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

Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study. Tagese Yakob Barata, Awoke Abiraham, Begidu Yakob, Mesfin Menza This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4620020/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 Figure 3 Figure 4 Background 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. Methods and Materials 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. Results 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 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 Negative 329 87.5 DM Rx Insulin 282 75.2 Noninsulin 74 19.7 Mixed 19 5.1 HTN Yes 153 40.7 No 223 59.3 Adherence Good 274 73.8 Fair 72 19.4 Poor 25 6.7 Retinopathy Yes 96 25.5 No 280 74.5 Comorbidity Yes 40 10.64 No 336 89.36 DM type Type I 117 29.79 Type II 249 70.21 Family History of DM Yes 138 37.23 No 227 62.77 Alcohol Yes 27 7.2 No 349 92.8 Smoking Yes 67 17.9 No 307 82.1 Exercise Yes 55 14.9 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) 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) 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) 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) 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) 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) 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) 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) 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) 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 ). Discussion 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 ). Conclusion 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. Abbreviations 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 Declarations 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. 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Hypertension and Its Associated Factors Among Type 2 Diabetes Mellitus Patients at Debre Tabor General Hospital, Northwest Ethiopia. Diabetes, metabolic syndrome and obesity : targets and therapy. 2020;13:1621-31. Lastra G, Syed S, Kurukulasuriya LR, Manrique C, Sowers JR. Type 2 Diabetes Mellitus and Hypertension: An Update. Endocrinology and Metabolism Clinics of North America. 2014;43(1):103-22. Grossman E, Messerli FH, Goldbourt U. High blood pressure and diabetes mellitus: are all antihypertensive drugs created equal? Archives of internal medicine. 2000;160(16):2447-52. Hu G, Jousilahti P, Tuomilehto J. Joint effects of history of hypertension at baseline and type 2 diabetes at baseline and during follow-up on the risk of coronary heart disease. European Heart Journal. 2007;28(24):3059-66. Miettinen H, Haffner SM, Lehto S, Rönnemaa T, Pyörälà K, Laakso M. Retinopathy predicts coronary heart disease events in NIDDM patients. Diabetes care. 1996;19(12):1445-8. Faglia E, Favales F, Calia P, Paleari F, Segalini G, Gamba PL, et al. Cardiac events in 735 type 2 diabetic patients who underwent screening for unknown asymptomatic coronary heart disease: 5-year follow-up report from the Milan Study on Atherosclerosis and Diabetes (MiSAD). Diabetes Care. 2002;25(11):2032-6. Klein R, Klein BE, Moss SE, Cruickshanks KJ. Association of ocular disease and mortality in a diabetic population. Archives of ophthalmology. 1999;117(11):1487-95. Juutilainen A, Lehto S, Rönnemaa T, Pyörälä K, Laakso M. Retinopathy predicts cardiovascular mortality in type 2 diabetic men and women. Diabetes care. 2007;30(2):292-9. Cheung N, Wang JJ, Klein R, Couper DJ, Sharrett AR, Wong TY. Diabetic Retinopathy and the Risk of Coronary Heart Disease. The Atherosclerosis Risk in Communities Study. 2007;30(7):1742-6. Association AD. Standards of Medical Care in Diabetes—2010. Diabetes Care. 2010;33(Supplement 1):S11-S61. Sowers JR. Recommendations for special populations: diabetes mellitus and the metabolic syndrome. American journal of hypertension. 2003;16(11 Pt 2):4 1 s- 1s- 5 s 5s . Grossman E, Messerli FH. Hypertension and diabetes. Advances in cardiology. 2008;45:82-106. ad SIA. Diabetes: An Old Disease, a New Insight. ad SIA, editor. Springer Science+Business Media, LLC/Springer Landes Bioscience; 2013. Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, et al. Causes of vision loss worldwide, 1990-2010: a systematic analysis. The Lancet Global health. 2013;1(6):e339-49. Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP, et al. Global data on visual impairment in the year 2002. Bulletin of the World Health Organization. 2004;82(11):844-51. Knowler WC, Bennett PH, Ballintine EJ. Increased incidence of retinopathy in diabetics with elevated blood pressure. A six-year follow-up study in Pima Indians. The New England journal of medicine. 1980;302(12):645-50. Fenwick E, Rees G, Pesudovs K, Dirani M, Kawasaki R, Wong TY, et al. Social and emotional impact of diabetic retinopathy: a review. Clinical & experimental ophthalmology. 2012;40(1):27-38. Zheng Y, He M, Congdon N. The worldwide epidemic of diabetic retinopathy. Indian journal of ophthalmology. 2012;60(5):428-31. Rein DB, Zhang P, Wirth KE, Lee PP, Hoerger TJ, McCall N, et al. The economic burden of major adult visual disorders in the United States. Arch Ophthalmol. 2006;124(12):1754-60. Heintz E, Wiréhn AB, Peebo BB, Rosenqvist U, Levin LA. Prevalence and healthcare costs of diabetic retinopathy: a population-based register study in Sweden. Diabetologia. 2010;53(10):2147-54. Yamazaki D, Hitomi H, Nishiyama A. Hypertension with diabetes mellitus complications. Hypertension research : official journal of the Japanese Society of Hypertension. 2018;41(3):147-56. Kim Y-S, Davis SC, Truijen J, Stok WJ, Secher NH, Van Lieshout JJ. Intensive blood pressure control affects cerebral blood flow in type 2 diabetes mellitus patients. Hypertension (Dallas, Tex : 1979). 2011;57(4):738-45. Turchin A, Goldberg SI, Shubina M, Einbinder JS, Conlin PR. Encounter frequency and blood pressure in hypertensive patients with diabetes mellitus. Hypertension (Dallas, Tex : 1979). 