worldwide. Hypertension accelerates the development of retinopathy in diabetic patients (33).

DR has a substantial negative impact on patients’ emotional well-being (34) and increases financial costs in health care systems. It is estimated that diabetes accounts for 11.6% of the annual health-care budgets in most countries, and DR makes a large contribution to this figure (35); for example, in the USA, the direct annual costs of DR were estimated to be USD $490 million in 2004 (36) and USD $93.6 per patient in Sweden (37).

Vascular complications are induced predominantly by lasting hyperglycaemia and arteriosclerosis. To inhibit these chronic complications, both glycemic and blood pressure control are important (38). Adequate cerebral perfusion in diabetic patients remains a concern because lowering blood pressure may lead to microvascular disease and impaired cerebrovascular autoregulation (39). Thus, not only glycemic control but also the optimal blood pressure goal for diabetic patients should be emphasized. Various strategies, such as more frequent patient visits (40) and home blood pressure monitoring (41), in patients with diabetes mellitus and hypertension have been shown to increase blood pressure goal accomplishment. Monitoring biomarkers is essential for targeted medical interventions. Health professionals monitor plasma glucose levels using HbA1c as a disease marker. Disease progression, as indicated by longitudinal HbA1c measurements, may affect the time of interest (retinopathy). Hence, poor glycemic control increases and hastens the risk of retinopathy. Therefore, the objective of this study was to identify predictors of DR among diabetic patients in public hospitals in Central and Southern Ethiopia.

Study Setting, Study Design and Period

A hospital-based retrospective follow-up study was conducted from January 1, 2015, to June 30, 2022, among adult diabetic patients in five selected public hospitals in the central and southern regions of Ethiopia from June 1/2023 to July 30/2023. Both regions were newly emerged regions in Ethiopia on the ward on 19 August 2023. Both regions have 19 zones and three special woreda in combination with an estimated total population of more than 16.9 million. During the year of this study, there were 52 public hospitals (compressive specialized, referral, general and primary) in the region (42, 43). The selected hospitals provide general and specialty health care services along with teaching and research activities. It also provides comprehensive diabetes-related services. The diabetic clinic is a former hospital clinic where care and follow-ups are given to patients with all types of diabetes.

Population

The source population was all adult diabetic patients who were followed up at chronic disease follow-up units in public hospitals in the central and southern regions of Ethiopia. Among those, selected newly diagnosed adult patients with diabetes from January 1, 2015, to June 30, 2022, composed the study population. All adult diabetic patients aged ≥ 18 years who were diagnosed between January 1, 2015, and June 30, 2022, were included in the study. However, those whose date of initiation was not recorded, who had gestational DM or who had DM or retinopathy at the same time of diagnosis were excluded.

Sample size calculation and sampling procedure

The sample size was determined using the Schoenfeld formula (44) with STATA 14 based on the power approach by considering predictors significantly associated with the incidence of ADRs from previous studies under the following assumptions:

Cox proportional hazard model, 95% confidence level, 80% power, 10% withdrawal probability
The Schoenfeld formula (Schoenfeld, 1983) was used for manual calculation:

$E$ = $\frac{(\frac{Z\alpha }{2}+ZB)2}{\text{P}1\text{P}2 (\text{l}\text{n}\text{H}\text{R})2}$ and $n$ =$\frac{E}{P\left(E\right)}$

where

n = Total sample size
𝐻𝑅 is the hazard ratio of selected covariates
P1 is the proportion of subjects in the exposure group.
P2 = 1 – P1 and
E = number of events
P (𝐸) is the probability of an event from a previous study.
The hazard ratios for three predictors significantly associated with diabetic retinopathy in a study conducted at the Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar (45), were calculated (Table 1).

Table 1

Minimum sample size calculated for predictors significantly associated with adult diabetic patients in public hospitals in the central and southern regions of Ethiopia.
Variables	AHR	Event	Probability of Event	Sample size
HTN Protein Uria LDL	2.5 4 3.2	38 17 24	0.287 0.287 0.287	376 198 257

Therefore, the final sample size for this study was 376. Patients who met the inclusion criteria were included in the study. The sampling procedure followed the same approach used in a similar study (46, 47). The study was conducted at five selected public hospitals. Initially, a sampling frame was created using the patient’s medical registration number from each hospital hypertension registration book. After that, the calculated sample size was proportionally distributed to each hospital. The study participants were then selected from each of the selected hospitals using a computer-generated specific random sampling method.

Variables of the study

The dependent variable was diabetic retinopathy status, and the independent variables were sociodemographic characteristics (sex, age, marital status, residence, education status, income, occupation, ethnicity, and family size); clinical factors (treatment adherence, diabetes duration, glucometer usage, number of visits, self-monitoring of blood glucose, lipid profile, BMI, haemoglobin level, creatinine level, number of anti-diabetic agents, family history of DM); behavioural factors (smoking, chat chewing, inadequate physical exercise); and comorbidities (pregnancy, presence of cataract surgery, obesity, puberty, other comorbidities).

Operational definition

Diabetic retinopathy was defined by both direct and indirect ophthalmoscopy assessments performed by physicians confirmed by fundus photography. DRR was defined as a microvascular complication of diabetes that was evaluated by clinical examination or indirect ophthalmoscopy by ophthalmologists and classified as present (yes) or absent (no) from the charts based on ophthalmologists' decisions (3).

The time to diabetic retinopathy was defined as the time between the time of diagnosis of DM and the time to the development of diabetic retinopathy. Adult diabetic patients who experienced an event of interest (diabetic retinopathy) during the follow-up period. Adult diabetic patients who were lost to follow-up, died or did not experience diabetic retinopathy at the end of follow-up were censored.

Data Collection Procedures

The data for this study are secondary data and were obtained from diabetic patients who were undergoing follow-up from January 1, 2019, to June 30, 2023. All necessary (sociodemographic, laboratory, clinical and epidemiological) data for this research were extracted from the patient’s intake form, follow-up card, chronic disease registration book, especially DM; any electronic data sources; clinical records, including laboratory results of biomarkers; and any relevant investigation using a structured and pretested questionnaire developed based on the follow-up chart applied in the hospital and reviewed literature in the English language. HMIS card numbers were utilized to identify individual patient cards or their data in the electronic database. The baseline and follow-up data were collected from the date that patients started regular follow-up treatment until the end of the study or the time of confirmation of the event or censoring during the study period. The data were collected by health workers working at the chronic disease follow-up unit who were carefully selected based on their experience and educational level.

Data quality control

To ensure the data quality, an appropriately designed and pretested data extraction checklist was used, and training and necessary explanations for the data collector about the objectives of the study and process of the data collection were given. Every day, the collected data were checked for completeness and consistency. Familiarization with the checklist and strict supervision were provided during the data collection.

Data processing and analysis

The collected data were entered into Epi-data version 4.6.0.2 and then exported to STATA version 14 for further analysis. Appropriate data management techniques were employed to ensure that the data were suitable for analysis. Descriptive statistics were calculated to describe the study population and are presented as the means, medians, IQRs, percentages, frequencies, standard deviations, text, figures, and tables. The Kaplan‒Meier survival function and log-rank test were used to assess the survival experience of the patients and to compare their survival experiences among different groups, respectively. The Cox proportional hazard (PH) assumption was checked using the Schoenfeld residuals test before fitting the survival model.

The goodness of fit of the selected model was assessed using the Cox-Snell residual technique. To identify factors that are associated with the time to DR, a P value less than 0.25 was considered the cut-off point for selecting variables for the final multivariable analysis. The final reduced model was identified based on a stepwise procedure. Finally, a variable with a P value less than 0.05 and a corresponding 95% confidence interval was used to declare the association.

Ethics consideration

Ethical approval and a letter of cooperation were obtained from the Institutional Review Board of Wachemo University, College of Medicine and Health Sciences, and the hospital was informed about the study objectives through a written letter. Informed consent was waived by the Institutional Review Committee of Wachemo University. Confidentiality was maintained at all levels of the study. The data were stored on a secured password protection system. All procedures were conducted based on the regulations, guidelines and principles of the Helsinki Declaration.

Socio-demographic characteristics of the respondents

In this study, data from 376 diabetic patients who were followed up between 2019 and 2023 were collected. The reported mean baseline age of the study subjects was 34.8 years, with a standard deviation of 10. Among the study subjects, half (189, 50.3%) were females, and the majority (197, 52.4%) were married. Approximately one-third (106, 28.2%) of the participants had a college education or above, followed by 103 (27.4%) (Table 2).