2010;56(1):68-74. Logan AG, Irvine MJ, McIsaac WJ, Tisler A, Rossos PG, Easty A, et al. Effect of home blood pressure telemonitoring with self-care support on uncontrolled systolic hypertension in diabetics. Hypertension (Dallas, Tex : 1979). 2012;60(1):51-7. "Central Ethiopia, Southern Ethiopia Regional States Established" (https://www.ena.et/web/eng/w/eng_3222547#:~:text=The%20Central%20Ethiopia%20region%20constitutes,Chief%20Administrator%20of20the%20region.) . www.ena.et. . "Ethiopia Regions"(http://www.worldstatesmen.org/Ethiopia_Regions.html) . Worldstatesmen.org. Retrieved September 17,2023. Schoenfeld DA. Sample-size formula for the proportional-hazards regression model. Biometrics. 1983:499-503. Takele MB, Boneya DJ, Alemu HA, Tsegaye TB, Birhanu MY, Alemu S, et al. Retinopathy among Adult Diabetics and Its Predictors in Northwest Ethiopia. Journal of Diabetes Research. 2022;2022:1362144. Sultan F. Magliah, Wedad Bardisi, Maha Al Attah3,4, Manal M. Khorsheed: The prevalence and risk factors for diabetic retinopathy in selected primary care centers during the 3‑year screening intervals; Journal of Family Medicine and Primary Care: Volume 7 : Issue 5 : 2018. Tilahun E, Workina A, Habtamu A, Tufa H,Abebe F, Fikadu A and Atomsa F (2024)Survival, incidence, and predictors of diabeticneuropathy among type 2 diabetic patients in hospitals of Addis Ababa.Front. Clin. Diabetes Healthc. 5:1386426.doi: 10.3389/fcdhc.2024.1386426. Azeze TK, Sisay MM, Zeleke EGJBrn. Incidence of diabetes retinopathy and determinants of time to diabetes retinopathy among diabetes patients at Tikur Anbessa Hospital, Ethiopia: a retrospective follow up study. 2018;11(1):1-6. Chisha Y, Terefe W, Assefa H. Incidence and factors associated with diabetic retinopathy among diabetic patients at arbaminch general hospital, gamo gofa Zone (longitudinal follow up data analysis). J Diabetol. 2017;8(1). doi:10.4103/jod.jod_6_17. Gebiso Roba Debele, Shuma Gosha Kanfe, Adisu Birhanu Weldesenbet, Galana Mamo Ayana, Wakuma Wakene Jifar, Temam Beshir Raru: Incidence of Diabetic Retinopathy and Its Predictors Among Newly Diagnosed Type 1 and Type 2 Diabetic Patients: A Retrospective Follow-up Study at Tertiary Health-care Setting of Ethiopia: 2021:14; Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. Kawasaki R, Tanaka S, Tanaka S, et al. Incidence and progression of diabetic retinopathy in Japanese adults with type 2 diabetes: 8 year follow-up study of the Japan Diabetes Complications Study (JDCS). Diabetologia. 2011;54(9):2288–2294. doi:10.1007/s00125-011-2199-0. Liu L, Wu J, Yue S, et al. Incidence density and risk factors for diabetic retinopathy within type 2 diabetes: a five-year cohort study in China (Report 1). Int J Environ Res Public Health. 2015;12 (7):7899–7909. doi:10.3390/ijerph120707899. Salinero-Fort MA, San Andres-Rebollo FJ, de Burgos-lunar C, Arrieta-Blanco FJ, Gomez-Campelo P, Group M. Four-year incidence of diabetic retinopathy in a Spanish cohort: the MADIABETES study. PLoS One. 2013;8(10):e76417. doi:10.1371/journal.pone.0 076417. Cikamatana L, Mitchell P, Rochtchina E, Foran S, Wang JJ. Five-year incidence and progression of diabetic retinopathy in a defined older population: the Blue Mountains Eye Study. Eye. 2007;21(4):465–471. doi:10.1038/sj.eye.6702771 Jones CD, Greenwood RH, Misra A, Bachmann MO. Incidence and progression of diabetic retinopathy during 17 years of a population-based screening program in England. Diabetes Care. 2012;35(3):592–596. doi:10.2337/dc11-0943. Bastawrous A, Mathenge W, Wing K, et al. The incidence of diabetes mellitus and diabetic retinopathy in a population-based cohort study of people age 50 years and over in Nakuru, Kenya. BMC Endocr Disord. 2017;17(1):19. doi:10.1186/s12902-017-0170-x Takele MB, Boneya DJ, Alemu HA, Tsegaye TB, Birhanu MY, Alemu S, et al. Retinopathy among Adult Diabetics and Its Predictors in Northwest Ethiopia. 2022;2022. Janghorbani M, Amini M, Ghanbari H, Safaiee H. Incidence of and risk factors for diabetic retinopathy in Isfahan, Iran. Ophthalmic Epidemiol. 2003;10(2):81-95. Sasso FC, Pafundi PC, Gelso A, Bono V, Costagliola C, Marfella R, et al. Relationship between albuminuric CKD and diabetic retinopathy in a real-world setting of type 2 diabetes: Findings from No blind study. Nutrition, metabolism, and cardiovascular diseases : NMCD. 2019;29(9):923-30. Wat N, Wong RLM, Wong IYH. Associations between diabetic retinopathy and systemic risk factors. Hong Kong Med J. 2016. doi:10.12809/hkmj164869. Broe R, Rasmussen ML, Frydkjaer-Olsen U, et al. The 16-year incidence, progression and regression of diabetic retinopathy in a young population-based Danish cohort with type 1 diabetes mellitus: the Danish cohort of pediatric diabetes 1987 (DCPD1987). Acta Diabetol. 2014;51(3):413–420. doi:10.1007/s00592-013-0527-1. Tomić M, Ljubić S, Kaštelan S, Gverović Antunica A, Jazbec A, Poljičanin T. Inflammation, haemostatic disturbance, and obesity: possible link to pathogenesis of diabetic retinopathy in type 2 diabetes. Mediators Inflamm. 2013;2013:1–10. doi:10.1155/2013/818671. Berihun L, Muluneh EKJEJoS, Technology. Correlates of time to microvascular complications among diabetes mellitus patients using parametric and nonparametric approaches: a case study of Ayder referral hospital, Ethiopia. 2017;10(1):65-80. Koopman RJ, Mainous AG, Diaz VA, Geesey ME. Changes in age at diagnosis of type 2 diabetes mellitus in the United States, 1988 to 2000. Ann Family Med. 2005;3(1):60–63. doi:10.1370/afm.214. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4620020","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318679383,"identity":"9954498a-dcaf-406c-8751-3117d1829b31","order_by":0,"name":"Tagese Yakob Barata","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACZijN2N4AJA0siNEC1cPYcwCkRYIUayQSwCRhDQbH+Q9+/FJRl9g88/nVDT8KJBj427sT8Gs5zMwsLXPmcGLj7Jyymz1Ah0mcObuBkBYGacm2AyAtaTd4gFoMJHIJamH+LfmvLrFx5pm0m3+I1MIm+bGBObFxBvux20TZInmY2cya4dhh48aeHLbbMgYSPAT9wnf+4OObP2rqZDe2H392880fGzn+9l78WhQOAGOGB8gwbOAxAAnw4FUOAvINwIj/AWIwsD8gqHoUjIJRMApGJgAALRNKv2SCvGAAAAAASUVORK5CYII=","orcid":"","institution":"Wolaita Zone Health Department","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tagese","middleName":"Yakob","lastName":"Barata","suffix":""},{"id":318679384,"identity":"afe694d4-f132-4439-842d-f4b5c97f8158","order_by":1,"name":"Awoke Abiraham","email":"","orcid":"","institution":"Wolaita Zone Health Department","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Awoke","middleName":"","lastName":"Abiraham","suffix":""},{"id":318679385,"identity":"fb8a4a10-44b3-41d9-8702-bbc133cd3993","order_by":2,"name":"Begidu Yakob","email":"","orcid":"","institution":"Wolaita Zone Health Department","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Begidu","middleName":"","lastName":"Yakob","suffix":""},{"id":318679386,"identity":"318697d0-06c8-4736-a333-19a575034105","order_by":3,"name":"Mesfin Menza","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mesfin","middleName":"","lastName":"Menza","suffix":""}],"badges":[],"createdAt":"2024-06-22 04:47:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4620020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4620020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60629120,"identity":"09108da8-76b0-4bfd-9ad3-d81e90991157","added_by":"auto","created_at":"2024-07-19 00:29:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44087,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence of retinopathy among DM patients at selected public hospital in central and southern, Ethiopia, 2023.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4620020/v1/42a55adeea0994dd5bace92a.png"},{"id":60629630,"identity":"33bef2a6-1f3d-4d74-9f18-a380799eada3","added_by":"auto","created_at":"2024-07-19 00:37:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25424,"visible":true,"origin":"","legend":"\u003cp\u003eOverall Kaplan Meier estimate of retinopathy among patients on DM at selected public hospitalin central and southern, Ethiopia, 2023.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4620020/v1/9b535b96da2c8c5aec53a4d5.png"},{"id":60629122,"identity":"e4458d79-d635-403d-b894-8c2bdaceb5a2","added_by":"auto","created_at":"2024-07-19 00:29:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38572,"visible":true,"origin":"","legend":"\u003cp\u003eLog-log survival plot by DM type and proteinuria for patients on DM, at selected public hospital in central and southern, Ethiopia, 2023.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4620020/v1/19be6fa5249f6f56d81e6f0e.png"},{"id":60630480,"identity":"75ffd828-65b0-4872-8fae-0d0aff5070b0","added_by":"auto","created_at":"2024-07-19 00:45:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26011,"visible":true,"origin":"","legend":"\u003cp\u003eCox-Snell residual plot of diabetic retinopathy among DM patients, patients at selected public hospital in central and southern, Ethiopia, 2023\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4620020/v1/764bb403785ca72a32543c5d.png"},{"id":83269877,"identity":"985b3163-2ad2-4feb-9137-69d9c0213e98","added_by":"auto","created_at":"2025-05-22 07:24:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1318137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4620020/v1/aa73fb11-81db-4b1b-bd31-b0fae23955fc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study.","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes retinopathy (DR) is the most prevalent cause of acquired blindness in adults and is one of the most life-threatening conditions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). One of the microcirculatory lesions of the general diabetic illness, diabetic retinopathy, is an ocular consequence of diabetes that can result in blindness (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It can be caused by changes in platelet aggregation, blood flow variations, or lesions in the artery walls (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The three main stages of diabetic macular edema are proliferative DR, early and severe non-proliferative DR, and diabetic macular edema (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). 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 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). 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 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetes 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 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). 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 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). According to the International Diabetes Federation (IDF) 2021, the global prevalence of diabetes among individuals aged 20 to 79 years is 537\u0026nbsp;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\u0026nbsp;million, and by 2045, it will reach 782\u0026nbsp;million. It is also estimated that more than 6.7\u0026nbsp;million people aged 20\u0026ndash;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 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). It has a pooled prevalence of 4.99% in Ethiopia (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobally, the number of diabetic patients increased from 108\u0026nbsp;million in 1980 to 422\u0026nbsp;million in 2014 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). 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%) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). 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\u0026nbsp;million deaths were directly caused by diabetes in 2019, and between 2000 and 2016, diabetes caused a 5% increase in premature mortality (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eType 2 diabetes is a chronic disease that has a significant global impact (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The presence of type 2 diabetes has been associated with an increased risk of depression and vice versa (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Patients with type 2 diabetes are at increased risk of both microvascular and macrovascular disease (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Type 2 diabetes often occurs concomitantly and increases the risk of vascular diseases independently. However, their coexistence dramatically increases the risk (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and is thus associated with retinopathy (\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetic 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% (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The incidence of diabetic retinopathy in patients with type 2 diabetes is twofold greater than that in patients without this disease (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Type II diabetes is often devastating and remains one of the leading causes of blindness (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). DR is a common cause of blindness and visual impairment in the working-age population (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). It is a major cause of blindness(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and causes 2.6% of blindness(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and 4.8% of visual impairment(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) worldwide. Hypertension accelerates the development of retinopathy in diabetic patients (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDR has a substantial negative impact on patients\u0026rsquo; emotional well-being (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) 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 \u003cspan\u003e$\u003c/span\u003e490\u0026nbsp;million in 2004 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and USD \u003cspan\u003e$\u003c/span\u003e93.6 per patient in Sweden (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVascular complications are induced predominantly by lasting hyperglycaemia and arteriosclerosis. To inhibit these chronic complications, both glycemic and blood pressure control are important (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Adequate cerebral perfusion in diabetic patients remains a concern because lowering blood pressure may lead to microvascular disease and impaired cerebrovascular autoregulation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). 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 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and home blood pressure monitoring (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), 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.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Setting, Study Design and Period\u003c/h2\u003e\n \u003cp\u003eA 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\u0026nbsp;million. During the year of this study, there were 52 public hospitals (compressive specialized, referral, general and primary) in the region (\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e). 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePopulation\u003c/h2\u003e\n \u003cp\u003eThe 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\u0026thinsp;\u0026ge;\u0026thinsp;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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eSample size calculation and sampling procedure\u003c/h2\u003e\n \u003cp\u003eThe sample size was determined using the Schoenfeld formula (\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e) 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:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eCox proportional hazard model, 95% confidence level, 80% power, 10% withdrawal probability\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe Schoenfeld formula (Schoenfeld, 1983) was used for manual calculation:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(E\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{(\\frac{Z\\alpha }{2}+ZB)2}{\\text{P}1\\text{P}2 (\\text{l}\\text{n}\\text{H}\\text{R})2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(n\\)\u003c/span\u003e\u003c/span\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{E}{P\\left(E\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ewhere\u003c/p\u003e\n \u003c/div\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;Total sample size\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e𝐻𝑅 is the hazard ratio of selected covariates\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eP1 is the proportion of subjects in the exposure group.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eP2\u0026thinsp;=\u0026thinsp;1 \u0026ndash; P1 and\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eE\u0026thinsp;=\u0026thinsp;number of events\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eP (𝐸) is the probability of an event from a previous study.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe hazard ratios for three predictors significantly associated with diabetic retinopathy in a study conducted at the Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar (\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e), were calculated (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMinimum sample size calculated for predictors significantly associated with adult diabetic patients in public hospitals in the central and southern regions of Ethiopia.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProbability of Event\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003cp\u003eProtein Uria\u003c/p\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTherefore, 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 (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e). The study was conducted at five selected public hospitals. Initially, a sampling frame was created using the patient\u0026rsquo;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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eVariables of the study\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eThe dependent variable was\u003c/strong\u003e diabetic retinopathy status, \u003cstrong\u003eand the independent\u003c/strong\u003e 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).