Table 2: Socio-demographic characteristics of the respondents with DM at follow-up at public hospitals in the central and southern regions of Ethiopia

Variables	Category	Frequency	Percent (%)
Sex	Male	187	49.73
Sex	Female	189	50.27
Marital status	Single	79	21.01
	Married	197	52.39
	Divorced	80	21.28
	Widowed	19	5.05
	Separated	1	0.27
Educational status	No formal education	86	22.87
	Primary	81	21.54
	Secondary	103	27.39
	College and above	106	28.19
Occupation	Gov’t employed	102	27.13
	Non-gov’t employed	74	19.68
	Farmer	17	4.52
	Student	29	7.71
	Housewife	79	21.01
	Other	75	19.95

Others: Daily labourers, homemakers, and self-employed individuals.

Baseline clinical and behavioural characteristics

The median duration of DM treatment reported by the respondents was 20.2 months, with IQRs of 18.4 and 29.3. Three-fourths (n=282; 75.2%) of the study participants were receiving insulin treatment, and approximately 73% of the respondents had good adherence to treatment. In this study, while 17.9% of the study participants reported that they had ever smoked tobacco products, nearly 12.5% of participants reported that they had ever drunk alcoholic drinks. Among the study subjects, one-fourth (96, 25.5%) of the respondents had developed retinopathy (Table 3).

Table 3: Baseline clinical and behavioural characteristics of diabetic patients on follow-up in public hospitals in the central and southern regions of Ethiopia

Variables	Category	Frequency	Percent (%)
BMI	Underweight	56	14.89
	Normal	303	80.59
	Obesity	17	4.52
Proteinuria	Positive	47	12.5
Proteinuria	Negative	329	87.5
DM Rx	Insulin	282	75.2
	Noninsulin	74	19.7
	Mixed	19	5.1
HTN	Yes	153	40.7
HTN	No	223	59.3
Adherence	Good	274	73.8
	Fair	72	19.4
	Poor	25	6.7
Retinopathy	Yes	96	25.5
Retinopathy	No	280	74.5
Comorbidity	Yes	40	10.64
Comorbidity	No	336	89.36
DM type	Type I	117	29.79
DM type	Type II	249	70.21
Family History of DM	Yes	138	37.23
Family History of DM	No	227	62.77
Alcohol	Yes	27	7.2
Alcohol	No	349	92.8
Smoking	Yes	67	17.9
Smoking	No	307	82.1
Exercise	Yes	55	14.9
Exercise	No	315	85.1

Incidence of Retinopathy

The patients were followed for a minimum of 2.8 months and a maximum of 59.3 months, with a median follow-up time of 19.3 months and an IQR of 16.6 to 26.9. Out of 376 study participants who were followed retrospectively for four years, 96 (25.5%) developed retinopathy. The incidence rate was 11.7/1000 PM (approximately twelve cases per 1000 person-months of observation), with a 95% CI of [0.0096, 0.0143] (Figure 1).

Survival probability of patients with Diabetes

The overall Kaplan-Meier survival curves at the onset of follow-up, the likelihood of surviving was high and equal to the upper bound of the survival probability, and it then began to decline. The curve steps down to a lower value at each time in the final graph. At the maximum censorship time, it ends (Figure 2).

Proportional Hazard Assumption

The proportional assumption was met in this investigation as evidenced by the predictors' estimated logs (−log (survival)) versus survival times being parallel. Nevertheless, as these are univariate analyses and do not indicate whether risks will remain proportionate in a model with numerous other factors, examining the log (−log (survival)) alone will not provide sufficient assurance of proportionality. However, they back up our proportionality claim (Figure 3). Schoenfeld residual test for the Global test was insignificant that indicating the proportional hazard assumption holds (table 4).

Table 4: Schoenfeld residual test to check proportional hazard assumptions

Variables	Chi-square	DF	p-value
Sex	0.2368	1	0.627
Adherence	2.3480	2	0.309
DM duration	0.4601	1	0.498
BMI	2.0119	1	0.156
Smoking	0.5432	1	0.461
Exercise	5.7536	3	0.124
Alcohol	0.0472	1	0.828
DM type	1.6116	1	0.204
DM Rx	0.2410	2	0.886
Family history of DM	9.9806	3	0.089
HTN	0.8340	1	0.361
Proteinuria	4.3536	3	0.144
Global	23.1233	17	0.145

Predictors of the incidence of Diabetic Retinopathy among Diabetic patients

Based on the p-value of the Bivariable Cox proportional hazard regression analysis, twelve variables with P value ≤ 0.25 were identified as potential candidate variables for the multivariable Cox proportional hazard regression model. These were sex, marital status, DM treatment, HTN, proteinuria, adherence, and DM type, Family history of DM, comorbidity, smoking, exercise, and alcohol. In Multivariable cox proportional hazard regression analysis, HTN, DM type, and proteinuria showed statistically significant associations with the incidence of Diabetic retinopathy (Table 5).