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eOperational definition\u003c/h2\u003e\n \u003cp\u003eDiabetic 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\u0026apos; decisions (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe time to diabetic retinopathy\u003c/strong\u003e 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eData Collection Procedures\u003c/h2\u003e\n \u003cp\u003eThe 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\u0026rsquo;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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eData quality control\u003c/h2\u003e\n \u003cp\u003eTo 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eData processing and analysis\u003c/h2\u003e\n \u003cp\u003eThe 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.\u003c/p\u003e\n \u003cp\u003eThe 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.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEthics consideration\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eand 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.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eSocio-demographic characteristics of the respondents\u003c/h2\u003e\n\u003cp\u003eIn 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%) \u003cstrong\u003e(Table 2)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2:\u0026nbsp;Socio-demographic\u0026nbsp;characteristics of\u0026nbsp;the\u0026nbsp;respondents with DM\u0026nbsp;at\u0026nbsp;follow-up at\u0026nbsp;public hospitals\u0026nbsp;in the central and southern regions\u0026nbsp;of Ethiopia\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.649350649350648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.967532467532468%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.785714285714285%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.649350649350648%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.967532467532468%\" valign=\"top\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.785714285714285%\" valign=\"top\"\u003e\n \u003cp\u003e49.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e50.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.649350649350648%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.967532467532468%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.785714285714285%\" valign=\"top\"\u003e\n \u003cp\u003e21.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e52.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e21.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.649350649350648%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eEducational status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.967532467532468%\" valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.785714285714285%\" valign=\"top\"\u003e\n \u003cp\u003e22.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e21.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e27.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eCollege and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e28.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.649350649350648%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5974025974026%\" valign=\"top\"\u003e\n \u003cp\u003eGov\u0026rsquo;t employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.967532467532468%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.785714285714285%\" valign=\"top\"\u003e\n \u003cp\u003e27.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eNon-gov\u0026rsquo;t employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e19.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eHousewife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e21.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11790393013101%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.85589519650655%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.02620087336245%\" valign=\"top\"\u003e\n \u003cp\u003e19.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eOthers\u003c/strong\u003e: Daily\u0026nbsp;labourers, homemakers, and self-employed\u0026nbsp;individuals.\u003c/p\u003e\n\u003ch2\u003eBaseline clinical and behavioural characteristics\u003c/h2\u003e\n\u003cp\u003eThe 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 \u003cstrong\u003e(Table 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3: Baseline clinical and behavioural characteristics of diabetic patients on follow-up\u0026nbsp;in public hospitals\u0026nbsp;in the central and southern regions\u0026nbsp;of Ethiopia\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e80.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eProteinuria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDM Rx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e75.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNoninsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e59.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAdherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRetinopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e89.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDM type\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e29.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eType II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e70.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFamily History of DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e37.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e62.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eExercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.675324675324674%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.88961038961039%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.162337662337663%\" valign=\"top\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.92857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.473214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.598214285714285%\" valign=\"top\"\u003e\n \u003cp\u003e85.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eIncidence of\u0026nbsp;Retinopathy\u003c/h2\u003e\n\u003cp\u003eThe 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] \u003cstrong\u003e(Figure 1)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSurvival probability of patients with Diabetes \u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe 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\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(Figure 2).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003eProportional Hazard Assumption\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe proportional assumption was met in this investigation as evidenced by the predictors\u0026apos; estimated logs (\u0026minus;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 (\u0026minus;log (survival)) alone will not provide sufficient assurance of proportionality. However, they back up our proportionality claim \u003cstrong\u003e(Figure 3).\u003c/strong\u003e Schoenfeld residual test for the Global test was insignificant that indicating the proportional hazard assumption holds (\u003cstrong\u003etable 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Schoenfeld residual test to check proportional hazard assumptions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003eChi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e0.2368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eAdherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e2.3480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eDM duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e0.4601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e2.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e0.5432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eExercise\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e5.7536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e0.0472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eDM type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e1.6116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eDM Rx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e0.2410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e9.9806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e0.8340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003eProteinuria\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e4.3536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.228525121555915%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.094003241491087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.1233\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.71799027552674%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.959481361426256%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.145\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003c/h2\u003e\n\u003ch2\u003ePredictors of the incidence of Diabetic Retinopathy among Diabetic patients\u003c/h2\u003e\n\u003cp\u003eBased on the p-value of the Bivariable Cox proportional hazard regression analysis, twelve variables with P value \u0026le; 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 \u003cstrong\u003e(Table 5)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e: Bivariate and Multivariable Cox proportional hazard regression analysis results for predictors affecting diabetic retinopathy\u0026nbsp;in selected public hospitals of Central and South region of Ethiopia.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"750\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCensored (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e145(77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e42(22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e135(71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e54(28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e1.35(0.90- 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.52(0.94 - 2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e63(79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e16(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e157(79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e40(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e0.97(0.54- 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e0.93(0.48 - 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eDivorced\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e49(61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e31(38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e1.81(0.98- 3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e0.97(0.49 - 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eWidowed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e11(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e9(45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e2.24(0.95- 5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e2.19(0.82 - 5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e250(88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e32(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eNon-insulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e28(37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e46(62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e6.92(4.39-10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.45(0.78 - 2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eMixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e2(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e17(89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e8.60(4.74- 15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.79(0.76 - 4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProteinuria\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e10(21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e37(78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e270(82.