Table 5: Bivariate and Multivariable Cox proportional hazard regression analysis results for predictors affecting diabetic retinopathy in selected public hospitals of Central and South region of Ethiopia.

Variables	Category	Censored (%)	Event (%)	CHR (95%CI)	AHR (95%CI)	p-value
Sex	Male	145(77.5)	42(22.5)	1(reference)	1(reference)
Sex	Female	135(71.4)	54(28.5)	1.35(0.90- 2.02)	1.52(0.94 - 2.46)	0.071
Marital Status	Single	63(79.7)	16(20.3)	1(reference)	1(reference)
	Married	157(79.7)	40(20.3)	0.97(0.54- 1.74)	0.93(0.48 - 1.81)	0.122
	Divorced	49(61.2)	31(38.8)	1.81(0.98- 3.31)	0.97(0.49 - 1.95)	0.091
	Widowed	11(55.0)	9(45.0)	2.24(0.95- 5.24)	2.19(0.82 - 5.82)	0.199
DM treatment	Insulin	250(88.6)	32(11.3)	1(reference)	1(reference)
	Non-insulin	28(37.8)	46(62.2)	6.92(4.39-10.9)	1.45(0.78 - 2.70)	0.099
	Mixed	2(10.5)	17(89.5)	8.60(4.74- 15.6)	1.79(0.76 - 4.09)	0.177
Proteinuria	Negative	10(21.3)	37(78.7)	1(reference)	1(reference)
Proteinuria	Positive	270(82.1)	59(17.9)	5.98(3.95- 9.07)	2.19(1.18-4.08)*	0.001
Hypertension	No	107(69.9)	46(30.1)	1(reference)	1(reference)
Hypertension	Yes	173(77.6)	50(22.4)	1.69(1.13 - 2.54)	2.23(1.39-3.55)*	0.002
Co morbidity	No	18(45.0)	22(55.0)	1(reference)	1(reference)
Co morbidity	Yes	262(77.9)	74(22.1)	2.21(1.37- 3.57)	1.16(0.66 - 2.07)	0.120
Family Hx of DM	No	45(38.7)	72(61.5)	1(reference)	1(reference)
Family Hx of DM	Yes	227(91.2)	22(8.8)	8.38(5.19- 13.5)	1.38(0.60 - 3.17)	0.096
DM type	Type I	208(91.6)	19(8.4)	1(reference)	1(reference)
DM type	Type II	61(44.2)	77(55.8)	7.64(4.62- 12.7)	2.89(1.19-7.05)*	0.031
Alcohol	No	11(44.0)	16(56.0)	1(reference)	1(reference)
Alcohol	Yes	269(77.1)	80(22.9)	5.88(3.91- 8.84)	1.28(0.61 - 2.67)	0.067
Smoking	Yes	261(85.0)	46(15.0)	1(reference)	1(reference)
Smoking	No	17(25.4)	50(74.6)	5.19(3.46- 7.79)	0.81(0.42 - 1.57)	0.072
Exercise	Yes	12(21.8)	43(78.2)	1(reference)	1(reference)
Exercise	No	263(83.5)	52(15.5)	0.17(0.11- 0.25)	1.62(0.82 - 3.22)	0.110

*p < 0.05 CI, confidence interval; Proteinuria, Hypertension, DM type 2

Model Adequacy

The Cox-Snell residual plot is approximately linear through the origin with a slope 1 which indicated that the fitted cox model is adequate (Figure 4).

This study sought to ascertain the incidence of diabetic retinopathy and its risk factors among DM patients in the central and southern regions of Ethiopia. According to this study, the cumulative density of diabetic retinopathy was 11.7 per 1000 adult-month observation. Similarly, the overall cumulative incidence of diabetic retinopathy was 25.5% (95% CI 20.1–31.3). This finding was compared with those of studies conducted in Addis Abeba, Ethiopia (48), Arbamich General Hospital, Ethiopia (49), Jimma Medical Center, Ethiopia (50), and Japan (51). This might be due to the use of a matching service delivery approach at the diabetic clinic in the facility. These studies revealed that the incidence was greater than that reported in studies conducted in China (52), Spain (53) and Australia (54). However, the findings of the present study were lower than those of studies in England (55) and Kenya (56). This difference might be due to the study period and study population used in the respective studies, as the follow-up years and screening programs could all contribute factors.