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e59(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e5.98(3.95- 9.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e2.19(1.18-4.08)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e107(69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e46(30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e173(77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e50(22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e1.69(1.13 - 2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e2.23(1.39-3.55)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo morbidity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e18(45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e22(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e262(77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e74(22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e2.21(1.37- 3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.16(0.66 - 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Hx of DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e45(38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e72(61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e227(91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e22(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e8.38(5.19- 13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.38(0.60 - 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eType I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e208(91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e19(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eType II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e61(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e77(55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e7.64(4.62- 12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e2.89(1.19-7.05)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e11(44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e16(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e269(77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e80(22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e5.88(3.91- 8.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.28(0.61 - 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e261(85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e46(15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e17(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e50(74.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e5.19(3.46- 7.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e0.81(0.42 - 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.2%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2%\" valign=\"top\"\u003e\n \u003cp\u003e12(21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.8%\" valign=\"top\"\u003e\n \u003cp\u003e43(78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.6%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e1(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92452830188679%\" valign=\"top\"\u003e\n \u003cp\u003e263(83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e52(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.754716981132077%\" valign=\"top\"\u003e\n \u003cp\u003e0.17(0.11- 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.81132075471698%\" valign=\"top\"\u003e\n \u003cp\u003e1.62(0.82 - 3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.49056603773585%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p \u0026lt; 0.05 CI, confidence interval;\u0026nbsp;Proteinuria,\u0026nbsp;Hypertension,\u0026nbsp;DM type 2\u003c/p\u003e\n\u003ch2\u003eModel Adequacy\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe Cox-Snell residual plot is approximately linear through the origin with a slope 1 which indicated that the fitted cox model is adequate (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis 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\u0026ndash;31.3). This finding \u003cb\u003ewas\u003c/b\u003e compared with \u003cb\u003ethose of\u003c/b\u003e studies conducted in Addis Abeba, Ethiopia (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), Arbamich General Hospital, Ethiopia (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), Jimma Medical Center, Ethiopia (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), and Japan (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). 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 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), Spain (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) and Australia (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). However, the findings of the present study were lower than those of studies in England (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) and Kenya (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eThis study identified proteinuria as a risk factor for diabetic retinopathy. The risk of diabetic retinopathy was 2.19, with a 95% CI (1.18\u0026ndash;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 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) and Iran (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). 