This study identified proteinuria as a risk factor for diabetic retinopathy. The risk of diabetic retinopathy was 2.19, with a 95% CI (1.18–4.08), which was greater among patients with positive proteinuria than among those with negative proteinuria. This finding is in line with those of retrospective cohort studies conducted at the Felege Hiwot Comprehensive Specialized Hospital, Ethiopia (57) and Iran (58). On the other hand, a study revealed that there was no difference between the presence and absence of DR in terms of the albumin excretion rate (AER) (45). This can be a result of the different study designs and study focuses (59).

Hypertension has been identified as another risk factor for the incidence of diabetic retinopathy among adult diabetic patients. The hazard of DR among DM patients with hypertensive comorbidities was 2.23 times greater than that among DM patients with no hypertensive comorbidities, with a 95% CI (1.39–3.55). This finding is consistent with those of studies conducted in Northwest Ethiopia (45), Arbamich General Hospital, Ethiopia (49), China (52), and Japan (51). This finding is also analogous to those of studies conducted in Hong Kong (60) and in Denmark (61). This significant link between the outcome variable and hypertension might be due to the repeated clinical coexistence of hypertension and DM (60). Hypertension itself might cause complications of DM, such as DR, through changes in the morphology of the vessel at the retina, such as haemorrhages, hard exudates and others (62).

In this study, patients with type 2 DM had a 2.89 (95% CI 1.19–7.05) greater probability of acquiring DR than those with type 1 DM. These findings are consistent with a study performed at Ayder Referral Hospital in Ethiopia (63) and in Addis Abeba, Ethiopia (48), which revealed that type 2 diabetes patients were more likely than type 1 diabetes patients to experience microvascular problems earlier in life. This might be because aging was more common among T2DM patients than among T1DM patients. Additionally, T2DM onset decreases with age, and microvascular diabetic complications can occur with comparable durations (64).

The cumulative incidence of retinopathy among diabetic patients was 25.5%, with a density of 11.7 per 1000 adult-month observations. For this study, the predicted median follow-up time was 57 months. Hypertension, proteinuria and diabetes type were identified as predictors of diabetic retinopathy. On the other hand, sex, marital status, DM treatment status, family history of DM, comorbidities, smoking status, exercise status, and alcohol consumption were not factors. To reduce diabetic retinopathy, we recommend that health professionals closely monitor and follow DM patients with hypertension and proteinuria. Considering the limitations of the current study, a prospective follow-up study is needed to identify all predictors of diabetic retinopathy among diabetic patients.

BMI; Body Mass Index, DM; Diabetes Mellitus, DR; Diabetic Retinopathy, HbA1c; Hemoglobin A1C, HMIS; Health Management and Information Systems, IQR; Interquartile Ranges, MRN; Medical Registration Number, PY; Person Year, T2DM; Type Two Diabetes Mellitus

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that there are no competing interests.

Funding

The authors declare(s) that no financial support was received for the research, authorship, and/or publication of this article.

Authors' contributions

Author contributions

TY: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing—original draft, writing—review and editing; BY: conceptualization, data curation, investigation, methodology, project administration, resources, software, supervision, validation, visualization; AA: investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing—original draft, writing—review and editing; MM: software, supervision, validation, visualization, writing—original draft, writing—review and editing

Acknowledgements

Our heartfelt gratitude goes to Wachemo University, College of Health and Medical Sciences for support with all necessary services. Additionally, we appreciate the support from hospital administrations and data collectors.

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Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study.

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Abstract

Background

Objective

Methods

Results

Conclusion

Figures

Background

Methods and Materials

Study Setting, Study Design and Period

Population

Sample size calculation and sampling procedure

Variables of the study

Operational definition

Data Collection Procedures

Data quality control

Data processing and analysis

Ethics consideration

Results

Socio-demographic characteristics of the respondents

Baseline clinical and behavioural characteristics

Incidence of Retinopathy

Survival probability of patients with Diabetes

Proportional Hazard Assumption

Predictors of the incidence of Diabetic Retinopathy among Diabetic patients

Model Adequacy

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1