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) (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). This can be a result of the different study designs and study focuses (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHypertension 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\u0026ndash;3.55). This finding is consistent with those of studies conducted in Northwest Ethiopia (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), Arbamich General Hospital, Ethiopia (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), China (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), and Japan (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). This finding is also analogous to those of studies conducted in Hong Kong (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e) and in Denmark (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). This significant link between the outcome variable and hypertension might be due to the repeated clinical coexistence of hypertension and DM (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). 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 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, patients with type 2 DM had a 2.89 (95% CI 1.19\u0026ndash;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 (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) and in Addis Abeba, Ethiopia (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), 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 (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe 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.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eBMI; 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\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or\u0026nbsp;analysed\u0026nbsp;during the current study\u0026nbsp;are\u0026nbsp;available from the corresponding author\u0026nbsp;upon\u0026nbsp;reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;declare(s) that no financial support was received for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTY:\u0026nbsp;conceptualization, data\u0026nbsp;curation,\u0026nbsp;formal\u0026nbsp;analysis,\u0026nbsp;funding\u0026nbsp;acquisition,\u0026nbsp;investigation, methodology, project\u0026nbsp;administration,\u0026nbsp;resources, software, supervision, validation, visualization, writing\u0026mdash;original draft, writing\u0026mdash;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\u0026mdash;original draft, writing\u0026mdash;review and editing; MM: software, supervision, validation, visualization, writing\u0026mdash;original draft, writing\u0026mdash;review\u0026nbsp;and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKobrin Klein BE. Overview of epidemiologic studies of diabetic retinopathy. Ophthalmic epidemiology. 2007;14(4):179-83.\u003c/li\u003e\n\u003cli\u003eChow S-C, Shao J, Wang H, Lokhnygina Y. Sample size calculations in clinical research: chapman and hall/CRC; 2017.\u003c/li\u003e\n\u003cli\u003eAzeze TK, Sisay MM, Zeleke EG. Incidence of diabetes retinopathy and determinants of time to diabetes retinopathy among diabetes patients at Tikur Anbessa Hospital, Ethiopia: a retrospective follow up study. BMC research notes. 2018;11(1):1-6.\u003c/li\u003e\n\u003cli\u003eLarsen HW. Diabetic retinopathy. An ophthalmoscopic study with a discussion of the morphologic changes and the pathogenetic factors in this disease. Acta Ophthalmol Suppl. 1960;Suppl 60:1-89.\u003c/li\u003e\n\u003cli\u003eDobree JH. 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Associations between diabetic retinopathy and systemic risk factors. Hong Kong Med J. 2016. doi:10.12809/hkmj164869.\u003c/li\u003e\n\u003cli\u003eBroe R, Rasmussen ML, Frydkjaer-Olsen U, et al. The 16-year incidence, progression and regression of diabetic retinopathy in a young population-based Danish cohort with type 1 diabetes mellitus: the Danish cohort of pediatric diabetes 1987 (DCPD1987). Acta Diabetol. 2014;51(3):413\u0026ndash;420. doi:10.1007/s00592-013-0527-1.\u003c/li\u003e\n\u003cli\u003eTomić M, Ljubić S, Ka\u0026scaron;telan S, Gverović Antunica A, Jazbec A, Poljičanin T. Inflammation, haemostatic disturbance, and obesity: possible link to pathogenesis of diabetic retinopathy in type 2 diabetes. Mediators Inflamm. 2013;2013:1\u0026ndash;10. doi:10.1155/2013/818671.\u003c/li\u003e\n\u003cli\u003eBerihun L, Muluneh EKJEJoS, Technology. Correlates of time to microvascular complications among diabetes mellitus patients using parametric and nonparametric approaches: a case study of Ayder referral hospital, Ethiopia. 2017;10(1):65-80.\u003c/li\u003e\n\u003cli\u003eKoopman RJ, Mainous AG, Diaz VA, Geesey ME. Changes in age at diagnosis of type 2 diabetes mellitus in the United States, 1988 to 2000. Ann Family Med. 2005;3(1):60\u0026ndash;63. doi:10.1370/afm.214.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"incidence, diabetic retinopathy, predictors, adult diabetic patients","lastPublishedDoi":"10.21203/rs.3.rs-4620020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4620020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetic 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.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to determine the incidence of diabetic retinopathy and its predictors among adult diabetic patients in public hospitals in Central and Southern Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA 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\u0026thinsp;=\u0026thinsp;2.19: 95% CI: 1.18, 4.08), hypertension (Yes) (AHR\u0026thinsp;=\u0026thinsp;2.23: 95% CI: 1.39, 3.55) and type II DM (AHR\u0026thinsp;=\u0026thinsp;2.89: 95% CI: 1.19, 7.05) were identified as significant predictors of diabetic retinopathy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"Incidence of Diabetic Retinopathy and Predictors among Adult Diabetic Patients in Central and Southern Ethiopia: A Multicentre Retrospective Cohort Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 00:29:15","doi":"10.21203/rs.3.rs-4620020/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3439a0a6-a6ee-47f7-926d-228674c918d2","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-22T07:23:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 00:29:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4620020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4620020","identity":"rs-4620020","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}